Trait Variance Research Articles

Developmentally cascading structures do not lose evolutionary potential, but compound developmental instability in rat molars.

Increasing variability down serially segmented structures, such as mammalian molar teeth and vertebrate limb segments, is a much-replicated pattern. The same phenotypic pattern has conflicting interpretations at different evolutionary scales. Macroevolutionary patterns are thought to reflect greater evolutionary potential in later-forming segments, but microevolutionary patterns are thought to reflect less evolutionary potential and greater phenotypic plasticity. We address this conflict by recalculating evolutionary potential (evolvability) from published mammalian molar data and directly measuring phenotypic plasticity from a controlled feeding experiment. Effects on lengths and widths are discordant in a way that suggests general growth pathways have a role in phenotypically plastic dental responses to nutrition. Effects on successive trait means do not necessarily increase downstream, contrary to long-standing hypotheses. We confirm prior findings of increasing non-inherited variance downstream, showing decoupling between effects on trait mean and variance. These patterns can be explained by a cascading model of tooth development compounding the effect of anatomically hyper-local developmental instability as an influence separate from general environmental effects on the developing embryo. When evaluated in terms of evolvability, not heritability, later-developing molars are equally or more evolvable than earlier-developing molars, aligning their microevolutionary potential with macroevolutionary patterns in other serially segmented structures.

Genetic Variability Studies in Early Segregating Generation of Cowpea [Vigna unguiculata (L.) Walp subsp unguiculata

Aims: In a self-pollinated crop like cowpea, most breeding approaches predominantly use different techniques of handling the segregating generations. It is important to select and increase the frequency of the desirable genotypes for efficient management of segregating generations and gradual improvement of important characters in the material. For the selection to be efficient, a complete knowledge of the features of the segregating material in terms of genetic variability with regard to important characters is crucial. So the objective of the study was to comprehend the genetic variability in segregating generations and to estimate the percentage of transgressive segregants. Study Design: The experiment was laid out in Augmented design II during kharif, 2023. Place and Duration of Study: The current experiment was undergone at the Botany garden, Department of Genetics and Plant Breeding, University of Agricultural Sciences, Dharwad, Karnataka to evaluate 36 F4 families and six checks during kharif 2023. Methodology: Observations were taken for ten yield and yield related traits viz., total yield (kg/ha), single plant yield (g), number of pods per plant, test weight (g), number of seeds per pod, pod length (cm), number of clusters per plant, number of pods per cluster, days to fifty per cent flowering and days to maturity. Analysis of variance (ANOVA) for yield and yield related traits of 36 F4 families of diverse crosses and pedigree along with checks in grain type cowpea was carried out in augmented design II as proposed by Federer and Raghavarao, 1975. Different genetic parameters were estimated to characterize the material for nature and amount of genetic variability. Percentage of transressive segregants was calculated using the mean±1 SD for all characters. Results: The variances between genotypes were shown to be highly significant among all the characters under study. All traits exhibited high heritability (>60%) except the trait number of pods per cluster. High heritability coupled with high genetic advance (>20%) was obtained in traits like total yield (kg/ha) (71.00% and 43.59%), single plant yield (g) (76.18% and 35.01%), number of pods per plant (71.98% and 33.49%), pod length (cm) (75.99% and 22.00%) and number of clusters per plant (64.90% and 30.02%), indicating the possibility of effective selection for these characters. Highest percentage of transgressive segregants was revealed for the traits total yield (19.44%) and days to fifty per cent flowering (19.44%). Conclusion: Sufficient amount of genetic variability present among the F4 families ensures profound scope of selection which in turn indicates possibility of improvement in the population.

Refining fine-mapping: Effect sizes and regional heritability.

