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Synchrony Genetics: Linking Ecological Mechanisms to Genetic Structure A framework for genetic inference in ecologically coupled systems

Spatial synchrony, correlated population dynamics across space, is a defining feature of ecological dynamics, shaping outbreaks, cycles, and waves across ecosystems. Yet its genetic consequences remain poorly resolved because classical population-genetic models assume demographic independence and equilibrium conditions that synchronised populations systematically violate. Here I introduce Synchrony Genetics, a general framework that treats ecological coupling as the causal process and spatial synchrony as only one observable manifestation of that coupling. The framework links the three canonical ecological coupling mechanisms, environmental (Moran-type) coupling, dispersal-driven coupling, interaction-mediated coupling, alone or in combination, to their characteristic genetic signatures. Under this view, genetic structure is not a static property of populations or a proxy for equilibrium connectivity, but an emergent indicator of how populations are ecologically coupled across space. These expectations are synthesised in a Prediction Matrix that maps coupling mechanisms to diagnostic contrasts across widely used genetic metrics, enabling mechanism attribution from genetic data alone or in combination with demographic information. By reframing genetic patterns as evidence of coupling mechanisms rather than equilibrium processes, Synchrony Genetics provides a mechanistic foundation for interpreting genetic data in spatially coherent systems where dispersal, demographic covariance, and ecological interactions jointly shape genetic signatures. More broadly, the framework establishes a new baseline for genetic inference in systems where ecological coupling violates demographic independence, repositioning genetic structure as mechanistic evidence of how populations are linked across space.

ecology↗

Genetic diversity in global chicken breeds as a function of genetic distance to the wild populations

Migration of populations from their founder population is expected to cause a reduction in genetic diversity and facilitates population differentiation between the populations and their founder population as predicted by the theory of genetic isolation by distance. Consistent with that, a model of expansion from a single founder predicts that patterns of genetic diversity in populations can be well explained by their geographic expansion from the founders, which is correlated to the genetic differentiation. To investigate this in the chicken, we have estimated the relationship between the genetic diversity in 172 domesticated chicken populations and their genetic distances to wild populations. We have found a strong inverse relationship whereby 87.5% of the variation in the overall genetic diversity of domesticated chicken can be explained by the genetic distance to the wild populations. We also investigated if different types of SNPs and genes present similar patterns of genetic diversity as the overall genome. Among different SNP classes, the non-synonymous ones were the most deviating from the overall genome. However, the genetic distances to wild populations still explained more variation in domesticated chicken diversity in all SNP classes ranging from 81.7 to 88.7%. The genetic diversity seemed to change at a faster rate within the chicken in genes that are associated with transmembrane transport, protein transport and protein metabolic processes, and lipid metabolic processes. In general, such genes are flexible to be manipulated according to the population needs. On the other hand, genes which the genetic diversity hardly changes despite the genetic distance to the wild populations are associated with major functions e.g. brain development. Therefore, changes in the genes may be detrimental to the chickens. These results contribute to the knowledge of different evolutionary patterns of different functional genomic regions in the chicken. Author summaryThe chicken was first domesticated about 6000 B.C. in Asia from the jungle fowl. Following domestication, chickens were taken to different parts of the world mainly by humans. Evolutionary forces such as selection and genetic drift have shaped diversification within the chicken species. In addition, new breeds or strains have been developed from crossbreeding programs facilitated by man. These events, together with other breeding practices, have led to genomic alterations causing genetic differentiation between the domesticated chickens and their ancestral/wild population as well as manipulation of the genetic diversity within the domesticated chickens. We investigated the relationship between 172 domesticated chicken populations from different selection, breeding and management backgrounds and their genetic distance to the wild type chickens. We found that the genetic diversity within the populations decreases with the increasing genetic distances to the wild types. Human manipulation of chicken genetic diversity has more effect on the genetic differentiation than simple geographic separations (through migrations) do. We further found that some genes associated with vital functions show evolutionary constraints or persistent selection across the populations and do not comply with this relationship i.e. the genetic diversity within the populations is constant despite the change in the genetic distance to the wild types.

