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Fast estimation of genetic correlation for Biobank-scale data

Yue Wu, Anna Yaschenko, Mohammadreza Hajy Heydary, Sriram Sankararaman
doi: https://doi.org/10.1101/525055
Yue Wu
1Department of Computer Science, UCLA
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Anna Yaschenko
3Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
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Mohammadreza Hajy Heydary
4Department of Computer Science, California State University, Fullerton
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Sriram Sankararaman
1Department of Computer Science, UCLA
2Department of Human Genetics, UCLA
5Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, California
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  • For correspondence: sriram@cs.ucla.edu
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Abstract

Genetic correlation, i.e., the proportion of phenotypic correlation across a pair of traits that can be explained by genetic variation, is an important parameter in efforts to understand the relationships among complex traits. The observation of substantial genetic correlation across a pair of traits, can provide insights into shared genetic pathways as well as providing a starting point to investigate causal relationships. Attempts to estimate genetic correlations among complex phenotypes attributable to genome-wide SNP variation data have motivated the analysis of large datasets as well as the development of sophisticated methods.

Bi-variate Linear Mixed Models (LMMs) have emerged as a key tool to estimate genetic correlation from datasets where individual genotypes and traits are measured. The bi-variate LMM jointly models the effect sizes of a given SNP on each of the pair of traits being analyzed. The parameters of the bi-variate LMM, i.e., the variance components, are related to the heritability of each trait as well as correlation across traits attributable to genotyped SNPs. However, inference in bi-variate LMMs, typically achieved by maximizing the likelihood, poses serious computational challenges.

We propose, RG-Cor, a scalable randomized Method-of-Moments (MoM) estimator of genetic correlations in bi-variate LMMs. RG-Cor leverages the structure of genotype data to obtain runtimes that scale sub-linearly with the number of individuals in the input dataset (assuming the number of SNPs is held constant). We perform extensive simulations to validate the accuracy and scalability of RG-Cor. RG-Cor can compute the genetic correlations on the UK biobank dataset consisting of 430, 000 individuals and 460, 000 SNPs in 3 hours on a stand-alone compute machine.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 20, 2019.
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Fast estimation of genetic correlation for Biobank-scale data
Yue Wu, Anna Yaschenko, Mohammadreza Hajy Heydary, Sriram Sankararaman
bioRxiv 525055; doi: https://doi.org/10.1101/525055
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Fast estimation of genetic correlation for Biobank-scale data
Yue Wu, Anna Yaschenko, Mohammadreza Hajy Heydary, Sriram Sankararaman
bioRxiv 525055; doi: https://doi.org/10.1101/525055

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