Genetic Imputation

Genotype imputation enables researchers and health tech applications to leverage the power of whole genome sequencing at the cost of genotyping. Imputation uses array variants as a scaffold to infer missing variants from a relevant reference panel.

However, the accuracy of imputation is crucial for all downstream applications. We developed Selphi, our genotype imputation tool to give genotype researchers the highest level of imputation accuracy.

As seen in Figure 3, Selfie outperforms leading imputation methods across all allele frequencies, performing exceedingly well for rare variants, where all other models struggle. This is of importance because rare variants are more likely to be of medical significance. Selphi also performs better in all tested super populations: Africans, East and South Asians, and Europeans.

Polygenic Risk Scores

Most common health conditions are highly polygenic. The science of Polygenic Risk Scoring (PRS) allows us to know an individual’s genetic predispositions for a myriad of common health conditions, including the top causes of death and health care expenses, such as cardiovascular disease or diabetes.

This predisposition data enables clinicians to focus on the areas of highest risk and to stratify patient populations.

Our Polygenic Risk Score (PRS) model is currently unnamed, but Figure 4 shows the results from our latest benchmarking against leading PRS methods. Additionally, these results are prior to any corrections or adjusting for covariates such as principal components for population structure, gender, age, etc., which will significantly increase statistical power and final prediction accuracy.

Companion Diagnostics Pipeline

As a proof of concept, we built a genomic companion diagnostic pipeline based on matching people's genetic data to lifestyle, diet, and vitamin and mineral recommendations. We followed and measured users’ biomarker data before and after using the platform. We show the results related to diabetes prevention in Figure 5.