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上海精神医学 2016年第2期28卷 115-120页

作者： Liu JY Tang W Chen GQ Lu Y Feng CY Tu XM Department of Biostatistics and Computational Biology University of Rochester Rochester NY USA Department of Biostatistics & Bioinformatics School of Public Health and Tropical Medicine Tulane University New Orleans LA USA VA Cooperative Studies Program Palo Alto Coordinating Center VA Palo Alto Health Care System Palo Alto CA USA Department of Biomedical Data Science Stanford University Stanford CA USA

一致性(agreement)和相关性(correlation)是两个广泛使用的概念,用来评估变量之间的关联。虽然二者相似且相关,但是它们代表关联完全不同的概念。评估变量之间的一致性假设变量测量的是相同的结构,而在变量测量完全不同的结构时也可以... 详细信息

一致性(agreement)和相关性(correlation)是两个广泛使用的概念,用来评估变量之间的关联。虽然二者相似且相关,但是它们代表关联完全不同的概念。评估变量之间的一致性假设变量测量的是相同的结构,而在变量测量完全不同的结构时也可以评估它们之间的相关性。这种概念上的差异就要求使用不同的统计方法,并且当评估一致性或相关性时,统计方法根据数据的分布和研究者的兴趣可能会有所不同。例如,Pearson相关性,作为评估连续变量之间相关性的一种普遍测量方法,只有用于符合线性关系的变量时才能提供有用的信息;当用于不符合线性关系的变量时就无法提供准确信息甚至会产生误导。同样地,内部相关性,作为一种评估连续变量之间一致性的常用方法,如果一致性不好的实质正好是研究兴趣所在,那么该测量就不能为研究者提供充分的信息。本报告回顾了一致性和相关性的概念,并讨论了几种常用方法在应用中的差异。

关键词：积差相关性内部一致性 Kendall's tau 非线性相关 Pearson's相关性 Spearman's rho

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DOMMINO 2.0: Integrating structurally resolved protein-, RNA-, and DNA-mediated Macromolecular interactions

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Database 2016年第2016期2016卷 bav114页

作者： Kuang, Xingyan Dhroso, Andi Han, Jing Ginger Shyu, Chi-Ren Korkin, Dmitry Informatics Institute University of Missouri Columbia MO United States Department of Computer Science Bioinformatics and Computational Biology Program Worcester Polytechnic Institute Worcester MA United States Department of Electrical and Computer Engineering United States Department of Computer Science University of Missouri Columbia MO United States Ohio State University Comprehensive Cancer Center Columbus OH United States

Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction's mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein-protein interactions or protein-DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1 040 000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43 000 RNA-mediated interactions, and ∼12 000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N-termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary intera

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A high-resolution transcriptome map of cell cycle reveals novel connections between periodic genes and cancer

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细胞研究（英文版） 2016年第8期26卷 946-962页

作者： Daniel Dominguez Yi-Hsuan Tsai Nicholas Gomez Deepak Kumar Jha Ian Davis Zefeng Wang Department of Pharmacology University of North Carolina at Chapel Hill Chapel Hill NC USA Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Pharmacology University of North Carolina at Chapel Hill Chapel Hill NC USA program in Bioinformatics and Computational Biology University of North Carolina at Chapel Hill Chapel Hill NC USA Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill NC USA Curriculum in Genetics and Molecular Biology University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Biochemistry and Biophysics University of North Carolina at Chapel Hill Chapel Hill NC USA Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Genetics University of North Carolina at Chapel HillChapel Hill NC USA Department of Pediatrics University of North Carolina at Chapel Hill Chapel Hill NC USA Carolina Center for Genome Sciences University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Pharmacology University of North Carolina at Chapel Hill Chapel Hill NC USA Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill NC USA Key Lab of Computational Biology CAS-MPG Partner Institute for Computational Biology Chinese Academy of Science Shanghai China

Progression through the cell cycle is largely dependent on waves of periodic gene expression, and the regulatory networks for these transcriptome dynamics have emerged as critical points of vulnerability in various aspects of tumor biology. Through RNA-sequencing of human cells during two continuous cell cycles (>2.3 billion paired reads), we identified over 1 000 mRNAs, non-coding RNAs and pseudogenes with periodic expression. Periodic transcripts are enriched in functions related to DNA metabolism, mitosis, and DNA damage response, indicating these genes likely represent putative cell cycle regulators. Using our set of periodic genes, we developed a new approach termed “mitotic trait” that can classify primary tumors and normal tissues by their transcriptome similarity to different cell cycle stages. By analyzing >4 000 tumor samples in The Cancer Genome Atlas (TCGA) and other expression data sets, we found that mitotic trait significantly correlates with genetic alterations, tumor subtype and, notably, patient survival. We further defined a core set of 67 genes with robust periodic expression in multiple cell types. Proteins encoded by these genes function as major hubs of protein-protein interaction and are mostly required for cell cycle progression. The core genes also have unique chromatin features including increased levels of CTCF/RAD21 binding and H3K36me3. Loss of these features in uterine and kidney cancers is associated with altered expression of the core 67 genes. Our study suggests new chromatin-associated mechanisms for periodic gene regulation and offers a predictor of cancer patient outcomes.

