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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

Reconstruction of networks with direct and indirect genetic effects

Genetic variance of a phenotypic trait can originate from direct genetic effects, or from indirect effects, i.e., through genetic effects on other traits, affecting the trait of interest. This distinction is often of great importance, for example when trying to improve crop yield and simultaneously controlling plant height. As suggested by Sewall Wright, assessing contributions of direct and indirect effects requires knowledge of (1) the presence or absence of direct genetic effects on each trait, and (2) the functional relationships between the traits. Because experimental validation of such relationships is often unfeasible, it is increasingly common to reconstruct them using causal inference methods. However, most of the current methods require all genetic variance to be explained by a small number of QTLs with fixed effects. Only few authors considered the missing heritability case, where contributions of many undetectable QTLs are modelled with random effects. Usually, these are treated as nuisance terms, that need to be eliminated by taking residuals from a multi-trait mixed model (MTM). But fitting such MTM is challenging, and it is impossible to infer the presence of direct genetic effects. Here we propose an alternative strategy, where genetic effects are formally included in the graph. This has important advantages: (1) genetic effects can be directly incorporated in causal inference, implemented via our PCgen algorithm, which can analyze many more traits and (2) we can test the existence of direct genetic effects and improve the orientation of edges between traits. Finally, we show that reconstruction is much more accurate if individual plant or plot data are used, instead of genotypic means. We have implemented the PCgen-algorithm in the R-package pcgen.

genetics↗
bioRxiv2019

Assessing digital phenotyping to enhance genetic studies of human diseases

Population-scale biobanks that combine genetic data and high-dimensional phenotyping for a large number of participants provide an exciting opportunity to perform genome-wide association studies (GWAS) to identify genetic variants associated with diverse quantitative traits and diseases. A major challenge for GWAS in population biobanks is ascertaining disease cases from heterogeneous data sources such as hospital records, digital questionnaire responses, or interviews. In this study, we use genetic parameters including genetic correlation to evaluate whether GWAS performed using cases in the UK Biobank ascertained from hospital records, questionnaire responses, and family history of diseases implicate similar disease genetics across a range of effect sizes. We find that hospital record and questionnaire GWAS largely identify similar genetic effects for many complex phenotypes and that combining together both phenotyping methods improves power to detect genetic associations. We also show that family GWAS using cases ascertained on family history of disease agrees with combined hospital record/questionnaire GWAS and that family history GWAS has better power to detect genetic associations for some phenotypes. Overall, this work demonstrates that digital phenotyping and unstructured phenotype data can be combined with structured data such as hospital records to identify cases for GWAS in biobanks and improve the ability of such studies to identify genetic associations.

genetics↗
bioRxiv2019

Genetic Mapping in Autohexaploid Sweet Potato with Low-coverage NGS-based Genotyping Data

Next-generation sequencing (NGS)-based genotyping methods can generate numerous genetic markers in a single experiment and have contributed to plant genetic mapping. However, the benefits of NGS-based methods are limited in autopolyploids as their genetic segregation mode is complex. Moreover, autopolyploids have large genomes and require abundant sequencing data to obtain sufficient genetic markers. There are several methods for genetic mapping in autopolyploids. These approaches may be impractical for plant genetic studies as they require large amounts of data and are not cost-effective. In the present study, we propose a simple strategy for genetic mapping of polyploids in a cost-effective manner. The allele dosage probabilities calculated from NGS read counts were used in association analyses to detect loci associated with specific phenotypes. This approach is superior to conventional methods of determining allele dosage, which usually result in the filtering of many genetic markers with low read depth. The validity of the strategy was demonstrated using real phenotype data from autohexaploid sweet potato populations to detect genetic loci for both qualitative and quantitative traits, the latter of which required the use of allele dosage probabilities for the detection of loci. We demonstrate that this proposed method is useful with reasonable NGS read counts.

