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Results for “genetics”

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bioRxiv2016

Associations of coffee genetic risk scores with coffee, tea and other beverages in the UK Biobank

BackgroundGenetic variants which determine amount of coffee consumed have been identified in genome-wide association studies (GWAS) of coffee consumption; these may help to further understanding of the effects of coffee on health outcomes. However, there is limited information about how these variants relate to caffeinated beverage consumption more generally.\n\nAimsTo improve phenotype definition for coffee consumption related genetic risk scores by testing their association with coffee, tea and other beverages.\n\nMethodsWe tested the associations of genetic risk scores for coffee consumption with beverage consumption in 114,316 individuals of European ancestry from the UK Biobank. Drinks were self-reported in a baseline questionnaire and in detailed 24 dietary recall questionnaires in a subset.\n\nResultsGenetic risk scores including two and eight single nucleotide polymorphisms (SNPs) explained up to 0.39%, 0.19% and 0.77% of the variance in coffee, tea and combined coffee and tea consumption respectively. A one standard deviation increase in the 8 SNP genetic risk score was associated with a 0.13 cup per day (95% CI: 0.12, 0.14), 0.12 cup per day (95%CI: 0.11, 0.14) and 0.25 cup per day (95% CI: 0.24, 0.27) increase in coffee, tea and combined tea and coffee consumption, respectively. Genetic risk scores also demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption. In 48,692 individuals with dietary recall data, the genetic risk scores were positively associated with coffee and tea, (apart from herbal teas) consumption, but did not show clear evidence for positive associations with other beverages. However, there was evidence that the genetic risk scores were associated with lower daily water consumption and lower overall drink consumption.\n\nConclusionsGenetic risk scores created from variants identified in coffee consumption GWAS associate more broadly with caffeinated beverage consumption and also with decaffeinated coffee and tea consumption.

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bioRxiv2016

Joint genetic analysis using variant sets reveals polygenic gene-context interactions

Joint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. Our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. We first use simulations to validate iSet before applying the model to the analysis of genotype-environment interactions in an eQTL study. Our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. Finally, we apply iSet to test for sub-group specific genetic effects in human lipid levels in a large human cohort, where we identify a gene-sex interaction for C-reactive protein that is missed by alternative methods.\n\nAuthor summaryGenetic effects on phenotypes can depend on external contexts, including environment. Statistical tests for identifying such interactions are important to understand how individual genetic variants may act in different contexts. Interaction effects can either be studied using measurements of a given phenotype in different contexts, under the same genetic backgrounds, or by stratifying a population into subgroups. Here, we derive a method based on linear mixed models that can be applied to both of these designs. iSet enables testing for interactions between context and sets of variants, and accounts for polygenic effects. We validate our model using simulations, before applying it to the genetic analysis of gene expression studies and genome-wide association studies of human blood lipid levels. We find that modeling interactions with variant sets offers increased power, thereby uncovering interactions that cannot be detected by alternative methods.

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bioRxiv2017

Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank

Genetic discovery from the multitude of phenotypes extractable from routine healthcare data has the ability to radically transform our understanding of the human phenome, thereby accelerating progress towards precision medicine. However, a critical question when analysing high-dimensional and heterogeneous data is how to interrogate increasingly specific subphenotypes whilst retaining statistical power to detect genetic associations. Here we develop and employ a novel Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to jointly analyse genetic variants against UK Biobank healthcare phenotypes. Our method displays a more than 20% increase in power to detect genetic effects over other approaches, such that we uncover the broader burden of genetic variation: we identify associations with over 2,000 diagnostic terms. We find novel associations with common immune-mediated diseases (IMD), we reveal the extent of genetic sharing between specific IMDs, and we expose differences in disease perception or diagnosis with potential clinical implications.

