Lecture3.pdf - RESEARCH ARTICLE Methods for fine-mapping...

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RESEARCH ARTICLE Methods for fine-mapping with chromatin and expression data Megan Roytman 1 * , Gleb Kichaev 1 , Alexander Gusev 2,3 , Bogdan Pasaniuc 1,4,5 * 1 Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, California, United States of America, 2 Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, United States of America, 3 Division of Genetics, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America, 4 Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America, 5 Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America * [email protected] (MR); [email protected] (BP) Abstract Recent studies have identified thousands of regions in the genome associated with chroma- tin modifications, which may in turn be affecting gene expression. Existing works have used heuristic methods to investigate the relationships between genome, epigenome, and gene expression, but, to our knowledge, none have explicitly modeled the chain of causality whereby genetic variants impact chromatin, which impacts gene expression. In this work we introduce a new hierarchical fine-mapping framework that integrates information across all three levels of data to better identify the causal variant and chromatin mark that are concor- dantly influencing gene expression. In simulations we show that our method is more accu- rate than existing approaches at identifying the causal mark influencing expression. We analyze empirical genetic, chromatin, and gene expression data from 65 African-ancestry and 47 European-ancestry individuals and show that many of the paths prioritized by our method are consistent with the proposed causal model and often lie in likely functional regions. Author summary Genome-wide association studies (GWAS) have revealed that the majority of variants associated with complex disease lie in noncoding regulatory sequences. More recent stud- ies have identified thousands of quantitative trait loci (QTLs) associated with chromatin modifications, which in turn are associated with changes in gene regulation. Thus, one proposed mechanism by which genetic variants act on trait is through chromatin, which may in turn have downstream effects on transcription. In this work, we propose a method that assumes a causal path from genetic variation to chromatin to expression and inte- grates information across all three levels of data in order to identify the causal variant and chromatin mark that are likely influencing gene expression. We demonstrate in simula- tions that our probabilistic approach produces well-calibrated posterior probabilities and outperforms existing methods with respect to SNP-, mark-, and overall path-mapping.
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