Rehabilitating a biological notion of race? A response to Sesardic
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- Taylor, P. Biol Philos (2011) 26: 469. doi:10.1007/s10539-011-9249-3
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The point Sesardic (Biol Philos 25: 143–162, 2010) makes about the possibility of distinguishing groups for which there is a lot of within-group variation is not sufficient to rehabilitate a biological concept of race. In this note, I sketch a number of issues that quickly arise once we delve more deeply into the relevant scientific knowledge, concepts, methods, and questions for inquiry.
KeywordsAncestryBiomedicineCluster analysisGenetic variationIQ scoresPhylogeneticsRace
Sesardic is a critic of positions accepted by liberal-left philosophers about heritability, genes, IQ test scores, and racial differences. The theme that recurs in his criticisms is that philosphers need to delve more deeply into the science as they consider their arguments (2000, 2005). The latest contribution in this spirit, Sesardic (2010), argues that “the biological notion of race [is not at all inconsistent] with what the best contemporary science tells us about human genetic variation.” In particular, the fact that variation at a range of genetic markers within a group is larger than variation between (the average of) the groups, does not mean that the groups cannot be distinguished using that genetic information.
Biology is more than genetic variation. For example, experience of racial discrimination by African-American women has been associated with higher risk of pre-term delivery of their babies even after controlling statistically for other factors that increase that risk (Mustillo et al. 2004). Race can be linked with biology even if races are not distinguishable on the basis of genetic differences. We can, however, put this point aside if we construe Sesardic to be focusing on a genetic concept of race, that is, one not dependent on social meanings given to surface appearances, many of which, such as skin color, are biological.
Suppose we imagine an original human gene pool that dispersed at some point of time from its origins in Africa around the world and was not subject to subsequent breeding among widely dispersed parts of the pool. Cluster analysis techniques could be used on genetic data to divide humans into, say, N groups. Such clustering techniques are sensitive to assumptions that determine whether groups are of roughly equal size or are a mix of a few large groups and many small ones. If we delineated groups that had similar amounts of within-group genetic variation, most of the N groups would be in Africa (Jorde and Wooding 2004). In other words, the traditional subdivisions of human races would have to be reformulated. However, experience using cluster analysis in large agricultural data sets (Cooper and Hammer 1996) suggests that many individuals cannot be consistently assigned to one group versus another; the grouping changes according to what traits (in our case, variants at genetic loci or SNPs) and how much each group is represented in the data set.
Perhaps molecular phylogenetic analysis would improve on cluster analysis by ensuring that individuals can be placed on a tree where closeness of two individuals is given by how many branch-points apart they are (Tishkoff et al. 2009). The researcher still has to decide whether to delineate groups of roughly equal size or a mix of a few large groups and many small ones. If we delineated groups that had similar amounts of within-group genetic variation, the same points as in #2 apply.
Suppose we now add migration subsequent to the initial dispersal, so that more than one group lives in some locality, but we assume that no interbreeding among the groups has taken place. Picking up on the last point in #2, if individuals from non-African groups outweighed those from African groups, as is the case in the United States, then how well could we recover from the data groups delineated in #2 (or 3)? That would be an empirical question, but the experience from agriculture warns us not to be optimistic about success.
Of course, in human history there has been considerable interbreeding along with migration subsequent to the initial dispersal from the place of human origin in Africa, including but not confined to the recent centuries of cross-Atlantic slavery and master–slave relations. How well could we recover from current individuals the one or more groups (as delineated in #2 or 3) that make up the individuals’ ancestries? Again, this is an empirical question. In a world of limited funds, biomedical researchers might well judge that useful results would be more likely to emerge from other avenues of inquiry, such as those indicated by biomedical correlates of socially defined race (i.e., not the groups that would emerge from the cluster or phylogenetic analyses in #2 or 3).
Perhaps, we could ask less than we have in #5. Rather than full recovery of original ancestries, we might seek simply want to predict whether an individual patient has some major biomedically relevant genes that differed, on average, among the original groups. These predictions would necessarily be probabilistic, and, if the clinical implications of the different genes diverged significantly, pressure would arise to test directly for their presence or absence. In any case, probabilistic or direct knowledge of the individual’s genetic profile would be limited in value given the recently-emerged consensus that most medically significant traits are associated with many genes of quite small effect (McCarthy et al. 2008). Moreover, the groups delineated in #2 or 3 would not match the traditional subdivisions of human races or those current in the US—there would be several different groups of African origin—so medical practitioners would need to disregard customary social assignments to racial groups, or even a patient’s self-assignment.
Suppose we put aside the difficulties raised in #1–6 and imagine a world in which we were able to use genetic information to assign humans to the original post-dispersal groups as reliably as in the statistics class we were able to assign individuals to male and female groups. What could we do with the knowledge that there is a difference between the average genetic profiles for groups A and B when there is large within-group variation for most genetic loci (at least, for those that vary within the human species)? Let me accentuate this question using the IQ test score case Sesardic (2000, 2005) has paid considerable attention to. Suppose we knew (which we do not) that only a certain small set of genes influenced IQ test scores. What could we do with the knowledge that there is a large difference between the average IQ test score for two groups and that this difference is smaller than the within-group variation? (To visualize this situation, imagine one of the axes in the Figure is IQ test score.) My personal answer is “very little”; I would not use this (hypothetical) ability to assign humans to original post-dispersal groups based on genetic profiles as grounds for using an individual’s membership in such a group to make educational or employment decisions for that individual. Sesardic’s answer to the question is not given; we do not know what he thinks would follow if a genetic view of race were to be rehabilitated along the lines he discusses.
There are clearly many issues to be delved deeply into before the relevant science about human genetic variation would support a genetic notion of human ancestral groupings. A fortiori, a genetic notion of socially and historically varying racial categories must lie, contra Sesardic (2010), well outside the scope of “what the best contemporary science tells us about human genetic variation.”