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class="o-input__droplist1 c-sort__page-input"><label for="c-sort2">Show:</label><select name="rows" id="c-sort2" form="facetForm"><option selected="" value="10">10</option><option value="20">20</option><option value="30">30</option></select></div></div><input type="hidden" name="start" form="facetForm" value="0"/><nav class="c-pagination"><ul><li><a href="" aria-label="you are on result set 1" class="c-pagination__item--current">1</a></li><li><a href="" aria-label="go to result set 2" class="c-pagination__item">2</a></li><li><a href="" aria-label="go to result set 3" class="c-pagination__item">3</a></li></ul></nav></div><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/62s7c4nk"><div class="c-clientmarkup">The Cys allele of the DRD2 Ser311Cys polymorphism has a dominant effect on risk for schizophrenia: Evidence from fixed- and random-effects meta-analyses</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AGlatt%2C%20Stephen%20J">Glatt, Stephen J</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AJonsson%2C%20E%20G">Jonsson, E G</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsd_postprints">UC San Diego Previously Published Works</a> (2006)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup"><p>Previously we derived independent estimates of the effect of the dopamine D2 receptor (DRD2) Ser311Cys polymorphism on risk for schizophrenia using fixed- and random-effects meta-analyses. Both analyses identified a significant association between the Cys allele and schizophrenia, but neither included all available data. Furthermore, genotype data were not evaluated in either analysis, thus precluding any determination of the mode of inheritance. The present study was conducted to resolve discrepancies between the existing meta-analyses, and provide more comprehensive and accurate estimates of the nature and magnitude of the influence of the Ser311Cys polymorphism on risk for schizophrenia. All discrepancies between the two sets of previously meta-analyzed studies were identified and resolved to the mutual satisfaction of the authors, and the final dataset was analyzed independently by fixed- and random-effects meta-analyses. A total of 27 samples, comprising 3,707 schizophrenia patients and 5,363 control subjects, were included in the analyses of allelic association, while smaller numbers of studies and subjects were included in each of the genotypic association analyses. A significant effect of the Cys allele was observed under both fixed-effects (odds ratio [OR] = 1.4; P = 0.002) and random-effects (OR = 1.4; P = 0.007) models. Cys/Ser heterozygotes were at elevated risk for schizophrenia when compared to Ser/Ser homozygotes (fixed- and random-effects OR = 1.4, p(s) >= 0.005), but Cys/Cys homozygotes were at no elevated risk relative to heterozygotes (OR = 1.0, p(s) >= 0.948). There was no evidence of heterogeneity, excessive influence of any single study, or publication bias in any of the analyses, suggesting that the effect of this DRD2 polymorphism on schizophrenia risk is reliable and uniform across populations, and our estimates of its magnitude are robust and accurate. (C) 2006 Wiley-Liss, Inc.</p></div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/62s7c4nk"><img src="/cms-assets/8104f4df94940d250b7ac0b27c7099af1ec8712ee0f3c6f850af327abab0a4b2" alt="Cover page: The Cys allele of the DRD2 Ser311Cys polymorphism has a dominant effect on risk for schizophrenia: Evidence from fixed- and random-effects meta-analyses"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/40r2g008"><div class="c-clientmarkup">Methodology in the GBD study of China</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3ALin%2C%20Ping-I">Lin, Ping-I</a>; </li><li><a href="/search/?q=author%3AGlatt%2C%20Stephen%20J">Glatt, Stephen J</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ATsuang%2C%20Ming%20T">Tsuang, Ming T</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsd_postprints">UC San Diego Previously Published Works</a> (2020)</div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/40r2g008"><img