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bioRxiv2023

Methodological Considerations When Using Polygenic Scores to Explore Parent-Offspring Genetic Nurturing Effects

BackgroundResearch has begun to explore the effects of parental genetic nurturing on offspring outcomes using polygenic scores (PGSs). However, there are concerns regarding potential biases due to confounding when mediating parental phenotypes are included. MethodsDepression, educational attainment and height PGSs were generated for 2680 biological parent-offspring trios using genome-wide association study (GWAS) meta-analysis summary statistics in a large population study: Generation Scotland. Regression and pathway models were estimated incorporating PGSs for both parents and offspring to explore direct (offspring PGS) and genetic nurturing (parental PGS) effects on psychological distress, educational attainment and height. Genetic nurturing via parental phenotypes were incorporated into the models. To explore sources of bias we conducted simulation analyses of 10,000 trios using combinations of PGS predictive accuracy and accounted variance. ResultsModels incorporating both offspring and parental PGSs suggested positive parental genetic nurturing effects on offspring educational attainment, but not psychological distress or height. In contrast, models additionally incorporating parental phenotypic information suggested positive parent phenotype mediated genetic nurturing effects were at play for all phenotypes explored as well as negative residual genetic nurturing effects for height. 10,000 parent-offspring trio effects (without genetic nurturing effects) were simulated. Simulations demonstrated that models incorporating parent and offspring PGSs resulted in genetic nurturing effects that were unbiased. However, adding parental phenotypes as mediating variables results in biased positive estimates of parent phenotype mediated genetic nurturing effects and negative estimates of residual genetic nurturing effects. Biased effects increased in magnitude as PGS accuracy and accounted variance decreased. These biases were only eliminated when PGSs were simulated to capture the entirety of trait genetic variance. ConclusionResults suggest that in the absence of PGSs that capture all genetic variance, parental phenotypes act as colliders in the same way as heritable environments. Relatively simple models combining parental and offspring PGSs can be used to detect genetic nurturing effects in complex traits. However, our findings suggest alternative methods should be utilised when aiming to identify mediating phenotypes and potentially modifiable parental nurturing effects.

genomics↗

bioRxiv2024

Genetic network analysis uncovers spatial variation in diversity and connectivity of a species presenting a continuous distribution

The conservation of genetic diversity and connectivity is essential for the long-term persistence and adaptive ability of a species. Recent calls have been made for the inclusion of genetic diversity and differentiation measures in the assessment, management, and conservation of species. However, the literature often lacks direction on how to do so for species with continuous distributions or no distinct breaks in genetic connectivity. There are many considerations to overcome when investigating genetic diversity and connectivity of such species. We combine multiple genetic network methodologies with more traditional population genetic analyses within a single framework to address the challenges of investigating population structure and quantifying variation in genetic diversity and connectivity of wide-ranging species with continuous distributions. We demonstrate the efficacy and applicability of our framework through a study on woodland caribou (Rangifer tarandus) occupying the boreal forest of Canada; a species of significant conservation concern. The dataset consisted of 4911 unique individuals genotyped at 9 microsatellite loci, which were subsequently partitioned into 103 spatial nodes to create a population-based genetic network. The Walktrap community detection algorithm was used to detect hierarchical population genetic structure within the study area and node-based network metrics such as mean inverse edge weight and clustering coefficient were used to quantify the variation in genetic connectivity across the range. Lastly, genetic diversity was assessed by calculating allelic richness and heterozygosity of the nodes making up the network. The community detection analysis identified two communities at the coarsest scale to nine communities at the optimal partition. A strong pattern of Isolation by Distance (IBD) was found across the range at multiple scales. Furthermore, signs of genetic erosion along the study areas southern boundaries were depicted by nodes presenting low genetic diversity and low centrality values. These results are important to the species status assessments in providing previously unavailable information on connectivity and diversity within and beyond the current local population units used in management. Our approach to quantify the patterns and extent of connectivity across the boreal range is comprehensive and could easily be adapted to other species. The results are robust and provide a solid foundation for the continued monitoring and recovery of the species.

