Network-guided sparse regression modeling for detection of gene-by-gene interactions | Zendy

Chen Lu | Zendy; Jeanne C. Latourelle | Zendy; George O'connor | Zendy; Josée Dupuis | Zendy; Eric D. Kolaczyk | Zendy

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Network-guided sparse regression modeling for detection of gene-by-gene interactions

Author(s) -

Chen Lu,

Jeanne C. Latourelle,

George O'connor,

Josée Dupuis,

Eric D. Kolaczyk

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt139

Subject(s) - heritability , missing heritability problem , framingham heart study , genome wide association study , regression , computer science , computational biology , genetic association , gene , data mining , biology , machine learning , statistics , genetic variants , genetics , mathematics , framingham risk score , medicine , single nucleotide polymorphism , disease , genotype , pathology

Genetic variants identified by genome-wide association studies to date explain only a small fraction of total heritability. Gene-by-gene interaction is one important potential source of unexplained total heritability. We propose a novel approach to detect such interactions that uses penalized regression and sparse estimation principles, and incorporates outside biological knowledge through a network-based penalty.

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