genetics↗

Integrating three genetic dimensions relating to piglet birth weight: direct and maternal effects on the mean and genetic control of residual variance

Uniformity of production traits is desired for different traits in livestock species, including the uniformity of within-litter birth weights in piglets. Birth weight (BW) in pigs is associated with increased vitality and survival until weaning. However, as uniformity of BW increases, the importance of initial weight decreases as competition between piglets decreases. The aim of this study was to estimate the direct and maternal genetic components of BW, jointly with the maternal genetic component of the residual variance for within-litter BW, and their genetic correlations. We used two distinct datasets of Swiss Large White pigs: 1) the experimental farm dataset and 2) the commercial farms dataset, comprising 43,135 and 23,313 records of individual piglet birth weight, respectively. For statistical analysis, the heteroscedastic (or canalising selection) model was used. This model assumes that both the mean BW and the residual variance are affected by systematic and random effects, with the residual variance being heterogeneous and partially under genetic control. Despite the best fitting model was the most complex one including both genetic effects for the mean trait, the results indicated that direct genetic effects, or correlations with such effects, are negligible. The genetic environmental variance for BW ranged between 0.071 and 0.131 for experimental farm and 0.037 to 0.094. The genetic correlation between the mean BW and its variability was always positive and ranged between 0.149 and 0.307 for the experimental farm and between 0.220 and 0.589 for the commercial farms. It is thus sufficient to model BW and its variability by including only the maternal genetic effect for both traits. In addition, even though moderate genetic correlations exist between the mean and the variance of BW, focusing selection on BW uniformity within litters would be preferable to creating a selection index for both traits simultaneously. ImplicationsMonitoring within-litter birth weight variability is important for efficient piglet production and welfare. Our findings suggest that maternal genetic effects are sufficient to model birth weight and its variability as genetic components of environmental variance. For both traits, direct genetic variance and its correlations with other components are negligible. However, since there are moderate genetic correlations between the mean and variance of birth weight, it is preferable to focus solely on selecting for within-litter birth weight uniformity rather than combining both traits in the selection index. This approach simplifies breeding strategies while maintaining the goal of improving piglets welfare. HighlightsO_LIData on individual piglet birth weight were collected on Swiss farms over 18 years C_LIO_LIHeteroscedastic animal models integrated three dimensions of genetic components C_LIO_LIThe direct genetic effect and its correlations are minimal and irrelevant for selection C_LIO_LIMaternal genetic effects are crucial for birth weight and its variability C_LIO_LISelection for uniformity is preferable to selection for both traits simultaneously C_LI

genetics↗

Understanding changes in genetic literacy over time and in genetic research participants

As genomic and personalized medicine becomes mainstream, assessing and understanding the publics genetic literacy is paramount. Because genetic research drives innovation in this area and involves much of the public, it is equally important to assess its impact on genetic literacy. We designed a survey to assess genetic literacy in three ways (familiarity, knowledge, and skills) and distributed it to two distinct samples: 2050 members of the general population, and 2023 individuals currently enrolled in a large-scale genetic research study. We compared these data to a similar survey implemented in 2013. The results indicate that familiarity with basic genetic terms in 2021 (M = 5.36, p<.001) and knowledge of genetic concepts in 2021 (M = 9.06, p = 0.002) is significantly higher compared to 2013 (M = 5.08; M = 8.72). Those currently enrolled in a genetic study were also significantly more familiar with genetic terms (M = 5.79, p<.001), more knowledgeable of genetic concepts (M = 10.27, p<.001), and scored higher in skills (M = 3.57, p<.001) than the general population (M = 4.98; M = 9.06; M = 2.65).The results suggest that genetic literacy is improving over time, though there is room for improvement. The data also suggest that participating in genetic research is one avenue for improving genetic literacy. We conclude that educational interventions are needed to ensure familiarity with and comprehension of basic genetic concepts. We also suggest further exploration of the impact of genetic research participation on genetic literacy to determine mechanisms for potential interventions.

