A Bayesian Framework for Multiple Trait Colocalization from Summary Association Statistics

Motivation: Most genetic variants implicated in complex diseases by genome‐wide association studies (GWAS) are non‐coding, making it challenging to understand the causative genes involved in disease. Integrating external information such as quantitative trait locus (QTL) mapping of molecular traits (e.g. expression, methylation) is a powerful approach to identify the subset of GWAS signals explained by regulatory effects. In particular, expression QTLs (eQTLs) help pinpoint the responsible gene among the GWAS regions that harbor many genes, while methylation QTLs (mQTLs) help identify the epigenetic mechanisms that impact gene expression which in turn affect disease risk. In this work, we propose multiple‐trait‐coloc (moloc), a Bayesian statistical framework that integrates GWAS summary data with multiple molecular QTL data to identify regulatory effects at GWAS risk loci. Results: We applied moloc to schizophrenia (SCZ) and eQTL/mQTL data derived from human brain tissue and identified 52 candidate genes that influence SCZ through methylation. Our method can be applied to any GWAS and relevant functional data to help prioritize disease associated genes. Availability and implementation: moloc is available for download as an R package (https://github.com/clagiamba/moloc). We also developed a web site to visualize the biological findings (icahn.mssm.edu/moloc). The browser allows searches by gene, methylation probe and scenario of interest. Supplementary information: Supplementary data are available at Bioinformatics online.

[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.