The importance of watching our weights: how the choice of weights for non-independent sib pairs can dramatically alter results

Evaluation of approaches to identify associated SNPs that explain the linkage evidence in nuclear families with affected siblings

Linkage analysis is often followed by association mapping to localize disease variants. In this paper, we evaluate approaches to determine how much of the observed linkage evidence, namely the identity-by-descent (IBD) sharing at the linkage peak, is explained by associated SNPs. We study several methods: Homozygote Sharing Tests (HST), Genotype Identity-by-Descent Sharing Test (GIST), and a permutation approach. We also propose a new approach, HSTMLB, combining HST and the Maximum Likelihood Binomial (MLB) linkage statistic. These methods can identify SNPs partially explaining the linkage peak, but only HST and HSTMLB can identify SNPs that do not fully explain the linkage evidence and be applied to multiple-SNPs. We contrast these methods with the association tests implemented in the software LAMP. In our simulations, GIST is more powerful at finding SNPs that partially explain the linkage peak, while HST and HSTMLB are equally powerful at identifying SNPs that do not fully explain the linkage peak. When applied to the North American Rheumatoid Arthritis Consortium data, HST and HSTMLB identify marker pairs that may fully explain the linkage peak on chromosome 6. In conclusion, HST and HSTMLB provide simple and flexible tools to identify SNPs that explain the IBD sharing at the linkage peak.

Multipoint linkage analysis of the pseudoautosomal regions, using affected sibling pairs

Affected sibling pairs are often the design of choice in linkage-analysis studies with the goal of identifying the genes that increase susceptibility to complex diseases. Methods for multipoint analysis based on sibling amount of sharing that is identical by descent are widely available, for both autosomal and X-linked markers. Such methods have the advantage of making few assumptions about the mode of inheritance of the disease. However, with this approach, data from the pseudoautosomal regions on the X chromosome pose special challenges. Same-sex sibling pairs will share, in that region of the genome, more genetic material identical by descent, with and without the presence of a disease-susceptibility gene. This increased sharing will be more pronounced for markers closely linked to the sex-specific region. For the same reason, opposite-sex sibling pairs will share fewer alleles identical by descent. Failure to take this inequality in sharing into account may result in a false declaration of linkage if the study sample contains an excess of sex-concordant pairs, or a linkage may be missed when an excess of sex-discordant pairs is present. We propose a method to take into account this expected increase/decrease in sharing when markers in the pseudoautosomal region are analyzed. For quantitative traits, we demonstrate, using the Haseman-Elston method, (1) the same inflation in type I error, in the absence of an appropriate correction, and (2) the inadequacy of permutation tests to estimate levels of significance when all phenotypic values are permuted, irrespective of gender. The proposed method is illustrated with a genome screen on 350 sibling pairs affected with type I diabetes.

Joint multipoint linkage analysis of multivariate qualitative and quantitative traits. II. Alcoholism and event-related potentials

The availability of robust quantitative biological markers that are correlated with qualitative psychiatric phenotypes can potentially improve the power of linkage methods to detect quantitative-trait loci influencing psychiatric disorders. We apply a variance-component method for joint multipoint linkage analysis of multivariate discrete and continuous traits to the extended pedigree data from the Collaborative Study on the Genetics of Alcoholism, in a bivariate analysis of qualitative alcoholism phenotypes and quantitative event-related potentials. Joint consideration of the DSM-IV diagnosis of alcoholism and the amplitude of the P300 component of the Cz event-related potential significantly increases the evidence for linkage of these traits to a chromosome 4 region near the class I alcohol dehydrogenase locus ADH3. A likelihood-ratio test for complete pleiotropy is significant, suggesting that the same quantitative-trait locus influences both risk of alcoholism and the amplitude of the P300 component.