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Nielsen, R.

Publications and source records attributed to Nielsen, R..

At least 55 records · Page 3Linked to original sources
bioRxiv2022

A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets

Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern Next-Generation Sequencing data. We here present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.

genomics↗
bioRxiv2022

Genetic risk for Multiple Sclerosis originated in Pastoralist Steppe populations

Multiple sclerosis (MS) is a modern neuro-inflammatory and -degenerative disease, which is most prevalent in Northern Europe. Whilst it is known that inherited risk to MS is located within or within close proximity to immune genes, it is unknown when, where and how this genetic risk originated 1. By using the largest ancient genome dataset from the Stone Age 2, along with new Medieval and post-Medieval genomes, we show that many of the genetic risk variants for MS rose to higher frequency among pastoralists located on the Pontic Steppe, and were brought into Europe by the Yamnaya-related migration approximately 5,000 years ago. We further show that these MS-associated immunogenetic variants underwent positive selection both within the Steppe population, and later in Europe, likely driven by pathogenic challenges coinciding with dietary, lifestyle, and population density changes. This study highlights the critical importance of this period as a determinant of modern immune responses and its subsequent impact on the risk of developing MS in a changing environment.

evolutionary biology↗
bioRxiv2022

The Selection Landscape and Genetic Legacy of Ancient Eurasians

The Holocene (beginning [~]12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical, and mental health of present-day populations. Using a dataset of >1600 imputed ancient genomes 1, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify major selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported, and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in >400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic, and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, while risk alleles for diabetes and Alzheimers disease are enriched for Western Hunter-gatherer ancestry. Our results suggest that ancient selection and migration were major contributors to the distribution of phenotypic diversity in present-day Europeans.

evolutionary biology↗
bioRxiv2022

Bayesian inference of admixture graphs on Native American and Arctic populations

Admixture graphs are mathematical structures that describe the ancestry of populations in terms of divergence and merging (admixing) of ancestral populations as a graph. An admixture graph consists of a graph topology, branch lengths, and admixture proportions. The branch lengths and admixture proportions can be estimated using numerous numerical optimization methods, but inferring the topology involves a combinatorial search for which no polynomial algorithm is known. In this paper, we present a reversible jump MCMC algorithm for sampling high-probability admixture graphs and show that this approach works well both as a heuristic search for a single best-fitting graph and for summarizing shared features extracted from posterior samples of graphs. We apply the method to 11 Native American and Siberian populations and exploit the shared structure of high-probability graphs to address the relationship between Saqqaq, Inuit, Koryaks, and Athabascans. Our analyses show that the Saqqaq is not a good proxy for the previously identified gene flow from Arctic people into the Na-Dene speaking Athabascans. Author SummaryOne way of summarizing historical relationships between genetic samples is by constructing an admixture graph. An admixture graph describes the demographic history of a set of populations as a directed acyclic graph representing population splits and mergers. The inference of admixture graphs is currently done via greedy search algorithms that may fail to find the global optimum. We here improve on these approaches by developing a novel MCMC sampling method, AdmixtureBayes, that can sample from the posterior distribution of admixture graphs. This enables an efficient search of the entire state space as well as the ability to report a level of confidence in the sampled graphs. We apply AdmixtureBayes to a set of Native American and Arctic genomes to reconstruct the demographic history of these populations and report posterior probabilities of specific admixture events. While some previous studies have identified the ancient Saqqaq culture as a source of introgression into Athabascans, we instead find that it is the Siberian Koryak population, not the Saqqaq, that serves as the best proxy for gene flow into Athabascans.

genetics↗
bioRxiv2022

Estimating population split times and migration rates from historical effective population sizes

The estimation of effective population sizes (Ne) through time is of fundamental interest in population genetics, but the interpretation of Ne as the effective number of breeding individuals in the population is challenged by the effect of population structure. In fact, variation in Ne reported in many studies may be a consequence of changes in migration rates between populations rather than changes in actual population size. We address this long-standing problem here by constructing joint models of population size changes, migration, and divergence that can adjust temporal estimates of Ne and estimate the actual Ne of a local deme connected to another population through migration. We also develop a method for estimating divergence times and migration rates taking into account complex scenarios of changing population sizes. We apply the method to previously published data from humans, and show that, when taking migration and changes in Ne into account, the estimated divergence between the San and Dinka populations is approximately 108 kya, and not 255 kya as reported in a previous study. Using simulations, we demonstrate that the previously reported and surprisingly old estimates of divergence between San and Dinka is in fact caused by a quantifiable estimation bias due to changes in Ne through time.

