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A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis.

ABSTRACT: Meta-analysis of trans-ethnic genome-wide association studies (GWAS) has proven to be a practical and profitable approach for identifying loci that contribute to the risk of complex diseases. However, the expected genetic effect heterogeneity cannot easily be accommodated through existing fixed-effects and random-effects methods. In response, we propose a novel random effect model for trans-ethnic meta-analysis with flexible modeling of the expected genetic effect heterogeneity across diverse populations. Specifically, we adopt a modified random effect model from the kernel regression framework, in which genetic effect coefficients are random variables whose correlation structure reflects the genetic distances across ancestry groups. In addition, we use the adaptive variance component test to achieve robust power regardless of the degree of genetic effect heterogeneity. Simulation studies show that our proposed method has well-calibrated type I error rates at very stringent significance levels and can improve power over the traditional meta-analysis methods. We reanalyzed the published type 2 diabetes GWAS meta-analysis (Consortium et al., 2014) and successfully identified one additional SNP that clearly exhibits genetic effect heterogeneity across different ancestry groups. Furthermore, our proposed method provides scalable computing time for genome-wide datasets, in which an analysis of one million SNPs would require less than 3 hours.

SUBMITTER: Shi J

PROVIDER: S-EPMC4996751 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis.

Shi Jingchunzi J Lee Seunggeun S

Biometrics 20160224 3

Meta-analysis of trans-ethnic genome-wide association studies (GWAS) has proven to be a practical and profitable approach for identifying loci that contribute to the risk of complex diseases. However, the expected genetic effect heterogeneity cannot easily be accommodated through existing fixed-effects and random-effects methods. In response, we propose a novel random effect model for trans-ethnic meta-analysis with flexible modeling of the expected genetic effect heterogeneity across diverse po ...[more]

PMID: 26916671

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Project description:Breast cancer is the most common solid organ malignancy and the most frequent cause of cancer death among women worldwide. Previous research has yielded insights into its genetic etiology, but there remains a gap in the understanding of genetic factors that contribute to risk, and particularly in the biological mechanisms by which genetic variation modulates risk. The National Cancer Institute's "Up for a Challenge" (U4C) competition provided an opportunity to further elucidate the genetic basis of the disease. Our group leveraged the seven datasets made available by the U4C organizers and data from the publicly available UK Biobank cohort to examine associations between imputed gene expression and breast cancer risk. In particular, we used reference datasets describing the breast tissue and whole blood transcriptomes to impute expression levels in breast cancer cases and controls. In trans-ethnic meta-analyses of U4C and UK Biobank data, we found significant associations between breast cancer risk and the expression of RCCD1 (joint p-value: 3.6x10-06) and DHODH (p-value: 7.1x10-06) in breast tissue, as well as a suggestive association for ANKLE1 (p-value: 9.3x10-05). Expression of RCCD1 in whole blood was also suggestively associated with disease risk (p-value: 1.2x10-05), as were expression of ACAP1 (p-value: 1.9x10-05) and LRRC25 (p-value: 5.2x10-05). While genome-wide association studies (GWAS) have implicated RCCD1 and ANKLE1 in breast cancer risk, they have not identified the remaining three genes. Among the genetic variants that contributed to the predicted expression of the five genes, we found 23 nominally (p-value < 0.05) associated with breast cancer risk, among which 15 are not in high linkage disequilibrium with risk variants previously identified by GWAS. In summary, we used a transcriptome-based approach to investigate the genetic underpinnings of breast carcinogenesis. This approach provided an avenue for deciphering the functional relevance of genes and genetic variants involved in breast cancer.

Project description:In a random effects meta-analysis model, true treatment effects for each study are routinely assumed to follow a normal distribution. However, normality is a restrictive assumption and the misspecification of the random effects distribution may result in a misleading estimate of overall mean for the treatment effect, an inappropriate quantification of heterogeneity across studies and a wrongly symmetric prediction interval.We focus on problems caused by an inappropriate normality assumption of the random effects distribution, and propose a novel random effects meta-analysis model where a Box-Cox transformation is applied to the observed treatment effect estimates. The proposed model aims to normalise an overall distribution of observed treatment effect estimates, which is sum of the within-study sampling distributions and the random effects distribution. When sampling distributions are approximately normal, non-normality in the overall distribution will be mainly due to the random effects distribution, especially when the between-study variation is large relative to the within-study variation. The Box-Cox transformation addresses this flexibly according to the observed departure from normality. We use a Bayesian approach for estimating parameters in the proposed model, and suggest summarising the meta-analysis results by an overall median, an interquartile range and a prediction interval. The model can be applied for any kind of variables once the treatment effect estimate is defined from the variable.A simulation study suggested that when the overall distribution of treatment effect estimates are skewed, the overall mean and conventional I 2 from the normal random effects model could be inappropriate summaries, and the proposed model helped reduce this issue. We illustrated the proposed model using two examples, which revealed some important differences on summary results, heterogeneity measures and prediction intervals from the normal random effects model.The random effects meta-analysis with the Box-Cox transformation may be an important tool for examining robustness of traditional meta-analysis results against skewness on the observed treatment effect estimates. Further critical evaluation of the method is needed.

Dataset Information

A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis.

Publications

A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis.

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