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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

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.
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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.

Mentions: A substantial percentage of SNPs (41%) we submitted for validation had scores below 0.6, and were excluded from being selected as tag SNPs. This meant coverage of SNPs was not complete, although in regions with high LD there was usually an alternative SNP to tag those with low validation scores. A second round of genotyping on a different platform will be performed for the HealthIron Study to attempt to capture these low validation uncaptured SNPs. Figure 1 shows that the relationship between validation scores for SNPs and "Gentrain" score (a measure of SNP performance automatically calculated by the Illumina BeadStudio software) is not strong. Half of the ten "unscorable" SNPs were Golden Gate validated (previously successful), i.e. given scores of 1.1; overall 35% of our selected SNPs had scores of 1.1. This suggests that validation/design scores above 0.6 do not predict genotyping performance, and that maximising average validation score may not have a large effect on SNP success rate.


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)

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

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

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.
Mentions: A substantial percentage of SNPs (41%) we submitted for validation had scores below 0.6, and were excluded from being selected as tag SNPs. This meant coverage of SNPs was not complete, although in regions with high LD there was usually an alternative SNP to tag those with low validation scores. A second round of genotyping on a different platform will be performed for the HealthIron Study to attempt to capture these low validation uncaptured SNPs. Figure 1 shows that the relationship between validation scores for SNPs and "Gentrain" score (a measure of SNP performance automatically calculated by the Illumina BeadStudio software) is not strong. Half of the ten "unscorable" SNPs were Golden Gate validated (previously successful), i.e. given scores of 1.1; overall 35% of our selected SNPs had scores of 1.1. This suggests that validation/design scores above 0.6 do not predict genotyping performance, and that maximising average validation score may not have a large effect on SNP success rate.

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