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bioRxiv2017

Genetic conflict with a parasitic nematode disrupts the legume-rhizobia mutualism

Genetic variation for partner quality in mutualisms is an evolutionary paradox. One possible resolution to this puzzle is that there is a tradeoff between partner quality and other fitness-related traits. Here, we tested whether a susceptibility to parasitism is one such tradeoff in the mutualism between legumes and nitrogen-fixing bacteria (rhizobia). We performed two greenhouse experiments with the legume Medicago truncatula. In the first, we inoculated each plant with the rhizobia Ensifer meliloti and with one of 40 genotypes of the parasitic root-knot nematode Meloidogyne hapla. In the second experiment, we inoculated all plants with rhizobia and half of the plants with a genetically variable population of nematodes. Using the number of nematode galls as a proxy for infection severity, we found that plant genotypes differed in susceptibility to nematode infection, and nematode genotypes differed in infectivity. Second, we showed that there was a genetic correlation between the number of mutualistic structures formed by rhizobia (nodules) and the number of parasitic structures formed by nematodes (galls). Finally, we found that nematodes disrupt the rhizobia mutualism: nematode-infected plants formed fewer nodules and had less nodule biomass than uninfected plants. Our results demonstrate that there is genetic conflict between attracting rhizobia and repelling nematodes in Medicago. If genetic conflict with parasitism is a general feature of mutualism, it could account for the maintenance of genetic variation in partner quality and influence the evolutionary dynamics of positive species interactions.\n\nImpact summaryCooperative species interactions, known as mutualisms, are vital for organisms from plants to humans. For example, beneficial microbes in the human gut are a necessary component of digestive health. However, parasites often infect their hosts via mechanisms that are extraordinarily similar to those used by mutualists, which may create a tradeoff between attracting mutualists and resisting parasites. In this study, we investigated whether this tradeoff exists, and how parasites impact mutualism function in the barrelclover Medicago truncatula, a close relative of alfalfa. Legumes like Medicago depend on nitrogen provided by mutualistic bacteria (rhizobia) to grow, but they are also infected by parasitic worms called nematodes, which steal plant nutrients. Both microorganisms live in unique structures (nodules and galls) on plant roots. We showed that the benefits of mutualism and the costs of parasitism are predicted by the number of mutualistic structures (nodules) and the number of parasitic structures (galls), respectively. Second, we found that there is a genetic tradeoff between attracting mutualists and repelling parasites in Medicago truncatula: plant genotypes that formed more rhizobia nodules also formed more nematode galls. Finally, we found that nematodes disrupt the rhizobia mutualism. Nematode-infected plants formed fewer rhizobia nodules and less total nodule biomass than uninfected plants. Our research addresses an enduring evolutionary puzzle: why is there so much variation in the benefits provided by mutualists when natural selection should weed out low-quality partners? Tradeoffs between benefits provided by mutualists and their susceptibility to parasites could resolve this paradox.

evolutionary biology↗

bioRxiv2017

The standard genetic code facilitates exploration of the space of functional nucleotide sequences

The standard genetic code is well known to be optimized for minimizing the phenotypic effects of single nucleotide substitutions, a property that was likely selected for during the emergence of a universal code. Given the fitness advantage afforded by high standing genetic diversity in a population in a dynamic environment, it is possible that selection to explore a large fraction of the space of functional proteins also occurred. To determine whether selection for such a property played a role during the emergence of the nearly universal genetic code, we investigated the number of functional variants of the Escherichia coli PhoQ protein explored at different time scales under translation using different genetic codes. We found that the standard genetic code is highly optimal for exploring a large fraction of the space of functional PhoQ variants at intermediate time scales as compared to random codes. Environmental changes, in response to which genetic diversity in a population provides a fitness advantage, are likely to have occurred at these intermediate time scales. Our results indicate that the ability of the standard code to explore a large fraction of the space of functional sequence variants arises from a balance between robustness and flexibility and is largely independent of the property of the standard code to minimize the phenotypic effects of mutations. We propose that selection to explore a large fraction of the functional sequence space while minimizing the phenotypic effects of mutations contributed towards the emergence of the standard code as the universal genetic code.

