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data-dom-id="Pill-react-component-a371cb9e-2a3f-4111-ad3a-eaab964c46bf"></div> <div id="Pill-react-component-a371cb9e-2a3f-4111-ad3a-eaab964c46bf"></div> </a></div></div><div class="external-links-container"><ul class="profile-links new-profile js-UserInfo-social"><li class="profile-profiles js-social-profiles-container"><i class="fa fa-spin fa-spinner"></i></li></ul></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Ritsert Jansen</h3></div><div class="js-work-strip profile--work_container" data-work-id="10058167"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058167/The_MAR_Mediated_Reduction_in_Position_Effect_Can_Be_Uncoupled_from_Copy_Number_Dependent_Expression_in_Transgenic_Plants"><img alt="Research paper thumbnail of The MAR-Mediated Reduction in Position Effect Can Be Uncoupled from Copy Number-Dependent Expression in Transgenic Plants" class="work-thumbnail" src="https://attachments.academia-assets.com/47550552/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058167/The_MAR_Mediated_Reduction_in_Position_Effect_Can_Be_Uncoupled_from_Copy_Number_Dependent_Expression_in_Transgenic_Plants">The MAR-Mediated Reduction in Position Effect Can Be Uncoupled from Copy Number-Dependent Expression in Transgenic Plants</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To study the role of matrix-associated regions (MARs) in establishing independent chromatin domai...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">To study the role of matrix-associated regions (MARs) in establishing independent chromatin domains in plants, two transgenes were cloned between chicken lysozyme A elements. These transgenes were the neomycin phosphotransferase (NPTII) gene under control of the nopaline synthase (nos) promoter and the P-glucuronidase (GUS) gene controlled by the double cauliflower mosaic virus (dCaMV) 35s promoter. The A elements are! supposed to establish an artificial chromatin domain upon integration into the plant DNA, resultlng in an independent unlt of transcriptlonal regulation. Such a domain is thought to be characterized by a correlated and position-independent, hence copy number-dependent, expression of the genes within the domain. The presence of MARs resulted ln a hlgher relative transformatlon efficiency, demonstrating MAR influence on NPTll gene expression. However, variatlon in NPTll gene expression was not significantly reduced. The selection bias for NPTll gene expression during transformation could not fully account for the lack of reduction in variation of NPTll gene expression. Topological interactions between the promoter and A element may interfere with the A element as a domain boundary. In contrast, the GUS gene on the same putative chromatin domain showed a highly significant reduction in variation of gene expression, as expected from previous results. Surprisingly, no copy number-dependent GUS gene expression was obsewed: all plants showed approximately the same GUS activity. We concluded, therefore, that dCaMV 35s-GUS gene expression in mature tobacco plants is ngulated by some form of dosage compensation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fd97cdb2c0903686b5e17c62f19784d5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550552,"asset_id":10058167,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550552/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058167"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058167"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058167; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058166"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058166/Identification_of_the_SAAT_gene_involved_in_strawberry_flavor_biogenesis_by_use_of_microarrays"><img alt="Research paper thumbnail of Identification of the SAAT gene involved in strawberry flavor biogenesis by use of microarrays" class="work-thumbnail" src="https://attachments.academia-assets.com/36186252/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058166/Identification_of_the_SAAT_gene_involved_in_strawberry_flavor_biogenesis_by_use_of_microarrays">Identification of the SAAT gene involved in strawberry flavor biogenesis by use of microarrays</a></div><div class="wp-workCard_item"><span>Plant Cell</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fruit flavor is a result of a complex mixture of numerous compounds. The formation of these compo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Fruit flavor is a result of a complex mixture of numerous compounds. The formation of these compounds is closely correlated with the metabolic changes occurring during fruit maturation. Here, we describe the use of DNA microarrays and appropriate statistical analyses to dissect a complex developmental process. In doing so, we have identified a novel strawberry alcohol acyltransferase ( SAAT ) gene that plays a crucial role in flavor biogenesis in ripening fruit. Volatile esters are quantitatively and qualitatively the most important compounds providing fruity odors. Biochemical evidence for involvement of the SAAT gene in formation of fruity esters is provided by characterizing the recombinant protein expressed in Escherichia coli . The SAAT enzyme showed maximum activity with aliphatic medium-chain alcohols, whose corresponding esters are major components of strawberry volatiles. The enzyme was capable of utilizing short-and medium-chain, branched, and aromatic acyl-CoA molecules as cosubstrates. The results suggest that the formation of volatile esters in fruit is subject to the availability of acyl-CoA molecules and alcohol substrates and is dictated by the temporal expression pattern of the SAAT gene(s) and substrate specificity of the SAAT enzyme(s).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="aed78a984d4c0378037b289478157464" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186252,"asset_id":10058166,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186252/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058166"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058166"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058166; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058165"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058165/Genetical_genomics_the_added_value_from_segregation"><img alt="Research paper thumbnail of Genetical genomics: the added value from segregation" class="work-thumbnail" src="https://attachments.academia-assets.com/47550545/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058165/Genetical_genomics_the_added_value_from_segregation">Genetical genomics: the added value from segregation</a></div><div class="wp-workCard_item"><span>Trends in Genetics</span><span>, 2001</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b02867511a62308a3b0c9b3943b9aafa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550545,"asset_id":10058165,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550545/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058165"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058165"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058165; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058164"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058164/Regulating_gene_expression_surprises_still_in_store"><img alt="Research paper thumbnail of Regulating gene expression: surprises still in store" class="work-thumbnail" src="https://attachments.academia-assets.com/47550569/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058164/Regulating_gene_expression_surprises_still_in_store">Regulating gene expression: surprises still in store</a></div><div class="wp-workCard_item"><span>Trends in Genetics</span><span>, 2004</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b381488f69066a606641abd604473ada" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550569,"asset_id":10058164,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550569/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058164"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058164"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058164; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058162"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058162/A_general_mixture_model_for_mapping_quantitative_trait_loci_by_using_molecular_markers"><img alt="Research paper thumbnail of A general mixture model for mapping quantitative trait loci by using molecular markers" class="work-thumbnail" src="https://attachments.academia-assets.com/36186245/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058162/A_general_mixture_model_for_mapping_quantitative_trait_loci_by_using_molecular_markers">A general mixture model for mapping quantitative trait loci by using molecular markers</a></div><div class="wp-workCard_item"><span>Theoretical and Applied Genetics</span><span>, 1992</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In a segregating population a quantitative trait may be considered to follow a mixture of (normal...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In a segregating population a quantitative trait may be considered to follow a mixture of (normal) distributions, the mixing proportions being based on Mendelian segregation rules. A general and flexible mixture model is proposed for mapping quantitative trait loci (QTLs) by using molecular markers. A method is discribed to fit the model to data. The model makes it possible to (1) analyse non-normally distributed traits such as lifetimes, counts or percentages in addition to normally distributed traits, (2) reduce environmental variation by taking into account the effects of experimental design factors and interaction between genotype and environment, (3) reduce genotypic variation by taking into account the effects of two or more QTLs simultaneously, (4) carry out a (combined) analysis of different population types, (5) estimate recombination frequencies between markers or use known marker distances, (6) cope with missing marker observations, (7) use markers as covariables in detection and mapping of QTLs, and finally to (8) implement the mapping in standard statistical packages.