Assessment of Penalized Regression for Genome-wide Association Studies

The data from genome-wide association studies (GWAS) in humans are still predominantly analyzed using single marker association methods. As an alternative to Single Marker Analysis (SMA), all or subsets of markers can be tested simultaneously. This approach requires a form of Penalized Regression (PR) as the number of SNPs is much larger than the sample size. Here we review PR methods in the context of GWAS, extend them to perform penalty parameter and SNP selection by False Discovery Rate (FDR) control, and assess their performance (including penalties incorporating linkage disequilibrium) in comparison with SMA. PR methods were compared with SMA on realistically simulated GWAS data consisting of genotype data from single and multiple chromosomes and a continuous phenotype and on real data. Based on our comparisons our analytic FDR criterion may currently be the best approach to SNP selection using PR for GWAS. We found that PR with FDR control provides substantially more power than SMA with genome-wide type-I error control but somewhat less power than SMA with Benjamini-Hochberg FDR control. PR controlled the FDR conservatively while SMA-BH may not achieve FDR control in all situations. Differences among PR methods seem quite small when the focus is on variable selection with FDR control. Incorporating LD into PR by adapting penalties developed for covariates measured on graphs can improve power but also generate morel false positives or wider regions for follow-up. We recommend using the Elastic Net with a mixing weight for the Lasso penalty near 0.5 as the best method.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Genetics, Bioinformatics, and Computational Biology

Department dc:contributor.department

Animal and Poultry Sciences

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2014

Author dc:creator

• Yi, Hui

Chair dc:contributor.committeechair

• Hoeschele, Ina

Committee members dc:contributor.committeemember

• Deng, Xinwei
• Saghai-Maroof, Mohammad A.
• Zhu, Hongxiao

• Genome-wide Association Study
• penalized regression
• false discovery rate
• linkage disequilibrium

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:3635

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/64845