evolutionary biology↗

bioRxiv2018

Genetic architecture of gene expression traits across diverse populations

For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n=233), Hispanic (HIS, n=352), and European (CAU, n=578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene, TACSTD2, was the same across populations with R2 > 0.86 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at .\n\nAuthor summaryMost genome-wide association studies (GWAS) have been conducted in populations of European ancestry leading to a disparity in understanding the genetics of complex traits between populations. For many complex traits, gene regulation is critical, given the consistent enrichment of regulatory variants among trait-associated variants. However, it is still unknown how the effects of these key variants differ across populations. We used data from MESA to study the underlying genetic architecture of gene expression by optimizing gene expression prediction within and across diverse populations. The populations with genotype and gene expression data available are from individuals with African American (AFA, n=233), Hispanic (HIS, n=352), and European (CAU, n=578) ancestry. After calculating the prediction performance, we found that there are many genes that were well predicted in one population are poorly predicted in another. We further show that a training set with ancestry similar to the test set resulted in better gene expression predictions, demonstrating the need to incorporate diverse populations in genomic studies. Our gene expression prediction models and performance statistics are publicly available to facilitate future transcriptome mapping studies in diverse populations.

genomics↗

bioRxiv2018

A meta-analysis of the diagnostic sensitivity and clinical utility of genome sequencing, exome sequencing and chromosomal microarray in children with suspected genetic diseases

IMPORTANCEGenetic diseases are a leading cause of childhood mortality. Whole genome sequencing (WGS) and whole exome sequencing (WES) are relatively new methods for diagnosing genetic diseases.\n\nOBJECTIVESCompare the diagnostic sensitivity (rate of causative, pathogenic or likely pathogenic genotypes in known disease genes) and rate of clinical utility (proportion in whom medical or surgical management was changed by diagnosis) of WGS, WES, and chromosomal microarrays (CMA) in children with suspected genetic diseases.\n\nDATA SOURCES AND STUDY SELECTIONSystematic review of the literature (January 2011 - August 2017) for studies of diagnostic sensitivity and/or clinical utility of WGS, WES, and/or CMA in children with suspected genetic diseases. 2% of identified studies met selection criteria.\n\nDATA EXTRACTION AND SYNTHESISTwo investigators extracted data independently following MOOSE/PRISMA guidelines.\n\nMAIN OUTCOMES AND MEASURESPooled rates and 95% Cl were estimated with a random-effects model. Metaanalysis of the rate of diagnosis was based on test type, family structure, and site of testing.\n\nRESULTSIn 36 observational series and one randomized control trial, comprising 20,068 children, the diagnostic sensitivity of WGS (0.41, 95% Cl 0.34-0.48, I2=44%) and WES (0.35, 95% Cl 0.31-0.39, I2=85%) were qualitatively greater than CMA (0.10, 95% Cl 0.08-0.12, I2=81%). Subgroup meta-analyses showed that the diagnostic sensitivity of WGS was significantly greater than CMA in studies published in 2017 (P<.0001, I2=13% and I2=40%, respectively), and the diagnostic sensitivity of WES was significantly greater than CMA in studies featuring within-cohort comparisons (P<001, I2=36%). Evidence for a significant difference in the diagnostic sensitivity of WGS and WES was lacking. In studies featuring within-cohort comparisons of singleton and trio WGS/WES, the likelihood of diagnosis was significantly greater for trios (odds ratio 2.04, 95% Cl 1.62-2.56, I2=12%; P<.0001). The diagnostic sensitivity of WGS/WES with hospital-based interpretation (0.41, 95% Cl 0.38-0.45, I2=50%) was qualitatively higher than that of reference laboratories (0.28, 95% Cl 0.24-0.32, I2=81%); this difference was significant in meta-analysis of studies published in 2017 (P=.004, I2=34% and I2=26%, respectively). The rates of clinical utility of WGS (0.27, 95% Cl 0.17-0.40, I2=54%) and WES (0.18, 95% Cl 0.13-0.24, I2-77%) were higher than CMA (0.06, 95% Cl 0.05-0.07, I2=42%); this difference was significant in meta-analysis of WGS vs CMA (P<.0001).\n\nCONCLUSIONS AND RELEVANCEIn children with suspected genetic diseases, the diagnostic sensitivity and rate of clinical utility of WGS/WES were greater than CMA. Subgroups with higher WGS/WES diagnostic sensitivity were trios and those receiving hospital-based interpretation. WGS/WES should be considered a first-line genomic test for children with suspected genetic diseases.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSWhat is the relative diagnostic sensitivity and clinical utility of different genome tests in children with suspected genetic diseases?\n\nFindingsWhole genome sequencing had greater diagnostic sensitivity and clinical utility than chromosomal microarrays. Testing parent-child trios had greater diagnostic sensitivity than proband singletons. Hospital-based testing had greater diagnostic sensitivity than reference laboratories.\n\nMeaningTrio genomic sequencing is the most sensitive diagnostic test for children with suspected genetic diseases.

