Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration

[1]  Paul Franken,et al.  Faculty Opinions recommendation of Harnessing Genetic Complexity to Enhance Translatability of Alzheimer's Disease Mouse Models: A Path toward Precision Medicine. , 2020 .

[2]  E. Chesler,et al.  Finding human gene-disease associations using a Network Enhanced Similarity Search (NESS) of multi-species heterogeneous functional genomics data , 2020, bioRxiv.

[3]  Shiva Kumar,et al.  Multi-omics Data Integration, Interpretation, and Its Application , 2020, Bioinformatics and biology insights.

[4]  P. Visscher,et al.  Cross-Species Integration of Transcriptomic Effects of Tobacco and Nicotine Exposure Helps to Prioritize Genetic Effects on Human Tobacco Consumption , 2019, bioRxiv.

[5]  W. Cahn,et al.  Evolutionary modifications in human brain connectivity associated with schizophrenia , 2019, Brain : a journal of neurology.

[6]  Stephen C. Grubb,et al.  Mouse Phenome Database: a data repository and analysis suite for curated primary mouse phenotype data , 2019, Nucleic Acids Res..

[7]  G. Lin,et al.  Comparative analysis of cellular expression pattern of schizophrenia risk genes in human versus mouse cortex , 2019, Cell & Bioscience.

[8]  O. Andreassen,et al.  The emerging pattern of shared polygenic architecture of psychiatric disorders, conceptual and methodological challenges. , 2019, Psychiatric genetics.

[9]  Anushya Muruganujan,et al.  Alliance of Genome Resources Portal: unified model organism research platform , 2019, Nucleic Acids Res..

[10]  A. McQuillin,et al.  Meta-analysis of problematic alcohol use in 435,563 individuals identifies 29 risk variants and yields insights into biology, pleiotropy and causality , 2019, bioRxiv.

[11]  Hinrich W. H. Göhlmann,et al.  A genetics-led approach defines the drug target landscape of 30 immune-related traits , 2019, Nature Genetics.

[12]  Harper B. Fauni,et al.  Connecting gene regulatory relationships to neurobiological mechanisms of brain disorders , 2019, bioRxiv.

[13]  P. Tsao,et al.  Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations , 2019, Nature Communications.

[14]  Thao T. T. Nguyen,et al.  A biologically-informed polygenic score identifies endophenotypes and clinical conditions associated with the insulin receptor function on specific brain regions , 2019, EBioMedicine.

[15]  Ryan D. Hernandez,et al.  Recovery of trait heritability from whole genome sequence data , 2019, bioRxiv.

[16]  J. Sutcliffe,et al.  A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data , 2019, Nature Neuroscience.

[17]  Shizhong Xu,et al.  Statistical power in genome-wide association studies and quantitative trait locus mapping , 2019, Heredity.

[18]  D. Geschwind,et al.  Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders , 2019, Cell.

[19]  H. de Wit,et al.  Genome-Wide Association Studies of Impulsive Personality Traits (BIS-11 and UPPS-P) and Drug Experimentation in up to 22,861 Adult Research Participants Identify Loci in the CACNA1I and CADM2 genes , 2019, The Journal of Neuroscience.

[20]  R. Marioni,et al.  Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions , 2019, Nature Neuroscience.

[21]  M. Huentelman,et al.  Harnessing Genetic Complexity to Enhance Translatability of Alzheimer’s Disease Mouse Models: A Path toward Precision Medicine , 2019, Neuron.

[22]  Hunna J. Watson,et al.  Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders , 2019, bioRxiv.

[23]  Daniel A. Skelly,et al.  Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples , 2018, Genetics.

[24]  John P. Rice,et al.  Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use , 2018, Nature Genetics.

[25]  Laurent Gil,et al.  Ensembl variation resources , 2018, Database J. Biol. Databases Curation.

[26]  S. Bacanu,et al.  Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data , 2018, bioRxiv.

[27]  Alkes L. Price,et al.  Modeling functional enrichment improves polygenic prediction accuracy in UK Biobank and 23andMe data sets , 2018, bioRxiv.

[28]  Arcadi Navarro,et al.  Replicability and Prediction: Lessons and Challenges from GWAS. , 2018, Trends in genetics : TIG.

[29]  A. Levey,et al.  Identification and therapeutic modulation of a pro-inflammatory subset of disease-associated-microglia in Alzheimer’s disease , 2018, Molecular Neurodegeneration.

[30]  A. Chen-Plotkin,et al.  The Post-GWAS Era: From Association to Function. , 2018, American journal of human genetics.