genetics↗

A Whole-Genome Approach Discovers Novel Genetic and Non-Genetic Variance Components Modulated by Lifestyle for Cardiovascular Health

Both genetic and non-genetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease (CVD) prevention. Here, we use a novel whole-genome framework to estimate genetic and non-genetic effects on--hence their predispositions to--cardiovascular risk and determine whether they vary with respect to lifestyle factors. We performed analyses on the Atherosclerosis Risk in Communities Study (ARIC, N=6,896-7,180) and validated findings using the UK Biobank (UKBB, N=14,076-34,538). Cardiovascular risk was measured using 23 traits in the ARIC and eight traits in the UKBB, such as body mass index (BMI), resting heart rate, white blood cell count and blood pressure; and lifestyle factors included information on physical activity, smoking, alcohol consumption and dietary intake. Physical activity altered both genetic and non-genetic effects on heart rate and BMI, genetic effects on HDL cholesterol level, and non-genetic effects on waist-to-hip ratio. Alcohol consumption altered both genetic and non-genetic effects on BMI, while smoking altered non-genetic effects on heart rate, pulse pressure, and white blood cell count. In addition, saturated fat intake modified genetic effects on BMI, and total daily energy intake modified non-genetic effects on waist-to-hip ratio. These results highlight the relevance of lifestyle changes for CVD prevention. We also stratified individuals according to their genetic predispositions and showed notable differences in the effects of lifestyle on cardiovascular risk across stratified groups, implying the need for individualizing lifestyle changes for CVD prevention. Finally, we showed that neglecting lifestyle modulation of genetic and non-genetic effects will on average reduce SNP heritability estimates of cardiovascular traits by a small yet significant amount, primarily owing to overestimation of residual variance. Thus, current SNP heritability estimates for cardiovascular traits, which commonly do not consider modulating effects of lifestyle covariates, are likely underestimated.

genetics↗

Large-scale interpretation of chemical-genetic interaction profiles using a genetic interaction network

Chemical-genetic interactions - observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes - contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. CG-TARGET compared favorably to a baseline enrichment approach across a variety of benchmarks, achieving similar accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. We applied CG-TARGET to a recent screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. Upon investigation of the compatibility of chemical-genetic and genetic interaction profiles, we observed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We present here a detailed characterization of the CG-TARGET method along with experimental validation of predicted biological process targets, focusing on inhibitors of tubulin polymerization and cell cycle progression. Our approach successfully demonstrates the use of genetic interaction networks in the functional annotation of compounds to biological processes.

systems biology↗

Investigating Genetic Heterogeneity in Major Depression Through Item-level Genetic Analyses of the PHQ-9

BackgroundMajor Depressive Disorder (MDD) is a clinically heterogeneous disorder. Previous large-scale genetic studies of MDD have explored genetic risk factors of MDD case-control status or aggregated sums of depressive symptoms, ignoring possible clinical or genetic heterogeneity. AimIn this study, we present the results of symptom-level genetic analyses and compare SNP-based heritability (h2 O_SCPCAPSNPC_SCPCAP) and genetic correlations across major depression symptoms. We further investigate genetic correlations with a range of psychiatric disorders and other associated traits. Methods: We have analysed data from the UK biobank and included 148,752 subjects of white British ancestry with genotype data who completed nine items of a self-rated measure of depression: the Patient Health Questionnaire (PHQ-9). Genome-Wide Association analyses were conducted for nine symptoms and two composite measures. LD score regression analysis was used to calculate SNP-based heritability (h2 O_SCPCAPSNPC_SCPCAP) and genetic correlations (rg) across symptoms and to investigate genetic correlations with 25 external phenotypes. Confirmatory factor analyses were applied to test whether one, two, or three-factor models best fit the pattern of genetic correlations across the nine symptoms. ResultsWe identified 9 novel genome-wide significant genomic loci, with no overlap in loci across depression symptoms. h2 O_SCPCAPSNPC_SCPCAP ranged from 3% (suicidal ideation) to 11% (fatigue). Genetic correlations range from 0.54 to 0.96 (all p < 1.39x10-3) with 30 of 36 correlations being significantly smaller than 1. A 3-factor model provided the best fit to the genetic correlation matrix, with factors representing "psychological", "neurovegetative", and "psychomotor / concentration" symptoms. The genetic correlations with external phenotypes showed large variation across the nine symptoms. DiscussionPatterns of h2 O_SCPCAPSNPC_SCPCAP and genetic correlations differed across the nine symptoms of depression. Our findings suggest that the large phenotypic heterogeneity observed for MDD is recapitulated at a genetic level. Future studies should investigate how genetic heterogeneity in MDD influences the efficacy of clinical interventions.