Recent statistical approaches have shown that the set of all available genetic variants explains considerably more phenotypic variance of complex traits and diseases than the individual variants that are robustly associated with these phenotypes. However, rapidly increasing sample sizes constantly improve detection and prioritization of individual variants driving the associations between genomic regions and phenotypes. Therefore, it is useful to routinely estimate how much phenotypic variance the detected variants explain for each region by taking into account the correlation structure of variants and the uncertainty in their causal status. Here we extend the FINEMAP software to estimate the effect sizes and regional heritability under the probabilistic model that assumes a handful of causal variants per region. Using the UK Biobank (UKB) data to simulate genomic regions, we demonstrate that FINEMAP provides higher precision and enables more detailed decomposition of regional heritability into individual variants than the variance component model implemented in BOLT or the fixed-effect model implemented in HESS, particularly when there are only a few causal variants in the fine-mapped region. Using data from 2,940 plasma proteins from the UKB study, we observed that on average FINEMAP identified 2.5 causal variants at an association signal and captured 36% more regional heritability than the variant with the lowest P-value. We estimate that in genomic regions with notable contribution to the total heritability, FINEMAP captures on average 13% and 40% more heritability than BOLT and HESS respectively. Our analysis shows how FINEMAP, BOLT and HESS relate to each other in cases where inference of a variant-level picture of the regional genetic architecture is possible.

Variance and covariance components of agronomic and quality traits assessed in tetraploid potato and their implications on practical breeding

Potato is a versatile food crop and major component of human nutrition worldwide. Model calculations and computer simulations can be used to optimize the resource allocation in potato breeding programs but require quantitative genetic parameters. The objectives of our study are to (i) estimate quantitative genetic parameters of the most important phenotypic traits in potato breeding programs, (ii) compare the importance of inter- vs. intra-population variance, (iii) quantify genotypic and phenotypic covariances among phenotypic traits, and (iv) examine the effect of a preselection in the single hills stage on variance and covariance components in later stages of the breeding program. Our study was based on a total of 1066 clones from three breeding programs which were evaluated in a non-orthogonal way in 15 environments for a total of 26 phenotypic traits. The examined traits showed an overall high to medium heritability, and variance analysis revealed trait-specific differences in the influence of the genotypic, environmental, and genotype-environment interaction effect. Accounting for heterogeneity in the residual variances between the 15 environments led to a significant improvement of the variance parameter estimation. The result of our study suggested that the first selection step at the single hills stage did not negatively impact the genetic variability of the target traits implying that the traits assessed in the earlier stages were not correlated with the traits influencing market success. Our results can be used as base for further simulation studies and, thus, help to optimize the resource allocation in breeding programs.

Professional Skepticism and Financial Statement Fraud Detection among Malaysian and Indonesian Auditors: A Cross-Cultural Analysis

Recent financial scandals highlight flaws in auditor quality, signalling a decline in professional skepticism, particularly in regions like Malaysia and Indonesia with diverse regulatory frameworks, cultures, and environments. This study examines the relationship between six professional skepticism traits and fraud detection by comparing Malaysian and Indonesian auditor utilising a questionnaire based on Hurtt’s 30-item scale to assess auditors’ professional skepticism. A purposive samples of 187 Malaysian auditors (MIA members) and 173 Indonesian auditors (IAPI members) was surveyed. Partial Least Squares Multi-Group Analysis (PLS-MGA) revealed significant differences between the two countries in two key traits: interpersonal understanding and suspension of judgment which weakened (improve) Malaysian (Indonesian) auditors’ ability to detect fraud respectively. Indonesian auditors, on the other hand, may benefit from developing these traits to enhance fraud detection. The variance in skepticism traits is attributed to differences in professional environments, with Malaysian auditors primarily from Big 4 firms, while Indonesian auditors are predominantly from non-Big 4 firms. Targeted training for Indonesian auditors, focusing on interpersonal understanding and suspension of judgment, could enhance their fraud detection capabilities. This study contributes to the literature by identifying cultural and professional differences as key factors affecting skepticism, providing a basis for strategies to improve auditing practices in Indonesia.

