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Shih-Chen Fu

Bio: Shih-Chen Fu is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Gene & Exome. The author has an hindex of 1, co-authored 1 publications receiving 1838 citations.

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Journal ArticleDOI
Silvia De Rubeis1, Xin-Xin He2, Arthur P. Goldberg1, Christopher S. Poultney1, Kaitlin E. Samocha3, A. Ercument Cicek2, Yan Kou1, Li Liu2, Menachem Fromer1, Menachem Fromer3, R. Susan Walker4, Tarjinder Singh5, Lambertus Klei6, Jack A. Kosmicki3, Shih-Chen Fu1, Branko Aleksic7, Monica Biscaldi8, Patrick Bolton9, Jessica M. Brownfeld1, Jinlu Cai1, Nicholas G. Campbell10, Angel Carracedo11, Angel Carracedo12, Maria H. Chahrour3, Andreas G. Chiocchetti, Hilary Coon13, Emily L. Crawford10, Lucy Crooks5, Sarah Curran9, Geraldine Dawson14, Eftichia Duketis, Bridget A. Fernandez15, Louise Gallagher16, Evan T. Geller17, Stephen J. Guter18, R. Sean Hill3, R. Sean Hill19, Iuliana Ionita-Laza20, Patricia Jiménez González, Helena Kilpinen, Sabine M. Klauck21, Alexander Kolevzon1, Irene Lee22, Jing Lei2, Terho Lehtimäki, Chiao-Feng Lin17, Avi Ma'ayan1, Christian R. Marshall4, Alison L. McInnes23, Benjamin M. Neale24, Michael John Owen25, Norio Ozaki7, Mara Parellada26, Jeremy R. Parr27, Shaun Purcell1, Kaija Puura, Deepthi Rajagopalan4, Karola Rehnström5, Abraham Reichenberg1, Aniko Sabo28, Michael Sachse, Stephen Sanders29, Chad M. Schafer2, Martin Schulte-Rüther30, David Skuse22, David Skuse31, Christine Stevens24, Peter Szatmari32, Kristiina Tammimies4, Otto Valladares17, Annette Voran33, Li-San Wang17, Lauren A. Weiss29, A. Jeremy Willsey29, Timothy W. Yu3, Timothy W. Yu19, Ryan K. C. Yuen4, Edwin H. Cook18, Christine M. Freitag, Michael Gill16, Christina M. Hultman34, Thomas Lehner35, Aarno Palotie36, Aarno Palotie3, Aarno Palotie24, Gerard D. Schellenberg17, Pamela Sklar1, Matthew W. State29, James S. Sutcliffe10, Christopher A. Walsh19, Christopher A. Walsh3, Stephen W. Scherer4, Michael E. Zwick37, Jeffrey C. Barrett5, David J. Cutler37, Kathryn Roeder2, Bernie Devlin6, Mark J. Daly24, Mark J. Daly3, Joseph D. Buxbaum1 
13 Nov 2014-Nature
TL;DR: Using exome sequencing, it is shown that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate of < 0.05, plus a set of 107 genes strongly enriched for those likely to affect risk (FDR < 0.30).
Abstract: The genetic architecture of autism spectrum disorder involves the interplay of common and rare variants and their impact on hundreds of genes. Using exome sequencing, here we show that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, plus a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic formation, transcriptional regulation and chromatin-remodelling pathways. These include voltage-gated ion channels regulating the propagation of action potentials, pacemaking and excitability-transcription coupling, as well as histone-modifying enzymes and chromatin remodellers-most prominently those that mediate post-translational lysine methylation/demethylation modifications of histones.

2,228 citations


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TL;DR: The remarkable range of discoveriesGWASs has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics are reviewed.
Abstract: Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.

2,669 citations

Journal ArticleDOI
15 Jun 2017-Cell
TL;DR: It is proposed that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways.

2,257 citations

Journal ArticleDOI
Naomi R. Wray1, Stephan Ripke2, Stephan Ripke3, Stephan Ripke4  +259 moreInstitutions (79)
TL;DR: A genome-wide association meta-analysis of individuals with clinically assessed or self-reported depression identifies 44 independent and significant loci and finds important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia.
Abstract: Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

1,898 citations

Journal ArticleDOI
James J. Lee1, Robbee Wedow2, Aysu Okbay3, Edward Kong4, Omeed Maghzian4, Meghan Zacher4, Tuan Anh Nguyen-Viet5, Peter Bowers4, Julia Sidorenko6, Julia Sidorenko7, Richard Karlsson Linnér3, Richard Karlsson Linnér8, Mark Alan Fontana9, Mark Alan Fontana5, Tushar Kundu5, Chanwook Lee4, Hui Li4, Ruoxi Li5, Rebecca Royer5, Pascal Timshel10, Pascal Timshel11, Raymond K. Walters12, Raymond K. Walters4, Emily A. Willoughby1, Loic Yengo6, Maris Alver7, Yanchun Bao13, David W. Clark14, Felix R. Day15, Nicholas A. Furlotte, Peter K. Joshi14, Peter K. Joshi16, Kathryn E. Kemper6, Aaron Kleinman, Claudia Langenberg15, Reedik Mägi7, Joey W. Trampush5, Shefali S. Verma17, Yang Wu6, Max Lam, Jing Hua Zhao15, Zhili Zheng18, Zhili Zheng6, Jason D. Boardman2, Harry Campbell14, Jeremy Freese19, Kathleen Mullan Harris20, Caroline Hayward14, Pamela Herd13, Pamela Herd21, Meena Kumari13, Todd Lencz22, Todd Lencz23, Jian'an Luan15, Anil K. Malhotra22, Anil K. Malhotra23, Andres Metspalu7, Lili Milani7, Ken K. Ong15, John R. B. Perry15, David J. Porteous14, Marylyn D. Ritchie17, Melissa C. Smart14, Blair H. Smith24, Joyce Y. Tung, Nicholas J. Wareham15, James F. Wilson14, Jonathan P. Beauchamp25, Dalton Conley26, Tõnu Esko7, Steven F. Lehrer27, Steven F. Lehrer28, Steven F. Lehrer29, Patrik K. E. Magnusson30, Sven Oskarsson31, Tune H. Pers10, Tune H. Pers11, Matthew R. Robinson6, Matthew R. Robinson32, Kevin Thom33, Chelsea Watson5, Christopher F. Chabris17, Michelle N. Meyer17, David Laibson4, Jian Yang6, Magnus Johannesson34, Philipp Koellinger8, Philipp Koellinger3, Patrick Turley12, Patrick Turley4, Peter M. Visscher6, Daniel J. Benjamin5, Daniel J. Benjamin29, David Cesarini29, David Cesarini33 
TL;DR: A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance ineducational attainment and 7–10% ofthe variance in cognitive performance, which substantially increases the utility ofpolygenic scores as tools in research.
Abstract: Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

1,658 citations

Journal ArticleDOI
22 Jun 2018-Science
TL;DR: It is demonstrated that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine, and it is shown that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures.
Abstract: Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

1,357 citations