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Not All Gene Scores Are Created Equally

GeneBlueprint patented gene prediction scores are proven to be a minimum of 14 times more predictive than other genetic prediction scores. Why is that important? Because, to make accurate recommendations, genetic prediction scores need to be to highly accurate. You would never build a house on a shaky foundation. Our solid gene scores are the foundation of our personalized health and wellness programs. Four factors contribute to why our genetic prediction scores are industry leading:


Genome-wide association study or GWAS measures and analyzes DNA from across the human genome in an effort to identify genetic risk factors that are common in the population. The ultimate goal of GWAS is to develop genetic prediction scores to identify the biological underpinnings for developing personalized health and wellness strategies.

2. Polygenic Scores

Polygenic scores are the best prediction method for the complex traits like carbohydrate tolerance. Carbohydrate Tolerance is the interplay of thousands genes not just one. Each gene is associated a weight and the weighted average determines the score. A gene score with only one gene loses it’s predictive power. No other direct-to-consumer company utilizes polygenic scores like we do.

3. Machine Learning

If you want to predict, for example, carbohydrate tolerance, you can run it through a machine-learning algorithm with data about past patterns and, if it has successfully “learned”, it will then do better at predicting future patterns.

4. Ethnic Diversity

The effect of genetic variants on human traits can vary depending on your ethnicity. Differences in Caucasian, African and Asian ancestry influence how genetic variants affect our traits. Failure to adjust for the effects of genetic ancestry will result in poorly calibrated genetic prediction scores that are less predictive.