evolutionary biology↗
bioRxiv2022

SCONCE2: jointly inferring single cell copy number profiles and tumor evolutionary distances

Single cell whole genome tumor sequencing can yield novel insights into the evolutionary history of somatic copy number alterations. Existing single cell copy number calling methods do not explicitly model the shared evolutionary process of multiple cells, and generally analyze cells independently. Additionally, existing methods for estimating tumor cell phylogenies using copy number profiles are sensitive to profile estimation errors. We present SCONCE2, a method for jointly calling copy number alterations and estimating pairwise distances for single cell sequencing data. Using simulations, we show that SCONCE2 has higher accuracy in copy number calling and phylogeny estimation than competing methods. We apply SCONCE2 to previously published single cell sequencing data to illustrate the utility of the method.

bioinformatics↗
bioRxiv2021

Evaluation of methods for the inference of ancestral recombination graphs

The ancestral recombination graph (ARG) is a structure that describes the joint genealogies of sampled DNA sequences along the genome. Recent computational methods have made impressive progress towards scalably estimating whole-genome genealogies. In addition to inferring the ARG, some of these methods can also provide ARGs sampled from a defined posterior distribution. Obtaining good samples of ARGs is crucial for quantifying statistical uncertainty and for estimating population genetic parameters such as effective population size, mutation rate, and allele age. Here, we use standard neutral coalescent simulations to benchmark the estimates of pairwise coalescence times from three popular ARG inference programs: ARGweaver, Relate, and tsinfer+tsdate. We compare 1) the true coalescence times to the inferred times at each locus; 2) the distribution of coalescence times across all loci to the expected exponential distribution; 3) whether the sampled coalescence times have the properties expected of a valid posterior distribution. We find that inferred coalescence times at each locus are most accurate in ARGweaver, and often more accurate in Relate than in tsinfer+tsdate. However, all three methods tend to overestimate small coalescence times and underestimate large ones. Lastly, the posterior distribution of ARGweaver is closer to the expected posterior distribution than Relates, but this higher accuracy comes at a substantial trade-off in scalability. The best choice of method will depend on the number and length of input sequences and on the goal of downstream analyses, and we provide guidelines for the best practices.

genomics↗
bioRxiv2021

SCONCE: A method for profiling Copy Number Alterations in Cancer Evolution using Single Cell Whole Genome Sequencing

Copy number alterations are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, copy number alterations are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content, and do not account for the stochastic evolutionary process. We present a theoretical framework to use tumor evolutionary history to accurately call copy number alterations in a principled manner. In order to model the tumor evolutionary process and account for technical noise from low coverage single cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public datasets and simulations, we show SCONCE accurately decodes copy number profiles, with broader implications for understanding tumor evolution. SCONCE is implemented in C++11 and is freely available from https://github.com/NielsenBerkeleyLab/sconce.

bioinformatics↗
bioRxiv2021

The genetic architecture of phenotypic diversity in the betta fish (Betta splendens)

The Betta fish displays a remarkable variety of phenotypes selected during domestication. However, the genetic basis underlying these traits remain largely unexplored. Here, we report a high-quality genome assembly and re-sequencing of 727 individuals representing diverse morphologies of the betta fish. We show that current breeds have a complex domestication history with extensive introgression with wild species. Using GWAS, we identify the genetic basis of multiple traits, including several coloration phenotypes, sex-determination which we map to DMRT1, and the long-fin phenotype which maps to KCNJ15. We identify a polygenic signal related to aggression with many similarities to human psychiatric traits, involving genes such as CACNB2 and DISC1. Our study provides a resource for developing the Betta fish as a genetic model for morphology and behavior in vertebrates.

genetics↗
bioRxiv2021

AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees

MotivationClustering is a fundamental task in the analysis of nucleotide sequences. Despite the exponential increase in the size of sequence databases of homologous genes, few methods exist to cluster divergent sequences. Traditional clustering methods have mostly focused on optimizing high speed clustering of highly similar sequences. We develop a phylogenetic clustering method which infers ancestral sequences for a set of initial clusters and then uses a greedy algorithm to cluster sequences. ResultsWe describe a clustering program AncestralClust, which is developed for clustering divergent sequences. We compare this method with other state-of-the-art clustering methods using datasets of homologous sequences from different species. We show that, in divergent datasets, AncestralClust has higher accuracy and more even cluster sizes than current popular methods. Availability and implementationAncestralClust is an Open Source program available at https://github.com/lpipes/ancestralclust. Contactlpipes@berkeley.edu Supplementary informationSupplementary figures and table are available online.