src="/cms-assets/f2bf314de4ccf4f64c540bf5de89585901b1b630ee679462d26ee0ede2923138" alt="Cover page: Methodology in the GBD study of China"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/2zh9686b"><div class="c-clientmarkup">Early onset bipolar disorder: possible linkage to chromosome 9q34</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AFaraone%2C%20S%20V">Faraone, S V</a>; </li><li><a href="/search/?q=author%3ALasky-Su%2C%20J">Lasky-Su, J</a>; </li><li><a href="/search/?q=author%3AGlatt%2C%20Stephen%20J">Glatt, Stephen J</a>; </li><li><a href="/search/?q=author%3AEerdewegh%2C%20P%20V">Eerdewegh, P V</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ATsuang%2C%20M%20T">Tsuang, M T</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsd_postprints">UC San Diego Previously Published Works</a> (2006)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup"><p>Objectives: Bipolar disorder (BD) is characterized by manic and depressive states that onset at various times in life. Research shows that early onset forms of BD are associated with a stronger genetic loading for the illness. We hypothesized that using age at onset to look at subsets of BD families in a genetic linkage analysis would prove useful in separating etiologically homogeneous BD sub-groups and subsequently identifying genetic susceptibility regions. Methods: We used the wave-I National Institute of Mental Health (NIMH) Genetics Initiative BD sample, which includes 540 individuals from 97 families with BD, in an ordered-subsets linkage analysis with age at onset of mania as the subset-identifying covariate. This analysis was performed using GENEHUNTER-PLUS followed by the ordered-subsets analysis program. This program generates empirical p-values for the subset with the largest LOD score to determine whether this value was significantly higher than the baseline LOD score using all families. Results: Three chromosomal regions resulted in LOD scores above 2.0: 2.21 (6q25), 3.21 (9q34), and 2.16 (20q11). The largest increase in LOD score was observed on chromosome 9q34 between markers D9S290 and D9S915 in the subset of 58 families that had mania onset before age 20. Families with a minimal mania onset less than 20 years had a significantly greater number of psychiatric comorbidities (p = 0.02) and a marginal increase in depressive symptoms (p = 0.10). Conclusions: Further investigation into chromosomal region 9q34 is necessary to determine whether this region may harbor a gene specific to families with a minimal age at onset of less than 20.</p></div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/2zh9686b"><img src="/cms-assets/34d22554095dd160350b44aec85f88ec2575a7068c65aca705ce68f2d67826cb" alt="Cover page: Early onset bipolar disorder: possible linkage to chromosome 9q34"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/8xv3r69h"><div class="c-clientmarkup">Adaptive combination of Bayes factors as a powerful method for the joint analysis of rare and common variants</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3ALin%2C%20Wan-Yu">Lin, Wan-Yu</a>; </li><li><a href="/search/?q=author%3AChen%2C%20Wei%20J">Chen, Wei J</a>; </li><li><a href="/search/?q=author%3ALiu%2C%20Chih-Min">Liu, Chih-Min</a>; </li><li><a href="/search/?q=author%3AHwu%2C%20Hai-Gwo">Hwu, Hai-Gwo</a>; </li><li><a href="/search/?q=author%3AMcCarroll%2C%20Steven%20A">McCarroll, Steven A</a>; </li><li><a href="/search/?q=author%3AGlatt%2C%20Stephen%20J">Glatt, Stephen J</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ATsuang%2C%20Ming%20T">Tsuang, Ming T</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsd_postprints">UC San Diego Previously Published Works</a> (2017)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Multi-marker association tests can be more powerful than single-locus analyses because they aggregate the variant information within a gene/region. However, combining the association signals of multiple markers within a gene/region may cause noise due to the inclusion of neutral variants, which usually compromises the power of a test. To reduce noise, the "adaptive combination of P-values" (ADA) method removes variants with larger P-values. However, when both rare