749 results on '"Devlin, Bernie"'
Search Results
2. Systematic evaluation of genome sequencing for the diagnostic assessment of autism spectrum disorder and fetal structural anomalies.
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Lowther, Chelsea, Valkanas, Elise, Giordano, Jessica, Wang, Harold, Currall, Benjamin, OKeefe, Kathryn, Pierce-Hoffman, Emma, Kurtas, Nehir, Whelan, Christopher, Hao, Stephanie, Weisburd, Ben, Jalili, Vahid, Fu, Jack, Wong, Isaac, Collins, Ryan, Zhao, Xuefang, Austin-Tse, Christina, Evangelista, Emily, Lemire, Gabrielle, Aggarwal, Vimla, Lucente, Diane, Gauthier, Laura, Tolonen, Charlotte, Sahakian, Nareh, Stevens, Christine, An, Joon-Yong, Dong, Shan, MacKenzie, Tippi, Devlin, Bernie, Gilmore, Kelly, Powell, Bradford, Brandt, Alicia, Vetrini, Francesco, DiVito, Michelle, MacArthur, Daniel, Hodge, Jennelle, ODonnell-Luria, Anne, Rehm, Heidi, Vora, Neeta, Levy, Brynn, Brand, Harrison, Wapner, Ronald, Talkowski, Michael, Norton, Mary, and Sanders, Stephan
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genome sequencing ,karyotype ,microarray ,exome sequencing ,structural variant ,autism spectrum disorder ,structural anomaly ,prenatal ,first-tier ,diagnostic ,Female ,Pregnancy ,Humans ,Autism Spectrum Disorder ,Pregnancy Trimester ,First ,Ultrasonography ,Prenatal ,Chromosome Mapping ,Exome - Abstract
Short-read genome sequencing (GS) holds the promise of becoming the primary diagnostic approach for the assessment of autism spectrum disorder (ASD) and fetal structural anomalies (FSAs). However, few studies have comprehensively evaluated its performance against current standard-of-care diagnostic tests: karyotype, chromosomal microarray (CMA), and exome sequencing (ES). To assess the clinical utility of GS, we compared its diagnostic yield against these three tests in 1,612 quartet families including an individual with ASD and in 295 prenatal families. Our GS analytic framework identified a diagnostic variant in 7.8% of ASD probands, almost 2-fold more than CMA (4.3%) and 3-fold more than ES (2.7%). However, when we systematically captured copy-number variants (CNVs) from the exome data, the diagnostic yield of ES (7.4%) was brought much closer to, but did not surpass, GS. Similarly, we estimated that GS could achieve an overall diagnostic yield of 46.1% in unselected FSAs, representing a 17.2% increased yield over karyotype, 14.1% over CMA, and 4.1% over ES with CNV calling or 36.1% increase without CNV discovery. Overall, GS provided an added diagnostic yield of 0.4% and 0.8% beyond the combination of all three standard-of-care tests in ASD and FSAs, respectively. This corresponded to nine GS unique diagnostic variants, including sequence variants in exons not captured by ES, structural variants (SVs) inaccessible to existing standard-of-care tests, and SVs where the resolution of GS changed variant classification. Overall, this large-scale evaluation demonstrated that GS significantly outperforms each individual standard-of-care test while also outperforming the combination of all three tests, thus warranting consideration as the first-tier diagnostic approach for the assessment of ASD and FSAs.
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- 2023
3. Spatiotemporal and genetic regulation of A-to-I editing throughout human brain development
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Cuddleston, Winston H, Fan, Xuanjia, Sloofman, Laura, Liang, Lindsay, Mossotto, Enrico, Moore, Kendall, Zipkowitz, Sarah, Wang, Minghui, Zhang, Bin, Wang, Jiebiao, Sestan, Nenad, Devlin, Bernie, Roeder, Kathryn, Sanders, Stephan J, Buxbaum, Joseph D, and Breen, Michael S
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Biological Sciences ,Bioinformatics and Computational Biology ,Congenital Structural Anomalies ,Genetics ,Neurosciences ,Pediatric ,1.1 Normal biological development and functioning ,Underpinning research ,Neurological ,Humans ,Animals ,Mice ,RNA Editing ,Inosine ,Adenosine ,Primates ,3' Untranslated Regions ,Brain ,Adenosine Deaminase ,CP: Molecular biology ,CP: Neuroscience ,RNA modifications ,RNA recoding ,brain maturation ,edQTLs ,hyper-editing ,late-fetal transition ,Biochemistry and Cell Biology ,Medical Physiology ,Biological sciences - Abstract
Posttranscriptional RNA modifications by adenosine-to-inosine (A-to-I) editing are abundant in the brain, yet elucidating functional sites remains challenging. To bridge this gap, we investigate spatiotemporal and genetically regulated A-to-I editing sites across prenatal and postnatal stages of human brain development. More than 10,000 spatiotemporally regulated A-to-I sites were identified that occur predominately in 3' UTRs and introns, as well as 37 sites that recode amino acids in protein coding regions with precise changes in editing levels across development. Hyper-edited transcripts are also enriched in the aging brain and stabilize RNA secondary structures. These features are conserved in murine and non-human primate models of neurodevelopment. Finally, thousands of cis-editing quantitative trait loci (edQTLs) were identified with unique regulatory effects during prenatal and postnatal development. Collectively, this work offers a resolved atlas linking spatiotemporal variation in editing levels to genetic regulatory effects throughout distinct stages of brain maturation.
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- 2022
4. Rare coding variation provides insight into the genetic architecture and phenotypic context of autism
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Fu, Jack M, Satterstrom, F Kyle, Peng, Minshi, Brand, Harrison, Collins, Ryan L, Dong, Shan, Wamsley, Brie, Klei, Lambertus, Wang, Lily, Hao, Stephanie P, Stevens, Christine R, Cusick, Caroline, Babadi, Mehrtash, Banks, Eric, Collins, Brett, Dodge, Sheila, Gabriel, Stacey B, Gauthier, Laura, Lee, Samuel K, Liang, Lindsay, Ljungdahl, Alicia, Mahjani, Behrang, Sloofman, Laura, Smirnov, Andrey N, Barbosa, Mafalda, Betancur, Catalina, Brusco, Alfredo, Chung, Brian HY, Cook, Edwin H, Cuccaro, Michael L, Domenici, Enrico, Ferrero, Giovanni Battista, Gargus, J Jay, Herman, Gail E, Hertz-Picciotto, Irva, Maciel, Patricia, Manoach, Dara S, Passos-Bueno, Maria Rita, Persico, Antonio M, Renieri, Alessandra, Sutcliffe, James S, Tassone, Flora, Trabetti, Elisabetta, Campos, Gabriele, Cardaropoli, Simona, Carli, Diana, Chan, Marcus CY, Fallerini, Chiara, Giorgio, Elisa, Girardi, Ana Cristina, Hansen-Kiss, Emily, Lee, So Lun, Lintas, Carla, Ludena, Yunin, Nguyen, Rachel, Pavinato, Lisa, Pericak-Vance, Margaret, Pessah, Isaac N, Schmidt, Rebecca J, Smith, Moyra, Costa, Claudia IS, Trajkova, Slavica, Wang, Jaqueline YT, Yu, Mullin HC, Cutler, David J, De Rubeis, Silvia, Buxbaum, Joseph D, Daly, Mark J, Devlin, Bernie, Roeder, Kathryn, Sanders, Stephan J, and Talkowski, Michael E
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Biological Sciences ,Bioinformatics and Computational Biology ,Genetics ,Autism ,Brain Disorders ,Pediatric ,Biotechnology ,Intellectual and Developmental Disabilities (IDD) ,Human Genome ,Mental Health ,Clinical Research ,Aetiology ,2.1 Biological and endogenous factors ,Mental health ,Autism Spectrum Disorder ,Autistic Disorder ,DNA Copy Number Variations ,Genetic Predisposition to Disease ,Humans ,Mutation ,Autism Sequencing Consortium ,Broad Institute Center for Common Disease Genomics ,iPSYCH-BROAD Consortium ,Medical and Health Sciences ,Developmental Biology ,Agricultural biotechnology ,Bioinformatics and computational biology - Abstract
Some individuals with autism spectrum disorder (ASD) carry functional mutations rarely observed in the general population. We explored the genes disrupted by these variants from joint analysis of protein-truncating variants (PTVs), missense variants and copy number variants (CNVs) in a cohort of 63,237 individuals. We discovered 72 genes associated with ASD at false discovery rate (FDR) ≤ 0.001 (185 at FDR ≤ 0.05). De novo PTVs, damaging missense variants and CNVs represented 57.5%, 21.1% and 8.44% of association evidence, while CNVs conferred greatest relative risk. Meta-analysis with cohorts ascertained for developmental delay (DD) (n = 91,605) yielded 373 genes associated with ASD/DD at FDR ≤ 0.001 (664 at FDR ≤ 0.05), some of which differed in relative frequency of mutation between ASD and DD cohorts. The DD-associated genes were enriched in transcriptomes of progenitor and immature neuronal cells, whereas genes showing stronger evidence in ASD were more enriched in maturing neurons and overlapped with schizophrenia-associated genes, emphasizing that these neuropsychiatric disorders may share common pathways to risk.
