Biology subjects

Meisner, J.

Publications and source records attributed to Meisner, J..

17 recordsLinked to original sources

bioRxiv2026

Mapping and rewiring the MYBPC3 promoter for rescue of haploinsufficiency driven hypertrophic cardiomyopathy

Autosomal dominant loss-of-function variants in the gene MYBPC3 are, collectively, the most common genetic cause of hypertrophic cardiomyopathy (HCM) and are a prototype of haploinsufficient human disease. Typical for haploinsufficiency-associated genes, hundreds of unique loss-of-function pathogenic variants have been reported for MYBPC3 - therapeutic gene editing to correct each of these variants poses major regulatory and logistical hurdles. Upregulating wild-type allele expression could offer a generalizable therapeutic strategy, but the capacity to modulate native MYBPC3 transcription is unknown. Here, we present a variant-agnostic approach to rescue haploinsufficiency by mapping and rationally redesigning the MYBPC3 promoter. Using massively parallel reporter assays (MPRAs) in human induced pluripotent stem cell-derived cardiomyocytes, we performed saturation mutagenesis of the MYBPC3 promoter at single base-pair resolution. This defined a new class of clinically relevant noncoding loss-of-function variants while revealing essential cis-regulatory grammar anchored by key transcription factor binding sites (TFBSs). Furthermore, by systematically screening thousands of variant combinations, modular promoter elements, and heterologous TFBS insertions, we identified synergistic sequence edits that drive robust increases in MYBPC3 expression. Together, our findings improve the clinical interpretation of noncoding variants and establish a scalable blueprint for promoter editing to treat MYBPC3-associated HCM and other haploinsufficient diseases.

molecular biology↗

bioRxiv2026

A generative model for dimensionality reduction with millions of features and few samples

MotivationIn this paper, we demonstrate that it is feasible to train a deep generative model for dimensionality reduction with millions of features using few samples, which makes this type of generative model a more versatile alternative to standard methods for dimensionality reduction. Specifically, we hypothesize that for a decoder-only model, the number of training samples required is almost independent of the feature dimensionality in most network architectures. ResultsThrough an extensive set of experiments on synthetic non-linear data, we validate this hypothesis. We also train the model on a downsampled version of the 1000 Genomes Project (1KGP) dataset to further assess its behavior under controlled reductions in sample size. Furthermore, we train a deep generative decoder (DGD) on a curated dataset from the International Cancer Genome Consortium (ICGC), which contains 4.4 million features. It is trained on approximately 4,000 samples and tested on 1,000 samples. The resulting latent representation exhibits clear clustering, and when methods are reduced to the same number of dimensions, it outperforms PCA and VAE for tumor type classification. Additionally, the DGD is computationally efficient and can be trained on a 16GB GPU. Availability and implementationCode is available at https://github.com/cpancott/ReceptiveDGD. Contactcorrado.pancotti@helmholtz-munich.de; akrogh@di.ku.dk Supplementary informationSupplementary data are available with this preprint.

bioinformatics↗

bioRxiv2025

Accurate and scalable genome-wide ancestry estimation using haplotype clusters

Unsupervised genome-wide ancestry estimation has been a staple in population and medical genetics for decades, and its importance continues to grow with the increasing number of large genetic cohorts of mixed ancestries. We propose an extension to the hapla framework that scales model-based ancestry estimation to unprecedented sample sizes by leveraging inferred haplotype clusters from phased genotype data. Our haplotype cluster-based approach is approximately 5x faster than the fastest model-free SNP-based approach on the harmonized Human Genome Diversity Project and 1000 Genomes Project dataset, while we further demonstrate it to be the most accurate method to date in an extensive simulation study. Our accurate ancestry estimates can help reduce health disparities and accelerate precision medicine efforts in the growing number of biobanks globally.