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bioRxiv2017

Dissecting the genetic architecture of shoot growth in carrot (Daucus carota L.) using a diallel mating design

Crop establishment in carrot (Daucus carota L.) is limited by slow seedling growth and delayed canopy closure, resulting in high management costs for weed control. Varieties with improved growth habit (i.e. larger canopy and increased shoot biomass) may help mitigate weed control, but the underlying genetics of these traits in carrot is unknown. This project used a diallel mating design coupled with recent Bayesian analytical methods to determine the genetic basis of carrot shoot growth. Six diverse carrot inbred lines with variable shoot size were crossed in WI in 2014. F1 hybrids, reciprocal crosses, and parental selfs were grown in a randomized complete block design (RCBD) with two blocks in CA (2015, 2016) and in WI (2015). Measurements included canopy height, canopy width, shoot biomass, and root biomass. General and specific combining abilities were estimated using Griffings Model I. In parallel, additive, inbreeding, epistatic, and maternal effects were estimated from a Bayesian linear mixed model, which is more robust to dealing with missing data, outliers, and theoretical constraints than traditional biometric methods. Both additive and non-additive effects significantly influenced shoot traits, with non-additive effects playing a larger role early in the growing season, when weed control is most critical. Results suggest that early season canopy growth and root size express hybrid vigor and can be improved through reciprocal recurrent selection.\n\nArticle SummaryBreeding for improved competitive ability is a priority in carrot, which suffers yield losses under weed pressure. However, improvement and in-depth genetic studies for these traits relies on knowledge of the underlying genetic architecture. This study estimated heritable and non-heritable components of carrot shoot growth from a diallel mating design using a Bayesian mixed model. Results directly contribute to improvement efforts by providing estimates of combining ability, identifying a useful tester line, and characterizing the genetic and non-genetic influences on traits for improved competitive ability in carrot.

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bioRxiv2017

A multi-parent advanced generation inter-cross population for genetic analysis of multiple traits in cowpea (Vigna unguiculata L. Walp.)

Development and analysis of Multiparent Advanced Generation Inter-Cross (MAGIC) populations have been conducted with several crop plants to harness the potential for dissecting the genetic structure of traits and improving breeding populations. We developed a first MAGIC population for cowpea (Vigna unguiculata L. Walp.) from eight founder parents which are genetically diverse and carry many abiotic and biotic stress resistance, seed quality and agronomic traits relevant to cowpea improvement in sub-Saharan Africa (SSA) where cowpea is vitally important in the human diet and in local economies. The eight parents were inter-crossed using structured matings to ensure the population would have balanced representation from each of the founder parents, followed by single-seed descent, resulting in 365 F8 recombinant inbred lines (RILs) each carrying a mosaic of genome blocks contributed from all founders. This was confirmed by SNP genotyping with the cowpea Illumina 60K iSelect BeadArray. Following filtering to eliminate duplicates, sister lines and accidental selfing events, a core set of 305 F8 RILs was chosen as the primary population. The F8 lines were on average 99.74% homozygous while also diverse in agronomic traits including flowering time, growth habit, maturity, yield potential and seed characteristics across environments. Trait-associated SNPs were identified for most of the parental traits. Loci with major effects on photoperiod sensitivity and seed size were also verified by genetic mapping in biparental RIL populations. The distribution of recombination frequency varied considerably between chromosomes, with recombination hotspots distributed mostly in the telomeric regions. Due to its broad genetic base, this cowpea MAGIC population promises breakthroughs in genetic gain and high-resolution genetic mapping for gene discovery, enhancement of breeding populations and, for some lines, direct releases as new varieties.

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bioRxiv2017

Genetic risk for schizophrenia influences substance use in emerging adulthood: an event-level polygenic prediction model

BackgroundEmerging adulthood is a peak period of risk for alcohol and illicit drug use. Recent advances in psychiatric genetics suggest that the co-occurrence of substance use and psychopathology arises, in part, from a shared genetic etiology. We sought to extend this research by investigating the influence of genetic risk for schizophrenia on trajectories of four substance use behaviors as they occurred across emerging adulthood.\n\nMethodYoung adult participants of non-Hispanic European descent provided DNA samples and completed daily reports of substance use for one month per year across four years (N=30,085 observations of N=342 participants). Polygenic scores for schizophrenia were included in two-level hierarchical linear models designed to test associations between genetic risk for schizophrenia, participant age, and four substance use phenotypes.\n\nResultsHere, we interpret results at p<.05 as suggestive and results at p<.005 as significant. Accordingly, our results suggest that polygenic scores for schizophrenia were positively associated with participants overall likelihood to engage in illicit drug use, but not alcohol-related substance use. Moreover, our results indicate that participants with a greater polygenic loading for schizophrenia experienced greater age-related increases in the likelihood of using substances across emerging adulthood.\n\nConclusionsThe present study used a novel combination of polygenic prediction and intensive longitudinal methods to characterize the influence of genetic risk for schizophrenia on patterns of age-related change in substance use across emerging adulthood. Results suggest that genetic risk for schizophrenia exerts both broad and developmentally-specific influences on substance use behaviors in a non-clinical population of young adults.

