A ‘recent’ (2017) article by RaceRealist/notpoliticallycorrect.me argues that the reason that East Africans are predominant in long-distance running competitions is due to physiological and genetic differences between populations. I’m a bit skeptical, so I’m going to examine some of the evidence.
Another important factor is their low BMI. Kenyans have the lowest BMIs in the world at 21.5,
A few notes. The first is that Kenyans don’t in fact have the lowest, but according to chart posted, have the 12th lowest BMI in the world. The second is that the figure is actually 21.6, not 21.5.
Moreover, if we are to posit genetic causes, it would be more likely that Africans have higher genotypic values for BMI (Tang et. al 2006).
But BMI (and other anthropometric variables) don’t seem to have an impact on extreme distance running (Knechtle et. al 2007), endurance running (Takeshima & Tanaka 1995), or sprint running (Morin et. al 2012). Studies have reported differing results on the relationship: Knechtle et. al (2012a) reported a relationship of body fat to race times in marathon and ultramarathon runners, but found BMI was positively related to times in marathoners and negatively related to times in ultramarathoners. Similarly, Knechtle et. al (2012b) did not find a significant relationship between BMI and marathon times. Rust et. al (2012b), (2012a) both found varying correlations between metrics of body mass and marathon times. It has been noted that the relationship between BMI and running times may also be the result of reverse causation: where more training leads to better times, but that training also reduces BMI (Jakcic et. al 2003; Mustelin et. al 2009; Vickers & Vertosick 2016). Hoffman (2008a) found that lower BMI was associated with faster running times in his sample of ultramarathoners, but that the BMIs of the finishers varied significantly.
Additionally, some studies don’t report differences in the body mass of actual African and Caucasian elite athletes, as compared to the general population [who may have differences] (Weston et. al 2000).
Whatever the relationship between BMI, body fat, and sprinting performance, it is likely to be complicated in a way that defies easy attribution of group differences.
Genetics, though, is the most likely explanation for African distance-running dominance (Vancini et al, 2014; see Scott and Pitsiladis, 2007 for alternative view that as of yet there are no genetic evidence for African running superiority).
In addition to Scott and Pitsiladis (2007), also review Harm et. al (2013), Onywera et. al (2006), Saltin et. al (1995a), Saltin et. al (1995b), Scott et. al (2005), Scott et. al (2009), Scott et. al (2010), and Wilber & Pitsiladis (2012).
Apparently, there is no indication that Kenyans possess a pulmonary system that confers a physiologic advantage over non-Kenyans (Larsen and Sheel, 2015
Moreover, there is no evidence that East Africans have capacity for VO₂max (Weston et. al 2000).
Similar results were seen in Switzerland, with male Africans being faster and younger than non-Africans (Aschmann et al, 2013).
There exists contrary evidence on the Switzerland question (Harm et. al 2013).
Jamaican sprinting dominance has been chalked up to numerous factors, most recently, symmetry of the knees and ankles (Trivers, Palestis, and Manning, 2013; Trivers et al, 2014). Trivers et al (2014) write in the Discussion:
While this research does seem to be interesting, sports scientist Ross Tucker has written about how the Trivers research is less than it seems. A few comments on the actual Trivers et. al (2014) paper. First we should note that their F-test only found significant differences between the athletes and the controls for the knees and ankles. I don’t think this finding means much, because there are so many differences between elite athletes and regular village-people, especially given body symmetry is known to covary with many other sport-relevant traits (Fink et. al 2014). Fluctuating asymmetry research itself is known to be very unreliable (Graham & Özener 2016), and it seems that that other research has not found replicable correlations (Oxford 2013). When they did the within-athlete comparison, they found a significant relationship between only knee asymmetry and performance, but this was only marginally significant (P=0.04). Moreover, the relationship lost significance in the full regression model when age was added as a covariate (Table 5), although collating the three types of symmetry together (not best practice & I suspect this was done as a form of HARKing) reattained significance (though again, very marginally; P=0.046). If we’re to posit this as one of the causes, I would be very cautious about the data and demand replication in more diverse samples.
It’s interesting to note that the mtDNA haplotype predicts success in African American sprinters, but not Jamaicans. In regards to mtDNA haplotypes, Jamaican sprinters had statistically similar mtDNA haplotypes, which suggests that the elite sprinters arose from the same source population which indicates that there is no population stratification or isolation on sprint performance. African American sprinters and non-sprinters, on the other hand, had statistically significant differences in mtDNA, which implies that maternal ancestry plays a part in sprinting performance.
One note about haplotypes is that they are notoriously unreplicable, especially on complex phenotypes like sport performance (Colhoun et. al 2003; Ikari et. al 2006; Lewis et. al 2010; Rasmussen-Torvik et. al 2005).
As everyone knows, you cannot teach speed (Lombardo and Deaner, 2014).
While the deliberate practice model is not empirically supported (Hambrick et. al 2016), there is an important role for practice in elite athletics (Issurin 2017), and the dichotomy between ‘born’ and ‘made’ likely isn’t useful (Hambrick et. al 2017).
In Afro-Caribbean adolescents, body height and stride number to body height ratio were the main determinants of sprint performance (Copaver, Hertogh, and Hue, 2012).
Body height being a predictor of sprint performance is nothing new; taller people have longer limbs; longer limbs cover more distance per step. Indeed, sprinters are taller than the American population, there is more variability in men than in women, sprinters have lower body mass and the height range excludes people who are really tall or really short (Uth, 2005).
But body mass is not a predictor of sprinting performance (Morin et. al 2012).
More broadly on Jamaicans, Deason (2017) suggests there are no differences in African ancestry between Jamaican athletes and controls, highly suggesting that the overrepresentation of Jamaican athletes is not due to genetics.
I’m going to refrain commenting on the ACTN3 issue, as that’s a very complicated and contentious issue within sports genetics that I don’t have the time or expertise to cover.
What does it mean for a group difference in sport performance to be genetic, or for it to be physiological? I have thought about this question for a while, and I’ve struggled to come up with an answer. One might think that you should be able to attribute the higher success of certain groups to higher frequencies of certain bodily features in those populations, but it is much more complicated than that. Given that athletes are not representative of their population, but are highly selected on numerous variables (both physiological and psychological, as well as sociological), it is not easy to infer the expected group difference in the athletic population from the group differences in a certain athletic-relevant trait in the general population. This depends on a number of things, including the distributions of the traits in both populations, the manner in which athletes are selected in each population, and the relative covariances of the traits with other athletic-relevant traits in both populations. At this point, I’m not sure there is a way to demonstrate that a group difference in performance is ‘genetic’ or ‘biological’.