and common variants are considered, it is not optimal to truncate variants according to their P-values. An alternative summary measure, the Bayes factor (BF), is defined as the ratio of the probability of the data under the alternative hypothesis to that under the null hypothesis. The BF quantifies the "relative" evidence supporting the alternative hypothesis. Here, we propose an "adaptive combination of Bayes factors" (ADABF) method that can be directly applied to variants with a wide spectrum of minor allele frequencies. The simulations show that ADABF is more powerful than single-nucleotide polymorphism (SNP)-set kernel association tests and burden tests. We also analyzed 1,109 case-parent trios from the Schizophrenia Trio Genomic Research in Taiwan. Three genes on chromosome 19p13.2 were found to be associated with schizophrenia at the suggestive significance level of 5 × 10-5.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/8xv3r69h"><img src="/cms-assets/b1679f834382f754e406d6f91ca82f65eb7ae5be0d28d2084389c6e0df8c534f" alt="Cover page: Adaptive combination of Bayes factors as a powerful method for the joint analysis of rare and common variants"/></a><a href="https://creativecommons.org/licenses/by/4.0/" class="c-scholworks__license"><img class="c-lazyimage" data-src="/images/cc-by-small.svg" alt="Creative Commons 'BY' version 4.0 license"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/4vf3p8wr"><div class="c-clientmarkup">Assessment of Lifespan Functioning Attainment (ALFA) scale: A quantitative interview for self-reported current and functional decline in schizophrenia</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AJoseph%2C%20Jamie">Joseph, Jamie</a>; </li><li><a href="/search/?q=author%3AKremen%2C%20William%20S">Kremen, William S</a>; </li><li><a href="/search/?q=author%3AGlatt%2C%20Stephen%20J">Glatt, Stephen J</a>; </li><li><a href="/search/?q=author%3AFranz%2C%20Carol%20E">Franz, Carol E</a>; </li><li><a href="/search/?q=author%3AChandler%2C%20Sharon%20D">Chandler, Sharon D</a>; </li><li><a href="/search/?q=author%3ALiu%2C%20Xiaohua">Liu, Xiaohua</a>; </li><li><a href="/search/?q=author%3AJohnson%2C%20Barbara%20K">Johnson, Barbara K</a>; </li><li><a href="/search/?q=author%3ATsuang%2C%20Ming%20T">Tsuang, Ming T</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ATwamley%2C%20Elizabeth%20W">Twamley, Elizabeth W</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsd_postprints">UC San Diego Previously Published Works</a> (2015)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Schizophrenia has been characterized as a disorder with poor outcomes across various functional domains, especially social and occupational functioning. Although these outcomes have been investigated based on patients' current functioning, few studies have considered the assessment of functional outcomes across the lifespan in schizophrenia. We developed a novel and brief scale of adulthood lifespan functioning, the Assessment of Lifespan Functioning Attainment (ALFA). We assessed current functioning and percentage of pre- and post-psychosis onset engagement for five functional domains including paid employment, living independently, romantic partnerships, close friendships, and recreational engagement with others. Pre-to post-psychosis functional decline was observed for all domains, with paid employment having the greatest decline (d = 2.68) and living independently having the least decline (d = .59). Our exploratory factor analysis suggests that a single factor accounted for the most variance in Pre-Psychosis Functioning in ALFA domains. Two factors explain the majority of variance in Post-Psychosis Functioning and Pre-to-Post Psychosis Decline: a sociability factor (close friendships and recreational engagement with others) and an independence factor (paid employment, living independently, romantic relationships). To our knowledge, this is the first study to report on a self-reported quantitative assessment of adult lifespan functioning in schizophrenia. The ALFA scale may be a useful tool for future research on functional outcomes in schizophrenia.