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bioRxiv2026

Interaction of combined stressors and genetic background on recombination rate plasticity in Drosophila melanogaster

Recombination is an important source of genetic variation and plays a central role in adaptation and genome stability. Although environmental conditions such as temperature can alter recombination rates, much less is known about how multiple environmental stressors act together. Here, we tested the effects of low temperature and hypoxia, both independently and in combination, on recombination in Drosophila melanogaster across five genetic backgrounds. Using a quadruple X-linked marker stock, we measured recombination in 50,209 male backcross progeny from 194 backcrosses exposed to four conditions: control, hypoxia, low temperature, and combined hypoxia and low temperature. Low temperature significantly altered recombination across all three chromosome intervals examined and affected total map length with an average increase of 5 cM. Genetic background strongly influenced the magnitude of altered recombination rate. Hypoxia alone did not significantly change total map length, but its effects differed among genetic backgrounds with significant variation in both the magnitude and direction with an average decrease of 0.96 cM. The response to combined hypoxia and low temperature was not simply the sum of the responses to each stressor alone, with an average increase of 6.78 cM, that also varied across genetic backgrounds in magnitude and direction as well as over time. DGRP_57 and DGRP_508 showed some of the largest increases in recombination under combined stress, whereas DGRP_491 showed a decrease. DGRP_391 was robust across all treatments. Despite these changes in crossover frequency, crossover interference remained largely stable across treatments, with only the y-cv-v interval pair showing a significant treatment-by-genotype interaction. Together, these results show that recombination responses to environmental stress depend strongly on genetic background and on the combination of stressors experienced. Our findings highlight the importance of considering both genetic variation and interacting environmental conditions when predicting how populations may respond to increasingly complex environmental change.

genetics↗

bioRxiv2026

X-Chromosomal STR-based analysis of genetic diversity, structure, and phylogeny in the Sri Lankan Vedda population

The Vedda people are regarded as the direct descendants of Sri Lanka's Mesolithic hunter-gatherers and represent one of South Asia's oldest surviving indigenous populations. X-chromosomal short tandem repeat (X-STR) analysis provides a powerful framework to examine their genetic distinctiveness, as the X chromosome exhibits sex-biased biparental inheritance and greater sensitivity to demographic and evolutionary forces compared to autosomal markers. However, no X-STR-based genetic investigations have yet been conducted on this population. In the present study, we successfully genotyped 50 unrelated Veddas individuals using a recently developed 16 X-STR multiplex PCR system optimized for Sri Lankan populations. Our analyses revealed a statistically significant genetic differentiation (2.43%) between the Veddas and the principal local ethnic groups; Sinhalese, Sri Lankan Tamils, Indian Tamils, and Moors exceeding previous estimates based on autosomal STR markers. Comparative analysis with 32 global populations positioned the Veddas as a genetically distinct and isolated group, separated from clusters representing South Asians, Europeans, Middle Easterners, and other indigenous populations such as Australian Aboriginals, Bhil Indians, and Kedayans, emphasizing their unique genetic heritage. Reduced allelic (115) and haplotype (62) counts, alongside significant linkage disequilibrium detected among eight pairs of X-STR loci, reflect the influence of genetic drift within this small, endogamous, and historically isolated population. These findings highlight the evolutionary and demographic distinctiveness of the Veddas and provide essential baseline data for future population genomic studies. Furthermore, the demonstrated high discriminatory power of the 16 X-STR loci supports their continued application in population genetics and anthropological research within this unique community.

genetics↗

bioRxiv2022

Genetic regulation of the human plasma proteome in 54,306 UK Biobank participants

The UK Biobank Pharma Proteomics Project (UKB-PPP) is a collaboration between the UK Biobank (UKB) and thirteen biopharmaceutical companies characterising the plasma proteomic profiles of 54,306 UKB participants. Here, we describe results from the first phase of UKB-PPP, including protein quantitative trait loci (pQTL) mapping of 1,463 proteins that identifies 10,248 primary genetic associations, of which 85% are newly discovered. We also identify independent secondary associations in 92% of cis and 29% of trans loci, expanding the catalogue of genetic instruments for downstream analyses. The study provides an updated characterisation of the genetic architecture of the plasma proteome, leveraging population-scale proteomics to provide novel, extensive insights into trans pQTLs across multiple biological domains. We highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement proteins, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug target discovery by extending the genetic proxied effect of PCSK9 levels on lipid concentrations, cardio- and cerebro-vascular diseases, and additionally disentangle specific genes and proteins perturbed at COVID-19 susceptibility loci. This public-private partnership provides the scientific community with an open-access proteomics resource of unprecedented breadth and depth to help elucidate biological mechanisms underlying genetic discoveries and accelerate the development of novel biomarkers and therapeutics.