bioinformatics↗
bioRxiv2020

The distribution of waiting distances in ancestral recombination graphs and its applications

The ancestral recombination graph (ARG) contains the full genealogical information of the sample, and many population genetic inference problems can be solved using inferred or sampled ARGs. In particular, the waiting distance between tree changes along the genome can be used to make inference about the distribution and evolution of recombination rates. To this end, we here derive an analytic expression for the distribution of waiting distances between tree changes under the sequentially Markovian coalescent model and obtain an accurate approximation to the distribution of waiting distances for topology changes. We use these results to show that some of the recently proposed methods for inferring sequences of trees along the genome provide strongly biased distributions of waiting distances. In addition, we provide a correction to an undercounting problem facing all available ARG inference methods, thereby facilitating the use of ARG inference methods to estimate temporal changes in the recombination rate.

evolutionary biology↗
bioRxiv2020

COAL_PHYRE: A Composite Likelihood Method for Estimating Species Tree Parameters from Genomic Data Using Coalescent Theory

2Genome-scale data are increasingly being used to infer phylogenetic trees. A major challenge in such inferences is that different regions of the genome may have local topologies that differ from the species tree due to incomplete lineage sorting (ILS). Another source of gene tree discrepancies is estimation errors arising from the randomness of the mutational process during sequence evolution. There are two major groups of methods for estimating species tree from whole-genome data: a set of full likelihood methods, which model both sources of variance, but do not scale to large numbers of independent loci, and a class of faster approximation methods which do not model the mutational variance. To bridge the gap between these two classes of methods, we present COAL_PHYRE (COmposite Approximate Likelihood for PHYlogenetic REconstruction), a composite likelihood based method for inferring population size and divergence time estimates of rooted species trees from aligned gene sequences. COAL_PHYRE jointly models coalescent variation across loci using the MSC and variation in local gene tree reconstruction using a normal approximation. To evaluate the accuracy and speed of the method, we compare against BPP, a powerful MCMC full-likelihood method, as well as ASTRAL-III, a fast approximate method. We show that COAL_PHYREs divergence time and population size estimates are more accurate than ASTRAL, and comparable to those obtained using BPP, with an order of magnitude decrease in computational time. We also present results on previously published data from a set of Gibbon species to evaluate the accuracy in topology and parameter inference on real data, and to illustrate the methods ability to analyze data sets which are prohibitively large for MCMC methods.

evolutionary biology↗
bioRxiv2020

Evolution of the genetic architecture of local adaptations under genetic rescue is determined by mutational load and polygenicity

Inbred populations often suffer from heightened mutational load and decreased fitness due to lower efficiency of purifying selection at small effective population size. Genetic rescue (GR) is a tool that is studied and deployed with the aim of increasing fitness of such inbred populations. The success of GR is known to depend on certain factors that may vary between different populations, such as their demographic history and distribution of dominance effects of mutations. While we understand the effects of these factors on the evolution of overall ancestry in the inbred population after GR, it is less clear what the effect is on local adaptations and their genetic architecture. To this end, we conduct a population genetic simulation study evaluating the effect of several different factors on the efficacy of GR including trait complexity (Mendelian vs. polygenic), dominance effects, and demographic history. We find that the effect on local adaptations depends highly on the mutational load at the time of GR, which is shaped dynamically by interactions between demographic history and dominance effects of deleterious variation. While local adaptations are generally restored post-GR in the long run, in the short term they are often compromised in the process of purging deleterious variation. We also show that while local adaptations are almost always fully restored, the degree to which ancestral genetic variation comprising the trait is replaced by donor variation can vary drastically, and is especially high for complex traits. Our results provide considerations for practical GR and its effects on trait evolution.

genetics↗
bioRxiv2020

The history and evolution of the Denisovan-EPAS1 haplotype in Tibetans

Recent studies suggest that admixture with archaic hominins played an important role in facilitating biological adaptations to new environments. For example, interbreeding with Denisovans facilitated the adaptation to high altitude environments on the Tibetan Plateau. Specifically, the EPAS1 gene, a transcription factor that regulates the response to hypoxia, exhibits strong signatures of both positive selection and introgression from Denisovans in Tibetan individuals. Interestingly, despite being geographically closer to the Denisova cave, East Asian populations do not harbor as much Denisovan ancestry as populations from Melanesia. Recently, two studies have suggested two independent waves of Denisovan admixture into East Asians, one of which is shared with South Asians and Oceanians. Here we leverage data from EPAS1 in 78 Tibetan individuals to interrogate which of these two introgression events introduced the EPAS1 beneficial sequence into the ancestral population of Tibetans, and we use the distribution of introgressed segment lengths at this locus to infer the timing of the introgression and selection event. We find that the introgression event unique to East Asians most likely introduced the beneficial haplotype into the ancestral population of Tibetans around 43,000 (15,700-60,000) years ago, and selection started 12,000 (1,925-50,000) years ago. Our estimates suggest that one of the most convincing examples of adaptive introgression is in fact selection acting on standing archaic variation.

