  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.

Quantitative Methods;Methodology