Efficient strategies for genome scanning using maximum-likelihood affected-sib-pair analysis.
Detection of linkage with a systematic genome scan in nuclear families including an affected sibling pair is an important initial step on the path to cloning susceptibility genes for complex genetic disorders, and it is desirable to optimize the efficiency of such studies. The aim is to maximize power while simultaneously minimizing the total number of genotypings and probability of type I error. One approach to increase efficiency, which has been investigated by other workers, is grid tightening: a sample is initially typed using a coarse grid of markers, and promising results are followed up by use of a finer grid. Another approach, not previously considered in detail in the context of an affected-sib-pair genome scan for linkage, is sample splitting: a portion of the sample is typed in the screening stage, and promising results are followed up in the whole sample. In the current study, we have used computer simulation to investigate the relative efficiency of two-stage strategies involving combinations of both grid tightening and sample splitting and found that the optimal strategy incorporates both approaches. In general, typing half the sample of affected pairs with a coarse grid of markers in the screening stage is an efficient strategy under a variety of conditions. If Hardy-Weinberg equilibrium holds, it is most efficient not to type parents in the screening stage. If Hardy-Weinberg equilibrium does not hold (e.g., because of stratification) failure to type parents in the first stage increases the amount of genotyping required, although the overall probability of type I error is not greatly increased, provided the parents are used in the final analysis.
Holmans
P
P
Department of Psychological Medicine, University of Wales College of Medicine, Cardiff, United Kingdom.
Craddock
N
N
D005787:Gene Frequency; D030342:Genetic Diseases, Inborn / Q000235:genetics; D008040:Genetic Linkage; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D016678:Genome; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic
D005819:Genetic Markers