genetics↗

bioRxiv2017

Shared Genetic Contributions to Atrial Fibrillation and Ischemic Stroke Risk

ObjectiveWe sought to assess whether genetic risk factors for atrial fibrillation can explain cardioembolic stroke risk.\n\nMethodsWe evaluated genetic correlations between a prior genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously-validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.\n\nResultsWe observed strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearsons r=0.77 and 0.76, respectively, across SNPs with p < 4.4 x 10-4 in the prior AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio (OR) per standard deviation (sd) = 1.40, p = 1.45x10-48), explaining [~]20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per sd = 1.07, p = 0.004), but no other primary stroke subtypes (all p > 0.1).\n\nConclusionsGenetic risk for AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.

genetics↗

bioRxiv2014

Genetic landscape of populations along the Silk Road: admixture and migration patterns

BackgroundThe ancient Silk Road has been a trading route between Europe and Central Asia from the 2nd century BCE to the 15th century CE. While most populations on this route have been characterized, the genetic background of others remains poorly understood, and little is known about past migration patterns. The scientific expedition \"Marco Polo\" has recently collected genetic and phenotypic data in six regions (Georgia, Armenia, Azerbaijan, Uzbekistan, Kazakhstan, Tajikistan) along the Silk Road to study the genetics of a number of phenotypes.\n\nResultsWe characterized the genetic structure of these populations within a worldwide context. We observed a West-East subdivision albeit the existence of a genetic component shared within Central Asia and nearby populations from Europe and Near East. We observed a contribution of up to 50% from Europe and Asia to most of the populations that have been analyzed. The contribution from Asia dates back to ~25 generations and is limited to the Eastern Silk Road. Time and direction of this contribution are consistent with the Mongolian expansion era.\n\nConclusionsWe clarified the genetic structure of six populations from Central Asia and suggested a complex pattern of gene flow among them. We provided a map of migration events in time and space and we quantified exchanges among populations. Altogether these novel findings will support the future studies aimed at understanding the genetics of the phenotypes that have been collected during the Marco Polo campaign, they will provide insights into the history of these populations, and they will be useful to reconstruct the developments and events that have shaped modern Eurasians genomes.

Genomics↗

bioRxiv2015

Catch me if you can: Adaptation from standing genetic variation to a moving phenotypic optimum

Adaptation lies at the heart of Darwinian evolution. Accordingly, numerous studies have tried to provide a formal framework for the description of the adaptive process. Out of these, two complementary modelling approaches have emerged: While so-called adaptive-walk models consider adaptation from the successive fixation of de-novo mutations only, quantitative genetic models assume that adaptation proceeds exclusively from pre-existing standing genetic variation. The latter approach, however, has focused on short-term evolution of population means and variances rather than on the statistical properties of adaptive substitutions. Our aim is to combine these two approaches by describing the ecological and genetic factors that determine the genetic basis of adaptation from standing genetic variation in terms of the effect-size distribution of individual alleles. Specifically, we consider the evolution of a quantitative trait to a gradually changing environment. By means of analytical approximations, we derive the distribution of adaptive substitutions from standing genetic variation, that is, the distribution of the phenotypic effects of those alleles from the standing variation that become fixed during adaptation. Our results are checked against individual-based simulations. We find that, compared to adaptation from de-novo mutations, (i) adaptation from standing variation proceeds by the fixation of more alleles of small effect; (ii) populations that adapt from standing genetic variation can traverse larger distances in phenotype space and, thus, have a higher potential for adaptation if the rate of environmental change is fast rather than slow.

