Sib-pair linkage analyses of nuclear family data: quantitative versus dichotomous disease classification

Assessing genomewide statistical significance in linkage studies

Assessment of genomewide statistical significance in multipoint linkage analysis is a thorny problem. The existing analytical solutions rely on strong assumptions (i.e., infinitely dense or equally spaced genetic markers that are fully informative and completely observed, and a single type of relative pair) which are rarely satisfied in real human studies, while simulation-based methods are computationally intensive and may not be applicable to complex data structures and sophisticated genetic models. Here, we propose a conceptually simple and numerically efficient Monte Carlo procedure for determining genomewide significance levels that is applicable to all linkage studies. The pedigree structure is completely general; the marker data are totally arbitrary in respect to number, spacing, informativeness, and missingness; the trait can be qualitative, quantitative, or multivariate; the alternative hypothesis can be two-sided or one-sided; and the statistic can be parametric or nonparametric. The usefulness of the proposed approach is demonstrated through extensive simulation studies and an application to the nuclear family data from the Tenth Genetic Analysis Workshop.

Identifying genes and genetic variation underlying human diseases and complex phenotypes via recombination mapping

Understanding the mechanisms by which DNA and DNA variation influence diseases, naturally occurring phenotypic variation, and complex biological systems, has been one of the major tasks associated with contemporary human genetics research. The identification and characterization of specific genetic variations that influence particular human diseases and phenotypes is complicated by the fact that most diseases and phenotypes are influenced by many genetic and environmental factors. Thus, the identification of any particular phenotypically relevant factor might be hampered as other relevant factors may obscure its individual effect. Over the years numerous methods and study designs have been described to identify disease causing genes and mutations. One in particular - meiotic or recombination mapping - has received considerable attention over the last 50 years, and has been used widely with varying degrees of success. This review describes the motivation behind, and problems associated with, recombination mapping, in terms of both linkage mapping and linkage disequilibrium mapping.

Sib-pair linkage analyses of alcoholism: dichotomous and quantitative measures

We hypothesized that a quantitative alcoholism trait would have greater power than the Collaborative Study on the Genetics of Alcoholism (COGA) dichotomous alcoholism traits, ALDX1 and ALDX2, to detect putative alcoholism loci. To test this, we performed nonparametric sib-pair linkage analysis to screen 285 polymorphic autosomal markers for evidence of linkage to ALDX1, ALDX2, and a quantitative trait, QUANT, defined from the 11 COGA latent class variables. We also examined the effects on the analyses of including covariates (sex, age, and pack-years of smoking) and of transforming QUANT (log and square root). ALDX1 and ALDX2 showed the greatest evidence for linkage to markers on chromosome 1, by both the affected sib-pair and the Haseman-Elston tests. Regions of interest were also identified on chromosomes 4, 8, 16, and 17. QUANT showed little evidence for linkage to any chromosomal region, having no more significant results than were expected by chance. Including covariates or transforming QUANT had little effect on the analyses. A quantitative trait based on all 37 latent class variables, with each variable appropriately weighted, may have had more power than QUANT to detect genomic regions of relevance to alcoholism.