genetics↗

Elucidating relationships between P.falciparum prevalence and measures of genetic diversity with a combined genetic-epidemiological model of malaria

There is an abundance of malaria genetic data being collected from the field, yet using this data to understand features of regional epidemiology remains a challenge. A key issue is the lack of models that relate parasite genetic diversity to epidemiological parameters. Classical models in population genetics characterize changes in genetic diversity in relation to demographic parameters, but fail to account for the unique features of the malaria life cycle. In contrast, epidemiological models, such as the Ross-Macdonald model, capture malaria transmission dynamics but do not consider genetics. Here, we have developed an integrated model encompassing both parasite evolution and regional epidemiology. We achieve this by combining the Ross-Macdonald model with an intra-host continuous-time Moran model, thus explicitly representing the evolution of individual parasite genomes in a traditional epidemiological framework. Implemented as a stochastic simulation, we use the model to explore relationships between measures of parasite genetic diversity and parasite prevalence, a widely-used metric of transmission intensity. First, we explore how varying parasite prevalence influences genetic diversity at equilibrium. We find that multiple genetic diversity statistics are correlated with prevalence, but the strength of the relationships depends on whether variation in prevalence is driven by host- or vector-related factors. Next, we assess the responsiveness of a variety of statistics to malaria control interventions, finding that those related to mixed infections respond quickly (~ months) whereas other statistics, such as nucleotide diversity, may take decades to respond. These findings provide insights into the opportunities and challenges associated with using genetic data to monitor malaria epidemiology. Author summaryKnowledge of how the prevalence of P.falciparum malaria varies, either between regions or through time, is critical to the operation of malaria control programs. Yet obtaining this information through traditional methods is fraught with challenges. Parasite genetic data is increasingly accessible, and may provide an alternative means to estimate P.falciparum prevalence in the field. However, our understanding of how the genetic diversity of parasite populations relates to prevalence is limited, and suitable models to guide our understanding are largely lacking. Here, we merge two classical models - the Ross-Macondald and the Moran - to produce a framework in which the relationships between parasite genetic diversity and prevalence can be explored. We find that several genetic diversity statistics are correlated with prevalence, although to differing degrees, and over different time scales. Overall, statistics related to mixed infection are robustly and rapidly responsive to changes in prevalence, suggesting they may be a useful focal point for the development of malaria surveillance methods that harness genetic data.

genomics↗

Animal models with group-specific additive genetic variances: extending genetic group models

