Biology subjects

Albrechtsen, A.

Publications and source records attributed to Albrechtsen, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Calibration improves estimation of linkage disequilibrium on low sample sizes

Linkage disequilibrium is a central statistic in population genetic studies, commonly measured by the squared correlation between pairs of genetic variants. An important drawback of this measure is its upward bias caused by a finite sample size. To handle this, different methods exist that correct for sample-size bias. However, because the correlation consists of a ratio, there is no unbiased method to compute it. In this work, we present a procedure to calibrate those methods using a non-parametric approach with simulated data. This is done with forward modeling to generate genotype matrices with known parameters, followed by an inverse mapping to recover estimates of the underlying parameters. Then, a mean-centering calibration is applied to the recovered estimate of the true parameter. This approach is applied to real and simulated data, showing consistent improvement in accuracy compared to other sample-size-aware methods. Furthermore, to study the effects on downstream analyses, we analyze the classification performance on LD pruning, where we also observe an improvement, particularly in extreme cases with low sample sizes of 5 or 10 individuals.

bioinformatics↗

bioRxiv2026

A time-calibrated phylogeny of hummingbirds supports stepwise diversification in the Andes

Hummingbirds (Trochilidae) represent the second largest avian family, with [~]356 species occupying diverse habitats across the Neotropical and Nearctic regions. Their extensive diversification includes notable adaptations to extreme environments, with nearly one-third of extant taxa belonging to the predominantly high-altitude Coquettes and Brilliants. Although recent work proposed a monophyletic Andean clade uniting these groups, consistent with rapid radiation during Andean orogeny, we find no support for this relationship. Using 2,949 nuclear loci sampled from 47 species spanning all nine major hummingbird clades, we recover a different evolutionary pattern: a stepwise sequence of diversification in which Brilliants are sister to a broader assemblage comprising Coquettes, the genus Patagona, Emeralds, Mountain Gems, and the recently diverged Bees. By assembling complete mitochondrial genomes, we additionally detect significant discordance between nuclear topologies and those derived from the mitogenome and Z chromosome. Analyses of gene-tree heterogeneity show that incomplete lineage sorting is pervasive across the phylogeny, with particularly strong impacts on branches associated with Andean diversification. Divergence-time estimation further indicates that the major Andean radiation--including Brilliants, Coquettes, Emeralds, Bees, and Mountain Gems--originated around [~]14 Ma, with the three younger clades diversifying [~]12 Ma, coinciding with both the mid-Miocene Andean uplift and the mid-Miocene Climate Transition that increased habitat heterogeneity and likely promoted rapid speciation. To support future phylogenomic efforts, we identify a reduced set of highly informative, independent protein-coding loci and present a near-complete species-level phylogeny constrained by our autosomal backbone. Our findings highlight the importance of integrating loci with distinct inheritance modes to detect and interpret phylogenetic incongruence in rapid radiations.

evolutionary biology↗

bioRxiv2025

From deserts to forests: sequencing of the giant forest hog completes the genomic history of the African Suids

The giant forest hog (GFH, Hylochoerus meinertzhageni), the largest and most elusive wild pig, is found in a disjunct range across equatorial Africa, primarily inhabiting forests and woodlands. It is the only remaining African suid species without whole-genome data. The GFH is currently classified into three subspecies, differing in skull morphology and size. Here, we present the first whole-genome data of nine GFHs; one from Guinea and eight Uganda, representing the two geographically most distant subspecies. This genomic evidence allowed us to resolve the previously uncertain placement of the GFH among African suids as a sister group to the warthogs (Phacochoerus spp.). Also, we found a deep evolutionary split between the two GFH populations resulting in a level of genetic differentiation (FST > 0.75), which is similar to or higher than values observed among species of African wild pigs. The GFH likely diverged from warthogs around 4.6 Mya while the two study populations of GFH diverged at least 0.5 Mya and, subsequently, experienced very dissimilar demographic trajectories. The individual from Guinea had half of the heterozygosity level of the Uganda population, and lower value compared to all the African suids, probably as a result of the long term low Ne and isolation. Due to the close affinity of GFH with the African rainforest biotic zone, our findings have implications for understanding the history of habitat change in Africa. They also indicate a very deep and previously underappreciated evolutionary divergence within the GFH that may have implications for its conservation.

