Biology subjects

Nielsen, R.

Publications and source records attributed to Nielsen, R..

At least 73 records · Page 4Linked to original sources

bioRxiv2020

Disentangling selection on genetically correlated polygenic traits using whole-genome genealogies

We present a full-likelihood method to estimate and quantify polygenic adaptation from contemporary DNA sequence data. The method combines population genetic DNA sequence data and GWAS summary statistics from up to thousands of nucleotide sites in a joint likelihood function to estimate the strength of transient directional selection acting on a polygenic trait. Through population genetic simulations of polygenic trait architectures and GWAS, we show that the method substantially improves power over current methods. We examine the robustness of the method under uncorrected GWAS stratification, uncertainty and ascertainment bias in the GWAS estimates of SNP effects, uncertainty in the identification of causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size. The method can quantify selection acting on correlated traits, fully controlling for pleiotropy even among traits with strong genetic correlation (|rg| = 80%; c.f. schizophrenia and bipolar disorder) while retaining high power to attribute selection to the causal trait. We apply the method to study 56 human polygenic traits for signs of recent adaptation. We find signals of directional selection on pigmentation (tanning, sunburn, hair, P=5.5e-15, 1.1e-11, 2.2e-6, respectively), life history traits (age at first birth, EduYears, P=2.5e-4, 2.6e-4, respectively), glycated hemoglobin (HbA1c, P=1.2e-3), bone mineral density (P=1.1e-3), and neuroticism (P=5.5e-3). We also conduct joint testing of 137 pairs of genetically correlated traits. We find evidence of widespread correlated response acting on these traits (2.6-fold enrichment over the null expectation, P=1.5e-7). We find that for several traits previously reported as adaptive, such as educational attainment and hair color, a significant proportion of the signal of selection on these traits can be attributed to correlated response, vs direct selection (P=2.9e-6, 1.7e-4, respectively). Lastly, our joint test uncovers antagonistic selection that has acted to increase type 2 diabetes (T2D) risk and decrease HbA1c (P=1.5e-5).

genetics↗

bioRxiv2020

Synonymous mutations and the molecular evolution of SARS-Cov-2 origins

Human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is most closely related, by average genetic distance, to two coronaviruses isolated from bats, RaTG13 and RmYN02. However, there is a segment of high amino acid similarity between human SARS-CoV-2 and a pangolin isolated strain, GD410721, in the receptor binding domain (RBD) of the spike protein, a pattern that can be caused by either recombination or by convergent amino acid evolution driven by natural selection. We perform a detailed analysis of the synonymous divergence, which is less likely to be affected by selection than amino acid divergence, between human SARS-CoV-2 and related strains. We show that the synonymous divergence between the bat derived viruses and SARS-CoV-2 is larger than between GD410721 and SARS-CoV-2 in the RBD, providing strong additional support for the recombination hypothesis. However, the synonymous divergence between pangolin strain and SARS-CoV-2 is also relatively high, which is not consistent with a recent recombination between them, instead it suggests a recombination into RaTG13. We also find a 14-fold increase in the dN/dS ratio from the lineage leading to SARS-CoV-2 to the strains of the current pandemic, suggesting that the vast majority of non-synonymous mutations currently segregating within the human strains have a negative impact on viral fitness. Finally, we estimate that the time to the most recent common ancestor of SARS-CoV-2 and RaTG13 or RmYN02 based on synonymous divergence, is 51.71 years (95% C.I., 28.11-75.31) and 37.02 years (95% C.I., 18.19-55.85), respectively.

evolutionary biology↗

bioRxiv2020

Divergence, gene flow and the origin of leapfrog geographic distributions: The history of color pattern variation in Phyllobates poison-dart frogs

