Linkage Disequilibrium in Humans: Models and Data

Abstract

In this review, we describe recent empirical and theoretical work on the extent of linkage disequilibrium (LD) in the human genome, comparing the predictions of simple population-genetic models to available data. Several studies report significant LD over distances longer than those predicted by standard models, whereas some data from short, intergenic regions show less LD than would be expected. The apparent discrepancies between theory and data present a challenge-both to modelers and to human geneticists-to identify which important features are missing from our understanding of the biological processes that give rise to LD. Salient features may include demographic complications such as recent admixture, as well as genetic factors such as local variation in recombination rates, gene conversion, and the potential segregation of inversions. We also outline some implications that the emerging patterns of LD have for association-mapping strategies. In particular, we discuss what marker densities might be necessary for genomewide association scans.

Alcoholism and alcohol drinking habits predicted from alcohol dehydrogenase genes

“…46,47 Linkage disequilibrium was expressed as r 2 and D 0 . 48,49 To study the association between ADH1B and ADH1C genotypes and amount of alcohol intake, the correlated mixed distribution model was applied (Mixcorr macro 50 ). This model handles data with clumping at zero and a lognormal distribution for nonzero values, and contains components to model the probability of a nonzero value and the mean of nonzero values, allowing for repeated measurements using random effects.…”

Using haplotype blocks to map human complex trait loci

“…Although this frequency bias is likely to be a minor problem if common diseases are caused by common variants (COMMON DISEASE -COMMON VARIANT HYPOTHESIS) [19], it will seriously limit the usefulness of the HapMap catalogue for diseases caused by rarer alleles [5,20]. Still, if recombination hotspot boundaries are highly specific and their location governed by cellular signals, such as sequence signatures or chromosome structure, as apparent in some organisms [21], some of the intervening block boundaries might be the same whether blocks are defined by common or rare alleles.…”

Linkage disequilibrium analysis of case–control data: an application to generalized aggressive periodontitis

“…5,6 In contrast to linkage studies, which look for coinheritance of chromosomal regions with disease in families, association studies look for differences in the frequency of genetic variants between unrelated affected individuals and controls, with the expectation that a riskconferring variant and/or alleles on the disease haplotype will be more common in patients. 7,8 However, within a candidate region, the patterns of association between markers and disease are known to be complex and unpredictable. For example, pairs of markers that are several kilobases apart might be in 'complete' LD, whereas nearby pairs of sites from the same region may be in weak LD.…”