Cardiac Valve Calcification as an Important Predictor for All-Cause Mortality and Cardiovascular Mortality in Long-Term Peritoneal Dialysis Patients

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Abstract
Quantitative genetics has evolved dramatically in the past century, and the proliferation of genetic data enables the characterization of complex interactions beyond the scope of its theoretical foundations. In this paper, we lay the foundations of an alternative formulation of quantitative genetics based on information theory. Information theory can provide sensitive measures of statistical dependencies among variables, and provides a natural mathematical language for an alternative view of quantitative genetics. In previous work we examined the information content of discrete functions and applied this formalism to the analysis of genetic data. We present here a set of relationships that both unifies the information measures for the set of discrete functions, and uses them to express key quantitative genetic relationships. Information theory measures of variable interdependency are used to identify significant interactions, and a general approach is described for inferring functional relationships within genotype and phenotype data. We present information-based measures of the genetic quantities: penetrance, heritability and degrees of statistical epistasis. Our scope here includes the consideration of three variable dependencies and independently segregating variants, which captures two locus effects, genetic interactions, and two phenotype pleiotropy. However, this formalism and general theory naturally applies to multi-variable interactions and higher-order complex dependencies, and can be adapted to account for population structure, linkage and non-randomly segregating markers. This paper therefore lays the initial groundwork for a full formulation of quantitative genetics based in information theory.