diseases, including cardiovascular disease, coronary artery disease, coronary heart disease, type 2 diabetes, hypertension, stroke, myocardial infarction, and non-alcoholic fatty liver disease. In the clinic, typically assessed risks of these conditions include blood lipids (triglyceride, and HDL, LDL and total cholesterol), glycemic traits (plasma glucose and insulin, HOMA-IR, beta cell function as HOMA-BC), obesity anthropometrics (BMI/obesity, adiposity, body weight, waist circumference, waist-to-hip ratio), vascular measures (diastolic and systolic blood pressure), and biomarkers of inflammation. Gene–environment interactions can modulate the adverse effects of an allele that confers increased risk of disease, or can exacerbate the genotype–phenotype relationship and increase risk, in a manner often referred to as nutrigenetics. A catalog of genetic variants that associate with these and related cardiometabolic phenotypes and modified by common environmental factors is available. Conversely, a disease study using breast cancer, type 2 diabetes, and rheumatoid arthritis shows that including GxE interactions in a risk prediction model does not improve risk identification. Examples In Drosophila: A classic example of gene–environment interaction was performed on Drosophila by Gupta and Lewontin in 1981. In their experiment they demonstrated that the mean bristle number on Drosophila could vary with changing temperatures. As seen in the graph to the right, different genotypes reacted differently to the changing environment. Each line represents a given genotype, and the slope of the line reflects the changing phenotype (bristle number) with changing temperature. Some individuals had an increase in bristle number with increasing temperature while others had a sharp decrease in bristle number with increasing temperature. This showed that the norms of reaction were not parallel for these flies, proving that gene–environment interactions exist. In plants: One very interesting approach about genotype by environment interaction strategies is its use in the selection of sugarcane cultivars adapted to different environments. In this article, they analyzed twenty sugarcane genotypes grown in eight different locations over two crop cycles to identify mega-environments related to higher cane yield, measured in tons of cane per hectare (TCH) and percentage of sucrose (Pol% cane) using biplot multivariate GEI models. The authors then created a novel strategy to study both yield variables in a two-way coupled strategy even though the results showed a mean negative correlation. Through coinertia analysis, it was possible to determine the best-fitted genotypes for both yield variables in all environments. The use of these novel strategies like coinertia in GEI, proved to be a great complement analysis