1. The animal model is a key tool in quantitative genetics and has been used extensively to estimate fundamental parameters, such as additive genetic variance, heritability, or inbreeding effects. An implicit assumption of animal models is that all founder individuals derive from a single population. This assumption is commonly violated, for instance in cross-bred livestock breeds, when an observed population receive immigrants, or when a meta-population is split into genetically differentiated subpopulations. Ignoring genetic differences among different source populations of founders may lead to biased parameter estimates, in particular for the additive genetic variance.\n\n2. To avoid such biases, genetic group models, extensions to the animal model that account for the presence of more than one genetic group, have been proposed. As a key limitation, the method to date only allows that the breeding values differ in their means, but not in their variances among the groups. Methodology previously proposed to account for group-specific variances included terms for segregation variance, which rendered the models infeasibly complex for application to most real study systems.\n\n3. Here we explain why segregation variances are often negligible when analyzing the complex polygenic traits that are frequently the focus of evolutionary ecologists and animal breeders. Based on this we suggest an extension of the animal model that permits estimation of group-specific additive genetic variances. This is achieved by employing group-specific relatedness matrices for the breeding value components attributable to different genetic groups. We derive these matrices by decomposing the full relatedness matrix via the generalized Cholesky decomposition, and by scaling the respective matrix components for each group. To this end, we propose a computationally convenient approximation for the matrix component that encodes for the Mendelian sampling variance. Although convenient, this approximation is not critical.\n\n4. Simulations and an example from an insular meta-population of house sparrows in Norway with three genetic groups illustrate that the method is successful in estimating group-specific additive genetic variances and that segregation variances are indeed negligible in the empirical example.\n\n5. Quantifying differences in additive genetic variance within and among populations is of major biological interest in ecology, evolution, and animal and plant breeding. The proposed method allows to estimate such differences for subpopulations that form a connected meta-population, which may also be useful to study temporal or spatial variation of additive genetic variance.

evolutionary biology↗

Isolation-by-Drift: Quantifying the Respective Contributions of Genetic Drift and Gene Flow in Shaping Spatial Patterns of Genetic Differentiation

O_LIPairwise measures of neutral genetic differentiation are supposed to contain information about past and on-going dispersal events and are thus often used as dependent variables in correlative analyses to elucidate how neutral genetic variation is affected by landscape connectivity. However, spatial heterogeneity in the intensity of genetic drift, stemming from variations in population sizes, may inflate variance in measures of genetic differentiation and lead to erroneous or incomplete interpretations in terms of connectivity. Here, we tested the efficiency of two distance-based metrics designed to capture the unique influence of spatial heterogeneity in local drift on genetic differentiation. These metrics are easily computed from estimates of effective population sizes or from environmental proxies for local carrying capacities, and allow us to introduce the hypothesis of Spatial-Heterogeneity-in-Effective-Population-Sizes (SHNe). SHNe can be tested in a way similar to isolation-by-distance or isolation-by-resistance within the classical landscape genetics hypothesis-testing framework.\nC_LIO_LIWe used simulations under various models of population structure to investigate the reliability of these metrics to quantify the unique contribution of SHNe in explaining patterns of genetic differentiation. We then applied these metrics to an empirical genetic dataset obtained for a freshwater fish (Gobio occitaniae).\nC_LIO_LISimulations showed that SHNe explained up to 60% of variance in genetic differentiation (measured as Fst) in the absence of gene flow, and up to 20% when migration rates were as high as 0.10. Furthermore, one of the two metrics was particularly robust to uncertainty in the estimation of effective population sizes (or proxies for carrying capacity). In the empirical dataset, the effect of SHNe on spatial patterns of Fst was five times higher than that of isolation-by-distance, uniquely contributing to 41% of variance in pairwise Fst. Taking the influence of SHNe into account also allowed decreasing the signal-to-noise ratio, and improving the upper estimate of effective dispersal distance.\nC_LIO_LIWe conclude that the use of SHNe metrics in landscape genetics will substantially improve the understanding of evolutionary drivers of genetic variation, providing substantial information as to the actual drivers of patterns of genetic differentiation in addition to traditional measures of Euclidean distance or landscape resistance.\nC_LI

Evolutionary Biology↗

Whole-genome sequencing revealed local genetic differences and multi-layered genetic structure in brown bear (Ursus arctos) populations

