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• W2139768049 abstract "Abstract Background: Large-scale candidate-gene and genome-wide association studies genotype multiple SNPs within or surrounding a gene, including both tag and functional SNPs. The immense amount of data generated in these studies poses new challenges to analysis. One particularly challenging yet important question is how to best use all genetic information to test whether a gene or a region is associated with the trait of interest. Methods: Here we propose a powerful gene-based Association Test by combining Optimally Weighted Markers (ATOM) within a genomic region. Due to variation in linkage disequilibrium, different markers often associate with the trait of interest at different levels. To appropriately apportion their contributions, we assign a weight to each marker that is proportional to the amount of information it captures about the trait locus. We analytically derive the optimal weights for both quantitative and binary traits, and describe a procedure for estimating the weights from a reference database such as the HapMap. Compared with existing approaches, our method has several distinct advantages, including (i) the ability to borrow information from an external database to increase power, (ii) the theoretical derivation of optimal marker weights and (iii) the scalability to simultaneous analysis of all SNPs in candidate genes and pathways. Results: Through extensive simulations and analysis of the FTO gene in our ongoing genome-wide association study on childhood obesity, we demonstrate that ATOM increases the power to detect genetic association as compared with several commonly used multi-marker association tests. Contact: mingyao@mail.med.upenn.edu; chun.li@vanderbilt.edu Supplementary information: Supplementary data are available at Bioinformatics online." @default.
• W2139768049 created "2016-06-24" @default.
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• W2139768049 date "2008-12-15" @default.
• W2139768049 modified "2023-10-15" @default.
• W2139768049 title "ATOM: a powerful gene-based association test by combining optimally weighted markers" @default.
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• W2139768049 doi "https://doi.org/10.1093/bioinformatics/btn641" @default.
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