bioinformatics↗

bioRxiv2025

Faroese Whole Genomes Provide Insight into Ancestry and Recent Selection

The Faroe Islands are home to descendants of a North Atlantic founder population with a unique history shaped by both migration and periods of relative isolation. Here, we investigate the genetic diversity, population structure, and demographic history of the islands by analyzing whole genome sequencing data from 40 participants in the Faroe Genome Project. This represents the first whole genome sequencing panel of this size from the Faroe Islands. We observed numerous putatively functional private alleles, including stop gain variants and high impact missense variants in the cohort. Faroese individuals had a higher proportion of their genomes contained in long runs of homozygosity than other European groups, including Finnish, suggesting a more recent or stronger bottleneck in the Faroese population. Signals of positive selection were identified at loci containing genes that play roles in vitamin D and dietary fat absorption and DNA repair, while increased diversity on lactase persistence haplotypes was observed. Fine-scale analysis of haplotype structure in present-day and ancient European genomes revealed genetic affinities with ancient Iron Age individuals from the North and West of Europe, providing evidence for potential contributions to the Faroese gene pool from Celtic and Viking populations as well as information about the temporal order in which these events happened. This study highlights the impact of evolutionary processes, such as ancient admixture, founder events, and positive selection, on the present-day genetic architecture of North Atlantic founder populations like the Faroe Islands.

genetics↗

bioRxiv2024

Faster model-based estimation of ancestry proportions

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. Likelihood-free approaches optimize a least square objective and have gained popularity in recent years due to their scalability. However, this comes at the cost of accuracy in the ancestry estimates in more complex admixture scenarios. We present a new model-based approach, fastmixture, which adopts aspects from likelihood-free approaches for parameter initialization, followed by a mini-batch expectation-maximization procedure to model the standard likelihood. In a simulation study, we demonstrate that the model-based approaches of fastmixture and ADMIXTURE are significantly more accurate than recent and likelihood-free approaches. We further show that fastmixture runs approximately 30x faster than ADMIXTURE on both simulated and empirical data from the 1000 Genomes Project such that our model-based approach scales to much larger sample sizes than previously possible. Our software is freely available at https://github.com/Rosemeis/fastmixture.

bioinformatics↗

bioRxiv2024

Fish predation induces drifting and emergence in an experimental stream mesocosm system

Predator-prey interactions are important drivers of adaptation in aquatic communities, shaping the behaviour of invertebrates with cascading effects on community dynamics. Behavioural responses, such as moving with the downstream current (drift) or altering the timing of emergence, can be initiated and reduces the risk of encountering predators. This study aimed to examine the effects of indirect and direct predation pressure on drift behaviour and emergence patterns of aquatic invertebrates. Using an experimental mesocosm setup (ExStream), we analysed how the number of drifting and emerging invertebrates changed with elevated levels of chemical cues from fish (indirect predation, Gasterosteus aculeatus and Cottus rhenanus) and subsequent additional direct predation by Cottus rhenanus over a 9-day period. Our findings show that most of the analysed invertebrate groups displayed noticeable responses to either indirect, direct, or both forms of predation. Furthermore, our study revealed a significant impact of predation on emergence patterns, reducing the number and the size of emergent invertebrates. Given the importance of drift and emergence in dispersion and in facilitating resource flows into terrestrial systems, our findings indicate a strong effect of predation on the communities.

ecology↗

bioRxiv2022

PCAone: fast and accurate out-of-core PCA framework for large scale biobank data

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. In this paper, we propose a novel Randomized Singular Value Decomposition (RSVD) algorithm implemented in PCAone, featuring a window-based optimization scheme that enables accelerated convergence while improving the accuracy. Additionally, PCAone incorporates out-of-core and multithreaded implementations for the existing Implicitly Restarted Arnoldi Method (IRAM) and RSVD. Through comprehensive evaluations using multiple large-scale real-world datasets in different fields, we demonstrate the advantage of PCAone over existing methods. The new algorithm achieves significantly faster computation time while maintaining accuracy comparable to the slower IRAM method. Notably, our analyses of UK Biobank, comprising around 0.5 million individuals and 6.1 million common SNPs, demonstrate that PCAone accurately computes the top 40 principal components within a 9 hours. This analysis effectively captures population structure, signals of selection, structural variants, and low recombination regions, utilizing less than 20 GB of memory and 20 CPU threads. Furthermore, when applied to single-cell RNA sequencing data featuring 1.3 million cells, PCAone, accurately capturing the top 40 principal components in 49 minutes. This performance represents a 10-fold improvement over state-of-the-art tools.