The geographic distribution of phenotypic variation among closely related populations is a valuable source of information about the evolutionary processes that generate and maintain biodiversity. Leapfrog distributions, in which phenotypically similar populations are disjunctly distributed and separated by one or more phenotypically distinct populations, represent geographic replicates for the existence of a phenotype, and are therefore especially informative. These geographic patterns have mostly been studied from phylogenetic perspectives to understand how common ancestry and divergent evolution drive their formation. Other processes, such as gene flow between populations, have not received as much attention. Here we investigate the roles of divergence and gene flow between populations in the origin and maintenance of a leapfrog distribution in Phyllobates poison frogs. We found evidence for high levels of gene flow between neighboring populations but not over long distances, indicating that gene flow between populations exhibiting the central phenotype may have a homogenizing effect that maintains their similarity, and that introgression between "leapfroging" taxa has not played a prominent role as a driver of phenotypic diversity in Phyllobates. Although phylogenetic analyses suggest that the leapfrog distribution was formed through independent evolution of the peripheral (i.e. leapfrogging) populations, the elevated levels of gene flow between geographically close populations poise alternative scenarios, such as the history of phenotypic change becoming decoupled from genome-averaged patterns of divergence, which we cannot rule out. These results highlight the importance of incorporating gene flow between populations into the study of geographic variation in phenotypes, both as a driver of phenotypic diversity and as a confounding factor of phylogeographic inferences.

evolutionary biology↗

bioRxiv2019

Massive haplotypes underlie ecotypic differentiation in sunflowers

Species often include multiple ecotypes that are adapted to different environments. But how do ecotypes arise, and how are their distinctive combinations of adaptive alleles maintained despite hybridization with non-adapted populations? Re-sequencing of 1506 wild sunflowers from three species identified 37 large (1-100 Mbp), non-recombining haplotype blocks associated with numerous ecologically relevant traits, and soil and climate characteristics. Limited recombination in these regions keeps adaptive alleles together, and we find that they differentiate several sunflower ecotypes; for example, they control a 77 day difference in flowering between ecotypes of silverleaf sunflower (likely through deletion of a FLOWERING LOCUS T homolog), and are associated with seed size, flowering time and soil fertility in dune-adapted sunflowers. These haplotypes are highly divergent, associated with polymorphic structural variants, and often appear to represent introgressions from other, possibly extinct, congeners. This work highlights a pervasive role of structural variation in maintaining complex ecotypic adaptation.

evolutionary biology↗

bioRxiv2019

Deviations from Hardy Weinberg Equilibrium at CCR5-Δ32 in Large Sequencing Data Sets

Previous analyses of the UK Biobank (UKB) genotyping array data in the CCR5-{Delta}32 locus show evidence for deviations from Hardy-Weinberg Equilibrium (HWE) and an increased mortality rate of homozygous individuals, consistent with a recessive deleterious effect of the deletion mutation. We here examine if similar deviations from HWE can be observed in the newly released UKB Whole Exome Sequencing (WES) data and in the sequencing data of the Genome Aggregation Database (gnomAD). We also examine the reliability of the genotype calls in the UKB array data. The UKB genotyping array probe targeting CCR5-{Delta}32 (rs62625034) and the WES of {Delta}32 are strongly correlated (r2 = 0.97). This contrasts to tag SNPs of CCR5-{Delta}32 in the UKB which have high missing data rates and imputation errors rates. We also show that, while different data sets are subject to different biases, both the UKB-WES and the gnomAD data have a deficiency of homozygous CCR5-{Delta}32 individuals compared to the HWE expectation (combined P-value < 0.01), consistent with an increased mortality rate in homozygotes. Finally, we perform a survival analysis on data from parents of UKB volunteers, that, while underpowered, is also consistent with the original report of a deleterious effect of CCR5-{Delta}32 in the homozygous state.

evolutionary biology↗

bioRxiv2019

On the distribution of tract lengths during adaptive introgression

Admixture is increasingly being recognized as an important factor in evolutionary genetics. The distribution of genomic admixture tracts, and the resulting effects on admixture linkage disequilibrium, can be used to date the timing of admixture between species or populations. However, the theory used for such prediction assumes selective neutrality despite the fact that many famous examples of admixture involve natural selection acting for or against admixture. In this paper, we investigate the effects of positive selection on the distribution of tract lengths. We develop a theoretical framework that relies on approximating the trajectory of the selected allele using a logistic function. By numerically calculating the expected allele trajectory, we also show that the approach can be extended to cases where the logistic approximation is poor due to the effects of genetic drift. Using simulations, we show that the model is highly accurate under most scenarios. We use the model to show that positive selection on average will tend to increase the admixture tract length. However, perhaps counter-intuitively, conditional on the allele frequency at the time of sampling, positive selection will actually produce shorter expected tract lengths. We discuss the consequences of our results in interpreting the timing of the introgression of EPAS1 from Denisovans into the ancestors of Tibetans.

