Our group developed a novel method for detecting eGenes, the genes whose expression levels are associated with variation at a particular genetic variant. Identification of eGenes is increasingly important to studies of expression quantitative trait loci (eQTLs), the genetic variants that affect gene expression. Mapped eGenes help guide eQTL studies of complex human disease. However, standard approaches cannot efficiently detect these complex features in today’s large genomic datasets. Func-eGene, which we describe and test in a recent Bioinformatics paper, significantly increases the statistical power of existing association study methods and detects more eGenes in comparison to standard approaches.
Standard statistical methods for classifying a gene as an eGene first perform association testing at all variants near the gene of interest, then use a permutation test to conduct multiple-testing correction for results. The permutation test effectively corrects for potential biases introduced by multiple testing and obtains a p value for each gene. However, the permutation test is computationally inefficient when processing the increasingly large sample sizes of today’s eQTL datasets and has become a computational bottleneck in eQTL studies.
Our new approach, Func-eGene, incorporates genomic annotation of variants to improve the computing power of eQTL studies. Variants located near gene transcription sites (TSSs), or near some histone modifications, often regulate gene expression. Standard approaches do not consider genomic annotations, but we found that annotation of these variants can help locate and associate more causal variants using less time and computing power. In order to do this, we expand upon the standard multithreshold association test that specifies different significance thresholds for each variant when correcting for multiple testing. Func-eGene increases power by assigning lower significance thresholds to variants that are likely to contribute to gene expression.
However, this association test still depends on the time-consuming permutation test and requires a known prior based on annotation for genetic variants. Func-eGene avoids these difficulties by reducing runtime and selecting an appropriate prior. To reduce runtime, we replace the permutation test with the Mvn-sampling procedure described in Sul et al. (2015). To find an appropriate prior, we run a grid search over possible sets of scores assigned to annotation categories. Func-eGene then seeks a set of scores that maximizes the number of eGenes and uses a cross-validation strategy to avoid data re-use and over-fitting. Thus, there are two ways to apply Func-eGene to eQTL data. Permutation Func-eGene uses the traditional permutation test to calculate the null density of the observed statistic, whereas Mvn Func-eGene relies on the Mvn-sampling procedure.
We applied our method to the liver Genotype-Tissue Expression (GTEx) dataset. We used genomic annotations of the following variants: distance from TSSs, DNase hypersensitivity sites, and six histone modifications. Notably, the distance from TSS annotation detected the highest number of candidate eGenes; using this annotation, our new method discovered 50% more candidate eGenes when compared to the standard permutation method. Our simulations show that Func-eGene successfully control the rate of false-positive associations when using either the permutation or the Mvn procedure. However, implementing Func-eGene with a traditional permutation test is inefficient. Instead, we can obtain the same results with considerably faster runtime when using Mvn sampling.
This project was led by Dat Duong and involved Jennifer Zou, Farhad Hormozdiari, and Jae Hoon Sul. The article is available at: http://bioinformatics.oxfordjournals.org/content/32/12/i156.abstract
The full citation to our paper is:
|(2016): Using genomic annotations increases statistical power to detect eGenes.. In: Bioinformatics, 32 (12), pp. i156-i163, 2016, ISSN: 1367-4811.|
FUNC-eGene was developed by Dat Duong and is available for download at: https://github.com/datduong/FUNC-eGene
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”).
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:
|(2016): Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models.. In: PLoS Genet, 12 (3), pp. e1005849, 2016, ISSN: 1553-7404.|
This approach uses our PyLMM software package available for download at: http://genetics.cs.ucla.edu/pylmm/.
Our group recently published a paper in G3 that presents a new method for interpreting meta-analysis of genomic studies. Our software, called ForestPMPlot, is a free, open-source, python-interfaced R package tool available for download from ZarLab Software. In our article, we demonstrate how ForestPMPlot facilitates interpretation of meta-analysis results by producing a plot that visualizes the heterogeneous genetic effects on the phenotype in different study conditions. We show an example analysis where our visualization framework leads to plausible interpretations of gene-by-environment interaction and multiple tissue eQTL, which would not have been straightforward with the traditional framework.
Meta-analysis has become a popular tool for increasing power in genetic association studies, yet it remains a methodological challenge. Genetic association studies can differ from each other in terms of environmental conditions, study design, population types and sizes, statistical noise, and analytical use of covariates. These factors produce different effect sizes between studies, a phenomenon called between-study heterogeneity. Correctly interpreting and accounting for heterogeneity in genetic association studies would give us a more accurate model of the true effects genetic variants have on traits under specific conditions.
Compared to traditional forest plotting techniques, ForestPMPlot visualizes a broader depth of information useful to interpretation of meta-analysis results. Specifically, our tool helps visualize differences in the effect sizes of genetic association studies and clarify why such studies exhibit heterogeneity for a particular phenotype and locus pair under different conditions. To distinguish studies with an effect from studies without an effect, we use the m-value framework. The m-value (Han and Eskin 2012; Kang et al. 2014) is the posterior probability that the effect exists in each study. In our paper, we explain how to compute an m-value and propose using the PM-plot framework (Han and Eskin 2012) to plot the P-values and m-values of each study together. The PM-Plot visualizes the relationship between m-values and P-values in a two-dimensional space, allowing a researcher to easily distinguish which study is predicted to have an effect, and which study is predicted not to have an effect.
We applied ForestPMPlot to a GWAS meta-analysis of 17 HDL mouse studies that have different environmental conditions, such as diet (e.g., high fat/low fat), and genetic knockouts, including homozygous deficiency in leptin receptor (db/db), LDL receptor knockouts, and Apoe gene knockouts. Here, we observe that two confidence intervals of effect estimates overlap each other when only considering the effect size estimates in forest plot format. This result is ambiguous if the observed heterogeneity is a result of stochastic errors. However, in the PM-Plot, we observe that the posterior probabilities are well segregated for these two studies (m-value: 0.93 vs. 0.03), allowing us to hypothesize that the SNP effects on HDL in these strains under the Western diet condition can be interacting with sex.
We continue to develop new applications for ForestPMPlot, and we hope that our tool will facilitate more accurate interpretations of meta-analysis in future genetic association research.
ForestPMPlot was developed by Eun Yong Kang and Yurang Park. The article is available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4938634/.
Visit the following page to download ForestPMPlot: http://genetics.cs.ucla.edu/meta_jemdoc/
The full citation to our paper is:
|(2016): ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity between Studies in Meta-analysis.. In: G3 (Bethesda), 6 (7), pp. 1793-8, 2016, ISSN: 2160-1836.|
This paper describes methods implemented based on research originally published by this group:
|(2012): Interpreting meta-analyses of genome-wide association studies.. In: PLoS Genet, 8 (3), pp. e1002555, 2012, ISSN: 1553-7404.|
|(2011): Random-Effects Model Aimed at Discovering Associations in Meta-Analysis of Genome-wide Association Studies.. In: Am J Hum Genet, 88 (5), pp. 586-98, 2011, ISSN: 1537-6605.|
We discussed these methods and papers in a 2013 blog post: http://zarlab.cs.ucla.edu/heterogeneity-and-meta-analysis/