genetics↗
bioRxiv2019

Towards an information theory of quantitative genetics

Quantitative genetics has evolved dramatically in the past century, and the proliferation of genetic data, in quantity as well as type, enables the characterization of complex interactions and mechanisms beyond the scope of its theoretical foundations. In this paper, we argue that revisiting the framework for analysis is important and we begin to lay the foundations of an alternative formulation of quantitative genetics based on information theory. Information theory can provide sensitive and unbiased measures of statistical dependencies among variables, and it provides a natural mathematical language for an alternative view of quantitative genetics. In previous work we examined the information content of discrete functions and applied this approach and methods to the analysis of genetic data. In this paper we present a framework built around a set of relationships that both unifies the information measures for the discrete functions and uses them to express key quantitative genetic relationships. Information theory measures of variable interdependency are used to identify significant interactions, and a general approach is described for inferring functional relationships within genotype and phenotype data. We present information-based measures of the genetic quantities: penetrance, heritability and degrees of statistical epistasis. Our scope here includes the consideration of both two- and three-variable dependencies and independently segregating variants, which captures additive effects, genetic interactions, and two phenotype pleiotropy. This formalism and the theoretical approach naturally applies to higher multi-variable interactions and complex dependencies, and can be adapted to account for population structure, linkage and non-randomly segregating markers. This paper thus focuses on presenting the initial groundwork for a full formulation of quantitative genetics based on information theory.

genetics↗
bioRxiv2019

Comparative population genetic structure of two Ixodidae ticks (Ixodes ovatus and Haemaphysalis flava) in Niigata Prefecture, Japan

Ixodid tick species such as Ixodes ovatus and Haemaphysalis flava are essential vectors of tick-borne diseases in Japan. In this study, we investigated the population genetic structures and gene flow of I. ovatus and H. flava as affected by the tick host mobility. We hypothesized that I. ovatus and H. flava may have differences in their genetic structure due to the low mobility of small rodent hosts of I. ovatus at the immature stage in contrast to the mediated dispersal of avian hosts for immature H. flava. We collected 307 adult I. ovatus and 220 adult H. flava from 29 and 17 locations across Niigata Prefecture, Japan. We investigated the genetic structure at two mitochondrial loci (cox1, 16S rRNA gene). For I. ovatus, pairwise FST and analysis of molecular variance (AMOVA) analyses of cox1 sequences indicated significant genetic variation among populations. Both cox1 and 16S rRNA markers showed non-significant genetic variation among locations for H. flava. The Bayesian tree and haplotype network of cox1 marker for I. ovatus samples in Niigata Prefecture found 3 genetic groups wherein most haplotypes in group 2 were distributed in low altitudinal areas. When we added cox1 sequences of I. ovatus from China to the phylogenetic analysis, three genetic groups (China 1, China 2, and Niigata and Hokkaido, Japan) were formed in the tree suggesting the potential for cryptic species in the genetic group in Japan. Our results support our hypothesis and suggest that the host preference of ticks at the immature stage may influence the genetic structure and gene flow of the ticks. This information is vital in understanding the tick-host interactions in the field to better understand the tick-borne disease transmission and in designing an effective tick control program.

genetics↗
bioRxiv2026

The multidimensional structure of wellbeing: genetic evidence from a multivariate twin study including the Mental Health Continuum

Wellbeing is commonly defined as the combination of feeling good and functioning well and typically conceptualized as two related but distinct components. Hedonic wellbeing emphasizes pleasure, happiness, and life satisfaction, while eudaimonic wellbeing focuses on meaning, personal growth, flourishing, and the realization of ones potential. The Mental Health Continuum-Short Form was developed as a comprehensive measure of wellbeing and includes three subscales assessing emotional, social, and psychological wellbeing. Although the Mental Health Continuum total score is often interpreted as an indicator of overall wellbeing, the underlying genetic structure of its three subscales and its genetic overlap with other commonly used wellbeing measures remains unclear. Using data from 5,212 individuals from the Netherlands Twin Register (72% female, mean age 36.4), we fitted multivariate twin models to examine the genetic architecture of the Mental Health Continuum and its associations with other wellbeing measures (quality of life, life satisfaction, subjective happiness, and flourishing). Results indicate that, at the genetic level, the Mental Health Continuum is best explained by its three distinct subscales rather than by a latent factor. When considering the Mental Health Continuum together with the other wellbeing measures, we found moderate to high genetic correlations (r = 0.52 - 0.83), indicating substantial overlap in the genetics underlying the wellbeing constructs. However, we did not find evidence for a single common genetic factor underlying all constructs. These findings highlight the multidimensional structure of wellbeing, but the moderate to high genetic correlations across measures suggest that it is important to align the level of measurement (phenotypic vs genetic) with the research question.

