Testing for Linkage underRobust Genetic Models

Abstract

Robust genetic models are used to assess linkage between a quantitative trait and genetic variation at a specific locus using allele-sharing data. Little is known about the relative performance of different possible significance tests under these models. Under the robust variance components model approach there are several alternatives: standard Wald and likelihood ratio tests, a quasilikelihood Wald test, and a Monte Carlo test. This paper reports on the relative performance (significance level and power) of the robust sibling pair test and the different alternatives under the robust variance components model. Simulations show that (1) for a fixed sample size of nuclear families, the variance components model approach is more powerful than the robust sibling pair approach; (2) when the number of nuclear families is at least ∼100 and heritability at the trait locus is moderate to high (>0.20) all tests based on the variance components model are equally effective; (3) when the number of nuclear families is less than ∼100 or heritability at the trait locus is low (<0.20), on balance, the Monte Carlo test provides the best power and is the most valid. The different testing procedures are applied to determine which are able to detect the known association between low density lipoprotein cholesterol and the common genotypes at the locus encoding apolipoprotein E. Results from this application show that the robust sibling pair method may be more effective in practice than that indicated by simulations.

Comparison of Multivariate Tests for Genetic Linkage

“…However, recent studies [33,34] have shown lack of convergence of the test for linkage to a limiting ¯2 distribution when the data are not normally distributed and there is a strong residual correlation among sibs, after allowing for the major gene effect. Methods to allow the construction of accurate tests for nonnormal data include data trimming [35], application of generalized estimating equations or robust variance estimation [22], or the construction of permutation tests [36]. Application of each of these approaches for multivariate data could be rather complex, and tests using either permutation tests or generalized estimating equations are computationally intensive.…”

Heritability of chronic venous disease

“…A 95% confidence interval for h 2 was derived as ±2 standard errors of the estimate. The statistical significance of h 2 was assessed by means of a likelihood ratio test (Guerra et al 1999). The influence of the two main CVD risk factors, namely sex and age, upon CEAP grade was estimated using the Kullback–Leibler deviance R 2 .…”

Ascertainment issues in variance components models

“…The maximum likelihood estimates of the components of variance can be obtained iteratively through the scoring method as described by de Andrade et al [1999]. Although the presence of a major gene induces non-normality, the parameter estimates of equation (2) obtained by the maximum likelihood method from unselected families are rather accurate [Amos et al, 1996;Guerra et al, 1999].…”