Practical Considerations Regarding the Use of Genotype and Pedigree Data to Model Relatedness in the Context of Genome-Wide Association Studies
Year: 2013
Type: article
Abstract: Genome-wide association studies of complex traits often are complicated by relatedness among individuals. Ignoring or inappropriately accounting for relatedness often results in inflated type I error rates. Either genotype or pedigree data can be used to estimate relatedness for use in mixed-models when undertaking quantitative trait locus mapping. We performed simulations to investigate methods for controlling type I error and optimizing power considering both full and partial pedigrees and, similarly, both sparse and dense marker coverage; we also examined real data sets. (1) When marker density was low, estimating relatedness by genotype data alone failed to control the type I error rate; (2) this was resolved by combining both genotype and pedigree data. (3) When sufficiently dense marker data were used to estimate relatedness, type I error was well controlled and power increased; however, (4) this was only true when the relatedness was estimated using genotype data that excluded genotypes on the chromosome currently being scanned for a quantitative trait locus.
Source: G3 Genes Genomes Genetics
Authors: Riyan Cheng, Clarissa C Parker, Mark Abney, Abraham A Palmer
Institutions: Australian National University, University of Chicago
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,709
Awards: G2800256649, Genome-wide association studies in outbred mice, Methods for Complex Trait Mapping in Isolated Populations, FINDING GENES THAT CAUSE QLT FOR FEAR LEARNING AND ANXIETY, G7099524749, Systems Genetic Analysis of Methamphetamine's Motivational Effects in a Mouse AIL, G776096440, G8908545868
OpenAlex ID: W1999935373