The impact of genotyping error on family-based analysis of quantitative traits.
Errors in genotyping can substantially influence the power to detect linkage using affected sib-pairs, but it is not clear what effect such errors have on quantitative trait analyses. Here we use Monte Carlo simulation to examine the influence of genotyping error on multipoint vs two-point analysis, variable map density, locus effect size and allele frequency in quantitative trait linkage and association studies of sib-pairs. The analyses are conducted using variance components methods. We contrast the effects of error on quantitative trait analyses with those on the affected sib-pair design. The results indicate that genotyping error influences linkage studies of affected sib pairs more severely than studies of quantitative traits in unselected sibs. In situations of modest effect size, 5% genotyping error eliminates all supporting evidence for linkage to a true susceptibility locus in affected pairs, but may only result in a loss of 15% of linkage information in random pairs. Multipoint analysis does not suffer substantially more than two-point analysis; for moderate error rates (< 5%), multipoint analysis with error is more powerful than two-point with no error. Map density does not appear to be an important factor for linkage analysis. QTL association analyses of common alleles are reasonably robust to genotyping error but power can be affected dramatically with rare alleles.
Abecasis
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GR
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
Cherny
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Cardon
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LR
D000483:Alleles; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D005787:Gene Frequency / Q000235:genetics; D008040:Genetic Linkage; D005838:Genotype; D006801:Humans; D008126:Lod Score; D016015:Logistic Models; D016555:Matched-Pair Analysis; D008957:Models, Genetic; D009010:Monte Carlo Method; D009679:Nuclear Family; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results