bioinformatics↗

bioRxiv2022

Modulation of the gut microbiome by novel synthetic glycans for the production of propionate and the reduction of cardiometabolic risk factors

BackgroundIncreasing evidence indicates that an altered gut microbiome participates in the development of cardiometabolic syndrome and associated risk factors, such as insulin resistance, dyslipidemia, and obesity, and that targeting the gut microbiome is a promising strategy to lower the risk for cardiometabolic diseases. Part of this reduction is mediated by specific metabolites generated by the gut microbiome. Propionate, a short-chain fatty acid (SCFA) produced from dietary glycans by certain gut microbes is known to exert multiple beneficial metabolic effects. Here, we identify KB39, a novel gut microbiome-targeting synthetic glycan selected for its strong propionate-producing capacity, and demonstrate its effects in vivo to reduce cardiometabolic disease using western diet-fed LDL receptor knock-out mice. MethodsEx vivo fermentation screening of a large library of synthetic glycan ensembles was performed using gut microbiome communities from healthy subjects and overweight patients with type 2 diabetes. A synthetic glycan identified for its high propionate-producing capacity (KB39) was then tested in vivo for effects on systemic, blood and cecal metabolic parameters in Ldlr-/- mice fed a western diet. ResultsEx vivo screening of ~600 synthetic glycans using human gut microbiota from healthy subjects and patients with type 2 diabetes identified a novel glycan (KB39) with high propionate-producing capacity that increased propionate contribution to total SCFA and propionate-producing bacterial taxa compared to negative control. In western diet-fed Ldlr-/- mice, KB39 treatment resulted in an enrichment in propiogenic bacteria and propionate biosynthetic genes in vivo and an increase in absolute and relative amounts of propionate in the cecum. This also resulted in significant decreases in serum total cholesterol, LDL-cholesterol, and insulin levels, as well as reduced hepatic triglycerides and cholesterol content compared to non-treated animals. Importantly, KB39 treatment significantly reduced atherosclerosis, liver steatosis and inflammation, upregulated hepatic expression of genes involved in fatty acid oxidation and downregulated transcriptional markers of inflammation, fibrosis and insulin resistance with only a mild lowering of body weight gain. ConclusionsOur data show that KB39, a novel synthetic glycan supporting a high propionate-producing microbiome, can reduce cardiometabolic risk factors and disease in mice and suggest this approach could be of benefit for the prevention or treatment of cardiometabolic diseases in humans. CLINICAL PERSPECTIVEWhat is new? O_LIA novel synthetic glycan, KB39, was selected from a library of compounds for its high propionate-producing capacity and beneficial effects on the human gut microbiome composition C_LIO_LIKB39 modulates the gut microbiome for high propionate production and significantly reduces cardiometabolic risk factors and disease in a murine model of cardiometabolic diseases C_LI What are the clinical implications? O_LIKB39, delivered orally, could be of benefit for the prevention or treatment of cardiometabolic diseases in humans C_LIO_LIThe efficacy of KB39 in mice compared to the clinical drug fenofibrate justifies further study in humans C_LI

microbiology↗

bioRxiv2022

Estimating the distribution of livestock in space and time: a time-series contribution to livestock density mapping