Distinct network patterns emerge from Cartesian and XOR epistasis models: a comparative network science analysis

BackgroundEpistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, significantly contributes to the phenotypic variance of complex traits. Traditionally, epistasis has been modeled using the Cartesian epistatic model, a multiplicative approach based on standard statistical regression. However, a recent study investigating epistasis in obesity-related traits has identified potential limitations of the Cartesian epistatic model, revealing that it likely only detects a fraction of the genetic interactions occurring in natural systems. In contrast, the exclusive-or (XOR) epistatic model has shown promise in detecting a broader range of epistatic interactions and revealing more biologically relevant functions associated with interacting variants. To investigate whether the XOR epistatic model also forms distinct network structures compared to the Cartesian model, we applied network science to examine genetic interactions underlying body mass index (BMI) in rats (Rattus norvegicus).ResultsOur comparative analysis of XOR and Cartesian epistatic models in rats reveals distinct topological characteristics. The XOR model exhibits enhanced sensitivity to epistatic interactions between the network communities found in the Cartesian epistatic network, facilitating the identification of novel trait-related biological functions via community-based enrichment analysis. Additionally, the XOR network features triangle network motifs, indicative of higher-order epistatic interactions. This research also evaluates the impact of linkage disequilibrium (LD)-based edge pruning on network-based epistasis analysis, finding that LD-based edge pruning may lead to increased network fragmentation, which may hinder the effectiveness of network analysis for the investigation of epistasis. We confirmed through network permutation analysis that most XOR and Cartesian epistatic networks derived from the data display distinct structural properties compared to randomly shuffled networks.ConclusionsCollectively, these findings highlight the XOR model’s ability to uncover meaningful biological associations and higher-order epistasis derived from lower-order network topologies. The introduction of community-based enrichment analysis and motif-based epistatic discovery emphasize network science as a critical approach for advancing epistasis research and understanding complex genetic architectures.

Transfer Learning Estimation and Transferability of LNC and LMA Across Different Datasets

Leaf mass per area (LMA) and leaf nitrogen concentration (LNC) are both essential parameters in plant ecology, which can reflect the growth status of plants. The features of LMA and LNC can be captured using spectral reflectance in a remote sensing approach. While the relationships between spectra and leaf trait variance across different species with estimation performance are unclear, the development of assessment and transferable models to predicate LMA and LNC are prevented. Hence, we analyzed the variance of raw spectra and spectral data difference with four pretreated approaches (SG—Savitzky–Golay filter, SNV—Standard Normalized Variate, MSC—Multiplicative Scatter Correction analysis, and normalize), LMA, and LNC over six remote sensing datasets by a transfer component analysis (TCA) approach. Spectra combined with the Successive Projections Algorithm (SPA) were also presented to extract wavelengths with higher important coefficients to minimize the redundancy of datasets. The variance of normalized spectra between different datasets showed a minor degree of variance, and LNC spectra variance was decreased by the SPA. The results also showed that a smaller LMA and LNC variance is presented over different datasets when the trait values with higher distribution probabilities are close to each other. The LNC and LMA estimation performance in transfer models established by partial least squares regression (PLS), support vector regression (SVR), extreme gradient boosting (XGB), and random forest regression (RFR) algorithms across different datasets were employed, in which the RFR transfer models performed good prediction results. The relationships between spectra and leaf trait variance and estimation performance in RFR transfer models over different datasets were evaluated. LMA distance has a significant influence on estimation performance in the transfer model, and the variance of spectra with all pretreated approaches showed a very significant effect on LNC accession performance. Furthermore, we proposed a weight coefficient of spectral data updating combined with the TCA and RFR approach (WDT-RFR) transfer model to improve transferability between datasets and promote estimation performance in the transfer model. Compared to the RFR transfer model using spectra without updating, the root mean square error (RMSE) of the WDT-RFR transfer model with 5% samples transferred to estimate LMA and LNC increased by 7.9% and 4.8% on average, respectively. The estimation results showed that our transfer model showed a superior estimation performance.

Illusory traits: Wrong but sometimes useful.

