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Statistical estimation of correlated genome associations to a quantitative trait network.

Kim S, Xing EP - PLoS Genet. (2009)

Bottom Line: Using simulated datasets based on the HapMap consortium and an asthma dataset, we compared the performance of our method with other methods based on single-marker analysis and regression-based methods that do not use any of the relational information in the traits.We found that our method showed an increased power in detecting causal variants affecting correlated traits.Our results showed that, when correlation patterns among traits in a QTN are considered explicitly and directly during a structured multivariate genome association analysis using our proposed methods, the power of detecting true causal SNPs with possibly pleiotropic effects increased significantly without compromising performance on non-pleiotropic SNPs.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.

ABSTRACT
Many complex disease syndromes, such as asthma, consist of a large number of highly related, rather than independent, clinical or molecular phenotypes. This raises a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical framework called graph-guided fused lasso (GFlasso) to directly and effectively incorporate the correlation structure of multiple quantitative traits such as clinical metrics and gene expressions in association analysis. Our approach represents correlation information explicitly among the quantitative traits as a quantitative trait network (QTN) and then leverages this network to encode structured regularization functions in a multivariate regression model over the genotypes and traits. The result is that the genetic markers that jointly influence subgroups of highly correlated traits can be detected jointly with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently and combined the results afterwards, our approach analyzes all of the traits jointly in a single statistical framework. This allows our method to borrow information across correlated phenotypes to discover the genetic markers that perturb a subset of the correlated traits synergistically. Using simulated datasets based on the HapMap consortium and an asthma dataset, we compared the performance of our method with other methods based on single-marker analysis and regression-based methods that do not use any of the relational information in the traits. We found that our method showed an increased power in detecting causal variants affecting correlated traits. Our results showed that, when correlation patterns among traits in a QTN are considered explicitly and directly during a structured multivariate genome association analysis using our proposed methods, the power of detecting true causal SNPs with possibly pleiotropic effects increased significantly without compromising performance on non-pleiotropic SNPs.

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Comparison of the computation time for lasso, , , and .(A) We varied the number of SNPs with the number of phenotypes fixed at 250. (B) We varied the number of phenotypes with the number of SNPs fixed at 50. The QTNs were obtained using threshold . The number of edges in the QTNs ranged from 900 to 950 in each case.
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pgen-1000587-g010: Comparison of the computation time for lasso, , , and .(A) We varied the number of SNPs with the number of phenotypes fixed at 250. (B) We varied the number of phenotypes with the number of SNPs fixed at 50. The QTNs were obtained using threshold . The number of edges in the QTNs ranged from 900 to 950 in each case.

Mentions: The scalability of our methods can be assessed from Figure 10, where the computation time for solving the optimization problem for lasso, , , and with fixed regularization parameters is shown. In Figure 10A, the number of traits in the QTN was fixed at 250, and the number of SNPs varied over the illustrated range. With 100 SNPs and 250 traits, the running time was around 20 minutes for the GFlasso methods, suggesting that a sliding-window scheme along the genome would be more reasonable for a whole-genome scan than considering all of the SNPs in a single model. Figure 10B shows the time cost over varying number of traits, with the total number of SNPs fixed at 50. We found that the GFlasso methods could handle at least hundreds of traits reasonably well. For a large dataset with more than several thousand traits, one might consider first breaking down the whole network into smaller components and then running GFlasso on each component separately.


Statistical estimation of correlated genome associations to a quantitative trait network.

Kim S, Xing EP - PLoS Genet. (2009)

Comparison of the computation time for lasso, , , and .(A) We varied the number of SNPs with the number of phenotypes fixed at 250. (B) We varied the number of phenotypes with the number of SNPs fixed at 50. The QTNs were obtained using threshold . The number of edges in the QTNs ranged from 900 to 950 in each case.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2719086&req=5

pgen-1000587-g010: Comparison of the computation time for lasso, , , and .(A) We varied the number of SNPs with the number of phenotypes fixed at 250. (B) We varied the number of phenotypes with the number of SNPs fixed at 50. The QTNs were obtained using threshold . The number of edges in the QTNs ranged from 900 to 950 in each case.
Mentions: The scalability of our methods can be assessed from Figure 10, where the computation time for solving the optimization problem for lasso, , , and with fixed regularization parameters is shown. In Figure 10A, the number of traits in the QTN was fixed at 250, and the number of SNPs varied over the illustrated range. With 100 SNPs and 250 traits, the running time was around 20 minutes for the GFlasso methods, suggesting that a sliding-window scheme along the genome would be more reasonable for a whole-genome scan than considering all of the SNPs in a single model. Figure 10B shows the time cost over varying number of traits, with the total number of SNPs fixed at 50. We found that the GFlasso methods could handle at least hundreds of traits reasonably well. For a large dataset with more than several thousand traits, one might consider first breaking down the whole network into smaller components and then running GFlasso on each component separately.

Bottom Line: Using simulated datasets based on the HapMap consortium and an asthma dataset, we compared the performance of our method with other methods based on single-marker analysis and regression-based methods that do not use any of the relational information in the traits.We found that our method showed an increased power in detecting causal variants affecting correlated traits.Our results showed that, when correlation patterns among traits in a QTN are considered explicitly and directly during a structured multivariate genome association analysis using our proposed methods, the power of detecting true causal SNPs with possibly pleiotropic effects increased significantly without compromising performance on non-pleiotropic SNPs.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.

ABSTRACT
Many complex disease syndromes, such as asthma, consist of a large number of highly related, rather than independent, clinical or molecular phenotypes. This raises a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical framework called graph-guided fused lasso (GFlasso) to directly and effectively incorporate the correlation structure of multiple quantitative traits such as clinical metrics and gene expressions in association analysis. Our approach represents correlation information explicitly among the quantitative traits as a quantitative trait network (QTN) and then leverages this network to encode structured regularization functions in a multivariate regression model over the genotypes and traits. The result is that the genetic markers that jointly influence subgroups of highly correlated traits can be detected jointly with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently and combined the results afterwards, our approach analyzes all of the traits jointly in a single statistical framework. This allows our method to borrow information across correlated phenotypes to discover the genetic markers that perturb a subset of the correlated traits synergistically. Using simulated datasets based on the HapMap consortium and an asthma dataset, we compared the performance of our method with other methods based on single-marker analysis and regression-based methods that do not use any of the relational information in the traits. We found that our method showed an increased power in detecting causal variants affecting correlated traits. Our results showed that, when correlation patterns among traits in a QTN are considered explicitly and directly during a structured multivariate genome association analysis using our proposed methods, the power of detecting true causal SNPs with possibly pleiotropic effects increased significantly without compromising performance on non-pleiotropic SNPs.

Show MeSH
Related in: MedlinePlus