genomics↗

bioRxiv2018

Genetic basis of thermal plasticity variation in Drosophila melanogaster body size

Body size is a quantitative trait that is closely associated to fitness and under the control of both genetic and environmental factors. While developmental plasticity for this and other traits is heritable and under selection, little is known about the genetic basis for variation in plasticity that can provide the raw material for its evolution. We quantified genetic variation for body size plasticity in Drosophila melanogaster by measuring thorax and abdomen length of females reared at two temperatures from a panel representing naturally segregating alleles, the Drosophila Genetic Reference Panel (DGRP). We found variation between genotypes for the levels and direction of thermal plasticity in size of both body parts. We then used a Genome-Wide Association Study (GWAS) approach to unravel the genetic basis of inter-genotype variation in body size plasticity, and used different approaches to validate selected QTLs and to explore potential pleiotropic effects. We found mostly \"private QTLs\", with little overlap between the candidate loci underlying variation in plasticity for thorax versus abdomen size, for different properties of the plastic response, and for size versus size plasticity. We also found that the putative functions of plasticity QTLs were diverse and that alleles for higher plasticity were found at lower frequencies in the target population. Importantly, a number of our plasticity QTLs have been targets of selection in other populations. Our data sheds light onto the genetic basis of inter-genotype variation in size plasticity that is necessary for its evolution.\n\nSignificance StatementThe environmental conditions under which development takes place can affect developmental outcomes and lead to the production of phenotypes adjusted to the environment adults will live in. This developmental plasticity, which can help organisms cope with environmental heterogeneity, is heritable and under selection. Plasticity can itself evolve, a process that will be partly dependent on the available genetic variation for this trait. Using a wild-derived D. melanogaster panel, we identified DNA sequence variants associated to variation in thermal plasticity for body size. We found that these variants correspond to a diverse set of gene functions. Furthermore, their effects differ between body parts and properties of the thermal response, which can, therefore, evolve independently. Our results shed new light onto a number of key questions about the long discussed genes for plasticity.

evolutionary biology↗

bioRxiv2018

Engineered Genetic Redundancy Relaxes Selective Constraints upon Endogenous Genes in Viral RNA Genomes