Psychological measures frequently show trait-like properties, and the ontological status of stable psychological traits has been discussed for decades. We argue that these properties can emerge from causal dynamics of time-varying processes, which are omitted from the analysis model, potentially leading to the estimation of traits that are, at least in part, illusory. Theories positing the importance of a large set of dynamic psychological causes across development are consistent with the existence of illusory traits. We show via simulation that even a linear system with many processes can generate a covariance matrix with trait-like properties. We then attempt to examine how illusory traits affect our conclusions drawn from a common statistical model, which assumes stable traits to analyze longitudinal panel data-a random-intercept cross-lagged panel model (RI-CLPM). We find that the RI-CLPM sometimes falsely detects the existence of traits in the presence of omitted processes, even when the data-generating model does not include any traits. However, in this scenario, the RI-CLPM estimates less causally biased autoregressive and cross-lagged effects than an analysis model, which does not assume traits (i.e., the cross-lagged panel model). The results indicate that the detection of trait variance should not be inferred as strong evidence for the existence of time-invariant trait causes. On the other hand, even when traits are illusory, statistical models assuming stable traits may sometimes be useful for causal inference. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Association Between Reduced Upstream Riparian Forest Cover and Impaired Development of Embryos From Eastern Hellbenders (Cryptobranchus alleganiensis)

ABSTRACT Populations of the Eastern Hellbender, (a large‐bodied, fully aquatic salamander) inhabiting stream reaches with low catchment‐wide riparian forest cover upstream, have experienced population declines and a shift toward a geriatric population age structure. These population declines and demographic shifts might be attributed to reduced embryo viability. Reduced egg quality/viability could negatively affect recruitment and has also been known to trigger filial cannibalism in other species. Therefore, we hypothesized that in comparison to high forest cover sites, hellbender eggs collected from low forest cover sites would have a greater incidence of developmental abnormalities and lower overall viability, and that this would predict whole‐clutch cannibalism by the attending male. We collected a subset of eggs (~20–35) from 99 clutches across sites with variable upstream riparian forest cover and reared these eggs through hatching in stream water under controlled laboratory conditions. At the same time, we monitored the fate of the remaining eggs from the same clutches in the field to document the frequency of whole‐clutch filial cannibalism. We found that eggs collected from sites with lower upstream forest cover had significantly shorter embryonic development times and produced a lower percentage of viable hatchlings (hatchlings with normal development times and morphology). The average modelled viability of hatchlings was 70% higher in sites with the highest forest cover compared to our sites with the lowest forest cover. In contrast to our predictions, we did not find evidence to suggest that egg viability in the lab predicted whole‐clutch cannibalism in the field. Although forest cover was a significant predictor of egg viability and underdevelopment, substantial variance in embryonic developmental traits was unaccounted for in our models suggesting that traits associated with adults (e.g., egg and/or sperm quality) may also play a role in determining developmental outcomes. Further experiments are needed to identify what factors (e.g., egg quality, water quality) disrupt the embryonic development of hellbenders as well as the proximate stimulus that causes adult male hellbenders to eat their young. Our results emphasise the importance of restoring and protecting riparian forest cover to conserve sensitive stream species.

Variation in flower morphology associated with higher bee diversity in urban green spaces.

Urbanization is a leading threat to biodiversity, but scientifically informed management of urban ecosystems can mitigate negative impacts. For wild bees, which are declining worldwide, careful consideration of flower choice in public and private green spaces could help preserve their diversity. While floral density and species richness are both linked to wild bee diversity, the mechanisms underlying these relationships are not fully understood. Here, we tested two hypotheses relating the influence of floral trait composition to bee species richness, which we have termed the within-trait diversity and optimal floral trait hypotheses. Specifically, we assessed whether variation in bee richness relates to variation in the weighted variance (trait diversity) and mean (optimal trait) of floral traits observed in urban green spaces across the city of Montreal, Canada. Our analyses focused on two floral traits relating to pollinator feeding success: nectar sugar concentration and corolla length. After accounting for variation in floral density among sites, bee richness was positively related to community-weighted variance in corolla length, supporting the within-trait diversity hypothesis. These findings suggest that management practices that increase the diversity of flower morphologies in urban green spaces can promote the persistence of wild bee communities in cities.

Eels' individual migratory behavior stems from a complex syndrome involving cognition, behavior, physiology, and life history.

