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There is great scientific and popular interest in understanding the genetic history of populations in the Americas. We wish to understand when different regions of the continent were inhabited, where settlers came from, and how current inhabitants relate genetically to earlier populations. Recent studies unraveled parts of the genetic history of the continent using genotyping arrays and uniparental markers. The 1000 Genomes Project provides a unique opportunity for improving our understanding of population genetic history by providing over a hundred sequenced low coverage genomes and exomes from Colombian (CLM), Mexican-American (MXL), and Puerto Rican (PUR) populations. Here, we explore the genomic contributions of African, European, and especially Native American ancestry to these populations. Estimated Native American ancestry is 48% in MXL, 25% in CLM, and 13% in PUR. Native American ancestry in PUR is most closely related to populations surrounding the Orinoco River basin, confirming the Southern America ancestry of the Taíno people of the Caribbean. We present new methods to estimate the allele frequencies in the Native American fraction of the populations, and model their distribution using a demographic model for three ancestral Native American populations. These ancestral populations likely split in close succession: the most likely scenario, based on a peopling of the Americas 16 thousand years ago (kya), supports that the MXL Ancestors split 12.2kya, with a subsequent split of the ancestors to CLM and PUR 11.7kya. The model also features effective populations of 62,000 in Mexico, 8,700 in Colombia, and 1,900 in Puerto Rico. Modeling Identity-by-descent (IBD) and ancestry tract length, we show that post-contact populations also differ markedly in their effective sizes and migration patterns, with Puerto Rico showing the smallest effective size and the earlier migration from Europe. Finally, we compare IBD and ancestry assignments to find evidence for relatedness among European founders to the three populations., Facultad de Ciencias Naturales y Museo, Instituto Multidisciplinario de Biología Celular

Accurate identification of haplotypes in sequenced human genomes can provide invaluable information about population demography and fine-scale correlations along the genome, thus empowering both population genomic and medical association studies. Yet phasing unrelated individuals remains a challenging problem. Incorporating available data from high throughput sequencing into traditional statistical phasing approaches is a promising avenue to alleviate these issues. We present a novel statistical method that expands on an existing graphical haplotype reconstruction method (shapeIT) to incorporate phasing information from paired-end read data. The algorithm harnesses the haplotype graph information estimated by shapeIT from genotypes across the population and refines haplotype likelihoods for a given individual to be compatible with the sequencing data. Applying the method to HapMap individuals genotyped on the Affymetrix Axiom chip at 7,745,081 SNPs and on a trio sequenced by Complete Genomics, we found that the inclusion of paired end read data significantly improved phasing, with reductions in switch error on the order of 4-15% against shapeIT across all panels. As expected, the improvements were found to be most significant at sites harboring rare variants; furthermore, we found that longer read sizes and higher throughput translated to greater decreases in switching error, as did higher variance in the size of the insert separating the two reads--suggesting that multi-platform next generation sequencing may be exploited to yield particularly accurate haplotypes. Overall, the phasing improvements afforded by this new method highlight the power of integrating sequencing read information and population genotype data for reconstructing haplotypes in unrelated individuals., Comment: 18 pages, 4 figures, 3 tables