ArticleBMC proceedings2009
Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes.
Article in BMC proceedings, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure.Genetic epidemiology · 2017Article
- Empirical Bayes scan statistics for detecting clusters of disease risk variants in genetic studies.Biometrics · 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
- Gene- or region-based analysis of genome-wide association studies.Genetic epidemiology · 2009Article
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Authors and funding
5 authors.
Funding
Abstract
Due to the high-dimensionality of single-nucleotide polymorphism (SNP) data, region-based methods are an attractive approach to the identification of genetic variation associated with a certain phenotype. A common approach to defining regions is to identify the most significant SNPs from a single-SNP association analysis, and then use a gene database to obtain a list of genes proximal to the identified SNPs. Alternatively, regions may be defined statistically, via a scan statistic. After categorizing SNPs as significant or not (based on the single-SNP association p-values), a scan statistic is useful to identify regions that contain more significant SNPs than expected by chance. Important features of this method are that regions are defined statistically, so that there is no dependence on a gene database, and both gene and inter-gene regions can be detected. In the analysis of blood-lipid phenotypes from the Framingham Heart Study (FHS), we compared statistically defined regions with those formed from the top single SNP tests. Although we missed a number of single SNPs, we also identified many additional regions not found as SNP-database regions and avoided issues related to region definition. In addition, analyses of candidate genes for high-density lipoprotein, low-density lipoprotein, and triglyceride levels suggested that associations detected with region-based statistics are also found using the scan statistic approach.
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Registered trials
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