Correcting for cryptic relatedness in population-based association studies of continuous traits.
Cryptic relatedness was suggested to be an important source of confounding in population-based association studies (PBAS). The magnitude and manner of cryptic relatedness affecting the performance of PBAS of continuous traits remain to be investigated. We simulated a set of related samples through biased sampling and inbreeding, and evaluated the power and type I error rates of simple association tests (SAT) without correcting for cryptic relatedness. We also used extended likelihood ratio tests (ELRT) to conduct PBAS accounting for cryptic relatedness, and compared it with genomic control (GC). Cryptic relatedness decreased the power as well as increased the type I error rates of SAT in both biased sampling and inbreeding models. The impact of cryptic relatedness on the performance of SAT appeared to be limited in the biased sampling model. However, cryptic relatedness in inbred populations may result in excessive false positive results of SAT. Compared with SAT and GC, ELRT obtained improved power and type I error rates under various scenarios. Ignoring cryptic relatedness may increase spurious association results in PBAS. Our ELRT provides a novel approach to control cryptic relatedness in PBAS of human continuous traits.
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, PR China.
Feng
F
Zhang
Hong-Wen
HW
Deng
D000465:Algorithms; D003198:Computer Simulation; D005192:Family Health; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D040641:Quantitative Trait Loci / Q000235:genetics; D018401:Sample Size; D035781:Siblings