Statistically robust approaches for sib-pair linkage analysis

Many traits that distinguish one individual from another, such as height or weight, are clearly heritable and yet vary continuously in populations. Co ntinuous, heritable variation in trait levels presumably reflects the segre gation of multiple genes, but elucidation of the genetic architecture of qu antitative traits has been limited. Haseman & Elston (1972) developed a gen etically robust method (HE) for detecting linkage to quantitative trait loc i using sib-pairs. The method is based on a simple linear regression of the squared sib-pairs trait difference on the proportion of alleles shared ide ntical by descent at a marker locus. Linkage is detected by a negative slop e which has been traditionally assessed by a standard t-test. Wan, Cohen & Guerra (1997) have shown that the standard t-test is robust to the violatio ns of the stochastic assumptions underlying the test. In practice, however, the standard t-test, based on least-squares regression, is sensitive to ou tliers. The presence of outliers in the data can lead to false positive and false negative linkage results. Accordingly we have developed and evaluate d a statistically robust procedure for the HE approach to linkage. The proc edure is based on robust regression. Simulation studies show that this robu st procedure has greater power than the standard t-test in the presence of outliers, and has similar power to the standard t-test in the absence of ou tliers. This robust procedure also shows greater power than rank-based appr oaches either in the absence or presence of outliers. To illustrate the met hods using real data, we reanalyse data from two lipoprotein systems that m otivated this work.