A multi-layered genetic structure shaped by multiple demographic events across different time periods has been well documented in some species. However, evaluating this pattern remains challenging in many wide-ranging wildlife because genome-wide datasets with broad geographic coverage are still scarce. Brown bears are genetically differentiated among regional populations, but the genomic data are still limited and the demographic events remain incompletely understood. Here, we generated whole-genome resequencing data from brown bears in Asian local populations previously reported to show significant variation and combined these data with ancient samples for population genomic analyses. Our results support previously reported demographic events, such as the early divergence of Central Asian populations. Furthermore, we found genetic differences among Asian brown bear populations, suggesting that population-specific demographic histories occurring at different times have contributed to their unique genetic characteristics. In the Central and West Asian populations, divergence times estimated from autosomal genomes differed by hundreds of thousands of years from those inferred from mitochondrial DNA and Y-chromosomal DNA haplotypes, suggesting that gene flow continued after regional dispersal. In addition, the Y-chromosomal phylogeny and f4 statistics indicated genetic differentiation between the Kazakhstan and Tibetan populations, likely resulting from male-biased introgression into the Tibetan population. Finaly, the Hokkaido and Etorofu (Iturup) populations retained alleles derived from hybridization with polar bears, likely due to the limited impact of post-LGM population expansion. These findings indicate that the genetic variation among brown bear populations is shaped by multiple demographic events occurring at different times, resulting in a multi-layered genetic structure. Significance StatementMulti-layered genetic structure is well known in some species, but remains poorly understood in wild species due to limited genomic data. In this study, we newly sequenced nine brown bears in Asia which included bears that genome data in their area is unreported. By combining these with modern and ancient genomes, we conducted population genomic analyses to characterize local genetic variation and detect hybridization with related species such as the polar bear. We identified (1) substantial divergence time gaps in Central and West Asian populations between autosomal genomes and mitochondrial and Y-chromosomal haplotypes, (2) male-biased introgression into the Tibetan population, and (3) limited impact of recent expansion between island and Eurasian continental populations. These findings suggest that the genetic structure of the populations has been shaped by multiple demographic events at different times, resulting in a multi-layered genetic structure and providing new insights into how wildlife genetic diversity is shaped.

evolutionary biology↗

Mechanism of evolution by genetic assimilation: Equivalence and independence of genetic mutation and epigenetic modulation in phenotypic expression

Conrad H. Waddington discovered the phenomenon of genetic assimilation through a series of experiments on fruit flies. In those experiments, artificially exerted environmental stress induced plastic phe-notypic changes in the fruit flies, but after some generations the same phenotypic variant started to appear without the environmental stress. Both the initial state (where the phenotypic changes were environmentally induced and plastic) and the final state (where the phenotypic changes were genetically fixed and constitutive) are experimental facts. However, it remains unclear how the environmentally induced phenotypic change in the first generation becomes genetically fixed in the central process of genetic assimilation itself. We have argued that the key to understanding the mechanism of genetic assimilation lies in epigenetics, and proposed the \"cooperative model\" in which the evolutionary process depends on both genetic and epigenetic factors. Evolutionary simulations based on the cooperative model reproduced the process of genetic assimilation. Detailed analysis of the trajectories has revealed genetic assimilation is a process in which epigenetically induced phe-notypic changes are incrementally and statistically replaced with multiple minor genetic mutations through natural selection. In this scenario, epigenetic and genetic changes may be considered as mutually independent but equivalent in terms of their effects on phenotypic changes. This finding rejects the common (and confused) hypothesis that epigenetically induced phe-notypic changes depend on genetic mutations. Furthermore, we argue that transgenerational epigenetic inheritance is not required for evolution by genetic assimilation.

evolutionary biology↗

Genetic correlations across genetically determined and developmentally plastic alternative reproductive tactics