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a1fad71eb16b21ffb96eff3da8764c9f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186245,"asset_id":10058162,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186245/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058162"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058162"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058162; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058160"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058160/Sequence_Polymorphisms_Cause_Many_False_cis_eQTLs"><img alt="Research paper thumbnail of Sequence Polymorphisms Cause Many False cis eQTLs" class="work-thumbnail" src="https://attachments.academia-assets.com/36186244/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058160/Sequence_Polymorphisms_Cause_Many_False_cis_eQTLs">Sequence Polymorphisms Cause Many False cis eQTLs</a></div><div class="wp-workCard_item"><span>PLOS One</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Many investigations have reported the successful mapping of quantitative trait loci (QTLs) for ge...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Many investigations have reported the successful mapping of quantitative trait loci (QTLs) for gene expression phenotypes (eQTLs). Local eQTLs, where expression phenotypes map to the genes themselves, are of especially great interest, because they are direct candidates for previously mapped physiological QTLs. Here we show that many mapped local eQTLs in genetical genomics experiments do not reflect actual expression differences caused by sequence polymorphisms in cis-acting factors changing mRNA levels. Instead they indicate hybridization differences caused by sequence polymorphisms in the mRNA region that is targeted by the microarray probes. Many such polymorphisms can be detected by a sensitive and novel statistical approach that takes the individual probe signals into account. Applying this approach to recent mouse and human eQTL data, we demonstrate that indeed many local eQTLs are falsely reported as ''cis-acting'' or ''cis'' and can be successfully detected and eliminated with this approach.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cb088cd12d12cb24d8434575e4aadcb8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186244,"asset_id":10058160,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186244/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058160"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058160"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058160; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058159"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058159/The_genetics_of_plant_metabolism"><img alt="Research paper thumbnail of The genetics of plant metabolism" class="work-thumbnail" src="https://attachments.academia-assets.com/47550574/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058159/The_genetics_of_plant_metabolism">The genetics of plant metabolism</a></div><div class="wp-workCard_item"><span>Nature Genetics</span><span>, 2006</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ba33a89db28bed584b4cda1f67e982a0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550574,"asset_id":10058159,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550574/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058159"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058159"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058159; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=10058159]").text(description); $(".js-view-count[data-work-id=10058159]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 10058159; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='10058159']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058157"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058157/Interval_mapping_of_multiple_quantitative_trait_loci"><img alt="Research paper thumbnail of Interval mapping of multiple quantitative trait loci" class="work-thumbnail" src="https://attachments.academia-assets.com/36186241/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058157/Interval_mapping_of_multiple_quantitative_trait_loci">Interval mapping of multiple quantitative trait loci</a></div><div class="wp-workCard_item"><span>Genetics</span><span>, 1993</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in s...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in segregating generations derived from crosses between inbred lines. The efficiency of detecting and the accuracy of mapping multiple QTLs by using genetic markers are much increased by employing multiple QTL models instead of the single QTL models (and no QTL models) used in interval mapping. However, the computational work involved with multiple QTL models is considerable when the number of QTLs is large. In this paper it is proposed to combine multiple linear regression methods with conventional interval mapping. This is achieved by fitting one QTL at a time in a given interval and simultaneously using (part of) the markers as cofactors to eliminate the effects of additional QTLs. It is shown that the proposed method combines the easy computation of the single QTL interval mapping method with much of the efficiency and accuracy of multiple QTL models.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="650e3f182dcde10bfed1c443edd9eada" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186241,"asset_id":10058157,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186241/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058157"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058157"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058157; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058155"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058155/A_penalized_likelihood_method_for_mapping_epistatic_quantitative_trait_loci_with_one_dimensional_genome_searches"><img alt="Research paper thumbnail of A penalized likelihood method for mapping epistatic quantitative trait loci with one-dimensional genome searches" class="work-thumbnail" src="https://attachments.academia-assets.com/36186239/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058155/A_penalized_likelihood_method_for_mapping_epistatic_quantitative_trait_loci_with_one_dimensional_genome_searches">A penalized likelihood method for mapping epistatic quantitative trait loci with one-dimensional genome searches</a></div><div class="wp-workCard_item"><span>Genetics</span><span>, 2002</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Epistasis is a common and important phenomenon, as indicated by results from a number of recent e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Epistasis is a common and important phenomenon, as indicated by results from a number of recent experiments. Unfortunately, the discovery of epistatic quantitative trait loci (QTL) is difficult since one must search for multiple QTL simultaneously in two or more dimensions. Such a multidimensional search necessitates many statistical tests, and a high statistical threshold must be adopted to avoid false positives. Furthermore, the large number of (interaction) parameters in comparison with the number of observations results in a serious danger of overfitting and overinterpretation of the data. In this article we present a new statistical framework for mapping epistasis in inbred line crosses. It is based on reducing the high dimensionality of the problem in two ways. First, epistatic QTL are mapped in a one-dimensional genome scan for high interactions between QTL and the genetic background. Second, the dimension of the search is bounded by penalized likelihood methods. We use simulated backcross data to illustrate the new approach.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c20cffe995ca2c242068569382bb775d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186239,"asset_id":10058155,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186239/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058155"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058155"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058155; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058154"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058154/Epigenome_dynamics_a_quantitative_genetics_perspective"><img alt="Research paper thumbnail of Epigenome dynamics: a quantitative genetics perspective" class="work-thumbnail" src="https://attachments.academia-assets.com/47550575/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058154/Epigenome_dynamics_a_quantitative_genetics_perspective">Epigenome dynamics: a quantitative genetics perspective</a></div><div class="wp-workCard_item"><span>Nature Reviews Genetics</span><span>, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The heritable basis of complex traits has long been assumed to rest on the stable transmission of...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The heritable basis of complex traits has long been assumed to rest on the stable transmission of multiple causative DNA sequence alleles from parents to offspring. This classical view is being challenged by the recent discovery that variation in chromatin states is highly abundant in experimental and natural populations 1-5 , and could therefore provide an additional source of phenotypic variation. Indeed, chromatin differences between individuals can exist independently of DNA sequence polymorphisms and can be transmitted across mitosis and meiosis in both mammals and plants, with phenotypic consequences at the level of the cell, tissue or whole organism 6-10 .