Fine mapping and accurate prediction of complex traits using Bayesian Variable Selection models applied to biobank-size data

Gustavo de Los Campos; Alexander Grueneberg; Scott Funkhouser; Paulino Pérez-Rodríguez; Anirban Samaddar

doi:10.1038/s41431-022-01135-5

Fine mapping and accurate prediction of complex traits using Bayesian Variable Selection models applied to biobank-size data

Eur J Hum Genet. 2023 Mar;31(3):313-320. doi: 10.1038/s41431-022-01135-5. Epub 2022 Jul 19.

Authors

Gustavo de Los Campos^{1

2

3}, Alexander Grueneberg^{4

5}, Scott Funkhouser⁶, Paulino Pérez-Rodríguez⁷, Anirban Samaddar^{8

5}

Affiliations

¹ Michigan State University, Department of Epidemiology & Biostatistics, East Lansing, MI, USA. gustavoc@msu.edu.
² Michigan State University, Department of Statistics & Probability, East Lansing, MI, USA. gustavoc@msu.edu.
³ Michigan State University, Institute for Quantitative Health Sciences and Engineering, East Lansing, MI, USA. gustavoc@msu.edu.
⁴ Michigan State University, Department of Epidemiology & Biostatistics, East Lansing, MI, USA.
⁵ Michigan State University, Institute for Quantitative Health Sciences and Engineering, East Lansing, MI, USA.
⁶ DNAnexus, Mountain View, CA, USA.
⁷ Colegio de Postgraduados, Montecillo, Mexico.
⁸ Michigan State University, Department of Statistics & Probability, East Lansing, MI, USA.

Abstract

Modern GWAS studies use an enormous sample size and ultra-high density SNP genotypes. These conditions reduce the mapping resolution of marginal association tests-the method most often used in GWAS. Multi-locus Bayesian Variable Selection (BVS) offers a one-stop solution for powerful and precise mapping of risk variants and polygenic risk score (PRS) prediction. We show (with an extensive simulation) that multi-locus BVS methods can achieve high power with a low false discovery rate and a much better mapping resolution than marginal association tests. We demonstrate the performance of BVS for mapping and PRS prediction using data from blood biomarkers from the UK-Biobank (~300,000 samples and ~5.5 million SNPs). The article is accompanied by open-source R-software that implement the methods used in the study and scales to biobank-sized data.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
Biological Specimen Banks*
Computer Simulation
Genome-Wide Association Study / methods
Humans
Multifactorial Inheritance*
Polymorphism, Single Nucleotide
Software

Abstract

Publication types

MeSH terms

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