Trait Variance Research Articles

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.

Importance and variability of the paternal component in sow reproductive traits.

Reproductive traits are an integral part of the goals of the breeding programs that contribute to the economic success of production. Reproductive phenotypes such as litter size, number of piglets born alive, or litter weight at birth are mainly attributed to females. Thus, the maternal components can be found by default in quantitative genetics' animal models. Still, paternal contribution to variance components should not be discarded. In this review, we indicate the importance of paternal effects in pig breeding by describing both the biology and genetics of boars' traits, the use of (non-)genetic service sire effects in quantitative genetic models for traits measured on females, and genes involved in male reproduction. We start by describing the important biological traits of boars that have the most important effect on their reproductive abilities, i.e., sexual maturity, sperm quality, and testes parameters. Then we move to the possible environmental effects that could affect those traits of boars (e.g., feed, temperature). The main part of the review in detail describes the genetics of boars' reproductive traits (i.e., heritability) and their direct effect on reproductive traits of females (i.e., genetic correlations). We then move to the use of both genetic and non-genetic service sire effects in quantitative models estimated as their percentage in the total variance of traits, which vary depending on the breed from 1 to 4.5% or from 1 to 2%, respectively. Finally, we focus on the description of candidate genes and confirmed mutations affecting male reproduction success: IGF2, Tgm8, ESR1, ZSWIM7, and ELMO1. In conclusion, the observed variance of paternal effects in female reproduction traits might come from various attributes of boars including biological and genetic aspects. Those attributes of boars should not be neglected as they contribute to the success of female reproductive traits.

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.

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).

Estimation of Genetic Parameters for Milk Production Rate and Its Stability in Holstein Population.

Milk production rate (MPR) refers to the rate of milk secretion per hour (kg/h), calculated from the harvested milk yield and milking interval, and it is considered an appropriate measure to evaluate the production potential of cows. The objective of this study was to estimate the phenotypic and genetic parameters of milk production rate traits. In this study, the milking records of 4760 Holstein cows were collected, and four milk yield traits and six milk production rate traits were defined. The MIXED procedure was used to detect the impacts of non-genetic effects on milk yield and milk production rate traits, including parity, measured season and lactation stage. Variance and covariance components for milk yield and milk production rate traits were estimated using a univariate linear repeatability model. Parity, measurement season and lactation stage had significant effects (p < 0.01) on milk yield, milk production rate and its stability. Milk yield and milk production traits had high heritability, and ranged from 0.25 to 0.39. The stability of milk production rate had low heritability (0.04~0.05). Milk production rate is beneficial for the devolving novel trait in dairy breeding and provides new insights for herd management and genetic selection of production performance of dairy cattle.

121 Copy number variation: From mapping to variance components in Holsteins

Abstract Copy number variations (CNVs) modify transcription levels mainly through changing genes and regulatory elements dosages. Hence, CNVs can contribute to the additive genetic variance of quantitative traits, which might not be fully captured by SNPs. Here, CNVs were mapped from 3,601 Holsteins and their contribution to complex traits was explored by i) quantitative trait loci (QTL) enrichment analysis, and ii) variance component analysis for metritis (1 = disease, 0 = no disease). The 4,113 CNVs derived from high-density (777k) genotypic information were compiled into 1,184 CNV regions (CNVRs) overlapping 7,832 QTLs reported in dairy cattle and available in the AnimalQTLdb. Fisher’s exact test was applied to perform the QTL enrichment analysis followed by False Discovery Rate (FDR) correction. Then, QTLs overlapping CNVRs were enriched for traits when the corrected P-value (PFDR) was lower than 0.05. For variance components estimation, genomic information from all CNVs loci was used to build the CNV-derived genomic relationship matrix (CNV_GRM). CNV loci showing double deletions, single deletions, and normal state in the population were coded as 0, 1, and 2, respectively, whereas CNV loci presenting normal state, single duplications, and double duplications were coded as 0, 1, and 2, respectively. Given that some CNVs presented both deletions and duplications at the same position in the population, these mixed-CNVs were treated as two distinct CNVs loci, one to represent deletions and the other to represent duplications. The variance components for metritis were estimated by fitting either only a SNP-derived GRM (SNP_GRM) or jointly SNP_GRM and CNV_GRM considering 3,272 cows in models that included the female category and farm-year-season as fixed effects. QTLs overlapped with CNVRs were enriched for milk (e.g., milk fat yield, milk yield, and milk protein percentage), reproduction (e.g., calving ease, non-return rate, and heat intensity), exterior (e.g., teat length, udder depth, and udder height), production (e.g., length of productive life, lifetime profit index, and net merit), and health traits (e.g., immune globulin G level, somatic cell, and bovine respiratory susceptibility). Regarding variance components, copy number variations were able to capture a fraction of the additive genetic variance not captured by SNPs alone, increasing the heritability estimate for metritis. In summary, our findings suggest that CNVs overlap QTLs associated with economically important traits more than only by chance and CNVs can capture additional additive genetic variance not fully captured by SNPs alone.

