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

Constantine CC, Gurrin LC, McLaren CE, Bahlo M, Anderson GJ, Vulpe CD, Forrest SM, Allen KJ, Gertig DM, HealthIron Investigato - BMC Med. Genet. (2008)

Bottom Line: We contrasted results from two tag SNP selection algorithms, LDselect and Tagger.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.A candidate gene approach should seek to maximise coverage, and this can be improved by adding to HapMap data any available sequencing data.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Centre for Molecular, Environmental, Genetic and Analytic (MEGA) Epidemiology, School of Population Health, The University of Melbourne, Melbourne, Australia. ccconsta@uci.edu

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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Related in: MedlinePlus

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.
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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.

Mentions: Here we present an empirical example of the effect of resequencing coverage on the selection of tag SNPs. Figure 2 shows the coverage of the transferrin gene (TF) by the four data sources. HealthIron resequencing data used only the exons and small amounts of the surrounding introns (at least 30 bp). The NHLBI RS&G data had a wider coverage (green) and SeattleSNPs coverage was nearly complete (black), with HapMap CEU SNPs in brown.


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

Constantine CC, Gurrin LC, McLaren CE, Bahlo M, Anderson GJ, Vulpe CD, Forrest SM, Allen KJ, Gertig DM, HealthIron Investigato - BMC Med. Genet. (2008)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2289803&req=5

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.
Mentions: Here we present an empirical example of the effect of resequencing coverage on the selection of tag SNPs. Figure 2 shows the coverage of the transferrin gene (TF) by the four data sources. HealthIron resequencing data used only the exons and small amounts of the surrounding introns (at least 30 bp). The NHLBI RS&G data had a wider coverage (green) and SeattleSNPs coverage was nearly complete (black), with HapMap CEU SNPs in brown.

Bottom Line: We contrasted results from two tag SNP selection algorithms, LDselect and Tagger.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.A candidate gene approach should seek to maximise coverage, and this can be improved by adding to HapMap data any available sequencing data.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Centre for Molecular, Environmental, Genetic and Analytic (MEGA) Epidemiology, School of Population Health, The University of Melbourne, Melbourne, Australia. ccconsta@uci.edu

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.

Show MeSH
Related in: MedlinePlus