</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b5b9d286f9a43c3ee21d9c4ea6fb4d2d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550575,"asset_id":10058154,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550575/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058154"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058154"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058154; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058153"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058153/Complex_plant_traits_Time_for_polygenic_analysis"><img alt="Research paper thumbnail of Complex plant traits: Time for polygenic analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/47550555/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058153/Complex_plant_traits_Time_for_polygenic_analysis">Complex plant traits: Time for polygenic analysis</a></div><div class="wp-workCard_item"><span>Trends in Plant Science</span><span>, 1996</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="71692e7632a77b68471d02265de7bf96" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550555,"asset_id":10058153,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550555/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058153"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058153"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058153; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058151"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058151/Reduced_Position_Effect_in_Mature_Transgenic_Plants_Conferred_by_the_Chicken_Lysozyme_Matrix_Associated_Region"><img alt="Research paper thumbnail of Reduced Position Effect in Mature Transgenic Plants Conferred by the Chicken Lysozyme Matrix-Associated Region" class="work-thumbnail" src="https://attachments.academia-assets.com/47550549/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058151/Reduced_Position_Effect_in_Mature_Transgenic_Plants_Conferred_by_the_Chicken_Lysozyme_Matrix_Associated_Region">Reduced Position Effect in Mature Transgenic Plants Conferred by the Chicken Lysozyme Matrix-Associated Region</a></div><div class="wp-workCard_item"><span>Trends in Genetics</span><span>, 1994</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Matrix-associated regions may be useful for studying the role of chromatin architecture in transg...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Matrix-associated regions may be useful for studying the role of chromatin architecture in transgene activity of transformed plants. The chicken lysoryme A element was shown to have specific affinity for tobacco nuclear matrices, and its influence on the variability of transgene expression in tobacco plants was studied. T-DNA constructs in which this element flanked either the P-glucuronidase (GUS) reporter gene or both reporter and selection gene were introduced in tobacco. The variation in GUS gene activity was reduced significantly among mature first-generation transgenic plants carrying the A element. Average GUS activity became somewhat higher, but the maximum attainable level of gene expression was similar for all three constructs. Transient gene expression assays showed that the A element did not contain general enhancer functions; therefore, its presence seemed to prevent the lower levels of transgene expression. The strongest reduction in variability was found in plants transformed with the construct carrying the A elements at the borders of the T-DNA. In this population, expression levels became copy number dependent.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0dcb4572db3f650ef6f9bc1fd830e4a6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550549,"asset_id":10058151,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550549/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058151"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058151"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058151; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058149"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058149/Mapping_Determinants_of_Gene_Expression_Plasticity_by_Genetical_Genomics_in_C_elegans"><img alt="Research paper thumbnail of Mapping Determinants of Gene Expression Plasticity by Genetical Genomics in C. elegans" class="work-thumbnail" src="https://attachments.academia-assets.com/47550579/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058149/Mapping_Determinants_of_Gene_Expression_Plasticity_by_Genetical_Genomics_in_C_elegans">Mapping Determinants of Gene Expression Plasticity by Genetical Genomics in C. elegans</a></div><div class="wp-workCard_item"><span>PLOS Genetics</span><span>, 2005</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Recent genetical genomics studies have provided intimate views on gene regulatory networks. Gene ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Recent genetical genomics studies have provided intimate views on gene regulatory networks. Gene expression variations between genetically different individuals have been mapped to the causal regulatory regions, termed expression quantitative trait loci. Whether the environment-induced plastic response of gene expression also shows heritable difference has not yet been studied. Here we show that differential expression induced by temperatures of 16 8C and 24 8C has a strong genetic component in Caenorhabditis elegans recombinant inbred strains derived from a cross between strains CB4856 (Hawaii) and N2 (Bristol). No less than 59% of 308 trans-acting genes showed a significant eQTL-by-environment interaction, here termed plasticity quantitative trait loci. In contrast, only 8% of an estimated 188 cis-acting genes showed such interaction. This indicates that heritable differences in plastic responses of gene expression are largely regulated in trans. This regulation is spread over many different regulators. However, for one group of trans-genes we found prominent evidence for a common master regulator: a transband of 66 coregulated genes appeared at 24 8C. Our results suggest widespread genetic variation of differential expression responses to environmental impacts and demonstrate the potential of genetical genomics for mapping the molecular determinants of phenotypic plasticity. Citation: Li Y, Á lvarez OA, Gutteling EW, Tijsterman M, Fu J, et al. (2006) Mapping determinants of gene expression plasticity by genetical genomics in C. elegans. PLoS Genet 2(12): e222.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="156110e8c478acfdae06ec79e98f0391" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550579,"asset_id":10058149,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550579/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058149"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058149"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058149; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058148"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058148/Regulatory_network_construction_in_Arabidopsis_by_using_genome_wide_gene_expression_quantitative_trait_loci"><img alt="Research paper thumbnail of Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci" class="work-thumbnail" src="https://attachments.academia-assets.com/47550557/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058148/Regulatory_network_construction_in_Arabidopsis_by_using_genome_wide_gene_expression_quantitative_trait_loci">Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci</a></div><div class="wp-workCard_item"><span>Proceedings of The National Academy of Sciences</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Accessions of a plant species can show considerable genetic differences that are analyzed effecti...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Accessions of a plant species can show considerable genetic differences that are analyzed effectively by using recombinant inbred line (RIL) populations. Here we describe the results of genomewide expression variation analysis in an RIL population of Arabidopsis thaliana. For many genes, variation in expression could be explained by expression quantitative trait loci (eQTLs). The nature and consequences of this variation are discussed based on additional genetic parameters, such as heritability and transgression and by examining the genomic position of eQTLs versus gene position, polymorphism frequency, and gene ontology. Furthermore, we developed an approach for genetic regulatory network construction by combining eQTL mapping and regulator candidate gene selection. The power of our method was shown in a case study of genes associated with flowering time, a well studied regulatory network in Arabidopsis. Results that revealed clusters of coregulated genes and their most likely regulators were in agreement with published data, and unknown relationships could be predicted.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2e9671ff25cf6da3e672fb54e43f6e04" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550557,"asset_id":10058148,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550557/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058148"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058148"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058148; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058147"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/10058147/Using_complex_plant_pedigrees_to_map_valuable_genes"><img alt="Research paper thumbnail of Using complex plant pedigrees to map valuable genes" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/10058147/Using_complex_plant_pedigrees_to_map_valuable_genes">Using complex plant pedigrees to map valuable genes</a></div><div class="wp-workCard_item"><span>Trends in Plant Science</span><span>, 2001</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Bayesian inference using Gibbs Sampling, http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml). .....