Genetic contributions to variation in human stature in prehistoric Europe

Cox, Samantha L.; Ruff, Christopher B.; Maier, Robert; Mathieson, Iain

doi:10.1101/690545

Cited by 10 publications

(23 citation statements)

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“…Despite statistical methods to control for population stratification, it continues to be a confounding factor in the analysis of GWAS results 22,41,42 and could inflate predictive power in European relative to non-European ancestry cohorts. To test this, we used effect sizes at PRS SNPs re-estimated within sibling pairs from the UK Biobank 28 . This approach should remove much of the effect of population.…”

Section: Resultsmentioning

confidence: 99%

“…GWAS results : We obtained UK Biobank summary statistics for height from the Neale Lab GWAS (round 2; https://www.nealelab.is/uk-biobank). We used a set of 13,586,591 autosomal SNPs that passed QC filters of INFO score > 0.8 and MAF > 0.0001 For some analyses we used between-sibling effect sizes estimated at a subset of 1,284,881 SNPs 28 . The number of individuals and SNPs per dataset are shown in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

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Polygenic scores for height in admixed populations

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Mathieson

2020

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11Polygenic risk scores (PRS) use the results of genome-wide association studies (GWAS) to predict quantitative 12 phenotypes or disease risk at an individual level. This provides a potential route to the use of genetic data in 13 personalized medical care. However, a major barrier to the use of PRS is that the majority of GWAS come from 14 cohorts of European ancestry. The predictive power of PRS constructed from these studies is substantially 15 lower in non-European ancestry cohorts, although the reasons for this are unclear. To address this question, 16we investigate the performance of PRS for height in cohorts with admixed African and European ancestry, 17 allowing us to evaluate ancestry-related differences in PRS predictive accuracy while controlling for 18 environment and cohort differences. We first show that that the predictive accuracy of height PRS increases 19 linearly with European ancestry and is largely explained by European ancestry segments of the admixed 20 genomes. We show that differences in allele frequencies, recombination rate, and marginal effect sizes across 21 ancestries all contribute to the decrease in predictive power, but none of these effects explain the decrease on 22 its own. Finally, we demonstrate that prediction for admixed individuals can be improved by using a linear 23 combination of PRS that includes ancestry-specific effect sizes, although this approach is at present limited by 24 the small size of non-European ancestry discovery cohorts. 60we lifted over SNP positions to hg19 using liftOver. For WHI, JHS and HRS, we flipped alleles to the positive 61 strand using the appropriate strand files from https://www.well.ox.ac.uk/~wrayner/strand/. We identified 62 individuals with admixed African ancestry in each cohort using a combination of genetic clustering and self-63 reported ancestry as follows: 64 65 3 UKB: This dataset contains several ancestry groups. We selected 8,813 individuals with African or admixed 66 African and European ancestry based on PCA ( Figure S1) and refer to them as UKB_afr. We further filtered this 67 set to contain individuals with at least 5% of African ancestry, resulting in 8,700 individuals ( Table 1). We 68 randomly selected 9,998 European ancestry individuals from the "White British" subset to use as a comparison 69 sample and refer to them as "UKB_eur". 71WHI: This dataset contains both African American and Hispanic participants. We ran unsupervised 72 ADMIXTURE 27 with k=3 and identified 7,285 individuals with self-reported "African American" ancestry with at 73 most 0.8 of the first ADMIXTURE component (which we interpret as reflecting European ancestry), and at most 74 0.05 of the second (which we interpret as reflecting Native American ancestry; Figure S2). We further filtered 75 this set to contain individuals with at least 5% of African ancestry and height between ±2 standard deviations 76 (sd) from the mean (see Figure S12), resulting in 6,863 individuals ( Table 1). We refer to them as "WHI_afr". 77 78 HRS: This dataset contains multip...

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Polygenic scores for height in admixed populations

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Mathieson

2020

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“…Large whole-genome ancient DNA datasets have, in the past few years, allowed us to track the evolution of variation associated with both simple and complex traits (65,66). The majority of samples are from Western Eurasia and we focus on that region, noting that a parallel process of selection for light skin pigmentation has acted in East Asian populations (9,67).…”

Section: Discussionmentioning

confidence: 99%

The evolution of skin pigmentation associated variation in West Eurasia

Mathieson

2020

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11Skin pigmentation is a classic example of a polygenic trait that has experienced directional 12 selection in humans. Genome-wide association studies have identified well over a hundred 13 pigmentation-associated loci, and genomic scans in present-day and ancient populations have 14 identified selective sweeps for a small number of light pigmentation-associated alleles in 15Europeans. It is unclear whether selection has operated on all the genetic variation associated 16 with skin pigmentation as opposed to just a small number of large-effect variants. Here, we 17 address this question using ancient DNA from 1158 individuals from West Eurasia covering a 18 period of 40,000 years combined with genome-wide association summary statistics from the UK 19Biobank. We find a robust signal of directional selection in ancient West Eurasians on skin 20 pigmentation variants ascertained in the UK Biobank, but find this signal is driven mostly by a 21 limited number of large-effect variants. Consistent with this observation, we find that a 22 polygenic selection test in present-day populations fails to detect selection with the full set of 23 variants; rather, only the top five show strong evidence of selection. Our data allow us to 24 disentangle the effects of admixture and selection. Most notably, a large-effect variant at 25SLC24A5 was introduced to Europe by migrations of Neolithic farming populations but 26 continued to be under selection post-admixture. This study shows that the response to selection 27 for light skin pigmentation in West Eurasia was driven by a relatively small proportion of the 28 variants that are associated with present-day phenotypic variation. 29 30 Significance 31 Some of the genes responsible for the evolution of light skin pigmentation in Europeans show 32 signals of positive selection in present-day populations. Recently, genome-wide association 33 studies have highlighted the highly polygenic nature of skin pigmentation. It is unclear whether 34 selection has operated on all of these genetic variants or just a subset. By studying variation in 35 over a thousand ancient genomes from West Eurasia covering 40,000 years we are able to study 36 both the aggregate behavior of pigmentation-associated variants and the evolutionary history of 37 individual variants. We find that the evolution of light skin pigmentation in Europeans was 38 driven by frequency changes in a relatively small fraction of the genetic variants that are 39 associated with variation in the trait today. 40 SLC45A2, TYR, and APBA2/OCA2). 51 52 Therefore, while the existence of selective sweeps at a handful of skin pigmentation loci is well-53 established, the evidence for polygenic selection-a coordinated shift in allele frequencies across 54 many trait-associated variants (11)-is less clear. Recently, genome-wide association studies 55 (GWAS) of larger samples and more diverse populations (12-15) have emphasized the polygenic 56 architecture of skin pigmentation. This raises the question of whether selection on skin 57 pigmen...

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“…For the biomechanical study, data from various European comparative samples were used, given the lack of comparative data from the study region. Because in Europe, modern humans have shown a general decline in skeletal robusticity since the Palaeolithic to the present interpreted as reflecting a long term trend for decreased habitual activity and mobility (Ruff et al 2015;Ruff 2018aRuff , 2018bCox et al, 2019), European comparative samples from a wide range of time periods were employed to help to put our results into context and offer preliminary interpretations of activity patterns (while recognising the limitations of our sample size).…”

Section: Methodsmentioning

confidence: 99%