Mixed Logistic Regression in Genome-Wide Association Studies

Mixed Linear Models ◽

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.

Mixed logistic regression in genome-wide association studies

BMC Bioinformatics ◽

10.1186/s12859-020-03862-2 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Jacqueline Milet ◽

David Courtin ◽

André Garcia ◽

Hervé Perdry

Keyword(s):

Logistic Regression ◽

Linear Models ◽

Cox Model ◽

Association Studies ◽

Scale Up ◽

Score Test ◽

R Package ◽

Genome Wide Association ◽

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).

bGWAS: an R package to perform Bayesian genome wide association studies

Bioinformatics ◽

10.1093/bioinformatics/btaa549 ◽

2020 ◽

Vol 36 (15) ◽

pp. 4374-4376

Author(s):

Ninon Mounier ◽

Zoltán Kutalik

Keyword(s):

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Biological Mechanisms ◽

Genome Wide ◽

Related Risk

Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

Methodological implementation of mixed linear models in multi-locus genome-wide association studies

Briefings in Bioinformatics ◽

10.1093/bib/bbx028 ◽

2017 ◽

Vol 18 (5) ◽

pp. 906-906 ◽

Cited By ~ 12

Author(s):

Yang-Jun Wen ◽

Hanwen Zhang ◽

Yuan-Li Ni ◽

Bo Huang ◽

Jin Zhang ◽

...

Keyword(s):

Linear Models ◽

Association Studies ◽

Genome Wide Association ◽

Mixed Linear Models ◽

Methodological implementation of mixed linear models in multi-locus genome-wide association studies

Briefings in Bioinformatics ◽

10.1093/bib/bbw145 ◽

2017 ◽

Vol 19 (4) ◽

pp. 700-712 ◽

Cited By ~ 71

Author(s):

Yang-Jun Wen ◽

Hanwen Zhang ◽

Yuan-Li Ni ◽

Bo Huang ◽

Jin Zhang ◽

...

Keyword(s):

Linear Models ◽

Association Studies ◽

Genome Wide Association ◽

Mixed Linear Models ◽

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

BMC Bioinformatics ◽

10.1186/1471-2105-14-166 ◽

2013 ◽

Vol 14 (1) ◽

Cited By ~ 20

Author(s):

Karolina Sikorska ◽

Emmanuel Lesaffre ◽

Patrick FJ Groenen ◽

Paul HC Eilers

Keyword(s):

Logistic Regression ◽

Association Studies ◽

Genome Wide Association ◽

GWASpro: a high-performance genome-wide association analysis server

Bioinformatics ◽

10.1093/bioinformatics/bty989 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2512-2514 ◽

Cited By ~ 4

Author(s):

Bongsong Kim ◽

Xinbin Dai ◽

Wenchao Zhang ◽

Zhaohong Zhuang ◽

Darlene L Sanchez ◽

...

Keyword(s):

High Performance ◽

Large Scale ◽

Linear Mixed Model ◽

Association Studies ◽

Learning Curves ◽

Experimental Designs ◽

Genome Wide Association ◽

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.

Multi-SNP mediation intersection-union test

Bioinformatics ◽

10.1093/bioinformatics/btz285 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4724-4729 ◽

Cited By ~ 4

Author(s):

Wujuan Zhong ◽

Cassandra N Spracklen ◽

Karen L Mohlke ◽

Xiaojing Zheng ◽

Jason Fine ◽

...

Keyword(s):

Association Studies ◽

R Package ◽

Alternative Methods ◽

Mediation Effects ◽

Coding Regions ◽

Genome Wide ◽

Plasma Adiponectin Level ◽

Intersection Union Test

Abstract Summary Tens of thousands of reproducibly identified GWAS (Genome-Wide Association Studies) variants, with the vast majority falling in non-coding regions resulting in no eventual protein products, call urgently for mechanistic interpretations. Although numerous methods exist, there are few, if any methods, for simultaneously testing the mediation effects of multiple correlated SNPs via some mediator (e.g. the expression of a gene in the neighborhood) on phenotypic outcome. We propose multi-SNP mediation intersection-union test (SMUT) to fill in this methodological gap. Our extensive simulations demonstrate the validity of SMUT as well as substantial, up to 92%, power gains over alternative methods. In addition, SMUT confirmed known mediators in a real dataset of Finns for plasma adiponectin level, which were missed by many alternative methods. We believe SMUT will become a useful tool to generate mechanistic hypotheses underlying GWAS variants, facilitating functional follow-up. Availability and implementation The R package SMUT is publicly available from CRAN at https://CRAN.R-project.org/package=SMUT. Supplementary information Supplementary data are available at Bioinformatics online.

SumVg: Total heritability explained by all variants in genome-wide association studies based on summary statistics with standard error estimates

10.1101/016857 ◽

2015 ◽

Author(s):

Hon-Cheong SO ◽

Pak C. SHAM

Keyword(s):

Error Estimates ◽

Standard Error ◽

Association Studies ◽

Parametric Bootstrap ◽

R Package ◽

Genome Wide Association ◽

Summary Statistics ◽

Genome Wide ◽

Key Questions

Genome-wide association studies (GWAS) have become increasingly popular these days and one of the key questions is how much heritability could be explained by all variants in GWAS. We have previously proposed an approach to answer this question, based on recovering the "true" z-statistics from a set of observed z-statistics. Only summary statistics are required. However, methods for standard error (SE) estimation are not available yet, thereby limiting the interpretation of the results. In this study we developed resampling-based approaches to estimate the SE and the methods are implemented in an R package. We found that delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. Methods to compute the sum of heritability explained and the corresponding SE are implemented in the R package SumVg, available at https://sites.google.com/site/honcheongso/software/var-totalvg

CVRMS: Cross-validated Rank-based Marker Selection for Genome-wide Prediction of Low Heritability

10.1101/756130 ◽

2019 ◽

Author(s):

Seongmun Jeong ◽

Jae-Yoon Kim ◽

Namshin Kim

Keyword(s):

Genetic Information ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Marker Selection ◽

Genome Wide ◽

Precise Prediction ◽

Human Animal ◽

Selection For

AbstractCVRMS is an R package designed to extract marker subsets from repeated rank-based marker datasets generated from genome-wide association studies or marker effects for genome-wide prediction (https://github.com/lovemun/CVRMS). CVRMS provides an optimized genome-wide biomarker set with the best predictability of phenotype by implemented ridge regression using genetic information. Applying our method to human, animal, and plant datasets with wide heritability (zero to one), we selected hundreds to thousands of biomarkers for precise prediction.

pyseer: a comprehensive tool for microbial pangenome-wide association studies

10.1101/266312 ◽

2018 ◽

Cited By ~ 1

Author(s):

John A Lees ◽

Marco Galardini ◽

Stephen D Bentley ◽

Jeffrey N Weiser ◽

Jukka Corander

Keyword(s):

Input Data ◽

Association Studies ◽

Genome Wide Association ◽

Supplementary Data ◽

New Methods ◽

Link Type ◽

AbstractSummaryGenome-wide association studies (GWAS) in microbes face different challenges to eukaryotes and have been addressed by a number of different methods. pyseer brings these techniques together in one package tailored to microbial GWAS, allows greater flexibility of the input data used, and adds new methods to interpret the association results.Availability and Implementationpyseer is written in python and is freely available at https://github.com/mgalardini/pyseer, or can be installed through pip. Documentation and a tutorial are available at http://[email protected] and [email protected] informationSupplementary data are available online.