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bioRxiv2017

Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression

Major depressive disorder (MDD) is a notably complex illness with a lifetime prevalence of 14%.1 It is often chronic or recurrent and is thus accompanied by considerable morbidity, excess mortality, substantial costs, and heightened risk of suicide.2-7 MDD is a major cause of disability worldwide.8 We conducted a genome-wide association (GWA) meta-analysis in 130,664 MDD cases and 330,470 controls, and identified 44 independent loci that met criteria for statistical significance. We present extensive analyses of these results which provide new insights into the nature of MDD. The genetic findings were associated with clinical features of MDD, and implicated prefrontal and anterior cingulate cortex in the pathophysiology of MDD (regions exhibiting anatomical differences between MDD cases and controls). Genes that are targets of antidepressant medications were strongly enriched for MDD association signals (P=8.5x10-10), suggesting the relevance of these findings for improved pharmacotherapy of MDD. Sets of genes involved in gene splicing and in creating isoforms were also enriched for smaller MDD GWA P-values, and these gene sets have also been implicated in schizophrenia and autism. Genetic risk for MDD was correlated with that for many adult and childhood onset psychiatric disorders. Our analyses suggested important relations of genetic risk for MDD with educational attainment, body mass, and schizophrenia: the genetic basis of lower educational attainment and higher body mass were putatively causal for MDD whereas MDD and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for MDD, and a continuous measure of risk underlies the observed clinical phenotype. MDD is not a distinct entity that neatly demarcates normalcy from pathology but rather a useful clinical construct associated with a range of adverse outcomes and the end result of a complex process of intertwined genetic and environmental effects. These findings help refine and define the fundamental basis of MDD.

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bioRxiv2017

Discovering genetic interactions bridging pathways in genome-wide association studies

Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, the global genetic networks mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discovered significant interactions in Parkinsons disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

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bioRxiv2017

The contribution of common genetic risk variants for ADHD to a general factor of childhood psychopathology

Attention-deficit/hyperactivity disorder (ADHD) is a heritable neurodevelopmental disorder, with common genetic risk variants implicated in the clinical diagnosis and symptoms of ADHD. However, given evidence of comorbidity and genetic overlap across neurodevelopmental and externalizing conditions, it remains unclear whether these genetic risk variants are ADHD-specific. The aim of this study was to evaluate the associations between ADHD genetic risks and related neurodevelopmental and externalizing conditions, and to quantify the extent to which any such associations can be attributed to a general genetic liability towards psychopathology. We derived ADHD polygenic risk scores (PRS) for 13,460 children aged 9 and 12 years from the Child and Adolescent Twin Study in Sweden, using results from an independent meta-analysis of genome-wide association studies of ADHD diagnosis and symptoms. Associations between ADHD PRS, a latent general psychopathology factor, and six latent neurodevelopmental and externalizing factors were estimated using structural equation modelling. ADHD PRS were statistically significantly associated with elevated levels of inattention, hyperactivity/impulsivity, autistic traits, learning difficulties, oppositional-defiant, and conduct problems (standardized regression coefficients=0.07-0.12). Only the association with specific hyperactivity/impulsivity remained significant after accounting for a general psychopathology factor, on which all symptoms loaded positively (standardized mean loading=0.61, range=0.32-0.91). ADHD PRS simultaneously explained 1% (p-value<0.001) of the variance in the general psychopathology factor and 0.50% (p-value<0.001) in the specific hyperactivity/impulsivity factor. Our results suggest that common genetic risk variants associated with ADHD have largely general pleiotropic effects on neurodevelopmental and externalizing traits in the general population, in addition to a specific association with hyperactivity/impulsivity symptoms.

