Inference of Population Structure using Dense Haplotype Data

Abstract

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/.

Recent advances in conservation and population genomics data analysis

“…Further, haplotype calling allows for the retention of low‐frequency variants, which may be useful for population structure assessment in recently diverged populations. Rare alleles (or haplotypes) reveal recombination events that generated alternative sequences of ancestry and thereby identify fine‐scale structure that would be missed when using independent marker approaches (Lawson, Hellenthal, Myers, & Falush, 2012). …”

The Italian genome reflects the history of Europe and the Mediterranean basin

“…The Italian population substructure was investigated with the model-based Bayesian cluster algorithm implemented in the combined software ChromoPainter-fineSTRUCTURE, 14 using a model that takes into account linkage disequilibrium (LD) between SNPs ('linked' model) with the default parameters. The results were reported as a heatmap (coancestry matrix) of pairwise similarity between subjects, expressed as the number of genomic segments inherited by the same source population.…”

“…13 The aim of this study was to investigate fine-scale Italian population genetic substructure. We used both the large single-nucleotide polymorphism (SNP) data set, which we collected from a wellcharacterized Italian sample, and the most recent haplotype-based population genetic algorithms, such as fineSTRUCTURE, 14 which are able to provide finer resolution of the genetic structure of populations, as was shown for the UK population by Leslie et al 15 Specifically, our aims were to test the feasibility of identifying differences at the microregional level within Italy, to compare and quantify the contributions of populations from Europe and the Mediterranean basin to the genetic composition of the Italians and to explore the historical events that led to the observed high genomic variability within Italy. Several analytical approaches were combined to obtain a complete portrait of 'the Italian genome', and to test the robustness of our results across different methodologies.…”

Distribution of ancestral chromosomal segments in admixed genomes and its implications for inferring population history and admixture mapping

“…With the availability of high-density single-nucleotide polymorphisms data in recent years, it is now feasible to infer population history based on the length of ancestral chromosomal segments (LACS). 1 In addition, a number of methods and software, such as PCAdmix 7 and ChromoPainter, 8 have been developed to identify ancestral chromosomal segments based on the high-density genomic data. 2,[9][10][11] Application of these methods to the empirical data has significantly increased our knowledge of population history and admixture processes.…”