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Genet Epidemiol. 2002 Feb;22(2):156-69.

Quantification of type I error probabilities for heterogeneity LOD scores.

Author information

1
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York, USA.

Abstract

Locus heterogeneity is a major confounding factor in linkage analysis. When no prior knowledge of linkage exists, and one aims to detect linkage and heterogeneity simultaneously, classical distribution theory of log-likelihood ratios does not hold. Despite some theoretical work on this problem, no generally accepted practical guidelines exist. Nor has anyone rigorously examined the combined effect of testing for linkage and heterogeneity and simultaneously maximizing over two genetic models (dominant, recessive). The effect of linkage phase represents another uninvestigated issue. Using computer simulation, we investigated type I error (P value) of the "admixture" heterogeneity LOD (HLOD) score, i.e., the LOD score maximized over both recombination fraction theta and admixture parameter alpha and we compared this with the P values when one maximizes only with respect to theta (i.e., the standard LOD score). We generated datasets of phase-known and -unknown nuclear families, sizes k = 2, 4, and 6 children, under fully penetrant autosomal dominant inheritance. We analyzed these datasets (1) assuming a single genetic model, and maximizing the HLOD over theta and alpha; and (2) maximizing the HLOD additionally over two dominance models (dominant vs. recessive), then subtracting a 0.3 correction. For both (1) and (2), P values increased with family size k; rose less for phase-unknown families than for phase-known ones, with the former approaching the latter as k increased; and did not exceed the one-sided mixture distribution xi = (1/2) chi1(2) + (1/2) chi2(2). Thus, maximizing the HLOD over theta and alpha appears to add considerably less than an additional degree of freedom to the associated chi1(2) distribution. We conclude with practical guidelines for linkage investigators.

PMID:
11788961
DOI:
10.1002/gepi.0155
[Indexed for MEDLINE]

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