Variance Polygenic Score for Clinical Trial Participant Selection
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Solution Overview
Problem
Current polygenic scores primarily focus on the genetic influence on the mean level of outcomes, failing to account for how environmental factors impact variability in traits, which is crucial for understanding genetic moderation and treatment sensitivity.
Innovation Solution
The development of variance polygenic scores (vPGS) that calculate genetic contributions to variability in outcomes, distinct from mean levels, by using regression analyses and quantitative trait loci (QTL) methods to generate scores that reflect dispersion and spread within familial relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polygenic scores focus on predicting mean levels of outcomes, then prediction accuracy for average trait levels is improved, but the ability to predict environmental sensitivity and variability is lost
Solution Approach 1:
The patent segments the polygenic score into two distinct components: a mean polygenic score (mPGS) for predicting average outcome levels and a variance polygenic score (vPGS) for predicting environmental sensitivity and variability. This segmentation allows each component to optimize for its specific predictive function without compromising the other.
Solution Approach 2:
The patent adds a new dimension to polygenic scoring by introducing variance prediction capability alongside the traditional mean prediction. The vPGS operates in a separate predictive dimension that captures genetic influences on variability and environmental sensitivity, transforming the single-dimensional mean prediction into a two-dimensional framework.
2Reliability
If clinical trials include all participants, then statistical power is maintained through sufficient sample size, but cost and time are increased
Solution Approach 1:
The patent applies preliminary action by using the variance polygenic score to pre-screen and select participants who are most likely to benefit from the treatment based on their genetic propensity for variability. This pre-selection before the trial begins ensures that the final sample consists of participants with high treatment sensitivity, maximizing statistical power while reducing the total number of participants needed.
Solution Approach 2:
The patent changes the selection parameter from random sampling to stratified sampling based on vPGS distribution. By selecting participants from extreme groups (high and low variance scores) rather than uniformly distributing them, the trial achieves higher statistical power with fewer participants, as the selected group has greater variability in response potential.
3Device complexity
If environmental factors are not considered in polygenic scoring, then the scoring model remains simple, but the ability to understand gene-environment interactions is compromised
Solution Approach 1:
The patent extracts the environmental sensitivity component from the traditional polygenic score by creating a separate vPGS that specifically captures genetic influences on variability. This extraction allows the model to maintain simplicity for mean prediction while separately incorporating environmental interaction information through the variance score, which can be used to predict how individuals respond to environmental factors.
Data Source
AI summary
Disclosed is a method for calculating genetic score based on genotypic information that predicts plasticity in a phenotype. More particularly, disclosed is an algorithm to calculate a particular type of genetic score based on basic genotypic information that is provided by commercially available technologies ranging from “SNP-chips” to whole genome sequencing. Polygenic scores— attempts to summarize the genetic propensity for or risk of a given phenotype (i.e., disease or trait)—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 Variation Polygenic Score (“vPGS”) disclosed herein 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. The disclosed vPGS does not predict the mean level but rather the dispersion around that mean. It likely also predicts individual changes in a phenotype over the lifecourse (e.g., whether an individual tends to fluctuate greatly in weight). The disclosed approach 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 or intervention on a particular trait, disease or other phenotype.