Evolutionary Biology↗

bioRxiv2015

Mutation is a sufficient and robust predictor of genetic variation for mitotic spindle traits in C. elegans

Different types of phenotypic traits consistently exhibit different levels of genetic variation in natural populations. There are two potential explanations: either mutation produces genetic variation at different rates, or natural selection removes or promotes genetic variation at different rates. Whether mutation or selection is of greater general importance is a longstanding unresolved question in evolutionary genetics. Where the input of genetic variation by mutation differs between traits, it is usually uncertain whether the difference is the result of different mutational effects (\"mutational robustness\") or different numbers of underlying loci (\"mutational target\"), although conventional wisdom favors the latter. We report mutational variances (VM) for 19 traits related to the first mitotic cell division in C. elegans, and compare them to the standing genetic variances (VG) for the same suite of traits in a worldwide collection C. elegans. Two robust conclusions emerge. First, the mutational process is highly repeatable: the correlation between VM in two independent sets of mutation accumulation lines is ~0.9. Second, VM for a trait is a very good predictor of VG for that trait: the correlation between VM and VG is ~0.9. This result is predicted for a population at mutation-selection balance; it is not predicted if balancing selection plays a primary role in maintaining genetic variation. Goodness- of-fit of the data to two simple models of mutation suggest that differences in mutational effects may be a more important cause of differences in VM between traits than differences in the size of the mutational target.

Evolutionary Biology↗

bioRxiv2016

Genetic and environmental risk for chronic pain and the contribution of risk variants for psychiatric disorders. Results from Generation Scotland: Scottish Family Health Study and UK Biobank

BackgroundChronic pain is highly prevalent worldwide and a significant source of disability, yet its genetic and environmental risk factors are poorly understood. Its relationship with psychiatric illness, and major depressive disorder (MDD) in particular, is of particular importance. We sought to test the contribution of genetic factors and shared and unique environment to risk of chronic pain and its correlation with MDD in Generation Scotland: Scottish Family Health Study (GS:SFHS). We then sought to replicate any significant findings in the UK Biobank study.\n\nMethodsUsing family-based mixed-model analyses, we examined the contribution of genetics and environment to chronic pain using spouse, sibling and household groups as measures of shared environment. We then examined the correlation between chronic pain and MDD and estimated the contribution of genetic factors and shared environment. Finally, we used data from two independent genome-wide association studies to test whether chronic pain has a polygenic risk architecture and examine whether genomic risk of psychiatric disorder predicted chronic pain and whether genomic risk of chronic pain predicted MDD.\n\nResultsChronic pain is a moderately heritable trait (narrow sense heritability = 38.4%) which is more likely to be concordant in spouses and partners (variance explained 18.7%). Chronic pain is positively correlated with depression (rho = 0.13, p = 2.72x10-68) and it shows a tendency to cluster within families for genetic reasons (genetic correlation rho = 0.51, p = 8.24x10-19). Polygenic risk profiles for pain, generated using independent GWAS data, predicted chronic pain in both GS:SFHS (maximum {beta} = 6.18x10-2, p = 4.3x10-4) and UK Biobank (maximum {beta} = 5.68 x 10-2, p < 3x10-4). Genomic risk of MDD is also significantly associated with chronic pain in both GS:SFHS (maximum {beta} = 6.62x10-2, p = 4.3x10-4) and UK Biobank (maximum {beta} = 2.56x10-2, p < 3x10-4).\n\nConclusionsGenetic factors and chronic pain in a partner or spouse contribute substantially to the risk of chronic pain in the general population. Chronic pain is genetically correlated with MDD, has a polygenic architecture and is predicted by polygenic risk of MDD.

Genomics↗

bioRxiv2016

Genome-wide characterization of genetic and functional dysregulation in autism spectrum disorder