[31]  I. Deary,et al.  Genome-wide association study meta-analysis of the Alcohol Use Disorder Identification Test (AUDIT) in two population-based cohorts (N=141,932) , 2018, bioRxiv.

[32]  Ting Qi,et al.  Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits , 2018, Nature Communications.

[33]  M. G. van der Wijst,et al.  Single-cell RNA sequencing identifies cell type-specific cis-eQTLs and co-expression QTLs , 2018, Nature Genetics.

[34]  A. Gusev,et al.  Probabilistic fine-mapping of transcriptome-wide association studies , 2017, Nature Genetics.

[35]  D. Posthuma,et al.  Functional mapping and annotation of genetic associations with FUMA , 2017, Nature Communications.

[36]  Kathryn S. Burch,et al.  Leveraging polygenic functional enrichment to improve GWAS power , 2017, bioRxiv.

[37]  Dennis A. Benson,et al.  GenBank , 2017, Nucleic Acids Res..

[38]  Elissa J. Chesler,et al.  Mouse Phenome Database: an integrative database and analysis suite for curated empirical phenotype data from laboratory mice , 2017, Nucleic Acids Res..

[39]  P. Visscher,et al.  Multi-trait analysis of genome-wide association summary statistics using MTAG , 2017, Nature Genetics.

[40]  Wei Liu,et al.  Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction , 2017, PLoS genetics.

[41]  R. Guigó,et al.  Comparative transcriptomics in human and mouse , 2017, Nature Reviews Genetics.

[42]  Vladimir I. Vladimirov,et al.  Genomewide Association Study of Alcohol Dependence Identifies Risk Loci Altering Ethanol‐Response Behaviors in Model Organisms , 2017, Alcoholism, clinical and experimental research.

[43]  Uwe Völker,et al.  Predicting brain structure in population‐based samples with biologically informed genetic scores for schizophrenia , 2017, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[44]  Peter K. Joshi,et al.  KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference , 2016, Proceedings of the National Academy of Sciences.

[45]  Tudor Groza,et al.  The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species , 2016, bioRxiv.

[46]  J. Crabbe Progress With Nonhuman Animal Models of Addiction. , 2016, Journal of studies on alcohol and drugs.

[47]  Steve D. M. Brown,et al.  High-throughput discovery of novel developmental phenotypes , 2017 .

[48]  Jens Hjerling-Leffler,et al.  Disentangling neural cell diversity using single-cell transcriptomics , 2016, Nature Neuroscience.

[49]  Andrew D. Rouillard,et al.  The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins , 2016, Database J. Biol. Databases Curation.

[50]  Hongyu Zhao,et al.  Leveraging functional annotations in genetic risk prediction for human complex diseases , 2016, bioRxiv.

[51]  Y. Ofran,et al.  How far from the SNP may the causative genes be? , 2016, Nucleic acids research.

[52]  Mary Goldman,et al.  Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics , 2016, Nature Communications.

[53]  F. Cunningham,et al.  The Ensembl Variant Effect Predictor , 2016, Genome Biology.

[54]  E. Birney,et al.  Ensembl regulation resources , 2016, Database J. Biol. Databases Curation.

[55]  Giulio Genovese,et al.  Schizophrenia risk from complex variation of complement component 4 , 2016, Nature.

[56]  Michael A. Langston,et al.  GeneWeaver: data driven alignment of cross-species genomics in biology and disease , 2015, Nucleic Acids Res..

[57]  Michael J. Purcaro,et al.  The PsychENCODE project , 2015, Nature Neuroscience.

[58]  Damian Smedley,et al.  Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency , 2015, Genetics in Medicine.

[59]  R. Weinshilboum,et al.  KnowEnG: a knowledge engine for genomics , 2015, J. Am. Medical Informatics Assoc..

[60]  P. Visscher,et al.  Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores , 2015, bioRxiv.

[61]  T. Lehtimäki,et al.  Integrative approaches for large-scale transcriptome-wide association studies , 2015, Nature Genetics.

[62]  M. Schatz,et al.  Big Data: Astronomical or Genomical? , 2015, PLoS biology.

[63]  E. Chesler,et al.  GeneWeaver: finding consilience in heterogeneous cross-species functional genomics data , 2015, Mammalian Genome.

[64]  G. Kempermann Faculty Opinions recommendation of Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. , 2015 .

[65]  Jun S. Liu,et al.  The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans , 2015, Science.

[66]  Daniel S. Himmelstein,et al.  Understanding multicellular function and disease with human tissue-specific networks , 2015, Nature Genetics.

[67]  Joris M. Mooij,et al.  MAGMA: Generalized Gene-Set Analysis of GWAS Data , 2015, PLoS Comput. Biol..

