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bioRxiv2026

Local genetic neighbourhoods but resilient gene flow across anthropogenic landscapes in the red campion (Silene dioica)

Background and AimsIn the plant kingdom, gene flow occurs through pollen and seed dispersal, shaping both within-population spatial genetic structure and among-population genetic differentiation. Anthropogenic land-use change can affect levels of gene flow by reducing pollinator abundance and altering pollen and seed dispersal pathways. Yet, how environmental features shape gene flow events in common herbaceous plants - a fundamental building block of many ecosystems - remains poorly understood. We address this gap by investigating how environmental context influences population genetic structure at regional and local scales in the red campion (Silene dioica). MethodsBy sampling 1,005 individuals from 29 populations across habitats ranging from semi-natural to strongly human-altered, we assessed whether population size and landscape composition influenced within-population genetic diversity and population genetic differentiation. At the local scale, we examined whether landscape composition affected fine-scale spatial genetic structure and pollen dispersal distances in a subset of six populations representing the two extremes of an anthropogenic gradient. Key ResultsNo effect of either demographic or landscape factors was found on levels of genetic diversity. We detected moderate levels of genetic differentiation among populations that matched an isolation-by-distance pattern, with high levels of population admixture, while landscape composition did not explain variation in population genetic differentiation. At the local scale, five of the six studied populations exhibited significant spatial genetic structuring, indicating distinct neighbourhoods at spatial scales less than 10 m. Paternity analyses based on 4,800 offspring further revealed predominantly short-distance pollen dispersal together with substantial immigration from external sources. Neither fine-scale genetic structure nor pollen dispersal distances differed between habitat types. ConclusionsAltogether, our results demonstrate a multimodal pattern of gene flow and a remarkable resilience of this common herbaceous species to anthropogenic habitat change, as substantial genetic connectivity is maintained despite insect-mediated pollen dispersal and gravity-driven seed dispersal.

plant biology↗

bioRxiv2020

The impact of global and local Polynesian genetic ancestry on complex traits in Native Hawaiians

Epidemiological studies of obesity, Type-2 diabetes (T2D), cardiovascular diseases and several common cancers have revealed an increased risk in Native Hawaiians compared to European- or Asian-Americans living in the Hawaiian islands. However, there remains a gap in our understanding of the genetic factors that affect the health of Native Hawaiians. To fill this gap, we studied the genetic risk factors at both the chromosomal and sub-chromosomal scales using genome-wide SNP array data on [~]4,000 Native Hawaiians from the Multiethnic Cohort. We estimated the genomic proportion of Native Hawaiian ancestry ("global ancestry," which we presumed to be Polynesian in origin), as well as this ancestral component along each chromosome ("local ancestry") and tested their respective association with binary and quantitative cardiometabolic traits. After attempting to adjust for non-genetic covariates evaluated through questionnaires, we found that per 10% increase in global Polynesian genetic ancestry, there is a respective 8.6%, and 11.0% increase in the odds of being diabetic (P = 1.65 10-4) and having heart failure (P = 2.18 10-4), as well as a 0.059 s.d. increase in BMI (P = 1.04 10-10). When testing the association of local Polynesian ancestry with risk of disease or biomarkers, we identified a chr6 region associated with T2D. This association was driven by an uniquely prevalent variant in Polynesian ancestry individuals. However, we could not replicate this finding in an independent Polynesian cohort from Samoa due to the small sample size of the replication cohort. In conclusion, we showed that Polynesian ancestry, which likely capture both genetic and lifestyle risk factors, is associated with an increased risk of obesity, Type-2 diabetes, and heart failure, and that larger cohorts of Polynesian ancestry individuals will be needed to replicate the putative association on chr6 with T2D. Author SummaryNative Hawaiians are one of the fastest growing ethnic minority in the U.S., and exhibit increased risk for metabolic and cardiovascular diseases. However, they are generally understudied, especially from a genetic perspective. To fill this gap, we studied the association of Polynesian genetic ancestry, at genomic and subgenomic scale, with quantitative and binary traits in self-identified Native Hawaiians. We showed that Polynesian ancestry, which likely capture both genetic and non-genetic risk factors related to Native Hawaiian people and culture are associated with increased risk for obesity, type-2 diabetes, and heart failure. While we do not endorse utilizing genetic information to supplant current standards of defining community membership through self-identity or genealogical records, our results suggest future studies could identify population-specific genetic susceptibility factors that may be useful in suggesting underlying biological mechanisms and reducing the disparity in disease interventions in Polynesian populations.