Autism spectrum disorder (ASD) is a range of major neurodevelopmental disabilities with a strong genetic basis. Yet, owing to extensive genetic heterogeneity, multiple modes of inheritance and limited study sizes, sequencing and quantitative genetics approaches have had limited success in characterizing the complex genetics of ASD. Currently, only a small fraction of potentially causal genes--about 65 genes out of an estimated severalhundred--are known based on strong genetic evidence. Hence, there isa critical need for complementary approaches to further characterize the genetic basis of ASD, enabling development of better screening and therapeutics. Here, we use a machine-learning approach based on a human brain-specific functional gene interaction network to present a genome-wide prediction of autism-associated genes, including hundreds of candidate genes for which there is minimal or no prior genetic evidence. Our approach is validated in an independent case-control sequencing study of approximately 2,500families. Leveraging these genome-wide predictions and the brain-specificnetwork, we demonstrate that the large set of ASD genes converges on a smaller number of key cellular pathways and specific developmental stages of the brain. Specifically, integration with spatiotemporal transcriptome expression data implicates early fetal and midfetal stages of the developing human brain in ASD etiology. Likewise, analysis of the connectivity of topautism genes in the brain-specific interaction network reveals the breadthof autism-associated functional modules, processes, and pathways in the brain. Finally, we identify likely pathogenic genes within the most frequent autism-associated copy-number-variants (CNVs) and propose genes and pathways that are likely mediators of autism across multiple CNVs. All the predictions, interactions, and functional insights from this work are available to biomedical researchers at asd.princeton.edu.

Genomics↗

bioRxiv2016

kWIP: The k-mer Weighted Inner Product, a de novo Estimator of Genetic Similarity

Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals or samples in an unbiased manner, preferably de novo. The rapid and unbiased estimation of genetic relatedness has the potential to overcome reference genome bias, to detect mix-ups early, and to verify that biological replicates belong to the same genetic lineage before conclusions are drawn using mislabelled, or misidentified samples.\n\nWe present the k-mer Weighted Inner Product (kWIP), an assembly-, and alignment-free estimator of genetic similarity. kWIP combines a probabilistic data structure with a novel metric, the weighted inner product (WIP), to efficiently calculate pairwise similarity between sequencing runs from their k-mer counts. It produces a distance matrix, which can then be further analysed and visualised. Our method does not require prior knowledge of the underlying genomes and applications include detecting sample identity and mix-up, non-obvious genomic variation, and population structure.\n\nWe show that kWIP can reconstruct the true relatedness between samples from simulated populations. By re-analysing several published datasets we show that our results are consistent with marker-based analyses. kWIP is written in C++, licensed under the GNU GPL, and is available from https://github.com/kdmurray91/kwip.\n\nAuthor SummaryCurrent analysis of the genetic similarity of samples is overly dependent on alignment to reference genomes, which are often unavailable and in any case can introduce bias. We address this limitation by implementing an efficient alignment free sequence comparison algorithm (kWIP). The fast, unbiased analysis kWIP performs should be conducted in preliminary stages of any analysis to verify experimental designs and sample metadata, catching catastrophic errors earlier.\n\nkWIP extends alignment-free sequence comparison methods by operating directly on sequencing reads. kWIP uses an entropy-weighted inner product over k-mers as a estimator of genetic relatedness. We validate kWIP using rigorous simulation experiments. We also demonstrate high sensitivity and accuracy even where there is modest divergence between genomes, and/or when sequencing coverage is low. We show high sensitivity in replicate detection, and faithfully reproduce published reports of population structure and stratification of microbiomes. We provide a reproducible workflow for replicating our validation experiments.\n\nkWIP is an efficient, open source software package. Our software is well documented and cross platform, and tutorial-style workflows are provided for new users.

Bioinformatics↗

bioRxiv2016

Habitat preference predicts genetic diversity and population divergence in Amazonian birds

The ecological traits of organisms may predict important evolutionary parameters such as genetic diversity, population genetic structure, and demographic history. Making these ecological-evolutionary links is difficult because robust, comparable genetic estimates are required from many species with differing ecologies. In Amazonian birds, differences in habitat preference are an important component of ecological diversity. A subset of Amazonian birds is restricted to forest edge and open forest along floodplains, whereas another subset occurs only in the interior of tall, upland forest. Here, we examine the link between habitat and evolutionary metrics using 20 pairs of closely related and co-distributed bird species in which one member of the pair occurs primarily in forest edge and floodplains, and the other occurs in upland forest interior. We use standardized geographic sampling and genomic data from the same set of 2,416 independent markers to estimate genetic diversity, population structure, and demographic history in each species. We find that species of upland forest have higher genetic diversity, greater divergence across the landscape, more genetically distinct populations, and deeper gene histories than floodplain species. Our results reveal that species ecology in the form of habitat preference is an important predictor of genetic diversity and divergence and suggest that floodplain and upland avifaunas in the Amazon may be on separate evolutionary trajectories and require different conservation strategies.

