Penalized regression for genome-wide association screening of sequence data.
H Zhou1, D H Alexander, M E Sehl
1Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA. hua_zhou@ncsu.edu
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 2, 2010
Summary
Whole exome and whole genome sequencing offer powerful insights into complex diseases. This study extends penalized regression to analyze both common and rare variants, improving statistical association testing for genetic studies.
Area of Science:
- Genetics and Genomics
- Statistical Genetics
- Bioinformatics
Background:
- Whole exome and whole genome sequencing are increasingly used to study common diseases and complex traits.
- Analyzing sequencing data presents challenges due to the large number of rare variants and their low frequencies.
- Current association studies often focus on common variants, necessitating new analytical approaches for sequencing data.
Purpose of the Study:
- To extend the penalized regression framework for model selection to accommodate both common and rare variants in sequencing data.
- To investigate the tradeoffs between different penalized regression strategies, including pure lasso, group penalties, and mixtures.
- To provide a robust statistical framework for analyzing genetic association studies using whole exome and whole genome sequencing data.
Main Methods:
- Extension of penalized regression framework (lasso and Euclidean penalties) for model selection.
- Incorporation of biological information through weighted penalties for gene or pathway grouping.
- Examination of tradeoffs between pure lasso, pure group penalties, and mixed penalty approaches.
- Implementation in the MENDEL statistical genetics software.
Main Results:
- The proposed penalized regression framework effectively handles both common and rare variants in sequencing data.
- Different penalty strategies (lasso, group, mixed) offer distinct advantages and tradeoffs in model selection.
- Computational and statistical benefits of lasso penalized estimation are preserved in the extended framework.
- The approach was successfully illustrated using both simulated and real genetic datasets.
Conclusions:
- Penalized regression provides a powerful and flexible framework for analyzing genetic association studies with sequencing data.
- The extended methods allow for the integrated analysis of common and rare variants, enhancing the study of complex traits.
- The developed strategy, implemented in MENDEL, offers a valuable tool for geneticists and bioinformaticians.
Related Concept Videos
01:11Genome-wide Association Studies-GWAS
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
02:46Genetic Screens
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...