genetics↗

bioRxiv2020

A cautionary note on the use of machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC). PCoA and HAC are sensitive to both the definition of genetic distance and algorithmic specification. Importantly, neither algorithm infers malaria parasite ancestry. As such, PCoA and HAC can inform (e.g. via exploratory data visualisation and hypothesis generation), but not answer comprehensively, key questions about malaria parasite ancestry. We illustrate the sensitivity of PCoA and HAC using 393 Plasmodium falciparum whole genome sequences collected from Cambodia and neighbouring regions (where antimalarial resistance has emerged and spread recently) and we provide tentative guidance for the use and interpretation of PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scientific question; (ii) a clear justification of analytical methods used to answer the scientific question along with discussion of any inferential limitations; (iii) publicly available genetic distance matrices when downstream analyses depend on them; and (iv) sensitivity analyses. To bridge the inferential disconnect between the output of non-inferential unsupervised learning algorithms and the scientific questions of interest, tailor-made statistical models are needed to infer malaria parasite ancestry. In the absence of such models speculative reasoning should feature only as discussion but not as results. Author summaryGenetic epidemiology studies of malaria attempt to characterise what is happening in malaria parasite populations. In particular, they are an important tool to track the spread of drug resistance and to validate genetic makers of drug resistance. To make sense of parasite genetic data, researchers usually characterise the population structure using statistical methods. This is most often done as a two step process. The first is a data reduction step, whereby the data are summarised into a distance matrix (each entry represents the genetic distance between two isolates) and then the distance matrix is input into an unsupervised machine learning algorithm. Principal coordinates analysis and hierarchical agglomerative clustering are the two most popular unsupervised machine learning algorithms used for this purpose in malaria genetic epidemiology. We illustrate that this procedure is sensitive to the choice of genetic distance and to the specification of the algorithms. These unsupervised methods are useful for exploratory data analysis but cannot be used to infer historical events. We provide some guidance on how to make genetic epidemiology analyses more transparent and reproducible.

genetics↗

bioRxiv2020

Estimating genetic nurture with summary statistics of multi-generational genome-wide association studies

Marginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a novel statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower BMI, less active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. Polygenic transmission disequilibrium test showed a significant over-transmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.

genetics↗

bioRxiv2021

Proprioceptive Genes as a Source of Genetic Variation Underlying Robustness for Flight Performance in Drosophila

A central challenge of quantitative genetics is partitioning phenotypic variation into genetic and non-genetic components. These non-genetic components are usually interpreted as environmental effects; however, variation between genetically identical individuals in a common environment can still exhibit phenotypic variation. A traits resistance to variation is called robustness, though the genetics underlying it are poorly understood. Accordingly, we performed an association study on a previously studied, whole organism trait: robustness for flight performance. Using 197 of the Drosophila Genetic Reference Panel (DGRP) lines, we surveyed variation across single nucleotide polymorphisms, whole genes, and epistatic interactions to find genetic modifiers robustness for flight performance. There was an abundance of genes involved in the development of sensory organs and processing of external stimuli, supporting previous work that processing proprioceptive cues is important for affecting variation in flight performance. Additionally, we tested insertional mutants for their effect on robustness using candidate genes found to modify flight performance. These results suggest several genes involved in modulating a trait mean are also important for affecting trait variance, or robustness, as well. Article SummaryWe sought to understand the genetic architecture of robustness (variation in a trait caused by non-genetic factors) for flight performance. We used 197 Drosophila Genetic Reference Panel (DGRP) lines to find significant individual variants and pairs of epistatic interactions, many of which were involved in proprioception. Additionally, we validated significant genes identified from a prior study for the mean of flight performance, showing genes affecting trait means may also affect trait robustness.

genetics↗

bioRxiv2022

Mate selection: a useful approach to maximize genetic gain and control inbreeding in genomic and conventional oil palm (Elaeis guineensis Jacq.) hybrid breeding

