Natural selection in humans (OK, 375,000 British people)


The above figure is from Evidence of directional and stabilizing selection in contemporary humans. I’ll be entirely honest with you: I don’t read every UK Biobank paper, but I do read those where Peter Visscher is a co-author. It’s in PNAS, and a draft which is not open access. But it’s a pretty interesting read. Nothing too revolutionary, but confirms some intuitions one might have.

The abstract:

Modern molecular genetic datasets, primarily collected to study the biology of human health and disease, can be used to directly measure the action of natural selection and reveal important features of contemporary human evolution. Here we leverage the UK Biobank data to test for the presence of linear and nonlinear natural selection in a contemporary population of the United Kingdom. We obtain phenotypic and genetic evidence consistent with the action of linear/directional selection. Phenotypic evidence suggests that stabilizing selection, which acts to reduce variance in the population without necessarily modifying the population mean, is widespread and relatively weak in comparison with estimates from other species.

The stabilizing selection part is probably the most interesting part for me. But let’s hold up for a moment, and review some of the major findings. The authors focused on ~375,000 samples which matched their criteria (white British individuals old enough that they are well past their reproductive peak), and the genotyping platforms had 500,000 markers. The dependent variable they’re looking at is reproductive fitness. In this case specifically, “rRLS”, or relative reproductive lifetime success.

With these huge data sets and the large number of measured phenotypes they first used the classical Lande and Arnold method to detect selection gradients, which leveraged regression to measure directional and stabilizing dynamics. Basically, how does change in the phenotype impact reproductive fitness? So, it is notable that shorter women have higher reproductive fitness than taller women (shorter than the median). This seems like a robust result. We’ve seen it before on much smaller sample sizes.

The results using phenotypic correlations for direction (β) and stabilizing (γ) selection are shown below separated by sex. The abbreviations are the same as above.


There are many cases where directional selection seems to operate in females, but not in males. But they note that that is often due to near zero non-significant results in males, not because there were opposing directions in selection. Height was the exception, with regression coefficients in opposite directions. For stabilizing selection there was no antagonistic trait.

A major finding was that compared to other organisms stabilizing selection was very weak in humans. There’s just not that that much pressure against extreme phenotypes. This isn’t entirely surprising. First, you have the issue of the weirdness of a lot of studies in animal models, with inbred lines, or wild populations selected for their salience. Second, prior theory suggests that a trait with lots of heritable quantitative variation, like height, shouldn’t be subject to that much selection. If it had, the genetic variation which was the raw material of the trait’s distribution wouldn’t be there.

Using more complex regression methods that take into account confounds, they pruned the list of significant hits. But, it is important to note that even at ~375,000, this sample size might be underpowered to detect really subtle dynamics. Additionally, the beauty of this study is that it added modern genomic analysis to the mix. Detecting selection through phenotypic analysis goes back decades, but interrogating the genetic basis of complex traits and their evolutionary dynamics is new.

To a first approximation, the results were broadly consonant across the two methods. But, there are interesting details where they differ. There is selection on height in females, but not in males. This implies that though empirically you see taller males with higher rLSR, the genetic variance that is affecting height isn’t correlated with rLSR, so selection isn’t occurring in this sex.

~375,000 may seem like a lot, but from talking to people who work in polygenic selection there is still statistical power to be gained by going into the millions (perhaps tens of millions?). These sorts of results are very preliminary but show the power of synthesizing classical quantitative genetic models and ways of thinking with modern genomics. And, it does have me wondering about how these methods will align with the sort of stuff I wrote about last year which detects recent selection on time depths of a few thousand years. The SDS method, for example, seems to be detecting selection for increasing height the world over…which I wonder is some artifact, because there’s a robust pattern of shorter women having higher fertility in studies going back decades.

Selection for pigmentation in Khoisan?

In the recent paper, Reconstructing Prehistoric African Population Structure, there was a section natural selection. Since my post on the paper was already very long I didn’t address this dynamic.

But now I want to highlight this section:

The functional category that displays the most extreme allele frequency differentiation between present day San and ancient southern Africans is ‘‘response to radiation’’ (Z = 3.3 compared to the genome-wide average). To control for the possibility that genes in this category show an inflated allele frequency differentiation in general, we computed the same statistic for the Mbuti central African rainforest hunter-gatherer group but found no evidence for selection affecting the response to radiation category.

We speculate that the signal for selection in the response to radiation category in the San could be due to exposure to sunlight associated with the life of the Khomani and Juj’hoan North people in the Kalahari Basin, which has become a refuge for hunter-gatherer populations in the last millenia due to encroachment by pastoralist and agriculturalist groups.

I’m a bit puzzled here, because the implication seems to be that the San populations are darker than they were in the past. And yet earlier this summer I saw a talk which strongly suggested that there was a selection in modern Bushman populations for the derived variant of SLC24A5, presumably introduced through admixture from East African populations with Eurasian admixture.

In comparison to their neighbors the San are quite light-skinned, so it’s a reasonable supposition that they have been subject to natural selection recently. The Hadza, in contrast, seem to have the same complexion as their Bantu neighbors.

So what’s point of demographic models which leave you scratching your head

There’s a new paper on Tibetan adaptation to high altitudes, Evolutionary history of Tibetans inferred from whole-genome sequencing. The focus of the paper is on the fact that more genes than have previously been analyzed seem to be the targets of natural selection. And I buy most of their analyses (not sure about the estimate of Denisovan ancestry being 0.4%…these sorts of things can be tricky).

But they fancy it up with a ∂a∂i model of population history, as well as using MSMC to account for gene flow. I don’t understand why they didn’t use something simpler like TreeMix, which can also handle more complex models. I guess because they wanted to focus on only a few populations?

Years ago I asked the developer of MSMC, Stephan Schiffels, if assuming an admixed population is not admixed might cause weird inferences. Why yes, it would. For example, admixed populations might show higher effective population since they’re pooling the histories of two separate populations. As for ∂a∂i, the model above leaves me literally scratching my head.

…predicted that the initial divergence between Han and Tibetan was much earlier, at 54kya (bootstrap 95% C.I 44 kya to 58 kya). However, for the first 45ky, the two populations maintained substantial gene flow (6.8×10-4 and 9.0×10-4 per generation per chromosome). After 9.4 kya (bootstrap 95% C.I 8.6 kya to 11.2 kya), the gene flow rate dramatically dropped (1.3×10-11 and 4×10-7 per generation per chromosome), which is consistent with the estimate from MSMC.

Mystifying. The separation between Chinese and Tibetans is pretty much immediately after modern humans arrive in East Asia. Then there’s a lot of reciprocal gene flow…which ends during the Holocene.

We’re being told here that there are two populations which persisted in some form for ~45,000 years. Is this believable? That these two populations maintained some sort of continuity, and, remained in close proximity to engage in gene flow. And then ~10,000 years ago the ancestors of the Tibetans separated from the ancestors of the modern Han Chinese.

The latter scenario I can imagine. It’s this ~45,000 year dance I’m confused by. If there is substantial gene flow between the two groups why did they keep enough distinctive drift to be separate populations?

With what we know about ancient DNA from Europe if we posited such a model for that continent we’d be way off. There’s been too many population turnovers. Is East Asia different? I’m moderately skeptical of that. I think perhaps researchers should be very aware of the limitations of ∂a∂i when it comes to fine-grained population genomic analyses.

Note: This is a cool paper, and this small section is not entirely relevant. Which is why I’m confused about it since it seems the weakest part of the analysis in terms of originality, and the least believable.