More than one billion people rely on livestock for income, nutrition, and social cohesion, however livestock keeping can facilitate disease transmission and contribute to climate change. While data on the distribution of livestock thus have broad utility across a range of applications, efforts to map the distribution of livestock on a large scale are limited to the Gridded Livestock of the World (GLW) project. We present a complimentary effort to map the distribution of cattle and pigs in Malawi, Uganda, Democratic Republic of Congo (DRC), and South Sudan. In contrast to GLW, which uses dasymmetric modeling applied to census data to produce time-stratified estimates of livestock counts and spatial density, our work uses complex survey data and distinct modeling methods to generate a time-series of livestock distribution, defining livestock density as the ratio of animals to humans. In addition to favorable cross-validation results and general agreement with national density estimates derived from external data on national human and livestock populations, our results demonstrate extremely good agreement with GLW-3 estimates, supporting the validity of both efforts. Our results furthermore offer a high-resolution time series result and employ a definition of density which is particularly well-suited to the study of livestock-origin zoonoses.

ecology↗

bioRxiv2022

Synthetic glycans that control gut microbiome structure mitigate colitis in mice

Relative abundances of bacterial species in the gut microbiome have been linked to many diseases. Species of gut bacteria are ecologically differentiated by their abilities to metabolize different glycans, making glycan delivery a powerful way to alter the microbiome to promote health. We describe the properties and therapeutic potential of chemically diverse synthetic glycans (SGs). Fermentation of SGs by gut microbiome cultures resulted in compound-specific shifts in taxonomic and metabolite profiles not observed with reference glycans, including prebiotics. Model enteric pathogens grow poorly on most SGs, potentially increasing their safety for at-risk populations. SGs increased survival, reduced weight loss, and improved clinical scores in mouse models of colitis. Synthetic glycans are thus a promising modality to improve health through selective changes to the gut microbiome.

microbiology↗

bioRxiv2021

Household transmission of SARS-CoV-2 from humans to dogs in Washington and Idaho: burden and risk factors

SARS-CoV-2 is believed to have emerged from an animal reservoir; however, the frequency of and risk factors for inter-species transmission remain unclear. We carried out a community-based study of pets in households with one or more confirmed SARS-CoV-2 infection in humans. Among 119 dogs and 57 cats with completed surveys, clinical signs consistent with SARS-CoV-2 were reported in 20 dogs (21%) and 19 cats (39%). Out of 81 dogs and 32 cats sampled for testing, 40% of dogs and 43% of cats were seropositive, and 5% of dogs and 8% of cats were PCR positive; this discordance may be due to delays in sampling. Respondents commonly reported close human-animal contact and willingness to take measures to prevent transmission to their pets. Reported preventative measures showed a slightly protective trend for both illness and seropositivity in pets, while sharing of beds and bowls had slight harmful effects.

microbiology↗

bioRxiv2021

Detecting Selection in Low-Coverage High-Throughput Sequencing Data using Principal Component Analysis

1Identification 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. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data. Here, we present two selections statistics which we have implemented in the PCAngsd framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes. Moreover, we show that PCAngsd outperform selection statistics obtained from called genotypes from low-coverage sequencing data.

bioinformatics↗

bioRxiv2020

Haplotype and Population Structure Inference using Neural Networks in Whole-Genome Sequencing Data

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. By utilizing Gaussian mixtures in a variational autoencoder framework, we are able to learn a low-dimensional latent space in which we cluster haplotypes along the genome in a highly scalable manner. We demonstrate that we can use haplotype clusters in the latent space to infer global population structure utilizing haplotype information by exploiting the generative properties of our framework. Based on fitted neural networks and its latent haplotype clusters, we can perform principal component analysis and estimate ancestry proportions based on a maximum likelihood framework. Using sequencing data from simulations and closely related human populations, we demonstrate that our approach is better at distinguishing closely related populations than standard admixture and principal component analysis software. We further show that HaploNet is fast and highly scalable by applying it to genotype array data of the UK Biobank.

bioinformatics↗

bioRxiv2020

Large-scale Inference of Population Structure in Presence of Missingness using PCA

