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Swiel, Y.

Publications and source records attributed to Swiel, Y..

3 recordsLinked to original sources

A high-coverage genome from a 200,000-year-old Denisovan

Denisovans, an extinct sister group of Neandertals who lived in Eastern Eurasia during the Middle and Late Pleistocene, are known only from a handful of skeletal remains and limited genetic data, including the high-coverage genome of a woman who lived [~]65,000 years ago. Here, we present a second high-quality Denisovan genome, reconstructed from a molar found at Denisova Cave. It belonged to a man who lived [~]200,000 years ago in a small Denisovan group. This group mixed with early Neandertals and was then replaced by Denisovans who had mixed with later Neandertals. We show that in addition Denisovans received gene flow from hominins that diverged before the split of the ancestors of Denisovans and modern humans. The two Denisovan genomes allow us to disentangle Denisovan ancestry in present-day humans revealing contributions from at least three distinct Denisovan groups. In particular, Oceanians and South Asians independently inherited DNA from a deeply diverged Denisovan population which was likely isolated in South Asia. This supports an early migration of the ancestors of Oceanians through South Asia followed by the later arrival of the ancestors of present-day South Asians. East Asians do not share this Denisovan component in their genomes, suggesting that their ancestors arrived independently, perhaps by a northerly route. Finally, the two high-quality Denisovan genomes allow us to refine the catalogue of genetic changes that arose on the Denisovan lineage, some of which were contributed to present-day humans.

genomics↗

Resolving the source of branch length variation in the Y chromosome phylogeny

Genetic variation in the non-recombining part of the human Y chromosome has provided important insight into the paternal history of human populations. However, a significant and yet unexplained branch length variation of Y chromosome lineages has been observed, notably amongst those that are highly diverged from the human reference Y chromosome. Understanding the origin of this variation, which has previously been attributed to changes in generation time, mutation rate, or efficacy of selection, is important for accurately reconstructing human evolutionary and demographic history. Here, we analyze Y chromosomes from present-day and ancient modern humans, as well as Neandertals, and show that branch length variation amongst human Y chromosomes cannot solely be explained by differences in demographic or biological processes. Instead, reference bias results in mutations being missed on Y chromosomes that are highly diverged from the reference used for alignment. We show that masking fast-evolving, highly divergent regions of the human Y chromosome mitigates the effect of this bias and enables more accurate determination of branch lengths in the Y chromosome phylogeny. Finally, we show that this approach allows us to estimate the age of ancient samples from Y chromosome sequence data and provide updated TMRCA estimates using the portion of the Y chromosome where the effect of reference bias is minimised.

genetics↗

FPGA Acceleration of GWAS Permutation Testing

MotivationGenome-wide association studies (GWASs) analyse genetic variation over the genomes of many individuals in an attempt to identify single nucleotide polymorphisms (SNPs) associated with complex phenotypes. To capture a large amount of genetic variation and increase the chance of detecting associated SNPs, modern GWASs include millions of SNPs sampled from thousands of individuals. The cumulative probability of false associations increases with the number of SNPs included in the analysis. A GWAS, therefore, needs to control the number of false associations. Permutation testing is a straightforward and accurate method of controlling the false positive rate, but it is very computationally expensive (and slow) so there is a need for a permutation testing accelerator that can process modern GWAS datasets in reasonable time. ResultsFPGAs (Field-Programmable Gate Arrays) are reconfigurable integrated circuits which provide a high level of parallelisation that can be harnessed to accelerate GWAS permutation testing. This work presents an accessible FPGA-based tool (designed to run on a cloud-based AWS EC2 FPGA instance) that accelerates GWAS permutation testing for continuous phenotypes. The tool implements two known GWAS permutation testing algorithms: maxT permutation testing and adaptive permutation testing. The speed of the FPGA-based tool was compared to the speed of PLINK (a popular CPU-based tool) running on 40 Intel Xeon 4114 CPU cores using an imputed breast cancer dataset of 13.7 million SNPs sampled from 3652 individuals. For 1000 maxT permutations, the FPGA-based algorithms run time was 22 minutes while PLINKs run time was almost 7 days; for 100 million adaptive permutations, the FPGA-based algorithms run time was 325 minutes and PLINKs run time was about 8.5 days. For 700 million adaptive permutations of the same dataset (an almost unfeasible workload for PLINK running on a 40-core CPU) the run time of the FPGA-accelerated algorithm was 33 hours. AvailabilityAn EC2 AMI (FPGA_perm) in the us-east-1 region is available. Instructions, source code and sample data are available at https://github.com/witseie/fpgaperm.

bioinformatics↗