genetics↗
bioRxiv2013

Routes for breaching and protecting genetic privacy

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.\n\nAbout the AuthorsYaniv Erlich is a Fellow at the Whitehead Institute for Biomedical Research. Erlich received his Ph.D. from Cold Spring Harbor Laboratory in 2010 and B.Sc. from Tel-Aviv University in 2006. Prior to that, Erlich worked in computer security and was responsible for conducting penetration tests on financial institutes and commercial companies. Dr. Erlichs research involves developing new algorithms for computational human genetics.\n\nArvind Narayanan is an Assistant Professor in the Department of Computer Science and the Center for Information Technology and Policy at Princeton. He studies information privacy and security. His research has shown that data anonymization is broken in fundamental ways, for which he jointly received the 2008 Privacy Enhancing Technologies Award. His current research interests include building a platform for privacy-preserving data sharing.\n\nSummaryO_LIBroad data dissemination is essential for advancements in genetics, but also brings to light concerns regarding privacy.\nC_LIO_LIPrivacy breaching techniques work by cross-referencing two or more pieces of information to gain new, potentially undesirable knowledge on individuals or their families.\nC_LIO_LIBroadly speaking, the main routes to breach privacy are identity tracing, attribute disclosure, and completion of sensitive DNA information.\nC_LIO_LIIdentity tracing exploits quasi-identifiers in the DNA data or metadata to uncover the identity of an unknown genetic dataset.\nC_LIO_LIAttribute disclosure techniques work on known DNA datasets. They use the DNA information to link the identity of a person with a sensitive phenotype.\nC_LIO_LICompletion techniques also work on known DNA data. They try to uncover sensitive genomic areas that were masked to protect the participant.\nC_LIO_LIIn the last few years, we have witnessed a rapid growth in the range of techniques and tools to conduct these privacy-breaching attacks. Currently, most of the techniques are beyond the reach of the general public, but can be executed by trained persons with varying degrees of effort.\nC_LIO_LIThere is considerable debate regarding risk management. One camp supports a pragmatic, ad-hoc approach of privacy by obscurity and the other supports a systematic, mathematically-backed approach of privacy by design.\nC_LIO_LIPrivacy by design algorithms include access control, differential privacy, and cryptographic techniques. So far, data custodians of genetic databases mainly adopted access control as a mitigation strategy.\nC_LIO_LINew developments in cryptographic techniques may usher in an additional arsenal of security by design techniques.\nC_LI

Genomics↗
bioRxiv2014

The Genetic Architecture of Gene Expression Levels in Wild Baboons

Gene expression variation is well documented in human populations and its genetic architecture has been extensively explored. However, we still know little about the genetic architecture of gene expression variation in other species, particularly our closest living relatives, the nonhuman primates. To address this gap, we performed an RNA sequencing (RNA-seq)-based study of 63 wild baboons, members of the intensively studied Amboseli baboon population in Kenya. Our study design allowed us to measure gene expression levels and identify genetic variants using the same data set, enabling us to perform complementary mapping of putative cis-acting expression quantitative trait loci (eQTL) and measurements of allele-specific expression (ASE) levels. We discovered substantial evidence for genetic effects on gene expression levels in this population. Surprisingly, we found more power to detect individual eQTL in the baboons relative to a HapMap human data set of comparable size, probably as a result of greater genetic variation, enrichment of SNPs with high minor allele frequencies, and longer-range linkage disequilibrium in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes. Interestingly, genes with eQTL significantly overlapped between the baboon and human data sets, suggesting that some genes may tolerate more genetic perturbation than others, and that this property may be conserved across species. Finally, we used a Bayesian sparse linear mixed model to partition genetic, demographic, and early environmental contributions to variation in gene expression levels. We found a strong genetic contribution to gene expression levels for almost all genes, while individual demographic and environmental effects tended to be more modest. Together, our results establish the feasibility of eQTL mapping using RNA-seq data alone, and act as an important first step towards understanding the genetic architecture of gene expression variation in nonhuman primates.

