A powerful and data‐adaptive test for rare‐variant–based gene‐environment interaction analysis

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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.

Section: Discussionmentioning

confidence: 99%

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

Yang

Kim

et al. 2019

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“…We review PrediXcan and demonstrate that a direct extension of PrediXcan to G × E is problematic, followed by the review of the aSPU and a data‐adaptive G × E (aGE) test as the basis of our proposed aGEw test (Pan et al, 2014; Yang et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…The aSPU test was originally developed to test genetic main effect for a set of rare variants (Pan et al, 2014), while the aGE test extended it to test G × E (Yang et al, 2019). The aSPU and aGE tests choose the most powerful test among a family of sum of powered score (SPU) tests, including burden and variance‐component tests, such that it can maintain high power under a wide range of association patterns.…”

Section: Methodsmentioning

confidence: 99%

“…First, high linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs) could be problematic for a gene‐based score test. Unlike the likelihood ratio and Wald tests, the score test only requires estimating the genetic main association effects under the null hypothesis of no G × E (Yang, Chen, Tang, Li, & Wei, 2019). However, the maximum‐likelihood estimation of the null models was not stable for many genes in our preliminary analysis of real data, which hinders its use in the genome‐wide G × E scan.…”

Section: Introductionmentioning

confidence: 99%

“…Besides the lack of optimal‐quality weights, weight misspecification could be caused by the unsatisfied imposed model assumptions. For example, the majority of eQTLs in a gene may not interact with the environmental variable, that is neutral variants in terms of G × E. Therefore, we propose a robust and powerful eQTL‐weighted data‐adaptive G × E (called aGEw) test, as an extension of the adaptive sum of powered test (aSPU; Pan, Kim, Zhang, Shen, & Wei, 2014) and our earlier work on G × E (Yang et al, 2019). Instead of a single tissue, we use weights from all available tissues in a tissue‐agnostic manner (Barbeira et al, 2019; Wainberg et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incorporating multiple sets of eQTL weights into gene‐by‐environment interaction analysis identifies novel susceptibility loci for pancreatic cancer

Yang

Tang

Risch

et al. 2020

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It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene‐by‐environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data‐adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case–Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome‐wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking‐related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.

Data‐adaptive and pathway‐based tests for association studies between somatic mutations and germline variations in human cancers

Chen,

Liang,

Wei

2023

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Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large‐scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data‐adaptive and pathway‐based tests based on the score statistic for association studies between somatic mutations and germline variations. Previous research has shown that two single‐nucleotide polymorphism (SNP)‐set‐based association tests, adaptive sum of powered score (aSPU) and data‐adaptive pathway‐based (aSPUpath) tests, increase the power in genome‐wide association studies (GWASs) with a single disease trait in a case–control study. We extend aSPU and aSPUpath to multi‐traits, that is, somatic mutations of multiple genes in a cohort study, allowing extensive information aggregation at both SNP and gene levels. ‐values from different parameters assuming varying genetic architecture are combined to yield data‐adaptive tests for somatic mutations and germline variations. Extensive simulations show that, in comparison with some commonly used methods, our data‐adaptive somatic mutations/germline variations tests can be applied to multiple germline SNPs/genes/pathways, and generally have much higher statistical powers while maintaining the appropriate type I error. The proposed tests are applied to a large‐scale real‐world International Cancer Genome Consortium whole genome sequencing data set of 2583 subjects, detecting more significant and biologically relevant associations compared with the other existing methods on both gene and pathway levels. Our study has systematically identified the associations between various germline variations and somatic mutations across different cancer types, which potentially provides valuable utility for cancer risk prediction, prognosis, and therapeutics.