Linkage detection adaptive to linkage disequilibrium: the disequilibrium maximum-likelihood-binomial test for affected-sibship data.
It has been demonstrated in the literature that the transmission/disequilibrium test (TDT) has higher power than the affected-sib-pair (ASP) mean test when linkage disequilibrium (LD) is strong but that the mean test has higher power when LD is weak. Thus, for ASP data, it seems clear that the TDT should be used when LD is strong but that the mean test or other linkage tests should be used when LD is weak or absent. However, in practice, it may be difficult to follow such a guideline, because the extent of LD is often unknown. Even with a highly dense genetic-marker map, in which some markers should be located near the disease-predisposing mutation, strong LD is not inevitable. Besides the genetic distance, LD is also affected by many factors, such as the allelic heterogeneity at the disease locus, the initial LD, the allelic frequencies at both disease locus and marker locus, and the age of the mutation. Therefore, it is of interest to develop methods that are adaptive to the extent of LD. In this report, we propose a disequilibrium maximum-binomial-likelihood (DMLB) test that incorporates LD in the maximum-binomial-likelihood (MLB) test. Examination of the corresponding score statistics shows that this method adaptively combines two sources of information: (a) the identity-by-descent (IBD) sharing score, which is informative for linkage regardless of the existence of LD, and (b) the contrast between allele-specific IBD sharing score, which is informative for linkage only in the presence of LD. For ASP data, the proposed test has higher power than either the TDT or the mean test when the extent of LD ranges from moderate to strong. Only when LD is very weak or absent is the DMLB slightly less powerful than the mean test; in such cases, the TDT has essentially no power to detect linkage. Therefore, the DMLB test is an interesting approach to linkage detection when the extent of LD is unknown.
Huang
J
J
Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA 52242, USA. jian-huang@uiowa.edu.
Jiang
Y
Y
D000483:Alleles; D003433:Crosses, Genetic; D005799:Genes, Dominant / Q000235:genetics; D005808:Genes, Recessive / Q000235:genetics; D030342:Genetic Diseases, Inborn / Q000235:genetics; D018740:Genetic Heterogeneity; D020022:Genetic Predisposition to Disease; D006579:Heterozygote; D006801:Humans; D016013:Likelihood Functions; D015810:Linkage Disequilibrium / Q000235:genetics*; D008957:Models, Genetic; D009154:Mutation; D009679:Nuclear Family; D018401:Sample Size