[68]  Albert-László Barabási,et al.  A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome , 2015, PLoS Comput. Biol..

[69]  Jack Euesden,et al.  PRSice: Polygenic Risk Score software , 2014, Bioinform..

[70]  Erik L. L. Sonnhammer,et al.  InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic , 2014, Nucleic Acids Res..

[71]  Mukul S. Bansal,et al.  A comparative encyclopedia of DNA elements in the mouse genome , 2014, Nature.

[72]  E. Eskin,et al.  Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies , 2014, PLoS genetics.

[73]  C. Spencer,et al.  Biological Insights From 108 Schizophrenia-Associated Genetic Loci , 2014, Nature.

[74]  Jeremy J. Jay,et al.  Identification of a QTL in Mus musculus for Alcohol Preference, Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics , 2014, Genetics.

[75]  Xia Li,et al.  LincSNP: a database of linking disease-associated SNPs to human large intergenic non-coding RNAs , 2014, BMC Bioinformatics.

[76]  Sandhya Ramrakha,et al.  The p Factor , 2014, Clinical psychological science : a journal of the Association for Psychological Science.

[77]  Damian Smedley,et al.  Improved exome prioritization of disease genes through cross-species phenotype comparison , 2014, Genome research.

[78]  Sivasankaran Rajamanickam,et al.  Scalable matrix computations on large scale-free graphs using 2D graph partitioning , 2013, 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[79]  Damian Smedley,et al.  PhenoDigm: analyzing curated annotations to associate animal models with human diseases , 2013, Database J. Biol. Databases Curation.

[80]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[81]  F. Dudbridge Power and Predictive Accuracy of Polygenic Risk Scores , 2013, PLoS genetics.

[82]  Gabriel Kliot,et al.  Streaming graph partitioning for large distributed graphs , 2012, KDD.

[83]  Raymond K. Auerbach,et al.  An Integrated Encyclopedia of DNA Elements in the Human Genome , 2012, Nature.

[84]  J. Crabbe,et al.  Translational behaviour‐genetic studies of alcohol: are we there yet? , 2012, Genes, brain, and behavior.

[85]  Michael A. Langston,et al.  Genetic Dissection of Acute Ethanol Responsive Gene Networks in Prefrontal Cortex: Functional and Mechanistic Implications , 2012, PloS one.

[86]  Michael A. Langston,et al.  GeneWeaver: a web-based system for integrative functional genomics , 2011, Nucleic Acids Res..

[87]  Morris A. Swertz,et al.  Bioinformatics tools and database resources for systems genetics analysis in mice—a short review and an evaluation of future needs , 2011, Briefings Bioinform..

[88]  N. Grahame,et al.  Derivation and Characterization of Replicate High- and Low-Alcohol Preferring Lines of Mice and a High-Drinking Crossed HAP Line , 2011, Behavior genetics.

[89]  S. Hyman,et al.  Animal models of neuropsychiatric disorders , 2010, Nature Neuroscience.

[90]  T. Mikkelsen,et al.  The NIH Roadmap Epigenomics Mapping Consortium , 2010, Nature Biotechnology.

[91]  H. Hakonarson,et al.  ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data , 2010, Nucleic acids research.

[92]  Jeremy J. Jay,et al.  Ontological Discovery Environment: a system for integrating gene-phenotype associations. , 2009, Genomics.

[93]  Monte Westerfield,et al.  Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation , 2009, PLoS biology.

[94]  John Wilbanks,et al.  'Omics Data Sharing , 2009, Science.

[95]  M. Gerstein,et al.  Unlocking the secrets of the genome , 2009, Nature.

[96]  Matthieu Latapy,et al.  Main-memory triangle computations for very large (sparse (power-law)) graphs , 2008, Theor. Comput. Sci..

[97]  Wei Wang,et al.  The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics , 2007, Mammalian Genome.

[98]  Robert W. Williams,et al.  Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function , 2005, Nature Genetics.

[99]  J. Crabbe,et al.  Chromosomal loci influencing chronic alcohol withdrawal severity , 2003, Mammalian Genome.

[100]  Pjotr Prins,et al.  GeneNetwork: A Toolbox for Systems Genetics. , 2017, Methods in molecular biology.

[101]  E. Nestler,et al.  Animal models of depression: molecular perspectives. , 2011, Current topics in behavioral neurosciences.

[102]  H. Kinoshita,et al.  Different blood acetaldehyde concentration following ethanol administration in a newly developed high alcohol preference and low alcohol preference rat model system. , 2002, Alcohol and alcoholism.

[103]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..