Genomic selection (GS) is the most effective method of marker-assisted selection for the genetic improvement of complex traits in plants and animals. Optimization approaches could be used in conjunction with GS to further increase its efficiency and to limit inbreeding, which can increase faster with GS. Mate selection (MS) typically uses a metaheuristic optimization algorithm, simulated annealing, to optimize the selection of individuals and their matings. However, in species with long breeding cycles, this cannot be studied empirically. Here, we investigated this aspect with forward genetic simulations on a high-performance computing cluster and massively parallel computing, considering the oil palm hybrid breeding example. We compared MS and simple methods of inbreeding management (limitation of the number of individuals selected per family, prohibition of self-fertilization and combination of these two methods), in terms of parental inbreeding and genetic progress over four generations of genomic selection and phenotypic selection. The results showed that, compared to the conventional method without optimization, MS could lead to significant decreases in inbreeding and increases in annual genetic progress, with the magnitude of the effect depending on MS parameters and breeding scenarios. The optimal solution retained by MS differed by five breeding characteristics from the conventional solution: selected individuals covering a broader range of genetic values, fewer individuals selected per full-sib family, decreased percentage of selfings, selfings preferentially made on the best individuals and unbalanced number of crosses among selected individuals, with the better an individual, the higher the number of times he is mated. Stronger slowing-down in inbreeding could be achieved with other methods but they were associated with a decreased genetic progress. We recommend that breeders use MS, with preliminary analyses to identify the proper parameters to reach the goals of the breeding program in terms of inbreeding and genetic gain. Author summaryOil palm is a tropical perennial plant and the world main source of vegetable fats. Oil palm breeding requires the estimation of the additive genetic value of the selection candidates to identify the best hybrid crosses, that will be released as commercial varieties. Genomic selection (GS) is a marker-assisted selection method that has the potential of boosting the rate of genetic progress for quantitative traits. However, GS could also be used in conjunction with approaches to optimize selection and mating, in order to maximize genetic progress while limiting inbreeding, which can increase faster with GS and can have negative consequences (inbreeding depression detrimental for seed production, loss of favorable alleles). This study compared several strategies to reach this goal. In particular, we considered mate selection (MS), a method that uses an algorithm optimizing the selection of individuals and their crosses. Our results showed that MS reduced inbreeding in parental populations and increased annual genetic progress. The originality of this work resides in the fact that, despite the high computational burden of MS, we applied it in a large simulated dataset and showed its efficiency in the context of genomic and conventional selection. Also, this is the first study to characterize, in terms of breeding rules, the optimized solution (i.e. the set of selected individuals and their mating design) retained by MS.

genetics↗

bioRxiv2023

Genetic Gains in IRRIs Rice Salinity Breeding and Elite Panel Development as a Future Breeding Resource

Genetic gain is a crucial parameter to check the breeding programs success and help optimize future breeding strategies for enhanced genetic gains. In this work, IRRIs historical data from the Philippines and Bangladesh of the salinity breeding program was used to estimate the genetic gains and identify the best lines based on higher breeding values for yield as a future genetic resource. Two-stage mixed-model approach accounting for experimental design factors and pedigrees was adopted to obtain the breeding values for yield and estimate genetic trends under the salinity conditions. A positive genetic trend of 0.1% per annum with a yield advantage of 1.52 kg/ha for the Philippines and 0.31% per annum with a yield advantage of 14.02 kg/ha for Bangladesh datasets was observed. For the released varieties, genetic gain was 0.12% per annum with a yield advantage of 2.2 kg/ha/year and 0.14% per annum with a yield advantage of 5.9 kg/ha/year, respectively. Further, based on higher breeding values for grain yield, a core set of the top 145 genotypes with higher breeding values of >2400 kg/ha in the Philippines and >3500 kg/ha in Bangladesh with a selection accuracy >0.4 were selected for formulating the elite breeding panel as a future breeding resource. Conclusively, higher genetic gains are pivotal in IRRIs rice salinity breeding program, which requires a holistic breeding approach with a major paradigm shift in breeding strategies to enhance genetic gains. Key MessageEstimating genetic gains and formulating a future salinity elite breeding panel for rice pave the way for developing better high-yielding salinity tolerant lines with enhanced genetic gains.