Variability within species is key for adaptability and biological evolution. To understand individualities in the context of animal movement, we focused on one of the most remarkable migrations-the journey of the endangered European eel from their birthplace in the Sargasso Sea to freshwater environments. Laboratory observations unveiled a continuum of diverse phenotypes of migrating eels: Some displayed a heightened tendency to swim against a constant water flow, while others a greater propensity to climb obstacles. Looking for the biological underpinnings of this migratory diversity, we characterized the eels' individual differences in traits of four key domains: life history, physiology, behavior, and cognition, among which we found significant variance and interconnectedness. Upon reducing this variance to its primary multivariate axes, we found that these predict the migratory types. Eels with 1) low exploration, high activity, low boldness, and high lateralization; 2) strong lateralization, enhanced quantitative abilities, short problem-solving time, high boldness, and low growth rates; or 3) enhanced problem-solving, reduced spatial learning, high cognitive flexibility, and shorter time to solve the cognitive tasks were more likely to display the climbing migratory type. Field sampling revealed how specific traits' combinations seemed to influence the distribution of eels once they begin to settle in the freshwater environment. Our study underscores the impressive diversity of individuals during this critical migration, emphasizing an intrinsic connection to multidomain trait variance. Preserving this diversity becomes paramount, as it likely contributes to the resilience and adaptability of endangered migratory species.

Abstract 4138407: Exome-wide association study of over a million individuals identified effector genes and variants for blood lipids

Background: Inherited hypercholesterolemia is an important Mendelian condition associated with early onset myocardial infarction. However, current genetic diagnostic criteria only partially explain severe clinical hypercholesterolemia. Furthermore, current pathogenicity categorizations do not consistently inform prognosis, but quantification is limited by scarcity of such rare variants. Aim: Exploiting populations scale genotype/phenotype data, to determine the architecture of the genetic variants associating with blood lipid levels with quantitative assessments. Methods: Here, we discovered and characterized rare coding alleles contributing to genetic dyslipidemia, a principal risk for coronary artery disease, among over a million individuals combining two large contemporary genetic datasets (Million Veteran Program N = 626,554 and UK Biobank, N = 431,026). Results: Testing 2,869,269 rare (minor allele frequency < 1%), coding variants, we identified 793 exome-wide significant associations ( P < 4.4 × 10 -11 ), including 8 previously undiscovered loci. Associated alleles are highly enriched in functional variant classes; high confidence predicted loss of function, deleterious missense, and cryptic splicing, showed significant and recessive associations, had similar effects across populations. Overall, rare variants contributed to additional > 16% of genetic trait variances on top of common genetic variants. The effect estimates of associated signals confirmed and refined curated pathogenic database, highlighted by the identification of African or South Asian specific pathogenic alleles previously controversial in its pathogenicity. Conclusion: This study provides resources and insights for understanding causal mechanisms and quantifying the expressivity of rare coding alleles for blood lipids by large scale sequencing study. Furthermore, identifying and characterizing population enriched variants, our study underscored the importance of multi-population rare variant study.

Which ecological factors influence the level of intraspecific diversity within post‐glacial fishes? A case study using Coregonus and Salvelinus

Abstract While it is likely that ecological context is important, the factors that facilitate and maintain variable levels of intraspecific diversity in Salmonidae fishes across environments remain unclear. Using a meta‐analysis of sympatric ecotype assemblages from two salmonid genera—Salvelinus and Coregonus—we evaluated the importance of ecological factors determining the number of sympatric ecotypes (i.e. 2–7) and the level of trait divergence between them. We found that ecotype diversity increased with lake depth and surface area in both Coregonus and Salvelinus. Further, diversity in Coregonus increased with latitude, while the number of ecotypes in Salvelinus assemblages was linked to climatic seasonality. In comparing the two genera, we found elevated divergence in traits related to ontogeny (i.e. age and body shape) in Salvelinus and gill raker count in Coregonus. Trait divergence in life history traits (i.e. age and body length) in Salvelinus increased with seasonality, whereas contrasting relationships of latitude to body length and gill rakers were found in Coregonus. We also found similar levels of divergence in trait variance in the two genera, suggesting that among‐ecotype differences in phenotypic variability are not more common in one genus than the other. Overall, ecosystem characteristics, including lake location, climate and morphometry, are clearly important for where these genera have diversified, but the variables that are most closely associated with intraspecific diversity differ between the two genera studied and depend on whether diversity is quantified using number of ecotypes or trait divergence.

