Defining linkeage disequilibrium patterns and tracts of extended homozygosity to compare populations and search for disease genes

Abstract

This project aimed to define linkage disequilibrium (LD) patterns and tracts of extended homozygosity in order to compare populations and search for disease genes. SNP genotype data were analysed on a Unix platform, using the programs LDMAP+, for linkage disequilibrium unit (LDU) map creation, and CHROMSCAN-cluster, for association mapping, as well as software written as part of this project, in the C programming language, for determining tracts of homozygosity and for autozygosity mapping. LDU maps were compared over populations showing similarity in LD structure. A cosmopolitan LDU map which represents the LD patterns of different population samples was produced and able to recover 91-95% of the information in the original population specific data. Genome-wide LDU maps were created, compared across populations, and compared with the linkage map to estimate effective bottleneck time (t), the time since the last major bottleneck for each population. This project also discovered an unanticipated amount of homozygosity in the outbred individuals genotyped in the HapMap project. Large homozygous tracts are expected in inbred individuals and this analysis was able to determine 3 individuals with high levels of homozygosity consistent with recent inbreeding. The relationship between tracts of homozygosity and LD was investigated, using the LDU maps, showing that long tracts of homozygosity are more likely to occur in regions of high LD where the underlying haplotypes are of limited diversity. The relationship shown between LD and homozygosity enabled a more powerful approach to autozygosity mapping of a recessive locus in a consanguineous pedigree affected by Congenital Nephrotic Syndrome. High density SNP genotyping of affected individuals pinpointed regions of homozygosity which segregate with the disease, with the advantage of using few individuals and without the need for statistical inference from linkage. The regions determined were then prioritised on the basis of LDU length, therefore adding weight to regions of true autozygosity over regions of homozygosity associated with high LD. This analysis successfully determined a region containing a strong candidate gene (PLCEi) which has subsequently been shown to be mutated in the affected individuals. Extending the search for disease genes to complex disease studies, a genome- wide association scan was carried out, using real case-control data with an undisclosed disease and utilising the LDU maps. A combination of the results from the multi-SNP approach of CHROMSCAN-cluster and single SNP results allowed selection of regions for follow up in a multi-stage analysis.

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