ecology↗

bioRxiv2024

Deep genetic substructure within bonobos

Establishing the genetic and geographic structure of populations is fundamental both to understand their evolutionary past and preserve their future, especially for endangered species. Nevertheless, the patterns of genetic population structure are unknown for most endangered species, including some of our closest living relatives. This is the case of bonobos (Pan paniscus) which together with chimpanzees (Pan troglodytes) are humans closest living relatives. Chimpanzees live across equatorial Africa and are classified into four subspecies (Groves, 2001), with some genetic population substructure even within subspecies. Conversely, bonobos live exclusively in the Democratic Republic of Congo and are considered a homogeneous group with low genetic diversity (Fischer et al. 2011) despite some population structure inferred from mtDNA. Nevertheless, mtDNA aside, their genetic structure remains unknown, hampering our understanding of the species and conservation efforts. Placing bonobos genetics in space is however challenging because, being endangered, only non-invasive sampling is possible for wild individuals. Here, we jointly analyse the exomes and mtDNA from 20 wild-born bonobos, the whole-genomes of 10 captive bonobos and the mtDNA of 61 wild individuals. We identify three genetically distinct bonobo groups of inferred Central, Western and Far-Western geographic origin within the bonobo range. We estimate the split time between the central and western populations to [~]145,000 years ago, and genetic differentiation to be in the order of that of the closest chimpanzee subspecies. We identify putative signatures of differential genetic adaptation among populations for genes associated with homeostasis, metabolism and the nervous system. Furthermore, our estimated long-term Ne for Far-West ([~]3,000) is among the lowest estimated for any great ape lineage. Our results highlight the need of attention to bonobo substructure, both in terms of research and conservation. Highlights- We identified three genetically distinct populations of bonobos, inferred as having Central, Western and Far-Western geographic origin within the species range. The estimated split time is [~]145,000 years ago for the Central and Western populations, and [~]60,000 years ago for the two Western populations. - The genetic differentiation between the Central and Far-Western bonobo populations is in the order of that between Central and Eastern chimpanzee subspecies, while the genetic differentiation among Western bonobo populations is similar to that among human groups. - Once substructure is accounted for, we infer a long-term effective population size (Ne) of only [~]3,000 for Far-Western bonobos, genetic isolation and inbreeding.

evolutionary biology↗

bioRxiv2018

Genetics of Cocaine and Methamphetamine Consumption and Preference in Drosophila melanogaster

Illicit use of psychostimulants, such as cocaine and methamphetamine, constitutes a significant public health problem. Whereas neural mechanisms that mediate the effects of these drugs are well-characterized, genetic factors that account for individual variation in susceptibility to substance abuse and addiction remain largely unknown. Drosophila melanogaster can serve as a translational model for studies on substance abuse, since flies have a dopamine transporter that can bind cocaine and methamphetamine, and exposure to these compounds elicits effects similar to those observed in people, suggesting conserved evolutionary mechanisms underlying drug responses. Here, we used the D. melanogaster Genetic Reference Panel to investigate the genetic basis for variation in psychostimulant drug consumption, to determine whether similar or distinct genetic networks underlie variation in consumption of cocaine and methamphetamine, and to assess the extent of sexual dimorphism and effect of genetic context on variation in voluntary drug consumption. Quantification of natural genetic variation in voluntary consumption, preference, and change in consumption and preference over time for cocaine and methamphetamine uncovered significant genetic variation for all traits, including sex-, exposure-and drug-specific genetic variation. Genome wide association analyses identified both shared and drug-specific candidate genes, which could be integrated in genetic interaction networks. We assessed the effects of ubiquitous RNA interference (RNAi) on consumption behaviors for 34 candidate genes: all affected at least one behavior. Finally, we utilized RNAi knockdown in the nervous system to implicate dopaminergic neurons and the mushroom bodies as part of the neural circuitry underlying experience-dependent development of drug preference.\n\nAUTHOR SUMMARYIllicit use of cocaine and methamphetamine is a major public health problem. Whereas the neurological effects of these drugs are well characterized, it remains challenging to determine genetic risk factors for substance abuse in human populations. The fruit fly, Drosophila melanogaster, presents an excellent model for identifying evolutionarily conserved genes that affect drug consumption, since genetic background and exposure can be controlled precisely. We took advantage of natural variation in a panel of inbred wild derived fly lines with complete genome sequences to assess the extent of genetic variation among these lines for voluntary consumption of cocaine and methamphetamine and to explore whether some genetic backgrounds might show experience-dependent development of drug preference. The drug consumption traits were highly variable among the lines with strong sex-, drug- and exposure time-specific components. We identified candidate genes and gene networks associated with variation in consumption of cocaine and methamphetamine and development of drug preference. Using tissue-specific suppression of gene expression, we were able to functionally implicate candidate genes that affected at least one consumption trait in at least one drug and sex. In humans, the mesolimbic dopaminergic projection plays a role in drug addiction. We asked whether in Drosophila the mushroom bodies could play an analogous role, as they are integrative brain centers associated with experience-dependent learning. Indeed, our results suggest that variation in consumption and development of preference for both cocaine and methamphetamine is mediated, at least in part, through a neural network that comprises dopaminergic projections to the mushroom bodies.

