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


CONCLUSIONS
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.
Constantine
Clare C
CC
The Centre for Molecular, Environmental, Genetic and Analytic (MEGA) Epidemiology, School of Population Health, The University of Melbourne, Melbourne, Australia. ccconsta@uci.edu
Gurrin
Lyle C
LC
McLaren
Christine E
CE
Bahlo
Melanie
M
Anderson
Gregory J
GJ
Vulpe
Chris D
CD
Forrest
Susan M
SM
Allen
Katrina J
KJ
Gertig
Dorota M
DM
D002874:Chromosome Mapping; D015331:Cohort Studies; D030541:Databases, Genetic; D005787:Gene Frequency; D005819:Genetic Markers / Q000235:genetics; D015894:Genome, Human; D006432:Hemochromatosis / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D007501:Iron / Q000378:metabolism*; D015810:Linkage Disequilibrium; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA; D016324:Sequence Tagged Sites; D014168:Transferrin / Q000235:genetics
D005819:Genetic Markers; D014168:Transferrin; D007501:Iron