Publication: Assessing statistical significance in multivariable genome wide association analysis
Assessing statistical significance in multivariable genome wide association analysis
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Buzdugan, L., Kalisch, M., Navarro, A., Schunk, D., Fehr, E., & Bühlmann, P. (2016). Assessing statistical significance in multivariable genome wide association analysis. Bioinformatics, 32(13), 1990–2000. https://doi.org/10.1093/bioinformatics/btw128
Abstract
Abstract
Abstract
Motivation: Although Genome Wide Association Studies (GWAS) genotype a very large number of single nucleotide polymorphisms (SNPs), the data is often analyzed one SNP at a time. The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS. Results: We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Our method yields p-values for
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Creators (Authors)
- Buzdugan, Laura
- Kalisch, Markus
- Navarro, Arcadi
- Schunk, Daniel
- Bühlmann, Peter
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Citations
Buzdugan, L., Kalisch, M., Navarro, A., Schunk, D., Fehr, E., & Bühlmann, P. (2016). Assessing statistical significance in multivariable genome wide association analysis. Bioinformatics, 32(13), 1990–2000. https://doi.org/10.1093/bioinformatics/btw128
