Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs

Tang, Zheng Zheng; Lin, Dan

doi:10.1016/j.ajhg.2015.05.001

Cited by 34 publications

(42 citation statements)

References 39 publications

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“…[17][18][19] Lee et al 20 compare available gene-based tests and discuss design strategies for RVASs. Meta-analysis frameworks have been proposed to combine individual variant score statistics across studies and reconstruct gene-based tests, 21 but this may lead to biases when selection, sequencing, and quality control differ between studies. Another framework for metaanalysis is combining gene-based association statistics, but the effect on power of meta-analyzing gene-based association results from two rare variant studies with different study designs remains a question.…”

Section: Introductionmentioning

confidence: 99%

Phenotypic extremes in rare variant study designs

et al. 2015

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Currently, next-generation sequencing studies aim to identify rare and low-frequency variation that may contribute to disease. For a given effect size, as the allele frequency decreases, the power to detect genes or variants of interest also decreases. Although many methods have been proposed for the analysis of such data, study design and analytic issues still persist in data interpretation. In this study we present sequencing data for ABCA1 that has known rare variants associated with high-density lipoprotein cholesterol (HDL-C). We contrast empirical findings from two study designs: a phenotypic extreme sample and a population-based random sample. We found differing strengths of association with HDL-C across the two study designs (P = 0.0006 with n = 701 phenotypic extremes vs P = 0.03 with n = 1600 randomly sampled individuals). To explore this apparent difference in evidence for association, we performed a simulation study focused on the impact of phenotypic selection on power. We demonstrate that the power gain for an extreme phenotypic selection study design is much greater in rare variant studies than for studies of common variants. Our study confirms that studying phenotypic extremes is critical in rare variant studies because it boosts power in two ways: the typical increases from extreme sampling and increasing the proportion of relevant functional variants ascertained and thereby tested for association. Furthermore, we show that when combining statistical evidence through meta-analysis from an extreme-selected sample and a second separate population-based random sample, power is lower when a traditional sample size weighting is used compared with weighting by the noncentrality parameter.

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Section: Introductionmentioning

confidence: 99%

Phenotypic extremes in rare variant study designs

et al. 2015

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“…Both approaches are known to suffer from drawbacks, albeit some investigators have argued that they are appropriate in the context of rare variants analysis (Tang & Lin, 2015). Some analysis approaches that have been used to counteract this problem include rank-normalization of both trait values and of residuals, which are then used in a partly adjusted two-stage procedure.…”

Section: Discussionmentioning

confidence: 99%

“…This means that entries of ϵ are matched with quantiles of the normal distribution, so that the transformed values maintain the same order (or rank) as the original residuals, but follow the normal N (0,1) distribution (Tang & Lin, 2015). This means that entries of ϵ are matched with quantiles of the normal distribution, so that the transformed values maintain the same order (or rank) as the original residuals, but follow the normal N (0,1) distribution (Tang & Lin, 2015).…”

Section: Two-stage Approaches For Genetic Association Analysismentioning

confidence: 99%

“…To address this problem, an approach that is widely used in genetic association studies (within the regression framework) is to apply a rank-normalization of trait values, followed by subsequent analysis of the rank-normalized trait as the analysis outcome (see e.g., Ashton & Borecki, 1987;X. In the context of rare variants, it was shown by Tang and Lin (2015) that applying rank-normalization on traits before any analysis and testing helps to control the rate of Type I errors and increase statistical power. In the context of rare variants, it was shown by Tang and Lin (2015) that applying rank-normalization on traits before any analysis and testing helps to control the rate of Type I errors and increase statistical power.…”

Section: Introductionmentioning

confidence: 99%

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A fully adjusted two‐stage procedure for rank‐normalization in genetic association studies

Sofer

Zheng

Gogarten

et al. 2019

Genetic Epidemiology

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When testing genotype–phenotype associations using linear regression, departure of the trait distribution from normality can impact both Type I error rate control and statistical power, with worse consequences for rarer variants. Because genotypes are expected to have small effects (if any) investigators now routinely use a two‐stage method, in which they first regress the trait on covariates, obtain residuals, rank‐normalize them, and then use the rank‐normalized residuals in association analysis with the genotypes. Potential confounding signals are assumed to be removed at the first stage, so in practice, no further adjustment is done in the second stage. Here, we show that this widely used approach can lead to tests with undesirable statistical properties, due to both combination of a mis‐specified mean–variance relationship and remaining covariate associations between the rank‐normalized residuals and genotypes. We demonstrate these properties theoretically, and also in applications to genome‐wide and whole‐genome sequencing association studies. We further propose and evaluate an alternative fully adjusted two‐stage approach that adjusts for covariates both when residuals are obtained and in the subsequent association test. This method can reduce excess Type I errors and improve statistical power.

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“…Tang and Lin (2015) has provided a thorough review of the existing methods for meta-analysis of RVs. Tang and Lin (2015) has provided a thorough review of the existing methods for meta-analysis of RVs.…”

Section: Adaptive Sum Of Powered Squares (Aspu)-meta Testmentioning

confidence: 99%

An adaptive test for meta‐analysis of rare variant association studies

Yang

Kim

et al. 2019

Genetic Epidemiology

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Single genome-wide studies may be underpowered to detect trait-associated rare variants with moderate or weak effect sizes. As a viable alternative, meta-analysis is widely used to increase power by combining different studies. The power of meta-analysis critically depends on the underlying association patterns and heterogeneity levels, which are unknown and vary from locus to locus. However, existing methods mainly focus on one or only a few combinations of the association pattern and heterogeneity level, thus may lose power in many situations. To address this issue, we propose a general and unified framework by combining a class of tests including and beyond some existing ones, leading to high power across a wide range of scenarios. We demonstrate that the proposed test is more powerful than some existing methods in simulation studies, then show their performance with the NHLBI Exome-Sequencing Project (ESP) data.One gene (B4GALNT2) was found by our proposed test, but not by others, to be statistically significantly associated with plasma triglyceride. The signal was driven by African-ancestry subjects but it was previously reported to be associated with coronary artery disease among European-ancestry subjects. We implemented our method in an R package aSPUmeta, publicly available at https://github. com/ytzhong/metaRV and will be on CRAN soon. K E Y W O R D SaSPU, genetic heterogeneity, statistical power, whole-exome sequencing, whole-genome sequencing *Tianzhong Yang and Junghi Kim contributed equally to this work.

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Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs

Cited by 34 publications

References 39 publications

Phenotypic extremes in rare variant study designs

Phenotypic extremes in rare variant study designs

A fully adjusted two‐stage procedure for rank‐normalization in genetic association studies

An adaptive test for meta‐analysis of rare variant association studies

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