A sharp eyed reader, Stuart Ritchie, who on forensic examination will be revealed as a member of the Deary gang, has drawn my attention to a paper entitled: “Genetic Similarities Within and Between Human Populations” by D. J. Witherspoon, S. Wooding, A. R. Rogers,E. E. Marchani, W. S. Watkins, M. A. Batzer and L. B. Jorde
(2007) Genetics Society of America. DOI: 10.1534/genetics.106.067355 http://www.genetics.org/content/176/1/351.full.pdf+html
This paper shows that the debate about “variation within races is bigger than variation between races” depends largely on the number of loci being analysed, and the assumptions being made about the significance of the revealed differences. They concentrate “on the frequency, v, with which a pair of random individuals from two different populations is genetically more similar than a pair of individuals randomly selected from any single population. We compare v to the error rates of several classification methods, using data sets that vary in number of loci, average allele frequency, populations sampled, and polymorphism ascertainment strategy. We demonstrate that classification methods achieve higher discriminatory power
than v because of their use of aggregate properties of populations. The number of loci analyzed is the most critical variable: with 100 polymorphisms, accurate classification is possible, but v remains sizable, even when using populations as distinct as sub-Saharan Africans and Europeans. Phenotypes controlled by
a dozen or fewer loci can therefore be expected to show substantial overlap between human populations. This provides empirical justification for caution when using population labels in biomedical settings, with broad implications for personalized medicine, pharmacogenetics, and the meaning of race.
I have abstracted the key distribution shown above. It seems to me a balanced presentation of the issue, and the heated debate may revolve round “it depends what you mean about race” as well as “it depends how good your data are when carrying out a discriminant function analysis”.