ArticleBMC bioinformatics2022
BICOSS: Bayesian iterative conditional stochastic search for GWAS.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- To linearize or not to linearize: That is the Mazur delay discounting question.Journal of mathematical psychology · 2026Article
- Impact of FDA-approved graphic front-of-package health warnings on attention, package choice, and quit intentions.American journal of health behavior · 2026Article
- BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data.BMC bioinformatics · 2023Article
- Bayesian bi-level variable selection for genome-wide survival study.Genomics & informatics · 2023Article
- BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies.BMC bioinformatics · 2023Article
- Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting BacteriumMicroorganisms · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundSingle 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).
resultsWe 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.
conclusionsWhen compared to widely used SMA, BICOSS provides higher recall of true SNPs while dramatically reducing false discovery rate.
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Registered trials
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