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bioRxiv2020

The Latent Genetic Structure of Impulsivity and its Relation to Internalizing Psychopathology

Factor analyses suggest that impulsivity traits that capture tendencies to act prematurely or take risks tap partially distinct constructs. We applied genomic structure equation modeling to evaluate the genetic factor structure of two well-established impulsivity questionnaires, using published genome-wide association study statistics from up to 22,861 participants. We also tested the hypotheses that delay discounting would be genetically separable from other impulsivity factors, and that emotionally-triggered facets of impulsivity (urgency) would be those most strongly genetically correlated with an internalizing latent factor. A five-factor model best fit the impulsivity data. Delay discounting was genetically distinct from these five factors. As expected, the two urgency subscales were most strongly related to an Internalizing Psychopathology latent factor. These findings provide empirical genetic evidence that impulsivity can be disarticulated into distinct categories of differential relevance for internalizing psychopathology. They also demonstrate how measured genetic markers can be used to inform theories of psychology/personality.

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bioRxiv2020

Mate selection provides similar genetic progress and average inbreeding than optimum contribution selection in the long-term

Optimum contribution selection (OCS) and mate selection (MS) are alternative strategies to maximize genetic gain under controlled rates of inbreeding. There is evidence in the literature that MS outperforms OCS in controlling inbreeding under the same expected genetic gain in the short-term. It is unclear, however, if the same would occur in the long-term. This study aimed to compare OCS and MS regarding short and long-term genetic progress and inbreeding, using simulated data. The structure of the simulated population aimed to mimic an aquaculture breeding program. Twenty discrete generations were simulated, considering 50 families and 2,000 offspring per generation, and a trait with a heritability of 0.3. OCS and MS were applied using a differential evolution (DE) algorithm, under an objective function that accounted for genetic merit, inbreeding of the future progeny and coancestry among selection candidates. For OCS, the optimization process consisted of selection based on optimum contribution followed by minimum inbreeding mating. Objective functions using different weights on coancestry were tested. For each application, 20 replicates were simulated and the results were compared based on their average. Both strategies, OCS and MS, were very effective in controlling inbreeding over the generations. In the short-term, MS was more efficient than OCS in controlling inbreeding under the same genetic gain. In the long-term, OCS and MS resulted in similar genetic progress and average inbreeding, under the same penalty on coancestry.

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bioRxiv2020

Vector genetics, insecticide resistance and gene drives: an agent-based modeling approach to evaluate malaria transmission and elimination

Vector control has been a key component in the fight against malaria for decades, and chemical insecticides are critical to the success of vector control programs worldwide. However, increasing resistance to insecticides threatens to undermine these efforts. Understanding the evolution and propagation of resistance is thus imperative to mitigating loss of intervention effectiveness. Additionally, accelerated research and development of new tools that can be deployed alongside existing vector control strategies is key to eradicating malaria in the near future. Methods such as gene drives that aim to genetically modify large mosquito populations in the wild to either render them refractory to malaria or impair their reproduction may prove invaluable tools. Mathematical models of gene flow in populations, which is the transfer of genetic information from one population to another through migration, can offer invaluable insight into the behavior and potential impact of gene drives as well as the spread of insecticide resistance in the wild. Here, we present the first multi-locus, agent-based model of vector genetics that accounts for mutations and a many-to-many mapping cardinality of genotypes to phenotypes to investigate gene flow, and the propagation of gene drives in Anopheline populations. This model is embedded within a large scale individual-based model of malaria transmission representative of a high burden, high transmission setting characteristic of the Sahel. Results are presented for the selection of insecticide-resistant vectors and the spread of resistance through repeated deployment of insecticide treated nets (ITNs), in addition to scenarios where gene drives act in concert with existing vector control tools such as ITNs. The roles of seasonality, spatial distribution of vector habitat and feed sites, and existing vector control in propagating alleles that confer phenotypic traits via gene drives that result in reduced transmission are explored. The ability to model a spectrum of vector species with different genotypes and phenotypes in the context of malaria transmission allows us to test deployment strategies for existing interventions that reduce the deleterious effects of resistance and allows exploration of the impact of new tools being proposed or developed. Author summaryVector control interventions are essential to the success of global malaria control and elimination efforts but increasing insecticide resistance worldwide threatens to derail these efforts. Releasing genetically modified mosquitoes that use gene drives to pass on desired genes and their associated phenotypic traits to the entire population within a few generations has been proposed to address resistance and other issues such as transmission heterogeneity that can sustain malaria transmission indefinitely. While the ethics and safety of these methods are being debated, mathematical models offer an efficient way of predicting the behavior and estimating the efficacy of these interventions if deployed to specific regions facing challenges to reaching elimination. We have developed a detailed mathematical model of vector genetics where specific genomes code for physical attributes that influence transmission and are affected by the surrounding environment. This is the first model to incorporate an individual-based multi-locus genetic model into a detailed individual-based model of malaria transmission. This model opens the door to investigate a number of subtle but important questions such as the effects of small numbers of mosquitoes in a region sustaining malaria transmission during the low transmission season, and the success of gene drives in regions where extant vector control interventions could kill off gene drive mosquitoes before establishment. Here, we investigate the reduced efficacy of current vector control measures in the presence of insecticide resistance and evaluate the likelihood of achieving local malaria elimination using gene drive mosquitoes released into a high transmission setting alongside other vector control measures.