genomics↗

bioRxiv2024

Rapid and accurate genotype imputation from low coverage short read, long read, and cell free DNA sequence

Inexpensive and accurate genotyping methods are essential to modern genomics and health risk prediction. Here we introduce QUILT2, a scalable read-aware imputation method that can efficiently use biobank scale haplotype reference panels. This allows for fast and accurate imputation using short reads, as well as long reads (e.g. ONT 1X r2 = 0.937 at common SNPs), linked-reads and ancient DNA. In addition, QUILT2 contains a methodological innovation that enables imputation of the maternal and fetal genome using cell free non-invasive prenatal testing (NIPT) data. Using a UK Biobank reference panel, we see accurate imputation of both mother (r2 = 0.966) and fetus (r2 = 0.465) at 0.25X (fetal fraction of 10%, common SNPs). Imputation gets increasingly accurate as coverage increases, with r2 of around 0.90 or above for both mother and fetus at 4.0X (mother r2 = 0.996, fetal r2 = 0.894). We show that this imputation enables powerful GWAS and accurate PRS for both mother and fetus, which creates both clinical opportunities, and if phenotypes can be collected alongside clinical NIPT, the potential for large GWAS.

bioinformatics↗

bioRxiv2024

Measuring linkage disequilibrium and improvement of pruning and clumping in structured populations

Standard measures of linkage disequilibrium (LD) are affected by admixture and population structure, such that loci that are not in LD within each ancestral population appear linked when considered jointly. The influence of population structure on LD can cause problems for downstream analysis methods, in particular those that rely on LD pruning or clumping. To address this issue, we propose a measure of LD that accommodates population structure using the top inferred principal components. We estimate LD from the correlation of geno-type residuals and prove that this LD measure remains unaffected by population structure when analyzing multiple populations jointly, even with admixed individuals. Based on this adjusted measure of LD, we can perform LD pruning to remove the correlation between markers for downstream analysis. Traditional LD pruning is more likely to remove markers with high differences in allele frequencies between populations, which biases measures for genetic differentiation and removes markers that are not in LD in the ancestral populations. Using data from moderately differentiated human populations and highly differentiated giraffe populations we show that traditional LD pruning biases FST and PCA but that this can be alleviated with the adjusted LD measure. In addition, we show the adjusted LD leads to better PCA when pruning and that LD clumping retains more sites and the retained sites have stronger associations.

genetics↗

bioRxiv2024

Extensive population structure highlights an apparent paradox of stasis in the impala (Aepyceros melampus)

Impalas are unusual among bovids because they have remained morphologically similar over millions of years - a phenomenon referred to as evolutionary stasis. Here, we sequenced 119 whole genomes from the two extant subspecies of impala, the common (Aepyceros melampus melampus) and black-faced (A. m. petersi) impala. We investigated the evolutionary forces working within the species to explore how they might be associated with its evolutionary stasis as a taxon. Despite being one of the most abundant bovid species, we found low genetic diversity overall, and a phylogeographic signal of spatial expansion from southern to eastern Africa. Contrary to expectations under a scenario of evolutionary stasis, we found pronounced genetic structure between and within the two subspecies with indications of ancient, but not recent, gene flow. Black-faced impala and eastern African common impala populations had more runs of homozygosity than common impala in southern Africa, and, using a proxy for genetic load, we found that natural selection is working less efficiently in these populations compared to the southern African populations. Together with the fossil record, our results are consistent with a fixed-optimum model of evolutionary stasis, in which impalas in the southern African core of the range are able to stay near their evolutionary fitness optimum as a generalist ecotone species, whereas eastern African impalas may struggle to do so due to the effects of genetic drift and reduced adaptation to the local habitat, leading to recurrent local extinction in eastern Africa and re-colonization from the South.