genetics↗

bioRxiv2023

The overlapping genetic architecture of psychiatric disorders and cortical brain structure

Both psychiatric vulnerability and cortical structure are shaped by the cumulative effect of common genetic variants across the genome. However, the shared genetic underpinnings between psychiatric disorders and brain structural phenotypes, such as thickness and surface area of the cerebral cortex, remains elusive. In this study, we employed pleiotropy-informed conjunctional false discovery rate analysis to investigate shared loci across genome-wide association scans of regional cortical thickness, surface area, and seven psychiatric disorders in approximately 700,000 individuals of European ancestry. Aggregating regional measures, we identified 50 genetic loci shared between psychiatric disorders and surface area, as well as 26 genetic loci shared with cortical thickness. Risk alleles exhibited bidirectional effects on both cortical thickness and surface area, such that some risk alleles for each disorder increased regional brain size while other risk alleles decreased regional brain size. Due to bidirectional effects, in many cases we observed extensive pleiotropy between an imaging phenotype and a psychiatric disorder even in the absence of a significant genetic correlation between them. The impact of genetic risk for psychiatric disorders on regional brain structure did exhibit a consistent pattern across highly comorbid psychiatric disorders, with 80% of the genetic loci shared across multiple disorders displaying consistent directions of effect. Cortical patterning of genetic overlap revealed a hierarchical genetic architecture, with the association cortex and sensorimotor cortex representing two extremes of shared genetic influence on psychiatric disorders and brain structural variation. Integrating multi-scale functional annotations and transcriptomic profiles, we observed that shared genetic loci were enriched in active genomic regions, converged on neurobiological and metabolic pathways, and showed differential expression in postmortem brain tissue from individuals with psychiatric disorders. Cumulatively, these findings provide a significant advance in our understanding of the overlapping polygenic architecture between psychopathology and cortical brain structure.

genetics↗

bioRxiv2023

The latitudinal trend in genetic diversity and distinctiveness of Quercus robur rear edge forest remnants calls for new conservation priorities

Knowledge of the spatial distribution of intraspecific genetic variation is essential for planning conservation actions, designing networks of protected areas, and informing possible assisted migration strategies. Although the Italian peninsula harbours unique genetic variation as a legacy of Quaternary migrations, only sporadic genetic information is available for forest tree species. Here, we present the first geographically comprehensive genetic characterisation of Quercus robur, an iconic broadleaved species of European floodplain forests, in an area which acted as a primary glacial refugium for the species. 745 individuals from 25 populations were sampled and genotyped with 16 microsatellite markers. Their genetic structure was assessed through various metrics of diversity and distinctiveness, as well as by Bayesian clustering and multivariate methods. The demographic history of inferred gene pools was evaluated through Approximate Bayesian Computation analysis. Genetic distinctiveness showed a decline with increasing latitude, while allelic richness reached its peak in central Italy. A south-to-north trend in the complexity of the genetic structure was observed, with peninsular Italy being characterised by intermingled gene pools in contrast to the relative homogeneity exhibited by northern populations. Demographic inference indicated that the southern gene pool has been genetically isolated since the penultimate interglacial, whereas populations from central Italy persisted locally in a mosaic of small refugia during the last glaciation. Our results brought out the complexity of the genetic structure of forest trees populations in southern Europe. At least three Q. robur refugia contributed to the genetic layout of extant populations after the last glacial period, but refugial areas were probably even more numerous in central Italy. Such a detailed characterization sheds new light on the priorities to be established for the conservation of highly fragmented Q. robur populations in an area rich in diversified genetic lineages.

genetics↗

bioRxiv2023

The genetic consequences of population marginality: a case study in maritime pine

