Update to Long and Kittles's “Human Genetic Diversity and the Nonexistence of Biological Races” (2003): Fixation on an Index

Abstract

Sewall Wright’s fixation index FST measured among samples of world populations is often 0.15 or less when computed as an average over many alleles or loci. To many, this result indicates that the genetic similarities among human populations far outweigh the differences. For example, a finding like this led Richard Lewontin to claim that human races have no genetic or taxonomic significance (Lewontin 1972). Despite the farreaching proclamations that researchers make from FST, few have questioned the validity of how it is applied or interpreted. Earlier in this decade, Rick Kittles and I took an unusually critical look at FST (Long and Kittles 2003). We analyzed a unique data set composed of short tandem repeat (STR) allele frequencies for eight loci genotyped in both humans and chimpanzees (Deka et al. 1995). These data made it possible to see how FST played out when no one could dispute taxonomic and genetic significance. The answer surprised us. FST was pretty close to the canonical 0.15 shown so many times for human populations. In our analysis, FST was 0.12 for humans, but for humans and chimpanzees together, FST rose only to 0.18. Indeed, we found one locus, D13S122, where the size range of human and chimpanzee alleles hardly overlapped, yet FST equaled 0.15 (Figure 1). We ultimately found that the genetic and statistical model underlying FST does not fit well to human populations. Specifically, human population structure strongly biases the outcome of analyses by violating two assumptions: first, that expected genetic diversity is the same in every population; and second, that divergence between all pairs of populations is equal and independent. These assumptions are explicit and clear in the major statistical papers on estimating FST (Cockerham 1969; Weir and Cockerham 1984; Weir and Hill 2002), but most researchers ignore them. More important, Kittles and I introduced a way to relax these assumptions by using generalized hierarchical models that nest smaller units, such as genes, into larger units, such as individuals, populations, and geographic regions. In our approach, it is possible to restate many hierarchical models as either expansions or reductions of each other, and by comparing a sequence of nested models, we are able to identify those