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Joint Modelling of Multiple Phenotypes in Genome-Wide Associations Studies

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Submitted By yotsa
Words 463
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Introduction
Genome-wide association study (GWAS) is now becoming a routine analysis when there are large-scale genotype data and phenotypes data available from a population. However, conventional statistical techniques used in GWAS have yielded very little information considering the amount of data available. Low statistical power due to weak genetic effects is a possible explanation for this but applying simplistic tests for association could be another reason. For instance, currently there is no established method for detecting pleiotropy, that is when a single genetic variant influences multiple outcomes, from genome-wide scans, and generally phenotypes, which are often strongly biologically related, are rarely modelled together. Therefore, a new method that can identify pleiotropic genetic loci and model related phenotypes from genome-wide association study is needed.
Method
In this project, a novel method for the detection of pleiotropic genetic effects based on the genome-wide association study approach is developed. The method models multiple phenotypes simultaneously and in doing so may improve the power to detect genetic variants effecting phenotypes as well as identifying pleiotropic effects. The Usual way of association testing between genetic variants and phenotypes of interest, where a single phenotype or trait is the response variable and a single nucleotide polymorphisms (SNP) is the predictor, is reversed such that SNP is the outcome and multiple phenotypes are modelled simultaneously as predictors. The regression models used in this method are logistic and ordinal regression, since SNP genotypes provide ordered categorical data with 2 or 3 categories. This method is called inverseREGRESS. The method is applied to data on nine metabolic phenotypes from the Northern Finland Birth Cohort of 1966 (NFBC1966), which were studied...

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