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- 2022
5. Evaluating and improving health equity and fairness of polygenic scores
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Zhang, Tianyu, Zhou, Geyu, Klei, Lambertus, Liu, Peng, Chouldechova, Alexandra, Zhao, Hongyu, Roeder, Kathryn, G’Sell, Max, and Devlin, Bernie
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- 2024
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6. De novo structural mutation rates and gamete-of-origin biases revealed through genome sequencing of 2,396 families
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Belyeu, Jonathan R, Brand, Harrison, Wang, Harold, Zhao, Xuefang, Pedersen, Brent S, Feusier, Julie, Gupta, Meenal, Nicholas, Thomas J, Brown, Joseph, Baird, Lisa, Devlin, Bernie, Sanders, Stephan J, Jorde, Lynn B, Talkowski, Michael E, and Quinlan, Aaron R
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Genetics ,Biotechnology ,Human Genome ,Pediatric ,Contraception/Reproduction ,Brain Disorders ,Intellectual and Developmental Disabilities (IDD) ,2.1 Biological and endogenous factors ,Aetiology ,Aging ,Autistic Disorder ,Bias ,DNA Copy Number Variations ,DNA Mutational Analysis ,Family ,Female ,Genome ,Human ,Germ Cells ,Germ-Line Mutation ,Humans ,Male ,Mutation Rate ,Paternal Age ,Point Mutation ,autism ,copy number variation ,de novo mutation ,genetic diversity ,genomic structure ,genomics ,germline mutation ,structural variation ,Biological Sciences ,Medical and Health Sciences ,Genetics & Heredity - Abstract
Each human genome includes de novo mutations that arose during gametogenesis. While these germline mutations represent a fundamental source of new genetic diversity, they can also create deleterious alleles that impact fitness. Whereas the rate and patterns of point mutations in the human germline are now well understood, far less is known about the frequency and features that impact de novo structural variants (dnSVs). We report a family-based study of germline mutations among 9,599 human genomes from 33 multigenerational CEPH-Utah families and 2,384 families from the Simons Foundation Autism Research Initiative. We find that de novo structural mutations detected by alignment-based, short-read WGS occur at an overall rate of at least 0.160 events per genome in unaffected individuals, and we observe a significantly higher rate (0.206 per genome) in ASD-affected individuals. In both probands and unaffected samples, nearly 73% of de novo structural mutations arose in paternal gametes, and we predict most de novo structural mutations to be caused by mutational mechanisms that do not require sequence homology. After multiple testing correction, we did not observe a statistically significant correlation between parental age and the rate of de novo structural variation in offspring. These results highlight that a spectrum of mutational mechanisms contribute to germline structural mutations and that these mechanisms most likely have markedly different rates and selective pressures than those leading to point mutations.
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- 2021
7. Comment on: What genes are differentially expressed in individuals with schizophrenia? A systematic review
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Hoffman, Gabriel E., Jaffe, Andrew E., Gandal, Michael J., Collado-Torres, Leonardo, Sieberts, Solveig K., Devlin, Bernie, Geschwind, Daniel H., Weinberger, Daniel R., and Roussos, Panos
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- 2023
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8. Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex.
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Werling, Donna M, Pochareddy, Sirisha, Choi, Jinmyung, An, Joon-Yong, Sheppard, Brooke, Peng, Minshi, Li, Zhen, Dastmalchi, Claudia, Santpere, Gabriel, Sousa, André MM, Tebbenkamp, Andrew TN, Kaur, Navjot, Gulden, Forrest O, Breen, Michael S, Liang, Lindsay, Gilson, Michael C, Zhao, Xuefang, Dong, Shan, Klei, Lambertus, Cicek, A Ercument, Buxbaum, Joseph D, Adle-Biassette, Homa, Thomas, Jean-Leon, Aldinger, Kimberly A, O'Day, Diana R, Glass, Ian A, Zaitlen, Noah A, Talkowski, Michael E, Roeder, Kathryn, State, Matthew W, Devlin, Bernie, Sanders, Stephan J, and Sestan, Nenad
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BrainVar ,DLPFC ,LOC101926933 RP11-298I3.1 AL132780.1 ENSG00000257285 ,PsychENCODE ,RHEBL1 ,dorsolateral prefrontal cortex ,fetal transition ,mTOR ,prenatal eQTL ,Biochemistry and Cell Biology ,Medical Physiology - Abstract
Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both "constant" and "temporal-predominant" eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology.
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- 2020
9. Publisher Correction: Gene expression imputation across multiple brain regions provides insights into schizophrenia risk
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Huckins, Laura M, Dobbyn, Amanda, Ruderfer, Douglas M, Hoffman, Gabriel, Wang, Weiqing, Pardiñas, Antonio F, Rajagopal, Veera M, Als, Thomas D, T. Nguyen, Hoang, Girdhar, Kiran, Boocock, James, Roussos, Panos, Fromer, Menachem, Kramer, Robin, Domenici, Enrico, Gamazon, Eric R, Purcell, Shaun, Demontis, Ditte, Børglum, Anders D, Walters, James TR, O’Donovan, Michael C, Sullivan, Patrick, Owen, Michael J, Devlin, Bernie, Sieberts, Solveig K, Cox, Nancy J, Im, Hae Kyung, Sklar, Pamela, and Stahl, Eli A
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Genetics ,Neurosciences ,Human Genome ,Serious Mental Illness ,Schizophrenia ,Brain Disorders ,Mental Health ,Biotechnology ,Mental health ,CommonMind Consortium ,Schizophrenia Working Group of the Psychiatric Genomics Consortium ,iPSYCH-GEMS Schizophrenia Working Group ,Biological Sciences ,Medical and Health Sciences ,Developmental Biology - Abstract
In the HTML version of the article originally published, the author group 'The Schizophrenia Working Group of the Psychiatric Genomics Consortium' was displayed incorrectly. The error has been corrected in the HTML version of the article.
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- 2019
10. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk
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Huckins, Laura M, Dobbyn, Amanda, Ruderfer, Douglas M, Hoffman, Gabriel, Wang, Weiqing, Pardiñas, Antonio F, Rajagopal, Veera M, Als, Thomas D, T. Nguyen, Hoang, Girdhar, Kiran, Boocock, James, Roussos, Panos, Fromer, Menachem, Kramer, Robin, Domenici, Enrico, Gamazon, Eric R, Purcell, Shaun, Demontis, Ditte, Børglum, Anders D, Walters, James TR, O’Donovan, Michael C, Sullivan, Patrick, Owen, Michael J, Devlin, Bernie, Sieberts, Solveig K, Cox, Nancy J, Im, Hae Kyung, Sklar, Pamela, and Stahl, Eli A
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Biological Sciences ,Genetics ,Mental Health ,Brain Disorders ,Human Genome ,Schizophrenia ,2.1 Biological and endogenous factors ,Aetiology ,Mental health ,Brain ,Case-Control Studies ,Gene Expression ,Genetic Predisposition to Disease ,Genome-Wide Association Study ,Genotype ,Humans ,Polymorphism ,Single Nucleotide ,Quantitative Trait Loci ,Risk ,Transcriptome ,CommonMind Consortium ,Schizophrenia Working Group of the Psychiatric Genomics Consortium ,iPSYCH-GEMS Schizophrenia Working Group ,Medical and Health Sciences ,Developmental Biology ,Agricultural biotechnology ,Bioinformatics and computational biology - Abstract
Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.
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- 2019
11. Identification of common genetic risk variants for autism spectrum disorder
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Grove, Jakob, Ripke, Stephan, Als, Thomas D, Mattheisen, Manuel, Walters, Raymond K, Won, Hyejung, Pallesen, Jonatan, Agerbo, Esben, Andreassen, Ole A, Anney, Richard, Awashti, Swapnil, Belliveau, Rich, Bettella, Francesco, Buxbaum, Joseph D, Bybjerg-Grauholm, Jonas, Bækvad-Hansen, Marie, Cerrato, Felecia, Chambert, Kimberly, Christensen, Jane H, Churchhouse, Claire, Dellenvall, Karin, Demontis, Ditte, De Rubeis, Silvia, Devlin, Bernie, Djurovic, Srdjan, Dumont, Ashley L, Goldstein, Jacqueline I, Hansen, Christine S, Hauberg, Mads Engel, Hollegaard, Mads V, Hope, Sigrun, Howrigan, Daniel P, Huang, Hailiang, Hultman, Christina M, Klei, Lambertus, Maller, Julian, Martin, Joanna, Martin, Alicia R, Moran, Jennifer L, Nyegaard, Mette, Nærland, Terje, Palmer, Duncan S, Palotie, Aarno, Pedersen, Carsten Bøcker, Pedersen, Marianne Giørtz, dPoterba, Timothy, Poulsen, Jesper Buchhave, Pourcain, Beate St, Qvist, Per, Rehnström, Karola, Reichenberg, Abraham, Reichert, Jennifer, Robinson, Elise B, Roeder, Kathryn, Roussos, Panos, Saemundsen, Evald, Sandin, Sven, Satterstrom, F Kyle, Davey Smith, George, Stefansson, Hreinn, Steinberg, Stacy, Stevens, Christine R, Sullivan, Patrick F, Turley, Patrick, Walters, G Bragi, Xu, Xinyi, Stefansson, Kari, Geschwind, Daniel H, Nordentoft, Merete, Hougaard, David M, Werge, Thomas, Mors, Ole, Mortensen, Preben Bo, Neale, Benjamin M, Daly, Mark J, and Børglum, Anders D
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Intellectual and Developmental Disabilities (IDD) ,Human Genome ,Pediatric ,Brain Disorders ,Autism ,Biotechnology ,Genetics ,Mental Health ,Prevention ,2.3 Psychological ,social and economic factors ,Aetiology ,2.1 Biological and endogenous factors ,Mental health ,Adolescent ,Autism Spectrum Disorder ,Case-Control Studies ,Child ,Child ,Preschool ,Denmark ,Female ,Genetic Predisposition to Disease ,Genome-Wide Association Study ,Humans ,Male ,Multifactorial Inheritance ,Phenotype ,Polymorphism ,Single Nucleotide ,Risk Factors ,Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium ,BUPGEN ,Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium ,23andMe Research Team ,Biological Sciences ,Medical and Health Sciences ,Developmental Biology - Abstract
Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.
