Comparison of Nonparametric Statistics for Detection of Linkage in
Nuclear Families: Single-Marker Evaluation
Sean Davis1 and Daniel E. Weeks1,2
1
Department of Human Genetics, University of Pittsburgh, Pittsburgh;
and
2
Wellcome Trust Centre for Human
Genetics, University of Oxford, Oxford
American Journal of Human Genetics, 61, 1431-1444 (Dec 1997)
Abstract
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.
Allele-sharing linkage analysis