evolutionary biology↗

bioRxiv2024

Sucrase isomaltase dysfunction influences dietary sucrose intake and preference

ObjectiveTo characterise the role of Sucrase-isomaltase (SI) in regulating dietary behaviours, such as sweet preference and food liking in Si knockout (Sis-KO) mice and in population-based cohorts from Greenland and the UK. DesignWe profiled the appetitive and post-ingestive response to dietary carbohydrates in SI knockout (Sis-KO) mice. Alongside this, we conducted detailed dietary analysis of 45 foods in two Greenlandic population-based cohorts (IHIT, n=2778 and 68 foods, and B2018, n=2203 and 45 foods) with the presence of a common (allele frequency = 14.3%) SI Loss of function (LoF) variant, c.273-274delAG. Finally, we explored the association between SI hypomorphic variants, liking of 140 foods, and sucrose content using data from 134,766 UKBB participants with exome sequencing and questionnaire data available. ResultsSucrose naive Sis-KO mice had a significantly reduced intake of dietary sucrose, and preference for 10% liquid sucrose, in two-bottle preference studies. Mechanistically, oral administration of the short-chain fatty acid acetate reduced sucrose-preference in wild-type mice. In Greenlandic LoF homozygous carriers we show that the previously reported reduction in sugar intake may primarily be explained by a lower intake of cake and pastries, and of candy and chocolate and that added sugar is the main factor explaining these associations. In the UKBB, a negative association with "cake icing", the food with the highest sucrose content per 100g, was detected in SI hypomorphic carriers, as well as in sensitivity analyses conducted only including carriers of known CSID LoF variants. Further, a negative linear relationship was also observed between the effect estimates of hypomorphic SI variants on food liking and the estimated sucrose content per 100g of 88 sucrose-containing foods, indicating that food dislike in SI carriers correlates with the amount of sucrose in food. ConclusionCollectively, we demonstrated that genetic variation in the SI gene is associated with significant changes in sucrose preference, characterised by a rapid avoidance of dietary sucrose in Sis-KO mice, as well as lower consumption and increased disliking of sucrose rich foods in Greenlanders and Europeans, respectively. This work demonstrates that genetic variation in the SI gene may impact physiology beyond the gastrointestinal tract and suggest the possibility to target SI to reduce the preference, and intake, of dietary sucrose with implications for digestive and metabolic health.

genetics↗

bioRxiv2024

Inferring drift, genetic differentiation, and admixture graphs from low-depth sequencing data

A number of popular methods for inferring the evolutionary relationship between populations require essentially two components: First, they require estimates of f2-statistics, or some quantity that is a linear combination of these. Second, they require estimates of the variability of the statistic in question. Examples of methods in this class include qpGraph and TreeMix. It is known, however, that these statistics are biased when based on genotype calls at low depth. Moreover, as we show, this leads to downstream inference of significantly distorted trees. To solve this problem, we demonstrate how to accurately and efficiently compute a broad class of statistics from low-depth whole-genome sequencing data, including estimates of their standard errors, by using the site frequency spectrum. In particular, we focus on f2 and the sample covariance of allele frequencies to show how this method leads to accurate estimate of drift when fitting trees using qpGraph and TreeMix with low-depth data. However, the same considerations lead to uncertainty estimates for a variety of other statistics, including heterozygosity, kinship estimates (e.g. King), and quantities relating to genetic differentiation such as Fst and Dxy.

bioinformatics↗

bioRxiv2023

Utilizing Non-Invasive Prenatal Test Sequencing Data Resource for Human Genetic Investigation