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bioRxiv2020

Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs

Indirect parental genetic effects may be defined as the influence of parental genotypes on offspring phenotypes over and above that which results from the transmission of genes from parents to children. However, given the relative paucity of large-scale family-based cohorts around the world, it is difficult to demonstrate parental genetic effects on human traits, particularly at individual loci. In this manuscript, we illustrate how parental genetic effects on offspring phenotypes, including late onset diseases, can be estimated at individual loci in principle using large-scale genome-wide association study (GWAS) data, even in the absence of parental genotypes. Our strategy involves creating "virtual" mothers and fathers by estimating the genotypic dosages of parental genotypes using physically genotyped data from relative pairs. We then utilize the expected dosages of the parents, and the actual genotypes of the offspring relative pairs, to perform conditional genetic association analyses to obtain asymptotically unbiased estimates of maternal, paternal and offspring genetic effects. We develop a freely available web application that quantifies the power of our approach using closed form asymptotic solutions. We implement our methods in a user-friendly software package IMPISH (IMputing Parental genotypes In Siblings and Half-Siblings) which allows users to quickly and efficiently impute parental genotypes across the genome in large genome-wide datasets, and then use these estimated dosages in downstream linear mixed model association analyses. We conclude that imputing parental genotypes from relative pairs may provide a useful adjunct to existing large-scale genetic studies of parents and their offspring.

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bioRxiv2020

Network and pathway expansion of genetic disease associations identifies successful drug targets

It is widely accepted that genetic evidence of disease association acts as a sound basis for the selection of drug targets for complex common diseases and that propagation of genetic evidence through gene or protein interaction networks can accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these beliefs for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand-receptor pairs are suitable for even naive guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein-protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence should be used for drug target identification.

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bioRxiv2020

How understudied populations have contributed to our understanding of Alzheimer's disease genetics

The majority of genome-wide association studies have been conducted using samples with a European genetic background. As a field, we acknowledge this limitation and the need to increase the diversity of populations studied. A major challenge when designing and conducting such studies is to assimilate large samples sizes so that we attain enough statistical power to detect variants associated with disease, particularly when trying to identify variants with low and rare minor allele frequencies. In this study, we aimed to illustrate the benefits, to genetic characterization of Alzheimers disease (AD), in researching currently understudied populations. This is important for both fair representation of world populations and the translatability of findings. To that end, we have conducted a literature search to understand the contributions of studies, on different populations, to AD genetics. We systematically quantified the number of studies identifying mutations in known disease-causing genes, in a world-wide manner, and discussed the contributions of research in understudied populations to the identification of novel genetic factors in this disease. Additionally, we compared the effects of genome-wide significant SNPs across populations by focusing on loci that show different association profiles between populations (a key example being APOE). This work functions to both highlight how understudied populations have furthered our understanding of AD genetics, and to help us gage our progress in understanding the genetic architecture of this disease in all populations.