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- 2019
12. Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder
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An, Joon-Yong, Lin, Kevin, Zhu, Lingxue, Werling, Donna M, Dong, Shan, Brand, Harrison, Wang, Harold Z, Zhao, Xuefang, Schwartz, Grace B, Collins, Ryan L, Currall, Benjamin B, Dastmalchi, Claudia, Dea, Jeanselle, Duhn, Clif, Gilson, Michael C, Klei, Lambertus, Liang, Lindsay, Markenscoff-Papadimitriou, Eirene, Pochareddy, Sirisha, Ahituv, Nadav, Buxbaum, Joseph D, Coon, Hilary, Daly, Mark J, Kim, Young Shin, Marth, Gabor T, Neale, Benjamin M, Quinlan, Aaron R, Rubenstein, John L, Sestan, Nenad, State, Matthew W, Willsey, A Jeremy, Talkowski, Michael E, Devlin, Bernie, Roeder, Kathryn, and Sanders, Stephan J
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Human Genome ,Genetics ,Biotechnology ,Intellectual and Developmental Disabilities (IDD) ,Mental Health ,Brain Disorders ,Autism ,Pediatric ,2.1 Biological and endogenous factors ,Aetiology ,Mental health ,Autism Spectrum Disorder ,Binding Sites ,Conserved Sequence ,DNA Mutational Analysis ,Genetic Loci ,Genetic Variation ,Humans ,Mutation ,Pedigree ,Promoter Regions ,Genetic ,Risk ,Transcription Factors ,General Science & Technology - Abstract
Whole-genome sequencing (WGS) has facilitated the first genome-wide evaluations of the contribution of de novo noncoding mutations to complex disorders. Using WGS, we identified 255,106 de novo mutations among sample genomes from members of 1902 quartet families in which one child, but not a sibling or their parents, was affected by autism spectrum disorder (ASD). In contrast to coding mutations, no noncoding functional annotation category, analyzed in isolation, was significantly associated with ASD. Casting noncoding variation in the context of a de novo risk score across multiple annotation categories, however, did demonstrate association with mutations localized to promoter regions. We found that the strongest driver of this promoter signal emanates from evolutionarily conserved transcription factor binding sites distal to the transcription start site. These data suggest that de novo mutations in promoter regions, characterized by evolutionary and functional signatures, contribute to ASD.
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- 2018
13. Schizophrenia-associated differential DNA methylation in brain is distributed across the genome and annotated to MAD1L1, a locus at which DNA methylation and transcription phenotypes share genetic variation with schizophrenia risk
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McKinney, Brandon C., McClain, Lora L., Hensler, Christopher M., Wei, Yue, Klei, Lambertus, Lewis, David A., Devlin, Bernie, Wang, Jiebiao, Ding, Ying, and Sweet, Robert A.
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- 2022
- Full Text
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14. Genome-wide association identifies the first risk loci for psychosis in Alzheimer disease
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DeMichele-Sweet, Mary Ann A., Klei, Lambertus, Creese, Byron, Harwood, Janet C., Weamer, Elise A., McClain, Lora, Sims, Rebecca, Hernandez, Isabel, Moreno-Grau, Sonia, Tárraga, Lluís, Boada, Mercè, Alarcón-Martín, Emilio, Valero, Sergi, Liu, Yushi, Hooli, Basavaraj, Aarsland, Dag, Selbaek, Geir, Bergh, Sverre, Rongve, Arvid, Saltvedt, Ingvild, Skjellegrind, Håvard K., Engdahl, Bo, Stordal, Eystein, Andreassen, Ole A., Djurovic, Srdjan, Athanasiu, Lavinia, Seripa, Davide, Borroni, Barbara, Albani, Diego, Forloni, Gianluigi, Mecocci, Patrizia, Serretti, Alessandro, De Ronchi, Diana, Politis, Antonis, Williams, Julie, Mayeux, Richard, Foroud, Tatiana, Ruiz, Agustin, Ballard, Clive, Holmans, Peter, Lopez, Oscar L., Kamboh, M. Ilyas, Devlin, Bernie, and Sweet, Robert A.
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- 2021
- Full Text
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15. An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder
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Werling, Donna M, Brand, Harrison, An, Joon-Yong, Stone, Matthew R, Zhu, Lingxue, Glessner, Joseph T, Collins, Ryan L, Dong, Shan, Layer, Ryan M, Markenscoff-Papadimitriou, Eirene, Farrell, Andrew, Schwartz, Grace B, Wang, Harold Z, Currall, Benjamin B, Zhao, Xuefang, Dea, Jeanselle, Duhn, Clif, Erdman, Carolyn A, Gilson, Michael C, Yadav, Rachita, Handsaker, Robert E, Kashin, Seva, Klei, Lambertus, Mandell, Jeffrey D, Nowakowski, Tomasz J, Liu, Yuwen, Pochareddy, Sirisha, Smith, Louw, Walker, Michael F, Waterman, Matthew J, He, Xin, Kriegstein, Arnold R, Rubenstein, John L, Sestan, Nenad, McCarroll, Steven A, Neale, Benjamin M, Coon, Hilary, Willsey, A Jeremy, Buxbaum, Joseph D, Daly, Mark J, State, Matthew W, Quinlan, Aaron R, Marth, Gabor T, Roeder, Kathryn, Devlin, Bernie, Talkowski, Michael E, and Sanders, Stephan J
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Biological Sciences ,Genetics ,Intellectual and Developmental Disabilities (IDD) ,Brain Disorders ,Pediatric ,Autism ,Biotechnology ,Mental Health ,Human Genome ,2.1 Biological and endogenous factors ,Aetiology ,Autism Spectrum Disorder ,Female ,Genetic Predisposition to Disease ,Genome ,Genome-Wide Association Study ,Humans ,INDEL Mutation ,Male ,Polymorphism ,Single Nucleotide ,Protein Isoforms ,Medical and Health Sciences ,Developmental Biology ,Agricultural biotechnology ,Bioinformatics and computational biology - Abstract
Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.
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- 2018
16. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap
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Gandal, Michael J, Haney, Jillian R, Parikshak, Neelroop N, Leppa, Virpi, Ramaswami, Gokul, Hartl, Chris, Schork, Andrew J, Appadurai, Vivek, Buil, Alfonso, Werge, Thomas M, Liu, Chunyu, White, Kevin P, Horvath, Steve, Geschwind, Daniel H, Sestan, Nenad, Vaccarino, Flora, Gerstein, Mark, Weissman, Sherman, Pochareddy, Sirisha, State, Matthew, Knowles, James, Farnham, Peggy, Akbarian, Schahram, Pinto, Dalila, Van Baekl, Harm, Dracheva, Stella, Jaffe, Andrew, Hyde, Thomas, Zandi, Peter, Crawford, Gregory, Sullivan, Pat, Thompson, Wesley Kurt, Mortensen, Preben Bo, Agerbo, Esben, Pedersen, Marianne Giørtz, Pedersen, Carsten Bøcker, Mors, Ole, Børglum, Anders D, Nordentoft, Merete, Hougaard, David M, Bybjerg-Grauholm, Jonas, Bækvad-Hansen, Marie, Martin, Alicia R, Dumont, Ashley, Stevens, Christine, Churchhouse, Claire, Howrigan, Daniel P, Palmer, Duncan S, Robinson, Elise B, Satterstrom, Kyle F, Cerrato, Felecia, Huang, Hailiang, Goldstein, Jacqueline, Moran, Jennifer, Julian, Joanna Martin, Kimberly, Maller, Patrick, Chambert, Turley, Patrick, Walters, Raymond, Belliveau, Rich, Ripke, Stephan, Poterba, Timothy, Daly, Mark J, Neale, Benjamin, Fromer, Menachem, Roussos, Panos, Johnson, Jessica S, Shah, Hardik R, Mahajan, Milind C, Schadt, Eric, Haroutunian, Vahram, Ruderfer, Douglas M, Buxbaum, Joseph D, Sieberts, Solveig K, Dang, Kristen, Logsdon, Ben, Mangravite, Lara M, Peters, Mette, Gur, Raquel E, Hahn, Chang-Gyu, Devlin, Bernie, Klei, Lambertus L, Lewis, David, Lipska, Barbara, Hirai, Keisuke, Toyoshiba, Hiroyoshi, and Domenici, Enrico
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Biological Sciences ,Biomedical and Clinical Sciences ,Genetics ,Biological Psychology ,Psychology ,Pharmacology and Pharmaceutical Sciences ,Serious Mental Illness ,Brain Disorders ,Human Genome ,Mental Health ,Schizophrenia ,Neurosciences ,Bipolar Disorder ,Depression ,Mental Illness ,2.1 Biological and endogenous factors ,Aetiology ,Mental health ,Good Health and Well Being ,Cerebral Cortex ,Gene Expression Profiling ,Gene Expression Regulation ,Genetic Predisposition to Disease ,Humans ,Mental Disorders ,Multifactorial Inheritance ,Nervous System Diseases ,Polymorphism ,Single Nucleotide ,Transcription ,Genetic ,CommonMind Consortium ,PsychENCODE Consortium ,iPSYCH-BROAD Working Group ,General Science & Technology - Abstract
The predisposition to neuropsychiatric disease involves a complex, polygenic, and pleiotropic genetic architecture. However, little is known about how genetic variants impart brain dysfunction or pathology. We used transcriptomic profiling as a quantitative readout of molecular brain-based phenotypes across five major psychiatric disorders-autism, schizophrenia, bipolar disorder, depression, and alcoholism-compared with matched controls. We identified patterns of shared and distinct gene-expression perturbations across these conditions. The degree of sharing of transcriptional dysregulation is related to polygenic (single-nucleotide polymorphism-based) overlap across disorders, suggesting a substantial causal genetic component. This comprehensive systems-level view of the neurobiological architecture of major neuropsychiatric illness demonstrates pathways of molecular convergence and specificity.