Alternative reproductive tactics occur when individuals of the same sex have a suite of morphological and/or behavioural traits that allow them to pursue different reproductive strategies. A common pattern is e.g. the existence of \"courter\" and \"sneaker\" tactics within males. We have previously argued that alternative reproductive tactics should be subject to genetic conflict over the phenotypic expression of traits, similar to sexual antagonism. In this process, which we called intra-locus tactical conflict, genetically determined tactics experience conflicting selection on a shared phenotypic trait, such as body size, but a positive genetic correlation between tactics in body size prevents either tactic from reaching its optimum. Recently, other authors have attempted to extend this idea to developmentally plastic alternative reproductive tactics, with mixed results. However, it is not clear whether we should expect intra-locus tactical conflict in developmentally plastic tactics or not. We have therefore run a series of simulation models investigating under what conditions we should expect to see positive estimates of the inter-tactical genetic correlation, since a positive genetic correlation is a prerequisite for the existence of intra-locus tactical conflict. We found that for autosomal, X-linked, and Y-linked genetically-determined tactics, estimated inter-tactical genetic correlations were generally high. However, for developmentally plastic tactics, the genetic correlation depends on the properties of the switching threshold between tactics. If it is fixed, then estimated genetic correlations are positive, but if there is genetic variation in the switch-point, then any sign and magnitude of estimated genetic correlation is possible, even for highly heritable traits where the true underlying correlation is perfect. This means that caution should be used when investigating genetic constraints in plastic phenotypes.

evolutionary biology↗

Observational epidemiological studies can mitigate genetic confounding with the genetic relatedness matrix

Observational studies are commonly used in psychology and epidemiology to identify risk factors correlated with health outcomes. However, these studies are vulnerable to confounding when shared genetic variation influences both the putative risk factor and outcome. Researchers have historically controlled for this type of genetic confounding using polygenic scores, but these scores are often noisy and biased estimators of a traits genetic component. Here, we develop a method that leverages a genetic relatedness matrix (GRM) to control genetic confounding when testing for non-genetic risk factors. In simulations, we find that our method outperforms existing approaches, particularly at sample sizes that are large by the standards of much human research but smaller than datasets often used in human genetics. We also demonstrate that existing methods are susceptible to poor GWAS portability, whereas our method is inherently robust to such concerns. Finally, we apply our method to the UK Biobank to re-analyze social risk factors for health outcomes in previously understudied cohorts. Significance statementLarge observational datasets are often used in epidemiology or social sciences to identify risk factors associated with health outcomes. However, these studies can be misleading when the putative risk factor and outcome are both complex genetic traits: a correlated genetic basis can create spurious associations between a putative risk factor and outcome. Existing approaches to address this problem typically rely on GWAS summary statistics, which require biobank-scale datasets. In this work, we introduce an alternative method that uses a genetic relatedness matrix (GRM) to control for genetic confounding directly. We show that our approach is closely related to existing methods but outperforms them in smaller datasets, making it an especially valuable tool for researchers who work on understudied traits and populations.

genetics↗

The fog of genetics: Known unknowns and unknown unknowns in the genetics of complex traits and diseases

Associating genetic variants with phenotypes is not only important to understand the underlying biology but also to identify potential drug targets for treating diseases. It is widely accepted that for most complex traits many associations remain to be discovered, the so-called "missing heritability." Yet missing heritability can be estimated, it is a known unknown, and we argue is only a fraction of the unknowns in genetics. The majority of possible genetic variants in the genome space are either too rare to be detected or even entirely absent from populations, and therefore do not contribute to estimates of phenotypic or genetic variability. We call these unknown unknowns in genetics the "fog of genetics." Using data from the 1000 Genomes Project we then show that larger genes with greater genetic diversity are more likely to be associated with human traits, demonstrating that genetic associations are biased towards particular types of genes and that the genetic information we are lacking about traits and diseases is potentially immense. Our results and model have multiple implications for how genetic variability is perceived to influence complex traits, provide insights on molecular mechanisms of disease and for drug discovery efforts based on genetic information.

genetics↗

Geographical restructuring of Arabidopsis thaliana’s genetic makeup in the Iberian Peninsula due to climate change based on genetic cluster membership

