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. 2008 Mar 20:9:18.
doi: 10.1186/1471-2350-9-18.

SNP selection for genes of iron metabolism in a study of genetic modifiers of hemochromatosis

Clare C Constantine  1 , Lyle C Gurrin, Christine E McLaren, Melanie Bahlo, Gregory J Anderson, Chris D Vulpe, Susan M Forrest, Katrina J Allen, Dorota M Gertig; HealthIron Investigators
Collaborators, Affiliations

SNP selection for genes of iron metabolism in a study of genetic modifiers of hemochromatosis

Clare C Constantine et al. BMC Med Genet. .

Abstract

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 r2 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 > or = 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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Figures

Figure 1
Figure 1
Scatter plot of SNP validation scores with Gentrain scores. Previously successful SNPs are given a score of 1.1, design scores between 0 and 1 are calculated by a proprietary algorithm based on the surrounding 200 bp.
Figure 2
Figure 2
Regions sequenced in three resequencing Caucasian data sets: (i) HealthIron in red; (ii) NHLBI RS&G in green; (iii) SeattleSNPs in black. The HapMap Phase 1 Caucasian (European) SNPs with MAF ≥ 3% rs numbers are shown. The TF gene appears in blue with the exons shown as bars. The arrows indicated the direction of transcription.
Figure 3
Figure 3
Frequency distribution of captured and uncaptured SNPs from Seattle resequencing of TF using HapMap tagSNPS. The large number of captured SNPs in the 40–45% range represents the strong block of LD which is captured by a single tagSNP.
Figure 4
Figure 4
A graphical representation of linkage disequilibrium patterns for the transferrin gene from SNP data on Caucasian populations: (a) HapMap (11 SNPs with MAF ≥ 3%); (b) HealthIron (12 SNPs); (c) NHLBI RS&G (43 SNPs); (d) SeattleSNPs (101 SNPs). These LD displays were generated using the default settings in HaploView.
Figure 5
Figure 5
Pattern of linkage disequilibrium across six genes and five population samples using Haploview default settings (with blocks removed).
See this image and copyright information in PMC

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