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Activity Number: 237 - SPEED:Statistical Methods for GWAs, Genetics, Genomics, and Other Omics Studies, Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #306574
Title: Leveraging EQTLs to Identify Tissue-Specific Genetic Subtype of Complex Trait
Author(s): Arunabha Majumdar* and Claudia Giambartolomei and Na Cai and Malika Kumar Freund and Bogdan Pasaniuc
Companies: University of California, Los Angeles and University of California, Los Angeles and European Bioinformatics Institute (EMBL-EBI) and University of California, Los Angeles and University of California, Los Angeles
Keywords: Subtype; Complex trait; Tissue; Genetics; Mixture model; Expectation-maximization
Abstract:

If multiple tissue or cell-type specific biological pathways underlie a phenotype, it can be possible to identify the subtype of the phenotype which is defined by heterogeneous tissue-specific genetic architecture of the trait. For example, an individual's obesity can be regulated more by the set of genes higher expressed in brain compared to the set of genes higher expressed in adipose and vice versa. We aim to learn about such subtype structure of a complex trait based on individual-level data of marginal phenotype and genotypes of sets of expression quantitative trait loci (eQTLs), each corresponding to the set of genes higher expressed in a tissue. We propose a method eGST to detect tissue-specific genetic subtypes of a trait. Simulations show that if such subtype structure exists eGST can retrieve it meaningfully. We implemented eGST to subtype body mass index (BMI) in the UK Biobank cohort integrating expression data from the GTEx consortium. It identified groups of individuals whose BMI were classified as adipose and brain specific genetic subtype. The tissue-specific subtype groups of individuals for BMI were found genetically and phenotypically heterogeneous in UK Biobank.


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