title: Exploration of methods to identify polymorphisms associated with variation in DNA repair capacity phenotypes.
abstract: Elucidating the relationship between polymorphic sequences and risk of common disease is a challenge. For example, although it is clear that variation in DNA repair genes is associated with familial cancer, aging and neurological disease, progress toward identifying polymorphisms associated with elevated risk of sporadic disease has been slow. This is partly due to the complexity of the genetic variation, the existence of large numbers of mostly low frequency variants and the contribution of many genes to variation in susceptibility. There has been limited development of methods to find associations between genotypes having many polymorphisms and pathway function or health outcome. We have explored several statistical methods for identifying polymorphisms associated with variation in DNA repair phenotypes. The model system used was 80 cell lines that had been resequenced to identify variation; 191 single nucleotide substitution polymorphisms (SNPs) are included, of which 172 are in 31 base excision repair pathway genes, 19 in 5 anti-oxidation genes, and DNA repair phenotypes based on single strand breaks measured by the alkaline Comet assay. Univariate analyses were of limited value in identifying SNPs associated with phenotype variation. Of the multivariable model selection methods tested: the easiest that provided reduced error of prediction of phenotype was simple counting of the variant alleles predicted to encode proteins with reduced activity, which led to a genotype including 52 SNPs; the best and most parsimonious model was achieved using a two-step analysis without regard to potential functional relevance: first SNPs were ranked by importance determined by random forests regression (RFR), followed by cross-validation in a second round of RFR modeling that included ever more SNPs in declining order of importance. With this approach six SNPs were found to minimize prediction error. The results should encourage research into utilization of multivariate analytical methods for epidemiological studies of the association of genetic variation in complex genotypes with risk of common diseases.
authors.affiliation: Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. jones20@llnl.gov
authors.forename: Irene M
authors.initials: IM
authors.lastname: Jones
authors.forename: Cynthia B
authors.initials: CB
authors.lastname: Thomas
authors.forename: Tina
authors.initials: T
authors.lastname: Xi
authors.forename: Harvey W
authors.initials: HW
authors.lastname: Mohrenweiser
authors.forename: David O
authors.initials: DO
authors.lastname: Nelson
mesh_terms: D002460:Cell Line; D053904:DNA Breaks, Single-Stranded; D004260:DNA Repair; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D015999:Multivariate Analysis; D010641:Phenotype; D011110:Polymorphism, Genetic; D020641:Polymorphism, Single Nucleotide; D018570:Risk Assessment