A simple and accurate method to determine genomewide significance for association tests in sequencing studies.

TitleA simple and accurate method to determine genomewide significance for association tests in sequencing studies.
Publication TypeJournal Article
Year of Publication2019
AuthorsLin, Dan-Yu
JournalGenet Epidemiol
Volume43
Issue4
Pagination365-372
Date Published2019 06
ISSN1098-2272
KeywordsExome, Genetic Association Studies, Genome, Human, Genotype, High-Throughput Nucleotide Sequencing, Humans, Models, Theoretical, Phenotype, Polymorphism, Single Nucleotide, Practice Guidelines as Topic, Reproducibility of Results, Whole Genome Sequencing
Abstract

Whole-exome sequencing (WES) and whole-genome sequencing (WGS) studies are underway to investigate the impact of genetic variants on complex diseases and traits. It is customary to perform single-variant association tests for common variants and region-based association tests for rare variants. The latter may target variants with similar or opposite effects, interrogate variants with different frequencies or different functional annotations, and examine a variety of regions. The large number of tests that are performed necessitates adjustment for multiple testing. The conventional Bonferroni correction is overly conservative as the test statistics are correlated. To address this challenge, we propose a simple and accurate method based on parametric bootstrap to assess genomewide significance. We show that the correlations of the test statistics are determined primarily by the genotypes, such that the same significance threshold can be used in different studies that share a common sequencing platform. We demonstrate the usefulness of the proposed method with WES data from the National Heart, Lung, and Blood Institute Exome Sequencing Project and WGS data from the 1000 Genomes Project. We recommend the p value of as the genomewide significance threshold for testing all common and low-frequency variants (MAFs 0.1%) in the human genome.

DOI10.1002/gepi.22183
Alternate JournalGenet Epidemiol
Original PublicationA simple and accurate method to determine genomewide significance for association tests in sequencing studies.
PubMed ID30623491
PubMed Central IDPMC6520182
Grant ListR01 HG009974 / HG / NHGRI NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Project: