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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

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.
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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.

Mentions: Figure 4 shows the additional detail that is available with increasing coverage of the SNP data in the same genomic region, from the coarse grain of the HapMap SNPs through to the fine grain of SeattleSNPs which approaches complete resequencing. While the major feature of a large block of LD on the righthand side of the display is apparent at all levels, there is much more detail with resequencing data and additional blocks of LD are revealed as more SNPs are added.


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)

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

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

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.
Mentions: Figure 4 shows the additional detail that is available with increasing coverage of the SNP data in the same genomic region, from the coarse grain of the HapMap SNPs through to the fine grain of SeattleSNPs which approaches complete resequencing. While the major feature of a large block of LD on the righthand side of the display is apparent at all levels, there is much more detail with resequencing data and additional blocks of LD are revealed as more SNPs are added.

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