FADS1 and the timing of human adaptation to agriculture


Open access FADS1 and the timing of human adaptation to agriculture, by Sara Mathieson & Iain Mathieson, bioRxiv (2018).


Variation at the FADS1/FADS2 gene cluster is functionally associated with differences in lipid metabolism and is often hypothesized to reflect adaptation to an agricultural diet. Here, we test the evidence for this relationship using both modern and ancient DNA data. We document pre-out-of-Africa selection for both the derived and ancestral FADS1 alleles and show that almost all the inhabitants of Europe carried the ancestral allele until the derived allele was introduced approximately 8,500 years ago by Early Neolithic farming populations. However, we also show that it was not under strong selection in these populations. Further, we find that this allele, and other proposed agricultural adaptations including variants at LCT/MCM6, SLC22A4 and NAT2, were not strongly selected until the Bronze Age, 2,000-4,000 years ago. Similarly, increased copy number variation at the salivary amylase gene AMY1 is not linked to the development of agriculture although in this case, the putative adaptation precedes the agricultural transition. Our analysis shows that selection at the FADS locus was not tightly linked to the development of agriculture. Further, it suggests that the strongest signals of recent human adaptation may not have been driven by the agricultural transition but by more recent changes in environment or by increased efficiency of selection due to increases in effective population size.

Interesting excerpt for the steppe-related expansion:

Allele frequency trajectories for other putative agricultural adaptation variants. As in Figure 2C, estimated allele frequency trajectories and selection coefficients in different ancient European populations. Significant selection coefficients are labelled.

In the case of FADS1 and all the other examples we investigated, the proposed agricultural adaption was either not temporally linked with agriculture or showed no evidence of selection in agricultural populations. Instead, most of the variants with any evidence of selection were only strongly selected at some point between the Bronze Age and the present day, that is, in a period starting 2000-4000 BP and continuing until the present. This time period is one in which there is relatively limited ancient DNA data, and so we are unable to determine the timing of selection any more accurately. Future research should address the question of why this recent time period saw the most rapid changes in apparently diet associated genes. One plausible hypothesis is that the change in environment at this time was actually more dramatic than the earlier change associated with agriculture. Another is that effective population sizes were so small before this time that selection did not operate efficiently on variants with small selection coefficients. For example, analysis of present-day genomes from the United Kingdom suggests that effective population size increased by a factor of 100-1000 in the past 4500 years (Browning and Browning 2015). Ancient effective population sizes less that 104 would suggest that those populations would not be able to efficiently select for variants with selection coefficients on the order of 10-4 or smaller. Larger ancient DNA datasets from the past 4,000 years will likely resolve this question.

This complexity of the reasons for selection reminded me of the comment by Narasimhan on lactase persistence expanding with steppe populations into Central Asia (based on data of the paper where he is the first author):

I always thought that to argue for natural selection in humans (viz. skin color, lactase persistence, etc.) was possible for archaic groups over tens of thousands of years, but that more recent selections would be very difficult to prove, in so far as historical population expansions involve more ‘artificial’ (i.e. man-made or man-caused) societal changes.

NOTE. I am probably more inclined to think about regional outbreaks (especially of new diseases) as one of the few potential short-term selection mechanisms in historical societies, because of their potential to create sudden bottlenecks of better fitted survivors.

I think recent works like these are showing a mixed situation, where maybe some traits were strongly selected for environmental reasons; but most of the time they were probably – like, say, Y-DNA haplogroup bottlenecks in Europe after the steppe-related expansions – due mostly to chance.

Demographic history and genetic adaptation in the Himalayan region

Open access Demographic history and genetic adaptation in the Himalayan region inferred from genome-wide SNP genotypes of 49 populations, by Arciero et al. Mol. Biol. Evol (2018), accepted manuscript (msy094).

Abstract (emphasis mine):

We genotyped 738 individuals belonging to 49 populations from Nepal, Bhutan, North India or Tibet at over 500,000 SNPs, and analysed the genotypes in the context of available worldwide population data in order to investigate the demographic history of the region and the genetic adaptations to the harsh environment. The Himalayan populations resembled other South and East Asians, but in addition displayed their own specific ancestral component and showed strong population structure and genetic drift. We also found evidence for multiple admixture events involving Himalayan populations and South/East Asians between 200 and 2,000 years ago. In comparisons with available ancient genomes, the Himalayans, like other East and South Asian populations, showed similar genetic affinity to Eurasian hunter-gatherers (a 24,000-year-old Upper Palaeolithic Siberian), and the related Bronze Age Yamnaya. The high-altitude Himalayan populations all shared a specific ancestral component, suggesting that genetic adaptation to life at high altitude originated only once in this region and subsequently spread. Combining four approaches to identifying specific positively-selected loci, we confirmed that the strongest signals of high-altitude adaptation were located near the Endothelial PAS domain-containing protein 1 (EPAS1) and Egl-9 Family Hypoxia Inducible Factor 1 (EGLN1) loci, and discovered eight additional robust signals of high-altitude adaptation, five of which have strong biological functional links to such adaptation. In conclusion, the demographic history of Himalayan populations is complex, with strong local differentiation, reflecting both genetic and cultural factors; these populations also display evidence of multiple genetic adaptations to high-altitude environments.

Population samples analysed in this study. A. Map of South and East Asia, highlighting the four regions examined, and the colour assigned to each. B. Samples from the Tibetan Plateau. C.Samples from Nepal. D. Samples from Bhutan and India. The circle areas are proportional to the sample sizes. The three letter population codes in B-D are defined in supplementary table S1.

