Unified Sequence-Based Association Tests Allowing for Multiple Functional Annotations and Meta-analysis of Noncoding Variation in Metabochip Data

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.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Yang

Kim

et al. 2019

“…However, if the selected values do not reflect the true model, then the corresponding test might be underpowered (Selyeong Lee et al, ; Ray et al, ). To overcome such issues, minimum p value‐based omnibus tests have been proposed, which aggregate results across different values of the parameters to produce robust results (Dutta et al, ; Engel et al, ; He, Xu, Lee, & Ionita‐Laza, ; Urrutia et al, ; Zhan et al, ). Here, we use the same strategy to formulate robust tests across different choices of

Σ_{S}

Σ_{P}

, and

Σ_{G}

.…”

Section: Methodsmentioning

confidence: 99%

Meta‐MultiSKAT: Multiple phenotype meta‐analysis for region‐based association test

Dutta

Taliun

Weinstock

et al. 2019

Self Cite

The power of genetic association analyses can be increased by jointly meta‐analyzing multiple correlated phenotypes. Here, we develop a meta‐analysis framework, Meta‐MultiSKAT, that uses summary statistics to test for association between multiple continuous phenotypes and variants in a region of interest. Our approach models the heterogeneity of effects between studies through a kernel matrix and performs a variance component test for association. Using a genotype kernel, our approach can test for rare‐variants and the combined effects of both common and rare‐variants. To achieve robust power, within Meta‐MultiSKAT, we developed fast and accurate omnibus tests combining different models of genetic effects, functional genomic annotations, multiple correlated phenotypes, and heterogeneity across studies. In addition, Meta‐MultiSKAT accommodates situations where studies do not share exactly the same set of phenotypes or have differing correlation patterns among the phenotypes. Simulation studies confirm that Meta‐MultiSKAT can maintain the type‐I error rate at the exome‐wide level of 2.5 × 10−6. Further simulations under different models of association show that Meta‐MultiSKAT can improve the power of detection from 23% to 38% on average over single phenotype‐based meta‐analysis approaches. We demonstrate the utility and improved power of Meta‐MultiSKAT in the meta‐analyses of four white blood cell subtype traits from the Michigan Genomics Initiative (MGI) and SardiNIA studies.

“…Although the linear (weighted) kernels are still most widely applied in genetic association studies, owing to its biological interpretability and computational efficiency, there have been recent attempts to design kernels that adapt to more complex signal structures by taking into account prior biological knowledge. For example, new variance component kernels have been proposed to accommodate functional annotations of noncoding variations in the mixed-effects model framework (Hao, Zeng, Zhang, & Zhou, 2018;Z. He, Xu, Lee, & Ionita-Laza, 2017).…”

Section: Variance Component Kernelsmentioning

confidence: 99%

A review of kernel methods for genetic association studies

Larson

Chen

Schaid

2019

Evaluating the association of multiple genetic variants with a trait of interest by use of kernel‐based methods has made a significant impact on how genetic association analyses are conducted. An advantage of kernel methods is that they tend to be robust when the genetic variants have effects that are a mixture of positive and negative effects, as well as when there is a small fraction of causal variants. Another advantage is that kernel methods fit within the framework of mixed models, providing flexible ways to adjust for additional covariates that influence traits. Herein, we review the basic ideas behind the use of kernel methods for genetic association analysis as well as recent methodological advancements for different types of traits, multivariate traits, pedigree data, and longitudinal data. Finally, we discuss opportunities for future research.