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Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants.

Won S, Kim W, Lee S, Lee Y, Sung J, Park T - BMC Bioinformatics (2015)

Bottom Line: The proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available.Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes.The proposed method may be more statistically efficient than the existing methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health Science, Seoul National University, Seoul, Korea. won1@snu.ac.kr.

ABSTRACT

Background: Many disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. Therefore, joint analysis of multiple phenotypes and multiple markers has been considered as an efficient strategy for genome-wide association analysis, and in this work we propose an omnibus family-based association test for the joint analysis of multiple genotypes and multiple phenotypes.

Results: The proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available. Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes.

Conclusions: The proposed method may be more statistically efficient than the existing methods. The application was developed in C++ and is available at the following URL: http://healthstat.snu.ac.kr/software/mfqls/ .

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Manhattan-plots for the results from the genome-wide association study. (a) BMI, (b) WHR and (c) logTG was analyzed with EMMAX based on Linear Mixed Model. (d) BMI, WHR and logTG were jointly analyzed using MFQLS.
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Fig7: Manhattan-plots for the results from the genome-wide association study. (a) BMI, (b) WHR and (c) logTG was analyzed with EMMAX based on Linear Mixed Model. (d) BMI, WHR and logTG were jointly analyzed using MFQLS.

Mentions: The QQ plots in Figure 6 show that the results for the EMMAX and MFQLS preserve the nominal significance level, and Manhattan plots in Figure 7 demonstrate that the results from MFQLS are more significant than the results from EMMAX. Genome-wide significance level with Bonferroni correction is 9.68 × 10–8 and Table 6 shows the results for SNPs of which p-values were less than 5 × 10–7 for EMMAX or MFQLS. rs651821 is an unique genome-wide significant result and the p-value of rs651821 derived by MFQLS was markedly less than those derived by EMMAX. P-values of rs17119975 and rs4417316 were larger than the significance level by Bonferroni correction but they are still expected to be promising candidate disease susceptible loci. In particular, the genetic positions of these three SNPs were similar, and we checked the linkage disequilibrium between theses SNPs with pairwise r2 from the Chinese and Japanese data in the HapMap Release 3. rs17119975 and rs4417316 were in linkage disequilibrium with r2 = 0.823, but r2 between rs651821 and the others are less than 0.01. Small p-values of rs17119975 and rs4417316 may be generated with the same genetic component even though both are located in different genes, and it should be noticed that the smallest p-value for rs17119975 and rs4417316 was found with MFQLS.Figure 6


Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants.

Won S, Kim W, Lee S, Lee Y, Sung J, Park T - BMC Bioinformatics (2015)

Manhattan-plots for the results from the genome-wide association study. (a) BMI, (b) WHR and (c) logTG was analyzed with EMMAX based on Linear Mixed Model. (d) BMI, WHR and logTG were jointly analyzed using MFQLS.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4339744&req=5

Fig7: Manhattan-plots for the results from the genome-wide association study. (a) BMI, (b) WHR and (c) logTG was analyzed with EMMAX based on Linear Mixed Model. (d) BMI, WHR and logTG were jointly analyzed using MFQLS.
Mentions: The QQ plots in Figure 6 show that the results for the EMMAX and MFQLS preserve the nominal significance level, and Manhattan plots in Figure 7 demonstrate that the results from MFQLS are more significant than the results from EMMAX. Genome-wide significance level with Bonferroni correction is 9.68 × 10–8 and Table 6 shows the results for SNPs of which p-values were less than 5 × 10–7 for EMMAX or MFQLS. rs651821 is an unique genome-wide significant result and the p-value of rs651821 derived by MFQLS was markedly less than those derived by EMMAX. P-values of rs17119975 and rs4417316 were larger than the significance level by Bonferroni correction but they are still expected to be promising candidate disease susceptible loci. In particular, the genetic positions of these three SNPs were similar, and we checked the linkage disequilibrium between theses SNPs with pairwise r2 from the Chinese and Japanese data in the HapMap Release 3. rs17119975 and rs4417316 were in linkage disequilibrium with r2 = 0.823, but r2 between rs651821 and the others are less than 0.01. Small p-values of rs17119975 and rs4417316 may be generated with the same genetic component even though both are located in different genes, and it should be noticed that the smallest p-value for rs17119975 and rs4417316 was found with MFQLS.Figure 6

Bottom Line: The proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available.Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes.The proposed method may be more statistically efficient than the existing methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health Science, Seoul National University, Seoul, Korea. won1@snu.ac.kr.

ABSTRACT

Background: Many disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. Therefore, joint analysis of multiple phenotypes and multiple markers has been considered as an efficient strategy for genome-wide association analysis, and in this work we propose an omnibus family-based association test for the joint analysis of multiple genotypes and multiple phenotypes.

Results: The proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available. Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes.

Conclusions: The proposed method may be more statistically efficient than the existing methods. The application was developed in C++ and is available at the following URL: http://healthstat.snu.ac.kr/software/mfqls/ .

Show MeSH