1Genetic redundancy, understood as the functional overlap of different genes, is a double-edge sword. At the one side, it is thought to serve as a robustness mechanism that buffers the deleterious effect of mutations hitting one of the redundant copies, thus resulting in pseudogenization. At the other side, it is considered as a source of genetic and functional innovation. In any case, genetically redundant genes are expected to show an acceleration in the rate of molecular evolution. Here we tackle the role of genetic redundancy in viral RNA genomes. To this end, we have evaluated the rates of compensatory evolution for deleterious mutations affecting an essential function, the suppression of RNA silencing plant defense, of tobacco etch potyvirus (TEV). TEV genotypes containing deleterious mutations in presence/absence of engineered genetic redundancy were evolved and the pattern of fitness and virulence recovery evaluated. Genetically redundant genotypes suffered less from the effect of deleterious mutations and showed relatively minor changes in fitness and virulence. By contrast, non-genetically redundant genotypes had very low fitness and virulence at the beginning of the evolution experiment that were fully recovered by the end. At the molecular level, the outcome depended on the combination of the actual mutations being compensated and the presence/absence of genetic redundancy. Reversions to wild-type alleles were the norm in the non-redundant genotypes while redundant ones either did not fix any mutation at all or showed a higher nonsynonymous mutational load.

evolutionary biology↗

bioRxiv2018

The structure of the genetic code as an optimal graph clustering problem

The standard genetic code (SGC) is the set of rules by which genetic information is translated into proteins, from codons, i.e. triplets of nucleotides, to amino acids. The questions about the origin and the main factor responsible for the present structure of the code are still under a hot debate. Various methodologies have been used to study the features of the code and assess the level of its potential optimality. Here, we introduced a new general approach to evaluate the quality of the genetic code structure. This methodology comes from graph theory and allows us to describe new properties of the genetic code in terms of conductance. This parameter measures the robustness of codon groups against the potential changes in translation of the protein-coding sequences generated by single nucleotide substitutions. We described the genetic code as a partition of an undirected and unweighted graph, which makes the model general and universal. Using this approach, we showed that the structure of the genetic code is a solution to the graph clustering problem. We presented and discussed the structure of the codes that are optimal according to the conductance. Despite the fact that the standard genetic code is far from being optimal according to the conductance, its structure is characterised by many codon groups reaching the minimum conductance for their size. The SGC represents most likely a local minimum in terms of errors occurring in protein-coding sequences and their translation.

systems biology↗

bioRxiv2018

Concordance Of Genetic Variation That Increases Risk For Tourette Syndrome And That Influences Its Underlying Neurocircuitry

BACKGROUNDThere have been considerable recent advances in understanding the genetic architecture of Tourette Syndrome (TS) as well as its underlying neurocircuitry. However, the mechanisms by which genetic variations that increase risk for TS - and its main symptom dimensions - influence relevant brain regions are poorly understood. Here we undertook a genome-wide investigation of the overlap between TS genetic risk and genetic influences on the volume of specific subcortical brain structures that have been implicated in TS.\n\nMETHODSWe obtained summary statistics for the most recent TS genome-wide association study (GWAS) from the TS Psychiatric Genomics Consortium Working Group (4,644 cases and 8,695 controls) and GWAS of subcortical volumes from the ENIGMA consortium (30,717 individuals). We also undertook analyses using GWAS summary statistics of key symptom factors in TS, namely social disinhibition and symmetry behaviour. SNP Effect Concordance Analysis (SECA) was used to examine genetic pleiotropy - the same SNP affecting two traits - and concordance - the agreement in SNP effect directions across these two traits. In addition, a conditional false discovery rate (FDR) analysis was performed, conditioning the TS risk variants on each of the seven subcortical and the intracranial brain volume GWAS. Linkage Disequilibrium Score Regression (LDSR) was used as validation of SECA.\n\nRESULTSSECA revealed significant pleiotropy between TS and putaminal (p=2x10-4) and caudal (p=4x10-4) volumes, independent of direction of effect, and significant concordance between TS and lower thalamic volume (p=1x10-3). LDSR lent additional support for the association between TS and thalamic volume (p=5.85x10-2). Furthermore, SECA revealed significant evidence of concordance between the social disinhibition symptom dimension and lower thalamic volume (p=1x10-3), as well as concordance between symmetry behaviour and greater putaminal volume (p=7x10-4). Conditional FDR analysis further revealed novel variants significantly associated with TS (p<8x10-7) when conditioning on intracranial (rs2708146, q=0.046; and rs72853320, q=0.035 and hippocampal (rs1922786, q=0.001 volumes respectively.\n\nCONCLUSIONThese data indicate concordance for genetic variations involved in disorder risk and subcortical brain volumes in TS. Further work with larger samples is needed to fully delineate the genetic architecture of these disorders and their underlying neurocircuitry.