Genomics↗
bioRxiv2015

Analysis of the optimality of the Standard Genetic Code.

Many theories have been proposed attempting to explain the origin of the genetic code. While strong reasons remain to believe that the genetic code evolved as a frozen accident, at least for the first few amino acids, other theories remain viable. In this work, we test the optimality of the standard genetic code against approximately 17 million genetic codes, and locate 18 which outperform the standard genetic code at the following three criteria: (a) robustness to point mutation; (b) robustness to frameshift mutation; and (c) ability to encode additional information in the coding region. We use a genetic algorithm to generate and score codes from different parts of the associated landscape, and are, as a result presumably more representative of the entire landscape. Our results show that while the genetic code is sub-optimal for robustness to frameshift mutation and the ability to encode additional information in the coding region, it is very strongly selected for robustness to point mutation. This coupled with the observation that the different performance indicator scores for a particular genetic code are seemingly negatively correlated, make the standard genetic code nearly optimal for the three criteria tested in this work.

Evolutionary Biology↗
bioRxiv2017

Signs of host genetic regulation in the microbiome composition in cattle

Previous studies have revealed certain genetic control by the host over the microbiome composition, although in many species the host genetic link controlling microbial composition is yet unknown. This potential association is important in livestock to study all factors and interactions that rule the effect of the microbiome in complex traits. This report aims to study whether the host genotype exerts any genetic control on the microbiome composition of the rumen in cattle. Data on 16S and 18S rRNA gene-based analysis of the rumen microbiome in 18 dairy cows from two different breeds (Holstein and Brown Swiss) were used. The effect of the genetic background of the animal (through the breed and Single Nucleotide Polymorphisms; SNP) on the relative abundance (RA) of archaea, bacteria and ciliates (with average relative abundance per breed >0.1%) was analysed using Bayesian statistics. In total, 13 genera were analysed for bacteria (5), archaea (1), and ciliates (7). All these bacteria and archaea genera showed association to the host genetic background both for breed and SNP markers, except RA for the genera Butyrivibrio and Ruminococcus that showed association with the SNP markers but not with the breed composition. Relative abundance of 57% (4/7) of ciliate analysed showed to be associated to the genetic background of the host. This host genetic link was observed in some genus of Trichostomatia family. For instance, the breed had a significant effect on Isotricha, Ophryoscolex and Polyplastron, and the SNP markers on Entodinium, Ophryoscolex and Polyplastron. In total, 77% (10/13) of microbes analysed showed to be associated to the host genetic background (either by breed or SNP genotypes). Further, the results showed a significant association between DGAT1, ACSF3, AGPAT3 and STC2 genes with the relative abundance Prevotella genus with a false discovery rate lower than 15%. The results in this study support the hypothesis and provide some evidence that there exist a host genetic component in cattle that can partially regulate the composition of the microbiome.

genomics↗
bioRxiv2017

A genome-wide polygenic approach to HIV uncovers link to inflammatory bowel disease and identifies potential novel genetic variants

Polygenic approaches using genome-wide data have been hugely successful in confirming and quantifying the heritability of complex human traits. Here, we highlight their ability to identify potential novel risk variants by looking for variants with pleiotropic effect in genetically overlapping phenotypes.\n\nWe used LD Score Regression in a sample of 6,315 HIV+ European individuals and 7,247 controls to test for phenotypes genetically overlapping with susceptibility to HIV-1 infection. Using LD Hub, a web tool that performs LD Score Regression, identified two phenotypes with significant genetic overlap: schizophrenia (rG =0.19, p=0.0007 and ulcerative colitis (rG=0.22, p= 0.0061). We further showed that the genetic overlap between HIV acquisition and schizophrenia is likely driven in part by their shared overlap with cannabis use and sexual behavior. BUMHBOX analyses suggested that these genetic overlaps were driven by genome-wide pleiotropy with HIV acquisition rather than heterogeneity within the HIV acquisition sample. The two diseases identified as genetically overlapping with HIV-1 acquisition have >100 associated variants, and we tested if any of them significantly associated with HIV acquisition. We observed three variants that exceeded our threshold for statistical significance. Two of these were eQTLs in whole blood for genes coding for proteins suspected to be involved in HIV biology: rs1819333 in CCR6 (p=0.0002) and rs4932178 in FURIN (p=0.00033). However, no signal was found for these variants in two smaller African samples totaling 1015 cases and 963 controls, though the mode of acquisition and genetic architecture of these populations differed.\n\nThese results highlight the ability to use polygenic methods to gain new insights into complex diseases and identify potential associations with individual variants. Crucially, the leveraging of existing, publically available data makes these methods a cost-effective approach. In this case, our results add to the evidence for the role of risk taking behavior and inflammation of the bowel in HIV acquisition.\n\nAuthor SummaryThe biology of what puts certain individuals at greater risk of HIV acquisition is poorly understood. Using several novel polygenic methods, we identify supporting evidence for two important factors leading to acquisition. First, the role of an individuals genetic predisposition to risk taking behaviours such as number of sexual partners, age at first sexual intercourse drug use, and mental health problems. Second, the role of gut inflammation, in particular a genetic overlap between HIV acquisition with inflammatory bowel disease and the potential role of CCR6 during infection.

