A coalescent approach to study linkage disequilibrium between single-nucleotide polymorphisms.

We present the results of extensive simulations that emulate the development and distribution of linkage disequilibrium (LD) between single-nucleotide polymorphisms (SNPs) and a gene locus that is phenotypically stratified into two classes (disease phenotype and wild-type phenotype). Our approach, based on coalescence theory, allows an explicit modeling of the demographic history of the population without conditioning on the age of the mutation, and serves as an efficient tool to carry out simulations. More specifically, we compare the influence that a constant population size or an exponentially growing population has on the amount of LD. These results indicate that attempts to locate single disease genes are most likely successful in small and constant populations. On the other hand, if we consider an exponentially growing population that started to expand from an initially constant population of reasonable size, then our simulations indicate a lower success rate. The power to detect association is enhanced if haplotypes constructed from several SNPs are used as markers. The versatility of the coalescence approach also allows the analysis of other relevant factors that influence the chances that a disease gene will be located. We show that several alleles leading to the same disease have no substantial influence on the amount of LD, as long as the differences between the disease-causing alleles are confined to the same region of the gene locus and as long as each allele occurs in an appreciable frequency. Our simulations indicate that mapping of less-frequent diseases is more likely to be successful. Moreover, we show that successful attempts to map complex diseases depend crucially on the phenotype-genotype correlations of all alleles at the disease locus. An analysis of lipoprotein lipase data indicates that our simulations capture the major features of LD occurring in biological data.