genomics↗

bioRxiv2019

Genetic Susceptibility to Multiple Sclerosis: Interactions between Conserved Extended Haplotypes of the MHC and other Susceptibility Regions

OBJECTIVETo study the accumulation of MS-risk resulting from different combinations of MS-associated conserved-extended-haplotypes of the MHC and three non-MHC risk-loci nearby genes EOMES, ZFP36L1, CLEC16A.\n\nBACKGROUNDDefining \"genetic-susceptibility\" as having a non-zero probability of developing MS, both theoretical considerations and epidemiological observations indicate that only 2.2-4.5% of northern-populations can possibly be \"genetically-susceptible\" to MS. Nevertheless, many haplotypes (both within the MHC and elsewhere) are unequivocally MS-associated and, yet, have population-frequencies of >20%. Such frequency-disparities underscore the complex-interactions that must occur between these \"risk-haplotypes\" and MS-susceptibility.\n\nDESIGN/MEHTODSThe WTCCC dataset was statistically-phased at the MHC and at three other susceptibility-regions. Haplotypes were stratified by their impact on \"MS-risk\". MS-associations for different combinations of \"risk-haplotypes\" were assessed. The appropriateness of both additive and multiplicative risk-accumulation models was determined.\n\nRESULTSCombinations of different \"risk-haplotypes\" produced an MS-risk that was considerably closer to an additive model than a multiplicative model. Nevertheless, neither of these simple probability-models adequately accounted for the accumulation of disease-risk in MS at these four loci.\n\nCONCLUSIONS\"Genetic-susceptibility\" to MS seems to depend upon the exact state at each \"risk-locus\" and upon specific gene-gene combinations across loci. Moreover, \"genetic-susceptibility\" is both rare in the population and, yet, is a necessary condition for MS to develop in any individual. In this sense, MS is a \"genetic\" disease. Nevertheless although, \"genetic-susceptibility\" is a necessary condition for MS to develop, environmental factors (whatever these may be) and stochastic processes are also necessary determinants of whether a \"genetically-susceptible\" individual will actually get MS.\n\nAuthor SummaryDefining a \"genetically-susceptible\" individual to be any person in the population who has any chance of developing multiple sclerosis (MS), we demonstrate that, at a theoretical level and using widely-accepted epidemiological observations, only 2.2-4.5% of individuals in northern populations can possibly be \"genetically susceptible\" to MS. Thus, more than 95.5% of individuals in these populations have no chance of getting MS, regardless of the environmental circumstances that they may experience.\n\nNevertheless, certain \"susceptibility-haplotypes\" (e.g., HLA-DRB1*15:01~DQB1*06:02) have a far greater carrier-frequency than 2.2-4.5%. Consequently, most carriers of these \"susceptibility-haplotypes\" have no chance of getting MS and, therefore, their \"susceptibility\" must arise from some combination of these haplotypes with other \"susceptibility-haplotypes\". By analyzing such combinatorial impacts at four susceptibility-loci, we found significant interactions both within and between the different \"susceptibility-haplotypes\", thereby confirming the relationship between \"genetic-susceptibility\" and specific gene-gene combinations.\n\nThe nature of \"genetic-susceptibility\" developed here is applicable to other complex genetic disorders. Indeed, any disease for which the MZ-twin concordance rate is substantially greater than the life-time risk in the general population, only a small fraction of the population can possibly be in the \"genetically-susceptible\" subset (i.e., have any chance of developing the disease).

epidemiology↗

bioRxiv2016

The Genetic Architecture of Quantitative Traits Cannot Be Inferred From Variance Component Analysis

Classical quantitative genetic analyses estimate additive and non-additive genetic and environmental components of variance from phenotypes of related individuals. The genetic variance components are defined in terms of genotypic values reflecting underlying genetic architecture (additive, dominance and epistatic genotypic effects) and allele frequencies. However, the dependency of the definition of genetic variance components on the underlying genetic models is not often appreciated. Here, we show how the partitioning of additive and non-additive genetic variation is affected by the genetic models and parameterization of allelic effects. We show that arbitrarily defined variance components often capture a substantial fraction of total genetic variation regardless of the underlying genetic architecture in simulated and real data. Therefore, variance component analysis cannot be used to infer genetic architecture of quantitative traits. The genetic basis of quantitative trait variation in a natural population can only be defined empirically using high resolution mapping methods followed by detailed characterization of QTL effects.

