id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-345696-gwbi6nlt	Álvarez-Castro, José M.	Gene–Environment Interaction in the Era of Precision Medicine – Filling the Potholes Rather Than Starting to Build a New Road	2020-10-06		.txt	text/plain	6217	261	37	(2019) provide a probabilistic approach based on a Bayesian framework to hierarchically model geneenvironment interaction, leading to a population-dependent index, C, called the genetic coefficient of the disease (at a population)-"a large C indicates large distinguishability of case genomes from control genomes." Then they illustrate the performance of the proposed methodology using a built-up example in which the disease susceptibility is by default very low (0.01) and it significantly increases due to either environmental (exposure) or genetic (risk allele) factors or both, to 0.4, 0.5, and 0.9, respectively. Using previous extensions of classical models of genetic effects (Álvarez-Castro and Yang, 2011; Alvarez-Castro and Crujeiras, 2019), the COIA regression framework for gene-environment interaction developed above and its implementation into an ARNOIA model can be extended to several, possibly multiallelic, loci with arbitrary epistasis and arbitrary departures from linkage equilibrium and simultaneously to several environmental variables with multiple environmental instances, with nonrandom associations (i.e., correlations) of environmental variables and of genotypes and environments.	./cache/cord-345696-gwbi6nlt.txt	./txt/cord-345696-gwbi6nlt.txt