关键词： cell cycle periodic transcriptome epigenetic regulation mitotic trait

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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

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Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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Author Correction: A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers

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Nature communications 2021年第1期12卷 2986页

作者： Juliette Coignard Michael Lush Jonathan Beesley Tracy A O'Mara Joe Dennis Jonathan P Tyrer Daniel R Barnes Lesley McGuffog Goska Leslie Manjeet K Bolla Muriel A Adank Simona Agata Thomas Ahearn Kristiina Aittomäki Irene L Andrulis Hoda Anton-Culver Volker Arndt Norbert Arnold Kristan J Aronson Banu K Arun Annelie Augustinsson Jacopo Azzollini Daniel Barrowdale Caroline Baynes Heko Becher Marina Bermisheva Leslie Bernstein Katarzyna Białkowska Carl Blomqvist Stig E Bojesen Bernardo Bonanni Ake Borg Hiltrud Brauch Hermann Brenner Barbara Burwinkel Saundra S Buys Trinidad Caldés Maria A Caligo Daniele Campa Brian D Carter Jose E Castelao Jenny Chang-Claude Stephen J Chanock Wendy K Chung Kathleen B M Claes Christine L Clarke J Margriet Collée Don M Conroy Kamila Czene Mary B Daly Peter Devilee Orland Diez Yuan Chun Ding Susan M Domchek Thilo Dörk Isabel Dos-Santos-Silva Alison M Dunning Miriam Dwek Diana M Eccles A Heather Eliassen Christoph Engel Mikael Eriksson D Gareth Evans Peter A Fasching Henrik Flyger Florentia Fostira Eitan Friedman Lin Fritschi Debra Frost Manuela Gago-Dominguez Susan M Gapstur Judy Garber Vanesa Garcia-Barberan Montserrat García-Closas José A García-Sáenz Mia M Gaudet Simon A Gayther Andrea Gehrig Vassilios Georgoulias Graham G Giles Andrew K Godwin Mark S Goldberg David E Goldgar Anna González-Neira Mark H Greene Pascal Guénel Lothar Haeberle Eric Hahnen Christopher A Haiman Niclas Håkansson Per Hall Ute Hamann Patricia A Harrington Steven N Hart Wei He Frans B L Hogervorst Antoinette Hollestelle John L Hopper Darling J Horcasitas Peter J Hulick David J Hunter Evgeny N Imyanitov Agnes Jager Anna Jakubowska Paul A James Uffe Birk Jensen Esther M John Michael E Jones Rudolf Kaaks Pooja Middha Kapoor Beth Y Karlan Renske Keeman Elza Khusnutdinova Johanna I Kiiski Yon-Dschun Ko Veli-Matti Kosma Peter Kraft Allison W Kurian Yael Laitman Diether Lambrechts Loic Le Marchand Jenny Lester Fabienne Lesueur Tricia Lindstrom Adria Lopez-Fernández Jennifer T Loud Craig Luccarini Arto Mannermaa Siranou Genetic Epidemiology of Cancer team Inserm U900 Paris France. Institut Curie Paris Paris France. Mines ParisTech Fontainebleau Paris France. Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge Cambridge UK. PSL University Paris Paris France. Paris Sud University Orsay France. Department of Genetics and Computational Biology QIMR Berghofer Medical Research Institute Brisbane QLD Australia. Centre for Cancer Genetic Epidemiology Department of Oncology University of Cambridge Cambridge UK. Family Cancer Clinic The Netherlands Cancer Institute Antoni van Leeuwenhoek hospital Amsterdam The Netherlands. Immunology and Molecular Oncology Unit Veneto Institute of Oncology IOV - IRCCS Padua Italy. Division of Cancer Epidemiology and Genetics National Cancer Institute National Institutes of Health Department of Health and Human Services Bethesda MD USA. Department of Clinical Genetics Helsinki University Hospital University of Helsinki Helsinki Finland. Fred A. Litwin Center for Cancer Genetics Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Toronto ON Canada. Department of Molecular Genetics University of Toronto Toronto ON Canada. Department of Epidemiology Genetic Epidemiology Research Institute University of California Irvine Irvine CA USA. Division of Clinical Epidemiology and Aging Research German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Gynaecology and Obstetrics University Hospital of Schleswig-Holstein Campus Kiel Christian-Albrechts University Kiel Kiel Germany. Institute of Clinical Molecular Biology University Hospital of Schleswig-Holstein Campus Kiel Christian-Albrechts University Kiel Kiel Germany. Department of Public Health Sciences and Cancer Research Institute Queen's University Kingston ON Canada. Department of Breast Medical Oncology University of Texas MD Anderson Cancer Center Houston TX USA. Department of Cancer Epidemiology Clinical Sciences

A Correction to this paper has been published: https://***/10.1038/s41467-021-23162-4.