Genetics↗

bioRxiv2017

Genomic analysis of family data reveals additional genetic effects on intelligence and personality

Pedigree-based analyses of intelligence have reported that genetic differences account for 50-80% of the phenotypic variation. For personality traits these effects are smaller, with 34-48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0% and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using [~]20 000 individuals in the Generation Scotland family cohort genotyped for [~]700 000 single nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWASs of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that [~]50% of differences in intelligence, and [~]40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence and education is consistent with mutation-selection balance.

genetics↗

bioRxiv2017

Modeling of a negative feedback mechanism explains age-dependent genetic architecture in reproduction in domesticated C. elegans strains

Most biological traits and common diseases have a strong but complex genetic basis, controlled by large numbers of genetic variants with small contributions to a trait or disease risk. The effect-size of most genetic variants is not absolute, but can depend on a number of factors including the age and genetic background of an organism. In order to understand the mechanisms that cause these changes, we are studying heritable trait differences between two domesticated strains of C. elegans. We previously identified a major effect locus, caused by a mutation in a component of the NURF chromatin remodeling complex, that regulated reproductive output in an age-dependent manner. The effect-size of this locus changes from positive to negative over the course of an animals reproductive lifespan. Using a previously published macroscale model of egg-laying rate in C. elegans, we show how time-dependent effect-size can be explained by an unequal use of sperm combined with negative feedback between sperm and ovulation rate. We validate a number of key predictions of this model using controlled mating experiments and quantification of oogenesis and sperm use. By incorporating this model into QTL mapping, we identify and partition new QTLs into specific aspects of the egg-laying process. Finally, we show how epistasis between two genetic variants is predicted by this modeling as a consequence of unequal use of sperm. This work demonstrates how modeling of multicellular communication systems can improve our ability to predict and understand the role of genetic variation on a complex phenotype. Negative autoregulatory feedback loops, common in transcriptional regulation, could play an important role in modifying genetic architecture in other traits.\n\nAUTHOR SUMMARYComplex traits are influenced not only by the individual effects of genetic variants, but also how these variants interact with the environment, age, and each other. While complex genetic architectures seem to be ubiquitous in natural traits, little is known about the mechanisms that cause them. Here we identify an example of age-dependent genetic architecture controlling the rate and timing of reproduction in the hermaphroditic nematode C. elegans. Using computational modeling, we demonstrate how this age-dependent genetic architecture can arise as a consequence of two factors: hormonal feedback on oocytes mediated by major sperm protein (MSP) released by sperm stored in the spermatheca and life history differences in sperm use caused by genetic variants. Our work also suggests how age-dependent epistasis can emerge from multicellular feedback systems.

genetics↗

bioRxiv2017

Inbred or Outbred? Genetic diversity in laboratory rodent colonies

Non-model rodents are widely used as subjects for both basic and applied biological research, but the genetic diversity of the study individuals is rarely quantified. University-housed colonies tend to be small and subject to founder effects and genetic drift and so may be highly inbred or show substantial genetic divergence from other colonies, even those derived from the same source. Disregard for the levels of genetic diversity in an animal colony may result in a failure to replicate results if a different colony is used to repeat an experiment, as different colonies may have fixed alternative variants. Here we use high throughput sequencing to demonstrate genetic divergence in three isolated colonies of Mongolian gerbil (Meriones unguiculatus) even though they were all established recently from the same source. We also show that genetic diversity in allegedly outbred colonies of non-model rodents (gerbils, hamsters, house mice, and deer mice) varies considerably from nearly no segregating diversity, to very high levels of polymorphism. We conclude that genetic divergence in isolated colonies may play an important role in the replication crisis. In a more positive light, divergent rodent colonies represent an opportunity to leverage genetically distinct individuals in genetic crossing experiments. In sum, awareness of the genetic diversity of an animal colony is paramount as it allows researchers to properly replicate experiments and also to capitalize on other, genetically distinct individuals to explore the genetic basis of a trait.