</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... Bayesian inference using Gibbs Sampling, <a href="http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml" rel="nofollow">http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml</a>). ... Suitable multivariate mixed-model analyses are described in Refs 26, 27. ... because single plant crosses can produce large sibships, and combining line-cross methods with pedigree ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058147"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058147"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058147; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058140"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058140/Major_genes_for_resistance_to_beet_necrotic_yellow_vein_virus_BNYVV_in_Beta_vulgaris"><img alt="Research paper thumbnail of Major genes for resistance to beet necrotic yellow vein virus (BNYVV) in Beta vulgaris" class="work-thumbnail" src="https://attachments.academia-assets.com/47550568/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058140/Major_genes_for_resistance_to_beet_necrotic_yellow_vein_virus_BNYVV_in_Beta_vulgaris">Major genes for resistance to beet necrotic yellow vein virus (BNYVV) in Beta vulgaris</a></div><div class="wp-workCard_item"><span>Euphytica</span><span>, 1996</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Inheritance of resistance to beet necrotic yellow vein virus (BNYVV) was studied in segregating F...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Inheritance of resistance to beet necrotic yellow vein virus (BNYVV) was studied in segregating F2 and backcross families obtained from crosses between resistant plants of the sugar beet selection Holly-1-4 or the wild beet accession Beta vulgaris subsp. maritima WB42 and susceptible parents. Greenhouse tests were carried out, in which seedlings were grown in a mixture of sand and infested soil. Virus concentrations of BNYVV in the rootlets were estimated by ELISA. To discriminate resistant and susceptible plants, mixtures of normal distributions were fitted to log10 virus concentrations, estimated for segregating F1, F2 and BC populations of both accessions. The hypothesis that Holly-1-4 contained one single dominant major gene was accepted. For WB42, results fitted with the hypotheses that resistance was based on either one (or more) dominant major gene(s) showing distorted segregation, or two complementary dominant genes, which are both required for resistance. Resistance from WB42 appeared to be more effective against BNYVV than resistance from Holly-1-4.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="924856c4a6a3e69af108953dc4d3cee8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550568,"asset_id":10058140,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550568/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058140; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058139"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058139/SELDI_TOF_mass_spectra_A_view_on_sources_of_variation"><img alt="Research paper thumbnail of SELDI-TOF mass spectra: A view on sources of variation" class="work-thumbnail" src="https://attachments.academia-assets.com/47550571/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058139/SELDI_TOF_mass_spectra_A_view_on_sources_of_variation">SELDI-TOF mass spectra: A view on sources of variation</a></div><div class="wp-workCard_item"><span>Journal of Chromatography B-analytical Technologies in The Biomedical and Life Sciences</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Adequate interpretation of mass spectrometry data can yield valuable biomarkers. However, spectru...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Adequate interpretation of mass spectrometry data can yield valuable biomarkers. However, spectrum interpretation is a complicated task. This paper reviews the various factors that determine a sample's spectrum and demonstrates the role of these factors in the interpretation process. We derive a simulation model that adequately predicts the expected spectrum based on known sample content and, in the reverse mode, obtain an analysis model that adequately fits an observed spectrum based on the hypothesized sources of variation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d2f42f0643d018b0d4febb8b992f119d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550571,"asset_id":10058139,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550571/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058139"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058139"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058139; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=10058139]").text(description); $(".js-view-count[data-work-id=10058139]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 10058139; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='10058139']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d2f42f0643d018b0d4febb8b992f119d" } } $('.js-work-strip[data-work-id=10058139]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":10058139,"title":"SELDI-TOF mass spectra: A view on sources of variation","internal_url":"https://www.academia.edu/10058139/SELDI_TOF_mass_spectra_A_view_on_sources_of_variation","owner_id":24489171,"coauthors_can_edit":true,"owner":{"id":24489171,"first_name":"Ritsert","middle_initials":null,"last_name":"Jansen","page_name":"RitsertJansen","domain_name":"rug","created_at":"2015-01-07T11:24:38.711-08:00","display_name":"Ritsert Jansen","url":"https://rug.academia.edu/RitsertJansen"},"attachments":[{"id":47550571,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/47550571/thumbnails/1.jpg","file_name":"SELDI-TOF_mass_spectra_a_view_on_sources20160726-9881-9yqg9a.pdf","download_url":"https://www.academia.edu/attachments/47550571/download_file","bulk_download_file_name":"SELDI_TOF_mass_spectra_A_view_on_sources.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/47550571/SELDI-TOF_mass_spectra_a_view_on_sources20160726-9881-9yqg9a-libre.pdf?1469597963=\u0026response-content-disposition=attachment%3B+filename%3DSELDI_TOF_mass_spectra_A_view_on_sources.pdf\u0026Expires=1739927119\u0026Signature=N5jlRZiJVxr87PGbZe9mm8nCUVXzwhb7L8Iwro3Ew0fKtZK9SCaDB-2HTZmT0T5y1AVMVPZdhpzgpXSiNyB8LPuypZRf3RvaiAv~KK7ZSjP5P8k~0uIeTmefcBi0dSsJq24Mwv0ZyhSVSmGZRbZeaujfvQOeLMTh~QfXJGULZxaFtNBzuYU79lUJzt19m4GCJgmNbpQV~qMnOSEFXvgD197hHCIEtvqQJQXnN-Adyt1yK5mBF7xGf6iMQlNuU~59sD8N743ZhTdP-sbJQSuymHWsS8ekI0Ar2kM3swLND6ATZsery9AQzy~Y2WGNJ0nnkVx7PtSdWwuwkdLXCwSDBw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058138"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058138/QTL_analysis_of_seed_dormancy_in_Arabidopsis_using_recombinant_inbred_lines_and_MQM_mapping"><img alt="Research paper thumbnail of QTL analysis of seed dormancy in Arabidopsis using recombinant inbred lines and MQM mapping" class="work-thumbnail" src="https://attachments.academia-assets.com/36186232/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058138/QTL_analysis_of_seed_dormancy_in_Arabidopsis_using_recombinant_inbred_lines_and_MQM_mapping">QTL analysis of seed dormancy in Arabidopsis using recombinant inbred lines and MQM mapping</a></div><div class="wp-workCard_item"><span>Heredity</span><span>, 1997</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The genetic differences for seed germination between two commonly used Arabidopsis thaliana ecoty...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The genetic differences for seed germination between two commonly used Arabidopsis thaliana ecotypes Ler and Col, both showing a low level of seed dormancy, were investigated. The analysis was performed with 98 recombinant inbred lines (RILs) derived from the cross between the two ecotypes, and these lines had previously been analysed for molecular marker composition by Lister and Dean (Norwich, UK). The analysis of germination was performed on seeds grown in three different maternal environments and each seed batch was tested in three different germination environments: in light, in darkness and in the presence of the gibberellin inhibitor paclobutrazol. Fourteen loci were identified using the multiple-QTL-model (MQM) procedure for mapping quantitative trait loci. At nine loci no significant interaction between the detection of the locus and environmental factors could be detected. However, three other distinct loci controlling the germination behaviour in the presence of the gibberellin inhibitor paclobutrazol had a much lower or no effect when germination was tested in water either in light or darkness. Two other loci affecting germination in darkness and/or light had practically no effect on germination in the presence of paclobutrazol.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b70e28f1980930489da8abea10593330" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186232,"asset_id":10058138,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186232/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058138"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058138"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058138; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="2380474" id="papers"><div class="js-work-strip profile--work_container" data-work-id="10058167"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058167/The_MAR_Mediated_Reduction_in_Position_Effect_Can_Be_Uncoupled_from_Copy_Number_Dependent_Expression_in_Transgenic_Plants"><img alt="Research paper thumbnail of The MAR-Mediated Reduction in Position Effect Can Be Uncoupled from Copy Number-Dependent Expression in Transgenic Plants" class="work-thumbnail" src="https://attachments.academia-assets.com/47550552/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058167/The_MAR_Mediated_Reduction_in_Position_Effect_Can_Be_Uncoupled_from_Copy_Number_Dependent_Expression_in_Transgenic_Plants">The MAR-Mediated Reduction in Position Effect Can Be Uncoupled from Copy Number-Dependent Expression in Transgenic Plants</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To study the role of matrix-associated regions (MARs) in establishing independent chromatin domai...