What makes each of us unique? The nine-banded armadillo as a model to study individuality

The human brain is the foundation of our identity as a species and as individuals. It is where our unique sensations, emotions, and thoughts arise. The same way no two individuals are alike, no two brains are identical. Understanding the expression of inter-individual differences in brain and behavior and their underlying biological mechanisms can profoundly influence neuroscience and the science of individuality. Here, we argue that the nine-banded armadillo is a unique organism for the study of how inter-individual differences are expressed in the mammalian brain. Our argument is based on the fascinating reproductive biology of armadillos, the only known mammals that always generate offspring that are genetic clones, and on how this characteristic can help understand the complex interplay between genetic, environmental, and stochastic factors in the biology of individuality. We will first review the sources of variance in brain-related traits and behavior, then the biology of armadillos, and finally how they can aid in understanding the origins of variance in brain structure and function. Finally, we will provide an overview of the type of studies that can be performed using armadillos and how these studies can advance the science of individuality.

Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program

The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.

The Effect of Temperature Variability on Biological Responses of Ectothermic Animals-A Meta-Analysis.

Climate change is altering temperature means and variation, and both need to be considered in predictions underpinning conservation. However, there is no consensus in the literature regarding the effects of temperature fluctuations on biological functions. Fluctuations may affect biological responses because of inequalities from non-linear responses, endocrine regulation or exposure to damaging temperatures. Here we establish the current state of knowledge of how temperature fluctuations impact biological responses within individuals and populations compared to constant temperatures with the same mean. We conducted a meta-analysis of 143 studies on ectothermic animals (1492 effect sizes, 118 species). In this study, 89% of effect sizes were derived from diel cycles, but there were no significant differences between diel cycles and shorter (<8 h) or longer (>48 h) cycles in their effect on biological responses. We show that temperature fluctuations have little effect overall on trait mean and variance. Nonetheless, temperature fluctuations can be stressful: fluctuations increased 'gene expression' in aquatic animals, which was driven mainly by increased hsp70. Fluctuating temperatures also decreased longevity, and increased amplitudes had negative effects on population responses in aquatic organisms. We conclude that mean temperatures and extreme events such as heat waves are important to consider, but regular (particularly diel) temperature fluctuations are less so.

The Inventory of Callous-Unemotional Traits (ICU) self-report version: Factor structure, measurement invariance, and predictive validity in justice-involved male adolescents.

The Inventory of Callous-Unemotional Traits (ICU) is a widely used measure of callous-unemotional (CU) traits that may aid in the assessment of the diagnostic specifier "with limited prosocial emotions," which has been added to diagnostic criteria for conduct disorder. Though there is substantial support for use of the ICU total score, the scale's factor structure has been highly debated. Inconsistencies in past factor analyses may be largely attributed to failure to control for method variance due to item wording (i.e., half of the items being worded in the callous direction and half worded in the prosocial direction). Thus, the present study used a multitrait-multimethod confirmatory factor analytic approach that models both trait and method variance to test the factor structure of the ICU self-report in a clinically relevant, high-risk sample of justice-involved male adolescents (N = 1,216). When comparing the fit of empirical and theoretical models, goodness of fit indices (χ² = 1105.877, df = 190, root-mean-square error of approximation = .063, comparative fit index = .916, Tucker-Lewis index = .878, standardized root-mean-square residual = .051) provided support for a hierarchical four-factor model (i.e., one overarching callous-unemotional factor, four latent trait factors) when accounting for method variance (i.e., covarying positively worded items). This factor structure is consistent with the way the ICU was constructed and with criteria for the limited prosocial emotions specifier. In addition, measurement invariance of this factor structure across age, race, and ethnicity was supported, and the predictive validity of the ICU was supported across these demographic groups in predicting self-reported antisocial behavior and rearrests over a 5-year period following an adolescent's first arrest. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

How much trait variance is captured in autobiographical memory ratings?

