A Bayesian Framework for Multiple Trait Colocalization from Summary Association Statistics
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Wen Zhang | Joseph K. Pickrell | Andrew E. Jaffe | Claudia Giambartolomei | Bogdan Pasaniuc | Huwenbo Shi | Joe Pickrell | James Boocock | Panos Roussos | Jimmy Zhenli Liu | Mads Hauberg | The CommonMind Consortium | A. Jaffe | Wen Zhang | B. Pasaniuc | Huwenbo Shi | M. Hauberg | P. Roussos | C. Giambartolomei | J. Boocock | J. Liu
[1] Daniel R Weinberger,et al. Mapping DNA methylation across development, genotype, and schizophrenia in the human frontal cortex , 2015, Nature Neuroscience.
[2] Xiaoquan Wen,et al. Integrating Molecular QTL Data into Genome-wide Genetic Association Analysis: Probabilistic Assessment of Enrichment and Colocalization , 2016 .
[3] C. Wallace,et al. Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics , 2013, PLoS genetics.
[4] Matthew Stephens,et al. BAYESIAN METHODS FOR GENETIC ASSOCIATION ANALYSIS WITH HETEROGENEOUS SUBGROUPS: FROM META-ANALYSES TO GENE-ENVIRONMENT INTERACTIONS. , 2011, The annals of applied statistics.
[5] Hongyu Zhao,et al. GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation , 2014, PLoS genetics.
[6] C. Wallace. Statistical Testing of Shared Genetic Control for Potentially Related Traits , 2013, Genetic epidemiology.
[7] Manolis Kellis,et al. Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases , 2016, Nucleic acids research.
[8] Eric E Schadt,et al. Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression. , 2017, American journal of human genetics.
[9] Mary D. Fortune,et al. Integration of disease association and eQTL data using a Bayesian colocalisation approach highlights six candidate causal genes in immune-mediated diseases , 2015, Human molecular genetics.
[10] J. Mill,et al. Methylation quantitative trait loci in the developing brain and their enrichment in schizophrenia-associated genomic regions , 2015, Nature neuroscience.
[11] P. Visscher,et al. Five years of GWAS discovery. , 2012, American journal of human genetics.
[12] E. Eskin,et al. Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies , 2014, PLoS genetics.
[13] Bogdan Pasaniuc,et al. Local genetic correlation gives insights into the shared genetic architecture of complex traits , 2016, bioRxiv.
[14] M. Stephens,et al. Bayesian statistical methods for genetic association studies , 2009, Nature Reviews Genetics.
[15] Eleazar Eskin,et al. Colocalization of GWAS and eQTL Signals Detects Target Genes , 2016 .
[16] Tomaz Berisa,et al. Detection and interpretation of shared genetic influences on 40 human traits , 2015 .
[17] Joseph K. Pickrell,et al. Detection and interpretation of shared genetic influences on 42 human traits , 2015, Nature Genetics.
[18] Joseph K. Pickrell. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits , 2013, bioRxiv.
[19] Benjamin Neale,et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights , 2016 .
[20] David A. Knowles,et al. RNA splicing is a primary link between genetic variation and disease , 2016, Science.
[21] Joel Dudley,et al. Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia , 2016 .
[22] Cleanthe Spanaki,et al. The Relationship of Common Risk Variants and Polygenic Risk for Schizophrenia to Sensorimotor Gating , 2016, Biological Psychiatry.
[23] M. Daly,et al. An Atlas of Genetic Correlations across Human Diseases and Traits , 2015, Nature Genetics.
[24] C. Spencer,et al. Biological Insights From 108 Schizophrenia-Associated Genetic Loci , 2014, Nature.
[25] Robin M. Murray,et al. An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation , 2016, Genome Biology.
[26] M. Pirinen,et al. Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies. , 2017, American journal of human genetics.
[27] Jon Wakefield,et al. Bayes factors for genome‐wide association studies: comparison with P‐values , 2009, Genetic epidemiology.
[28] Daniel Marbach,et al. Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics , 2016, PLoS Comput. Biol..
[29] M. O’Donovan,et al. Pleiotropic effects of trait-associated genetic variation on DNA methylation: utility for refining , 2018 .
[30] P. Farnham,et al. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome , 2015, Epigenetics & Chromatin.
[31] J. T. Williams,et al. SK2 and SK3 expression differentially affect firing frequency and precision in dopamine neurons , 2012, Neuroscience.
[32] P. Visscher,et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets , 2016, Nature Genetics.