Predicting Physics Students’ Academic Performance: The Impact of Attitude, Self-Efficacy, and Personality Traits

The study examined the predictive impact of attitude, self-efficacy, and personality traits on the academic performance of high school students in physics. A multistage random sampling technique was used to select 210 research subjects between the ages of 16 and 22 (SD=1.80) from three high schools in the Berekum East Municipality of Ghana. Three standardised questionnaires as primary data sources, namely: Attitude Toward Physics Learning (ATPL), Physics Learning Self-Efficacy (PLSE), and Students’ Personality Trait Inventory (SPTI), and additionally, Students’ Physics Mock Examinations scores as a secondary data source, were used for data collection and analysis through multiple regression. Data collected from respondents were compiled, sorted, edited and coded into the Statistical Package for the Social Sciences (SPSS) version 23. The results revealed a joint impact of 21.100% of the variance in attitude, self-efficacy, and personality traits in predicting the academic performance of students in physics. Furthermore, the multiple regression analysis revealed that student attitudes, self-efficacy, and personality traits significantly impact student academic performance in physics. The analysis further revealed that students’ personality traits best explained students’ academic performance in Physics (β = 0.232, t = 3.423; p < 0.050), followed by self-efficacy (β = 0.192, t = 2.738; p < 0.050), and attitude (β = 0.121, t = 2.011; p < 0.05). Based on the results obtained, the study recommended that physics students in the Berekum East Municipality’s attitudes, self-efficacy, and personality traits should be given greater attention by physics teachers, and other stakeholders because of their positive impact on students’ academic performance in physics.

Disentangling the shared and unique aspects of clinical and subclinical socially aversive traits relevant for interpersonal personality dysfunction.

Most socially and/or ethically aversive traits from clinical and broad personality research overlap to a large degree. For the latter, however, the association with interpersonal personality dysfunction (IPD) is understudied. Moreover, it is also unclear to what extent the associations of aversive traits with IPD are due to their shared versus unique aspects. We investigate these questions based on a theoretical framework that comprehensively describes the shared variance of all aversive traits. To this end, we concurrently measured 20 aversive traits from clinical and broad personality research together with their common core. Results from five studies (four of them preregistered, total N = 4,847) revealed that all aversive traits are associated with IPD and that most do so (only) due to their common core. Only three traits offered additional aspects beyond the common core relevant for IPD. The results inform debates about whether to include more traits in the Alternative Model for Personality Disorders. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Quantitative omnigenic model discovers interpretable genome-wide associations

As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of nontranscriptional (including environmental) effects.

Genetic Analysis of Combining Ability and Gene Action in Sunflower (Helianthus annuus L.) Hybrids through Line × Tester Design

This study was conducted to evaluate the combining ability of six cytoplasmic male sterile (CMS) lines and nine testers using the line × tester method. Biometrical data were recorded for ten agronomic traits: days to 50% flowering, days to maturity, plant height, head diameter, seed filling percentage, 100-seed weight, volume weight, hull content, oil content, and seed yield per plant. The results indicated that specific combining ability (SCA) variance was higher than general combining ability (GCA) variance for most traits, except for days to 50 percent flowering and seed filling percentage, suggesting that non-additive gene action predominantly governed the inheritance of these traits. The GCA of parents did not always align with their per se performance, indicating that GCA should be the primary criterion for selecting parents in breeding programs. Three CMS lines, CMS-103A, CMS-10A and CMS-112A, along with testers EC-75717, EC-502036, EC-279309-1 and R-271-1, were identified as good general combiners for seed yield and yield-contributing traits. Among the hybrids, CMS-243A × EC-279309-1, CMS-243A × EC-75717 and CMS-103A × EC-75717 exhibited the highest SCA effects for seed yield per plant. These findings underscore the importance of selecting superior general combiners for traits like oil content and seed yield to develop high-yielding sunflower hybrids.

