Recent advances in the study of fine-scale population structure in humans

Abstract

Empowered by modern genotyping and large samples, population structure can be accurately described and quantified even when it only explains a fraction of a percent of total genetic variance. This is especially relevant and interesting for humans, where fine-scale population structure can both confound disease-mapping studies and reveal the history of migration and divergence that shaped our species' diversity. Here we review notable recent advances in the detection, use, and understanding of population structure. Our work addresses multiple areas where substantial progress is being made: improved statistics and models for better capturing differentiation, admixture, and the spatial distribution of variation; computational speed-ups that allow methods to scale to modern data; and advances in haplotypic modeling that have wide ranging consequences for the analysis of population structure. We conclude by outlining four important open challenges: The limitations of discrete population models, uncertainty in individual origins, the incorporation of both fine-scale structure and ancient DNA in parametric models, and the development of efficient computational tools, particularly for haplotype-based methods.If one assumes humans across the globe are a single randomly mating population, it would result in only a 5-15% average error when predicting the proportion of observed heterozygotes at a locus. This closeness to an idealized randomly mating population is one vestige of how little evolutionary time has passed since the common origin of all humans in Africa. The departure from random mating predictions due to population differentiation has a classic quantitative measure, called F ST , which appropriately takes on values of 5-15% in global samples of human populations [1][2][3]. If one zooms in within continental regions of the globe, F ST tends to be even lower, regularly taking values below 1%, a threshold which we use here informally to define "fine-scale structure". A triumph of the large datasets available to contemporary population geneticists is that they allow fine-scale structure to be detected and dissected to reveal population relationships (Figure 1).The reason large datasets allow fine-scale structure to be dissected is a statistical phenomenon [4]. Subtle differences in average pairwise similarity become more apparent as the scale of a dataset increases (Figure 2). For principal component analysis (PCA), Patterson et al. [5] have argued that structure reveals itself much like a phase change in physics; namely if the product of the number of genetic markers (m) and individuals (n) is greater than 1/(F ST 2 ) then structure will be evident. The great fortune of human geneticists is that novel technologies have made it affordable to amass datasets large enough to characterize even very subtle population structure.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/073221 d...

Geographic patterns of human allele frequency variation: a variant-centric perspective

“…We term our figures "GeoVar" plots as they help reveal the geographic distribution of sets of variants. GeoVar plots can complement other methods of visualizing population structure, including: plots of pairwise genetic distance, dimensionality-reduction approaches such as PCA, admixture proportion estimates such as STRUCTURE, and explicitly spatial methods that use the sampling locations of individuals (Guillot et al 2009;Novembre and Peter 2016;Bradburd and Ralph 2019). These previously developed methods help reveal population structure, infer genetic ancestry, and measure historical migration patterns.…”

“…As our figures show, this is not the case. Rather, it should be kept in mind that most fine-scale personal ancestry tests work using genotyping arrays and combining evidence from subtle fluctuations in the allele frequencies of many common variants (Novembre and Peter 2016). Finally, another related misconception is that two humans from different regions of the world differ mainly due to alleles that are typical of each region.…”

“…For clarity, we modify the original 1KGP groupings slightly for this project (by including several samples from the Americas in the AMR grouping, see Box 1). While human population structure can be dissected at much finer scales than these groups (e.g., Leslie et al 2015;Novembre and Peter 2016), the regional groupings we use are a practical and instructive starting point-as we will show, several key features of human evolutionary history become apparent, and many misconceptions about human differentiation can be addressed efficiently with this coarse approach (see Discussion). As any such groupings are necessarily arbitrary, we also show results without using regional groupings to calculate frequencies (see section Finer-scale resolution of variant distributions below).…”

“…Many commonly used analysis methods, such as principal components analysis (PCA) or admixture analysis, do a poor job of conveying absolute levels of differentiation (McVean 2009;Lawson, van Dorp, and Falush 2018). Observing the genetic clustering of individuals into groups can give a misleading impression of "deep" differentiation between populations, even when the signal comes from subtle allele frequency deviations at a large number of loci (Patterson, Price, and Reich 2006;McVean 2009;Novembre and Peter 2016). Related misconceptions can arise from observing how direct-to-consumer genetic ancestry tests apportion ancestry to broad continental regions.…”

Anthropological perspectives on genomic data, genetic ancestry, and race

“…A variety of other studies have also noted global patterns of substructure when considering geographically distant groups (Elhaik et al, ; Santos et al, ; Shriver et al, ). In more recent studies that use fine‐scale, high‐resolution genotyping of millions of genetic markers, population substructure has also been detected across local geographic regions (Novembre & Peter, ). In a article by Uren et al (), for example, they sought to learn more about the demographic history of the indigenous populations of southern Africa known as the KhoeSan and the Nama.…”