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An efficient weighted tag SNP-set analytical method in genome-wide association studies.

Yan B, Wang S, Jia H, Liu X, Wang X - BMC Genet. (2015)

Bottom Line: The intensive simulation results show that the power of weighted tag SNP-set-based test is much higher than that of weighted original SNP-set-based test and that of un-weighted tag SNP-set-based test.The simulation results indicate the method of selecting tag SNP-set impacts the power greatly and the power of our proposed method is the highest.From the analysis of simulated replicated data sets, we came to a conclusion that weighted tag SNP-set-based test is a powerful SNP-set test in GWAS.

View Article: PubMed Central - PubMed

Affiliation: College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong, 266590, China. 890612@163.com.

ABSTRACT

Background: Single-nucleotide polymorphism (SNP)-set analysis in Genome-wide association studies (GWAS) has emerged as a research hotspot for identifying genetic variants associated with disease susceptibility. But most existing methods of SNP-set analysis are affected by the quality of SNP-set, and poor quality of SNP-set can lead to low power in GWAS.

Results: In this research, we propose an efficient weighted tag-SNP-set analytical method to detect the disease associations. In our method, we first design a fast algorithm to select a subset of SNPs (called tag SNP-set) from a given original SNP-set based on the linkage disequilibrium (LD) between SNPs, then assign a proper weight to each of the selected tag SNP respectively and test the joint effect of these weighted tag SNPs. The intensive simulation results show that the power of weighted tag SNP-set-based test is much higher than that of weighted original SNP-set-based test and that of un-weighted tag SNP-set-based test. We also compare the powers of the weighted tag SNP-set-based test based on four types of tag SNP-sets. The simulation results indicate the method of selecting tag SNP-set impacts the power greatly and the power of our proposed method is the highest.

Conclusions: From the analysis of simulated replicated data sets, we came to a conclusion that weighted tag SNP-set-based test is a powerful SNP-set test in GWAS. We also designed a faster algorithm of selecting tag SNPs which include most of information of original SNP-set, and a better weighted function which can describe the status of each tag SNP in GWAS.

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Related in: MedlinePlus

Power comparisons of different SNP-sets for KBAT. This shows the power comparisons of KBAT, KBAT-tag, Weighted KBAT and Weighted KBAT-tag at the significant level of 0.05.
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Fig1: Power comparisons of different SNP-sets for KBAT. This shows the power comparisons of KBAT, KBAT-tag, Weighted KBAT and Weighted KBAT-tag at the significant level of 0.05.

Mentions: To evaluate the powers of KBAT, KBAT-tag, weighted KBAT and weighted KBAT-tag, we simulate 1000 replicated data sets in scenarios 2–9. Figure 1 plots the powers of them in scenario 2. As a whole, the powers of the tag SNP-set-based tests on the basis of KBAT are higher than the corresponding original SNP-set-based tests. That is to say, the selected tag SNP plays an important role in increasing the power of statistical test by obtaining information from the SNPs with high LD. But when we regard the 6th, 7th, 8th and 9th SNP respectively as the causal SNP, the powers of tests based on tag SNP-set are evidently lower than the one based on original SNP-set of KBAT. We think the main reason is the high LD between the SNPs. Namely, the very high LD exists between multi-SNPs and the causal SNP. This makes the test power reduce due to losing too much information when forming the tag SNP-set. Obviously, each tag SNP in the tag SNP-set plays a different role in detecting disease association. Therefore we come to an idea that each SNP in the tag SNP-set is assigned a different value weighted by the χ2 statistic of this SNP. Figure 1 shows that, in the weighted case, the power of test based on tag SNP-set is better than that based on original SNP-set.Figure 1


An efficient weighted tag SNP-set analytical method in genome-wide association studies.

Yan B, Wang S, Jia H, Liu X, Wang X - BMC Genet. (2015)

Power comparisons of different SNP-sets for KBAT. This shows the power comparisons of KBAT, KBAT-tag, Weighted KBAT and Weighted KBAT-tag at the significant level of 0.05.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4373116&req=5

Fig1: Power comparisons of different SNP-sets for KBAT. This shows the power comparisons of KBAT, KBAT-tag, Weighted KBAT and Weighted KBAT-tag at the significant level of 0.05.
Mentions: To evaluate the powers of KBAT, KBAT-tag, weighted KBAT and weighted KBAT-tag, we simulate 1000 replicated data sets in scenarios 2–9. Figure 1 plots the powers of them in scenario 2. As a whole, the powers of the tag SNP-set-based tests on the basis of KBAT are higher than the corresponding original SNP-set-based tests. That is to say, the selected tag SNP plays an important role in increasing the power of statistical test by obtaining information from the SNPs with high LD. But when we regard the 6th, 7th, 8th and 9th SNP respectively as the causal SNP, the powers of tests based on tag SNP-set are evidently lower than the one based on original SNP-set of KBAT. We think the main reason is the high LD between the SNPs. Namely, the very high LD exists between multi-SNPs and the causal SNP. This makes the test power reduce due to losing too much information when forming the tag SNP-set. Obviously, each tag SNP in the tag SNP-set plays a different role in detecting disease association. Therefore we come to an idea that each SNP in the tag SNP-set is assigned a different value weighted by the χ2 statistic of this SNP. Figure 1 shows that, in the weighted case, the power of test based on tag SNP-set is better than that based on original SNP-set.Figure 1

Bottom Line: The intensive simulation results show that the power of weighted tag SNP-set-based test is much higher than that of weighted original SNP-set-based test and that of un-weighted tag SNP-set-based test.The simulation results indicate the method of selecting tag SNP-set impacts the power greatly and the power of our proposed method is the highest.From the analysis of simulated replicated data sets, we came to a conclusion that weighted tag SNP-set-based test is a powerful SNP-set test in GWAS.

View Article: PubMed Central - PubMed

Affiliation: College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong, 266590, China. 890612@163.com.

ABSTRACT

Background: Single-nucleotide polymorphism (SNP)-set analysis in Genome-wide association studies (GWAS) has emerged as a research hotspot for identifying genetic variants associated with disease susceptibility. But most existing methods of SNP-set analysis are affected by the quality of SNP-set, and poor quality of SNP-set can lead to low power in GWAS.

Results: In this research, we propose an efficient weighted tag-SNP-set analytical method to detect the disease associations. In our method, we first design a fast algorithm to select a subset of SNPs (called tag SNP-set) from a given original SNP-set based on the linkage disequilibrium (LD) between SNPs, then assign a proper weight to each of the selected tag SNP respectively and test the joint effect of these weighted tag SNPs. The intensive simulation results show that the power of weighted tag SNP-set-based test is much higher than that of weighted original SNP-set-based test and that of un-weighted tag SNP-set-based test. We also compare the powers of the weighted tag SNP-set-based test based on four types of tag SNP-sets. The simulation results indicate the method of selecting tag SNP-set impacts the power greatly and the power of our proposed method is the highest.

Conclusions: From the analysis of simulated replicated data sets, we came to a conclusion that weighted tag SNP-set-based test is a powerful SNP-set test in GWAS. We also designed a faster algorithm of selecting tag SNPs which include most of information of original SNP-set, and a better weighted function which can describe the status of each tag SNP in GWAS.

Show MeSH
Related in: MedlinePlus