genomics↗
bioRxiv2017

Partitioning plant genetic and environmental drivers of above and belowground community assembly

Host-plant genetic variation affects the diversity and composition of associated above and belowground communities. Most evidence supporting this view is derived from studies within a single common garden, thereby constraining the range of biotic and abiotic environmental conditions that might directly or indirectly (via phenotypic plasticity) affect communities. If natural variability in the environment renders host-plant genetic effects on associated communities unimportant, then studying the community-level consequences of genetic variation may not be warranted. We addressed this knowledge gap by planting a series of common gardens consisting of 10 different clones (genotypes) of the willow Salix hookeriana in a coastal dune ecosystem and manipulated natural variation in ant-aphid interactions (biotic) and wind exposure (abiotic) in two separate experiments. We then quantified the responses of associated species assemblages both above (foliar arthropods) and belowground (rhizosphere fungi and bacteria). In addition, we quantified plant phenotypic responses (plant growth, leaf quality, and root quality) to tease apart the effects of genetic variation, phenotypic plasticity, and direct environmental effects on associated communities. In the ant-aphid experiment, we found that willow genotype explained more variation in foliar arthropod communities than aphid additions and proximity to aphid-tending ant mounds. However, aphid additions modified willow genetic effects on arthropod community composition by attracting other aphid species to certain willow genotypes. In the wind experiment, wind exposure explained more variation than willow genotype in structuring communities of foliar arthropods and rhizosphere bacteria. Still, willow genotype had strong effect sizes on several community properties of arthropods and fungi, indicating that host-plant genetic variation remains important. Across both experiments, genetic variation in plant traits was more important than phenotypic plasticity in structuring associated communities. The relative importance of genetic variation vs. direct environmental effects though depended on the type of environmental gradient (G > E-aphid, but E-wind > G). Taken together, our results suggest that host-plant genetic variation is an important driver of above and belowground biodiversity, despite natural variation in the biotic and abiotic environment.

ecology↗
bioRxiv2017

Effects of multiple sources of genetic drift on pathogen variation within hosts

Changes in pathogen genetic variation within hosts alter the severity and spread of infectious diseases, with important implications for clinical disease and public health. Genetic drift may play a strong role in shaping pathogen variation, but analyses of drift in pathogens have oversimplified pathogen population dynamics, either by considering dynamics only at a single scale (within hosts, between hosts), or by making drastic simplifying assumptions (host immune systems can be ignored, transmission bottlenecks are complete). Moreover, previous studies used genetic data to infer the strength of genetic drift, whereas we test whether the genetic drift imposed by pathogen population processes can be used to explain genetic data. We first constructed and parameterized a mathematical model of gypsy moth baculovirus dynamics that allows genetic drift to act within and between hosts. We then quantified the genome-wide diversity of baculovirus populations within each of 143 field-collected gypsy moth larvae using Illumina sequencing. Finally, we determined whether the genetic drift imposed by host-pathogen population dynamics in our model explains the levels of pathogen diversity in our data. We found that when the model allows drift to act at multiple scales, including within hosts, between hosts, and between years, it can accurately reproduce the data, but when the effects of drift are simplified by neglecting transmission bottlenecks and stochastic variation in virus replication within hosts, the model fails. A de novo mutation model and a purifying selection model similarly fail to explain the data. Our results show that genetic drift can play a strong role in determining pathogen variation, and that mathematical models that account for pathogen population growth at multiple scales of biological organization can be used to explain this variation.