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. With exceptional sensitivity, specificity, and safety, NIPT has gained global adoption, exceeding ten million tests, establishing it as one of the largest human genetic resources. This resource holds immense potential for exploring population genetic variations and their correlations with phenotypes. Here, we present comprehensive methods tailored for analyzing large, low-depth NIPT genetic datasets, involving customized algorithms and software for genetic variation detection, genotype imputation, and genome-wide association analysis. Through evaluations, we demonstrate that, when integrated with appropriate probabilistic models and population-specific haplotype reference panels, accurate allele frequency estimation and high genotype imputation accuracy (0.8 to 0.9) are achievable for genetic variants with alternative allele frequencies between 0.01 and 0.05, at sequencing depths of 0.1x to 0.25x. Additionally, we attained an R-square exceeding 0.9 for estimating genetic effect sizes across various sequencing platforms. These findings establish a robust theoretical and practical foundation for leveraging NIPT data in advancing medical genetic studies, not only in realms of maternal and child health, but also for long-term health outcomes. HighlightsO_LIIntroduction of probabilistic model integration for analyzing large-scale, low-pass non-invasive prenatal test (NIPT) sequencing data C_LIO_LIEvaluation of protocols for variant detection, genotype imputation, and genome-wide association analyses with NIPT data C_LI

bioinformatics↗

bioRxiv2023

African bush pigs exhibit porous species boundaries and appeared in Madagascar concurrently with human arrival

Several African mammals exhibit a phylogeographic pattern where closely related taxa are split between West/Central and East/Southern Africa, but their evolutionary relationships and histories remain controversial. Bushpigs (Potamochoerus larvatus) and red river hogs (P. porcus) are recognised as separate species due to morphological distinctions, a perceived lack of interbreeding at contact, and putatively old divergence times, but historically, they were considered conspecific. Moreover, the presence of Malagasy bushpigs as the sole large terrestrial mammal shared with the African mainland raises intriguing questions about its origin and arrival in Madagascar. Analyses of 67 whole genomes revealed a genetic continuum between the two species, with putative signatures of historical gene flow, variable FST values, and a recent divergence time (<500,000 years). Thus, our study challenges key arguments for splitting Potamochoerus into two species and suggests their speciation might be incomplete. Our findings also indicate that Malagasy bushpigs diverged from southern African populations and underwent a limited bottleneck 1,000-5,000 years ago, concurrent with human arrival in Madagascar. These results shed new light on the evolutionary history of an iconic and widespread African genus and provide insight into the longstanding biogeographic puzzle surrounding the bushpigs presence in Madagascar.

evolutionary biology↗

bioRxiv2023

Evaluation of population structure inferred by principal component analysis or the admixture model

Principal component analysis (PCA) is commonly used in genetics to infer and visualize population structure and admixture between populations. PCA is often interpreted in a way similar to inferred admixture proportions, where it is assumed that individuals belong to one of several possible populations or are admixed between these populations. We propose a new method to assess the statistical fit of PCA (interpreted as a model spanned by the top principal components) and to show that violations of the PCA assumptions affect the fit. Our method uses the chosen top principal components to predict the genotypes. By assessing the covariance (and the correlation) of the residuals (the differences between observed and predicted genotypes), we are able to detect violation of the model assumptions. Based on simulations and genome wide human data we show that our assessment of fit can be used to guide the interpretation of the data and to pinpoint individuals that are not well represented by the chosen principal components. Our method works equally on other similar models, such as the admixture model, where the mean of the data is represented by linear matrix decomposition.

genetics↗

bioRxiv2022

Estimating admixture pedigrees of recent hybrids without a contiguous reference genome

The genome of recently admixed individuals or hybrids have characteristic genetic patterns that can be used to learn about their recent admixture history. One of these are patterns of interancestry heterozygosity, which can be inferred from SNP data from either called genotypes or genotype likelihoods, without the need for information on genomic location. This makes them applicable to a wide range of data that are often used in evolutionary and conservation genomic studies, such as low-depth sequencing mapped to scaffolds and reduced representation sequencing. Here we implement maximum likelihood estimation of interancestry heterozygosity patterns using two complementary models. We furthermore develop apoh (Admixture Pedigrees Of Hybrids), a software that uses estimates of paired ancestry proportions to detect recently admixed individuals or hybrids, and to find the most compatible recent admixture pedigree. It furthermore calculates several hybrid indices that make it easier to identify and rank possible admixture pedigrees that could give rise to the estimated patterns. We implemented apoh both as a command line tool and as a Graphical User Interface that allows the user to automatically and interactively explore, rank and visualize compatible recent admixture pedigrees, and calculate the different summary indices. We validate the performance of the method using admixed family trios from the 1000 Genomes Project. In addition, we show its applicability on identifying recent hybrids from RAD-seq data of Grants gazelle (Nanger granti and Nanger petersii) and whole genome low depth data of waterbuck (Kobus ellipsiprymnus) which shows complex admixture of up to four populations.

