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      Ancient DNA HLA typing reveals significant shifts in frequency in Europe since the Neolithic

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          Abstract

          Computational HLA typing has surged as a cost-effective strategy to uncover questions regarding the evolution of the HLA system, enabling immunogenic characterization from ancient DNA (aDNA) data. Nevertheless, it remains to be seen whether these methods are suitable for analyzing aDNA generated without target-enrichment. To investigate this, we evaluated the performance of five HLA typing tools using present-day data with simulated profiles typical of aDNA, as well as from high-coverage aDNA genomes downsampled at different read depths. We found that characterization of Class I genes at the first field resolution is feasible at read depths as low as 2x, where it retains an accuracy of ≈ 80%. Next, we used this insight to characterize HLA evolution in Europe from 154 ancient genomes by detecting allele frequency changes throughout distinct prehistoric European populations. We observed important shifts in alleles associated with infectious and autoimmune diseases, most of which are found by contrasting the HLA landscape of Neolithic Farmers to that of present-day. Interestingly, several of these observations are in line with findings that have been previously reported by target-enrichment-based studies. Our results highlight the feasibility of applying HLA typing on shotgun aDNA data to examine the evolution of this loci during important transitions.

          Supplementary Information

          The online version contains supplementary material available at 10.1038/s41598-024-82449-w.

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          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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            Fast and accurate long-read alignment with Burrows–Wheeler transform

            Motivation: Many programs for aligning short sequencing reads to a reference genome have been developed in the last 2 years. Most of them are very efficient for short reads but inefficient or not applicable for reads >200 bp because the algorithms are heavily and specifically tuned for short queries with low sequencing error rate. However, some sequencing platforms already produce longer reads and others are expected to become available soon. For longer reads, hashing-based software such as BLAT and SSAHA2 remain the only choices. Nonetheless, these methods are substantially slower than short-read aligners in terms of aligned bases per unit time. Results: We designed and implemented a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), to align long sequences up to 1 Mb against a large sequence database (e.g. the human genome) with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more accurate than BLAT, and is several to tens of times faster than both. Availability: http://bio-bwa.sourceforge.net Contact: rd@sanger.ac.uk
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              Fast model-based estimation of ancestry in unrelated individuals.

              Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
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                Author and article information

                Contributors
                fsanchez@liigh.unam.mx
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 February 2025
                20 February 2025
                2025
                : 15
                : 6161
                Affiliations
                [1 ]International Laboratory for Human Genome Research, Universidad Nacional Autónoma de México (UNAM), ( https://ror.org/01tmp8f25) Querétaro, México
                [2 ]Human Evolution, Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, ( https://ror.org/048a87296) Uppsala, Sweden
                Article
                82449
                10.1038/s41598-024-82449-w
                11842861
                39979344
                158dc31f-b5d6-40cd-b2d2-d259c553c06b
                © The Author(s) 2025

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 13 June 2024
                : 5 December 2024
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                © Springer Nature Limited 2025

                Uncategorized
                evolutionary genetics,population genetics
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                evolutionary genetics, population genetics

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