Inference of Population Structure using Dense Haplotype Data

By Daniel J. Lawson, Garrett Hellenthal, S. Myers, Daniel Falush. 2012

1,159 citations recorded by the source corpus.

The advent of genome-wide dense variation data provides an opportunity to investigate ancestry in unprecedented detail, but presents new statistical challenges. We propose a novel inference framework that aims to efficiently capture information on population structure provided by patterns of haplotype similarity. Each individual in a sample is considered in turn as a recipient, whose chromosomes are reconstructed using chunks of DNA donated by the other individuals. Results of this “chromosome painting” can be summarized as a “coancestry matrix,” which directly reveals key information about ancestral relationships among individuals. If markers are viewed as independent, we show that this matrix almost completely captures the information used by both standard Principal Components Analysis (PCA) and model-based approaches such as STRUCTURE in a unified manner. Furthermore, when markers are in linkage disequilibrium, the matrix combines information across successive markers to increase the ability to discern fine-scale population structure using PCA. In parallel, we have developed an efficient model-based approach to identify discrete populations using this matrix, which offers advantages over PCA in terms of interpretability and over existing clustering algorithms in terms of speed, number of separable populations, and sensitivity to subtle population structure. We analyse Human Genome Diversity Panel data for 938 individuals and 641,000 markers, and we identify 226 populations reflecting differences on continental, regional, local, and family scales. We present multiple lines of evidence that, while many methods capture similar information among strongly differentiated groups, more subtle population structure in human populations is consistently present at a much finer level than currently available geographic labels and is only captured by the haplotype-based approach. The software used for this article, ChromoPainter and fineSTRUCTURE, is available from http://www.paintmychromosomes.com/.

Subfields: Genetics

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Citations in the corpus

Genome-Wide Inference of Ancestral Recombination Graphs (2013, background): “ l 1;Ds l 1;N l 1), for 1 l < K, have been derived previously as [91], P(C l =bjC l 1 =a;Ds l 1 =t;N l 1)= a å k=b exp k(k 1) 4N l 1 t (2k 1)( 1)k b b!(k b)!(k+b 1) k 1 Õ y=0 (b+y)(a y) a+y : (15) Hidden Markov Model Hidden Markov Model for Full Threading Problem Let us ﬁrst consider the comple”
Genome-wide genetic data on ~500,000 UK Biobank participants (2017, background): “Fine-scale population structure is known to exist within the UK [38] but methods for detecting such subtle structure [39] available at the”
Testing for Associations between Loci and Environmental Gradients Using Latent Factor Mixed Models (2012, background): “Although finer grain population structure could also be evaluated (Lawson et al. 2012), our choice was again motivated by a trade-off between accuracy and run-time.”
Demographic History and Genetic Adaptation in the Himalayan Region Inferred from Genome-Wide SNP Genotypes of 49 Populations (2018, neutral): “0.6 (Lawson et al. 2012) analyses.”
Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data (2012, methodology): “Several additional migration events in the human data have not been previously examined in detail, but are consistent with previous clustering analysis of these populations [7,20,21].”
Genomic analysis of diversity, population structure, virulence, and antimicrobial resistance in Klebsiella pneumoniae, an urgent threat to public health (2015, methodology): “KpI lineages were defined based on patristic distances in the ML tree using RAMI (38), supported by a phylogeny-free approach using fineSTRUCTURE (39).”
Fast and Efficient Estimation of Individual Ancestry Coefficients (2014, methodology): “The approach used by sNMF is based on theoretical connections between likelihood approaches, PCA, and NMF methods (Ding et al. 2008; Engelhardt and Stephens 2010; Lawson et al. 2012; Parry and Wang 2013).”
Ancient human genomes suggest three ancestral populations for present-day Europeans (2013, methodology): “ ≤ n. We set L to include Stuttgart, Loschbour, MA1, Onge, Karitiana, Mbuti and R to include 23 modern European populations who fit the model of SI14 and had admixture proportions within the interval [0,1] for the method with minimal modeling assumptions (SI17). Admixture proportions for Stuttgart i”
Geographic variation and bias in polygenic scores of complex diseases and traits in Finland (2018, methodology): “Here, we reproduced this analysis using CHROMOPAINTER and FineSTRUCTURE (Lawson et al. 2012) with our current sample of 2,376 individuals to estimate both phenotypic and polygenic score differences between these two populations.”