Relevant excerpts:

Genetic affinity to ancestral populations

We explored the genetic affinity between the Himalayan populations and five ancient genomes using f3-outgroup statistics. Himalayans show greater affinity to Eurasian hunter-gatherers (MA-1, a 24,000- year-old Upper Palaeolithic Siberian), and the related Bronze Age Yamnaya, than to European farmers (5,500-4,800 years ago; Fig. 5A) or to European hunter-gatherers (La Braña, 7,000 years ago; Fig. 5B), like other South and East Asian populations. We further explored the affinity of Himalayan populations by comparing them with the 45,000-year-old Upper Palaeolithic hunter-gatherer (Ust’-Ishim) and each of MA-1, La Braña, or Yamnaya. Himalayan individuals cluster together with other East Asian populations and show equal distance from Ust’-Ishim and the other ancient genomes, probably because Ust’-Ishim belongs to a much earlier period of time (supplementary fig. S15). We also explored genetic affinity between modern Himalayan populations and five ancient Himalayans (3,150 1,250 years old) from Nepal. The ancient individuals cluster together with modern Himalayan populations in a worldwide PCA (supplementary fig. S16), and the f3-outgroup statistics show modern high-altitude populations have the closest affinity with these ancient Himalayans, suggesting that these ancient individuals could represent a proxy for the first populations residing in the region (supplementary fig. S17 and supplementary table S4). Finally, we explored the genetic affinity of Himalayan samples with the archaic genomes of Denisovans and Neanderthals (Skoglund and Jakobsson 2011), and found that they show a similar sharing pattern with Denisovans and Neanderthals to the other South and East Asian populations. Individuals belonging to four Nepalese, one Cambodian, and three Chinese populations show the highest Denisovan sharing (after populations from Australia and Papua New Guinea) but these values are not significantly greater than other South and East Asian populations (supplementary figs. S18 and S19).

Genetic structure of the Himalayan region populations from analyses using unlinked SNPs. A. PCA of the Himalayan and HGDP-CEPH populations. Each dot represents a sample, coded by region as indicated. The Himalayan region samples lie between the HGDP-CEPH East Asian and South Asian samples on the right-hand side of the plot. B. PCA of the Himalayan populations alone. Each dot represents a sample, coded by country or region as indicated. Most samples lie on an arc between Bhutanese and Nepalese samples; Toto (India) are seen as extreme outlier in the bottom left corner, while Dhimal (Nepal) and Bodo (India) also form outliers.

NOTE. The variance explained in the PCA graphics seems to be too high. This happened recently also with the Damgaard et al. (2018) papers (see here the comment by Iosif Lazaridis).

Similarities and differences between high-altitude Himalayan

The most striking example is provided by the Toto from North India, an isolated tribal group with the lowest genetic diversity of the Himalayan populations examined here, indicated by the smallest long-term Ne (supplementary fig. S5), and a reported census size of 321 in 1951 (Mitra 1951), although their numbers have subsequently increased. Despite this extreme substructure, shared common ancestry among the high-altitude populations (Fig. 2C and Fig. 3) can be detected, and the Nepalese in general are distinguished from the Bhutanese and Tibetans (Fig. 2C) and they also cluster separately (Fig. 3). In a worldwide context, they share an ancestral component with South Asians (supplementary fig. S2). On the other hand, the Tibetans do not show detectable population substructure, probably due to a much more recent split in comparison with the other populations (Fig. 2C and supplementary fig. S6). The genetic similarity between the high-altitude populations, including Tibetans, Sherpa and Bhutanese, is also supported by their clustering together on the phylogenetic tree, the PCA generated from the co-ancestry matrix generated by fineSTRUCTURE (supplementary fig. S10 and S11), the lack of statistical significance for most of the D-statistics tests (Yoruba, Han; high-altitude Himalayan 1, high-altitude Himalayan 2), and the absence of correlation between the increased genetic affinity to lowland East Asians and the spatial location of the Himalayan populations (supplementary figs. S12 and S13). Together, these results suggest the presence of a single ancestral population carrying advantageous variants for high-altitude adaptation that separated from lowland East Asians, and then spread and diverged into different populations across the Himalayan region. (…)

Recent admixture events

Genetic structure of the Himalayan region populations from analyses using unlinked SNPs. C. ADMIXTURE (K values of 2 to 6, as indicated) analysis of the Himalayan samples. Note that most increases in the value of K result in single population being distinguished. Population codes in C are defined in supplementary table S1.

Himalayan populations show signatures of recent admixture events, mainly with South and East Asian populations as well as within the Himalayan region itself. Newar and Lhasa show the oldest signature of admixture, dated to between 2,000 and 1,000 years ago. Majhi and Dhimal display signatures of admixture within the last 1,000 years. Chetri and Bodo show the most recent admixture events, between 500 and 200 years ago (Fig. 4, supplementary tables S3). The comparison between the genetic tree and the linguistic association of each Himalayan population highlights the agreement between genetic and linguistic sub-divisions, in particular in the Bhutanese and Tibetan populations. Nepalese populations show more variability, with genetic sub-clusters of populations belonging to different linguistic affiliations (Fig. 3B). Modern high-altitude Himalayans show genetic affinity with ancient genomes from the same region (supplementary fig. S17), providing additional support for the idea of an ancient high-altitude population that spread across the Himalayan region and subsequently diverged into several of the present-day populations. Furthermore, Himalayan populations show a similar pattern of allele sharing with Denisovans as other South-East Asian populations (supplementary fig. S18 and S19). Overall, geographical isolation, genetic drift, admixture with neighbouring populations and linguistic subdivision played important roles in shaping the genetic variability we see in the Himalayan region today.