How Mixture of Genetic Traits Shaped Europeans

Ancestral genomic contributions to complex traits in contemporary Europeans


Summary
The contemporary European genetic makeup formed in the last 8,000 years when local Western Hunter-Gatherers (WHGs) mixed with incoming Anatolian Neolithic farmers and Pontic Steppe pastoralists.1, 2, 3 This encounter combined genetic variants with distinct evolutionary histories and, together with new environmental challenges faced by the post-Neolithic Europeans, unlocked novel adaptations.4 Previous studies inferred phenotypes in these source populations, using either a few single loci5, 6, 7 or polygenic scores based on genome-wide association studies,8, 9, 10 and investigated the strength and timing of natural selection on lactase persistence or height, among others.6,11,12 However, how ancient populations contributed to present-day phenotypic variation is poorly understood. Here, we investigate how the unique tiling of genetic variants inherited from different ancestral components drives the complex traits landscape of contemporary Europeans and quantify selection patterns associated with these components. Using matching individual-level genotype and phenotype data for 27 traits in the Estonian biobank13 and genotype data directly from the ancient source populations, we quantify the contributions from each ancestry to present-day phenotypic variation in each complex trait. We find substantial differences in ancestry for eye and hair color, body mass index, waist/hip circumferences, and their ratio, height, cholesterol levels, caffeine intake, heart rate, and age at menarche. Furthermore, we find evidence for recent positive selection linked to four of these traits and, in addition, sleep patterns and blood pressure. Our results show that these ancient components were differentiated enough to contribute ancestry-specific signatures to the complex trait variability displayed by contemporary Europeans.

Keywords
Human genetics
population genetics
complex traits
ancestry
biobank
Europe
Estonia

Results
We identified 27 complex traits of interest, based on information availability in the Estonian Biobank13 (EstBB) and genome-wide association studies (GWAS) catalog.14 EstBB contains matching genotype and phenotype information for individuals from a relatively homogeneous population, which contains all three ancestry components found in Europe, with the proportion of remnant Hunter-Gatherer ancestry among the highest in Europe and an additional minor (<5%) Siberian component associated with Iron Age movements.15,16 In order to associate specific ancient European ancestry components with predicted phenotypes, we introduce covA, a measure of the relative similarity between any contemporary individual and the ancestries that contributed to its genetic makeup. For each sample in the EstBB, we computed its covA with each of the ancestral source populations, focusing on genomic regions potentially connected to each trait. We then used covA as a predictor to model traits, also in comparison with the same statistic computed for the whole genome. Finally, we test if those regions associated with the genetic contribution from a specific ancestry experienced a post-admixture selective pressure on top of the observed local unbalance in contributing ancestries.

covA measures similarity with ancestral groups
Here we introduce covA, the covariance between allele frequency (p) in a contemporary individual i (i.e., its allele dosage) and a given ancestral population j with respect to the contemporary and ancient average frequencies respectively):

The covA statistic is expected to be high when the allele frequencies of the individual i and the ancestry j are similar in comparison with the differences within the contemporary population and across the ancestries that contributed to its genetic makeup. Furthermore, covA can be computed averaging over the contribution of multiple single nucleotide polymorphisms (SNPs), either across the whole genome or for specific regions of interest.

In order to test the potential of covA to distinguish between genetic contributions from different ancestries, we simulated polygenic traits in a modern population composed of three ancestral groups and verified that when predicting simulated traits, covA estimated coefficient correlates well with their ancestral specificity (Pearson’s correlation coefficient ρ = 0.919-0.937, Figure S1A). See STAR Methods for further discussion of properties and simulation details, including the definition of ancestral specificity.

For each EstBB individual, we computed genome-wide covA between the individual and each of the ancestries among Western Hunter-Gatherers (WHG), Neolithic Farmers from Anatolia (Anatolia_N), Yamnaya Pastoralists from the Pontic Steppes (Yamnaya), and Siberian (Siberia). We defined these ancestry groups based on genetic and chronological proximity to a set of identified focal individuals (see STAR Methods and Table S1 for a list of the ancient genomes assigned to each group). As expected, covAs calculated on the different ancestries are strongly interdependent because they include as term the average ancestral frequency and because of varying grades of similarity among the ancestries for historical demographic reasons (see covA joint distributions in Figures S1C–S1F). In particular, covA tends to be negatively correlated between different ancestral components with the exception of Yamnaya and WHG, reflecting complex demographic relationships between the two, involving WHG-like Eastern Hunter Gatherer ancestry presence in Yamnaya.2,3,17 Furthermore, although the Estonian population is considered relatively genetically uniform, some geographic differences exist with the southeastern inland counties having higher haplotype sharing with Latvians, Lithuanians, and Russians compared with the rest of the country, as recently shown in Pankratov et al., 2020.18 This result is also mirrored in our analyses with median covA for WHG being higher in southeastern inland counties (see Figure S1G). Conversely, as shown by median covA for the Siberian component in Figure S1I, the Siberian ancestry seems to be more abundant in northeast Estonia, consistently with Finnish ancestry shown by Pankratov and colleagues.18 Yamnaya and Anatolia_N covAs are instead more evenly distributed (Figures S1H and S1J).

Phenotype-associated genomic regions show specific ancestry similarity patterns

Next, we used covAs computed on the candidate regions as a predictor to model traits and asked whether they showed significantly different regression coefficients when compared to 50 size-matching random genomic sets; this was found true in 11 out of 27 traits (double-sided Z-test, Benjamini-Hochberg false discovery rate (FDR) = 0.05), see Z-scores in Figure 2. This analysis has the advantage of automatically controlling for virtually all potential confounders that apply to the genome in its entirety—e.g., social, economic, and cultural statuses—thus allowing us to not include any such covariates in the model. In addition, this analysis pinpoints genetic signals that are likely to be functionally connected to the trait. Among others, blood cholesterol levels are shown to be positively correlated with similarity to Yamnaya in cholesterol-associated regions with respect to the rest of the genome, while the opposite is true for WHG.




Since covA exhibits a high correlation across ancestries, we avoided implementing a model with largely multicollinear predictors including covA for all ancestries and instead adopted separate models for each ancestry, complementing them with a regression on covA independent components (ICs) (Figure 2B). We used the loadings from a principal component (PC) analysis on whole genome covAs (Figure 2C) to transform region-specific covAs into ICs. This, though not returning actual PCs in each candidate region, drastically reduces the collinearity (highest variance inflation factor = 1.62 in hair color 50kb candidate regions), while allowing simpler interpretation and, crucially, cross-region comparisons required for Z-scores computation. While covAs (Figure 2A) highlight the overall excess or lack of certain ancestries in relation with a given phenotype but are largely intertwined, ICs (Figure 2B) can be interpreted as independent axes defined by 2 or 3 covAs. We therefore adopted ICs to discriminate significant ancestry-trait associations, as they are independent variables in a comprehensive predictive model. Significant results, interpreted in light of the ICs, are summarized in Table S4 and discussed below. Among others, this analysis confirms the association between cholesterol levels and the Yamnaya-WHG axis previously mentioned.