evolutionary biology↗

bioRxiv2017

Fitness, Physical Activity, And Cardiovascular Disease: Longitudinal And Genetic Analyses In The UK Biobank Study

BackgroundExercise is inversely related with cardiovascular disease (CVD), but large-scale studies of incident CVD events are lacking. Moreover, little is known about genetic determinants of fitness and physical activity, and modifiable effects of exercise in individuals with elevated genetic risk of CVD. Finally, causal analyses of exercise traits are limited.\n\nMethodsWe estimated associations of grip strength, physical activity, and cardiorespiratory fitness with CVD and all-cause death in up to 502,635 individuals from the UK Biobank. We also examined these associations in individuals with different genetic burden on coronary heart disease (CHD) and atrial fibrillation (AF). Finally, we performed genome-wide association study (GWAS) of grip strength and physical activity, as well as Mendelian randomization analysis to assess the causal role of grip strength in CHD.\n\nFindingsGrip strength, physical activity, and cardiorespiratory fitness showed strong inverse associations with incident cardiovascular events and all-cause death (for composite CVD; HR, 0.78, 95% CI, 0.77-0.80; HR, 0.94, 95% CI, 0.93-0.95, and HR, 0.67, 95% CI, 0.63-0.71, per SD change, respectively). We observed stronger associations of grip strength with CHD and AF for individuals in the lowest tertile of genetic risk (Pinteraction = 0.006, Pinteraction = 0.03, respectively), but the inverse associations were present in each category of genetic risk. We report 27 novel genetic loci associated with grip strength and 2 loci with physical activity, with the strongest associations in FTO (rs56094641, P=3.8x10-24) and SMIM2 (rs9316077, P=1.4x10-8), respectively. By use of Mendelian randomization, we provide evidence that grip strength is causally related to CHD.\n\nInterpretationMaintaining physical strength is likely to prevent future cardiovascular events, also in individuals with elevated genetic risk for CVD.\n\nFundingNational Institutes of Health (1 R01 HL135313-01), Knut and Alice Wallenberg Foundation (2013.0126), and the Finnish Cultural Foundation.

epidemiology↗

bioRxiv2019

Spatial heterogeneity tunes the behavior of bacterial genetic circuits

Intracellular spatial heterogeneity is frequently observed in bacteria, where the chromosome occupies part of the cell’s volume and a circuit’s DNA often localizes within the cell. How this heterogeneity affects core processes and genetic circuits is still poorly understood. In fact, commonly used ordinary differential equation (ODE) models of genetic circuits assume a well-mixed ensemble of molecules and, as such, do not capture spatial aspects. Reaction-diffusion partial differential equation (PDE) models have been only occasionally used since they are difficult to integrate and do not provide mechanistic understanding of the effects of spatial heterogeneity. In this paper, we derive a reduced ODE model that captures spatial effects, yet has the same dimension as commonly used well-mixed models. In particular, the only difference with respect to a well-mixed ODE model is that the association rate constant of binding reactions is multiplied by a coefficient, which we refer to as the binding correction factor (BCF). The BCF depends on the size of interacting molecules and on their location when fixed in space and it is equal to unity in a well-mixed ODE model. The BCF can be used to investigate how spatial heterogeneity affects the behavior of core processes and genetic circuits. Specifically, our reduced model indicates that transcription and its regulation are more effective for genes located at the cell poles than for genes located on the chromosome. The extent of these effects depends on the value of the BCF, which we found to be close to unity. For translation, the value of the BCF is always greater than unity, it increases with mRNA size, and, with biologically relevant parameters, is substantially larger than unity. Our model has broad validity, has the same dimension as a well-mixed model, yet it incorporates spatial heterogeneity. This simple-to-use model can be used to both analyze and design genetic circuits while accounting for spatial intracellular effects.HighlightsIntracellular spatial heterogeneity modulates the effective association rate constant of binding reactions through a binding correction factor (BCF) that fully captures spatial effectsThe BCF depends on molecules size and location (if fixed) and can be determined experimentallySpatial heterogeneity may be detrimental or exploited for genetic circuit designTraditional well-mixed models can be appropriate despite spatial heterogeneityStatement of significance A general and simple modeling framework to determine how spatial heterogeneity modulates the dynamics of gene networks is currently lacking. To this end, this work provides a simple-to-use ordinary differential equation (ODE) model that can be used to both analyze and design genetic circuits while accounting for spatial intracellular effects. We apply our model to several core biological processes and determine that transcription and its regulation are more effective for genes located at the cell poles than for genes located on the chromosome and this difference increases with regulator size. For translation, we predict the effective binding between ribosomes and mRNA is higher than that predicted by a well-mixed model, and it increases with mRNA size. We provide examples where spatial effects are significant and should be considered but also where a traditional well-mixed model suffices despite severe spatial heterogeneity. Finally, we illustrate how the operation of well-known genetic circuits is impacted by spatial effects.Competing Interest StatementThe authors have declared no competing interest.View Full Text

