Power and robustness of a score test for linkage analysis of quantitative traits using identity by descent data on sib pairs

Recent advances in human quantitative-trait-locus mapping: comparison of methods for selected sibling pairs

During the past few years, there has been a great deal of new work on methods for mapping quantitative-trait loci by use of sibling pairs and sibships. There are several new methods based on linear regression, as well as several more that are based on score statistics. In theory, most of the new methods should be relatively robust to violations of distributional assumptions and to selected sampling, but, in practice, there has been little evaluation of how the methods perform on selected samples. We survey most of the new regression-based statistics and score statistics and propose a few minor variations on the score statistics. We use simulation to evaluate the type I error and the power of all of the statistics, considering (a) population samples of sibling pairs and (b) sibling pairs ascertained on the basis of at least one sibling with a trait value in the top 10% of the distribution. Most of the statistics have correct type I error for selected samples. The statistics proposed by Xu et al. and by Sham and Purcell are generally the most powerful, along with one of our score statistic variants. Even among the methods that are most powerful for "nice" data, some are more robust than others to non-Gaussian trait models and/or misspecified trait parameters.

Score test for mapping quantitative-trait loci with sibships of arbitrary size when the dominance effect is not negligible

In the linakge analysis of quantitative traits, an additive model that assumes no dominance effect is often adopted. Intuitively, when the no-dominance-effect assumption does not hold, such a practice does not make efficient use of the data, and its power to detect linkage can be improved. Here we introduce a score statistic for detecting quantitative trait loci when the dominance effect is not neglible or the dominance effect is a concern. This statistic is derived from a normal likelihood function for sibships of arbitrary size. In the derivation, the inherent genetic constraints on model parameters are fully taken into consideration. This score statistic is asymptotically equivalent to the corresponding likelihood ratio statistic, but it is much easier to compute. The asymptotic distribution of this statistic is derived, which is a mixture of chi(0) (2), chi(1) (2), and chi(2) (2). Weights for distribution components are functions of the informativeness of the marker data. The type I error rate and the power of the proposed statistic in finite sample are evaluated via simulations.

A score-statistic approach for the mapping of quantitative-trait loci with sibships of arbitrary size

The Haseman-Elston method is widely used for the mapping of quantitative-trait loci. However, this method does not use all the information in the data, because it only considers the sib-pair trait-value difference. In addition, the Haseman-Elston method was developed for independent sib pairs; its generalization to nonindependent sib pairs is not straightforward. Here we introduce a score test statistic derived from a normal likelihood based on multiplex sibship data, conditional on identical-by-descent sharing statuses. This score test is asymptotically equivalent to the corresponding likelihood-ratio test, but it is much easier to implement. Because the proposed test uses all of the trait values, it makes more efficient use of the data than does the Haseman-Elston method. The proposed test is naturally applicable to sibships of arbitrary size. The finite-sample properties of the proposed score statistic are evaluated via simulations.

Methods for linkage analysis of quantitative trait loci in humans

This paper reviews linkage analysis methods for detecting loci associated with quantitative traits in humans. All such methods are based on the underlying principle that family members who have similar trait values should have higher than expected levels of sharing of genetic material (identity by descent) near the genes that influence those traits. A number of different statistical methods for testing that association between shared trait values and shared identity by descent have been developed over the past 30 or more years. These different types of tests are reviewed here, with emphasis on their theory and derivations. Robustness and power are also discussed.