BulkLMM: Real-time genome scans for multiple quantitative traits using linear mixed models
Abstract Number:
3829
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Zifan Yu (1), Gregory Farage (1), Robert Williams (1), Karl Broman (2), Saunak Sen (1)
Institutions:
(1) University of Tennessee Health Science Center, N/A, (2) University of Wisconsin-Madison, N/A
Co-Author(s):
Saunak Sen
University of Tennessee Health Science Center
First Author:
Zifan Yu
University of Tennessee Health Science Center
Presenting Author:
Saunak Sen
University of Tennessee Health Science Center
Abstract Text:
Genetic studies often collect data using high-throughput phenotyping. That has led to the need for fast genomewide scans for large number of traits using linear mixed models (LMMs). Computing the scans one by one on each trait is time consuming. We have developed new algorithms for performing genome scans on a large number of quantitative traits using LMMs. Our method, BulkLMM, speeds up the computation by orders of magnitude compared to one trait at a time scans. On a mouse BXD Liver Proteome data with more than 35,000 traits and 7,000 markers, BulkLMM completed in a few seconds. We use vectorized, multi-threaded operations and regularization to improve optimization, and numerical approximations to speed up the computations. Our software implementation in the Julia programming language also provides permutation testing for LMMs and is available at
https://github.com/senresearch/BulkLMM.jl.
Keywords:
GWAS|eQTL|LMM|Julia| |
Sponsors:
Section on Statistical Computing
Tracks:
Computationally Intensive Methods
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