genetics↗

bioRxiv2020

Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits

Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions, which could shed unique light on etiologic sharing and provide additional mechanistic insights into the genetic basis of complex traits compared to global genetic correlation. However, accurate estimation of local genetic correlation remains challenging, in part due to extensive linkage disequilibrium in local genomic regions and pervasive sample overlap across studies. We introduce SUPERGNOVA, a unified framework to estimate both global and local genetic correlations using summary statistics from genome-wide association studies. Through extensive simulations and analyses of 30 complex traits, we demonstrate that SUPERGNOVA substantially outperforms existing methods and identifies 150 trait pairs with significant local genetic correlations. In particular, we show that the positive, consistently-identified, yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically-distinct genetic signatures with bidirectional local genetic correlations. We believe that statistically-rigorous local genetic correlation analysis could accelerate progress in complex trait genetics research.

genetics↗

bioRxiv2020

Mendelian imputation of parental genotypes for genome-wide estimation of direct and indirect genetic effects

Associations between genotype and phenotype derive from four sources: direct genetic effects, indirect genetic effects from relatives, population stratification, and correlations with other variants affecting the phenotype through assortative mating. Genome-wide association studies (GWAS) of unrelated individuals have limited ability to distinguish the different sources of genotype-phenotype association, confusing interpretation of results and potentially leading to bias when those results are applied – in genetic prediction of traits, for example. With genetic data on families, the randomisation of genetic material during meiosis can be used to distinguish direct genetic effects from other sources of genotype-phenotype association. Genetic data on siblings is the most common form of genetic data on close relatives. We develop a method that takes advantage of identity-by-descent sharing between siblings to impute missing parental genotypes. Compared to no imputation, this increases the effective sample size for estimation of direct genetic effects and indirect parental effects by up to one third and one half respectively. We develop a related method for imputing missing parental genotypes when a parent-offspring pair is observed. We provide the imputation methods in a software package, SNIPar (single nucleotide imputation of parents), that also estimates genome-wide direct and indirect effects of SNPs. We apply this to a sample of 45,826 White British individuals in the UK Biobank who have at least one genotyped first degree relative. We estimate direct and indirect genetic effects for ∼5 million genome-wide SNPs for five traits. We estimate the correlation between direct genetic effects and effects estimated by standard GWAS to be 0.61 (S.E. 0.09) for years of education, 0.68 (S.E. 0.10) for neuroticism, 0.72 (S.E. 0.09) for smoking initiation, 0.87 (S.E. 0.04) for BMI, and 0.96 (S.E. 0.01) for height. These results suggest that GWAS based on unrelated individuals provides an inaccurate picture of direct genetic effects for certain human traits.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genetics↗

bioRxiv2021

Strategies to assure optimal trade-offs among competing objectives for genetic improvement of soybean

Plant breeding is a decision making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize rate of genetic improvement and minimize loss of useful genetic variance. For commercial plant breeders competition in the marketplace forces greater emphasis on maximizing immediate genetic improvements. In contrast public plant breeders have an opportunity, perhaps an obligation, to place greater emphasis on minimizing loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short term genetic gains from Genomic Selection (GS) are much greater than Phenotypic Selection (PS), while PS provides better long term genetic gains because PS retains useful genetic diversity during the early cycles of selection. With limited resources must a soybean breeder choose between the two extreme responses provided by GS or PS? Or is it possible to develop novel breeding strategies that will provide a desirable compromise between the competing objectives? To address these questions, we decomposed breeding strategies into decisions about selection methods, mating designs and whether the breeding population should be organized as family islands. For breeding populations organized into islands decisions about possible migration rules among family islands were included. From among 60 possible strategies, genetic improvement is maximized for the first five to ten cycles using GS, a hub network mating design in breeding populations organized as fully connected family islands and migration rules allowing exchange of two lines among islands every other cycle of selection. If the objectives are to maximize both short-term and long-term gains, then the best compromise strategy is similar except a genomic mating design, instead of a hub networked mating design, is used. This strategy also resulted in realizing the greatest proportion of genetic potential of the founder populations. Weighted genomic selection applied to both non-isolated and island populations also resulted in realization of the greatest proportion of genetic potential of the founders, but required more cycles than the best compromise strategy.