AimMarginal tree populations, either those located at the edges of the species range or in suboptimal environments, are often a valuable genetic resource for biological conservation. However, there is a lack of knowledge about the genetic consequences of populations marginality, estimated across entire species ranges. Our study addresses this gap by providing information about several genetic indicators and their variability in marginal and core populations identified using quantitative marginality indices. LocationSouthwestern Europe and North Africa. MethodsUsing 10,185 SNPs across 82 populations of maritime pine (Pinus pinaster Ait.), a widespread and economically important conifer characterised by a fragmented range, we modelled the relationship of seven genetic indicators potentially related to population evolutionary resilience, namely genetic diversity (based on all SNPs and two kind of outlier loci), inbreeding, genetic differentiation, recessive genetic load and genomic offset, with population geographical, demo-historical and ecological marginality (as estimated by nine quantitative indices). Models were constructed for both regional (introducing gene pool as random factor) and range-wide spatial scales. ResultsWe showed a trend towards decreasing overall genetic diversity (albeit not necessarily for genetic diversity estimates based on outlier loci) and increasing differentiation with geographic marginality, supporting the centre-periphery hypothesis (CPH). Moreover, we found no correlation between population inbreeding and marginality, while geographically marginal populations had a lower recessive genetic load (only models without the gene pool effect) and ecologically marginal populations had a higher genomic offset, suggesting higher maladaptation to future climate. Main conclusionsOverall, our results indicate that marginal populations could be at higher risk of maladaptation to climate change than core populations, despite reduced levels of genetic load, a risk that is exacerbated by typically small effective population sizes and increasing human impact.

genetics↗

bioRxiv2024

Rapid loss of genetic variation and increased inbreeding in small and isolated populations of Norwegian wild reindeer

Wildlife responses to habitat loss and fragmentation are a central concern in the management and conservation of biodiversity. Small and isolated populations are vulnerable, both due to demographic and genetic mechanisms, which are often linked. Thus, understanding how (changes in) genetic diversity, effective population sizes, and levels of inbreeding relate to population size and degree of isolation is key for developing effective conservation strategies. High-density Single Nucleotide Polymorphism (SNP) arrays represent an increasingly cost-efficient tool to achieve the data needed for such analysis. Here, we present the development of a novel 625k SNP array for reindeer Rangifer tarandus and apply this array to assess conservation genetic issues across thirteen Norwegian wild reindeer populations of varying size, isolation, and genetic origin (i.e., semi-domesticated reindeer origin or a mix of wild reindeer and semi-domesticated reindeer origins). Many of these populations are currently completely isolated, with no gene flow from other populations. We genotyped n = 510 individuals sampled by hunters and found that variation in population size across the populations largely predicted their (recent loss of) genetic variation (observed heterozygosity, Ho), as well as effective population size (Ne) and (change in) level of recent inbreeding. For the smallest and most isolated populations, with total population sizes of <50-100 individuals and a high and increasing level of recent inbreeding, estimated loss of genetic variation was as high as 3-10% over the time span of a generation or less, and estimated Ne was as low as six individuals. With the current level of isolation and associated lack of gene flow, and considering their already low genetic diversity, these populations are hardly viable - neither demographically nor genetically - in the long term. These results have direct relevance for the management of Norwegian wild reindeer, recently red-listed as Near Threatened. Yet, these genetic challenges, characterizing many of the small wild reindeer populations in Norway, have been largely ignored by management thus far. Mitigation efforts such as reducing barriers would introduce substantial conservation dilemma due to the aim of avoiding further spread of chronic wasting disease (CWD), as well as potential further domestic introgression into populations with genetically wild reindeer (or mixed) origin. Nevertheless, our cost-efficient and high-density SNP array especially designed for reindeer and caribou offers a powerful genetic tool to include in future monitoring, providing important contributions to management and conservation decisions.

genetics↗

bioRxiv2025

Beyond Recombination: Exploring the Impact of Meiotic Frequency on Genome-wide Genetic Diversity