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">To study the role of matrix-associated regions (MARs) in establishing independent chromatin domains in plants, two transgenes were cloned between chicken lysozyme A elements. These transgenes were the neomycin phosphotransferase (NPTII) gene under control of the nopaline synthase (nos) promoter and the P-glucuronidase (GUS) gene controlled by the double cauliflower mosaic virus (dCaMV) 35s promoter. The A elements are! supposed to establish an artificial chromatin domain upon integration into the plant DNA, resultlng in an independent unlt of transcriptlonal regulation. Such a domain is thought to be characterized by a correlated and position-independent, hence copy number-dependent, expression of the genes within the domain. The presence of MARs resulted ln a hlgher relative transformatlon efficiency, demonstrating MAR influence on NPTll gene expression. However, variatlon in NPTll gene expression was not significantly reduced. The selection bias for NPTll gene expression during transformation could not fully account for the lack of reduction in variation of NPTll gene expression. Topological interactions between the promoter and A element may interfere with the A element as a domain boundary. In contrast, the GUS gene on the same putative chromatin domain showed a highly significant reduction in variation of gene expression, as expected from previous results. Surprisingly, no copy number-dependent GUS gene expression was obsewed: all plants showed approximately the same GUS activity. We concluded, therefore, that dCaMV 35s-GUS gene expression in mature tobacco plants is ngulated by some form of dosage compensation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fd97cdb2c0903686b5e17c62f19784d5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550552,"asset_id":10058167,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550552/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058167"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058167"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058167; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058166"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058166/Identification_of_the_SAAT_gene_involved_in_strawberry_flavor_biogenesis_by_use_of_microarrays"><img alt="Research paper thumbnail of Identification of the SAAT gene involved in strawberry flavor biogenesis by use of microarrays" class="work-thumbnail" src="https://attachments.academia-assets.com/36186252/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058166/Identification_of_the_SAAT_gene_involved_in_strawberry_flavor_biogenesis_by_use_of_microarrays">Identification of the SAAT gene involved in strawberry flavor biogenesis by use of microarrays</a></div><div class="wp-workCard_item"><span>Plant Cell</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fruit flavor is a result of a complex mixture of numerous compounds. The formation of these compo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Fruit flavor is a result of a complex mixture of numerous compounds. The formation of these compounds is closely correlated with the metabolic changes occurring during fruit maturation. Here, we describe the use of DNA microarrays and appropriate statistical analyses to dissect a complex developmental process. In doing so, we have identified a novel strawberry alcohol acyltransferase ( SAAT ) gene that plays a crucial role in flavor biogenesis in ripening fruit. Volatile esters are quantitatively and qualitatively the most important compounds providing fruity odors. Biochemical evidence for involvement of the SAAT gene in formation of fruity esters is provided by characterizing the recombinant protein expressed in Escherichia coli . The SAAT enzyme showed maximum activity with aliphatic medium-chain alcohols, whose corresponding esters are major components of strawberry volatiles. The enzyme was capable of utilizing short-and medium-chain, branched, and aromatic acyl-CoA molecules as cosubstrates. The results suggest that the formation of volatile esters in fruit is subject to the availability of acyl-CoA molecules and alcohol substrates and is dictated by the temporal expression pattern of the SAAT gene(s) and substrate specificity of the SAAT enzyme(s).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="aed78a984d4c0378037b289478157464" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186252,"asset_id":10058166,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186252/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058166"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058166"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058166; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058164"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058164/Regulating_gene_expression_surprises_still_in_store"><img alt="Research paper thumbnail of Regulating gene expression: surprises still in store" class="work-thumbnail" src="https://attachments.academia-assets.com/47550569/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058164/Regulating_gene_expression_surprises_still_in_store">Regulating gene expression: surprises still in store</a></div><div class="wp-workCard_item"><span>Trends in Genetics</span><span>, 2004</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b381488f69066a606641abd604473ada" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550569,"asset_id":10058164,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550569/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058164"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058164"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058164; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058162"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058162/A_general_mixture_model_for_mapping_quantitative_trait_loci_by_using_molecular_markers"><img alt="Research paper thumbnail of A general mixture model for mapping quantitative trait loci by using molecular markers" class="work-thumbnail" src="https://attachments.academia-assets.com/36186245/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058162/A_general_mixture_model_for_mapping_quantitative_trait_loci_by_using_molecular_markers">A general mixture model for mapping quantitative trait loci by using molecular markers</a></div><div class="wp-workCard_item"><span>Theoretical and Applied Genetics</span><span>, 1992</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In a segregating population a quantitative trait may be considered to follow a mixture of (normal...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In a segregating population a quantitative trait may be considered to follow a mixture of (normal) distributions, the mixing proportions being based on Mendelian segregation rules. A general and flexible mixture model is proposed for mapping quantitative trait loci (QTLs) by using molecular markers. A method is discribed to fit the model to data. The model makes it possible to (1) analyse non-normally distributed traits such as lifetimes, counts or percentages in addition to normally distributed traits, (2) reduce environmental variation by taking into account the effects of experimental design factors and interaction between genotype and environment, (3) reduce genotypic variation by taking into account the effects of two or more QTLs simultaneously, (4) carry out a (combined) analysis of different population types, (5) estimate recombination frequencies between markers or use known marker distances, (6) cope with missing marker observations, (7) use markers as covariables in detection and mapping of QTLs, and finally to (8) implement the mapping in standard statistical packages.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a1fad71eb16b21ffb96eff3da8764c9f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186245,"asset_id":10058162,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186245/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058162"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058162"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058162; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058160"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058160/Sequence_Polymorphisms_Cause_Many_False_cis_eQTLs"><img alt="Research paper thumbnail of Sequence Polymorphisms Cause Many False cis eQTLs" class="work-thumbnail" src="https://attachments.academia-assets.com/36186244/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058160/Sequence_Polymorphisms_Cause_Many_False_cis_eQTLs">Sequence Polymorphisms Cause Many False cis eQTLs</a></div><div class="wp-workCard_item"><span>PLOS One</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Many investigations have reported the successful mapping of quantitative trait loci (QTLs) for ge...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Many investigations have reported the successful mapping of quantitative trait loci (QTLs) for gene expression phenotypes (eQTLs). Local eQTLs, where expression phenotypes map to the genes themselves, are of especially great interest, because they are direct candidates for previously mapped physiological QTLs. Here we show that many mapped local eQTLs in genetical genomics experiments do not reflect actual expression differences caused by sequence polymorphisms in cis-acting factors changing mRNA levels. Instead they indicate hybridization differences caused by sequence polymorphisms in the mRNA region that is targeted by the microarray probes. Many such polymorphisms can be detected by a sensitive and novel statistical approach that takes the individual probe signals into account. Applying this approach to recent mouse and human eQTL data, we demonstrate that indeed many local eQTLs are falsely reported as ''cis-acting'' or ''cis'' and can be successfully detected and eliminated with this approach.