bioinformatics↗

bioRxiv2022

Persistent gene flow suggests an absence of reproductive isolation in an African antelope speciation model

African antelope diversity is a globally unique vestige of a much richer world-wide Pleistocene megafauna. Despite this, the evolutionary processes leading to the prolific radiation of African antelopes are not well understood. Here, we sequenced 145 whole genomes from both subspecies of the waterbuck, an African antelope believed to be in the process of speciation. We investigated genetic structure and population divergence and found evidence of a mid-Pleistocene separation on either side of the eastern Great Rift Valley, consistent with vicariance caused by a rain shadow along the so-called Kingdons Line. However, we also found pervasive evidence of not only isolated and recent, but also widespread historical gene flow across the Rift Valley barrier. By inferring the genome-wide landscape of variation among subspecies, we found 14 genomic regions of elevated differentiation, including a locus that may be related to each subspecies distinctive coat pigmentation pattern. We investigated these regions as candidate speciation islands. However, we observed no significant reduction in gene flow in these regions, nor any indications of selection against hybrids. Altogether, these results suggest a pattern whereby climatically driven vicariance is the most important process driving the African antelope radiation, and suggest that reproductive isolation may not set in until very late in the divergence process.

evolutionary biology↗

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

Estimation of site frequency spectra from low-coverage sequencing data using stochastic EM reduces overfitting, runtime, and memory usage

The site frequency spectrum (SFS) is an important summary statistic in population genetics used for inference on demographic history and selection. However, estimation of the SFS from called genotypes introduce bias when working with low-coverage sequencing data. Methods exist for addressing this issue, but sometimes suffer from two problems. First, they can have very high computational demands, to the point that it may not be possible to run estimation for genome-scale data. Second, existing methods are prone to overfitting, especially for multi-dimensional SFS estimation. In this article, we present a stochastic expectation-maximisation algorithm for inferring the SFS from NGS data that addresses these challenges. We show that this algorithm greatly reduces runtime and enables estimation with constant, trivial RAM usage. Further, the algorithm reduces overfitting and thereby improves downstream inference. An implementation is available at github.com/malthesr/winsfs.

bioinformatics↗

bioRxiv2021

Joint identification of sex and sex-linked scaffolds in non-model organisms using low depth sequencing data

Being able to assign sex to individuals and identify autosomal and sex-linked scaffolds are essential in most population genomic analyses. Non-model organisms often have genome assemblies at scaffold level and lack characterization of sex-linked scaffolds. Previous methods to identify sex and sex-linked scaffolds have relied on e.g. sequence similarity between the non-model organism and a closely related species or prior knowledge about the sex of the samples to identify sex-linked scaffolds. In the latter case, the difference in depth of coverage between the autosomes and the sex chromosomes are used. Here we present Sex Assignment Through Coverage (SATC), a method to identify sample sex and sex-linked scaffolds from NGS data. The method only requires a scaffold level reference assembly and sampling of both sexes with whole genome sequencing (WGS) data. We use the sequencing depth distribution across scaffolds to jointly identify: i) male and female individuals and ii) sex-linked scaffolds. This is achieved through projecting the scaffold depths into a low-dimensional space using principal component analysis (PCA) and subsequent Gaussian mixture clustering. We demonstrate the applicability of our method using data from five mammal species and a bird species complex. The method is open source and freely available at https://github.com/popgenDK/SATC

evolutionary biology↗

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↗