ArticleBMC proceedings2011
Gene-based partial least-squares approaches for detecting rare variant associations with complex traits.
Article in BMC proceedings, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Identifying rare and common variants with Bayesian variable selection.BMC proceedings · 2016Article
- Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics.BMC genomics · 2014Article
- Evaluation of logistic Bayesian LASSO for identifying association with rare haplotypes.BMC proceedings · 2014Article
- A PLSPM-based test statistic for detecting gene-gene co-association in genome-wide association study with case-control design.PloS one · 2013Article
- Article
- Incorporating biological information into association studies of sequencing data.Genetic epidemiology · 2011Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis.
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Registered trials
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