New computational method simplifies the analysis of gene-environmental interactions

Scientists from Weill Cornell Medicine and the Ithaca campus at Cornell University have designed a novel computational technique for analyzing environmental and genetic interactions and how they affect disease risk.

New computational method simplifies the analysis of gene-environmental interactions
Image Caption: Weill Cornell Medicine

Published in the American Journal of Human Genetics journal on January 7th, 2021, the study makes the process of identifying these interactions relatively less challenging and shows their significance in establishing diabetes risk and body mass index (BMI).

Our study demonstrates that your genes matter and the environment matters and that the interaction of the two can increase risk for disease.”

Dr Olivier Elemento, Study Co-Senior Author and Professor of Computational Genomics in Computational Biomedicine, Weill Cornell Medicine

Dr. Elemento is also a professor of physiology and biophysics, associate director of the HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, and director of the Caryl and Israel Englander Institute for Precision Medicine at Weill Cornell Medicine.

Analyzing the interactions between genes and environment usually poses a major computational challenge, stated Andrew Marderstein, the lead author of the study and a doctoral candidate in the Weill Cornell Graduate School of Medical Sciences, whose study was performed both in Dr. Elemento’s laboratory in New York City and Dr. Andrew Clark’s laboratory in Ithaca, allowing him to have direct access to computational biology and population health know-how.

Genotype-environment interaction can be thought of as the situation where some genotypes are much more sensitive to environmental insults than others. These are exactly the cases where changes in the diet or other exposures might have the biggest improvement in health, but only for a subset of individuals.”

Dr Andrew Clark, Study Co-Senior Author, Cornell University

Dr Clark is also the Jacob Gould Schurman Professor of Population Genetics in the Department of Molecular Biology and Genetics in the College of Arts & Sciences and a Nancy and Peter Meinig Family Investigator at Cornell University.

The countless numbers of inherited genetic variations, or genetic variants, identified between people in a population, and different environmental factors and lifestyle, like exercise, smoking, and varied eating habits, can be examined for combined impacts in various ways.

When scientists test for interactions between genes and the environment, they usually study scores of data points in a pairwise manner, implying that they evaluate a single genetic variant and its interaction with a single environmental factor at a time. An analysis like this can turn out to be quite labor-intensive, Marderstein added.

The novel computational technique prioritizes and evaluates a smaller number of genome variants—or the entire set of genetic material present in the body—for gene-environment interactions.

Marderstein added, “We condensed a problem with analyzing 10 million different genetic variants to essentially analyzing only tens of variants in different regions of the genome.”

Although a normal genetic association study may look at whether one genetic variant could cause an average change in BMI, the study evaluated which genetic variants were related to individuals who are likely to have a lower BMI or higher BMI.

The team discovered that searching for DNA sections connected with the variance in a human trait, known as a variance quantitative trait locus or vQTL, allowed them to more easily detect the interactions between the genes and the environment. Most importantly, the vQTLs related to BMI were also probably linked to diseases that have huge environmental influences.

Marderstein added that another field of study in which the novel computational technique may prove useful in establishing how a person may react to a particular drug on the basis of gene-environment interactions.

According to Dr. Elemento, the study of social determinants of health, which means an individual’s social and environmental conditions, like educational attainment and poverty level, is a third area that the team is interested in exploring.

On the whole, investigators in the field of precision medicine are realizing that they can sequence a DNA of a person, apart from evaluating environmental factors, like physical activity and air quality, to gain a better interpretation of whether the person is at risk of developing a particular disease.

The idea down the line is to use these concepts in the clinic. This is part of the evolution of precision medicine, where we can now sequence somebody's genome very easily and then potentially analyze all of the variants in the genetic landscape that correlate with the risk of developing particular conditions.”

Dr Olivier Elemento, Study Co-Senior Author and Professor of Computational Genomics in Computational Biomedicine, Weill Cornell Medicine

Source:
Journal reference:

Marderstein, A. R., et al. (2021) Leveraging phenotypic variability to identify genetic interactions in human phenotypes. American Journal of Human Genetics. doi.org/10.1016/j.ajhg.2020.11.016.

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