evolutionary biology↗
bioRxiv2017

Desert Tortoises in the Genomic Age: Population Genetics and the Landscape

The California Department of Fish and Wildlife (CDFW) provided research funds to study the conservation genomics and landscape genomics of the Mojave desert tortoise, Gopherus agassizii, in response to the Desert Renewable Energy Conservation Plan (DRECP). To do this, we consolidated tissue samples of the desert tortoise from across the species range within California and southern Nevada, generated a DNA dataset consisting of full genomes of 270 tortoises, and analyzed the way in which the environment of the desert tortoise has determined modern patterns of relatedness and genetic diversity across the landscape. Here we present the implications of these results for the conservation and landscape genomics of the desert tortoise. Our work strongly indicates that several well-defined genetic groups exist within the species, including a primary north-south genetic discontinuity at the Ivanpah Valley and another separating western from eastern Mojave samples. We also use existing desert tortoise habitat modeling data with a novel extension of genetic \"resistance distance\" using geographic maps of continuous space to predict the relative impacts of five proposed development alternatives within the DRECP and rank them with respect to their likely impacts on desert tortoise gene flow and connectivity in the Mojave. Finally, we analyzed the impacts of each of the 214 distinct proposed development area \"chunks,\" derived from the proposed development polygons, and ranked each chunk in terms of its range-wide impacts on desert tortoise gene flow.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC=\"FIGDIR/small/195743_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (147K):\norg.highwire.dtl.DTLVardef@b60416org.highwire.dtl.DTLVardef@1c649f2org.highwire.dtl.DTLVardef@120e625org.highwire.dtl.DTLVardef@e59b34_HPS_FORMAT_FIGEXP M_FIG C_FIG PrefaceO_ST_ABSContextC_ST_ABSThe following document is a report that was submitted to the California Department of Fish and Wildlife, describing a series of analyses to help understand the impacts of several alternative spatial configurations of renewable energy development on gene flow of the federally threatened Mojave desert tortoise. These development alternatives were the centerpiece of the Desert Renewable Energy Conservation Plan (DRECP), a landscape-level land use planning initiative undertaken by the Bureau of Land Management (BLM), U.S. Fish and Wildlife Service (USFWS), California Energy Commission (CEC), and the California Department of Fish and Wildlife (CDFW). We were tasked by the California Department of Fish and Wildlife with providing a detailed analysis of these alternative plans on desert tortoise gene flow, and submitted the report for the public comment period for the initial implementation of the DRECP.\n\nFuture PlansO_ST_ABSCurrent state of landscape-level planning for the Mojave desert tortoiseC_ST_ABSThe five proposed land use configuration alternatives analyzed in the subsequent report include public and private lands spread across several counties in California. Shortly after the end of the DRECPs public comment period, the government agencies that developed the DRECP announced that they would be splitting its implementation into two phases: one that deals with land use decisions on BLM-controlled lands and one that deals with non-BLM areas (Sahagun 2015).\n\nPhase I of the DRECP was approved by the Bureau of Land Management on September 14, 2016 (U.S. Bureau of Land Management 2016). This phase includes land use planning decisions for BLM-administered lands. Specifically, 388,000 acres of public lands were designated as development focus areas (DFAs). In applications for leasing lands for renewable energy development, DFAs will not require the same degree of environmental evaluation prior to permitting, as theyve already been evaluated in the context of the DRECP. The application process for renewable energy development within DFAs will be streamlined to encourage development in these areas. Phase I also designated a total of 6,527,000 acres for natural resource conservation. This includes California Desert National Conservation Lands, Areas of Critical Environmental Concern, and Wildlife Allocations. A further 2,691,000 acres were designated for recreation under Phase I. Phase II of the DRECP is currently under development in conjunction with county-level governments to extend this landscape-level planning beyond BLM-administered lands.\n\nAuthor ContributionsThis was a collaborative report. Evan McCartney-Melstad performed the simulations of the low-coverage full genome approach (see Figure 2); conducted all of the laboratory work to generate the genome sequences; performed all of the bioinformatic analyses to bring the raw sequence data to the various stages required for different analyses; wrote the software to quickly estimate pairwise genetic relationships between individuals using read count data in low coverage sequence data (www.github.com/atcg/cPWP); performed some of the population genetic analyses; and wrote and edited several sections of the report.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC=\"FIGDIR/small/195743_fig2.gif\" ALT=\"Figure 2\">\nView larger version (16K):\norg.highwire.dtl.DTLVardef@30a49forg.highwire.dtl.DTLVardef@187c3d8org.highwire.dtl.DTLVardef@4abe98org.highwire.dtl.DTLVardef@126febe_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO Comparison of two different sequencing approaches in their ability to differentiate very slightly differentiated populations (Fst=0.001)\n\nC_FIG Peter Ralph (in collaboration with Gideon Bradburd and Erik Lundgren) invented and implemented the random walk-based gene flow model that we used to estimate reductions in gene flow due to development, and also developed the theory behind the read-based pairwise pi and genetic covariance estimation used here, in addition to writing and editing several sections of the report. Gideon Bradburd also performed some of the population genetic analyses and wrote and edited several sections of the report. Jannet Vu collected and curated the spatial environmental data and generated the maps that are included in the report (Figures 10, A10-A13), and also wrote Appendices I and IV. Bridgette Hagerty, Fran Sandmeier, Chava Weitzman, and C. Richard Tracy contributed approximately 1,000 desert tortoise blood samples that they collected (at great effort), in addition to knowledge of tortoise ecology and conservation, as well as the results of previous microsatellite-based genetic analyses and editing of the report. H. Bradley Shaffer wrote and edited several sections of the report, and is listed as the lead author for his role in conceiving of and obtaining funding support for the project.\n\nO_FIG O_LINKSMALLFIG WIDTH=154 HEIGHT=200 SRC=\"FIGDIR/small/195743_fig10.gif\" ALT=\"Figure 10\">\nView larger version (82K):\norg.highwire.dtl.DTLVardef@11e99acorg.highwire.dtl.DTLVardef@1faf2f5org.highwire.dtl.DTLVardef@64c440org.highwire.dtl.DTLVardef@1907837_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 10.C_FLOATNO Spatial configuration of the proposed development chunks (see Appendix 4).\n\nC_FIG