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- 2018
17. A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk
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Yurko, Ronald, G’Sell, Max, Roeder, Kathryn, and Devlin, Bernie
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- 2020
18. A Unified Statistical Framework for Single Cell and Bulk RNA Sequencing Data
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Zhu, Lingxue, Lei, Jing, Devlin, Bernie, and Roeder, Kathryn
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Statistics - Applications - Abstract
Recent advances in technology have enabled the measurement of RNA levels for individual cells. Compared to traditional tissue-level bulk RNA-seq data, single cell sequencing yields valuable insights about gene expression profiles for different cell types, which is potentially critical for understanding many complex human diseases. However, developing quantitative tools for such data remains challenging because of high levels of technical noise, especially the "dropout" events. A "dropout" happens when the RNA for a gene fails to be amplified prior to sequencing, producing a "false" zero in the observed data. In this paper, we propose a Unified RNA-Sequencing Model (URSM) for both single cell and bulk RNA-seq data, formulated as a hierarchical model. URSM borrows the strength from both data sources and carefully models the dropouts in single cell data, leading to a more accurate estimation of cell type specific gene expression profile. In addition, URSM naturally provides inference on the dropout entries in single cell data that need to be imputed for downstream analyses, as well as the mixing proportions of different cell types in bulk samples. We adopt an empirical Bayes approach, where parameters are estimated using the EM algorithm and approximate inference is obtained by Gibbs sampling. Simulation results illustrate that URSM outperforms existing approaches both in correcting for dropouts in single cell data, as well as in deconvolving bulk samples. We also demonstrate an application to gene expression data on fetal brains, where our model successfully imputes the dropout genes and reveals cell type specific expression patterns.
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- 2016
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19. Testing High Dimensional Covariance Matrices, with Application to Detecting Schizophrenia Risk Genes
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Zhu, Lingxue, Lei, Jing, Devlin, Bernie, and Roeder, Kathryn
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Statistics - Methodology ,Statistics - Applications - Abstract
Scientists routinely compare gene expression levels in cases versus controls in part to determine genes associated with a disease. Similarly, detecting case-control differences in co-expression among genes can be critical to understanding complex human diseases; however statistical methods have been limited by the high dimensional nature of this problem. In this paper, we construct a sparse-Leading-Eigenvalue-Driven (sLED) test for comparing two high-dimensional covariance matrices. By focusing on the spectrum of the differential matrix, sLED provides a novel perspective that accommodates what we assume to be common, namely sparse and weak signals in gene expression data, and it is closely related with Sparse Principal Component Analysis. We prove that sLED achieves full power asymptotically under mild assumptions, and simulation studies verify that it outperforms other existing procedures under many biologically plausible scenarios. Applying sLED to the largest gene-expression dataset obtained from post-mortem brain tissue from Schizophrenia patients and controls, we provide a novel list of genes implicated in Schizophrenia and reveal intriguing patterns in gene co-expression change for Schizophrenia subjects. We also illustrate that sLED can be generalized to compare other gene-gene "relationship" matrices that are of practical interest, such as the weighted adjacency matrices., Comment: 25 pages, 5 figures, 3 tables
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- 2016
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20. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders
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Weiner, Daniel J, Wigdor, Emilie M, Ripke, Stephan, Walters, Raymond K, Kosmicki, Jack A, Grove, Jakob, Samocha, Kaitlin E, Goldstein, Jacqueline I, Okbay, Aysu, Bybjerg-Grauholm, Jonas, Werge, Thomas, Hougaard, David M, Taylor, Jacob, Skuse, David, Devlin, Bernie, Anney, Richard, Sanders, Stephan J, Bishop, Somer, Mortensen, Preben Bo, Børglum, Anders D, Smith, George Davey, Daly, Mark J, and Robinson, Elise B
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Biological Sciences ,Genetics ,Autism ,Brain Disorders ,Prevention ,Mental Health ,Pediatric ,Intellectual and Developmental Disabilities (IDD) ,2.4 Surveillance and distribution ,Aetiology ,2.1 Biological and endogenous factors ,Mental health ,Good Health and Well Being ,Adult ,Autism Spectrum Disorder ,Child ,Cohort Studies ,Educational Status ,Ethnicity ,Family Health ,Female ,Genetic Association Studies ,Genetic Predisposition to Disease ,Genetic Variation ,Genetics ,Behavioral ,Humans ,Intellectual Disability ,Intelligence ,Male ,Multifactorial Inheritance ,Phenotype ,Risk Factors ,Schizophrenia ,Sequence Deletion ,iPSYCH-Broad Autism Group ,Psychiatric Genomics Consortium Autism Group ,Medical and Health Sciences ,Developmental Biology ,Agricultural biotechnology ,Bioinformatics and computational biology - Abstract
Autism spectrum disorder (ASD) risk is influenced by common polygenic and de novo variation. We aimed to clarify the influence of polygenic risk for ASD and to identify subgroups of ASD cases, including those with strongly acting de novo variants, in which polygenic risk is relevant. Using a novel approach called the polygenic transmission disequilibrium test and data from 6,454 families with a child with ASD, we show that polygenic risk for ASD, schizophrenia, and greater educational attainment is over-transmitted to children with ASD. These findings hold independent of proband IQ. We find that polygenic variation contributes additively to risk in ASD cases who carry a strongly acting de novo variant. Lastly, we show that elements of polygenic risk are independent and differ in their relationship with phenotype. These results confirm that the genetic influences on ASD are additive and suggest that they create risk through at least partially distinct etiologic pathways.
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- 2017
21. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples
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Kosmicki, Jack A, Samocha, Kaitlin E, Howrigan, Daniel P, Sanders, Stephan J, Slowikowski, Kamil, Lek, Monkol, Karczewski, Konrad J, Cutler, David J, Devlin, Bernie, Roeder, Kathryn, Buxbaum, Joseph D, Neale, Benjamin M, MacArthur, Daniel G, Wall, Dennis P, Robinson, Elise B, and Daly, Mark J
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Biological Sciences ,Genetics ,Brain Disorders ,Neurosciences ,Biotechnology ,Intellectual and Developmental Disabilities (IDD) ,Aetiology ,2.1 Biological and endogenous factors ,Autism Spectrum Disorder ,Exome ,Genetic Predisposition to Disease ,Genetic Variation ,Humans ,Intellectual Disability ,Neurodevelopmental Disorders ,Phenotype ,Medical and Health Sciences ,Developmental Biology ,Agricultural biotechnology ,Bioinformatics and computational biology - Abstract
Recent research has uncovered an important role for de novo variation in neurodevelopmental disorders. Using aggregated data from 9,246 families with autism spectrum disorder, intellectual disability, or developmental delay, we found that ∼1/3 of de novo variants are independently present as standing variation in the Exome Aggregation Consortium's cohort of 60,706 adults, and these de novo variants do not contribute to neurodevelopmental risk. We further used a loss-of-function (LoF)-intolerance metric, pLI, to identify a subset of LoF-intolerant genes containing the observed signal of associated de novo protein-truncating variants (PTVs) in neurodevelopmental disorders. LoF-intolerant genes also carry a modest excess of inherited PTVs, although the strongest de novo-affected genes contribute little to this excess, thus suggesting that the excess of inherited risk resides in lower-penetrant genes. These findings illustrate the importance of population-based reference cohorts for the interpretation of candidate pathogenic variants, even for analyses of complex diseases and de novo variation.
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- 2017
22. A Pleiotropic Missense Variant in SLC39A8 Is Associated With Crohn’s Disease and Human Gut Microbiome Composition
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Li, Dalin, Achkar, Jean-Paul, Haritunians, Talin, Jacobs, Jonathan P, Hui, Ken Y, D'Amato, Mauro, Brand, Stephan, Radford-Smith, Graham, Halfvarson, Jonas, Niess, Jan-Hendrik, Kugathasan, Subra, Büning, Carsten, Schumm, L Philip, Klei, Lambertus, Ananthakrishnan, Ashwin, Aumais, Guy, Baidoo, Leonard, Dubinsky, Marla, Fiocchi, Claudio, Glas, Jürgen, Milgrom, Raquel, Proctor, Deborah D, Regueiro, Miguel, Simms, Lisa A, Stempak, Joanne M, Targan, Stephan R, Törkvist, Leif, Sharma, Yashoda, Devlin, Bernie, Borneman, James, Hakonarson, Hakon, Xavier, Ramnik J, Daly, Mark, Brant, Steven R, Rioux, John D, Silverberg, Mark S, Cho, Judy H, Braun, Jonathan, McGovern, Dermot PB, and Duerr, Richard H
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Biomedical and Clinical Sciences ,Clinical Sciences ,Nutrition and Dietetics ,Genetics ,Nutrition ,Inflammatory Bowel Disease ,Autoimmune Disease ,Human Genome ,Clinical Research ,Digestive Diseases ,Crohn's Disease ,Aetiology ,2.1 Biological and endogenous factors ,Oral and gastrointestinal ,Alleles ,Case-Control Studies ,Cation Transport Proteins ,Colitis ,Ulcerative ,Crohn Disease ,Female ,Gastrointestinal Microbiome ,Genetic Pleiotropy ,Genotype ,Humans ,Male ,Mutation ,Missense ,Risk Factors ,Inflammatory Bowel Diseases ,Microbiota ,Neurosciences ,Paediatrics and Reproductive Medicine ,Gastroenterology & Hepatology ,Clinical sciences ,Nutrition and dietetics - Abstract
Background & aimsGenome-wide association studies have identified 200 inflammatory bowel disease (IBD) loci, but the genetic architecture of Crohn's disease (CD) and ulcerative colitis remain incompletely defined. Here, we aimed to identify novel associations between IBD and functional genetic variants using the Illumina ExomeChip (San Diego, CA).MethodsGenotyping was performed in 10,523 IBD cases and 5726 non-IBD controls. There were 91,713 functional single-nucleotide polymorphism loci in coding regions analyzed. A novel identified association was replicated further in 2 independent cohorts. We further examined the association of the identified single-nucleotide polymorphism with microbiota from 338 mucosal lavage samples in the Mucosal Luminal Interface cohort measured using 16S sequencing.ResultsWe identified an association between CD and a missense variant encoding alanine or threonine at position 391 in the zinc transporter solute carrier family 39, member 8 protein (SLC39A8 alanine 391 threonine, rs13107325) and replicated the association with CD in 2 replication cohorts (combined meta-analysis P = 5.55 × 10(-13)). This variant has been associated previously with distinct phenotypes including obesity, lipid levels, blood pressure, and schizophrenia. We subsequently determined that the CD risk allele was associated with altered colonic mucosal microbiome composition in both healthy controls (P = .009) and CD cases (P = .0009). Moreover, microbes depleted in healthy carriers strongly overlap with those reduced in CD patients (P = 9.24 × 10(-16)) and overweight individuals (P = 6.73 × 10(-16)).ConclusionsOur results suggest that an SLC39A8-dependent shift in the gut microbiome could explain its pleiotropic effects on multiple complex diseases including CD.