genomics↗

bioRxiv2019

VolcanoFinder: genomic scans for adaptive introgression

Recent research shows that introgression between closely-related species is an important source of adaptive alleles for a wide range of taxa. Typically, detection of adaptive introgression from genomic data relies on comparative analyses that require sequence data from both the recipient and the donor species. However, in many cases, the donor is unknown or the data is not currently available. Here, we introduce a genome-scan method--VolcanoFinder--to detect recent events of adaptive introgression using polymorphism data from the recipient species only.\n\nVolcanoFinder detects adaptive introgression sweeps from the pattern of excess intermediate-frequency polymorphism they produce in the flanking region of the genome, a pattern which appears as a volcano-shape in pairwise genetic diversity.\n\nUsing coalescent theory, we derive analytical predictions for these patterns. Based on these results, we develop a composite-likelihood test to detect signatures of adaptive introgression relative to the genomic background. Simulation results show that VolcanoFinder has high statistical power to detect these signatures, even for older sweeps and for soft sweeps initiated by multiple migrant haplotypes. Finally, we implement VolcanoFinder to detect archaic introgression in European and sub-Saharan African human populations, and uncovered interesting candidates in both populations, such as TSHR in Europeans and TCHH-RPTN in Africans. We discuss their biological implications and provide guidelines for identifying and circumventing artifactual signals during empirical applications of VolcanoFinder.\n\nAuthor summaryThe process by which beneficial alleles are introduced into a species from a closely-related species is termed adaptive introgression. We present an analytically-tractable model for the effects of adaptive introgression on non-adaptive genetic variation in the genomic region surrounding the beneficial allele. The result we describe is a characteristic volcano-shaped pattern of increased variability that arises around the positively-selected site, and we introduce an open-source method VolcanoFinder to detect this signal in genomic data. Importantly, VolcanoFinder is a population-genetic likelihood-based approach, rather than a comparative-genomic approach, and can therefore probe genomic variation data from a single population for footprints of adaptive introgression, even from a priori unknown and possibly extinct donor species.

evolutionary biology↗

bioRxiv2019

A likelihood method for estimating present-day human contamination in ancient DNA samples using low-depth haploid chromosome data

1MotivationThe presence of present-day human contaminating DNA fragments is one of the challenges defining ancient DNA (aDNA) research. This is especially relevant to the ancient human DNA field where it is difficult to distinguish endogenous molecules from human contaminants due to their genetic similarity. Recently, with the advent of high-throughput sequencing and new aDNA protocols, hundreds of ancient human genomes have become available. Contamination in those genomes has been measured with computational methods often developed specifically for these empirical studies. Consequently, some of these methods have not been implemented and tested while few are aimed at low-depth data, a common feature in aDNA datasets.\n\nResultsWe develop a new X-chromosome-based maximum likelihood method for estimating present-day human contamination in low-depth sequencing data. We implement our method for general use, assess its performance under conditions typical of ancient human DNA research, and compare it to previous nuclear data-based methods through extensive simulations. For low-depth data, we show that existing methods can produce unusable estimates or substantially underestimate contamination. In contrast, our method provides accurate estimates for a depth of coverage as low as 0.5x on the X-chromosome when contamination is below 25%. Moreover, our method still yields meaningful estimates in very challenging situations, i.e., when the contaminant and the target come from closely related populations or with increased error rates. With a running time below five minutes, our method is applicable to large scale aDNA genomic studies.\n\nAvailability and implementationThe method is implemented in C++ and R and is freely available in https://github.com/sapfo/contaminationX.\n\nContactmorenomayar@gmail.com, annasapfo.malaspinas@unil.ch.

