Limb girdle muscular dystrophy type 2A (CAPN3): mapping using allelic association

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

Several studies have shown a role for the involvement of interleukin (IL)-1 gene cluster polymorphisms in the risk of periodontal diseases. In the present study, we tested polymorphisms, derived from genes of the IL-1 cluster, for association with generalized aggressive periodontitis (GAP) through both allelic association and by constructing a linkage disequilibrium (LD) map of the 2q13-14 disease candidate region. The IL-1RN (VNTR) genotype distribution observed was significantly different in GAP and control subjects (P=0.019). We also observed some evidence for an association between GAP and the IL-1B(+3953) polymorphism (P=0.039). The pattern of association in the region, represented as an LD map, identifies a recombination hot area between the IL-1B(+3953) and IL-1B(-511) polymorphisms. Multilocus modelling of association with disease gives a location for the peak association at the IL-1B(+3953) marker, although support for the peak is not significant. Haplotype analysis identifies a IL-1B(+3953)-IL-1B(-511) haplotype as having the lowest P-value in the region. Recognition of the presence of a recombination hot area between the IL-1B(+3953) and IL-1B(-511) polymorphisms will have an important bearing on future efforts to develop higher resolution SNP analysis in this region for both this and other diseases for which this cluster is implicated.

The optimal measure of linkage disequilibrium reduces error in association mapping of affection status

We have developed a simple yet powerful approach for disease gene association mapping by linkage disequilibrium (LD). This method is unique because it applies a model with evolutionary theory that incorporates a parameter for the location of the causal polymorphism. The method exploits LD maps, which assign a location in LD units (LDU) for each marker. This approach is based on single marker tests within a composite likelihood framework, which avoids the heavy Bonferroni correction through multiple testing. As a proof of principle, we tested an 890 kb region flanking the CYP2D6 gene associated with poor drug-metabolizing activity in order to refine the localization of a causal mutation. Previous LD mapping studies using single markers and haplotypes have identified a 390 kb significant region associated with the poor drug-metabolizing phenotype on chromosome 22. None of the 27 Single nucleotide polymorphisms was within the gene. Using a metric LDU map, the commonest functional polymorphism within the gene was located at 14.9 kb from its true location, surrounded within a 95% confidence interval of 172 kb. The kb map had a relative efficiency of 33% compared with the LDU map. Our findings indicate that the support interval and location error are smaller than any published results. Despite the low resolution and the strong LD in the region, our results provide evidence of the substantial utility of LDU maps for disease gene association mapping. These tests are robust to large numbers of markers and are applicable to haplotypes, diplotypes, whole-genome association or candidate region studies.

Positional cloning by linkage disequilibrium

Recently, metric linkage disequilibrium (LD) maps that assign an LD unit (LDU) location for each marker have been developed (Maniatis et al. 2002). Here we present a multiple pairwise method for positional cloning by LD within a composite likelihood framework and investigate the operating characteristics of maps in physical units (kb) and LDU for two bodies of data (Daly et al. 2001; Jeffreys et al. 2001) on which current ideas of blocks are based. False-negative indications of a disease locus (type II error) were examined by selecting one single-nucleotide polymorphism (SNP) at a time as causal and taking its allelic count (0, 1, or 2, for the three genotypes) as a pseudophenotype, Y. By use of regression and correlation, association between every pseudophenotype and the allelic count of each SNP locus (X) was based on an adaptation of the Malecot model, which includes a parameter for location of the putative gene. By expressing locations in kb or LDU, greater power for localization was observed when the LDU map was fitted. The efficiency of the kb map, relative to the LDU map, to describe LD varied from a maximum of 0.87 to a minimum of 0.36, with a mean of 0.62. False-positive indications of a disease locus (type I error) were examined by simulating an unlinked causal SNP and the allele count was used as a pseudophenotype. The type I error was in good agreement with Wald's likelihood theorem for both metrics and all models that were tested. Unlike tests that select only the most significant marker, haplotype, or haploset, these methods are robust to large numbers of markers in a candidate region. Contrary to predictions from tagging SNPs that retain haplotype diversity, the sample with smaller size but greater SNP density gave less error. The locations of causal SNPs were estimated with the same precision in blocks and steps, suggesting that block definition may be less useful than anticipated for mapping a causal SNP. These results provide a guide to efficient positional cloning by SNPs and a benchmark against which the power of positional cloning by haplotype-based alternatives may be measured.

Allelic association with SNPs: metrics, populations, and the linkage disequilibrium map

Comparison of different metrics, using three large samples of haplotypes from different populations, demonstrates that rho is the most efficient measure of association between pairs of single nucleotide polymorphisms (SNPs). Pairwise data can be modeled, using composite likelihood, to describe the decline in linkage disequilibrium with distance (the Malecot model). The evidence from more isolated populations (Finland, Sardinia) suggests that linkage disequilibrium extends to 427-893 kb but, even in samples representative of large heterogeneous populations, such as CEPH, the extent is 385 kb or greater. This suggests that isolated populations are not essential for linkage disequilibrium mapping of common diseases with SNPs. The in parameter of the Malecot model (recombination and time), evaluated at each SNP, indicates regions of the genome with extensive and less extensive disequilibrium (low and high values of in respectively). When plotted against the physical map, the regions with extensive and less extensive linkage disequilibrium may correspond to recombination cold and hot spots. This is discussed in relation to the Xq25 cytogenetic band and the HFE gene region.

Genetic epidemiology of single-nucleotide polymorphisms

On the causal hypothesis, most genetic determinants of disease are single-nucleotide polymorphisms (SNPs) that are likely to be selected as markers for positional cloning. On the proximity hypothesis, most disease determinants will not be included among markers but may be detected through linkage disequilibrium with other SNPs. In that event, allelic association among SNPs is an essential factor in positional cloning. Recent simulation based on monotonic population expansion suggests that useful association does not usually extend beyond 3 kb. This is contradicted by significant disequilibrium at much greater distances, with corresponding reduction in the number of SNPs required for a cost-effective genome scan. A plausible explanation is that cyclical expansions follow population bottlenecks that establish new disequilibria. Data on more than 1,000 locus pairs indicate that most disequilibria trace to the Neolithic, with no apparent difference between haplotypes that are random or selected through a major disease gene. Short duration may be characteristic of alleles contributing to disease susceptibility and haplotypes characteristic of particular ethnic groups. Alleles that are highly polymorphic in all ethnic groups may be older, neutral, or advantageous, in weak disequilibrium with nearby markers, and therefore less useful for positional cloning of disease genes. Significant disequilibrium at large distance makes the number of suitably chosen SNPs required for genome screening as small as 30,000, or 1 per 100 kb, with greater density (including less common SNPs) reserved for candidate regions.