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/4vf3p8wr"><img src="/cms-assets/bfa884e90139dde2f1c683f886e18b4da97c9930cdaed117dec54a35cfd65902" alt="Cover page: Assessment of Lifespan Functioning Attainment (ALFA) scale: A quantitative interview for self-reported current and functional decline in schizophrenia"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/0669h8pm"><div class="c-clientmarkup">Predictors of current functioning and functional decline in schizophrenia</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AJoseph%2C%20Jamie">Joseph, Jamie</a>; </li><li><a href="/search/?q=author%3AKremen%2C%20William%20S">Kremen, William S</a>; </li><li><a href="/search/?q=author%3AFranz%2C%20Carol%20E">Franz, Carol E</a>; </li><li><a href="/search/?q=author%3AGlatt%2C%20Stephen%20J">Glatt, Stephen J</a>; </li><li><a href="/search/?q=author%3Avan%20de%20Leemput%2C%20Joyce">van de Leemput, Joyce</a>; </li><li><a href="/search/?q=author%3AChandler%2C%20Sharon%20D">Chandler, Sharon D</a>; </li><li><a href="/search/?q=author%3ATsuang%2C%20Ming%20T">Tsuang, Ming T</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ATwamley%2C%20Elizabeth%20W">Twamley, Elizabeth W</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsd_postprints">UC San Diego Previously Published Works</a> (2017)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Positive, negative, and cognitive symptoms of schizophrenia may affect functional outcomes. However, these factors alone do not account for a large percentage of variance in outcomes. We investigated demographic, cognitive, symptom, and functional capacity predictors of current functional status in 280 outpatients with schizophrenia or schizoaffective disorder. Functional decline over the lifespan was also examined in a subset of participants. Stepwise regressions modeled predictors of current functional status and functional decline as measured by the Assessment of Lifespan Functioning Attainment (ALFA). ALFA functional domains included paid employment, independence in living situation, romantic relationships, close friendships, and recreational engagement. More severe depressive symptoms were consistently associated with worse current community integration (lower levels of close friendships and recreational engagement). Better working memory performance was associated with higher rates of current paid employment. There were no consistent modifiable predictors of decline in functioning, but women reported less functional decline in the domains of employment and close friendships than men. Better cognitive performance was associated with less decline in living independence and romantic relationships, but more decline in paid employment and recreational engagement. Increased assessment and treatment of comorbid depressive symptoms may improve functional outcomes in people with schizophrenia.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/0669h8pm"><img src="/cms-assets/952174bc269b1d2b522d2e1b1c27e95331b3f71de63097a7f0171ae3f3b5b84d" alt="Cover page: Predictors of current functioning and functional decline in schizophrenia"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/65f5c00t"><div class="c-clientmarkup">BrainGENIE: The Brain Gene Expression and Network Imputation Engine</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AHess%2C%20Jonathan%20L">Hess, Jonathan L</a>; </li><li><a href="/search/?q=author%3AQuinn%2C%20Thomas%20P">Quinn, Thomas P</a>; </li><li><a href="/search/?q=author%3AZhang%2C%20Chunling">Zhang, Chunling</a>; </li><li><a href="/search/?q=author%3AHearn%2C%20Gentry%20C">Hearn, Gentry C</a>; </li><li><a href="/search/?q=author%3AChen%2C%20Samuel">Chen, Samuel</a>; </li><li><a href="/search/?q=author%3AKong%2C%20Sek%20Won">Kong, Sek Won</a>; </li><li><a href="/search/?q=author%3ACairns%2C%20Murray">Cairns, Murray</a>; </li><li><a href="/search/?q=author%3ATsuang%2C%20Ming%20T">Tsuang, Ming T</a>; </li><li><a href="/search/?q=author%3AFaraone%2C%20Stephen%20V">Faraone, Stephen V</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AGlatt%2C%20Stephen%20J">Glatt, Stephen J</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsd_postprints">UC San Diego Previously Published