evolutionary biology↗
bioRxiv2020

Convergent evolution of increased urine concentrating ability in desert mammals

One of the most celebrated textbook examples of physiological adaptations to desert environments is the unique ability that desert mammals have to produce hyperosmotic urine. Commonly perceived as an adaptation mainly observed in small rodents, the extent to which urine concentrating ability has independently evolved in distinct lineages, including medium-sized and large desert mammals, has not previously been assessed using modern phylogenetic approaches. Here, we explicitly test the general hypothesis that desert-dwelling mammals have evolved increased ability to concentrate urine compared to non-desert species, controlling for body mass and other covariates. Phylogenetic generalized least-squares models show that the mean aridity index of a species distribution range largely predicts its urine concentrating ability, even when accounting for body mass differences and phylogenetic correlations. In contrast, we find much weaker correlations between mass-adjusted basal metabolic rate and environmental variables.

evolutionary biology↗
bioRxiv2020

Inferring Adaptive Introgression Using Hidden Markov Models

Adaptive introgression - the flow of adaptive genetic variation between species or populations - has attracted significant interest in recent years and it has been implicated in a number of cases of adaptation, from pesticide resistance and immunity, to local adaptation. Despite this, methods for identification of adaptive introgression from population genomic data are lacking. Here, we present Ancestry_HMM-S, a Hidden Markov Model based method for identifying genes undergoing adaptive introgression and quantifying the strength of selection acting on them. Through extensive validation, we show that this method performs well on moderately sized datasets for realistic population and selection parameters. We apply Ancestry_HMM-S to a dataset of an admixed Drosophila melanogaster population from South Africa and we identify 17 loci which show signatures of adaptive introgression, four of which have previously been shown to confer resistance to insecticides. Ancestry_HMM-S provides a powerful method for inferring adaptive introgression in datasets that are typically collected when studying admixed populations. This method will enable powerful insights into the genetic consequences of admixture across diverse populations. Ancestry_HMM-S can be downloaded from https://github.com/jesvedberg/Ancestry_HMM-S/.

evolutionary biology↗
bioRxiv2020

Non-additive polygenic models improve predictions of fitness traits in three eukaryote model species

To describe a living organism it is often said that "the whole is greater than the sum of its parts". In genetics, we may also think that the effect of multiple mutations on an organism is greater than their additive individual effect, a phenomenon called epistasis or multiplicity. Despite the last decades discovery that many disease- and fitness-related traits are polygenic, or controlled by many genetic variants, it is still debated whether the effects of individual genes combine additively or not. Here we develop a flexible likelihood framework for genome-wide associations to fit complex traits such as fitness under both additive and non-additive polygenic architectures. Analyses of simulated datasets under different true additive, multiplicative, or other epistatic models, confirm that our method can identify global non-additive selection. Applying the model to experimental datasets of wild type lines of Arabidopsis thaliana, Drosophila melanogaster, and Saccharomyces cerevisiae, we find that fitness is often best explained with non-additive polygenic models. Instead, a multiplicative polygenic model appears to better explain fitness in some experimental environments. The statistical models presented here have the potential to improve prediction of phenotypes, such as disease susceptibility, over the standard methods for calculating polygenic scores which assume additivity.

evolutionary biology↗
bioRxiv2020

Assessing uncertainty in the rooting of the SARS-CoV-2 phylogeny

The rooting of the SARS-CoV-2 phylogeny is important for understanding the origin and early spread of the virus. Previously published phylogenies have used different rootings that do not always provide consistent results. We investigate several different strategies for rooting the SARS-CoV-2 tree and provide measures of statistical uncertainty for all methods. We show that methods based on the molecular clock tend to place the root in the B clade, while methods based on outgroup rooting tend to place the root in the A clade. The results from the two approaches are statistically incompatible, possibly as a consequence of deviations from a molecular clock or excess back-mutations. We also show that none of the methods provide strong statistical support for the placement of the root in any particular edge of the tree. Our results suggest that inferences on the origin and early spread of SARS-CoV-2 based on rooted trees should be interpreted with caution.

evolutionary biology↗