From The Broad Institute:

Study highlights need to increase diversity within genetic data sets NEWS / 03.29.19

By Namrata Sengupta Diversifying population-level genetic data beyond Europeans will expand the power of polygenic scores.

“Population-level genetic data” is here being used to mean what most people would think of as race-level genetic data: Europeans vs. East Asians vs. sub-Saharans.

Polygenic scores can predict a person’s risk for conditions like coronary artery disease, breast cancer, and type 2 diabetes (T2D) with great accuracy, even in patients who lack common warning signs. This new genome analysis tool holds promise for physicians, who may be able to intervene earlier to help prevent common disease for at-risk individuals. According to a new study, however, polygenic scores developed by studying Europeans do a better job at predicting disease risk for people of European ancestry than for those of other ancestries.

In other words, genetic differences among the major races are big enough that you can’t trust that you can safely generalize for blacks and East Asians from a sample of Europeans. Sometimes you can generalize from whites to nonwhites, but often you can’t generalize.

Researchers from the Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH) led a team that used large-scale genetic data from UK Biobank to develop prediction scores for height, body mass index, T2D, and certain other traits and diseases. The researchers found that polygenic scores, calculated based on data from UK Biobank, had a 4.5 times higher prediction accuracy for people of European ancestry than those of African ancestry, and two times higher accuracy than those of East Asian ancestry. “From a clinical context, this means that current polygenic scores are significantly better in predicting the risk of common diseases for people of European ancestry than those of African ancestry,” said Alicia Martin, the lead author of the study and an affiliate of the Program in Medical and Population Genetics (MPG) and the Stanley Center for Psychiatric Research at the Broad Institute. … However, Martin and her team also developed separate polygenic scores using data from the BioBank Japan Project, an East Asian data set, and found that scores calculated from this data set were almost 50 percent more accurate in predicting disease risk for East Asians than scores based on UK Biobank data. “This further confirms that risk predictors are more precise if they are drawn from genetic data derived from a similar ancestry,” Martin said. “It is crucial that researchers should recruit more minority populations in future genetic studies and also make data from such studies accessible and open. Failure to do this will lead to further inequities in our healthcare system.”

So, racial genetic differences, at least among traditionally recognized major human races, such as Europeans, East Asians, and sub-Saharan Africans, are large enough that genetic modeling ought to be performed on each race separately to be satisfactorily accurate enough.