genomics↗

bioRxiv2018

Genetic characterization of chytrids isolated from larval amphibians collected in central and east Texas

Chytridiomycosis, an emerging infectious disease caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd), has caused amphibian population declines worldwide. Bd was first described in the 1990s and there are still geographic gaps in the genetic analysis of this globally distributed pathogen. Relatively few genetic studies have focused on regions where Bd exhibits low virulence, potentially creating a bias in our current knowledge of the pathogens genetic diversity. Disease-associated declines have not been recorded in Texas (USA), yet Bd has been detected on amphibians in the state. These strains have not been isolated and characterized genetically; therefore, we isolated, cultured, and genotyped Bd from central Texas and compared isolates to a panel of previously genotyped strains distributed across the Western Hemisphere. We also isolated other chytrids not known to infect amphibians from east Texas. To identify larval amphibian hosts, we sequenced part of the COI gene. Among 37 Bd isolates from Texas, we detected 19 unique multi-locus genotypes, but found no genetic structure associated with host species, Texas localities, or across North America. Isolates from central Texas exhibit high diversity and genetically cluster with Bd-GPL isolates from the western U.S. that have caused amphibian population declines. This study genetically characterizes isolates of Bd from the south central U.S. and adds to the global knowledge of Bd genotypes.

evolutionary biology↗

bioRxiv2018

A century of decline: loss of genetic diversity in a southern African lion-conservation stronghold

AimThere is a dearth of evidence that determines the genetic diversity of populations contained within present-day protected areas compared with their historic state prior to large-scale species declines, making inferences about a species conservation genetic status difficult to assess. The aim of this paper is to demonstrate the use of historic specimens to assess the change in genetic diversity over a defined spatial area.\n\nLocationLike other species, African lion populations (Panthera leo) are undergoing dramatic contractions in range and declines in numbers, motivating the identification of a number of lion conservation strongholds across East and southern Africa. We focus on one such stronghold, the Kavango-Zambezi transfrontier conservation area (KAZA) of Botswana, Namibia, Zambia and Zimbabwe.\n\nMethodsWe compare genetic diversity between historical museum specimens, collected during the late 19th and early 20th century, with samples from the modern extant population. We use 16 microsatellite markers and sequence 337 base pairs of the hypervariable control region (HVR1) of the mitochondrial genome. We use bootstrap resampling to allow for comparisons between the historic and modern data.\n\nResultsWe show that the genetic diversity of the modern population was reduced by 12% to 17%, with a reduction in allelic diversity of approximately 15%, compared to historic populations, in addition to having lost a number of mitochondrial haplotypes. We also identify reduced allelic diversity and a number of ghost alleles in the historical samples no longer present in the extant population.\n\nMain ConclusionsWe argue a rapid decline in allelic richness after 1895 suggests the erosion of genetic diversity coincides with the rise of a European colonial presence and the outbreak of rinderpest in the region. Our results support the need to improved connectivity between protected areas in order to prevent further loss of genetic diversity in the region.

zoology↗

bioRxiv2018

Genome-wide association study of anti-Mullerian hormone levels in pre-menopausal women of late reproductive age and relationship with genetic determinants of reproductive lifespan

