Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits

Abstract

Improving inference in large-scale genetic data linked to electronic medical record data requires the development of novel computationally efficient regression methods. Here, the authors develop a Bayesian approach for association analyses to improve SNP-heritability estimation, discovery, fine-mapping and genomic prediction.We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only = 95% probability of contributing >= 0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.

Published in

Nature Communications
2021, volume: 12, number: 1, article number: 6972
Publisher: NATURE PORTFOLIO

SLU Authors

• Rönnegård, Lars

  • Department of Animal Biosciences, Swedish University of Agricultural Sciences
    
  • Dalarna University

Global goals (SDG)

SDG3 Good health and well-being

UKÄ Subject classification

Bioinformatics (Computational Biology)
Genetics and Genomics

Publication identifier

• DOI: https://doi.org/10.1038/s41467-021-27258-9

Permanent link to this page (URI)

https://res.slu.se/id/publ/114699