An integrative approach to disease gene discovery combining genetic variation, gene expression, and epigenetics.

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PROJECT SUMMARY ABSTRACT Genome-wide association studies (GWASs) have uncovered hundreds of thousands of disease-associated genetic variations, but a remarkable disconnect persists between GWAS findings and biological insight required for clinical treatments and medicine advancement. Pinpointing the functional consequences of variants found in GWASs is complicated by linkage disequilibrium (LD) and the inability to interpret non-coding variations. Systematic genetic analysis of high-dimensional molecular and cellular datasets such as transcriptomics, epigenomics, proteomics, and metabolomics, offers the potential to bridge the gap from complex trait association to relevant biological processes yet poses unsolved computational and analytical challenges. The candidate proposes to address major gaps in existing methodologies for mapping the genetic basis of molecular phenotypes and integrating multi-omics data to improve disease gene discovery by developing a suite of open-source statistical methods and publicly available analytical resources. The candidate will: 1) develop a novel scalable statistical method to detect genome-wide expression quantitative trait loci (eQTL) using large- scale bulk or single-cell RNA sequencing (scRNA-seq) data with an extension for rare variants; 2) assemble and analyze more than 24 readily available bulk and scRNA-seq data sets for a comprehensive repository containing cis- and trans-eQTLs of both common and rare variations; 3) develop an integrative method to improve power for disease gene discovery by combining epigenetics, genome-wide eQTLs, and genetic variations. The proposed research and training plan were carefully designed to confer expertise in four domains: 1) transcriptomics and epigenomics, 2) statistical methods development, 3) large-scale data analysis and tools, and 4) professional development. These skills are fundamental to the candidate’s goal of becoming a leading investigator who develops and applies statistical methods to understand molecular mechanisms of complex diseases and traits. In addition to research training, the candidate will take coursework to gain greater expertise in transcriptomics and functional genomics, participate in regular seminars, attend workshops and conferences, and gain mentorship and teaching experience. All research will be conducted in the Analytic and Translational Genetics Unit at Massachusetts General Hospital and the Broad Institute with mentorship from renowned scientists Drs. Benjamin Neale and Mark Daly. Additional guidance from leading experts Drs. Xihong Lin, Ramnik Xavier, Kristin Ardlie, and Bradley Bernstein will ensure exceptional guidance and support. Overall, the training environment is outstanding, the mentors and advisors are world-class, the proposed studies address an urgent unmet need, and the additional skills gained in this award will poise the candidate to establish independent leadership in leveraging statistical genetics and large-scale multi-omics data for disentangling the etiology of complex diseases.