Comparison of nonparametric statistics for detection of linkage in nuclear families: single-marker evaluation.
We have evaluated 23 different statistics, from a total of 10 popular software packages for model-free linkage analysis of nuclear-family data, by applying them to single-marker data simulated under several two-locus disease models. The statistics that we examined fall into two broad categories: (1) those that test directly for increased identity-by-state or identity-by-descent sharing (by use of the programs APM, Genetic Analysis System [GAS] SIBSTATE and SIBDES, SAGE SIBPAL, ERPA, SimIBD, and Genehunter NPL) and (2) those that are based on likelihood-ratio tests and that report LOD scores (by use of the programs Splink, SIBPAIR, Mapmaker/Sibs, ASPEX, and GAS SIBMLS). For each of eight two-locus disease models, we analyzed six data sets; the first three data sets consisted of two-child families with both sibs affected and zero, one, or both parents typed, whereas the other three data sets consisted of four-child families with at least two affected sibs and zero, one, or both parents typed. We report false-positive rates, overall rank by power, and the power for each statistic. We give rough recommendations regarding which programs provide the most powerful tests for linkage, as well as the programs to be avoided under certain conditions. For the likelihood-ratio-based statistics, we examined the effects of various treatments of sibships with multiple affected individuals. Finally, we explored the use of some simple two-of-three composite statistics and found that such tests are of only marginal benefit over the most powerful single statistic.
Davis
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Department of Human Genetics, University of Pittsburgh, PA 15261, USA.
Weeks
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D000328:Adult; D002648:Child; D005069:Evaluation Studies as Topic; D005189:False Positive Reactions; D005192:Family Health; D008040:Genetic Linkage; D005819:Genetic Markers; D006801:Humans; D008957:Models, Genetic; D009679:Nuclear Family; D012984:Software; D018709:Statistics, Nonparametric
D005819:Genetic Markers