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.

Expression reflects population structure

“…The identification of population structure in expression data suggests that it should be interesting to extend population genetic methods such as [17] to population transcriptomics. The example of joint analysis of expression and genotype data can be extended to include other datatypes via an extension of CCA to more than two matrices [8,18,19,20], and the coupling of PCA to CCA could also be extended to a hierarchical factor analysis method.…”

Dutch population structure across space, time and GWAS design

“…Haplotype sharing has also revealed genetic affinities between populations, enabling inference of historical admixture events using modern populations as proxies for ancestral admixing sources 12 . Furthermore, geographic information can be integrated to model genetic similarity as a function of spatial distance 13 to infer demographic mobility within or between populations; one approach uses the Wishart distribution to estimate and map a surface of effective migration rates based on deviations from a pure isolation by distance model 14 , allowing migrational cold spots to be inferred which may derive from geographical boundaries such as rivers and mountains. Almost half of the area of the Netherlands is reclaimed from the sea and its contemporary land surface is densely subdivided by human-made waterways and naturally-occurring rivers, including the Rhine (Dutch: Rijn ), Meuse ( Maas ), Waal and IJssel.…”

Performance of Model Based Multifactor Dimensionality Reduction Methods for Epistasis Detection by Controlling Population Structure

“…PCA allows data transformation to a new coordinate system such that the projection of the data along the first new coordinate has the largest variance, the second principal component has the second largest variance, and so on. The relative straightforwardness of PCA, its ease of use, the availability of efficient algorithms and its ability to detect individuals with unusual or differential ancestry [28,[54][55][56][57][58] has made PCA among the most heavily used strategies in the context of genetic association studies in structured populations. Once principal components are obtained, several choices can be made to use these for the purpose of confounding correction in GWAS.…”