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cb088cd12d12cb24d8434575e4aadcb8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186244,"asset_id":10058160,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186244/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058160"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058160"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058160; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "cb088cd12d12cb24d8434575e4aadcb8" } } $('.js-work-strip[data-work-id=10058160]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":10058160,"title":"Sequence Polymorphisms Cause Many False cis eQTLs","internal_url":"https://www.academia.edu/10058160/Sequence_Polymorphisms_Cause_Many_False_cis_eQTLs","owner_id":24489171,"coauthors_can_edit":true,"owner":{"id":24489171,"first_name":"Ritsert","middle_initials":null,"last_name":"Jansen","page_name":"RitsertJansen","domain_name":"rug","created_at":"2015-01-07T11:24:38.711-08:00","display_name":"Ritsert Jansen","url":"https://rug.academia.edu/RitsertJansen"},"attachments":[{"id":36186244,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/36186244/thumbnails/1.jpg","file_name":"PLoSOne_published.pdf","download_url":"https://www.academia.edu/attachments/36186244/download_file","bulk_download_file_name":"Sequence_Polymorphisms_Cause_Many_False.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/36186244/PLoSOne_published-libre.pdf?1420662090=\u0026response-content-disposition=attachment%3B+filename%3DSequence_Polymorphisms_Cause_Many_False.pdf\u0026Expires=1739927119\u0026Signature=EWVLAZnJYWwDnQZjkTLLHJNVbl57w-XZPBy7gxnajM5FmcD-a6K-z1s0DpY6fbvCdoV6ZmZA3ETrik4ijiscozCswG7HwFoRB10ETjYcq25cr8EqDS3OKAFxM9b9Ne8YFJfRmTJR6GPrb7Pq~glpsoIbhfcM1KaB4tHYrS51oIb6JwExfB9P4m6kUeEzPliqdeO12MzPzOp15igWlPE4E3z21z~nkx0BAUyOFUhWyiGcgHqu02xKSYl0BjPJUGzyHZbZcCIoOGXu-D112whBO9s2dEIEjBgO4f-vhLXTb-vP9XrQWDIPzDWx2tQi1m3pMn6PaSvoR~j5HxahY7W5lw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058159"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058159/The_genetics_of_plant_metabolism"><img alt="Research paper thumbnail of The genetics of plant metabolism" class="work-thumbnail" src="https://attachments.academia-assets.com/47550574/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058159/The_genetics_of_plant_metabolism">The genetics of plant metabolism</a></div><div class="wp-workCard_item"><span>Nature Genetics</span><span>, 2006</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ba33a89db28bed584b4cda1f67e982a0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550574,"asset_id":10058159,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550574/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058159"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058159"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058159; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=10058159]").text(description); $(".js-view-count[data-work-id=10058159]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 10058159; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='10058159']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ba33a89db28bed584b4cda1f67e982a0" } } $('.js-work-strip[data-work-id=10058159]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":10058159,"title":"The genetics of plant metabolism","internal_url":"https://www.academia.edu/10058159/The_genetics_of_plant_metabolism","owner_id":24489171,"coauthors_can_edit":true,"owner":{"id":24489171,"first_name":"Ritsert","middle_initials":null,"last_name":"Jansen","page_name":"RitsertJansen","domain_name":"rug","created_at":"2015-01-07T11:24:38.711-08:00","display_name":"Ritsert Jansen","url":"https://rug.academia.edu/RitsertJansen"},"attachments":[{"id":47550574,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/47550574/thumbnails/1.jpg","file_name":"The_genetics_of_plant_metabolism._Nat_Ge20160726-4987-ok0x4w.pdf","download_url":"https://www.academia.edu/attachments/47550574/download_file","bulk_download_file_name":"The_genetics_of_plant_metabolism.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/47550574/The_genetics_of_plant_metabolism._Nat_Ge20160726-4987-ok0x4w-libre.pdf?1469597989=\u0026response-content-disposition=attachment%3B+filename%3DThe_genetics_of_plant_metabolism.pdf\u0026Expires=1739927119\u0026Signature=g2BkY132MmDh8sphRrycKrxAZJTrRVqUbVWhudbSOYt8042131SlbEn~fAFaU5FWjfuxHKp1V2~M1-pcy8i9Gxhc98JH8NU-Meo10IlD9Nn1Y57fMmxbmWZQIppgMj1qX7hIdO8hxSmA5AuZXQ0~v4jbGWzQb0ZMHkS4~SjaAJCTGI0qNxIfenj0JH5lid~L7iP~h7OgntQHquJyAEE9nXURG7DhOxcPbYKaOUHP0SmZH2L6GLGAR24hKBaWRxLWRl89wBFLFhxYs3zs-O4vqmQmc0WSu5mVXOSSVKwDoQ6g-IaIwKw0RLuMfCg2fq1EEUoGHtP9UsRyitx0O4~BlQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058157"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058157/Interval_mapping_of_multiple_quantitative_trait_loci"><img alt="Research paper thumbnail of Interval mapping of multiple quantitative trait loci" class="work-thumbnail" src="https://attachments.academia-assets.com/36186241/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058157/Interval_mapping_of_multiple_quantitative_trait_loci">Interval mapping of multiple quantitative trait loci</a></div><div class="wp-workCard_item"><span>Genetics</span><span>, 1993</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in s...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in segregating generations derived from crosses between inbred lines. The efficiency of detecting and the accuracy of mapping multiple QTLs by using genetic markers are much increased by employing multiple QTL models instead of the single QTL models (and no QTL models) used in interval mapping. However, the computational work involved with multiple QTL models is considerable when the number of QTLs is large. In this paper it is proposed to combine multiple linear regression methods with conventional interval mapping. This is achieved by fitting one QTL at a time in a given interval and simultaneously using (part of) the markers as cofactors to eliminate the effects of additional QTLs. It is shown that the proposed method combines the easy computation of the single QTL interval mapping method with much of the efficiency and accuracy of multiple QTL models.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="650e3f182dcde10bfed1c443edd9eada" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186241,"asset_id":10058157,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186241/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058157"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058157"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058157; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058155"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058155/A_penalized_likelihood_method_for_mapping_epistatic_quantitative_trait_loci_with_one_dimensional_genome_searches"><img alt="Research paper thumbnail of A penalized likelihood method for mapping epistatic quantitative trait loci with one-dimensional genome searches" class="work-thumbnail" src="https://attachments.academia-assets.com/36186239/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058155/A_penalized_likelihood_method_for_mapping_epistatic_quantitative_trait_loci_with_one_dimensional_genome_searches">A penalized likelihood method for mapping epistatic quantitative trait loci with one-dimensional genome searches</a></div><div class="wp-workCard_item"><span>Genetics</span><span>, 2002</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Epistasis is a common and important phenomenon, as indicated by results from a number of recent e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Epistasis is a common and important phenomenon, as indicated by results from a number of recent experiments. Unfortunately, the discovery of epistatic quantitative trait loci (QTL) is difficult since one must search for multiple QTL simultaneously in two or more dimensions. Such a multidimensional search necessitates many statistical tests, and a high statistical threshold must be adopted to avoid false positives. Furthermore, the large number of (interaction) parameters in comparison with the number of observations results in a serious danger of overfitting and overinterpretation of the data. In this article we present a new statistical framework for mapping epistasis in inbred line crosses. It is based on reducing the high dimensionality of the problem in two ways. First, epistatic QTL are mapped in a one-dimensional genome scan for high interactions between QTL and the genetic background. Second, the dimension of the search is bounded by penalized likelihood methods. We use simulated backcross data to illustrate the new approach.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c20cffe995ca2c242068569382bb775d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186239,"asset_id":10058155,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186239/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058155"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058155"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058155; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058154"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058154/Epigenome_dynamics_a_quantitative_genetics_perspective"><img alt="Research paper thumbnail of Epigenome dynamics: a quantitative genetics perspective" class="work-thumbnail" src="https://attachments.academia-assets.com/47550575/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058154/Epigenome_dynamics_a_quantitative_genetics_perspective">Epigenome dynamics: a quantitative genetics perspective</a></div><div class="wp-workCard_item"><span>Nature Reviews Genetics</span><span>, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The heritable basis of complex traits has long been assumed to rest on the stable transmission of...