Works</a> (2023)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/65f5c00t"><img src="/cms-assets/4bcc10369313ca8387dbd636aaec9fa8d4283e18b9f545ea6f92bfd4e7422f5b" alt="Cover page: BrainGENIE: The Brain Gene Expression and Network Imputation Engine"/></a><a href="https://creativecommons.org/licenses/by/4.0/" class="c-scholworks__license"><img class="c-lazyimage" data-src="/images/cc-by-small.svg" alt="Creative Commons 'BY' version 4.0 license"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/5gc3691w"><div class="c-clientmarkup">Solving for X: Evidence for sex‐specific autism biomarkers across multiple transcriptomic studies</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3ALee%2C%20Samuel%20C">Lee, Samuel C</a>; </li><li><a href="/search/?q=author%3AQuinn%2C%20Thomas%20P">Quinn, Thomas P</a>; </li><li><a href="/search/?q=author%3ALai%2C%20Jerry">Lai, Jerry</a>; </li><li><a href="/search/?q=author%3AKong%2C%20Sek%20Won">Kong, Sek Won</a>; </li><li><a href="/search/?q=author%3AHertz%E2%80%90Picciotto%2C%20Irva">Hertz‐Picciotto, Irva</a>; </li><li><a href="/search/?q=author%3AGlatt%2C%20Stephen%20J">Glatt, Stephen J</a>; </li><li><a href="/search/?q=author%3ACrowley%2C%20Tamsyn%20M">Crowley, Tamsyn M</a>; </li><li><a href="/search/?q=author%3AVenkatesh%2C%20Svetha">Venkatesh, Svetha</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ANguyen%2C%20Thin">Nguyen, Thin</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucd_postprints">UC Davis Previously Published Works</a> (2019)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers and characterize their function, we present an analysis of a single paired-end postmortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both postmortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/5gc3691w"><img src="/cms-assets/79bd8b215293a4c1ee5e9289e03d72c07b5e38ccab9bb0039c86f8a12809edd8" alt="Cover page: Solving for X: Evidence for sex‐specific autism biomarkers across multiple transcriptomic studies"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/4b91s6c1"><div class="c-clientmarkup">Schizophrenia, autism spectrum disorders and developmental disorders share specific disruptive coding mutations</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3ARees%2C%20Elliott">Rees, Elliott</a>; </li><li><a href="/search/?q=author%3ACreeth%2C%20Hugo%20DJ">Creeth, Hugo DJ</a>; </li><li><a href="/search/?q=author%3AHwu%2C%20Hai-Gwo">Hwu, Hai-Gwo</a>; </li><li><a href="/search/?q=author%3AChen%2C%20Wei%20J">Chen, Wei J</a>; </li><li><a href="/search/?q=author%3ATsuang%2C%20Ming">Tsuang, Ming</a>; </li><li><a href="/search/?q=author%3AGlatt%2C%20Stephen%20J">Glatt, Stephen J</a>; </li><li><a href="/search/?q=author%3ARey%2C%20Romain">Rey, Romain</a>; </li><li><a href="/search/?q=author%3AKirov%2C%20George">Kirov, George</a>; </li><li><a href="/search/?q=author%3AWalters%2C%20James%20TR">Walters, James TR</a>; </li><li><a href="/search/?q=author%3AHolmans%2C%20Peter">Holmans, Peter</a>; </li><li><a href="/search/?q=author%3AOwen%2C%20Michael%20J">Owen, Michael J</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AO%E2%80%99Donovan%2C%20Michael%20C">O’Donovan, Michael C</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsd_postprints">UC San Diego Previously Published Works</a> (2021)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">People with schizophrenia are enriched for rare coding variants in genes associated with neurodevelopmental disorders, particularly autism spectrum disorders and intellectual disability. However, it is unclear if the same changes to gene function that increase risk to neurodevelopmental disorders also do so for schizophrenia. Using data from 3444 schizophrenia trios and 37,488 neurodevelopmental disorder trios, we show that within shared risk genes, de novo variants in schizophrenia and neurodevelopmental disorders are generally of the same functional category, and that specific de novo variants observed in neurodevelopmental disorders are enriched in schizophrenia (P = 5.0 × 10<sup>-6</sup>). The latter includes variants known to be pathogenic for syndromic disorders, suggesting that schizophrenia be included as a characteristic of those syndromes. Our findings imply that, in part, neurodevelopmental disorders and schizophrenia have shared molecular aetiology, and therefore likely overlapping pathophysiology, and support the hypothesis that at least some forms of schizophrenia lie on a continuum of neurodevelopmental disorders.