Phenotype Doesnt Reveal Someones Race and Background

It is important to note that ethnicity has no objective measure or score, and there are no universally agreed ethnic categories. Ethnicity entails much more than the phenotype observable by the clinician, and should not be assumed based on any factors such as appearance, name, skin colour or ...

We have focused here on the practices and processes of GWAS in great detail to show how some geneticists are constructing new ways to measure ‘genetic ancestry’ differences, and we analyze how this emerging concept of ancestry does and does not relate to notions of race. Some medical geneticists conducting GWAS have attempted to search for genetic markers for disease by developing a technology called EIGENSTRAT to help them to avoid the use of race categories. They affirmed that their genetic analyses ‘were not about race’—whether defined by ideas about phenotypic characteristics like skin color or self-described categories.

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Principal component analysis (PCA) was performed on the All of Us and UKB pharmacogenomic variants using the FastPCA program implemented in PLINK v2.0, run with the “approx” modifier for the top 25 principal components (PCs) [40,41]. Pharmacogenomic PCA data were used to predict participant race and ethnicity using machine learning classifiers, with race/ethnicity as class labels and the top 25 PC-values as feature vectors. K-nearest neighbors (k-NN), random forest (RF), and support vector machine (SVM) classifier methods were implemented using the scikit-learn machine learning library v1.1.2 for Python [42]. All three methods were implemented with randomized searches to determine optimal prediction hyperparameters (training) and 5-fold cross-validation (CV) to measure prediction accuracy (testing).

Craniometric traits show a level of among-region differentiation comparable to genetic markers, with high levels of variation within populations as well as a correlation between phenotypic and geographic distance. Craniometric variation is geographically structured, allowing high levels of classification accuracy when comparing crania from different parts of the world. Nonetheless, the boundaries in global variation are not abrupt and do not fit a strict view of the race concept; the number of races and the cutoffs used to define them are arbitrary.

Dispersal of archaic and modern humans traced over 300,000 years and across continents provides an increasingly clear story of human genotypic and phenotypic variation. Statistical modeling of linkage disequilibrium, haplotypes, mitochondrial DNA, and Y-DNA from wide-ranging geographic ancestries has failed to yield any consistent association with race.