genetics↗

bioRxiv2021

The dynamic effect of regulatory genetic variation on the in vivo ER stress transcriptional response

Genotype x Environment (GxE) interactions occur when environmental conditions drastically change the effect of a genetic variant. In order to truly understand the effect of genetic variation, we need to incorporate multiple environments into our analyses. Many variants, under steady state conditions, may be silent or even have the opposite effect under stress conditions. This study uses an in vivo mouse model to investigate how the effect of genetic variation changes with tissue type and cellular stress. Endoplasmic reticulum (ER) stress occurs when misfolded proteins accumulate in the ER. This triggers the unfolded protein response (UPR), a large transcriptional response which attempts to return the cell to homeostasis. This transcriptional response, despite being a well conserved, basic cellular process, is highly variable across different genetic backgrounds, making it an ideal system to study GxE effects. In this study, we sought to better understand how genetic variation alters expression across tissues, in the presence and absence of ER stress. The use of different mouse strains and their F1s allow us to also identify context specific cis- and trans-regulatory mechanisms underlying variable transcriptional responses. We found hundreds of genes that respond to ER stress in a tissue- and/or genotype-dependent manner. Genotype-dependent ER stress-responsive genes are enriched for processes such as protein folding, apoptosis, and protein transport, indicating that some of the variability occurs in canonical ER stress factors. The majority of regulatory mechanisms underlying these variable transcriptional responses derive from cis-regulatory variation and are unique to a given tissue or ER stress state. This study demonstrates the need for incorporating multiple environments in future studies to better elucidate the effect of any particular genetic factor in basic biological pathways, like the ER stress response. Author SummaryThe effect of genetic variation is dependent on environmental context. Here we use genetically diverse mouse strains to understand how genetic variation interacts with stress state to produce variable transcriptional profiles. In this study, we take advantage of the endoplasmic reticulum (ER) stress response which is a large transcriptional response to misfolded proteins. Using this system, we uncovered tissue- and ER stress-specific effects of genetic variation on gene expression. Genes with genotype-dependent variable expression levels in response to ER stress were enriched for canonical ER stress functions, such as protein folding and transport. These variable effects of genetic variation are driven by unique sets of regulatory variation that are only active under context-specific circumstances. The results of this study highlight the importance of including multiple environments and genetic backgrounds when studying the ER stress response and other cellular pathways.

genetics↗

bioRxiv2022

Multiple invasions, Wolbachia and human-aided transport drive the genetic variability of Aedes albopictus in the Iberian Peninsula

The Asian tiger mosquito, Aedes albopictus, is a highly invasive species that has been spreading rapidly throughout tropical and temperate regions worldwide since the late 1970s. On the Iberian Peninsula, it was first recorded in 2004 near Barcelona. Since then, the species has spread along the Mediterranean coast and is now colonising the Peninsulas inner territories. As with other species of the genus Aedes, the spread of the tiger mosquito has been linked to global shipping routes and road networks. In particular, the transport of adult mosquitoes by car has been shown to augment its natural spreading capacity by orders of magnitude. Much remains unknown, however, about the impact of human-mediated dispersal on the genetic variability of this species. This study aimed to ascertain the factors that have contributed to the spread and the current genetic variation of Ae. albopictus across the Iberian Peninsula, through complex dispersal mechanisms and endosymbiosis with bacteria like Wolbachia. We do so through population genetic analysis of mitochondrial (COI) and nuclear (ITS2) DNA sequences. Overall, both COI and ITS2 markers showed a lack of genetic structure among sampled regions and the presence of worldwide dominant haplotypes, suggesting a pattern of multiple introductions from abroad. We found extremely low levels of variation in COI compared to ITS2, and this lack of mitochondrial polymorphism is likely explained by high Wolbachia prevalence (79%). Multilevel models revealed that greater mosquito fluxes (estimated from commuting patterns and tiger mosquito population distribution) and spatial proximity between sampling sites were associated with lower ITS2 genetic distance, suggesting that rapid short- and medium-distance dispersal is facilitated by humans through vehicular traffic. This study highlights the significant role of human transportation in shaping the genetic attributes of Ae. albopictus and promoting regional gene flow, and underscores the need for a territorially integrated surveillance across scales of this disease-carrying mosquito. Author SummaryThe tiger mosquito, Aedes albopictus, is one of the most invasive species in the world. Native to the tropical forests of Southeast Asia, over the past 30 years it has rapidly spread throughout tropical and temperate regions of the world. Its dramatic expansion has resulted in public health concerns as a consequence of its vector competence for at least 16 viruses. Previous studies showed that Ae. albopictus spread has been facilitated by human-mediated transportation, but much remains unknown about how this has affected its genetic attributes. Here we examined the factors that contributed to shaping the current genetic constitution of Ae. albopictus in the Iberian Peninsula, where the species was first found in 2004, by combining population genetics and Bayesian modelling. We found that both mitochondrial and nuclear DNA markers showed a lack of genetic structure and the presence of worldwide dominant haplotypes, suggesting regular introductions from abroad. Mitochondrial DNA showed little genetic diversity compared to nuclear DNA, likely explained by infection with maternally transmitted bacteria of the genus Wolbachia. Our models indicated that human transportation plays a role in shaping Ae. albopictus nuclear genetic structure by means of passive dispersal of adult tiger mosquitoes through the road network.

