ArticleThe annals of applied statistics
A FAST ALGORITHM FOR DETECTING GENE-GENE INTERACTIONS IN GENOME-WIDE ASSOCIATION STUDIES.
Article in The annals of applied statistics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 31 citations in OpenAlex.
- Review on GPU accelerated methods for genome-wide SNP-SNP interactions.Molecular genetics and genomics : MGG · 2024Review
- Bayesian bi-level variable selection for genome-wide survival study.Genomics & informatics · 2023Article
- Sparse group variable selection for gene-environment interactions in the longitudinal study.Genetic epidemiology · 2022Article
- Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects.Scientific reports · 2021Article
- Article
- Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect.DNA research : an international journal for rapid publication of reports on genes and genomes · 2019Article
- Overlapping group screening for detection of gene-gene interactions: application to gene expression profiles with survival trait.BMC bioinformatics · 2018Article
- An Ultrahigh-Dimensional Mapping Model of High-order Epistatic Networks for Complex Traits.Current genomics · 2018Article
- Article
- Detection of Epistasis for Flowering Time Using Bayesian Multilocus Estimation in a Barley MAGIC Population.Genetics · 2018Article
- Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.Scientific reports · 2016Article
- A block mixture model to map eQTLs for gene clustering and networking.Scientific reports · 2016Article
- A selective overview of feature screening for ultrahigh-dimensional data.Science China. Mathematics · 2015Article
- Mapping complex traits as a dynamic system.Physics of life reviews · 2015Review
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
Abstract
With the recent advent of high-throughput genotyping techniques, genetic data for genome-wide association studies (GWAS) have become increasingly available, which entails the development of efficient and effective statistical approaches. Although many such approaches have been developed and used to identify single-nucleotide polymorphisms (SNPs) that are associated with complex traits or diseases, few are able to detect gene-gene interactions among different SNPs. Genetic interactions, also known as epistasis, have been recognized to play a pivotal role in contributing to the genetic variation of phenotypic traits. However, because of an extremely large number of SNP-SNP combinations in GWAS, the model dimensionality can quickly become so overwhelming that no prevailing variable selection methods are capable of handling this problem. In this paper, we present a statistical framework for characterizing main genetic effects and epistatic interactions in a GWAS study. Specifically, we first propose a two-stage sure independence screening (TS-SIS) procedure and generate a pool of candidate SNPs and interactions, which serve as predictors to explain and predict the phenotypes of a complex trait. We also propose a rates adjusted thresholding estimation (RATE) approach to determine the size of the reduced model selected by an independence screening. Regularization regression methods, such as LASSO or SCAD, are then applied to further identify important genetic effects. Simulation studies show that the TS-SIS procedure is computationally efficient and has an outstanding finite sample performance in selecting potential SNPs as well as gene-gene interactions. We apply the proposed framework to analyze an ultrahigh-dimensional GWAS data set from the Framingham Heart Study, and select 23 active SNPs and 24 active epistatic interactions for the body mass index variation. It shows the capability of our procedure to resolve the complexity of genetic control.
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