AimTo assess the effects of climate change on genetic lineages of Arabidopsis thaliana at the admixed population level by directly modelling genetic cluster membership values to predict potential genetic cluster memberships across the Iberian Peninsula.\n\nLocationIberian Peninsula\n\nMethodsWe used a dataset of 274 accessions structured in four genetic clusters as inferred from 250 nuclear single-nucleotide polymorphisms with Bayesian clustering methods. We predicted the change in percentages of genetic cluster membership at a population level and the changes in potential suitability across the study area by combining parametric (Beta regression) and non-parametric (Recursive trees) methods.\n\nResultsClimate change will affect genetic lineages of Arabidopsis thaliana differently. Genetic clusters GC1 and GC2 will suffer a substantive reduction of their respective suitable areas while GC3 and GC4 will expand northward. At the population level, except for GC4, the rest of the lineages will undergo a genetic turnover for many of their populations.\n\nMain conclusionsA. thaliana in the Iberian Peninsula will undergo a major internal genetic restructuring and range change due to climate change. Genetic lineages of Arabidopsis thaliana in the Iberian Peninsula will be affected differently which reinforce the need for taking into account intraspecific genetic variation when modelling species distribution. Despite limited predictive power of individual statistical models, the combination of distinct models can compensate this shortcoming.

ecology↗

Unraveling the Genetic Architecture of Cryptic Genetic Variation

Cryptic genetic variation (CGV), hidden under most conditions, is the repressed genetic potential that can facilitate adaptation and evolution. The conditional manifestation of CGV has been claimed to explain the background dependence of causal loci as well as missing heritability. However, despite being proposed over 60 years ago, the genetic architecture and regulation of CGV and its contribution towards regulation of complex traits remains unclear. Using linkage mapping of mean and variance effects, we have identified loci that regulate phenotypic manifestation of standing genetic variation in a previously published dataset of biparental Saccharomyces cerevisiae population grown in 34 diverse environments. Based on our results we propose the existence of a gradient of buffering states for a population determined by the environment. Most environments show a tight buffering with additive, independent causal loci with little epistasis. However, as this buffering is disrupted, the underlying highly interconnected environment-specific genetic interactome is revealed such that each causal locus is a part of this network. Interspersed within these networks are generalist capacitors that regulate CGV across multiple environments, with one allele behaving as a capacitor and the other as a potentiator. Our study demonstrates the connecting link between architecture of hidden and visible genetic variation and uncovers the genetic networks which potentially underlie all complex traits. Our study establishes CGV as a significant contributor to phenotypic variation, and provides evidence for a predictable pattern underlying gene-gene and gene-environment interactions that can explain background dependence and missing heritability in complex traits and diseases.\n\nSUMMARYThe phenotypic effects of cryptic genetic variation (CGV) are mostly hidden and manifested only under certain rare conditions and have the potential to facilitate adaptation. However, little is understood about its genetic regulation. We performed variance QTL mapping to understand the regulation of phenotypic manifestation of standing genetic variation in a biparental yeast population. We propose a model describing the connecting link between visible variation and CGV. We identify generalist capacitors and environment-specific networks that potentially underlie all phenotypes. This fresh approach of mapping causal loci can solve the long-standing mystery of missing heritability in complex traits and diseases.

Genetics↗

Genetic Dependence and Genetic Diseases

The human life depends on the function of proteins that are encoded by about twenty-thousand genes. The gene-disease associations in majority genes are unknown and the mechanisms underlying pathogenicity of genes/variants and common diseases remain unclear. We studied how human life depends on the genes, i.e., the genetic-dependence, which was classified as genetic-dependent nature (GDN, vital consequence of abolishing a gene), genetic-dependent quantity (GDQ, quantitative genetic function required for normal life), and genetic-dependent stage (GDS, temporal expression pattern). Each gene differs in genetic-dependent features, which determines the gene-disease association extensively. The GDN is associated with the pathogenic potential/feature of genes and the strength of pathogenicity. The GDQ-damage relation determines the pathogenicity of variants and subsequently the pathogenic genotype, phenotype spectrum, and inheritance of variants. The GDS is mainly associated with the onset age/evolution/outcome and the nature of genetic disorders (disease/susceptibility). The varied and quantitative genetic-dependent feature of genome explains common mild phenotype/susceptibility. The genetic-dependence discloses the mechanisms underlying pathogenicity of gene/variants and common diseases. One sentence summaryGenetic dependent feature differs in genes and determines pathogenicity of genes/variants and the clinical features of genetic diseases.

genetics↗