Efficient and accurate multiple-phenotype regression method for high dimensional data considering population structure

Jong Wha (Joanne) Joo developed an approach to simultaneously analyze multiple phenotypes in a genome-wide association studies (GWAS) dataset. She introduces this new methodology, referred to as GAMMA (Generalized Analysis of Molecular variance for Mixed model Analysis), in a paper recently published in Genetics.

GWASs have identified many genetic variants involved in traits and development of human diseases by examining for correlation of a single phenotype and individual genotype one phenotype at a time. Since initial development of the standard GWAS approach, GWAS data collection has become larger in scale and higher in resolution. Today’s large-scale datasets include expression data and often contain thousands of phenotypes per individual. Performing the standard single-phenotype analysis on these datasets is slow and potentially fails to detect unmeasured aspects of complex biological networks.

Analyzing many phenotypes simultaneously increases the power to detect more variants and capture previously unmeasured aspects of the genome. However, standard GWAS approaches capable of simultaneously testing multiple phenotypes fail to account for the distorting effects of population structure, a phenomenon present in large cohorts that inevitably contain individuals sharing common ancestry from multiple populations. As a result, standard GWAS approaches either fail to detect true effects or produce many false positive identifications.

GAMMA is an efficient, robust approach capable of simultaneously analyzing many phenotypes while correcting for population structure. GAMMA uses the principles behind existing linear mixed models to analyze for many phenotypes simultaneously and a multiple regression technique to correct for population structure.

Joanne’s paper presents the results of testing GAMMA for accuracy in three scenarios: a simulated dataset containing population structure, a yeast dataset containing many trans-regulatory hotspots, and a complex gut microbiome dataset. In the simulated study using data implanted with true population structure effects, GAMMA accurately identifies these true effects without producing false positives. In the simulation with yeast data, GAMMA successfully corrected for the bias of technical artifacts such as batch effects and identified significant signals on most of the putative hotspots. In the third test, Joanne and her team assesses GAMMA’s ability to perform a multiple-phenotypes analysis with microbiome data. Here, results identified nine loci likely to have true biological mechanisms in the taxa.

In each scenario, results of GAMMA were compared to those of the standard t-test, EMMA, and MDMR. The standard t-test and EMMA failed to identify true variants, because the phenotypic effects in each example is smaller than the amount these methods are powered to detect. MDMR produced no significant signals in the yeast dataset and identified many false associations in the simulated and gut microbiome datasets. Both GAMMA and MDMR have sufficient power to detect small association signals in these complex datasets, but only GAMMA successfully corrects for population structure.

This project was led by Joanne Joo and involved Eun Yong Kang and Farhad Hormozdiari. The article is available at: http://www.genetics.org/content/204/4/1379.

GAMMA was developed by Joanne Joo, Eun Yong Kang, Elin Org, Nick Furlotte, Brian Parks, Aldons J. Lusis, and Eleazar Eskin. Visit the following page to download GAMMA: http://genetics.cs.ucla.edu/GAMMA/

The full citation to our paper is:

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The results of GAMMA and three standard GWAS methods applied to a simulated dataset. The x-axis shows SNP locations and the y-axis shows log10p-value of associations between each SNP and all the genes. Blue arrows show the location of the true trans-regulatory hotspots.

Review Article: Population Structure in Genetic Studies: Confounding Factors and Mixed Models

Bioinformatics is a rapidly growing field comprised of multiple academic disciplines. The work of quantitative geneticists is often not well understood by scholars conducting other types of research in Genetics. In response to this information gap, we are launching a series of reviews that are aimed to make common problems in computational biology research accessible to anyone in Genetics. We hope these reviews help researchers in Genetics better understand the scope and applicability of each other’s work, and serve as study guides for students taking college courses on the subject matter.

Today we made available on bioRxiv the first paper in this series, our review of population structure and relatedness in association studies. A genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to effectively test for association while correcting for population structure is a computational and statistical challenge. Our review motivates the problem of population structure in association studies using laboratory mouse strains and how it can cause false positives associations. We then motivate mixed models in the context of unmodeled factors.

