You are here

Association score testing for rare variants and binary traits in family data with shared controls.

TitleAssociation score testing for rare variants and binary traits in family data with shared controls.
Publication TypeJournal Article
Year of Publication2019
AuthorsSaad M, Wijsman EM
JournalBrief Bioinform
Date Published2019 01 18
KeywordsCase-Control Studies, Computational Biology, Computer Simulation, Feasibility Studies, Female, Gene Frequency, Genetic Variation, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Humans, Linear Models, Linkage Disequilibrium, Male, Models, Genetic, Pedigree, Quantitative Trait Loci, Sequence Analysis, DNA

Genome-wide association studies have been an important approach used to localize trait loci, with primary focus on common variants. The multiple rare variant-common disease hypothesis may explain the missing heritability remaining after accounting for identified common variants. Advances of sequencing technologies with their decreasing costs, coupled with methodological advances in the context of association studies in large samples, now make the study of rare variants at a genome-wide scale feasible. The resurgence of family-based association designs because of their advantage in studying rare variants has also stimulated more methods development, mainly based on linear mixed models (LMMs). Other tests such as score tests can have advantages over the LMMs, but to date have mainly been proposed for single-marker association tests. In this article, we extend several score tests (χcorrected2, WQLS, and SKAT) to the multiple variant association framework. We evaluate and compare their statistical performances relative with the LMM. Moreover, we show that three tests can be cast as the difference between marker allele frequencies (AFs) estimated in each of the group of affected and unaffected subjects. We show that these tests are flexible, as they can be based on related, unrelated or both related and unrelated subjects. They also make feasible an increasingly common design that only sequences a subset of affected subjects (related or unrelated) and uses for comparison publicly available AFs estimated in a group of healthy subjects. Finally, we show the great impact of linkage disequilibrium on the performance of all these tests.

Pubmed Link
Alternate JournalBrief. Bioinformatics
PubMed ID28968627
PubMed Central IDPMC6357552
Grant ListP50 AG005136 / AG / NIA NIH HHS / United States
R01 MH094293 / MH / NIMH NIH HHS / United States
U01 AG049507 / AG / NIA NIH HHS / United States

Theme by Danetsoft and Danang Probo Sayekti inspired by Maksimer