evolutionary biology↗
bioRxiv2020

Congruent population genetic structures and divergence histories in anther-smut fungi and their host plants Silene italica and the S. nutans species complex

The study of population genetic structure congruence between hosts and pathogens gives important insights into their shared phylogeographic and coevolutionary histories. We studied the population genetic structure of castrating anther-smut fungi (Microbotryum genus) and of their host plants, the Silene nutans species complex, and the morphologically and genetically close S. italica, which can be found in sympatry. Phylogeographic population genetic structure related to persistence in separate glacial refugia has been recently revealed in the S. nutans plant species complex across Western Europe, identifying several distinct lineages. We genotyped 171 associated plant-pathogen pairs of anther-smut fungi and their host plant individuals using microsatellite markers and plant chloroplastic SNPs. We found clear differentiation between fungal populations parasitizing S. nutans and S. italica plants. The population genetic structure of fungal strains parasitizing the S. nutans plant species complex mirrored the host plant genetic structure, suggesting that the pathogen was isolated in glacial refugia together with its host and/or that it has specialized on the plant genetic lineages. Using random forest approximate Bayesian computation (ABC-RF), we found that the divergence history of the fungal lineages on S. nutans was congruent with the one previously inferred for the host plant and likely occurred with ancient but no recent gene flow. Genome sequences confirmed the genetic structure and the absence of recent gene flow between fungal genetic lineages. Our analyses of host-pathogen individual pairs contribute to a better understanding of co-evolutionary histories between hosts and pathogens in natural ecosystems, in which such studies are still scarce.

evolutionary biology↗
bioRxiv2020

Unassisted recovery without genetic bottleneck in regionally extinct peregrine falcons (Falco peregrinus) from southern England

