期刊
BMC MEDICAL GENETICS
卷 9, 期 -, 页码 -出版社
BMC
DOI: 10.1186/1471-2350-9-18
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资金
- NCI NIH HHS [P30 CA062203] Funding Source: Medline
- NHLBI NIH HHS [R01 HL083328, N01HC05190, N01-HC-05190] Funding Source: Medline
- NIDDK NIH HHS [1R01 DK061885-01A2, R01 DK061885] Funding Source: Medline
- NIMHD NIH HHS [P60 MD00222, P60 MD000222] Funding Source: Medline
Background: 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. Methods: 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. Results: Validation/ design scores above 0.6 were not strongly correlated with SNP performance as estimated by Gentrain score. We contrasted results from two tag SNP selection algorithms, LDselect and Tagger. Varying r(2) from 0.5 to 1.0 produced a near linear correlation with the number of tag SNPs required. 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. Resequencing can reveal adjacent SNPs ( within 60 bp) which may affect assay performance. We report the number of SNPs present within the region of six of our larger candidate genes, for different versions of stock genotyping assays. Conclusion: A candidate gene approach should seek to maximise coverage, and this can be improved by adding to HapMap data any available sequencing data. 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.
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