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- 2016
23. Maternal Effects as Causes of Risk for Obsessive-Compulsive Disorder
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Mahjani, Behrang, Klei, Lambertus, Hultman, Christina M., Larsson, Henrik, Devlin, Bernie, Buxbaum, Joseph D., Sandin, Sven, and Grice, Dorothy E.
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- 2020
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24. Age dependent association of inbreeding with risk for schizophrenia in Egypt
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McClain, Lora, Mansour, Hader, Ibrahim, Ibtihal, Klei, Lambertus, Fathi, Warda, Wood, Joel, Kodavali, Chowdari, Maysterchuk, Alina, Wood, Shawn, El-Chennawi, Farha, Ibrahim, Nahed, Eissa, Ahmed, El-Bahaei, Wafaa, El Sayed, Hanan, Yassein, Amal, Tobar, Salwa, El-Boraie, Hala, El-Sheshtawy, Eman, Salah, Hala, Ali, Ahmed, Erdin, Serkan, Devlin, Bernie, Talkowski, Michael, and Nimgaonkar, Vishwajit
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- 2020
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25. Comparison of gene expression in living and postmortem human brain
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Collado-Torres, Leonardo, primary, Klei, Lambertus, additional, Liu, Chunyu, additional, Kleinman, Joel E, additional, Hyde, Thomas M, additional, Geschwind, Daniel H, additional, Gandal, Michael J, additional, Devlin, Bernie, additional, and Weinberger, Daniel R, additional
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- 2023
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26. T63. CROSS-DISORDER ANALYSIS OF AUTISM AND ADHD USING RARE VARIANTS: INSIGHTS FROM DANISH IPSYCH EXOMES
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Duan, Jinjie, primary, Grove, Jakob, additional, Demontis, Ditte, additional, Satterstrom, F. Kyle, additional, Fu, Jack, additional, Carey, Caitlin, additional, Sanders, Stephan J., additional, Devlin, Bernie, additional, Roeder, Kathryn, additional, Buxbaum, Joseph, additional, Robinson, Elise, additional, Talkowski, Michael, additional, Neale, Benjamin, additional, Daly, Mark, additional, and Børglum, Anders, additional
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- 2023
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27. Cohort profile: Epidemiology and Genetics of Obsessive–compulsive disorder and chronic tic disorders in Sweden (EGOS)
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Mahjani, Behrang, Dellenvall, Karin, Grahnat, Anna-Carin Säll, Karlsson, Gun, Tuuliainen, Aki, Reichert, Jennifer, Mahjani, Christina G., Klei, Lambertus, De Rubeis, Silvia, Reichenberg, Abraham, Devlin, Bernie, Hultman, Christina M., Buxbaum, Joseph D., Sandin, Sven, and Grice, Dorothy E.
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- 2020
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28. Heterogeneous Trajectories of Problematic Alcohol Use, Depressive Symptoms, and their Co-Occurrence in Young Adults with and without Childhood ADHD
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Wang, Frances L., Pedersen, Sarah L., Devlin, Bernie, Gnagy, Elizabeth M., Pelham, Jr, William E., and Molina, Brooke S.G.
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- 2020
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29. Semisoft clustering of single-cell data
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Zhu, Lingxue, Lei, Jing, Klei, Lambertus, Devlin, Bernie, and Roeder, Kathryn
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- 2019
30. How rare and common risk variation jointly affect liability for autism spectrum disorder
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Klei, Lambertus, McClain, Lora Lee, Mahjani, Behrang, Panayidou, Klea, De Rubeis, Silvia, Grahnat, Anna-Carin Säll, Karlsson, Gun, Lu, Yangyi, Melhem, Nadine, Xu, Xinyi, Reichenberg, Abraham, Sandin, Sven, Hultman, Christina M., Buxbaum, Joseph D., Roeder, Kathryn, and Devlin, Bernie
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- 2021
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31. Prevalence and phenotypic impact of rare potentially damaging variants in autism spectrum disorder
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Mahjani, Behrang, De Rubeis, Silvia, Gustavsson Mahjani, Christina, Mulhern, Maureen, Xu, Xinyi, Klei, Lambertus, Satterstrom, F. Kyle, Fu, Jack, Talkowski, Michael E., Reichenberg, Abraham, Sandin, Sven, Hultman, Christina M., Grice, Dorothy E., Roeder, Kathryn, Devlin, Bernie, and Buxbaum, Joseph D.
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- 2021
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32. Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci
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Sanders, Stephan J, He, Xin, Willsey, A Jeremy, Ercan-Sencicek, A Gulhan, Samocha, Kaitlin E, Cicek, A Ercument, Murtha, Michael T, Bal, Vanessa H, Bishop, Somer L, Dong, Shan, Goldberg, Arthur P, Jinlu, Cai, Keaney, John F, Klei, Lambertus, Mandell, Jeffrey D, Moreno-De-Luca, Daniel, Poultney, Christopher S, Robinson, Elise B, Smith, Louw, Solli-Nowlan, Tor, Su, Mack Y, Teran, Nicole A, Walker, Michael F, Werling, Donna M, Beaudet, Arthur L, Cantor, Rita M, Fombonne, Eric, Geschwind, Daniel H, Grice, Dorothy E, Lord, Catherine, Lowe, Jennifer K, Mane, Shrikant M, Martin, Donna M, Morrow, Eric M, Talkowski, Michael E, Sutcliffe, James S, Walsh, Christopher A, Yu, Timothy W, Consortium, Autism Sequencing, Ledbetter, David H, Martin, Christa Lese, Cook, Edwin H, Buxbaum, Joseph D, Daly, Mark J, Devlin, Bernie, Roeder, Kathryn, and State, Matthew W
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Biological Psychology ,Biomedical and Clinical Sciences ,Neurosciences ,Psychology ,Pediatric ,Prevention ,Biotechnology ,Brain Disorders ,Genetics ,Mental Health ,Intellectual and Developmental Disabilities (IDD) ,Human Genome ,Autism ,2.1 Biological and endogenous factors ,Aetiology ,Mental health ,Autism Spectrum Disorder ,Female ,Genetic Loci ,Genetic Variation ,Humans ,Male ,Protein Interaction Maps ,Autism Sequencing Consortium ,Cognitive Sciences ,Neurology & Neurosurgery ,Biological psychology - Abstract
Analysis of de novo CNVs (dnCNVs) from the full Simons Simplex Collection (SSC) (N = 2,591 families) replicates prior findings of strong association with autism spectrum disorders (ASDs) and confirms six risk loci (1q21.1, 3q29, 7q11.23, 16p11.2, 15q11.2-13, and 22q11.2). The addition of published CNV data from the Autism Genome Project (AGP) and exome sequencing data from the SSC and the Autism Sequencing Consortium (ASC) shows that genes within small de novo deletions, but not within large dnCNVs, significantly overlap the high-effect risk genes identified by sequencing. Alternatively, large dnCNVs are found likely to contain multiple modest-effect risk genes. Overall, we find strong evidence that de novo mutations are associated with ASD apart from the risk for intellectual disability. Extending the transmission and de novo association test (TADA) to include small de novo deletions reveals 71 ASD risk loci, including 6 CNV regions (noted above) and 65 risk genes (FDR ≤ 0.1).
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- 2015
33. Genome-wide association study of corticobasal degeneration identifies risk variants shared with progressive supranuclear palsy.