The peregrine falcon (Falco peregrinus) has been affected by persecution, pollution, trade and habitat degradation, but it is considered a flagship conservation success story because of successful reintroductions. However, in the UK there were never formal reintroduction programmes for peregrine falcons, and it appears that UK populations - and specifically the Sussex peregrines of the English south coast - recently recovered from a population crash unassisted. To study this, we obtained samples from contemporary populations in southern England, Ireland, continental Europe, domestic-bred peregrine falcons, and from England pre-population crash. Using microsatellite and mtDNA control region data, the genetic diversity and structure, signatures of genetic bottlenecks, and potential origin of the Sussex peregrines was investigated. We found low levels of genetic diversity across all peregrine falcon populations, low but significant genetic differentiation among all populations, and a few private alleles, indicating some level of genetic structure in European peregrines. Although we could not pinpoint the origin of the Sussex peregrines, the data suggests that it is not likely to have originated from escaped domestic birds or from adjacent European populations. The results obtained here parallel other studies on peregrines elsewhere showing low genetic diversity but genetic structure. We conclude that not enough time elapsed for genetic erosion to occur due to the population bottleneck, and that at least for the Sussex peregrines there is no need for genetic conservation by wild-take and subsequent captive breeding programmes as long as current protection measures remain in place.

evolutionary biology↗
bioRxiv2020

AlphaPart - R implementation of the method for partitioning genetic trends

BackgroundIn this paper we present the AlphaPart R package, an open-source software that implements a method for partitioning breeding values and genetic trends to identify sources of genetic gain. Breeding programmes improve populations for a set of traits, which can be measured with a genetic trend calculated from averaged year of birth estimated breeding values of selection candidates. While sources of the overall genetic gain are generally known, their realised contributions are hard to quantify in complex breeding programmes. The aim of this paper is to present the AlphaPart R package and demonstrate it with a simulated pig breeding example. ResultsThe package includes the main partitioning function AlphaPart, that partitions the breeding values and genetic trends by analyst defined paths, and a set of functions for handling data and results. The package is freely available from CRAN repository at http://CRAN.R-project.org/package=AlphaPart. We demonstrate the use of the package by examining the genetic gain in a pig breeding example, in which the multiplier achieved higher breeding values than the nucleus for traits measured and selected in the multiplier. The partitioning analysis revealed that these higher values depended on the accuracy and intensity of selection in the multiplier and the extent of gene flow from the nucleus. For traits measured only in the nucleus, the multiplier achieved comparable or smaller genetic gain than the nucleus depending on the amount of gene flow. ConclusionsAlphaPart implements a method for partitioning breeding values and genetic trends and provides a useful tool for quantifying the sources of genetic gain in breeding programmes. The use of AlphaPart will help breeders to better understand or improve their breeding programmes.

genomics↗
bioRxiv2020

Range reduction of the Oblong Rocksnail, Leptoxis compacta, shapes riverscape genetic patterns

Many freshwater gastropod species face extinction, including 79% of species in the family Pleuroceridae. The Oblong Rocksnail, Leptoxis compacta, is a narrow range endemic pleurocerid from the Cahaba River basin in central Alabama that has seen rapid range contraction in the last 100 years. Such a decline is expected to negatively affect genetic diversity in the species. However, precise patterns of genetic variation and gene flow across the restricted range of L. compacta are unknown. This lack of information limits our understanding of human impacts on the Cahaba River system and Pleuroceridae. Here, we show that L. compacta has likely seen a species-wide decline in genetic diversity, but remaining populations have relatively high genetic diversity. We also report a contemporary range extension compared to the last published survey. Leptoxis compacta does not display an isolation by distance pattern, contrasting patterns seen in many riverine taxa. Our findings also indicate that historical range contraction has resulted in the absence of common genetic patterns seen in many riverine taxa like isolation by distance as the small distribution of L. compacta allows for relatively unrestricted gene flow across its remaining range despite limited dispersal abilities. Two collection sites had higher genetic diversity than others, and broodstock sites for future captive propagation and reintroduction efforts should utilize sites identified here as having the highest genetic diversity. Broadly, our results support the hypothesis that range contraction will result in the reduction of species-wide genetic diversity, and common riverscape genetic patterns cannot be assumed to be present in species facing extinction risk.

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