Abstract
An analytic method is described for estimating phenotypic correlations between pairs of members of specific relationships in pedigrees. In estimating correlations, this new method allows simultaneous adjustment for available covariates such as age, gender, environmental factors, and variables reflecting ascertainment mode, through mean- and variance-regression models. The estimated correlations and regression coefficients corresponding to covariates are consistent and asymptotically normally distributed. Differing from a full-likelihood approach, this new method does not require the assumption of a particular joint distribution of phenotypes from a pedigree, such as the multivariate normal distribution, but instead only requires correct specification of mean- and variance-regression models. Within this framework, missing data, if they are missing completely at random, can be ignored without biasing estimates. The method is illustrated by an application using nevus-count data from 28 Utah kinships. The results from the analysis are that covariate-adjusted nevus counts are correlated between parents and children (correlation .22; P less than .001) and between siblings (correlation .32; P less than .001), while the correlation of -.04 between husband and wife is not significantly different (P = .31) from 0. This result is consistent with a genetic etiology of nevus count.
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Selected References
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