Analysis of phenotype-genotype associations using genomic informational field theory (GIFT).
Jonathan A D Wattis, Sian M Bray, Panagiota Kyratzi, Cyril Rauch
Author Information
Jonathan A D Wattis: Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK. Electronic address: Jonathan.Wattis@nottingham.ac.uk.
Sian M Bray: School of Life Sciences, University Park, Nottingham NG7 2RD, UK. Electronic address: Sian.Bray@nottingham.ac.uk.
Panagiota Kyratzi: Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK; Vetinary Academic Building, Sutton Bonington Campus, University of Nottingham, Sutton Bonington, Leicestershire LE12 5RD, UK.
Cyril Rauch: Vetinary Academic Building, Sutton Bonington Campus, University of Nottingham, Sutton Bonington, Leicestershire LE12 5RD, UK. Electronic address: Cyril.Rauch@nottingham.ac.uk.
We show how field- and information theory can be used to quantify the relationship between genotype and phenotype in cases where phenotype is a continuous variable. Given a sample population of phenotype measurements, from various known genotypes, we show how the ordering of phenotype data can lead to quantification of the effect of genotype. This method does not assume that the data has a Gaussian distribution, it is particularly effective at extracting weak and unusual dependencies of genotype on phenotype. However, in cases where data has a special form, (eg Gaussian), we observe that the effective phenotype field has a special form. We use asymptotic analysis to solve both the forward and reverse formulations of the problem. We show how p-values can be calculated so that the significance of correlation between phenotype and genotype can be quantified. This provides a significant generalisation of the traditional methods used in genome-wide association studies GWAS. We derive a field-strength which can be used to deduce how the correlations between genotype and phenotype, and their impact on the distribution of phenotypes.
