Cargando…
Bayesian multiple logistic regression for case-control GWAS
Genetic variants in genome-wide association studies (GWAS) are tested for disease association mostly using simple regression, one variant at a time. Standard approaches to improve power in detecting disease-associated SNPs use multiple regression with Bayesian variable selection in which a sparsity-...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329526/ https://www.ncbi.nlm.nih.gov/pubmed/30596640 http://dx.doi.org/10.1371/journal.pgen.1007856 |
_version_ | 1783386843298922496 |
---|---|
author | Banerjee, Saikat Zeng, Lingyao Schunkert, Heribert Söding, Johannes |
author_facet | Banerjee, Saikat Zeng, Lingyao Schunkert, Heribert Söding, Johannes |
author_sort | Banerjee, Saikat |
collection | PubMed |
description | Genetic variants in genome-wide association studies (GWAS) are tested for disease association mostly using simple regression, one variant at a time. Standard approaches to improve power in detecting disease-associated SNPs use multiple regression with Bayesian variable selection in which a sparsity-enforcing prior on effect sizes is used to avoid overtraining and all effect sizes are integrated out for posterior inference. For binary traits, the logistic model has not yielded clear improvements over the linear model. For multi-SNP analysis, the logistic model required costly and technically challenging MCMC sampling to perform the integration. Here, we introduce the quasi-Laplace approximation to solve the integral and avoid MCMC sampling. We expect the logistic model to perform much better than multiple linear regression except when predicted disease risks are spread closely around 0.5, because only close to its inflection point can the logistic function be well approximated by a linear function. Indeed, in extensive benchmarks with simulated phenotypes and real genotypes, our Bayesian multiple LOgistic REgression method (B-LORE) showed considerable improvements (1) when regressing on many variants in multiple loci at heritabilities ≥ 0.4 and (2) for unbalanced case-control ratios. B-LORE also enables meta-analysis by approximating the likelihood functions of individual studies by multivariate normal distributions, using their means and covariance matrices as summary statistics. Our work should make sparse multiple logistic regression attractive also for other applications with binary target variables. B-LORE is freely available from: https://github.com/soedinglab/b-lore. |
format | Online Article Text |
id | pubmed-6329526 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63295262019-01-30 Bayesian multiple logistic regression for case-control GWAS Banerjee, Saikat Zeng, Lingyao Schunkert, Heribert Söding, Johannes PLoS Genet Research Article Genetic variants in genome-wide association studies (GWAS) are tested for disease association mostly using simple regression, one variant at a time. Standard approaches to improve power in detecting disease-associated SNPs use multiple regression with Bayesian variable selection in which a sparsity-enforcing prior on effect sizes is used to avoid overtraining and all effect sizes are integrated out for posterior inference. For binary traits, the logistic model has not yielded clear improvements over the linear model. For multi-SNP analysis, the logistic model required costly and technically challenging MCMC sampling to perform the integration. Here, we introduce the quasi-Laplace approximation to solve the integral and avoid MCMC sampling. We expect the logistic model to perform much better than multiple linear regression except when predicted disease risks are spread closely around 0.5, because only close to its inflection point can the logistic function be well approximated by a linear function. Indeed, in extensive benchmarks with simulated phenotypes and real genotypes, our Bayesian multiple LOgistic REgression method (B-LORE) showed considerable improvements (1) when regressing on many variants in multiple loci at heritabilities ≥ 0.4 and (2) for unbalanced case-control ratios. B-LORE also enables meta-analysis by approximating the likelihood functions of individual studies by multivariate normal distributions, using their means and covariance matrices as summary statistics. Our work should make sparse multiple logistic regression attractive also for other applications with binary target variables. B-LORE is freely available from: https://github.com/soedinglab/b-lore. Public Library of Science 2018-12-31 /pmc/articles/PMC6329526/ /pubmed/30596640 http://dx.doi.org/10.1371/journal.pgen.1007856 Text en © 2018 Banerjee et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Banerjee, Saikat Zeng, Lingyao Schunkert, Heribert Söding, Johannes Bayesian multiple logistic regression for case-control GWAS |
title | Bayesian multiple logistic regression for case-control GWAS |
title_full | Bayesian multiple logistic regression for case-control GWAS |
title_fullStr | Bayesian multiple logistic regression for case-control GWAS |
title_full_unstemmed | Bayesian multiple logistic regression for case-control GWAS |
title_short | Bayesian multiple logistic regression for case-control GWAS |
title_sort | bayesian multiple logistic regression for case-control gwas |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329526/ https://www.ncbi.nlm.nih.gov/pubmed/30596640 http://dx.doi.org/10.1371/journal.pgen.1007856 |
work_keys_str_mv | AT banerjeesaikat bayesianmultiplelogisticregressionforcasecontrolgwas AT zenglingyao bayesianmultiplelogisticregressionforcasecontrolgwas AT schunkertheribert bayesianmultiplelogisticregressionforcasecontrolgwas AT sodingjohannes bayesianmultiplelogisticregressionforcasecontrolgwas |