Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates
| العنوان: | Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates |
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| المؤلفون: | Pak C. Sham, Hon-Cheong So |
| المصدر: | Behavior Genetics |
| بيانات النشر: | Springer Science and Business Media LLC, 2011. |
| سنة النشر: | 2011 |
| مصطلحات موضوعية: | Genes, Recessive, Genome-wide association study, Quantitative trait locus, Biology, Genome-wide association studies, Polymorphism, Single Nucleotide, 01 natural sciences, Association, 010104 statistics & probability, 03 medical and health sciences, Software, Genetic model, Statistics, Covariate, Genetics, Humans, Computer Simulation, Genetics(clinical), 0101 mathematics, Additive model, Genetics (clinical), Ecology, Evolution, Behavior and Systematics, Original Research, Genes, Dominant, 030304 developmental biology, Statistical hypothesis testing, 0303 health sciences, Models, Statistical, Models, Genetic, business.industry, Computational Biology, Genetic Variation, Genomics, Models, Theoretical, Genome-Wide Association Study - methods, Genetic models, Computational Biology - methods, business, Algorithms, Genome-Wide Association Study, Type I and type II errors |
| الوصف: | The association of genetic variants with outcomes is usually assessed under an additive model, for example by the trend test. However, misspecification of the genetic model will lead to a reduction in power. More robust tests for association might therefore be preferred. A useful approach is to consider the maximum of the three test statistics under additive, dominant and recessive models (MAX3). The p-value however has to be adjusted to maintain the type I error rate. Previous studies and software on robust association tests have focused on binary traits without covariates. In this study we developed an analytic approach to robust association tests using MAX3, allowing for quantitative or binary traits as well as covariates. The p-values from our theoretical calculations match very well with those from a bootstrap resampling procedure. The methodology is implemented in the R package RobustSNP which is able to handle both small-scale studies and GWAS. The package and documentation are available at http://sites.google.comTest/ site/honcheongso/software/robustsnp. © The Author(s) 2011. published_or_final_version Springer Open Choice, 21 Feb 2012 |
| تدمد: | 1573-3297 0001-8244 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::834dd341e352f6b65e81839141cecbe9Test https://doi.org/10.1007/s10519-011-9450-9Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....834dd341e352f6b65e81839141cecbe9 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15733297 00018244 |
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