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The heritable basis of complex traits has long been assumed to rest on the stable transmission of multiple causative DNA sequence alleles from parents to offspring. This classical view is being challenged by the recent discovery that variation in chromatin states is highly abundant in experimental and natural populations 1-5 , and could therefore provide an additional source of phenotypic variation. Indeed, chromatin differences between individuals can exist independently of DNA sequence polymorphisms and can be transmitted across mitosis and meiosis in both mammals and plants, with phenotypic consequences at the level of the cell, tissue or whole organism 6-10 .</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b5b9d286f9a43c3ee21d9c4ea6fb4d2d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550575,"asset_id":10058154,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550575/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058154"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058154"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058154; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058153"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058153/Complex_plant_traits_Time_for_polygenic_analysis"><img alt="Research paper thumbnail of Complex plant traits: Time for polygenic analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/47550555/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058153/Complex_plant_traits_Time_for_polygenic_analysis">Complex plant traits: Time for polygenic analysis</a></div><div class="wp-workCard_item"><span>Trends in Plant Science</span><span>, 1996</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="71692e7632a77b68471d02265de7bf96" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550555,"asset_id":10058153,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550555/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058153"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058153"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058153; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058151"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058151/Reduced_Position_Effect_in_Mature_Transgenic_Plants_Conferred_by_the_Chicken_Lysozyme_Matrix_Associated_Region"><img alt="Research paper thumbnail of Reduced Position Effect in Mature Transgenic Plants Conferred by the Chicken Lysozyme Matrix-Associated Region" class="work-thumbnail" src="https://attachments.academia-assets.com/47550549/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058151/Reduced_Position_Effect_in_Mature_Transgenic_Plants_Conferred_by_the_Chicken_Lysozyme_Matrix_Associated_Region">Reduced Position Effect in Mature Transgenic Plants Conferred by the Chicken Lysozyme Matrix-Associated Region</a></div><div class="wp-workCard_item"><span>Trends in Genetics</span><span>, 1994</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Matrix-associated regions may be useful for studying the role of chromatin architecture in transg...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Matrix-associated regions may be useful for studying the role of chromatin architecture in transgene activity of transformed plants. The chicken lysoryme A element was shown to have specific affinity for tobacco nuclear matrices, and its influence on the variability of transgene expression in tobacco plants was studied. T-DNA constructs in which this element flanked either the P-glucuronidase (GUS) reporter gene or both reporter and selection gene were introduced in tobacco. The variation in GUS gene activity was reduced significantly among mature first-generation transgenic plants carrying the A element. Average GUS activity became somewhat higher, but the maximum attainable level of gene expression was similar for all three constructs. Transient gene expression assays showed that the A element did not contain general enhancer functions; therefore, its presence seemed to prevent the lower levels of transgene expression. The strongest reduction in variability was found in plants transformed with the construct carrying the A elements at the borders of the T-DNA. In this population, expression levels became copy number dependent.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0dcb4572db3f650ef6f9bc1fd830e4a6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550549,"asset_id":10058151,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550549/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058151"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058151"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058151; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058149"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058149/Mapping_Determinants_of_Gene_Expression_Plasticity_by_Genetical_Genomics_in_C_elegans"><img alt="Research paper thumbnail of Mapping Determinants of Gene Expression Plasticity by Genetical Genomics in C. elegans" class="work-thumbnail" src="https://attachments.academia-assets.com/47550579/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058149/Mapping_Determinants_of_Gene_Expression_Plasticity_by_Genetical_Genomics_in_C_elegans">Mapping Determinants of Gene Expression Plasticity by Genetical Genomics in C. elegans</a></div><div class="wp-workCard_item"><span>PLOS Genetics</span><span>, 2005</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Recent genetical genomics studies have provided intimate views on gene regulatory networks. Gene ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Recent genetical genomics studies have provided intimate views on gene regulatory networks. Gene expression variations between genetically different individuals have been mapped to the causal regulatory regions, termed expression quantitative trait loci. Whether the environment-induced plastic response of gene expression also shows heritable difference has not yet been studied. Here we show that differential expression induced by temperatures of 16 8C and 24 8C has a strong genetic component in Caenorhabditis elegans recombinant inbred strains derived from a cross between strains CB4856 (Hawaii) and N2 (Bristol). No less than 59% of 308 trans-acting genes showed a significant eQTL-by-environment interaction, here termed plasticity quantitative trait loci. In contrast, only 8% of an estimated 188 cis-acting genes showed such interaction. This indicates that heritable differences in plastic responses of gene expression are largely regulated in trans. This regulation is spread over many different regulators. However, for one group of trans-genes we found prominent evidence for a common master regulator: a transband of 66 coregulated genes appeared at 24 8C. Our results suggest widespread genetic variation of differential expression responses to environmental impacts and demonstrate the potential of genetical genomics for mapping the molecular determinants of phenotypic plasticity. Citation: Li Y, Á lvarez OA, Gutteling EW, Tijsterman M, Fu J, et al. (2006) Mapping determinants of gene expression plasticity by genetical genomics in C. elegans. PLoS Genet 2(12): e222.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="156110e8c478acfdae06ec79e98f0391" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550579,"asset_id":10058149,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550579/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058149"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058149"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058149; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058148"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058148/Regulatory_network_construction_in_Arabidopsis_by_using_genome_wide_gene_expression_quantitative_trait_loci"><img alt="Research paper thumbnail of Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci" class="work-thumbnail" src="https://attachments.academia-assets.com/47550557/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058148/Regulatory_network_construction_in_Arabidopsis_by_using_genome_wide_gene_expression_quantitative_trait_loci">Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci</a></div><div class="wp-workCard_item"><span>Proceedings of The National Academy of Sciences</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Accessions of a plant species can show considerable genetic differences that are analyzed effecti...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Accessions of a plant species can show considerable genetic differences that are analyzed effectively by using recombinant inbred line (RIL) populations. Here we describe the results of genomewide expression variation analysis in an RIL population of Arabidopsis thaliana. For many genes, variation in expression could be explained by expression quantitative trait loci (eQTLs). The nature and consequences of this variation are discussed based on additional genetic parameters, such as heritability and transgression and by examining the genomic position of eQTLs versus gene position, polymorphism frequency, and gene ontology. Furthermore, we developed an approach for genetic regulatory network construction by combining eQTL mapping and regulator candidate gene selection. The power of our method was shown in a case study of genes associated with flowering time, a well studied regulatory network in Arabidopsis. Results that revealed clusters of coregulated genes and their most likely regulators were in agreement with published data, and unknown relationships could be predicted.