By choosing a subset of maximally informative SNPs, or "tag" SNPs, to represent these haplotypes, the number of SNPs to be genotyped in a larger sample can be reduced without losing the ability to capture most of the variation, and in particular any association between unmeasured "causal" alleles and the disease outcome measured on individuals in the sample. These segments, or haplotypes, are combinations of particular SNP alleles on the same chromosome that tend to segregate together. This is due to the phenomenon of linkage disequilibrium (LD), or non-random association of SNP alleles at the population level, due to the sharing by multiple individuals of ancestral chromosomal segments. We can, however, reduce the genotyping burden by exploiting the strong correlation between some SNPs that are close together on the genome. Many genes have a large number of SNPs, and it is acknowledged that there are more than 10 million SNPs across the human genome, making it impossible for cost-effective genotyping of all of them in studies of disease, even in very small samples. Single nucleotide polymorphisms (SNPs), changes in a single base pair of the DNA sequence, are the most frequently occurring form of variation in the human genome. Tag SNP software must be fast and flexible to data changes, since tag SNP selection involves iteration as investigators seek to satisfy the competing demands of coverage within and between populations, and typability on the technology platform chosen. ConclusionĪ candidate gene approach should seek to maximise coverage, and this can be improved by adding to HapMap data any available sequencing data. We report the number of SNPs present within the region of six of our larger candidate genes, for different versions of stock genotyping assays. Resequencing can reveal adjacent SNPs (within 60 bp) which may affect assay performance. We examined the pattern of linkage disequilibrium of three levels of resequencing coverage for the transferrin gene and found HapMap phase 1 tag SNPs capture 45% of the ≥ 3% MAF SNPs found in SeattleSNPs where there is nearly complete resequencing.
Varying r 2 from 0.5 to 1.0 produced a near linear correlation with the number of tag SNPs required. We contrasted results from two tag SNP selection algorithms, LDselect and Tagger. Validation/design scores above 0.6 were not strongly correlated with SNP performance as estimated by Gentrain score. We combined our own and publicly available resequencing data with HapMap to maximise our coverage to select 384 SNPs in candidate genes suitable for typing on the Illumina platform. We report our experience of selecting tag SNPs in 35 genes involved in iron metabolism in a cohort study seeking to discover genetic modifiers of hereditary hemochromatosis.
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