Abstract：
:Variance components analysis provides an efficient method for performing linkage analysis for quantitative traits. However, power and type 1 error of variance components-based likelihood ratio testing may be affected when phenotypic data are nonnormally distributed (especially with high values of kurtosis) and there is moderate to high correlation among the siblings. Winsorization can reduce the effect of outliers on statistical analyses. Here, we considered the effect of winsorization on variance components-based tests. We considered the likelihood ratio test (LRT), the Wald test, and some robust variance components tests. We compared these tests with Haseman-Elston least squares-based tests. We found that power to detect linkage is significantly increased after winsorization of the nonnormal phenotypes. Winsorization does not greatly diminish the type 1 error for the variance components-based tests for markedly nonnormal data. A robust version of the LRT that adjusts for sample kurtosis showed the best power for nonnormal data. Finally, phenotype winsorization of nonnormal data reduces the bias in estimation of the major gene variance component.
journal_name
Behav Genetjournal_title
Behavior geneticsauthors
Shete S,Beasley TM,Etzel CJ,Fernández JR,Chen J,Allison DB,Amos CIdoi
10.1023/B:BEGE.0000013729.26354.dasubject
Has Abstractpub_date
2004-03-01 00:00:00pages
153-9issue
2eissn
0001-8244issn
1573-3297pii
478050journal_volume
34pub_type
杂志文章abstract：:There is considerable evidence that sex differences in spatial ability exist in adults, with males outperforming females at every age after puberty. It is difficult, however, to find sex differences in children younger than 13. This is due in part to the lack of adequate measures of spatial ability for use with childr...
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