A nonparametric test for association with multiple loci in the retrospective case-control study
| Autor: | Shufang Deng, Liming Li, Yue-Qing Hu, Chan Wang, Leiming Sun |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Linkage disequilibrium Epidemiology Population Genome-wide association study Single-nucleotide polymorphism Computational biology Biology Polymorphism Single Nucleotide Statistics Nonparametric 03 medical and health sciences 0302 clinical medicine Health Information Management Humans Disease education Retrospective Studies 030304 developmental biology Genetic association 0303 health sciences education.field_of_study Nonparametric statistics Heritability Genetic Loci Case-Control Studies Pairwise comparison Algorithms 030217 neurology & neurosurgery Genome-Wide Association Study |
| Zdroj: | Statistical Methods in Medical Research. 29:589-602 |
| ISSN: | 1477-0334 0962-2802 |
| DOI: | 10.1177/0962280219842892 |
| Popis: | The genome-wide association studies aim at identifying common or rare variants associated with common diseases and explaining more heritability. It is well known that common diseases are influenced by multiple single nucleotide polymorphisms (SNPs) that are usually correlated in location or function. In order to powerfully detect association signals, it is highly desirable to take account of correlations or linkage disequilibrium (LD) information among multiple SNPs in testing for association. In this article, we propose a test SLIDE that depicts the difference of the average multi-locus genotypes between cases and controls and derive its variance–covariance matrix in the retrospective design. This matrix is composed of the pairwise LD between SNPs. Thus SLIDE can borrow the strength from an external database in the population of interest with a few thousands to hundreds of thousands individuals to improve the power for detecting association. Extensive simulations show that SLIDE has apparent superiority over the existing methods, especially in the situation involving both common and rare variants, both protective and deleterious variants. Furthermore, the efficiency of the proposed method is demonstrated in the application to the data from the Wellcome Trust Case Control Consortium. |
| Databáze: | OpenAIRE |
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