Anti-Mullerian hormone (AMH) is required for sexual differentiation in the fetus, and in adult females AMH is produced by growing ovarian follicles. Consequently, AMH levels are correlated with ovarian reserve, declining towards menopause when the oocyte pool is exhausted. A previous genome-wide association study identified three genetic variants in and around the AMH gene that explained 25% of variation in AMH levels in adolescent males but did not identify any genetic associations reaching genome-wide significance in adolescent females. To explore the role of genetic variation in determining AMH levels in women of late reproductive age, we carried out a genome-wide meta-analysis in 3,344 pre-menopausal women from five cohorts (median age 44-48 years at blood draw). A single genetic variant, rs16991615, previously associated with age at menopause, reached genome-wide significance at P=3.48x10-10, with a per allele difference in age-adjusted inverse normal AMH of 0.26 SD (95% CI [0.18,0.34]). We investigated whether genetic determinants of female reproductive lifespan were more generally associated with pre-menopausal AMH levels. Genetically-predicted age at menarche had no robust association but genetically-predicted age at menopause was associated with lower AMH levels by 0.18 SD (95% CI [0.14,0.21]) in age-adjusted inverse normal AMH per one-year earlier age at menopause. Our findings support the hypothesis that AMH is a valid measure of ovarian reserve in pre-menopausal women and suggest that the underlying biology of ovarian reserve results in a causal link between pre-menopausal AMH levels and menopause timing.

genomics↗

bioRxiv2018

Extensive genetic divergence between recently evolved sympatric Arctic charr morphs

The availability of diverse ecological niches can promote adaptation of trophic specializations and related traits, as has been repeatedly observed in evolutionary radiations of freshwater fish. The role of genetics, environment and history in ecologically driven divergence and adaptation, can be studied on adaptive radiations or populations showing ecological polymorphism. Salmonids, especially the Salvelinus genus, are renowned for both phenotypic diversity and polymorphism. Arctic charr (Salvelinus alpinus) invaded Icelandic streams during the glacial retreat (about 10,000 years ago) and exhibits many instances of sympatric polymorphism. Particularly well studied are the four morphs in Lake Thingvallavatn in Iceland. The small benthic (SB), large benthic (LB), planktivorous (PL) and piscivorous (PI) charr differ in many regards, including size, form and life history traits. To investigate relatedness and genomic differentiation between morphs we identified variable sites from RNA-sequencing data from three of those morphs, and verified 22 variants in population samples. The data reveal genetic differences between the morphs, with the two benthic morphs being more similar and the PL-charr more genetically different. The markers with high differentiation map to all linkage groups, suggesting ancient and pervasive genetic separation of these three morphs. Furthermore, GO analyses suggest differences in collagen metabolism, odontogenesis and sensory systems between PL-charr and the benthic morphs. Genotyping in population samples from all four morphs confirms the genetic separation and indicates that the PI-charr are less genetically distinct than the other three morphs. The genetic separation of the other three morphs indicates certain degree of reproductive isolation. The extent of gene flow between the morphs and the nature of reproductive barriers between them remain to be elucidated.

evolutionary biology↗

bioRxiv2019

The genetic basis of cis-regulatory divergence between the subspecies of cultivated rice (Oryza sativa)

Cultivated rice consists of two subspecies, Indica and Japonica, that exhibit well-characterized differences at the morphological and genetic levels. However, the differences between these subspecies at the transcriptome level remains largely unexamined. Here, we provide a comprehensive characterization of transcriptome divergence and cis-regulatory variation within rice using transcriptome data from 91 accessions from a rice diversity panel (RDP1). The transcriptomes of the two subspecies of rice are highly divergent. The expression and genetic diversity was significantly lower within Japonica relative to Indica, which is consistent with the known population bottleneck during Japonica domestication. Moreover, 1,860 and 1,325 genes showed differences in heritability in the broad and narrow sense respectively, between the subspecies, which was driven largely by environmental and genetic effects rather than differences in phenotypic variability. We leveraged high-density genotypic data and transcript levels to identify cis-regulatory variants that may explain the genetic divergence between the subspecies. We identified significantly more eQTL that were specific to the Indica subspecies compared to Japonica, suggesting that the observed differences in expression and genetic variability also extends to cis-regulatory variation. We next explored the potential causes of this cis-regulatory divergence by assessing local genetic diversity for cis-eQTL. Local genetic diversity around subspecies-specific cis-eQTL was significantly lower than genome-wide averages in subspecies lacking the eQTL, suggesting that selective pressures may have shaped regulatory variation in each subspecies. This study provides the first comprehensive characterization of transcriptional and cis-regulatory variation in cultivated rice, and could be an important resource for future studies.

