Thursday, April 18, 2019

Thursday, April 18, 2019 — 2:30 PM EDT

Regional-level genetic association testing under genomic partitioning adapted to local linkage disequilibrium

Motivated by characterizations of genomic architecture where multiple-variant analysis can uncover novel associations missed by single-variant analysis, we consider computationally efficient regression-based testing methods for regional genomic discovery, including genomic partitioning, that are feasible for genome-wide processing. To address the challenging question of how to specify appropriate regional units, we apply a novel haplotype block detection algorithm that uses interval graph modeling to cluster correlated variants and partition the genome into a large number of non-overlapping and quasi-independent linkage disequilibrium block regions. Within each block, we specify multiple-variant global test statistics with reduced dimension that maybe subject to multi-level testing. I will discuss some of the theoretical and practical issues we face in applications to quantitative trait and disease status analyses using dense genotyping/imputation genome-wide association study data.

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