genomics↗

bioRxiv2019

An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data

Most current methods for detecting natural selection from DNA sequence data are limited in that they are either based on summary statistics or a composite likelihood, and as a consequence, do not make full use of the information available in DNA sequence data. We here present a new importance sampling approach for approximating the full likelihood function for the selection coefficient. The method treats the ancestral recombination graph (ARG) as a latent variable that is integrated out using previously published Markov Chain Monte Carlo (MCMC) methods. The method can be used for detecting selection, estimating selection coefficients, testing models of changes in the strength of selection, estimating the time of the start of a selective sweep, and for inferring the allele frequency trajectory of a selected or neutral allele. We perform extensive simulations to evaluate the method and show that it uniformly improves power to detect selection compared to current popular methods such as nSL and SDS, under various demographic models and can provide reliable inferences of allele frequency trajectories under many conditions. We also explore the potential of our method to detect extremely recent changes in the strength of selection. We use the method to infer the past allele frequency trajectory for a lactase persistence SNP (MCM6) in Europeans. We also study a set of 11 pigmentation-associated variants. Several genes show evidence of strong selection particularly within the last 5,000 years, including ASIP, KITLG, and TYR. However, selection on OCA2/HERC2 seems to be much older and, in contrast to previous claims, we find no evidence of selection on TYRP1.\n\nAuthor summaryCurrent methods to study natural selection using modern population genomic data are limited in their power and flexibility. Here, we present a new method to infer natural selection that builds on recent methodological advances in estimating genome-wide genealogies. By using importance sampling we are able to efficiently estimate the likelihood function of the selection coefficient. We show our method improves power to test for selection over competing methods across a diverse range of scenarios, and also accurately infers the selection coefficient. We also demonstrate a novel capability of our model, using it to infer the alleles frequency over time. We validate these results with a study of a lactase persistence SNP in Europeans, and also study a set of 11 pigmentation-associated variants.

genetics↗

bioRxiv2019

A Bayesian framework for inferring the influence of sequence context on single base modifications

The probability of single base modifications (mutations and DNA/RNA modifications) is expected to be highly influenced by the flanking nucleotides that surround them, known as the sequence context. This phenomenon may be mainly attributed to the enzyme that modifies or mutates the genetic material, since most enzymes tend to have specific sequence contexts that dictate their activity. Thus, identification of context effects may lead to the discovery of additional editing sites or unknown enzymatic factors. Here, we develop a statistical model that allows for the detection and evaluation of the effects of different sequence contexts on mutation rates from deep population sequencing data. This task is computationally challenging, as the complexity of the model increases exponentially as the context size increases. We established our novel Bayesian method based on sparse model selection methods, with the leading assumption that the number of actual sequence contexts that directly influence mutation rates is minuscule compared to the number of possible sequence contexts. We show that our method is highly accurate on simulated data using pentanucleotide contexts, even when accounting for noisy data. We next analyze empirical population sequencing data from polioviruses and detect a significant enrichment in sequence contexts associated with deamination by the cellular deaminases ADAR 1/2. In the current era, where next generation sequencing data is highly abundant, our approach can be used on any population sequencing data to reveal context-dependent base alterations, and may assist in the discovery of novel mutable sites or editing sites.

bioinformatics↗

bioRxiv2019

Ohana: detecting selection in multiple populations by modelling ancestral admixture components

One of the most powerful and commonly used methods for detecting local adaptation in the genome is the identification of extreme allele frequency differences between populations. In this paper, we present a new maximum likelihood method for finding regions under positive selection. The method is based on a Gaussian approximation to allele frequency changes and it incorporates admixture between populations. The method can analyze multiple populations simultaneously and retains power to detect selection signatures specific to ancestry components that are not representative of any extant populations. We evaluate the method using simulated data and compare it to related methods based on summary statistics. We also apply it to human genomic data and identify loci with extreme genetic differentiation between major geographic groups. Many of the genes identified are previously known selected loci relating to hair pigmentation and morphology, skin and eye pigmentation. We also identify new candidate regions, including various selected loci in the Native American component of admixed Mexican-Americans. These involve diverse biological functions, like immunity, fat distribution, food intake, vision and hair development.