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Kouri, Naomi, Ross, Owen A, Dombroski, Beth, Younkin, Curtis S, Serie, Daniel J, Soto-Ortolaza, Alexandra, Baker, Matthew, Finch, Ni Cole A, Yoon, Hyejin, Kim, Jungsu, Fujioka, Shinsuke, McLean, Catriona A, Ghetti, Bernardino, Spina, Salvatore, Cantwell, Laura B, Farlow, Martin R, Grafman, Jordan, Huey, Edward D, Ryung Han, Mi, Beecher, Sherry, Geller, Evan T, Kretzschmar, Hans A, Roeber, Sigrun, Gearing, Marla, Juncos, Jorge L, Vonsattel, Jean Paul G, Van Deerlin, Vivianna M, Grossman, Murray, Hurtig, Howard I, Gross, Rachel G, Arnold, Steven E, Trojanowski, John Q, Lee, Virginia M, Wenning, Gregor K, White, Charles L, Höglinger, Günter U, Müller, Ulrich, Devlin, Bernie, Golbe, Lawrence I, Crook, Julia, Parisi, Joseph E, Boeve, Bradley F, Josephs, Keith A, Wszolek, Zbigniew K, Uitti, Ryan J, Graff-Radford, Neill R, Litvan, Irene, Younkin, Steven G, Wang, Li-San, Ertekin-Taner, Nilüfer, Rademakers, Rosa, Hakonarsen, Hakon, Schellenberg, Gerard D, and Dickson, Dennis W
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Cerebral Cortex ,Humans ,Basal Ganglia Diseases ,Supranuclear Palsy ,Progressive ,Neurodegenerative Diseases ,Kinesin ,SOS1 Protein ,tau Proteins ,Myelin Proteins ,Case-Control Studies ,Polymorphism ,Single Nucleotide ,Adult ,Aged ,Aged ,80 and over ,Middle Aged ,Female ,Male ,Genome-Wide Association Study ,Young Adult ,RNA ,Long Noncoding ,and over ,Polymorphism ,Single Nucleotide ,RNA ,Long Noncoding ,Supranuclear Palsy ,Progressive - Abstract
Corticobasal degeneration (CBD) is a neurodegenerative disorder affecting movement and cognition, definitively diagnosed only at autopsy. Here, we conduct a genome-wide association study (GWAS) in CBD cases (n=152) and 3,311 controls, and 67 CBD cases and 439 controls in a replication stage. Associations with meta-analysis were 17q21 at MAPT (P=1.42 × 10(-12)), 8p12 at lnc-KIF13B-1, a long non-coding RNA (rs643472; P=3.41 × 10(-8)), and 2p22 at SOS1 (rs963731; P=1.76 × 10(-7)). Testing for association of CBD with top progressive supranuclear palsy (PSP) GWAS single-nucleotide polymorphisms (SNPs) identified associations at MOBP (3p22; rs1768208; P=2.07 × 10(-7)) and MAPT H1c (17q21; rs242557; P=7.91 × 10(-6)). We previously reported SNP/transcript level associations with rs8070723/MAPT, rs242557/MAPT, and rs1768208/MOBP and herein identified association with rs963731/SOS1. We identify new CBD susceptibility loci and show that CBD and PSP share a genetic risk factor other than MAPT at 3p22 MOBP (myelin-associated oligodendrocyte basic protein).
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- 2015
34. A genome-wide association study of autism using the Simons Simplex Collection: Does reducing phenotypic heterogeneity in autism increase genetic homogeneity?
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Chaste, Pauline, Klei, Lambertus, Sanders, Stephan J, Hus, Vanessa, Murtha, Michael T, Lowe, Jennifer K, Willsey, A Jeremy, Moreno-De-Luca, Daniel, Yu, Timothy W, Fombonne, Eric, Geschwind, Daniel, Grice, Dorothy E, Ledbetter, David H, Mane, Shrikant M, Martin, Donna M, Morrow, Eric M, Walsh, Christopher A, Sutcliffe, James S, Lese Martin, Christa, Beaudet, Arthur L, Lord, Catherine, State, Matthew W, Cook, Edwin H, and Devlin, Bernie
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Humans ,Genetic Predisposition to Disease ,Family ,Autistic Disorder ,Phenotype ,Polymorphism ,Single Nucleotide ,Female ,Male ,Genetic Variation ,Genome-Wide Association Study ,Autism Spectrum Disorder ,Autism ,GWAS ,Genetics ,Heterogeneity ,Power ,Genetic Testing ,Pediatric ,Intellectual and Developmental Disabilities (IDD) ,Mental Health ,Human Genome ,Brain Disorders ,Biological Sciences ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Psychiatry - Abstract
BackgroundPhenotypic heterogeneity in autism has long been conjectured to be a major hindrance to the discovery of genetic risk factors, leading to numerous attempts to stratify children based on phenotype to increase power of discovery studies. This approach, however, is based on the hypothesis that phenotypic heterogeneity closely maps to genetic variation, which has not been tested. Our study examines the impact of subphenotyping of a well-characterized autism spectrum disorder (ASD) sample on genetic homogeneity and the ability to discover common genetic variants conferring liability to ASD.MethodsGenome-wide genotypic data of 2576 families from the Simons Simplex Collection were analyzed in the overall sample and phenotypic subgroups defined on the basis of diagnosis, IQ, and symptom profiles. We conducted a family-based association study, as well as estimating heritability and evaluating allele scores for each phenotypic subgroup.ResultsAssociation analyses revealed no genome-wide significant association signal. Subphenotyping did not increase power substantially. Moreover, allele scores built from the most associated single nucleotide polymorphisms, based on the odds ratio in the full sample, predicted case status in subsets of the sample equally well and heritability estimates were very similar for all subgroups.ConclusionsIn genome-wide association analysis of the Simons Simplex Collection sample, reducing phenotypic heterogeneity had at most a modest impact on genetic homogeneity. Our results are based on a relatively small sample, one with greater homogeneity than the entire population; if they apply more broadly, they imply that analysis of subphenotypes is not a productive path forward for discovering genetic risk variants in ASD.
- Published
- 2015
35. De Novo Insertions and Deletions of Predominantly Paternal Origin Are Associated with Autism Spectrum Disorder
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Dong, Shan, Walker, Michael F, Carriero, Nicholas J, DiCola, Michael, Willsey, A Jeremy, Ye, Adam Y, Waqar, Zainulabedin, Gonzalez, Luis E, Overton, John D, Frahm, Stephanie, Keaney, John F, Teran, Nicole A, Dea, Jeanselle, Mandell, Jeffrey D, Bal, Vanessa Hus, Sullivan, Catherine A, DiLullo, Nicholas M, Khalil, Rehab O, Gockley, Jake, Yuksel, Zafer, Sertel, Sinem M, Ercan-Sencicek, A Gulhan, Gupta, Abha R, Mane, Shrikant M, Sheldon, Michael, Brooks, Andrew I, Roeder, Kathryn, Devlin, Bernie, State, Matthew W, Wei, Liping, and Sanders, Stephan J
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Biological Sciences ,Bioinformatics and Computational Biology ,Genetics ,Autism ,Human Genome ,Intellectual and Developmental Disabilities (IDD) ,Brain Disorders ,Mental Health ,Pediatric ,Aetiology ,2.1 Biological and endogenous factors ,Mental health ,Child ,Child Development Disorders ,Pervasive ,DNA ,DNA-Binding Proteins ,Female ,Fragile X Mental Retardation Protein ,Frameshift Mutation ,GTP-Binding Proteins ,Humans ,Male ,Nerve Tissue Proteins ,Pedigree ,Phenotype ,Sequence Deletion ,Sex Factors ,Biochemistry and Cell Biology ,Medical Physiology ,Biological sciences - Abstract
Whole-exome sequencing (WES) studies have demonstrated the contribution of de novo loss-of-function single-nucleotide variants (SNVs) to autism spectrum disorder (ASD). However, challenges in the reliable detection of de novo insertions and deletions (indels) have limited inclusion of these variants in prior analyses. By applying a robust indel detection method to WES data from 787 ASD families (2,963 individuals), we demonstrate that de novo frameshift indels contribute to ASD risk (OR = 1.6; 95% CI = 1.0-2.7; p = 0.03), are more common in female probands (p = 0.02), are enriched among genes encoding FMRP targets (p = 6 × 10(-9)), and arise predominantly on the paternal chromosome (p < 0.001). On the basis of mutation rates in probands versus unaffected siblings, we conclude that de novo frameshift indels contribute to risk in approximately 3% of individuals with ASD. Finally, by observing clustering of mutations in unrelated probands, we uncover two ASD-associated genes: KMT2E (MLL5), a chromatin regulator, and RIMS1, a regulator of synaptic vesicle release.
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- 2014
36. Refining genetically inferred relationships using treelet covariance smoothing
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Crossett, Andrew, Lee, Ann B., Klei, Lambertus, Devlin, Bernie, and Roeder, Kathryn
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Statistics - Applications - Abstract
Recent technological advances coupled with large sample sets have uncovered many factors underlying the genetic basis of traits and the predisposition to complex disease, but much is left to discover. A common thread to most genetic investigations is familial relationships. Close relatives can be identified from family records, and more distant relatives can be inferred from large panels of genetic markers. Unfortunately these empirical estimates can be noisy, especially regarding distant relatives. We propose a new method for denoising genetically - inferred relationship matrices by exploiting the underlying structure due to hierarchical groupings of correlated individuals. The approach, which we call Treelet Covariance Smoothing, employs a multiscale decomposition of covariance matrices to improve estimates of pairwise relationships. On both simulated and real data, we show that smoothing leads to better estimates of the relatedness amongst distantly related individuals. We illustrate our method with a large genome-wide association study and estimate the "heritability" of body mass index quite accurately. Traditionally heritability, defined as the fraction of the total trait variance attributable to additive genetic effects, is estimated from samples of closely related individuals using random effects models. We show that by using smoothed relationship matrices we can estimate heritability using population-based samples. Finally, while our methods have been developed for refining genetic relationship matrices and improving estimates of heritability, they have much broader potential application in statistics. Most notably, for error-in-variables random effects models and settings that require regularization of matrices with block or hierarchical structure., Comment: Published in at http://dx.doi.org/10.1214/12-AOAS598 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
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- 2012
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37. GemTools: A fast and efficient approach to estimating genetic ancestry
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Klei, Lambertus, Kent, Brian P., Melhem, Nadine, Devlin, Bernie, and Roeder, Kathryn
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Statistics - Applications - Abstract
To uncover the genetic basis of complex disease, individuals are often measured at a large number of genetic variants (usually SNPs) across the genome. GemTools provides computationally efficient tools for modeling genetic ancestry based on SNP genotypes. The main algorithm creates an eigenmap based on genetic similarities, and then clusters subjects based on their map position. This process is continued iteratively until each cluster is relatively homogeneous. For genetic association studies, GemTools matches cases and controls based on genetic similarity., Comment: 5 pages, 1 figure
- Published
- 2011
38. Transcriptome alterations are enriched for synapse-associated genes in the striatum of subjects with obsessive-compulsive disorder
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Piantadosi, Sean C., McClain, Lora L., Klei, Lambertus, Wang, Jiebiao, Chamberlain, Brittany L., Springer, Sara A., Lewis, David A., Devlin, Bernie, and Ahmari, Susanne E.
