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. 2013 Jul 1;29(13):i171-9.
doi: 10.1093/bioinformatics/btt238.

Efficient Network-Guided Multi-Locus Association Mapping With Graph Cuts

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Free PMC article

Efficient Network-Guided Multi-Locus Association Mapping With Graph Cuts

Chloé-Agathe Azencott et al. Bioinformatics. .
Free PMC article

Abstract

Motivation: As an increasing number of genome-wide association studies reveal the limitations of the attempt to explain phenotypic heritability by single genetic loci, there is a recent focus on associating complex phenotypes with sets of genetic loci. Although several methods for multi-locus mapping have been proposed, it is often unclear how to relate the detected loci to the growing knowledge about gene pathways and networks. The few methods that take biological pathways or networks into account are either restricted to investigating a limited number of predetermined sets of loci or do not scale to genome-wide settings.

Results: We present SConES, a new efficient method to discover sets of genetic loci that are maximally associated with a phenotype while being connected in an underlying network. Our approach is based on a minimum cut reformulation of the problem of selecting features under sparsity and connectivity constraints, which can be solved exactly and rapidly. SConES outperforms state-of-the-art competitors in terms of runtime, scales to hundreds of thousands of genetic loci and exhibits higher power in detecting causal SNPs in simulation studies than other methods. On flowering time phenotypes and genotypes from Arabidopsis thaliana, SConES detects loci that enable accurate phenotype prediction and that are supported by the literature.

Availability: Code is available at http://webdav.tuebingen.mpg.de/u/karsten/Forschung/scones/.

Supplementary information: Supplementary data are available at Bioinformatics online.

Figures

Fig. 1.
Fig. 1.
Small examples of the three types of networks considered
Fig. 2.
Fig. 2.
Graph for the s/ t-min-cut formulation of the selection of networks of genetic markers
Fig. 3.
Fig. 3.
Real CPU runtime comparison between univariate linear regression, ncLasso, nonoverlapping group Lasso and SConES, from 100 to 25 000 SNPs (left) and from 100 to 200 000 SNPs (right). ‘ncLasso’ refers to the original implementation suggested by Li and Li (2008) and ‘ncLasso (accelerated)’ to the incidence-matrix-based implementation we use here. After 3 weeks, nonoverlapping group Lasso and ncLasso had not finished running for 50 000 SNPs. The accelerated version of ncLasso ran out of memory for ≥150 000 SNPs
Fig. 4.
Fig. 4.
Power and FDR of SConES, compared with state-of-the-art Lasso algorithms and a baseline univariate linear regression, in three different data simulation scenarios. Best methods are closest to the upper-left corner. Numbers denote the number of SNPs selected by the method
Fig. 5.
Fig. 5.
Cross-validated predictivity (measured as Pearson’s squared correlation coefficient between actual phenotype and phenotype predicted by a ridge-regression over the selected SNPs) of SConES compared with that of Lasso, groupLasso and ncLasso. Horizontal bars indicate cross-validated BLUP predictivity

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