genomics↗

bioRxiv2019

Fast estimation of genetic correlation for Biobank-scale data

Genetic correlation is an important parameter in efforts to understand the relationships among complex traits. Current methods that analyze individual genotype data for estimating genetic correlation are challenging to scale to large datasets. Methods that analyze summary data, while being computationally efficient, tend to yield estimates of genetic correlation with reduced precision. We propose, SCORE, a randomized method of moments estimator of genetic correlation that is both scalable and accurate. SCORE obtains more precise estimates of genetic correlations relative to summary-statistic methods that can be applied at scale achieving a 50% reduction in standard error relative to LD-score regression (LDSC) and a 26% reduction relative to high-definition likelihood (HDL) (averaged over all simulations). The efficiency of SCORE enables computation of genetic correlations on the UK biobank dataset consisting of {approx} 300K individuals and {approx} 500K SNPs in a few hours (orders of magnitude faster than methods that analyze individual data such as GCTA). Across 780 pairs of traits in 291, 273 unrelated white British individuals in the UK Biobank, SCORE identifies significant genetic correlation between 200 additional pairs of traits over LDSC (beyond the 245 pairs identified by both).

bioinformatics↗

bioRxiv2019

Worldwide impacts of human on animal genetic diversity

Human impacts on genetic diversity are poorly understood yet critical to understanding the evolutionary capacity of the worlds biodiversity. We used global maps of land use and human density to assess human impacts on the intraspecific genetic diversity of 15,946 species of birds, fishes, insects, and mammals over time and across four spatial scales worldwide. We analyzed 164,518 mitochondrial cytochrome c oxidase subunit I (COI) sequences to quantify changes in genetic diversity between 1980-2016. We found temporal change in genetic diversity, with both increasing and decreasing trends observed. The magnitude and sign of human impacts on genetic diversity depended on scale and taxon. In contrast, latitude was a strong predictor of diversity in fish, insects, and mammals. Our analyses provide a first worldwide picture of human impacts on animal genetic diversity. A global effort to systematically monitor genetic diversity is needed to fill the gaps in taxonomic and geographic coverage in this dataset.

evolutionary biology↗

bioRxiv2019

The intersectional genetics landscape for human

The human body is made up of hundreds, perhaps thousands of cell types and states, most of which are currently inaccessible genetically. Genetic accessibility carries significant diagnostic and therapeutic potential by allowing the selective delivery of genetic messages or cures to cells. Research in model organisms has shown that single regulatory element (RE) activities are seldom cell type specific, limiting their usage in genetic systems designed to restrict gene expression posteriorly to their delivery to cells. Intersectional genetic approaches can increase the number of genetically accessible cells. A typical intersectional method acts like an AND logic gate by converting the input of two or more active REs into a single synthetic output, which becomes unique for that cell. Here, we systematically assessed the intersectional genetics landscape of human using a curated subset of cells from a large RE usage atlas obtained by Cap Analysis of Gene Expression Sequencing (CAGE-Seq) of thousands of primary and cancer cells (the FANTOM5 consortium atlas). We developed the heuristics and algorithms to retrieve and quality rank AND gate intersections intra- and inter-individually. We find that >90% of the 154 primary cell types surveyed can be distinguished from each other with as little as 3 to 4 active REs, with quantifiable safety and robustness. We call these minimal intersections of active REs with cell-type diagnostic potential "Versatile Entry Codes" (VEnCodes). We show that VEnCodes could be found for 100% of the 158 cancer cell types surveyed, and that most of these are highly robust to intra- and interindividual variation. Our tools for generating and quality-ranking VEnCodes can be adapted to other RE usage databases and to other intersectional methods using alternative Boolean logic operations. Our work demonstrate the potential of intersectional approaches for future gene delivery technologies in human.

bioinformatics↗

bioRxiv2019

The complex genetic architecture of recombination and structural variation in wheat uncovered using a large 8-founder MAGIC population