synthetic biology↗

bioRxiv2020

Quantitative genetics of temperature performance curves of Neurospora crassa

Earths temperature is increasing due to anthropogenic CO2 emissions; and organisms need either to adapt to higher temperatures, migrate into colder areas, or face extinction. Temperature affects nearly all aspects of an organisms physiology via its influence on metabolic rate and protein structure, therefore genetic adaptation to increased temperature may be much harder to achieve compared to other abiotic stresses. There is still much to be learned about the evolutionary potential for adaptation to higher temperatures, therefore we studied the quantitative genetics of growth rates in different temperatures that make up the thermal performance curve of the fungal model system Neurospora crassa. We studied the amount of genetic variation for thermal performance curves and examined possible genetic constraints by estimating the G-matrix. We observed a substantial amount of genetic variation for growth in different temperatures, and most genetic variation was for performance curve elevation. Contrary to common theoretical assumptions, we did not find strong evidence for genetic trade-offs for growth between hotter and colder temperatures. We also simulated short term evolution of thermal performance curves of N. crassa, and suggest that they can have versatile responses to selection.

evolutionary biology↗

bioRxiv2020

Synchronized genetic activities in Alzheimer's brains revealed by heterogeneity-capturing network analysis

It is becoming increasingly evident that the efficacy of single-gene computational analyses for complex traits is nearly exhausted and future advances hinge on unraveling the intricate combinatorial interactions among multiple genes. However, the discovery of modules of genes working in concert to manifest a complex trait has been crippled by combinatorial complexity, genetic heterogeneity, and validation biases. We introduce Maestro, a novel network approach that employs a multifaceted correlation measure, which captures heterogeneity, and a rigorous validation method. Maestros utilization for Alzheimers disease (AD) reveals an expression pattern that has virtually zero probability of simultaneous expression by an individual, assuming independence. Yet this pattern is exhibited by 19.0% of AD cases and 7.3% of controls, establishing an unprecedented pattern of synchronized genetic activities in the human brain. This pattern is significantly associated with AD, with an odds ratio of 3.0. This study substantiates Maestros power for discovery of orchestrated genetic activities underlying complex traits. More generally, Maestro can be applied in diverse domains in which heterogeneity exists. HighlightsO_LISynchronized genetic activities associated with Alzheimers disease C_LIO_LINovel vector-based correlation measure that captures genetic heterogeneity C_LIO_LIEnhanced network model for revealing combinatorial genetic interactions C_LIO_LIPro-survival genetic activities associated with Alzheimers disease C_LIO_LIGeneral approach for revealing patterns in data subject to heterogeneity C_LI

bioinformatics↗

bioRxiv2020

Examination of the efficacy of small genetic panels in genomic conservation of companion animal populations

