Review and Evaluation of Methods Correcting for Population Stratification with a Focus on Underlying Statistical Principles
Year: 2008
Type: review
Abstract: When two or more populations have been separated by geographic or cultural boundaries for many generations, drift, spontaneous mutations, differential selection pressures and other factors may lead to allele frequency differences among populations. If these 'parental' populations subsequently come together and begin inter-mating, disequilibrium among linked markers may span a greater genetic distance than it typically does among populations under panmixia [see glossary]. This extended disequilibrium can make association studies highly effective and more economical than disequilibrium mapping in panmictic populations since less marker loci are needed to detect regions of the genome that harbor phenotype-influencing loci. However, under some circumstances, this process of intermating (as well as other processes) can produce disequilibrium between pairs of unlinked loci and thus create the possibility of confounding or spurious associations due to this population stratification. Accordingly, researchers are advised to employ valid statistical tests for linkage disequilibrium mapping allowing conduct of genetic association studies that control for such confounding. Many recent papers have addressed this need. We provide a comprehensive review of advances made in recent years in correcting for population stratification and then evaluate and synthesize these methods based on statistical principles such as (1) randomization, (2) conditioning on sufficient statistics, and (3) identifying whether the method is based on testing the genotype-phenotype covariance (conditional upon familial information) and/or testing departures of the marginal distribution from the expected genotypic frequencies.
Source: Human Heredity
DOI: 10.1159/000119107
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
Funders: National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Environmental Health Sciences, National Institute of Arthritis and Musculoskeletal and Skin Diseases, U.S. National Library of Medicine, National Center for Research Resources
Awards: G1197929714, GENETIC MOUSE MODELS FOR CHRONIC INFLAMMATORY DISEASE, G151419470, G1582319558, G162670763, Multiple Imputation in Genetic Studies, UAB Biostatistics Pre-doctoral Training Program, G2936269400, Training Program in Rheumatic Diseases Research, Genetic and Environmental Determinants of Triglycerides, The molecular genetic analysis of human obesity, Center for Nutrient-Gene Interaction in Cancer Prevention, Investigating Obesity and Chronic Disease-Related Risk Factors of Alaska Natives, Genome-wide Structured Association Testing & Regional Admixture Mapping, Haplotype Analysis in Linkage Disequilibrium Mapping, Program Project in the Genetics of SLE, Predictors of Treatment Response in Early Aggressive RA, STATISTICAL GENETICS FOR OBESITY &NUTRITION RESEARCHERS, Positional Gene Identification of Complex Traits, Diet, genetics, and CVD risk factor response in Blacks, HyperGEN: Genetics of Left Ventricular Hypertrophy, G864137628, UAB Nutrition Obesity Research Center (NORC)
OpenAlex ID: W2097210466