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- 2021
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39. Characterizing runs of homozygosity and their impact on risk for psychosis in a population isolate
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Melhem, Nadine M, Lu, Cong, Dresbold, Cara, Middleton, Frank A, Klei, Lambertus, Wood, Shawn, Faraone, Stephen V, Vinogradov, Sophia, Tiobech, Josepha, Yano, Victor, Roeder, Kathryn, Byerley, William, Myles‐Worsley, Marina, and Devlin, Bernie
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Pharmacology and Pharmaceutical Sciences ,Biomedical and Clinical Sciences ,Brain Disorders ,Schizophrenia ,Clinical Research ,Mental Health ,Aetiology ,2.1 Biological and endogenous factors ,Mental health ,Alleles ,Case-Control Studies ,Genetic Predisposition to Disease ,Genome ,Human ,Haplotypes ,Homozygote ,Humans ,Palau ,Polymorphism ,Single Nucleotide ,Psychotic Disorders ,Risk Factors ,homozygosity ,ROH ,schizophrenia ,psychosis ,Genetics ,Clinical Sciences ,Neurosciences ,Clinical sciences - Abstract
An increased abundance of runs of homozygosity (ROH) has been associated with risk for various diseases, including schizophrenia. Here we investigate the characteristics of ROH in Palau, an Oceanic population, evaluating whether these characteristics are related to risk for psychotic disorders and the nature of this association. To accomplish these aims we evaluate a sample of 203 cases with schizophrenia and related psychotic disorders-representing almost complete ascertainment of affected individuals in the population-and contrast their ROH to that of 125 subjects chosen to function as controls. While Palauan diagnosed with psychotic disorders tend to have slightly more ROH regions than controls, the distinguishing features are that they have longer ROH regions, greater total length of ROH, and their ROH tends to co-occur more often at the same locus. The nature of the sample allows us to investigate whether rare, highly penetrant recessive variants generate such case-control differences in ROH. Neither rare, highly penetrant recessive variants nor individual common variants of large effect account for a substantial proportion of risk for psychosis in Palau. These results suggest a more nuanced model for risk is required to explain patterns of ROH for this population.
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- 2014
40. Modest impact on risk for autism spectrum disorder of rare copy number variants at 15q11.2, specifically breakpoints 1 to 2.
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Chaste, Pauline, Sanders, Stephan, Mohan, Kommu, Klei, Lambertus, Song, Youeun, Murtha, Michael, Hus, Vanessa, Lowe, Jennifer, Willsey, Arthur, Moreno-De-Luca, Daniel, Yu, Timothy, Fombonne, Eric, Geschwind, Daniel, Grice, Dorothy, Ledbetter, David, Lord, Catherine, Mane, Shrikant, Martin, Donna, Morrow, Eric, Walsh, Christopher, Sutcliffe, James, State, Matthew, Martin, Christa, Devlin, Bernie, Beaudet, Arthur, Cook, Edwin, and Kim, Soo-Jeong
- Subjects
15q11.2 ,autism ,deletion ,duplication ,penetrance ,Adult ,Child ,Child Development Disorders ,Pervasive ,Chromosome Deletion ,Chromosomes ,Human ,Pair 15 ,DNA Copy Number Variations ,Female ,Genetic Predisposition to Disease ,Humans ,Male - Abstract
The proximal region of chromosome 15 is one of the genomic hotspots for copy number variants (CNVs). Among the rearrangements observed in this region, CNVs from the interval between the common breakpoints 1 and 2 (BP1 and BP2) have been reported cosegregating with autism spectrum disorder (ASD). Although evidence supporting an association between BP1-BP2 CNVs and autism accumulates, the magnitude of the effect of BP1-BP2 CNVs remains elusive, posing a great challenge to recurrence-risk counseling. To gain further insight into their pathogenicity for ASD, we estimated the penetrance of the BP1-BP2 CNVs for ASD as well as their effects on ASD-related phenotypes in a well-characterized ASD sample (n = 2525 families). Transmission disequilibrium test revealed significant preferential transmission only for the duplicated chromosome in probands (20T:9NT). The penetrance of the BP1-BP2 CNVs for ASD was low, conferring additional risks of 0.3% (deletion) and 0.8% (duplication). Stepwise regression analyses suggest a greater effect of the CNVs on ASD-related phenotype in males and when maternally inherited. Taken together, the results are consistent with BP1-BP2 CNVs as risk factors for autism. However, their effect is modest, more akin to that seen for common variants. To be consistent with the current American College of Medical Genetics guidelines for interpretation of postnatal CNV, the BP1-BP2 deletion and duplication CNVs would probably best be classified as variants of uncertain significance (VOUS): they appear to have an impact on risk, but one so modest that these CNVs do not merit pathogenic status.
- Published
- 2014
41. DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics.
- Author
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Liu, Li, Lei, Jing, Sanders, Stephan, Willsey, Arthur, Kou, Yan, Cicek, Abdullah, Klei, Lambertus, Lu, Cong, He, Xin, Li, Mingfeng, Muhle, Rebecca, Maayan, Avi, Noonan, James, Sestan, Nenad, McFadden, Kathryn, State, Matthew, Buxbaum, Joseph, Devlin, Bernie, and Roeder, Kathryn
- Abstract
BACKGROUND: De novo loss-of-function (dnLoF) mutations are found twofold more often in autism spectrum disorder (ASD) probands than their unaffected siblings. Multiple independent dnLoF mutations in the same gene implicate the gene in risk and hence provide a systematic, albeit arduous, path forward for ASD genetics. It is likely that using additional non-genetic data will enhance the ability to identify ASD genes. METHODS: To accelerate the search for ASD genes, we developed a novel algorithm, DAWN, to model two kinds of data: rare variations from exome sequencing and gene co-expression in the mid-fetal prefrontal and motor-somatosensory neocortex, a critical nexus for risk. The algorithm casts the ensemble data as a hidden Markov random field in which the graph structure is determined by gene co-expression and it combines these interrelationships with node-specific observations, namely gene identity, expression, genetic data and the estimated effect on risk. RESULTS: Using currently available genetic data and a specific developmental time period for gene co-expression, DAWN identified 127 genes that plausibly affect risk, and a set of likely ASD subnetworks. Validation experiments making use of published targeted resequencing results demonstrate its efficacy in reliably predicting ASD genes. DAWN also successfully predicts known ASD genes, not included in the genetic data used to create the model. CONCLUSIONS: Validation studies demonstrate that DAWN is effective in predicting ASD genes and subnetworks by leveraging genetic and gene expression data. The findings reported here implicate neurite extension and neuronal arborization as risks for ASD. Using DAWN on emerging ASD sequence data and gene expression data from other brain regions and tissues would likely identify novel ASD genes. DAWN can also be used for other complex disorders to identify genes and subnetworks in those disorders.
- Published
- 2014
42. Heritable Composite Phenotypes Defined by Combinations of Conduct Problem, Depression, and Temperament Features: Contributions to risk for Alcohol Problems
- Author
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Wang, Frances L., primary, Klei, Lambertus, additional, Devlin, Bernie, additional, Molina, Brooke S.G., additional, and Chassin, Laurie, additional
- Published
- 2023
- Full Text
- View/download PDF
43. Evaluating and Improving Health Equity and Fairness of Polygenic Scores
- Author
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Zhang, Tianyu, primary, Klei, Lambertus, additional, Liu, Peng, additional, Chouldechova, Alexandra, additional, Roeder, Kathryn, additional, G’Sell, Max, additional, and Devlin, Bernie, additional
- Published
- 2023
- Full Text
- View/download PDF
44. A UNIFIED STATISTICAL FRAMEWORK FOR SINGLE CELL AND BULK RNA SEQUENCING DATA
- Author
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Zhu, Lingxue, Lei, Jing, Devlin, Bernie, and Roeder, Kathryn
- Published
- 2018
45. Coexpression Networks Implicate Human Midfetal Deep Cortical Projection Neurons in the Pathogenesis of Autism
- Author
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Willsey, A Jeremy, Sanders, Stephan J, Li, Mingfeng, Dong, Shan, Tebbenkamp, Andrew T, Muhle, Rebecca A, Reilly, Steven K, Lin, Leon, Fertuzinhos, Sofia, Miller, Jeremy A, Murtha, Michael T, Bichsel, Candace, Niu, Wei, Cotney, Justin, Ercan-Sencicek, A Gulhan, Gockley, Jake, Gupta, Abha R, Han, Wenqi, He, Xin, Hoffman, Ellen J, Klei, Lambertus, Lei, Jing, Liu, Wenzhong, Liu, Li, Lu, Cong, Xu, Xuming, Zhu, Ying, Mane, Shrikant M, Lein, Ed S, Wei, Liping, Noonan, James P, Roeder, Kathryn, Devlin, Bernie, Sestan, Nenad, and State, Matthew W
- Subjects
Biological Sciences ,Bioinformatics and Computational Biology ,Genetics ,Neurosciences ,Mental Health ,Intellectual and Developmental Disabilities (IDD) ,Pediatric ,Brain Disorders ,Clinical Research ,Autism ,Human Genome ,2.1 Biological and endogenous factors ,Aetiology ,Neurological ,Mental health ,Animals ,Brain ,Child Development Disorders ,Pervasive ,Exome ,Female ,Fetus ,Gene Expression Profiling ,Genetic Predisposition to Disease ,Genome-Wide Association Study ,Humans ,Male ,Mice ,Mutation ,Neurons ,Prefrontal Cortex ,Sequence Analysis ,DNA ,Medical and Health Sciences ,Developmental Biology ,Biological sciences ,Biomedical and clinical sciences - Abstract
Autism spectrum disorder (ASD) is a complex developmental syndrome of unknown etiology. Recent studies employing exome- and genome-wide sequencing have identified nine high-confidence ASD (hcASD) genes. Working from the hypothesis that ASD-associated mutations in these biologically pleiotropic genes will disrupt intersecting developmental processes to contribute to a common phenotype, we have attempted to identify time periods, brain regions, and cell types in which these genes converge. We have constructed coexpression networks based on the hcASD "seed" genes, leveraging a rich expression data set encompassing multiple human brain regions across human development and into adulthood. By assessing enrichment of an independent set of probable ASD (pASD) genes, derived from the same sequencing studies, we demonstrate a key point of convergence in midfetal layer 5/6 cortical projection neurons. This approach informs when, where, and in what cell types mutations in these specific genes may be productively studied to clarify ASD pathophysiology.