An important aim of population genetics is to elucidate the processes affecting genetic diversity across regions of the genome and across species. Canonical population genetic models of sexually reproducing species define the rate of meiotic recombination in terms of the frequency of recombination events per site per sexual generation. This paper studies the interplay of several factors with respect to their effects on neutral genetic diversity in a facultatively sexual, diploid, unicellular species such as yeast. The relevant factors are the prevalence of meiosis versus mitosis, the recombination rate, and the selection and dominance coefficients at loci under positive selection. We assume that many generations of mitotic cell divisions are interspersed with episodes of sexual reproduction, in which all individuals in the population undergo meiosis, followed by random matings among the resulting gametes. Our findings reveal that a single hard selective sweep can reduce neutral nucleotide site diversity across the entire genome, provided that the frequency of meiotic events is sufficiently low, and that the effects of a selective sweep on levels of neutral diversity at sites linked to the target of selection can be markedly different from those predicted by standard models of sweeps based on obligate sexuality. Species that reproduce by facultative sex are thus likely to exhibit unusual patterns of genetic diversity. Author summaryIn this study, we explored how different sexual strategies influence genetic diversity. Specifically, we looked at how the balance between sexual reproduction (which involves meiosis) and asexual reproduction (which involves mitosis) affect genetic variation in a species like yeast. Our research focused on the effects of recombination rates and the role of selective sweeps -- when a beneficial genetic variant spreads rapidly through a population -- on genetic diversity. We found that, in species with facultative sex, a selective sweep can dramatically reduce genetic diversity across the genome, but only if sexual reproduction occurs infrequently. We compared our findings to existing models and also developed a new mathematical framework for understanding the effects of sweeps when sexual reproduction is rare. The fact that facultative sex can reduce genetic diversity over the entire genome is likely to complicate inferences about population size and evolutionary dynamics in species with mixed reproductive strategies.

genetics↗

bioRxiv2025

Optimization of wheat breeding programs using an evolutionary algorithm achieves enhanced genetic gain through strategic resource allocation

Plant breeding is a complex process that involves trade-offs among competing breeding objectives and limited resources. Despite the necessity for optimization of breeding program design, the inherent complexity can make optimization challenging. Most often, a predefined set of scenarios is compared or a single parameter of a breeding scheme is assessed in depth. In a previous study, we developed an optimization pipeline, utilizing stochastic simulations and an evolutionary algorithm, suitable for the joint optimization of multiple class and continuous parameters. Here, we assess the applicability of our framework to realistic plant breeding schemes. For this, a wheat line breeding scheme simulated using AlphaSimR and a wheat hybrid breeding scheme simulated using MoBPS were considered. Both schemes were further optimized using our optimization pipeline. When aiming to maximize genetic gain while maintaining a fixed budget, the breeding program design suggested by our optimization pipeline results in 32.6% higher genetic gain compared to a proposed baseline wheat line breeding program. When adjusting the breeding objective to put 20% of the weight on the maintenance of genetic diversity, genetic gain still increased by 4.5% while maintaining 9.1% higher genetic diversity compared to the baseline. Similarly, 4.6%/8.8% higher genetic gains for the male/female part of the hybrid breeding scheme compared to its baseline scenario were obtained. Results highlight the importance of optimizing breeding program design to improve breeding efficiency, with the suggested pipeline offering breeders a powerful framework to refine breeding designs, balance breeding goals, and enhance competitiveness, profitability, and sustainability. Core Ideas Evolutionary algorithm can improve breeding design by optimizing many breeding decisions simultaneously Our framework is compatible with various backend simulators, enabling broad application across platforms Optimized designs enhance genetic gain and diversity, demonstrating greater overall breeding program efficiency Genetic gain in wheat line program increased by over 30% compared to a baseline program without additional costs Plain Language Summary Modern plant breeding programs must make many complex decisions, such as how many plants to test or where to grow them while staying with tight budgets. These choices are often connected and involve trade-offs between goals like improving genetic gain, maintaining diversity, and reducing costs. In this study, we used an evolutionary algorithm framework to evaluate thousands of breeding program designs and identify the most effective ones. The optimized programs outperformed the respective baseline programs, increasing genetic gain by up to 32% and preserving 9% more genetic diversity - all without additional costs. This flexible framework can support a wide range of breeding decisions and adapt to different program types, providing breeders with a powerful tool to improve outcomes and use resources more efficiently.

genetics↗

bioRxiv2025

Genetic correlation-guided mega-analysis of DO mice provides mechanistic insight and candidate genes for age-related pathologies