In many ways dogs are an ideal model for the study of genetic erosion and population recovery, problems of major concern in the field of conservation genetics. Genetic diversity in many dog breeds has been declining systematically since the beginning of the 1800s, when modern breeding practices came into fashion. As such, inbreeding in domestic dog breeds is substantial and widespread and has led to an increase in recessive deleterious mutations of high effect as well as general inbreeding depression. Pedigrees can in theory be used to guide breeding decisions, though are often incomplete and do not reflect the full history of inbreeding. Small microsatellite panels are also used in some cases to choose mating pairs to produce litters with low levels of inbreeding. However, the long-term impact of such practices have not been thoroughly evaluated. Here, we use forward simulation on a model of the dog genome to examine the impact of using limited markers panels to guide pairwise mating decisions on genome-wide population level genetic diversity. Our results suggest that in unsupervised mating schemes, where breeding decisions are made at the pairwise-rather than population-level, such panels can lead to accelerated loss of genetic diversity compared to random mating at regions of the genome unlinked to panel markers and demonstrate the importance of genome-wide genetic panels for managing and conserving genetic diversity in dogs and other companion animals.

evolutionary biology↗

bioRxiv2020

Using spatial genetics to quantify mosquito dispersal for control programs

BackgroundHundreds of millions of people get a mosquito-borne disease every year, of which nearly one million die. Mosquito-borne diseases are primarily controlled and mitigated through the control of mosquito vectors. Accurately quantified mosquito dispersal in a given landscape is critical for the design and optimization of the control programs, yet the field experiments that measure dispersal of mosquitoes recaptured at certain distances from the release point (mark-release-recapture MRR studies) are challenging for such small insects and often unrepresentative of the insects true field behavior. Using Singapore as a study site, we show how mosquito dispersal patterns can be characterized from the spatial analyses of genetic relatedness among individuals sampled over a short time span without interruption of their natural behaviors. Methods and FindingsWe captured ovipositing females of Aedes aegypti, a major arboviral disease vector, across floors of high-rise apartment blocks and genotyped them using thousands of genome-wide SNP markers. We developed a methodology that produces a dispersal kernel for distance that results from one generation of successful breeding (effective dispersal), using the distances separating full siblings, 2nd and 3rd degree relatives (close kin). In Singapore, the estimated dispersal distance kernel was exponential (Laplacian), giving the mean effective dispersal distance (and dispersal kernel spread {sigma}) of 45.2 m (95%CI: 39.7-51.3 m), and 10% probability of dispersal >100 m (95%CI: 92-117 m). Our genetic-based estimates matched the parametrized dispersal kernels from the previously reported MRR experiments. If few close-kin are captured, a conventional genetic isolation-by-distance analysis can be used, and we show that it can produce {sigma} estimates congruent with the close-kin method, conditioned on the accurate estimation of effective population density. We also show that genetic patch size, estimated with the spatial autocorrelation analysis, reflects the spatial extent of the dispersal kernel tail that influences e.g. predictions of critical radii of release zones and Wolbachia wave speed in mosquito replacement programs. ConclusionsWe demonstrate that spatial genetics (the newly developed close-kin analysis, and conventional IBD and spatial autocorrelation analyses) can provide a detailed and robust characterization of mosquito dispersal that can guide operational vector control decisions. With the decreasing cost of next generation sequencing, acquisition of spatial genetic data will become increasingly accessible, and given the complexities and criticisms of conventional MRR methods, but the central role of dispersal measures in vector control programs, we recommend genetic-based dispersal characterization as the more desirable means of parameterization.

ecology↗

bioRxiv2020

Conservation genomics reveals low genetic diversity and multiple parentage in the threatened freshwater mussel, Margaritifera hembeli

Margaritifera hembeli is a federally threatened freshwater mussel species restricted to three central Louisiana drainages. Currently, management efforts are being formulated without an understanding of population-level genetic patterns, which could result in sub-optimal conservation outcomes. In particular, information about riverscape genetic patterns is needed to design effective propagation and reintroduction plans. We apply a genomic approach (RADseq) to assess genetic diversity and structure among four wild populations sampled from across the species range. We also assess the genetic diversity of a captively reared cohort produced from a single female. We recovered population differentiation between individuals sampled to the north and south of the Red River. All sites had similarly low levels of heterogeneity and other measures of genetic diversity. The captive cohort displayed higher levels of genetic diversity than expected and likely represents a case of multiple paternity. Future propagation efforts will likely be able to produce genetically diverse cohorts from a small number of wild-caught females, and we recommend future reintroduction efforts utilize brooders within the sub-drainage closest to the reintroduction effort.

genomics↗