bioinformatics↗

bioRxiv2019

The California Environmental DNA \"CALeDNA\" Program

Global change is leading to habitat shifts that threaten species persistence throughout Californias unique ecosystems. Baseline biodiversity data provide opportunities for ecosystems to be managed for community complexity and connectivity. In 2017, the University of California Conservation Genomics Consortium launched the California Environmental DNA (CALeDNA) program, a community science initiative monitoring Californias biodiversity through environmental DNA (eDNA)--DNA shed from organisms through fur, mucus, spores, pollen, etc. Community scientists collect soil and sediment samples, then researchers analyze the eDNA in the samples and share results with the public. The results are catalogues of thousands of organisms per sample, ranging from microbes to mammals. The CALeDNA website presents biodiversity inventories in a platform designed for the public and researchers alike, as well as user-friendly analysis tools and educational modules. Here, we present CALeDNA as a scalable community science framework that can harmonize with future biodiversity research and education initiatives.

scientific communication and education↗

bioRxiv2018

Joint Estimation of Pedigrees and Effective Population Size Using Markov Chain Monte Carlo

Pedigrees provide the genealogical relationships among individuals at a fine resolution and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of the short-term effective population size (Ne), which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and Ne from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals with the use of a composite likelihood, which significantly increases computational efficiency. We show on simulated data that our method is able to jointly estimate relationships up to first cousins and Ne with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.

genetics↗

bioRxiv2018

A map of climate change-driven natural selection in Arabidopsis thaliana

Through the lens of evolution, climate change is an agent of directional selection that forces populations to change and adapt, or face extinction. Current assessments of the risks associated with climate change1,2, however, do not typically take into account that natural selection can dramatically impact the genetic makeup of populations3. We made use of extensive genome information in Arabidopsis thaliana and measured how rainfall-manipulation affected the fitness of 517 natural lines grown in Spain and Germany. This allowed us to directly infer selection at the genetic level4. Natural selection was particularly strong in the hot-dry Spanish location, killing 63% of lines and significantly changing the frequency of [~]5% of all genome-wide variants. A significant proportion of this selection over variants could be predicted from climate (mis)match between experimental sites and the geographic areas of where variants are found (R2=29-52%). Field-validated predictions across the species range indicated that Mediterranean and Western Siberia populations -- at the edges of the species environmental limits -- currently experience the strongest climate-driven selection, and Central Europeans the weakest. With rapidly increasing droughts and rising temperatures in Europe5, we forecast a wave of directional selection moving North, putting many native A thaliana populations at evolutionary risk.

evolutionary biology↗

bioRxiv2018

Estimating the timing of multiple admixture pulses during local ancestry inference.

Admixture, the mixing of genetically distinct populations, is increasingly recognized as a fundamental biological process. One major goal of admixture analyses is to estimate the timing of admixture events. Whereas most methods today can only detect the most recent admixture event, here we present coalescent theory and associated software that can be used to estimate the timing of multiple admixture events in an admixed population. We extensively validate this approach and evaluate the conditions under which it can succesfully distinguish one from two-pulse admixture models. We apply our approach to real and simulated data of Drosophila melanogaster. We find evidence of a single very recent pulse of cosmopolitan ancestry contributing to African populations as well as evidence for more ancient admixture among genetically differentiated populations in sub-Saharan Africa. These results suggest our method can quantify complex admixture histories involving genetic material introduced by multiple discrete admixture pulses. The new method facilitates the exploration of admixture and its contribution to adaptation, ecological divergence, and speciation.

