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Asymptotic tests of association with multiple SNPs in linkage disequilibrium.

ABSTRACT: We consider detecting associations between a trait and multiple single nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD). To maximize the use of information contained in multiple SNPs while minimizing the cost of large degrees of freedom (DF) in testing multiple parameters, we first theoretically explore the sum test derived under a working assumption of a common association strength between the trait and each SNP, testing on the corresponding parameter with only one DF. Under the scenarios that the association strengths between the trait and the SNPs are close to each other (and in the same direction), as considered by Wang and Elston [Am. J. Hum. Genet. [2007] 80:353-360], we show with simulated data that the sum test was powerful as compared to several existing tests; otherwise, the sum test might have much reduced power. To overcome the limitation of the sum test, based on our theoretical analysis of the sum test, we propose five new tests that are closely related to each other and are shown to consistently perform similarly well across a wide range of scenarios. We point out the close connection of the proposed tests to the Goeman test. Furthermore, we derive the asymptotic distributions of the proposed tests so that P-values can be easily calculated, in contrast to the use of computationally demanding permutations or simulations for the Goeman test. A distinguishing feature of the five new tests is their use of a diagonal working covariance matrix, rather than a full covariance matrix as used in the usual Wald or score test. We recommend the routine use of two of the new tests, along with several other tests, to detect disease associations with multiple linked SNPs.

SUBMITTER: Pan W

PROVIDER: S-EPMC2732754 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

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Asymptotic tests of association with multiple SNPs in linkage disequilibrium.

Pan Wei W

Genetic epidemiology 20090901 6

We consider detecting associations between a trait and multiple single nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD). To maximize the use of information contained in multiple SNPs while minimizing the cost of large degrees of freedom (DF) in testing multiple parameters, we first theoretically explore the sum test derived under a working assumption of a common association strength between the trait and each SNP, testing on the corresponding parameter with only one DF. Under the s ...[more]

PMID: 19170135

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Project description:Single nucleotide polymorphisms (SNPs) may be correlated due to linkage disequilibrium (LD). Association studies look for both direct and indirect associations with disease loci. In a Random Forest (RF) analysis, correlation between a true risk SNP and SNPs in LD may lead to diminished variable importance for the true risk SNP. One approach to address this problem is to select SNPs in linkage equilibrium (LE) for analysis. Here, we explore alternative methods for dealing with SNPs in LD: change the tree-building algorithm by building each tree in an RF only with SNPs in LE, modify the importance measure (IM), and use haplotypes instead of SNPs to build a RF.We evaluated the performance of our alternative methods by simulation of a spectrum of complex genetics models. When a haplotype rather than an individual SNP is the risk factor, we find that the original Random Forest method performed on SNPs provides good performance. When individual, genotyped SNPs are the risk factors, we find that the stronger the genetic effect, the stronger the effect LD has on the performance of the original RF. A revised importance measure used with the original RF is relatively robust to LD among SNPs; this revised importance measure used with the revised RF is sometimes inflated. Overall, we find that the revised importance measure used with the original RF is the best choice when the genetic model and the number of SNPs in LD with risk SNPs are unknown. For the haplotype-based method, under a multiplicative heterogeneity model, we observed a decrease in the performance of RF with increasing LD among the SNPs in the haplotype.Our results suggest that by strategically revising the Random Forest method tree-building or importance measure calculation, power can increase when LD exists between SNPs. We conclude that the revised Random Forest method performed on SNPs offers an advantage of not requiring genotype phase, making it a viable tool for use in the context of thousands of SNPs, such as candidate gene studies and follow-up of top candidates from genome wide association studies.

Project description:Background and Aims: This study was aim to investigate the relationship between the four intron SNPs (rs3087404, rs2029167, rs2029166 and rs7296239) of SMUG1 and the susceptibility of cervical squamous cell carcinoma. Methods: Four SMUG1 intron SNPs (rs3087404, rs2029167, rs2029166 and rs7296239) were genotyped by MA-PCR in 400 CSCCs, 400 CIN III and 1200 controls. qRT-PCR and Western blot were used to detect the SMUG1 mRNA and protein expression. Results: Interestingly, we found that the homozygous GG of rs3087404 had a significantly increased risk of CIN III [OR=1.78(1.27-2.51), P= 0.001] and CSCCs [OR=4.04(2.94-5.55), P=0.000]. The individuals with G allele or G carrier (AG +GG) at rs3087404 were at higher risk for CSCCs [OR=1.34 (1.04-1.71), P= 0.022]. Similarly, the homozygous GG of rs2029167 also had an increased risk of CIN III [OR=2.56 (1.91-3.43), P= 0.000] and CSCCs [OR=4.05(3.02-5.44), P=0.000]. The individuals with G allele or G carrier (AG +GG) at rs2029167 were at higher risk for CINIII [OR=1.41(1.10-1.80), P= 0.006] and CSCCs [OR=1.91 (1.48-2.47), P= 0.000]. In HR-HPV positive group, both the homozygous GG of rs3087404 and the homozygous GG of rs2029167 had an increased risk to CIN III and CSCC. Stratified analysis of the number of sexual partners and the age of first sexual intercourse found that the rs3087404 (A/G) had a particularly high level of enrichment in the CIN III or CSCCs groups. About the rs2029167 (A/G), we only found a particularly high level of enrichment grouping by the number of sexual partners in the CIN III and CSCCs groups. Meanwhile, we also found that there is a correlation between the SNPs of SMUG1 rs3087404 (A/G) and rs2029167 (A/G) with tumor cell differentiation and family heredity. But we didn't find that there was an association between the deferent genotypes of SMUG1 rs2029166 and rs7296239 with SMUG1 gene mRNA or protein expression. During the linkage disequilibrium analysis between rs3087404 (A/G) and rs2029167 (A/G), the genotype with AA-GG [OR=3.14(1.95-5.05)], AG-GG [OR=2.45(1.58-3.89)], GG-AA [OR=2.24(1.28-3.90)] and GG-AG [OR=2.58(1.54-4.32)] significantly increased the risk of CIN III. More notably, this risk is much greater in CSCCs: AA-GG [OR=7.13(4.03-12.61)], AG-GG [OR=7.22(4.21-12.38)], GG-AA [OR=8.60(4.73-15.63)], GG-AG [OR=9.64(5.43-17.13)]. Additionally, most GG (rs3087404) genotypes were linkage GG-AG (44/77, 80/140) in the CIN III and CSCCs, while most GG (rs2029167) genotypes were linkage genotype AG-GG (79/145, 112/184) in the CIN III and CSCCs, respectively. Conclusions: These findings suggested that there was association between the two genetic polymorphisms of SMUG1 rs3087404(A/G) and rs2029167(A/G) with the susceptibility of CIN III and CSCCs, and there was a linkage disequilibrium between the rs3087404 with the rs2029167 in CIN III and CSCCs. This particular linkage disequilibrium can be used as predictive biomarkers of CIN III and CSCC.

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Asymptotic tests of association with multiple SNPs in linkage disequilibrium.

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Asymptotic tests of association with multiple SNPs in linkage disequilibrium.

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