Dataset Information

Comparison of tagging single-nucleotide polymorphism methods in association analyses.

ABSTRACT: Several methods to identify tagging single-nucleotide polymorphisms (SNPs) are in common use for genetic epidemiologic studies; however, there may be loss of information when using only a subset of SNPs. We sought to compare the ability of commonly used pairwise, multimarker, and haplotype-based tagging SNP selection methods to detect known associations with quantitative expression phenotypes. Using data from HapMap release 21 on unrelated Utah residents with ancestors from northern and western Europe (CEPH-Utah, CEU), we selected tagging SNPs in five chromosomal regions using ldSelect, Tagger, and TagSNPs. We found that SNP subsets did not substantially overlap, and that the use of trio data did not greatly impact SNP selection. We then tested associations between HapMap genotypes and expression phenotypes on 28 CEU individuals as part of Genetic Analysis Workshop 15. Relative to the use of all SNPs (n = 210 SNPs across all regions), most subset methods were able to detect single-SNP and haplotype associations. Generally, pairwise selection approaches worked extremely well, relative to use of all SNPs, with marked reductions in the number of SNPs required. Haplotype-based approaches, which had identified smaller SNP subsets, missed associations in some regions. We conclude that the optimal tagging SNP method depends on the true model of the genetic association (i.e., whether a SNP or haplotype is responsible); unfortunately, this is often unknown at the time of SNP selection. Additional evaluations using empirical and simulated data are needed.

SUBMITTER: Goode EL

PROVIDER: S-EPMC2367496 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Comparison of tagging single-nucleotide polymorphism methods in association analyses.

Goode Ellen L EL Fridley Brooke L BL Sun Zhifu Z Atkinson Elizabeth J EJ Nord Alex S AS McDonnell Shannon K SK Jarvik Gail P GP de Andrade Mariza M Slager Susan L SL

BMC proceedings 20071218

Several methods to identify tagging single-nucleotide polymorphisms (SNPs) are in common use for genetic epidemiologic studies; however, there may be loss of information when using only a subset of SNPs. We sought to compare the ability of commonly used pairwise, multimarker, and haplotype-based tagging SNP selection methods to detect known associations with quantitative expression phenotypes. Using data from HapMap release 21 on unrelated Utah residents with ancestors from northern and western ...[more]

PMID: 18466560

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Project description:The association between growth differentiation factor 5 (GDF5), single nucleotide polymorphism (SNP) rs143383 and lumbar disc degeneration (LDD) was investigated. A total of 210 patients with LDD (observation group) and 320 patients without lumbar diseases (control group) diagnosed in Shanghai General Hospital of Nanjing Medical University from August 2013 to March 2017 were randomly selected. Then, deoxyribonucleic acid (DNA) was extracted from the blood of each patient, and Taq-man fluorescent quantitative polymerase chain reaction (qPCR) technique was used to detect rs143383 in GFD5 gene. The frequency of different genotypes in observation group and control group was counted, and the associations between different SNP genotypes and the incidence of LDD were analyzed. Good genotyping results were found in both LDD patient group and control group. There were no significant differences in distribution frequency of TT and TC genotypes at site rs143383 between LDD patient group and control group (P>0.05), but the distribution frequency of CC genotype at site rs143383 in LDD patient group had a statistically significant difference from that in control group (P<0.05). In dominant models, odds ratio (OR) of (TC+CC/TT) was 1.195 (P=0.532). In recessive models, OR of (CC/TT+TC) was 4.333 (P=0.028). In co-dominant models, ORs of (TC/TT) and (CC/TT) were 0.967 and 4.43, respectively (P=0.99). The differences in 3 genotypes showed no statistical significance among different pathological grades (Grade I to V) (?2=1.034, P=0.998), and there was no statistically significant difference in T and C (?2=0.012, P=0.999). Pathological grades in dominant models, recessive models and over dominant models were analyzed, and no statistically significant difference was found (P>0.05). In conclusion, CC mutant type at rs143383 in GDF5 gene has a strong association with the incidence of LDD, and a high prevalence risk, but it has no evident correlation with pathological grades.

Dataset Information

Comparison of tagging single-nucleotide polymorphism methods in association analyses.

Publications

Comparison of tagging single-nucleotide polymorphism methods in association analyses.

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