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/4b91s6c1"><img src="/cms-assets/c4bac6c4c55d425458ea09049cf9c9e9362b0b1742611a680582719e5189af8c" alt="Cover page: Schizophrenia, autism spectrum disorders and developmental disorders share specific disruptive coding mutations"/></a><a href="https://creativecommons.org/licenses/by/4.0/" class="c-scholworks__license"><img class="c-lazyimage" data-src="/images/cc-by-small.svg" alt="Creative Commons 'BY' version 4.0 license"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/1vq461f1"><div class="c-clientmarkup">Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined‐samples mega‐analysis</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3ATylee%2C%20Daniel%20S">Tylee, Daniel S</a>; </li><li><a href="/search/?q=author%3AHess%2C%20Jonathan%20L">Hess, Jonathan L</a>; </li><li><a href="/search/?q=author%3AQuinn%2C%20Thomas%20P">Quinn, Thomas P</a>; </li><li><a href="/search/?q=author%3ABarve%2C%20Rahul">Barve, Rahul</a>; </li><li><a href="/search/?q=author%3AHuang%2C%20Hailiang">Huang, Hailiang</a>; </li><li><a href="/search/?q=author%3AZhang%E2%80%90James%2C%20Yanli">Zhang‐James, Yanli</a>; </li><li><a href="/search/?q=author%3AChang%2C%20Jeffrey">Chang, Jeffrey</a>; </li><li><a href="/search/?q=author%3AStamova%2C%20Boryana%20S">Stamova, Boryana S</a>; </li><li><a href="/search/?q=author%3ASharp%2C%20Frank%20R">Sharp, Frank R</a>; </li><li><a href="/search/?q=author%3AHertz%E2%80%90Picciotto%2C%20Irva">Hertz‐Picciotto, Irva</a>; </li><li><a href="/search/?q=author%3AFaraone%2C%20Stephen%20V">Faraone, Stephen V</a>; </li><li><a href="/search/?q=author%3AKong%2C%20Sek%20Won">Kong, Sek Won</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AGlatt%2C%20Stephen%20J">Glatt, Stephen J</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucd_postprints">UC Davis Previously Published Works</a> (2017)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. © 2016 Wiley Periodicals, Inc.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/1vq461f1"><img src="/cms-assets/9fc82ca3ea0e7811f02f420dd07ad12f44f65604a3cf4e65adaf6b9673ed0c1c" alt="Cover page: Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined‐samples mega‐analysis"/></a></div></section><nav class="c-pagination"><ul><li><a href="" aria-label="you are on result set 1" class="c-pagination__item--current">1</a></li><li><a href="" aria-label="go to result set 2" class="c-pagination__item">2</a></li><li><a href="" aria-label="go to result set 3" class="c-pagination__item">3</a></li></ul></nav></section></main></form></div><div><div class="c-toplink"><a href="javascript:window.scrollTo(0, 0)">Top</a></div><footer class="c-footer"><nav class="c-footer__nav"><ul><li><a href="/">Home</a></li><li><a href="/aboutEschol">About eScholarship</a></li><li><a href="/campuses">Campus Sites</a></li><li><a href="/ucoapolicies">UC Open Access Policy</a></li><li><a href="/publishing">eScholarship Publishing</a></li><li><a href="https://www.cdlib.org/about/accessibility.html">Accessibility</a></li><li><a href="/privacypolicy">Privacy Statement</a></li><li><a href="/policies">Site Policies</a></li><li><a href="/terms">Terms of Use</a></li><li><a href="/login"><strong>Admin Login</strong></a></li><li><a href="https://help.escholarship.org"><strong>Help</strong></a></li></ul></nav><div class="c-footer__logo"><a href="/"><img class="c-lazyimage" data-src="/images/logo_footer-eschol.svg" alt="eScholarship, University of California"/></a></div><div class="c-footer__copyright">Powered by the<br/><a href="http://www.cdlib.org">California Digital Library</a><br/>Copyright © 2017<br/>The Regents of the University of California</div></footer></div></div></div></div> <script src="/js/vendors~app-bundle-2aefc956e545366a5d4e.js"></script> <script src="/js/app-bundle-4477d7630fb8c6f70662.js"></script> </body> </html>