evolutionary biology↗

bioRxiv2018

Inferring the ancestry of parents and grandparents from genetic data

Inference of admixture proportions is a classical statistical problem in population genetics. Standard methods implicitly assume that both parents of an individual have the same admixture fraction. However, this is rarely the case in real data. In this paper we show that the distribution of admixture tract lengths in a genome contains information about the admixture proportions of the ancestors of an individual. We develop a Hidden Markov Model (HMM) framework for estimating the admixture proportions of the immediate ancestors of an individual, i.e. a type of decomposition of an individuals admixture proportions into further subsets of ancestral proportions in the ancestors. Based on a genealogical model for admixture tracts, we develop an efficient algorithm for computing the sampling probability of the genome from a single individual, as a function of the admixture proportions of the ancestors of this individual. This allows us to perform probabilistic inference of admixture proportions of ancestors only using the genome of an extant individual. We perform extensive simulations to quantify the error in the estimation of ancestral admixture proportions under various conditions. To illustrate the utility of the method, we apply it to real genetic data. Author summaryAncestry inference is an important problem in genetics and is used commercially by a number of companies affecting millions of consumers of genetic ancestry tests. In this paper, we show that it is possible, not only to estimate the ancestry fractions of an individual, but also, with some uncertainty, to estimate the ancestry fractions of an individuals ancestors. For example, if an individual traces his/her ancestry 50% to Asia and 50% to Europe, it is possible to distinguish between the individual having two parents that each are 50:50 composites of Asian and European ancestry, or one parent from Asia and one from Europe. It is likewise also possible to make inferences about grandparents. We present a computationally efficient method for making such inferences called PedMix. PedMix is based on a probabilistic model for the descendant and the recent ancestors. PedMix infers admixture proportions of recent ancestors (parents, grandparents or even great grandparents) using whole-genome genetic variation data from a focal individual. Results on both simulated and real data show that PedMix performs reasonably well in most scenarios.

genetics↗

bioRxiv2017

A population phylogenetic view of mitochondrial heteroplasmy

The mitochondrion has recently emerged as an active player in a myriad of cellular processes. Additionally, it was recently shown that more than 200 diseases are known to be linked to variants in mitochondrial DNA or in nuclear genes interacting with mitochondria. This has reinvigorated interest in its biology and population genetics. Mitochondrial heteroplasmy, or genotypic variation of mitochondria within an individual, is now understood to be common in humans and important in human health. However, it is still not possible to make quantitative predictions about the inheritance of heteroplasmy and its proliferation within the body, partly due to the lack of an appropriate model. Here, we present a population-genetic framework for modeling mitochondrial heteroplasmy as a process that occurs on an ontogenetic phylogeny, with genetic drift and mutation changing heteroplasmy frequencies during the various developmental processes represented in the phylogeny. Using this framework, we develop a Bayesian inference method for inferring rates of mitochondrial genetic drift and mutation at different stages of human life. Applying the method to previously published heteroplasmy frequency data, we demonstrate a severe effective germline bottleneck comprised of the cumulative genetic drift occurring between the divergence of germline and somatic cells in the mother and the separation of germ layers in the offspring. Additionally, we find that the two somatic tissues we analyze here undergo tissue-specific bottlenecks during embryogenesis, less severe than the effective germline bottleneck, and that these somatic tissues experience little additional genetic drift during adulthood. We conclude with a discussion of possible extensions of the ontogenetic phylogeny framework and its possible applications to other ontogenetic processes in addition to mitochondrial heteroplasmy.

genomics↗

bioRxiv2017

On The Number Of Siblings And p-th Cousins In A Large Population Sample

1.The number of individuals in a random sample with close relatives in the sample is a quantity of interest when designing Genome Wide Association Studies (GWAS) and other cohort based genetic, and non-genetic, studies. In this paper, we develop expressions for the distribution and expectation of the number of p-th cousins in a sample from a population of size N under two diploid Wright-Fisher models. We also develop simple asymptotic expressions for large values of N. For example, the expected proportion of individuals with at least one p-th cousin in a sample of K individuals, for a diploid dioecious Wright-Fisher model, is approximately 1 - e-(22p-1)K/N. Our results show that a substantial fraction of individuals in the sample will have at least a second cousin if the sampling fraction (K/N) is on the order of 10-2. This confirms that, for large cohort samples, relatedness among individuals cannot easily be ignored.

evolutionary biology↗