ArticleGenetic epidemiology2013
Identification of grouped rare and common variants via penalized logistic regression.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Article
- Longitudinal data analysis for rare variants detection with penalized quadratic inference function.Scientific reports · 2017Article
- Detecting association of rare and common variants based on cross-validation prediction error.Genetic epidemiology · 2017Article
- Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure.Genetic epidemiology · 2017Article
- EM Adaptive LASSO-A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes.Frontiers in genetics · 2016Article
- Fine-mapping additive and dominant SNP effects using group-LASSO and fractional resample model averaging.Genetic epidemiology · 2015Article
- Clique-Based Clustering of Correlated SNPs in a Gene Can Improve Performance of Gene-Based Multi-Bin Linear Combination Test.BioMed research international · 2015Article
- Personalized medicine in hematology - A landmark from bench to bed.Computational and structural biotechnology journal · 2014Review
- Regularized rare variant enrichment analysis for case-control exome sequencing data.Genetic epidemiology · 2014Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
In spite of the success of genome-wide association studies in finding many common variants associated with disease, these variants seem to explain only a small proportion of the estimated heritability. Data collection has turned toward exome and whole genome sequencing, but it is well known that single marker methods frequently used for common variants have low power to detect rare variants associated with disease, even with very large sample sizes. In response, a variety of methods have been developed that attempt to cluster rare variants so that they may gather strength from one another under the premise that there may be multiple causal variants within a gene. Most of these methods group variants by gene or proximity, and test one gene or marker window at a time. We propose a penalized regression method (PeRC) that analyzes all genes at once, allowing grouping of all (rare and common) variants within a gene, along with subgrouping of the rare variants, thus borrowing strength from both rare and common variants within the same gene. The method can incorporate either a burden-based weighting of the rare variants or one in which the weights are data driven. In simulations, our method performs favorably when compared to many previously proposed approaches, including its predecessor, the sparse group lasso [Friedman et al., 2010].
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.