The first step in calculating a common genetic score is determining the number of risk alleles an individual has that are associated with a particular disease or trait. This number is then multiplied by each respective variant’s reported effect size.

This value is then transformed such that when measured in many individuals in the general population, the average score is zero, and the standard deviation in the population equals 1 (‘unit-variance’). This effectively makes every common genetic score returned to Nucleus customers a “Z” or “standardized” score.

The scaling and centering is based on allele frequencies taken from relevant and user-matched populations, primarily the 1000 Genomes Project (2,3). This means that Z-scores, as given, should be interpreted as being relative to users of the same ancestry group. Given that most GWAS are primarily performed with people of white European ancestry, this approach is one way Nucleus is addressing the current ancestry biases in polygenic scores (4).

The exact source of the GWAS association data differs for each report. They include scores from the Polygenic Score Catalog (5), in-house trained scores, and scores based directly on published results from GWAS studies. This is explained in greater detail in Folkersen et al. (2020) (6), but the guiding principle is to always provide the most recent, well-established, and predictive scores for each disease and trait.