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2e9671ff25cf6da3e672fb54e43f6e04" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550557,"asset_id":10058148,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550557/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058148"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058148"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058148; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058147"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/10058147/Using_complex_plant_pedigrees_to_map_valuable_genes"><img alt="Research paper thumbnail of Using complex plant pedigrees to map valuable genes" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/10058147/Using_complex_plant_pedigrees_to_map_valuable_genes">Using complex plant pedigrees to map valuable genes</a></div><div class="wp-workCard_item"><span>Trends in Plant Science</span><span>, 2001</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Bayesian inference using Gibbs Sampling, http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml). .....</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... Bayesian inference using Gibbs Sampling, <a href="http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml" rel="nofollow">http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml</a>). ... Suitable multivariate mixed-model analyses are described in Refs 26, 27. ... because single plant crosses can produce large sibships, and combining line-cross methods with pedigree ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058147"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058147"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058147; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058140"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058140/Major_genes_for_resistance_to_beet_necrotic_yellow_vein_virus_BNYVV_in_Beta_vulgaris"><img alt="Research paper thumbnail of Major genes for resistance to beet necrotic yellow vein virus (BNYVV) in Beta vulgaris" class="work-thumbnail" src="https://attachments.academia-assets.com/47550568/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058140/Major_genes_for_resistance_to_beet_necrotic_yellow_vein_virus_BNYVV_in_Beta_vulgaris">Major genes for resistance to beet necrotic yellow vein virus (BNYVV) in Beta vulgaris</a></div><div class="wp-workCard_item"><span>Euphytica</span><span>, 1996</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Inheritance of resistance to beet necrotic yellow vein virus (BNYVV) was studied in segregating F...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Inheritance of resistance to beet necrotic yellow vein virus (BNYVV) was studied in segregating F2 and backcross families obtained from crosses between resistant plants of the sugar beet selection Holly-1-4 or the wild beet accession Beta vulgaris subsp. maritima WB42 and susceptible parents. Greenhouse tests were carried out, in which seedlings were grown in a mixture of sand and infested soil. Virus concentrations of BNYVV in the rootlets were estimated by ELISA. To discriminate resistant and susceptible plants, mixtures of normal distributions were fitted to log10 virus concentrations, estimated for segregating F1, F2 and BC populations of both accessions. The hypothesis that Holly-1-4 contained one single dominant major gene was accepted. For WB42, results fitted with the hypotheses that resistance was based on either one (or more) dominant major gene(s) showing distorted segregation, or two complementary dominant genes, which are both required for resistance. Resistance from WB42 appeared to be more effective against BNYVV than resistance from Holly-1-4.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="924856c4a6a3e69af108953dc4d3cee8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550568,"asset_id":10058140,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550568/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058140; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058139"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058139/SELDI_TOF_mass_spectra_A_view_on_sources_of_variation"><img alt="Research paper thumbnail of SELDI-TOF mass spectra: A view on sources of variation" class="work-thumbnail" src="https://attachments.academia-assets.com/47550571/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058139/SELDI_TOF_mass_spectra_A_view_on_sources_of_variation">SELDI-TOF mass spectra: A view on sources of variation</a></div><div class="wp-workCard_item"><span>Journal of Chromatography B-analytical Technologies in The Biomedical and Life Sciences</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Adequate interpretation of mass spectrometry data can yield valuable biomarkers. However, spectru...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Adequate interpretation of mass spectrometry data can yield valuable biomarkers. However, spectrum interpretation is a complicated task. This paper reviews the various factors that determine a sample's spectrum and demonstrates the role of these factors in the interpretation process. We derive a simulation model that adequately predicts the expected spectrum based on known sample content and, in the reverse mode, obtain an analysis model that adequately fits an observed spectrum based on the hypothesized sources of variation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d2f42f0643d018b0d4febb8b992f119d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":47550571,"asset_id":10058139,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/47550571/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058139"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058139"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058139; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=10058139]").text(description); $(".js-view-count[data-work-id=10058139]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 10058139; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='10058139']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d2f42f0643d018b0d4febb8b992f119d" } } $('.js-work-strip[data-work-id=10058139]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":10058139,"title":"SELDI-TOF mass spectra: A view on sources of variation","internal_url":"https://www.academia.edu/10058139/SELDI_TOF_mass_spectra_A_view_on_sources_of_variation","owner_id":24489171,"coauthors_can_edit":true,"owner":{"id":24489171,"first_name":"Ritsert","middle_initials":null,"last_name":"Jansen","page_name":"RitsertJansen","domain_name":"rug","created_at":"2015-01-07T11:24:38.711-08:00","display_name":"Ritsert Jansen","url":"https://rug.academia.edu/RitsertJansen"},"attachments":[{"id":47550571,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/47550571/thumbnails/1.jpg","file_name":"SELDI-TOF_mass_spectra_a_view_on_sources20160726-9881-9yqg9a.pdf","download_url":"https://www.academia.edu/attachments/47550571/download_file","bulk_download_file_name":"SELDI_TOF_mass_spectra_A_view_on_sources.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/47550571/SELDI-TOF_mass_spectra_a_view_on_sources20160726-9881-9yqg9a-libre.pdf?1469597963=\u0026response-content-disposition=attachment%3B+filename%3DSELDI_TOF_mass_spectra_A_view_on_sources.pdf\u0026Expires=1739927119\u0026Signature=N5jlRZiJVxr87PGbZe9mm8nCUVXzwhb7L8Iwro3Ew0fKtZK9SCaDB-2HTZmT0T5y1AVMVPZdhpzgpXSiNyB8LPuypZRf3RvaiAv~KK7ZSjP5P8k~0uIeTmefcBi0dSsJq24Mwv0ZyhSVSmGZRbZeaujfvQOeLMTh~QfXJGULZxaFtNBzuYU79lUJzt19m4GCJgmNbpQV~qMnOSEFXvgD197hHCIEtvqQJQXnN-Adyt1yK5mBF7xGf6iMQlNuU~59sD8N743ZhTdP-sbJQSuymHWsS8ekI0Ar2kM3swLND6ATZsery9AQzy~Y2WGNJ0nnkVx7PtSdWwuwkdLXCwSDBw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="10058138"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/10058138/QTL_analysis_of_seed_dormancy_in_Arabidopsis_using_recombinant_inbred_lines_and_MQM_mapping"><img alt="Research paper thumbnail of QTL analysis of seed dormancy in Arabidopsis using recombinant inbred lines and MQM mapping" class="work-thumbnail" src="https://attachments.academia-assets.com/36186232/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/10058138/QTL_analysis_of_seed_dormancy_in_Arabidopsis_using_recombinant_inbred_lines_and_MQM_mapping">QTL analysis of seed dormancy in Arabidopsis using recombinant inbred lines and MQM mapping</a></div><div class="wp-workCard_item"><span>Heredity</span><span>, 1997</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The genetic differences for seed germination between two commonly used Arabidopsis thaliana ecoty...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The genetic differences for seed germination between two commonly used Arabidopsis thaliana ecotypes Ler and Col, both showing a low level of seed dormancy, were investigated. The analysis was performed with 98 recombinant inbred lines (RILs) derived from the cross between the two ecotypes, and these lines had previously been analysed for molecular marker composition by Lister and Dean (Norwich, UK). The analysis of germination was performed on seeds grown in three different maternal environments and each seed batch was tested in three different germination environments: in light, in darkness and in the presence of the gibberellin inhibitor paclobutrazol. Fourteen loci were identified using the multiple-QTL-model (MQM) procedure for mapping quantitative trait loci. At nine loci no significant interaction between the detection of the locus and environmental factors could be detected. However, three other distinct loci controlling the germination behaviour in the presence of the gibberellin inhibitor paclobutrazol had a much lower or no effect when germination was tested in water either in light or darkness. Two other loci affecting germination in darkness and/or light had practically no effect on germination in the presence of paclobutrazol.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b70e28f1980930489da8abea10593330" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":36186232,"asset_id":10058138,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/36186232/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="10058138"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="10058138"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10058138; 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