genetics↗

bioRxiv2023

The Genetic Architecture of Dietary Iron Overload and Associated Pathology in Mice

Tissue iron overload is a frequent pathologic finding in multiple disease states including non-alcoholic fatty liver disease (NAFLD), neurodegenerative disorders, cardiomyopathy, diabetes, and some forms of cancer. The role of iron, as a cause or consequence of disease progression and observed phenotypic manifestations, remains controversial. In addition, the impact of genetic variation on iron overload related phenotypes is unclear, and the identification of genetic modifiers is incomplete. Here, we used the Hybrid Mouse Diversity Panel (HMDP), consisting of over 100 genetically distinct mouse strains optimized for genome-wide association studies and systems genetics, to characterize the genetic architecture of dietary iron overload and pathology. Dietary iron overload was induced by feeding male mice (114 strains, 6-7 mice per strain on average) a high iron diet for six weeks, and then tissues were collected at 10-11 weeks of age. Liver metal levels and gene expression were measured by ICP-MS/ICP-AES and RNASeq, and lipids were measured by colorimetric assays. FaST-LMM was used for genetic mapping, and Metascape, WGCNA, and Mergeomics were used for pathway, module, and key driver bioinformatics analyses. Mice on the high iron diet accumulated iron in the liver, with a 6.5 fold difference across strain means. The iron loaded diet also led to a spectrum of copper deficiency and anemia, with liver copper levels highly positively correlated with red blood cell count, hemoglobin, and hematocrit. Hepatic steatosis of various severity was observed histologically, with 52.5 fold variation in triglyceride levels across the strains. Liver triglyceride and iron mapped most significantly to an overlapping locus on chromosome 7 that has not been previously associated with either trait. Based on network modeling, significant key drivers for both iron and triglyceride accumulation are involved in cholesterol biosynthesis and oxidative stress management. To make the full data set accessible and useable by others, we have made our data and analyses available on a resource website. Author summaryThe response to a high iron diet is determined in part by genetic factors. We now report the responses to such a diet in a diverse set of inbred strains of mice, known as the Hybrid Mouse Diversity Panel, that enables high resolution genetic mapping and systems genetics analyses. The levels of iron in the liver varied about >5 fold across the strains, with genetic variation explaining up to 74% of the variation in liver iron. Pathologies included copper deficiency, anemia, and fatty liver, with liver triglycerides varying over 50 fold among the strains. Genetic mapping and network modeling identified significant genetic loci and pathways underlying the response to diet.

genetics↗

bioRxiv2024

Characterizing population structure and documenting rapid loss of genetic diversity in Chiricahua Leopard Frogs (Lithobates chiricahuensis) with high throughput microsatellite genotyping

The use of molecular markers to assess genetic diversity has become a common component of recovery action plans for threatened and endangered species. In this study, we use an unusually large number of microsatellite markers (N=91) to characterize the genetic variation of Chiricahua Leopard Frogs (Lithobates chiricahuensis) across their range in order to understand their distribution of genetic variation, identify genetic bottlenecks, and measure genetic changes over time in a single, highly-managed population. Populations were best divided into three genetically distinct clusters, with the southeastern Arizona and New Mexico populations forming distinct genetic clusters. While there is moderate genetic variation distributed across the sampled populations, each population on its own shows relatively low allelic diversity. Most populations displayed strong genetic signals of recent genetic bottlenecks or a deficiency of heterozygous genotypes that is typically associated with frequent inbreeding. Populations that have a history of no management through translocations harbored the greatest number of unique alleles and overall allelic richness, especially in a subset of the Mexican populations. Finally, long-term cohort sampling at one specific site (the Southwestern Research Station in Portal, Arizona) allowed us to demonstrate how rapidly genetic diversity can decrease across a matter of years in a population with few founders. This work shows how microsatellite markers can provide important context for conservation agencies, but even a large suite of markers beyond what is typical may not be enough for populations that are extremely bottlenecked and have low levels of standing genetic diversity.

