RT Journal Article
SR Electronic
T1 SNP Variable Selection by Generalized Graph Domination
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 396085
DO 10.1101/396085
A1 Sun, Shuzhen
A1 Miao, Zhuqi
A1 Ratcliffe, Blaise
A1 Campbell, Polly
A1 Pasch, Bret
A1 El-Kassaby, Yousry A.
A1 Balasundaram, Balabhaskar
A1 Chen, Charles
YR 2018
UL http://biorxiv.org/content/early/2018/08/20/396085.abstract
AB High-throughput sequencing technology has revolutionized both medical and biological research by generating exceedingly large numbers of genetic variants. The resulting datasets share a number of common characteristics that might lead to poor generalization capacity. Concerns include noise accumulated due to the large number of predictors, sparse information regarding the p ≫ n problem, and overfitting and model mis-identification resulting from spurious collinearity. Additionally, complex correlation patterns are present among variables. As a consequence, reliable variable selection techniques play a pivotal role in predictive analysis, generalization capability, and robustness in clustering, as well as interpretability of the derived models.K-dominating set, a parameterized graph-theoretic generalization model, was used to model SNP (single nucleotide polymorphism) data as a similarity network and searched for representative SNP variables. In particular, each SNP was represented as a vertex in the graph, (dis)similarity measures such as correlation coefficients or pairwise linkage disequilibrium were estimated to describe the relationship between each pair of SNPs; a pair of vertices are adjacent, i.e. joined by an edge, if the pairwise similarity measure exceeds a user-specified threshold. A minimum K-dominating set in the SNP graph was then made as the smallest subset such that every SNP that is excluded from the subset has at least k neighbors in the selected ones. The strength ofk-dominating set selection in identifying independent variables, and in culling representative variables that are highly correlated with others, was demonstrated by a simulated dataset. The advantages of k-dominating set variable selection were also illustrated in two applications: pedigree reconstruction using SNP profiles of 1,372 Douglas-fir trees, and species delineation for 226 grasshopper mouse samples. A C++ source code that implements SNP-SELECT and uses Gurobi™ optimization solver for the k-dominating set variable selection is available (https://github.com/transgenomicsosu/SNP-SELECT).