A pseudolikelihood approach for assessing genetic association in case–control studies with unmeasured population structure
| Autor: | Kung Yee Liang, Yong Chen, W. H. Linda Kao, Kathleen C. Barnes, Terri H. Beaty, Pan Tong |
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| Rok vydání: | 2020 |
| Předmět: |
Genetic Markers
Statistics and Probability Pseudolikelihood education.field_of_study Epidemiology Population Population structure Case-control study Computational biology Biology Latent class model Bias Gene Frequency Health Information Management Genetic marker Case-Control Studies Humans education Spurious relationship Probability Genetic association |
| Zdroj: | Statistical Methods in Medical Research. 29:3153-3165 |
| ISSN: | 1477-0334 0962-2802 |
| DOI: | 10.1177/0962280220921212 |
| Popis: | The case–control study design is one of the main tools for detecting associations between genetic markers and diseases. It is well known that population substructure can lead to spurious association between disease status and a genetic marker if the prevalence of disease and the marker allele frequency vary across subpopulations. In this paper, we propose a novel statistical method to estimate the association in case–control studies with unmeasured population substructure. The proposed method takes two steps. First, the information on genomic markers and disease status is used to infer the population substructure; second, the association between the disease and the test marker adjusting for the population substructure is modeled and estimated parametrically through polytomous logistic regression. The performance of the proposed method, relative to the existing methods, on bias, coverage probability and computational time, is assessed through simulations. The method is applied to an end-stage renal disease study in African Americans population. |
| Databáze: | OpenAIRE |
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