Genetic ancestry inference using support vector machines, and the active emergence of a unique American population

Haasl, Ryan J.; McCarty, Catherine A.; Payseur, Bret A.

doi:10.1038/ejhg.2012.258

Cited by 15 publications

(17 citation statements)

References 39 publications

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“…We projected Estonian as well as other European and West Asian aDNA sequences [8,30] to principal component axes constructed from the Human Origins data of modern samples [30] (Figure 2A). Projecting ancient samples may introduce some shrinkage bias [33,34], for example the shift of Kudrukü la3 toward 0;0. However, as also evident by our permutation test (see STAR Methods and Figure S3), this does not have a noteworthy effect on our results.…”

Section: Genome-wide Snapshot Of the Estonian Neolithicmentioning

confidence: 99%

Extensive Farming in Estonia Started through a Sex-Biased Migration from the Steppe

et al. 2017

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The transition from hunting and gathering to farming in Europe was brought upon by arrival of new people carrying novel material culture and genetic ancestry. The exact nature and scale of the transition-both material and genetic-varied in different parts of Europe [1-7]. Farming-based economies appear relatively late in Northeast Europe, and the extent to which they involve change in genetic ancestry is not fully understood due to the lack of relevant ancient DNA data. Here we present the results from new low-coverage whole-genome shotgun sequence data from five hunter-gatherers and five first farmers of Estonia whose remains date to 4,500 to 6,300 years before present. We find evidence of significant differences between the two groups in the composition of autosomal as well as mtDNA, X chromosome, and Y chromosome ancestries. We find that Estonian hunter-gatherers of Comb Ceramic culture are closest to Eastern hunter-gatherers, which is in contrast to earlier hunter-gatherers from the Baltics, who are close to Western hunter-gatherers [8, 9]. The Estonian first farmers of Corded Ware culture show high similarity in their autosomes with European hunter-gatherers, Steppe Eneolithic and Bronze Age populations, and European Late Neolithic/Bronze Age populations, while their X chromosomes are in addition equally closely related to European and Anatolian and Levantine early farmers. These findings suggest that the shift to intensive cultivation and animal husbandry in Estonia was triggered by the arrival of new people with predominantly Steppe ancestry but whose ancestors had undergone sex-specific admixture with early farmers with Anatolian ancestry.

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Section: Genome-wide Snapshot Of the Estonian Neolithicmentioning

confidence: 99%

Extensive Farming in Estonia Started through a Sex-Biased Migration from the Steppe

et al. 2017

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“…For each analyzed population ( n = 25), we generated a set of ARBs to limit issues related with PCA projection due to the reported shrinkage phenomenon (Haasl et al. 2013), which can be only partially mitigated in the case of samples with high missingness.…”

Section: Methodsmentioning

confidence: 99%

Ancestry-Specific Analyses Reveal Differential Demographic Histories and Opposite Selective Pressures in Modern South Asian Populations

Yelmen

Mondal

Marnetto

et al. 2019

Molecular Biology and Evolution

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Genetic variation in contemporary South Asian populations follows a northwest to southeast decreasing cline of shared West Eurasian ancestry. A growing body of ancient DNA evidence is being used to build increasingly more realistic models of demographic changes in the last few thousand years. Through high-quality modern genomes, these models can be tested for gene and genome level deviations. Using local ancestry deconvolution and masking, we reconstructed population-specific surrogates of the two main ancestral components for more than 500 samples from 25 South Asian populations and showed our approach to be robust via coalescent simulations. Our f3 and f4 statistics–based estimates reveal that the reconstructed haplotypes are good proxies for the source populations that admixed in the area and point to complex interpopulation relationships within the West Eurasian component, compatible with multiple waves of arrival, as opposed to a simpler one wave scenario. Our approach also provides reliable local haplotypes for future downstream analyses. As one such example, the local ancestry deconvolution in South Asians reveals opposite selective pressures on two pigmentation genes (SLC45A2 and SLC24A5) that are common or fixed in West Eurasians, suggesting post-admixture purifying and positive selection signals, respectively.

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“…This is particularly advantageous with the proposed sparsely-regularized admixture models, which are often more computationally intensive than the non-regularized admixture models. Finally, some methods can even accommodate complex admixtures, such as support vector machines (Haasl et al, 2013;Durand et al, 2014). Comparison of our methods with support vector machines was not evaluated in the present study but can be of interest for future studies.…”

Section: Estimation Of Gbc For Composite Animalsmentioning

confidence: 99%

Estimation of Genomic Breed Composition for Purebred and Crossbred Animals Using Sparsely Regularized Admixture Models

Wang

et al. 2020

Front. Genet.

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A variety of statistical methods, such as admixture models, have been used to estimate genomic breed composition (GBC). These methods, however, tend to produce non-zero components to reference breeds that shared some genomic similarity with a test animal. These non-essential GBC components, in turn, offset the estimated GBC for the breed to which it belongs. As a result, not all purebred animals have 100% GBC of their respective breeds, which statistically indicates an elevated false-negative rate in the identification of purebred animals with 100% GBC as the cutoff. Otherwise, a lower cutoff of estimated GBC will have to be used, which is arbitrary, and the results are less interpretable. In the present study, three admixture models with regularization were proposed, which produced sparse solutions through suppressing the noise in the estimated GBC due to genomic similarities. The regularization or penalty forms included the L1 norm penalty, minimax concave penalty (MCP), and smooth clipped absolute deviation (SCAD). The performances of these regularized admixture models on the estimation of GBC were examined in purebred and composite animals, respectively, and compared to that of the non-regularized admixture model as the baseline model. The results showed that, given optimal values for λ, the three sparsely regularized admixture models had higher power and thus reduced the false-negative rate for the breed identification of purebred animals than the non-regularized admixture model. Of the three regularized admixture models, the two with a non-convex penalty outperformed the one with L1 norm penalty. In the Brangus, a composite cattle breed, estimated GBC were roughly comparable among the four admixture models, but all the four models underestimated the GBC for these composite animals when non-ancestral breeds were included as the reference. In conclusion, the admixture models with sparse regularization gave more parsimonious, consistent and interpretable results of estimated GBC for purebred animals than the nonregularized admixture model. Nevertheless, the utility of regularized admixture models for estimating GBC in crossbred or composite animals needs to be taken with caution.

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Genetic ancestry inference using support vector machines, and the active emergence of a unique American population

Cited by 15 publications

References 39 publications

Extensive Farming in Estonia Started through a Sex-Biased Migration from the Steppe

Extensive Farming in Estonia Started through a Sex-Biased Migration from the Steppe

Ancestry-Specific Analyses Reveal Differential Demographic Histories and Opposite Selective Pressures in Modern South Asian Populations

Estimation of Genomic Breed Composition for Purebred and Crossbred Animals Using Sparsely Regularized Admixture Models

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