Tag: metaanalysis

Discovering Genetic Variation that Affects Expression in Multiple Tissues

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Over the past several years, Genome Wide Association Studies (GWAS) have discovered hundreds of genetic variants involved in complex diseases(10.1056/NEJMra0905980).  The vast majority of these variants do not lie in the protein coding regions of genes and thus do not affect what the gene produces, but instead likely affect how the genes are regulated.  For this reason, the study of how genetic variation affect gene activity levels (referred to as expression levels) has been a major focus of research for many years.  Genetic variation that affects gene expression are referred to as expression quantitative trait loci (eQTL)(10.1038/nrg2969).

Several studies collect expression from multiple tissues which leads to the question of whether or not the same genetic variants affect expression in multiple tissues(10.1038/ng.2653).  Another way to ask this question is: Are eQTLs tissue specific or not tissue specific?

A challenge in this type of analysis is that an eQTL may affect expression in multiple tissues, but because of small sample sizes, the eQTL will only be detected in one of the tissues.  Thus, traditional techniques for eQTLs will systematically be biased against detecting eQTLs in multiple tissues.

Jae-Hoon Sul and Buhm Han in our group developed a method to address this issue which builds upon recent methods in random effects meta-analysis(10.1016/j.ajhg.2011.04.014),(10.1371/journal.pgen.1002555).  To apply these methods we first analyze each tissue separately and then use the meta-analysis method to combine the results of each tissue.  Since our methods are specifically designed to handle “heterogeneity” which is that the effect size can be different in each study, our method is able to perform well when the effect is present in all of the tissues or just some of the tissues.  More information about our meta-analysis research is here.

The full citation of our paper is here:

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Over the past few years, our group has published several papers on methods for eQTL analysis.  Our other paper on eQTL analysis include:

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Heterogeneity and Meta-Analysis

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Figure5_crohns_forest_small

Visualizing heterogeneity in meta-analyses of GWAS. The left panel shows a forest plot which shows the predicted effect size and standard error for each study. The right panel shows a PM-plot which for each study plots the p-value on the y-axis and the m-value on the x-axis. M-values have the following interpretations: Small m-value (e.g. < 0.1) suggest the study does not have an effect. Large m-value (e.g. > 0.9) suggest the study is predicted to have an effect. Otherwise the prediction is ambiguous.

Over the past couple of years, a major focus of our group has been on meta-analysis. These efforts have been led by Buhm Han who is a graduate of our group and now a post-doc at the Broad Institute.

Meta-Analysis is a statistical method to combine the results of many statistical studies.  Meta-analysis has the advantage that the statistical power of the combination of the studies is much higher than the statistical power of any individual studies.  In fact, the majority of the recently identified genetic variants associated with complex diseases have been discovered using meta-analysis (10.1146/annurev-genom-091212-153520) since most of the effect sizes of these variants are too small to discover in the sample sizes of the individual studies.

Standard meta-analysis techniques assume what is referred to as the “fixed effect model” (FE). In the FE model, the effect size in each study is assumed to the the same. In the case of genetic association studies, this is an unrealistic assumption because the studies are often collected in very different populations which are subject to very different environmental conditions. An alternate model is the “random effects model” (RE) where the effect size are assumed to be different in each study and the effect sizes are modeled as being drawn from a distribution with an estimated mean and variance. This difference in effect sizes between studies is referred to as “heterogeneity.”

Buhm Han, in our group, made two contributions related to heterogeneity in meta-analysis. In his first paper, he noticed that previous approaches for hypothesis testing using the RE model did not correctly model the null hypothesis and led to a significant loss in power(10.1016/j.ajhg.2011.04.014). His second paper presented a method for helping interpret meta-analysis studies to identify in which studies an effect is present and in which studies an effect is not present(10.1371/journal.pgen.1002555).  One aspect of the interpretation framework is the m-value which can be used to identify in which studies an effect is present and a summary of the heterogeneity of the meta-analysis can be visualized utilizing a PM-plot (see figure).

The methods are implemented in the software that Buhm developed, METASOFT, available at http://genetics.cs.ucla.edu/meta/.

The full citations to his papers are below:

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