BackgroundIdentifying the genetic architecture of complex traits requires access to populations with sufficient genetic diversity and recombination. Multi-parent Advanced Generation InterCross (MAGIC) populations are a powerful resource due to their balanced population structure, allelic diversity and enhanced recombination. However, implementing a MAGIC population in complex polyploids such as wheat is not trivial, as wheat harbours many introgressions, inversions and other genetic factors that interfere with linkage mapping.\n\nResultsBy utilising a comprehensive crossing strategy, additional rounds of mixing and novel genotype calling approaches, we developed a bread wheat eight parent MAGIC population made up of more than 3000 fully genotyped recombinant inbred lines derived from 2151 distinct crosses, and achieved a dense genetic map covering the complete genome. Further rounds of inter-crossing led to increased recombination in inbred lines, as expected. The comprehensive and novel approaches taken in the development and analysis of this population provide a platform for genetic discovery in bread wheat. We identify previously unreported structural variation highlighted by segregation distortion, along with the identification of epistatic allelic interactions between specific founders. We demonstrate the ability to conduct high resolution QTL mapping using the number of recombination events as a trait, and identify several significant QTLs explaining greater than 50% of the variance.\n\nConclusionsWe report on a novel and effective resource for genomic and trait exploration in hexaploid wheat, that can be used to detect small genetic effects and epistatic interactions due to the high level of recombination and large number of lines. The interactions and genetic effects identified provide a basis for ongoing research to understand the basis of allelic frequencies across the genome, particularly where economically important loci are involved.

plant biology↗

bioRxiv2019

Identifying Brain Region Connectivity using Steiner Minimal Tree Approximation and a Genetic Algorithm

BackgroundComputation and visualization of connectivity among the brain regions is vital for tasks such as disease identification and drug discovery. An effective visualization can aid clinicians and biologists to perform these tasks addressing a genuine research and industrial need. In this paper, we present SMT-Neurophysiology, a web-based tool in a form of an approximation to the Steiner Minimal Tree (SMT) algorithm to search neurophysiology partonomy and connectivity graph in order to find biomedically-meaningful paths that could explain, to neurologists and neuroscientists, the mechanistic relationship, for example, among specific neurophysiological examinations. We also present SMT-Genetic, a web-based tool in a form of a Genetic Algorithm (GA) to find better paths than SMT-Neurophysiology.\n\nResultsWe introduce an approximation to the SMT algorithm to identify the most parsimonious connectivity among the brain regions of interest. We have implemented our algorithm as a highly interactive web application called SMT-Neurophysiology that enables such computation and visualization. It operates on brain region connectivity dataset curated from the Neuroscience Information Framework (NIF) for four species - human, monkey, rat and bird. We present two case studies on finding the most biomedically-meaningful solutions that identifies connections among a set of brain regions over a specific route. The case studies demonstrate that SMT-Neurophysiology is able to find connection among brain regions of interest. Furthermore, SMT-Neurophysiology is modular and generic in nature allowing the underlying connectivity graph to model any data on which the tool can operate. In order to find better connections among a set of brain regions than SMT-Neurophysiology, we have implemented a web-based tool in a form of a GA called SMT-Genetic. We present further three case studies where SMT-Genetic finds better connections among a set of brain regions than SMT-Neurophysiology. SMT-Genetic gives better connections because SMT-Genetic finds global optimum whereas SMT-Neurophysiology finds local optimum although execution time of SMT-Genetic is higher than SMT-Neurophysiology.\n\nConclusionOur analysis would provide key insights to clinical investigators about potential mechanisms underlying a particular neurological disease. The web-based tools and the underlying data are useful to clinicians and scientists to understand neurological disease mechanisms; discover pharmacological or surgical targets; and design diagnostic or therapeutic clinical trials. The source codes and links to the live tools are available at https://github.com/dewancse/connected-brain-regions and https://github.com/dewancse/SMT-Genetic.

bioinformatics↗