To read the full review, download our paper: http://biorxiv.org/content/early/2016/12/07/092106.

This review was written by Lana Martin and Eleazar Eskin. We welcome feedback; please e-mail Lana if you have comments or questions: lana [dot] martin [at] ucla [dot] edu.

Body weight phenotypes of 38 inbred mouse strains from the Mouse Phenome Database generated by The Jackson Laboratory. The distribution of mice body weights shows two clades of mice have very different body weights.

Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models

This year, our group published a paper in PLOS Genetics that describes our efforts to better understand and correct for population structure when computing gene-by-environment (GEI) statistics in genome-wide association studies (GWASs). We use simulated and actual GWAS datasets to demonstrate that population structure, the relatedness of individuals within a cohort, inflates test statistics for both GEIs and genetic variants. We present a novel mixed model method capable of improving accuracy when computing GEI statistics in GWAS. This method can be efficiently applied to GWAS datasets containing thousands of individuals and hundreds of thousands of SNPs.

GWASs have discovered many genetic variants associated with complex traits and diseases, yet these genetic variants explain only a small fraction of phenotypic variance in the human genome. Other sources of phenotypic variance include discrete environmental factors and GEIs, complex interactions between an individual’s genetic material and environmental factors. Recent GEI association analyses have demonstrated the importance of GEIs in complex traits and disease development. Identification of these causal GEIs would provide insight into disease pathways, particularly the effects of environmental factors in disease risk, and guide development of novel diagnostic tools and personalized therapies.

Several methodological challenges have limited successful identification of causal GEIs. As with standard GWAS approaches, GxE GWASs are prone to produce an inflated number of associations due to population structure. Unlike standard GWASs, we lack a method designed to avoid detection of these spurious associations when computing GEI statistics. Accounting for genetic similarity with a standard GWAS approach does control inflation of test statistics for causal SNPs, but does not control inflation of associated GEIs. Simultaneously accounting for both similarities would control both types of population structure known to confound GWASs—false associations caused by SNPs under selection and those caused by the remaining SNPs.

Our linear mixed model approach introduces two random effects and takes into account two types of similarities between individuals: overlap in the genome itself and overlap in genetic expression caused by complex interactions between genes and environment. We use a pair of kinship matrices corresponding to the two types of similarity to include these two random effects in the model and correct for population structure.

In order to better understand false associations in GxE GWASs, we compare our approach to two standard approaches. We apply the three methods to two large genomic datasets, one human and one mouse, that are known to contain population structure and have many quantitative phenotypes to test effect of GEIs. We use a standard GWAS method that does not correct for population structure (defined as “OLS” in our paper) and an approach that performs population structure correction for only SNP statistics (“One RE”). The last approach is our proposed mixed model approach that uses both genetic and GxE kinship to correct for population structure on both SNP and GEI statistics (“Two RE”).


Distribution of inflation factors of GEI statistics on HMDP GxE GWAS data. (A) Inflation factor for each phenotype with no population structure correction (OLS), population structure correction for SNP statistics (One RE), and population structure correction for both SNP and GEI statistics (Two RE). (B) QQ plot of one of the phenotypes (free fatty acids, ffa), showing the distributions of p-values of GEI statistics for the three methods.

In both datasets, even a moderate amount of population structure causes spurious GEIs when using standard approaches for identifying GEI in GWAS. While the One RE approach reduces inflation of test statistics on SNPs (see Supplement S1 Figure), it has almost the same or slightly higher inflation factors on GxE statistics when compared to OLS. Results from both datasets suggest that our approach effectively controls population structure when computing statistics for GEIs and genetic variants. We hope our method is useful advancing our understanding of how life-history influences an individual’s disease risk.

This project was led by Jae Hoon Sul and involved Michael Bilow. The article is available at: http://dx.doi.org/10.1371/journal.pgen.1005849

The full citation to our paper is: 

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This approach uses our PyLMM software package available for download at: http://genetics.cs.ucla.edu/pylmm/.