publication . Article . 2009

Combining least absolute shrinkage and selection operator (LASSO) and principal-components analysis for detection of gene-gene interactions in genome-wide association studies

D'Angelo Gina M; Rao DC; Gu C Charles;
Open Access English
  • Published: 01 Dec 2009 Journal: BMC Proceedings, volume 3, issue Suppl 7, page S62 (issn: 1753-6561, Copyright policy)
  • Publisher: Springer Nature
Abstract
<p>Abstract</p> <p>Variable selection in genome-wide association studies can be a daunting task and statistically challenging because there are more variables than subjects. We propose an approach that uses principal-component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) to identify gene-gene interaction in genome-wide association studies. A PCA was used to first reduce the dimension of the single-nucleotide polymorphisms (SNPs) within each gene. The interaction of the gene PCA scores were placed into LASSO to determine whether any gene-gene signals exist. We have extended the PCA-LASSO approach using the bootstrap to estimate the s...
Subjects
arXiv: Quantitative Biology::GenomicsQuantitative Biology::Molecular Networks
free text keywords: General Biochemistry, Genetics and Molecular Biology, General Medicine, Lasso (statistics), Logistic regression, Feature selection, Genetic association, Data mining, computer.software_genre, computer, Medicine, business.industry, business, Generalized linear model, Bootstrapping (electronics), Genome-wide association study, Bioinformatics, Principal component analysis, Proceedings, R, Science, Q
Related Organizations
Funded by
NIH| Genetic Analysis of Common Diseases: An Evaluation
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01GM031575-22
  • Funding stream: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
,
NIH| Washington University Institute of Clinical and Translational Sciences (UL1)
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 3UL1RR024992-03S4
  • Funding stream: NATIONAL CENTER FOR RESEARCH RESOURCES

Li, KC. Sliced inverse regression for dimension reduction. J Am Stat Assoc. 1991; 86: 316-327 [DOI]

Tibshirani, R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol. 1996; 58: 267-288

Efron, B, Hastie, T, Johnstone, I, Tibshirani, R. Least angle regression. Ann Stat. 2004; 32: 407-499 [OpenAIRE] [DOI]

Malo, N, Libiger, O, Schork, NJ. Accommodating linkage disequilibrium in genetic-association analyses via ridge regression. Am J Hum Genet. 2008; 82: 375-85 [OpenAIRE] [PubMed] [DOI]

Steyerberg, EW, Eijkemans, MJC, Habbema, JDF. Application of shrinkage techniques in logistic regression analysis: a case study. Stat Neerl. 2001; 55: 76-88 [OpenAIRE] [DOI]

Lokhorst, J. The LASSO and Generalised Linear Models. Honors Project. 1999

Jolliffe, IT. Principal Component Analysis. 1986

Shi, W, Lee, KE, Wahba, G. Detecting disease-causing genes by LASSO-patternsearch algorithm. BMC Proc. 2007; 1 (suppl 1): S60 [OpenAIRE] [PubMed] [DOI]

Newton, JL, Harney, SMJ, Wordsworth, BP, Brown, MA. A review of the MHC genetics of rheumatoid arthritis. Genes Immun. 2004; 5: 151-157 [OpenAIRE] [PubMed] [DOI]

Carlton, VEH, Hu, X, Chokkalingam, AP, Schrodi, SJ, Brandon, R, Alexander, HC, Chang, M, Catanese, JJ, Leong, DU, Ardlie, KG, Kastner, DL, Seldin, MF, Criswell, LA, Gregersen, PK, Beasley, E, Thomson, G, Amos, CI, Begovich, AB. PTPN22 genetic variation: evidence for multiple variants associated with rheumatoid arthritis. Am J Hum Genet. 2005; 77: 567-581 [OpenAIRE] [PubMed] [DOI]

Meier, L, Geer, S van de, Bühlmann, P. The group lasso for logistic regression. J R Stat Soc Series B Stat Methodol. 2008; 70: 53-71

Davison, AC, Hinkley, DV. Bootstrap Methods and Their Application. 1997

Benjamini, Y, Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995; 57: 289-300

Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue