High-dimensional multi-trait GWAS by reverse prediction of genotypes
- URL: http://arxiv.org/abs/2111.00108v1
- Date: Fri, 29 Oct 2021 22:34:35 GMT
- Title: High-dimensional multi-trait GWAS by reverse prediction of genotypes
- Authors: Muhammad Ammar Malik, Adriaan-Alexander Ludl, Tom Michoel
- Abstract summary: Reverse regression is a promising approach to perform multi-trait GWAS in high-dimensional settings.
We analyzed different machine learning methods for reverse regression in multi-trait GWAS.
Model feature coefficients correlated with the strength of association between variants and individual traits, and were predictive of true trans-eQTL target genes.
- Score: 3.441021278275805
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-trait genome-wide association studies (GWAS) use multi-variate
statistical methods to identify associations between genetic variants and
multiple correlated traits simultaneously, and have higher statistical power
than independent univariate analysis of traits. Reverse regression, where
genotypes of genetic variants are regressed on multiple traits simultaneously,
has emerged as a promising approach to perform multi-trait GWAS in
high-dimensional settings where the number of traits exceeds the number of
samples. We extended this approach and analyzed different machine learning
methods (ridge regression, random forests and support vector machines)for
reverse regression in multi-trait GWAS, using genotypes, gene expression data
and ground-truth transcriptional regulatory networks from the DREAM5 SysGen
Challenge and from a cross between two yeast strains to evaluate methods. We
found that genotype prediction performance, in terms of root mean squared error
(RMSE), allowed to distinguish between genomic regions with high and low
transcriptional activity. Moreover, model feature coefficients correlated with
the strength of association between variants and individual traits, and were
predictive of true trans-eQTL target genes, with complementary findings across
methods.
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