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Articles by Jonas Meisner
The genetic diversity of Indonesian cattle has been shaped by multiple introductions and adaptive introgression - Nature Communications
Abstract Genetic diversity is a crucial resource in livestock, determining their traits and ability to respond to selection. Indonesian cattle are unique due to their history of admixture involving both zebu (Bos indicus) and banteng (B. javanicus), and may therefore contain novel cattle genetic resources. We generated whole genome sequences from 126 Indonesian cattle, 51 domesticated banteng and three captive banteng.
Tracing the evolutionary history of the CCR5delta32 deletion via ancient and modern genomes
Keywords CCR5 CCR5delta32 HIV-1 infection resistance pleiotropy haplotype recombination ancient genomes evolution positive selection immune genes Introduction Humans have been exposed to pathogens over the course of our evolutionary history, and adaptations to them have left numerous signatures in our genomes.1,2,3 In recent years, evidence for selection has been found in genes involved in the development of tolerance against intracellular pathogens and in the inflammatory response against...
Genetic architecture in Greenland is shaped by demography, structure and selection - Nature
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Abstract Greenlandic Inuit and other indigenous populations are underrepresented in genetic research1,2, leading to inequity in healthcare opportunities. To address this, we performed analyses of sequenced or imputed genomes of 5,996 Greenlanders with extensive phenotypes. We quantified their historical population bottleneck and how it has shaped their genetic architecture to have fewer, but more common, variable sites.
By Frederik Filip Stæger, Mette Andersen, Zilong Li, Shixu He, Cindy G Santander, Kristian Ebbesen Hanghøj, Emil Jørsboe, Sara Stinson Verified, Renzo F. Balboa, Jonas Meisner, Allan Linneberg, Niels Grarup, Rasmus Nielsen, Marit E. Jørgensen, Torben Frøstrup Hansen, Ida Moltke, Jasmin Pernille Hjerresen
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Nature
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Faster model-based estimation of ancestry proportions
Abstract Ancestry estimation from genotype data in unrelated individuals has become an essential tool in population and medical genetics to understand demographic population histories and to model or correct for population structure. The ADMIXTURE software is a widely used model-based approach to account for population stratification, however, it struggles with convergence issues and does not scale to modern human datasets or the large number of variants in whole-genome sequencing data.
Faster model-based estimation of ancestry proportions
Abstract Ancestry estimation from genotype data in unrelated individuals has become an essential tool in population and medical genetics to understand demographic population histories and to model or correct for population structure. The ADMIXTURE software is a widely used model-based approach to account for population stratification, however, it struggles with convergence issues and does not scale to modern human datasets or the large number of variants in whole-genome sequencing data.
Faster model-based estimation of ancestry proportions
Abstract Ancestry estimation from genotype data in unrelated individuals has become an essential tool in population and medical genetics to understand demographic population histories and to model or correct for population structure. The ADMIXTURE software is a widely used model-based approach to account for population stratification, however, it struggles with convergence issues and does not scale to modern human datasets or to the large number of variants in whole-genome sequencing data.
Leveraging haplotype information in heritability estimation and polygenic prediction
The authors have declared no competing interest. Lundbeck Foundation (R380-2021-1225 and R278-2018-1411) and the Novo Nordisk Foundation (NNF14CC0001 and NNF23SA0084103). I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Fast and accurate out-of-core PCA framework for large scale biobank data
1 1Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, 2200 København, Denmark; 2Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital, 2100 København, Denmark; 3Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 København, Denmark Corresponding authors: zilong.dk{at}gmail.com, aalbrechtsen{at}bio.ku.dk Footnotes [Supplemental material is available for this article.] Article...
Fast and accurate out-of-core PCA framework for large scale biobank data
Abstract Principal Component Analysis (PCA) is widely utilized in statistics, machine learning, and genomics for dimensionality reduction and uncovering low-dimensional latent structure. To address the challenges posed by ever-growing data size, fast and memory-efficient PCA methods have gained prominence.
PCAone: fast and accurate out-of-core PCA framework for large scale biobank data
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
PCAone: fast and accurate out-of-core PCA framework for large scale biobank data
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
PCAone: fast and accurate out-of-core PCA framework for large scale biobank data
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
Haplotype and Population Structure Inference using Neural Networks in Whole-Genome Sequencing Data
Abstract Accurate inference of population structure is important in many studies of population genetics. Here we present, HaploNet, a method for performing dimensionality reduction and clustering of genetic data. The method is based on local clustering of phased haplotypes using neural networks from whole-genome sequencing or dense genotype data.
Detecting Selection in Low-Coverage High-Throughput Sequencing Data using Principal Component Analysis
1 Abstract Identification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation.
Large-scale Inference of Population Structure in Presence of Missingness using PCA
Large-scale Inference of Population Structure in Presence of Missingness using PCA Jonas Meisner, Anders Albrechtsen, Siyang Liu, Mingxi Huang bioRxiv 2020.04.29.067496; doi: https://doi.org/10.1101/2020.04.29.067496
Testing for Hardy-Weinberg Equilibrium in Structured Populations using NGS Data
Testing for Hardy-Weinberg Equilibrium in Structured Populations using NGS Data Jonas Meisner, Anders Albrechtsen bioRxiv 468611; doi: https://doi.org/10.1101/468611
Inferring Population Structure and Admixture Proportions in Low Depth Next-Generation Sequencing Data
Inferring Population Structure and Admixture Proportions in Low Depth Next-Generation Sequencing Data Jonas Meisner, Anders Albrechtsen bioRxiv 302463; doi: https://doi.org/10.1101/302463
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