Diversity Outbred (DO) mice are a powerful model system for mapping complex traits due to their high genetic diversity and mapping resolution. However, while there are extensive tools available for standard genetic analysis in DO mice, fewer techniques have been implemented to facilitate integrated, cross-study analysis. Here, we implement Haseman-Elston regression to estimate genetic correlations among 7,233 phenotypes measured across eleven independent DO mouse studies. We used this network of genetic correlations to cluster phenotypes according to shared genetics, which enhanced the power to detect quantitative trait loci (QTL). This approach empowered the detection of 884 QTL for 383 meta-phenotypes, explaining an average of 40.36% of the total genetic variance per mega-analysis. We leveraged this network for insights into specific areas of biology, including lifespan, frailty, immune composition, histological and functional lung phenotypes, and histological phenotypes of the aorta. We found the genetics of lifespan to share limited correlation with the genetics of frailty but stronger correlation with the genetics of immune cell composition. Additionally, mega-analyses driven by genetic correlations identified candidate genes (e.g. Cdkn2b) associated with degraded extracellular matrix in the aorta. Finally, an ensemble of genetic analyses implicated pulmonary neuroendocrine cell signaling and/or differentiation as a key driver of multiple lung pathophenotypes.

genetics↗

bioRxiv2025

Conservation of the genetic interaction network between two yeast species

Genetic interactions are essential to decipher the complex relationship between genotype and phenotype, which is key to addressing fundamental biology questions and developing therapies for human diseases. However, the scarcity of genetic interaction data in most species hinders research progress. Understanding the conservation of genetic interactions across species can partially bridge the current data gap. Previous conservation studies focused on subsets of genes and relied on the incomplete genetic networks available at the time, limiting the generalization of their findings. This study systematically quantifies the conservation of the genetic interaction networks between Saccharomyces cerevisiae and Schizosaccharomyces pombe using a comprehensive global genetic interaction map. Conservation is evaluated at three levels of increasing complexity: gene connectivity, individual genetic interactions, and genetic interaction profiles. Overall, the analyses show significant conservation across species at all levels, particularly among functionally related genes, 1:1 orthologs, and less diverged orthologs. However, the functional redundancy caused by gene duplication decreases cross-species conservation, resulting in lower connectivity, loss of individual interactions, and enrichment for trigenic interactions. This disruptive effect can be partially reversed upon divergence of the duplicated pair. Additionally, this work provides predictions for genetic interactions in S. pombe and trigenic interactions in S. cerevisiae. Together, the findings presented here offer a framework to explore the genetic interaction landscape in species lacking such experimental data, including cancer cell lines once large-scale genetic interaction data for human becomes available.

genetics↗

bioRxiv2018

Genetically increased serum calcium levels reduce Alzheimer’s disease risk

IMPORTANCE Alzheimers disease (AD) is the leading cause of disability in the elderly. It has been a long time about the calcium hypothesis of AD on the basis of emerging evidence since 1994. However, most studies focused on the association between calcium homeostasis and AD, and concerned the intracellular calcium concentration. Only few studies reported reduced serum calcium levels in AD. Until now, it remains unclear whether serum calcium levels are genetically associated with AD risk.\n\nOBJECTIVE To evaluate the genetic association between increased serum calcium levels and AD risk\n\nDESIGN, SETTING, AND PARTICIPANTS We performed a Mendelian randomization study to investigate the association of increased serum calcium with AD risk using the genetic variants from the large-scale serum calcium genome-wide association study (GWAS) dataset (N=61,079 individuals of European descent) and the large-scale AD GWAS dataset (N=54,162 individuals including 17,008 AD cases and 37,154 controls of European descent). Inverse-variance weighted meta-analysis (IVW) was used to provide a combined estimate of the genetic association. Meanwhile, we selected the weighted median regression and MR-Egger regression as the complementary analysis methods to examine the robustness of the IVW estimate.\n\nEXPOSURES Genetic predisposition to increased serum calcium levels\n\nMAIN OUTCOMES AND MEASURES The risk of AD.\n\nRESULTS We selected 6 independent genetic variants influencing serum calcium levels as the instrumental variables. IVW analysis showed that a genetically increased serum calcium level (per 1 standard deviation (SD) increase 0.5-mg/dL) was significantly associated with a reduced AD risk (OR=0.56, 95% CI: 0.34-0.94, P=5.00E-03). Meanwhile, both the weighted median estimate (OR=0.60, 95% CI: 0.34-1.06, P=0.08) and MR-Egger estimate (OR=0.66, 95% CI: 0.26-1.67, P=0.381) were consistent with the IVW estimate in terms of direction and magnitude.\n\nCONCLUSIONS AND RELEVANCE We provided evidence that genetically increased serum calcium levels could reduce the risk of AD. Meanwhile, randomized controlled study should be further conducted to assess the effect of serum calcium levels on AD risk, and further clarify whether diet calcium intake or calcium supplement, or both could reduce the risk of AD.\n\nKey PointsQuestion Is there a genetic relationship between elevated serum calcium levels and the risk of Alzheimers disease?\n\nFindings This Mendelian randomization study showed that the genetically increased serum calcium levels were associated with the reduced risk of Alzheimers disease.\n\nMeaning These findings provide evidence that genetically increased serum calcium levels could reduce the risk of Alzheimers disease.

