Linkage analysis of a derived glucose phenotype in the Genetic Analysis Workshop 13 simulated data using a variety of Haseman-Elston based regression methods

Genome-wide scan for loci influencing quantitative immune response traits in the Belém family study: comparison of methods and summary of results

Here we report the results from a genome-wide linkage scan to identify genes and chromosomal regions that influence quantitative immune response traits, using multi-case leprosy and tuberculosis families from north-eastern Brazil. Total plasma IgE, antigen-specific IgG to Mycobacterium leprae soluble antigen (MLSA), M. tuberculosis soluble antigen (MTSA) and M. tuberculosis purified protein derivative (PPD), and antigen-specific lymphocyte proliferation (stimulation index or SI) and interferon-gamma (IFN-gamma) release to MLSA and PPD, were measured in 16 tuberculosis (184 individuals) and 21 leprosy (177 individuals) families. The individuals were genotyped at 382 autosomal microsatellite markers across the genome. The adjusted immune-response phenotypes were analysed using a variety of variance components and regression-based methods. These analyses highlighted a number of practical issues and problems with regard to implementation of the methods and, interestingly, differences were observed between several standard statistical and genetic analysis packages used. From this we determined that, for this set of traits in these pedigrees, significant p values for linkage using variance components analysis, supported by significance using the Visscher-Hopper modification of the Haseman-Elston method, provided the most compelling evidence for linkage. Using these criteria, linkage (5.8 x 10(-5) < p < 0.008) was seen for: total plasma IgE on chromosome 2; IgG to MLSA on chromosomes 8, 17 and 21; IgG to PPD on chromosome 12; SI to PPD on chromosome 1; IFN-gamma to MLSA on chromosomes 6, 7, 10, 12 and 14; and IFN-gamma to PPD on chromosomes 1, 16 and 19.

Genetic analysis of phenotypes derived from longitudinal data: Presentation Group 1 of Genetic Analysis Workshop 13

The participants of Presentation Group 1 used the GAW13 data to derive new phenotypes, which were then analyzed for linkage and, in one case, for association to the genetic markers. Since the trait measurements ranged over longer time periods, the participants looked at the time dependence of particular traits in addition to the trait itself. The phenotypes analyzed with the Framingham data can be roughly divided into 1) body weight-related traits, which also include a type 2 diabetes progression trait, and 2) traits related to systolic blood pressure. Both trait classes are associated with metabolic syndrome. For traits related to body weight, linkage was consistently identified by at least two participating groups to genetic regions on chromosomes 4, 8, 11, and 18. For systolic blood pressure, or its derivatives, at least two groups obtained linkage for regions on chromosomes 4, 6, 8, 11, 14, 16, and 19. Five of the 13 participating groups focused on the simulated data. Due to the rather sparse grid of microsatellite markers, an association analysis for several traits was not successful. Linkage analysis of hypertension and body mass index using LODs and heterogeneity LODs (HLODs) had low power. For the glucose phenotype, a combination of random coefficient regression models and variance component linkage analysis turned out to be strikingly powerful in the identification of a trait locus simulated on chromosome 5. Haseman-Elston regression methods, applied to the same phenotype, had low power, but the above-mentioned chromosome 5 locus was not included in this analysis.