BICOSS: Bayesian iterative conditional stochastic search for GWAS

Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control. In addition, when a few variants have large effect sizes, SMA has low statistical p...

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Bibliographic Details
Published in:BMC bioinformatics 2022-11, Vol.23 (1), p.1-475, Article 475
Main Authors: Williams, Jacob, Ferreira, Marco A. R, Ji, Tieming
Format: Article
Language:English
Subjects:
Analysis
Approximation
Bayesian analysis
Bayesian method
Bayesian statistical decision theory
Genetic algorithms
Genetic diversity
Genetic variance
Genome-wide association studies
Genomes
GWAS
Iterative methods
Mathematical models
Methods
Model selection
Nucleotides
Phenotypes
Probability
Recall
Regression analysis
Simulation
Single nucleotide polymorphisms
Single-nucleotide polymorphism
Statistical analysis
Stochasticity
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Summary:Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control. In addition, when a few variants have large effect sizes, SMA has low statistical power to detect small and medium effect sizes, leading to low recall of true causal single nucleotide polymorphisms (SNPs). We present the Bayesian Iterative Conditional Stochastic Search (BICOSS) method that controls false discovery rate and increases recall of variants with small and medium effect sizes. BICOSS iterates between a screening step and a Bayesian model selection step. A simulation study shows that, when compared to SMA, BICOSS dramatically reduces false discovery rate and allows for smaller effect sizes to be discovered. Finally, two real world applications show the utility and flexibility of BICOSS. When compared to widely used SMA, BICOSS provides higher recall of true SNPs while dramatically reducing false discovery rate.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-022-05030-0