A New Tool for Gene x Environment (GxE) Research - Variation Polygenic Score (vPGS)
Docket # 21-3797
Researchers in the Department of Sociology at Princeton University have developed an algorithm to calculate a particular type of genetic score, Variation Polygenic Score – vPGS, based on genotypic information that is provided by commercially available technologies ranging from “SNP-chips” to whole genome sequencing.
Polygenic scores attempt to summarize the genetic propensity for, or risk, of a given phenotype, (i.e. disease or trait) and have been around for more than a decade. They aim to predict the level of a trait i.e. how tall or short someone may be or what their blood pressure or BMI might be. The vPGS is different. Its purpose is not to predict whether someone who scores higher or lower on the vPGS will be, for instance, heavier or lighter or have a higher or lower IQ. Rather, it is formulated to predict variation. As an example, if we divided people into two groups based on having a high vPGS or a low vPGS, they may have the same average height of five-foot- nine-inches. But those in the low vPGS group may be all clustered around that mean level of 5’9” (say, +/- 2 inches). However, those in the high vPGS group may have a much broader range, say 5’9” +/- 4 inches. (Actually, the range would be expressed in standard deviations, but inches are used here to illustrate the point.) Moreover, a traditional PGS for body mass index (BMI) might predict one group to have a BMI of 28 and another group to have one of 23. Our vPGS again does not predict the mean level but rather the dispersion around that mean. It likely also predicts individual changes in BMI over the lifecourse (i.e. whether an individual tends to fluctuate greatly in weight). We have also demonstrated that vPGS is very suited for gene-environment interaction studies: that is, it is a good measure of the genetic propensity to be influenced by the environment for a particular trait.
vPGS can be utilized by pharmaceutical companies and in other experimental settings. As an example, since efficacy of pharmaceuticals (or any treatment) is based on the difference between the treatment group (who receives the drug) and the control group (who receives a placebo), when there is a high “placebo” effect, that makes it all the more difficult to demonstrate a true effect of the treatment. Companies who run trials, then, sometimes try to determine who is likely to demonstrate a big placebo effect and eliminate them from the subject pool. The vPGS offers a different, complementary approach: By scoring potential subjects for an experimental blood pressure medication (for instance) on their vPGS for blood pressure, trials can retain only those subjects with a high vPGS; that is, subjects whose genetic architecture likely makes them more “sensitive” to treatments. This, in turn, may magnify effects, increase statistically power and lower the costs of trials (by requiring fewer subjects to demonstrate a statistically significant effect). This approach need not be limited to pharmaceutical interventions but can be applied to any randomized controlled trial. Other applications may be in risk screening in clinical and non-clinical settings where the vPGS may be helpful as well.
• Improved clinical design for drug development
• Prediction of who will respond to soci-behavorial treatments
• Applicable in any randomized controlled trial, or any outcome that has been measured in datasets with genetic information
• Costs savings in any randomized controlled trial
The impact of late-career job loss and genetic risk on body mass index: Evidence from variance polygenic scores, Lauren L. Schmitz, Julia Goodwin, Jiacheng Miao, Qiongshi Lu &, Dalton Conley , Scientific Reports, volume 11, Article number: 7647 (2021
Polygenic Scores for Plasticity: A New Tool for Studying Gene-Environment Interplay Rebecca Johnson, Ramina Sotoudeh, and Dalton Conley, bioRxiv preprint doi: https://doi.org/10.1101/2020.08.30.274530
Intellectual Property Status
Patent protection is pending. Industrial collaborators are sought for the further development and commercialization of this technology.
Dalton Conley is the Henry Putnam University Professor in Sociology. He holds PhDs in both sociology and biology. Conley’s scholarship has primarily dealt with the intergenerational social and genetic transmission of socioeconomic and health status from parents to children. He has been the recipient of Guggenheim, Robert Wood Johnson Foundation and Russell Sage Foundation fellowships as well as a CAREER Award and the Alan T. Waterman Award from the National Science Foundation. He is an elected fellow of the American Academy of Arts and Sciences and an elected member of the National Academy of Sciences.
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