- Published
- 2013
46. Adjusting head circumference for covariates in autism: clinical correlates of a highly heritable continuous trait.
- Author
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Chaste, Pauline, Klei, Lambertus, Sanders, Stephan, Murtha, Michael, Hus, Vanessa, Lowe, Jennifer, Willsey, Arthur, Moreno-De-Luca, Daniel, Yu, Timothy, Fombonne, Eric, Geschwind, Daniel, Grice, Dorothy, Ledbetter, David, Lord, Catherine, Mane, Shrikant, Lese Martin, Christa, Martin, Donna, Morrow, Eric, Walsh, Christopher, Sutcliffe, James, State, Matthew, Devlin, Bernie, Cook, Edwin, and Kim, Soo-Jeong
- Subjects
ASD ,IQ ,autism spectrum disorder ,body metrics ,genetic ancestry ,head circumference ,Adult ,Autistic Disorder ,Body Weights and Measures ,Child ,Family ,Female ,Head ,Humans ,Intelligence ,Male ,Quantitative Trait ,Heritable - Abstract
BACKGROUND: Brain development follows a different trajectory in children with autism spectrum disorders (ASD) than in typically developing children. A proxy for neurodevelopment could be head circumference (HC), but studies assessing HC and its clinical correlates in ASD have been inconsistent. This study investigates HC and clinical correlates in the Simons Simplex Collection cohort. METHODS: We used a mixed linear model to estimate effects of covariates and the deviation from the expected HC given parental HC (genetic deviation). After excluding individuals with incomplete data, 7225 individuals in 1891 families remained for analysis. We examined the relationship between HC/genetic deviation of HC and clinical parameters. RESULTS: Gender, age, height, weight, genetic ancestry, and ASD status were significant predictors of HC (estimate of the ASD effect = .2 cm). HC was approximately normally distributed in probands and unaffected relatives, with only a few outliers. Genetic deviation of HC was also normally distributed, consistent with a random sampling of parental genes. Whereas larger HC than expected was associated with ASD symptom severity and regression, IQ decreased with the absolute value of the genetic deviation of HC. CONCLUSIONS: Measured against expected values derived from covariates of ASD subjects, statistical outliers for HC were uncommon. HC is a strongly heritable trait, and population norms for HC would be far more accurate if covariates including genetic ancestry, height, and age were taken into account. The association of diminishing IQ with absolute deviation from predicted HC values suggests HC could reflect subtle underlying brain development and warrants further investigation.
- Published
- 2013
47. Improving power in genome-wide association studies: weights tip the scale
- Author
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Roeder, Kathryn, Devlin, Bernie, and Wasserman, Larry
- Subjects
Mathematics - Statistics Theory ,Quantitative Biology - Quantitative Methods - Abstract
Genome-wide association analysis has generated much discussion about how to preserve power to detect signals despite the detrimental effect of multiple testing on power. We develop a weighted multiple testing procedure that facilitates the input of prior information in the form of groupings of tests. For each group a weight is estimated from the observed test statistics within the group. Differentially weighting groups improves the power to detect signals in likely groupings. The advantage of the grouped-weighting concept, over fixed weights based on prior information, is that it often leads to an increase in power even if many of the groupings are not correlated with the signal. Being data dependent, the procedure is remarkably robust to poor choices in groupings. Power is typically improved if one (or more) of the groups clusters multiple tests with signals, yet little power is lost when the groupings are totally random. If there is no apparent signal in a group, relative to a group that appears to have several tests with signals, the former group will be down-weighted relative to the latter. If no groups show apparent signals, then the weights will be approximately equal. The only restriction on the procedure is that the number of groups be small, relative to the total number of tests performed.
- Published
- 2007
48. Joint evaluation of serum C-Reactive Protein levels and polygenic risk scores as risk factors for schizophrenia
- Author
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Nimgaonkar, Vishwajit L., Dickerson, Faith, Pouget, Jennie G., Chowdari, Kodavali, O’Dushlaine, Colm, Wood, Joel, Klei, Lambertus, Devlin, Bernie, and Yolken, Robert
- Published
- 2018
- Full Text
- View/download PDF
49. Intellectual Disability Is Associated with Increased Runs of Homozygosity in Simplex Autism
- Author
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Gamsiz, Ece D, Viscidi, Emma W, Frederick, Abbie M, Nagpal, Shailender, Sanders, Stephan J, Murtha, Michael T, Schmidt, Michael, Consortium, Simons Simplex Collection Genetics, Triche, Elizabeth W, Geschwind, Daniel H, State, Matthew W, Istrail, Sorin, Cook, Edwin H, Devlin, Bernie, and Morrow, Eric M
- Subjects
Biological Sciences ,Genetics ,Intellectual and Developmental Disabilities (IDD) ,Pediatric ,Autism ,Mental Health ,Brain Disorders ,Clinical Research ,Human Genome ,Aetiology ,2.1 Biological and endogenous factors ,Mental health ,Child ,Child Development Disorders ,Pervasive ,Chromosomes ,Human ,Female ,Genetic Association Studies ,Genetic Diseases ,Inborn ,Genetic Predisposition to Disease ,Genetics ,Population ,Homozygote ,Humans ,Intellectual Disability ,Intelligence Tests ,Male ,Pedigree ,Phenotype ,Sex Factors ,Simons Simplex Collection Genetics Consortium ,Medical and Health Sciences ,Genetics & Heredity ,Biological sciences ,Biomedical and clinical sciences ,Health sciences - Abstract
Intellectual disability (ID), often attributed to autosomal-recessive mutations, occurs in 40% of autism spectrum disorders (ASDs). For this reason, we conducted a genome-wide analysis of runs of homozygosity (ROH) in simplex ASD-affected families consisting of a proband diagnosed with ASD and at least one unaffected sibling. In these families, probands with an IQ ≤ 70 show more ROH than their unaffected siblings, whereas probands with an IQ > 70 do not show this excess. Although ASD is far more common in males than in females, the proportion of females increases with decreasing IQ. Our data do support an association between ROH burden and autism diagnosis in girls; however, we are not able to show that this effect is independent of low IQ. We have also discovered several autism candidate genes on the basis of finding (1) a single gene that is within an ROH interval and that is recurrent in autism or (2) a gene that is within an autism ROH block and that harbors a homozygous, rare deleterious variant upon analysis of exome-sequencing data. In summary, our data suggest a distinct genetic architecture for participants with autism and co-occurring intellectual disability and that this architecture could involve a role for recessively inherited loci for this autism subgroup.
- Published
- 2013
50. Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes.
- Author
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He, Xin, Sanders, Stephan, Liu, Li, De Rubeis, Silvia, Lim, Elaine, Sutcliffe, James, Schellenberg, Gerard, Gibbs, Richard, Daly, Mark, Buxbaum, Joseph, State, Matthew, Devlin, Bernie, and Roeder, Kathryn
- Subjects
Bayes Theorem ,Case-Control Studies ,Child Development Disorders ,Pervasive ,Exome ,Genetic Predisposition to Disease ,Genetic Variation ,Humans ,Likelihood Functions ,Models ,Theoretical ,Mutation ,Risk Factors ,Sequence Analysis ,DNA - Abstract
De novo mutations affect risk for many diseases and disorders, especially those with early-onset. An example is autism spectrum disorders (ASD). Four recent whole-exome sequencing (WES) studies of ASD families revealed a handful of novel risk genes, based on independent de novo loss-of-function (LoF) mutations falling in the same gene, and found that de novo LoF mutations occurred at a twofold higher rate than expected by chance. However successful these studies were, they used only a small fraction of the data, excluding other types of de novo mutations and inherited rare variants. Moreover, such analyses cannot readily incorporate data from case-control studies. An important research challenge in gene discovery, therefore, is to develop statistical methods that accommodate a broader class of rare variation. We develop methods that can incorporate WES data regarding de novo mutations, inherited variants present, and variants identified within cases and controls. TADA, for Transmission And De novo Association, integrates these data by a gene-based likelihood model involving parameters for allele frequencies and gene-specific penetrances. Inference is based on a Hierarchical Bayes strategy that borrows information across all genes to infer parameters that would be difficult to estimate for individual genes. In addition to theoretical development we validated TADA using realistic simulations mimicking rare, large-effect mutations affecting risk for ASD and show it has dramatically better power than other common methods of analysis. Thus TADAs integration of various kinds of WES data can be a highly effective means of identifying novel risk genes. Indeed, application of TADA to WES data from subjects with ASD and their families, as well as from a study of ASD subjects and controls, revealed several novel and promising ASD candidate genes with strong statistical support.
- Published
- 2013
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