genetics↗

bioRxiv2018

Clustering of Type 2 Diabetes Genetic Loci by Multi-Trait Associations Identifies Disease Mechanisms and Subtypes

BackgroundType 2 diabetes (T2D) is a heterogeneous disease for which 1) disease-causing pathways are incompletely understood and 2) sub-classification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity. First, we aimed to categorize genetic loci into groups representing likely disease mechanistic pathways. Second, we asked whether the novel clusters of genetic loci we identified have any broad clinical consequence, as assessed in four independent cohorts of individuals with T2D.\n\nMethods and FindingsIn an effort to identify mechanistic pathways driven by established T2D genetic loci, we applied Bayesian nonnegative matrix factorization clustering to genome-wide association results for 94 independent T2D genetic loci and 47 diabetes-related traits. We identified five robust clusters of T2D loci and traits, each with distinct tissue-specific enhancer enrichment based on analysis of epigenomic data from 28 cell types. Two clusters contained variant-trait associations indicative of reduced beta-cell function, differing from each other by high vs. low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity-mediated (high BMI, waist circumference), \"lipodystrophy-like\" fat distribution (low BMI, adiponectin, HDL-cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster GRSs were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease, and stroke risk. We evaluated the potential for clinical impact of these clusters in four studies containing participants with T2D (METSIM, N=487; Ashkenazi, N=509; Partners Biobank, N=2,065; UK Biobank N=14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with ~30% of all participants assigned to just one cluster top decile.\n\nConclusionOur approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.

genetics↗

bioRxiv2018

Non-uniform genetic effect sizes of variants associated with refractive error suggests gene-gene or gene-environment interactions are pervasive

Refractive error is a complex ocular trait controlled by genetic and environmental factors. Genome-wide association studies (GWAS) have identified approximately 150 genetic variants associated with refractive error. Among the known environmental factors, education, near-work and time spent outdoors have been demonstrated to have the strongest associations. Currently, the extent of gene-environment or gene-gene interactions in myopia is unknown. Here we show that the majority of genetic variants associated with refractive error show evidence of effect size heterogeneity, which is a hallmark feature of genetic interactions. Using conditional quantile regression, we observed that 88% of genetic variants associated with refractive error have at least nominally-significant non-uniform, non-linear profiles across the refractive error distribution. SNP effects tend to be strongest at the phenotype extremes and have weaker effects in emmetropes. A parsimonious explanation for these findings is that gene-environment or gene-gene interactions in refractive error are pervasive.\n\nAuthor summaryThe prevalence of myopia (nearsightedness) in the United States and East Asia has almost doubled in the past 30 years. Such a rapid rise in prevalence cannot be explained by genetics, which implies that environmental (lifestyle) risk factors play a major role. Nevertheless, diverse approaches have suggested that genetics is also important, and indeed approximately 150 distinct genetic risk loci for myopia have been discovered to date. One attractive explanation for the evidence implicating both genes and environment in myopia is gene-environment (GxE) interaction (a difference in genetic effect in individuals exposed to a high vs. low level of an environmental risk factor). Past studies aiming to discover GxE interactions in myopia have met with limited success, perhaps because information on lifestyle exposures during childhood has rarely been available. Here we used an agnostic approach that does not require information about specific lifestyle exposures in order to detect signatures of GxE interaction. We found compelling evidence for widespread genetic interactions in myopia, with 88% of 150 known myopia genetic susceptibility loci showing an interaction signature. These findings suggest that GxE interactions in myopia are pervasive.

genetics↗