Gene–environment interaction
A norm of reaction is a graph that shows the relationship between genes and environmental factors when phenotypic differences are continuous.
[3] These interactions are of particular interest to genetic epidemiologists for predicting disease rates and methods of prevention with respect to public health.
Fisher sought to eliminate interaction from statistical studies as it was a phenomenon that could be removed using a variation in scale.
Hogben believed that the interaction should be investigated instead of eliminated as it provided information on the causation of certain elements of development.
Arthur Jensen published the study “How much can we boost IQ and scholastic achievement?”, which amongst much criticism also faced contention by scientists Richard Lewontin and David Layzer.
In contrast to previous debates, Moffitt and Caspi were now using the statistical analysis to prove that interaction existed and could be used to uncover the mechanisms of a vulnerability trait.
Contention came from Zammit, Owen and Lewis who reiterated the concerns of Fisher in that the statistical effect was not related to the developmental process and would not be replicable with a difference of scale.
The biometric (or statistical) conception has its origins in research programs that seek to measure the relative proportions of genetic and environmental contributions to phenotypic variation within populations.
For example, the PKU gene results in higher levels of phenylalanine than normal which in turn causes mental retardation.
The polygenic nature of complex phenotypes suggests single candidate studies could be ineffective in determining the various smaller scale effects from the large number of influencing gene variants.
A polygenic score is generated using the alleles associated with a trait and their respective weights based on effect and examined in combination with environmental exposure.
An effective approach to this all-encompassing study occurs in two-steps where the genome is first filtered using gene-level tests and pathway based gene set analyses.
Studies are suggested to produce inaccurate results due to the investigation of multiple phenotypes and environmental factors in individual experiments.
[15] There are two different models for the scale of measurement that helps determine if gene–environment interaction exists in a statistical context.
The same study suggests taking a life course approach to determining genetic sensitivity to environmental influences within the scope of mental illnesses.
[19] There may be significant public health benefits in using gene by environment interactions to prevent or cure disease.
[19] Therefore, the clinical importance of pharmacogenetics and gene by environment interactions comes from the possibility that genomic, along with environmental information, will allow more accurate predictions of an individual's drug response.
These other factors include the diet and specific nutrients within the diet, physical activity, alcohol and tobacco use, sleep (bed time, duration), and any of a number of exposures (or exposome), including toxins, pollutants, sunlight (latitude north–south of the equator), among any number of others.
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.
[22] A catalog of genetic variants that associate with these and related cardiometabolic phenotypes and modified by common environmental factors is available.