genetics↗

bioRxiv2024

Machine learning model to predict risk assessment of a child inheriting a genetic disorder

Advancements in Machine Learning (ML) have revolutionised precision medicine, particularly in predicting and preventing genetic disorders. This study provides a comprehensive analysis of ML models designed for the risk assessment of genetic disorders in children, based on clinical history and pedigree analysis. Leveraging a diverse dataset of familial genetic profiles, clinical outcomes, and medical histories, we employed ML algorithms to identify inheritance patterns, assess genetic risks for Mendelian disorders, and predict disease recurrence. The analysis incorporates several ML algorithms, including Gradient Boosting, XGBoost, Random Forest, Logistic Regression, Naive Bayes, and Support Vector Machines (SVM), with the Gradient Boosting model achieving the highest mean cross-validation score of over 0.99. Designed for primary care physicians and healthcare professionals, this model aids in genetic counselling by predicting genetic disorder recurrence based on family history. The paper also addresses ethical and legal considerations, emphasising the importance of genetic counselling and informed decision-making. This tool is intended to support, not replace, medical professionals. This work advances personalised risk assessment for Mendelian single-gene disorders, including autosomal dominant, autosomal recessive, and X-linked recessive disorders, contributing to the field of genomic medicine and facilitating effective family planning strategies. Author summaryGenetic disorders are posing significant socio-economic, health, and psychological burdens due to the risk of inheritance, which can impact future generations with health complications. Genetic counselling has emerged as an important tool to help manage this issue, offering guidance to susceptible families. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have introduced new avenues for predicting the risk of genetic disorders with greater accuracy. The aim was to leverage machine learning models that would predict the risk assessment for single gene chromosomal Mendelian disorders. To train these models, patient data was collected from a clinical geneticist at a renowned hospital, all of whom had confirmed genetic testing results. This data was used to train the models that predicted the likelihood of the next child inheriting a genetic disorder. The initial results are promising, demonstrating a good fit with the data. However, it should be noted that larger sample sizes are needed to improve the accuracy of any model. With more extensive data, its predictive capabilities can be significantly enhanced. This tool has the potential to be a resource for genetic counsellors and primary healthcare physicians, aiding them in providing more accurate risk assessments and personalised guidance to families.

genetics↗

bioRxiv2017

Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection

This study evaluates optimal cross selection for balancing selection and maintenance of genetic diversity in two-part plant breeding programs with rapid recurrent genomic selection. The two-part program reorganizes a conventional breeding program into population improvement component with recurrent genomic selection to increase the mean of germplasm and product development component with standard methods to develop new lines. Rapid recurrent genomic selection has a large potential, but is challenging due to genotyping costs or genetic drift. Here we simulate a wheat breeding program for 20 years and compare optimal cross selection against truncation selection in the population improvement with one to six cycles per year. With truncation selection we crossed a small or a large number of parents. With optimal cross selection we jointly optimised selection, maintenance of genetic diversity, and cross allocation with AlphaMate program. The results show that the two-part program with optimal cross selection delivered the largest genetic gain that increased with the increasing number of cycles. With four cycles per year optimal cross selection had 78% (15%) higher long-term genetic gain than truncation selection with a small (large) number of parents. Higher genetic gain was achieved through higher efficiency of converting genetic diversity into genetic gain; optimal cross selection quadrupled (doubled) efficiency of truncation selection with a small (large) number of parents. Optimal cross selection also reduced the drop of genomic selection accuracy due to the drift between training and prediction populations. In conclusion, optimal cross-selection enables optimal management and exploitation of population improvement germplasm in two-part programs.\n\nKey messageOptimal cross selection increases long-term genetic gain of two-part programs with rapid recurrent genomic selection. It achieves this by optimising efficiency of converting genetic diversity into genetic gain through reducing the loss of genetic diversity and reducing the drop of genomic prediction accuracy with rapid cycling.

genetics↗