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HBS-Tools for Hairpin Bisulfite Sequencing Data Processing and Analysis

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Advances in bioinformatics 2015年第1期2015卷 760423页

作者： Sun, Ming-An Velmurugan, Karthik Raja Keimig, David Xie, Hehuang Epigenomics and Computational Biology Lab Virginia Bioinformatics Institute Virginia Tech Blacksburg 24061 VA United States Interdisciplinary Ph.D. Program in Genomics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24061 VA United States Department of Biological Sciences Virginia Tech Blacksburg 24061 VA United States

The emerging genome-wide hairpin bisulfite sequencing (hairpin-BS-Seq) technique enables the determination of the methylation pattern for DNA double strands simultaneously. Compared with traditional bisulfite sequencing (BS-Seq) techniques, hairpin-BS-Seq can determine methylation fidelity and increase mapping efficiency. However, no computational tool has been designed for the analysis of hairpin-BS-Seq data yet. Here we present HBS-tools, a set of command line based tools for the preprocessing, mapping, methylation calling, and summarizing of genome-wide hairpin-BS-Seq data. It accepts paired-end hairpin-BS-Seq reads to recover the original (pre-bisulfite-converted) sequences using global alignment and then calls the methylation statuses for cytosines on both DNA strands after mapping the original sequences to the reference genome. After applying to hairpin-BS-Seq datasets, we found that HBS-tools have a reduced mapping time and improved mapping efficiency compared with state-of-the-art mapping tools. The HBS-tools source scripts, along with user guide and testing data, are freely available for download. © 2015 Ming-an Sun et al.

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Cover Image, Volume 84, Issue 12

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Proteins: Structure, Function, and bioinformatics 2016年第12期84卷

作者： Hyuntae Na Guang Song Department of Computer Science Penn State Harrisburg Middletown Pennsylvania 17057 Department of Computer Science Iowa State University Ames Iowa 50011 Program of Bioinformatics and Computational Biology Iowa State University Ames Iowa 50011 L. H. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames Iowa 50011

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Donor Selection for Hematopoietic Stem Cell Transplant Using Cost-Sensitive SVM

Donor Selection for Hematopoietic Stem Cell Transplant Using...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Adarsh Sivasankaran Vladimir Cherkassky Mark Albrecht Eric Williams Martin Maiers Biomedical Informatics and Computational Biology Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota U.S.A. Bioinformatics Research National Marrow Donor Program Minneapolis Minnesota U.S.A.

ISBN: (纸本)9781509002887

Donor selection for Hematopoietic Stem Cell Transplant often requires physicians to manually select 3 to 5 donors from a list of 100s of genetically compatible donors as identified by HLA-based matching algorithms. The decision process is complicated by a lack of strict guidelines governing a "secondary" selection process, which is based upon non-HLA donor attributes. Our research is aimed at modeling this "secondary" decision process which can help physicians choose the right donors, based on donor attributes and historical choice behavior. Proposed black box models will help in improving selection consistency.

关键词： Medical services Support vector machines Data models Stem cells Mathematical model Numerical models Training

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Comprehensive Functional Characterization of Germline ETV6 Variants Associated with Inherited Predisposition to Acute Lymphoblastic Leukemia in Children

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BLOOD 2016年第22期128卷 1085-1085页

作者： Nishii, Rina Baskin, Rebekah Moriyama, Takaya Hoshitsuki, Keito Metzger, Monika L. Wu, Gang Devidas, Meenakshi Yang, Wenjian Tedrick, Paige Quinn, Emily Gastier-Foster, Julie M. Raetz, Elizabeth Larsen, Eric Martin, Paul L. Bowman, W. Paul Winick, Naomi Takagi, Masatoshi Pui, Ching-Hon Evans, William E. Zhang, Jinghui Relling, Mary V. Hunger, Stephen P. Mullighan, Charles Loh, Mignon L. Nichols, Kim E. Yang, Jun J. Hematological Malignancies Program and Department of Pharmaceutical Sciences St. Jude Children's Research Hospital Memphis TN Department of Pediatrics and Developmental Biology Tokyo Medical and Dental University Graduate School of Medicine Tokyo Japan Department of Oncology St. Jude Children's Research Hospital Memphis TN School of Pharmacy University of Pittsburgh Pittsburgh PA Department of Computational Biology St. Jude Children's Research Hospital Memphis TN Department of Biostatistics College of Medicine Public Health and Health Professions University of Florida Gainesville FL Department of Pathology and Laboratory Medicine Nationwide Children's Hospital Columbus OH Huntsman Cancer Institute University of Utah Salt Lake City UT Maine Children's Cancer Program Scarborough ME Department of Pediatrics Duke University Durham NC Cook Children's Medical Center Fort Worth TX Pediatric Hematology Oncology University of Texas Southwestern Medical Center Dallas TX Oncology St. Jude Children's Research Hospital Memphis TN Department of Computational Biology and Bioinformatics St. Jude Children's Research Hospital Memphis TN

1085

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

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.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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