BackgroundPrincipal component analysis (PCA) is a commonly used tool in genetics to capture and visualize population structure. Due to technological advances in sequencing, such as the widely used non-invasive prenatal test, massive datasets of ultra-low coverage sequencing are being generated. These datasets are characterized by having a large amount of missing genotype information. We present EMU, a method for inferring population structure in the presence of rampant non-random missingness. ResultsWe show through simulations that several commonly used PCA methods can not handle missing data arisen from various sources, which leads to biased results as individuals are projected into the PC space based on their amount of missingness. In terms of accuracy, EMU outperforms an existing method that also accommodates missingness while being competitively fast. We further tested EMU on around 100K individuals of the Phase 1 dataset of the Chinese Millionome Project, that were shallowly sequenced to around 0.08x. From this data we are able to capture the population structure of the Han Chinese and to reproduce previous analysis in a matter of CPU hours instead of CPU years. ConclusionsEMUs capability to accurately infer population structure in the presence of missingness will be of increasing importance with the rising number of large-scale genetic datasets. EMU is written in Python and is freely available at https://github.com/Rosemeis/emu/.

genetics↗

bioRxiv2018

Genomic diversity and novel genome-wide association with fruit morphology in Capsicum, from 746k polymorphic sites.

BackgroundCapsicum is one of the major vegetable crops grown world-wide. Current subdivision in clades and species is based on morphological traits and coarse sets of genetic markers. Fruits broad variability has been driven by breeding programs and has been mainly studied by linkage analysis.\n\nResultsWe discovered 746k variable sites by sequencing 1.8% of the genome in a collection of 373 accessions belonging to 11 Capsicum species from 51 countries. We describe genomic variation at population-level, confirm major subdivision in clades and species, and show that the known subdivision of C. annuum in two groups separates large and bulky fruits form small ones. In C. annuum, we identify four novel loci associated with phenotypes determining the fruit shape, including a non-synonymous mutation in the gene Longifolia 1-like (CA03g16080).\n\nConclusionsOur collection covers all the economically important species of Capsicum widely used in breeding programs, and represent the widest and largest study so far in terms of the number of species and genetic variants analyzed. We identified a large set of markers that can be used for population genetic studies and genetic association analyses. Our results foster fine genetic association studies and foresee genomic variability at population-level.

genetics↗

bioRxiv2018

Testing for Hardy-Weinberg Equilibrium in Structured Populations using NGS Data

Testing for Hardy-Weinberg Equilibrium (HWE) is a common practice for quality control in genetic studies. Variable sites violating HWE may be identi[fi]ed as technical errors in the sequencing or genotyping process, or they may be of special evolutionary interest. Large-scale genetic studies based on next-generation sequencing (NGS) methods have become more prevalent as cost is decreasing but these methods are still associated with statistical uncertainty. The large-scale studies usually consist of samples from diverse ancestries that make the existence of some degree of population structure almost inevitable. Precautions are therefore needed when analyzing these datasets, as population structure causes deviations from HWE. Here we propose a method that takes population structure into account in the testing for HWE, such that other factors causing deviations from HWE can be detected. We show the effectiveness of our method in NGS data, as well as in genotype data, for both simulated and real datasets, where the use of genotype likelihoods enables us to model the uncertainty for low-depth sequencing data.

genetics↗

bioRxiv2018

Inferring Population Structure and Admixture Proportions in Low Depth Next-Generation Sequencing Data

We here present two methods for inferring population structure and admixture proportions in low depth next generation sequencing data. Inference of population structure is essential in both population genetics and association studies and is often performed using principal component analysis or clustering-based approaches. Next-generation sequencing methods provide large amounts of genetic data but are associated with statistical uncertainty for especially low depth sequencing data. Models can account for this uncertainty by working directly on genotype likelihoods of the unobserved genotypes. We propose a method for inferring population structure through principal component analysis in an iterative approach of estimating individual allele frequencies, where we demonstrate improved accuracy in samples with low and variable sequencing depth for both simulated and real datasets. We also use the estimated individual allele frequencies in a fast non-negative matrix factorization method to estimate admixture proportions. Both methods have been implemented in the PCAngsd framework available at http://www.popgen.dk/software/.

genetics↗