Testing for genetic association in the presence of linkage and gene-covariate interactions.

In order to study family-based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human Heredity 64, 5-15) by adding a genotypic effect to the mean. The corresponding score test is a weighted family-based association tests statistic, where the weight depends on the linkage effect and on other genetic and shared environmental effects. For testing of genetic association in the presence of gene-covariate interaction, we propose a linear regression method where the family-specific score statistic is regressed on family-specific covariates.

Influence of genotyping error in linkage mapping for complex traits--an analytic study.

Background: Despite the current trend towards large epidemiological studies of unrelated individuals, linkage studies in families are still thoroughly being utilized as tools for disease gene mapping. The use of the single-nucleotide-polymorphisms (SNP) array technology in genotyping of family data has the potential to provide more informative linkage data. Nevertheless, SNP array data are not immune to genotyping error which, as has been suggested in the past, could dramatically affect the evidence for linkage especially in selective designs such as affected sib pair (ASP) designs.

Adjusting for sex and anti-CCP levels in linkage analysis of rheumatoid arthritis.

We incorporate population effects of sex and antibodies directed against cyclic citrullinated peptides (anti-CCP) into the linkage analysis of rheumatoid arthritis (RA) with microsatellites data provided by the North American Rheumatoid Arthritis Consortium in Genetic Analysis Workshop 15.The method stems from a generalized linear mixed model that incorporates the marginal population effects of important covariates. The resulting test for linkage is based on a score test in a pseudo-likelihood of this model.

Linkage analyses of rheumatoid arthritis and related quantitative phenotypes: the GAW15 experience.

The group that formed on the theme of linkage analyses of rheumatoid arthritis RA and related phenotypes (Group 10) in the Genetic Analysis Workshop 15 comprised 18 sets of investigators. Two data sets were available: one was a real set provided by the North American Rheumatoid Arthritis Consortium and collaborators in Canada, France (European Consortium Of Rheumatoid Arthritis Families) and the UK; the other was a simulated data set modelled after the real data set. Whereas a majority of the investigators analyzed the RA affection status as a binary phenotype, a few contributions considered data on correlated quantitative traits such as anti-cyclic citrullinated peptide and rheumatoid factor-immunoglobulin M.

Gene by environment interactions.

This paper summarizes the contributions of group 8 to the Genetic Analysis Workshop 15. Group 8 focused on ways to address the possibility that genetic and environmental effects on phenotype may not be independent, but instead may interact in ways that could play important roles in determining phenotype. Among the eight contributors to this group, all three data sets (expression data, rheumatoid arthritis data, and simulated data) were analyzed.

Confirmation of dyslexia susceptibility loci on chromosomes 1p and 2p, but not 6p in a Dutch sib-pair collection.

In this study, we attempted to confirm genetic linkage to developmental dyslexia and reading-related quantitative traits of loci that have been shown to be associated with dyslexia in previous studies. In our sample of 108 Dutch nuclear families, the categorical trait showed strongest linkage to 1p36 (NPL-LOD = 2.1).

Locally weighted transmission/disequilibrium test for genetic association analysis.

The transmission/disequilibrium test statistic has been used for assessing genetic association in affected-parent trios. In the presence of multiple tightly linked marker loci where local dependency may exist, haplotypes are reconstructed statistically to estimate the joint effects of these markers. In this manuscript, we propose an alternative to the haplotype approach by taking a weighted average of multiple loci, where the weight is proportional to the product of (1-2X recombination fraction) and the linkage disequilibrium between markers.

Modeling the effect of an associated single-nucleotide polymorphism in linkage studies.

For linkage analysis in affected sibling pairs, we propose a regression model to incorporate information from a disease-associated single-nucleotide polymorphism located under the linkage peak. This model can be used to study if the associated single-nucleotide polymorphism marker partly explains the original linkage peak. Two sources of information are used for performing this task, namely the genotypes of the parents and the genotypes of the siblings.