GWAS on your notebook: fast semi-parallel linear and logistic regression for genome-wide association studies

Abstract Background Mixed linear models (MLM) have been widely used to account for population structure in case-control genome-wide association studies, the status being analyzed as a quantitative phenotype. Chen et al. proved in 2016 that this method is inappropriate in some situations and proposed GMMAT, a score test for the mixed logistic regression (MLR). However, this test does not produces an estimation of the variants’ effects. We propose two computationally efficient methods to estimate the variants’ effects. Their properties and those of other methods (MLM, logistic regression) are evaluated using both simulated and real genomic data from a recent GWAS in two geographically close population in West Africa. Results We show that, when the disease prevalence differs between population strata, MLM is inappropriate to analyze binary traits. MLR performs the best in all circumstances. The variants’ effects are well evaluated by our methods, with a moderate bias when the effect sizes are large. Additionally, we propose a stratified QQ-plot, enhancing the diagnosis of p values inflation or deflation when population strata are not clearly identified in the sample. Conclusion The two proposed methods are implemented in the R package milorGWAS available on the CRAN. Both methods scale up to at least 10,000 individuals. The same computational strategies could be applied to other models (e.g. mixed Cox model for survival analysis).

Download Full-text

Mixed Logistic Regression in Genome-Wide Association Studies

10.1101/2020.01.17.910109 ◽

2020 ◽

Author(s):

Jacqueline Milet ◽

Hervé Perdry

Keyword(s):

Logistic Regression ◽

Linear Models ◽

Association Studies ◽

Score Test ◽

R Package ◽

Genome Wide Association ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Mixed Linear Models ◽

Genome Wide

AbstractMotivationMixed linear models (MLM) have been widely used to account for population structure in case-control genome-wide association studies, the status being analyzed as a quantitative phenotype. Chen et al. proved that this method is inappropriate and proposed a score test for the mixed logistic regression (MLR). However this test does not allow an estimation of the variants’ effects.ResultsWe propose two computationally efficient methods to estimate the variants’ effects. Their properties are evaluated on two simulations sets, and compared with other methods (MLM, logistic regression). MLR performs the best in all circumstances. The variants’ effects are well evaluated by our methods, with a moderate bias when the effect sizes are large. Additionally, we propose a stratified QQ-plot, enhancing the diagnosis of p-values inflation or deflation, when population strata are not clearly identified in the sample.AvailabilityAll methods are implemented in the R package milorGWAS available at https://github.com/genostats/[email protected] informationSupplementary data are available at Bioinformatics online.

Download Full-text

Detecting latent exposure in genome-wide association studies using a breakpoint model for logistic regression

Statistical Methods in Medical Research ◽

10.1177/0962280218776385 ◽

2018 ◽

Vol 28 (6) ◽

pp. 1781-1792

Author(s):

Flora Alarcon ◽

Gregory Nuel

Keyword(s):

Logistic Regression ◽

Control Method ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Powerful Method ◽

Challenging Problem ◽

Standard Methods ◽

Gene Environment ◽

Genome Wide

Detecting gene-environment (G × E) interactions in the context of genome-wide association studies (GWAS) is a challenging problem since standard methods generally present a lack of power. An additional difficulty arises from the fact that the causal exposure is seldom observed and only a proxy of this exposure is observed. This leads to an additional drop in terms of power and it explains the failure of standard methods in detecting interactions, even very strong ones. In this article, we consider the latent exposure as a source of heterogeneity and we propose a new powerful method, named “Breakpoint Model for Logistic Regression” (BMLR), based on a breakpoint model, in order to detect G × E interactions when causal exposure is unobserved. First, the BMLR method is compared to the ordered-subset analysis for case-control method, which has been developed for the same purpose, through simulations. This highlights the ability of BMLR to detect the heterogeneity, and therefore, to detect interaction with latent exposure. Finally, the BMLR method is compared to standard methods, such as Plink, to perform a GWAS on a published realistic benchmark.

Download Full-text

A Differential Privacy Preserving Approach for Logistic Regression in Genome-Wide Association Studies

2019 International Conference on Networking and Network Applications (NaNA) ◽

10.1109/nana.2019.00040 ◽

2019 ◽

Author(s):

Ziwei Han ◽

Laifeng Lu ◽

Hai Liu

Keyword(s):

Logistic Regression ◽

Differential Privacy ◽

Association Studies ◽

Privacy Preserving ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

Download Full-text

A Two-Stage Penalized Logistic Regression Approach to Case-Control Genome-Wide Association Studies

Journal of Probability and Statistics ◽

10.1155/2012/642403 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Jingyuan Zhao ◽

Zehua Chen

Keyword(s):

Logistic Regression ◽

Association Studies ◽

Case Control ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Two Stage ◽

Genome Wide ◽

Regression Approach ◽

Penalized Logistic Regression ◽

Selection Stage

We propose a two-stage penalized logistic regression approach to case-control genome-wide association studies. This approach consists of a screening stage and a selection stage. In the screening stage, main-effect and interaction-effect features are screened by usingL1-penalized logistic like-lihoods. In the selection stage, the retained features are ranked by the logistic likelihood with the smoothly clipped absolute deviation (SCAD) penalty (Fan and Li, 2001) and Jeffrey’s Prior penalty (Firth, 1993), a sequence of nested candidate models are formed, and the models are assessed by a family of extended Bayesian information criteria (J. Chen and Z. Chen, 2008). The proposed approach is applied to the analysis of the prostate cancer data of the Cancer Genetic Markers of Susceptibility (CGEMS) project in the National Cancer Institute, USA. Simulation studies are carried out to compare the approach with the pair-wise multiple testing approach (Marchini et al. 2005) and the LASSO-patternsearch algorithm (Shi et al. 2007).

Download Full-text