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bioRxiv2020

Response of Regulatory Genetic Variation in Gene Expression to Environmental Change in Drosophila melanogaster

The genetics of phenotypic responses to changing environments remains elusive. Using whole genome quantitative gene expression as a model, we studied how the genetic architecture of regulatory variation in gene expression changed in a population of fully sequenced inbred Drosophila melanogaster strains when flies developed at different environments (25 {degrees}C and 18 {degrees}C). We found a substantial fraction of the transcriptome exhibited genotype by environment interaction, implicating environmentally plastic genetic architecture of gene expression. Genetic variance in expression increased at 18 {degrees}C relative to 25 {degrees}C for most genes that had a change in genetic variance. Although the majority of expression quantitative trait loci (eQTLs) for the gene expression traits in the two environments were shared and had similar effects, analysis of the environment-specific eQTLs revealed enrichment of binding sites for two transcription factors. Finally, although genotype by environment interaction in gene expression could potentially disrupt genetic networks, the co-expression networks were highly conserved across environments. Genes with higher network connectivity were under stronger stabilizing selection, suggesting that stabilizing selection on expression plays an important role in promoting network robustness.

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bioRxiv2020

Cultural Evolution of Genetic Heritability

Behavioral genetics and cultural evolution have both revolutionized our understanding of human behavior, but largely independently of each other. Here we reconcile these two fields using a dual inheritance approach, which offers a more nuanced understanding of the interaction between genes and culture, and a resolution to several long-standing puzzles. For example, by neglecting how human environments are extensively shaped by cultural dynamics, behavioral genetic approaches systematically inflate heritability estimates and thereby overestimate the genetic basis of human behavior. A WEIRD (Western, educated, industrialized, rich, democratic) gene problem obscures this inflation. Considering both genetic and cultural evolutionary forces, heritability scores become less a property of a trait and more a moving target that responds to cultural and social changes. Ignoring cultural evolutionary forces leads to an over-simplified model of gene-to-phenotype causality. When cumulative culture functionally overlaps with genes, genetic effects become masked, or even reversed, and the causal effect of an identified gene is confounded with features of the cultural environment, specific to a particular society at a particular time. This framework helps explain why it is easier to discover genes for deficiencies than genes for abilities. With this framework, we predict the ways in which heritability should differ between societies, between socioeconomic levels within some societies but not others, and over the life course. An integrated cultural evolutionary behavioral genetics cuts through the nature-nurture debate and elucidates controversial topics such as general intelligence.

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bioRxiv2020

Towards genetic modification of plant-parasitic nematodes: delivery of macromolecules to adults and expression of exogenous mRNA in second stage juveniles.

Plant-parasitic nematodes are a continuing threat to food security, causing an estimated 100 billion USD in crop losses each year. The most problematic are the obligate sedentary endoparasites (primarily root knot nematodes and cyst nematodes). Progress in understanding their biology is held back by a lack of tools for functional genetics: forward genetics is largely restricted to studies of natural variation in populations, and reverse genetics is entirely reliant on RNA interference. There is an expectation that the development of functional genetic tools would accelerate the progress of research on plant-parasitic nematodes, and hence the development of novel control solutions. Here, we develop some of the foundational biology required to deliver a functional genetic tool kit in plant-parasitic nematodes. We characterise the gonads of male Heterodera schachtii and Meloidogyne hapla in the context of spermatogenesis. We test and optimise various methods for the delivery, expression, and/or detection of exogenous nucleic acids in plant-parasitic nematodes. We demonstrate that delivery of macromolecules to cyst and root knot nematode male germlines is difficult, but possible. Similarly, we demonstrate the delivery of oligonucleotides to root knot nematode gametes. Finally, we develop a transient expression system in plant-parasitic nematodes by demonstrating the delivery and expression of exogenous mRNA encoding various reporter genes throughout the body of H. schachtii juveniles using lipofectamine-based transfection. We anticipate these developments to be independently useful, will expedite the development of genetic modification tools for plant-parasitic nematodes, and ultimately catalyze research on a group of nematodes that threaten global food security.

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