Machine learning-driven polygenic risk scores for bipolar disorder, depression, and panic disorder

Polygenic Risk Score (PRS) is a computational tech- nique that uses various genomic data to simultaneously analyze an individ- ual’s genetic risk for particular illnesses or traits. However, the traditional PRS computation has a few weaknesses, including its limited capacity to account for just a portion of trait variance, susceptibility to overfitting, and insufficient ability to discriminate among the larger population. Machine Learning (ML) methods offer a promising alternative to the traditional method by avoiding the problem of overfitting and improving accuracy. This study aims to develop an ML model for improved PRS calculation. We used the summary statistics for three mentals diseases, bipolar, depression, and panic disorder, from the Psychiatric Genomics Consortium (PGC) as a disease reference. We also obtained actual genotype data of individuals from OpenSNP, which includes both case and control samples. This data is used for predicting scores. The suggested approach, called Polygenic Risk Score Neural Network (PRSNN), calculates the PRS using weight vectors that estimate the relevance of each single nucleotide polymorphism (SNP) with a particular phenotype by deep learning model as an alternative to the traditional method. This study aims to develop a machine learning model, called PRSNN, for improved calculation of Polygenic Risk Scores (PRS). The PRSNN method outperforms the conventional method in identifying individuals at risk of mental disease. A novel deep-learning approach, named as PRSNN, is proposed for generating PRSs. The results demonstrate that it outperforms the traditional method of computing PRS for complex diseases. Further upgrades for this tool are required to overcome the current limitations, including lack of validation with external data from different ancestries, which may limit the applicability of the PRSNN method across diverse populations, and the small sample size, which may affect the results.

Community Trait Variation Drives Selection on Species Diversity Through Feedback With Predator Density.

Identifying the processes underlying community assembly and dynamics remains a central goal in ecology. Although much research has been devoted to analyzing how environments affect species diversity, fewer studies have resolved the link between the fundamental process of ecological selection and species diversity. It has been suggested that identifying ecological selection by estimating changes in community-weighted variance (CWV) and mean (CWM) of functional traits may help to identify more general rules of community assembly. Here, we asked whether and how selection by predation and competition affect species diversity, and how this is determined by the initial CWV and CWM for traits governing species interactions, as in our case: Competitiveness and defense against a predator. We tracked experimental five-species phytoplankton communities in the presence and absence of a rotifer predator over time. We manipulated the initial community composition so that communities shared at least three of the five species but differed in CWV and CWM for defense against predation. We found that species diversity was highest with higher initial trait distributions and that temporal changes in diversity correlated with trait selection. The initial distributions determined the form of selection over time, with directional selection for defense and competitiveness, followed by reduced selection and an increase in niche availability when the initial trait distribution was low or high. For intermediate initial trait distributions, we observed directional selection in only one trait, followed by stabilizing selection. Differences and changes in selection for defense, competitiveness, and species diversity correlated with the changes in predator density over time. This suggests that the initial trait distribution determined species diversity through a feedback loop with changes in selection on traits and predator density. Overall, our study shows that identifying ecological selection on functional traits can provide a mechanistic understanding of community assembly.

Genome-wide association analysis of flowering date in a collection of cultivated olive tree.

Flowering date in perennial fruit trees is an important trait for fruit production. Depending on the winter and spring temperatures, flowering of olive may be advanced, delayed, or even suppressed. Deciphering the genetic control of flowering date is thus key to help selecting cultivars better adapted to the current climate context. Here, we investigated the genetic determinism of full flowering date stage in cultivated olive based on capture sequencing data of 318 genotypes from the worldwide olive germplasm bank of Marrakech, Morocco. The genetic structure of this collection was organized in three clusters that were broadly attributed to eastern, central, and western Mediterranean regions, based on the presumed origin of genotypes. Flowering dates, collected over 7 years, were used to estimate the genotypic best linear unbiased predictors, which were then analyzed in a genome-wide association study. Loci with small effects were significantly associated with the studied trait, by either a single- or a multi-locus approach. The three most robust loci were located on chromosomes 01 and 04, and on a scaffold, and explained 7.1%, 6.2%, and 6.5% of the trait variance, respectively. A significantly higher accuracy in the best linear unbiased predictors of flowering date prediction was reported with Ridge- compared to LASSO-based genomic prediction model. Along with genomic association results, this suggests a complex polygenic determinism of flowering date, as seen in many other fruit perennials. These results and the screening of associated regions for candidate genes open perspectives for further studies and breeding programs targeting flowering date.