AbstractThis study examined the trait‐variance in autobiographical memory recollection (AMR). Participants (N = 397) provided ratings on eight autobiographical memories elicited using cue words. Multilevel random coefficients modeling revealed that 43% of the variance in memory ratings was due to individual differences, while the remaining 57% was related to other factors. Scores on the Brief Autobiographical Recollection Test (Brief ART; Berntsen et al.), a measure of metacognitive evaluations of autobiographical memory, were significantly correlated with mean memory ratings. However, they only explained 34% of the variance in mean ratings, and 15% of the variance in the ratings of individual memories, suggesting that metacognitive evaluations do not fully coincide with actual memory experiences. These findings suggest that AMR is a complex phenomenon influenced by both stable individual differences and situational factors. Multilevel modeling provides a rigorous approach to gaining a more nuanced understanding of AMR by disentangling various sources of variance.

A scalable adaptive quadratic kernel method for interpretable epistasis analysis in complex traits.

Our knowledge of the contribution of genetic interactions (epistasis) to variation in human complex traits remains limited, partly due to the lack of efficient, powerful, and interpretable algorithms to detect interactions. Recently proposed approaches for set-based association tests show promise in improving the power to detect epistasis by examining the aggregated effects of multiple variants. Nevertheless, these methods either do not scale to large Biobank data sets or lack interpretability. We propose QuadKAST, a scalable algorithm focused on testing pairwise interaction effects (quadratic effects) within small to medium-sized sets of genetic variants (window size ≤100) on a trait and provide quantified interpretation of these effects. Comprehensive simulations show that QuadKAST is well-calibrated. Additionally, QuadKAST is highly sensitive in detecting loci with epistatic signals and accurate in its estimation of quadratic effects. We applied QuadKAST to 52 quantitative phenotypes measured in ≈300,000 unrelated white British individuals in the UK Biobank to test for quadratic effects within each of 9515 protein-coding genes. We detect 32 trait-gene pairs across 17 traits and 29 genes that demonstrate statistically significant signals of quadratic effects (accounting for the number of genes and traits tested). Across these trait-gene pairs, the proportion of trait variance explained by quadratic effects is comparable to additive effects, with five pairs having a ratio >1. Our method enables the detailed investigation of epistasis on a large scale, offering new insights into its role and importance.

Accounting for cellular-level variation in lysis: implications for virus-host dynamics.

Viral impacts on microbial populations depend on interaction phenotypes-including viral traits spanning the adsorption rate, latent period, and burst size. The latent period is a key viral trait in lytic infections. Defined as the time from viral adsorption to viral progeny release, the latent period of bacteriophage is conventionally inferred via one-step growth curves in which the accumulation of free virus is measured over time in a population of infected cells. Developed more than 80 years ago, one-step growth curves do not account for cellular-level variability in the timing of lysis, potentially biasing inference of viral traits. Here, we use nonlinear dynamical models to understand how individual-level variation of the latent period impacts virus-host dynamics. Our modeling approach shows that inference of the latent period via one-step growth curves is systematically biased-generating estimates of shorter latent periods than the underlying population-level mean. The bias arises because variability in lysis timing at the cellular level leads to a fraction of early burst events, which are interpreted, artefactually, as an earlier mean time of viral release. We develop a computational framework to estimate latent period variability from joint measurements of host and free virus populations. Our computational framework recovers both the mean and variance of the latent period within simulated infections including realistic measurement noise. This work suggests that reframing the latent period as a distribution to account for variability in the population will improve the study of viral traits and their role in shaping microbial populations.IMPORTANCEQuantifying viral traits-including the adsorption rate, burst size, and latent period-is critical to characterize viral infection dynamics and develop predictive models of viral impacts across scales from cells to ecosystems. Here, we revisit the gold standard of viral trait estimation-the one-step growth curve-to assess the extent to which assumptions at the core of viral infection dynamics lead to ongoing and systematic biases in inferences of viral traits. We show that latent period estimates obtained via one-step growth curves systematically underestimate the mean latent period and, in turn, overestimate the rate of viral killing at population scales. By explicitly incorporating trait variability into a dynamical inference framework that leverages both virus and host time series, we provide a practical route to improve estimates of the mean and variance of viral traits across diverse virus-microbe systems.

Gender and Specialty Influence on Narcissism in University Students

Background: Narcissistic personality traits have garnered increasing attention in psychological research, particularly within academic environments where student behavior and performance are critical. Understanding these traits and their influencing factors is essential for improving student well-being and academic outcomes. Specific Background: The impact of gender and field of study on narcissistic traits among university students remains underexplored. Kernberg's (1970) theoretical framework provides a basis for investigating these traits through empirical measures. Knowledge Gap: While previous studies have examined narcissism broadly, there is limited research focusing specifically on how gender and academic specialization affect narcissistic traits among university students. Aims: This study aimed to identify narcissistic personality traits among university students and examine variations based on gender and field of study using a newly developed Narcissistic Personality Scale. Results: The research, involving 376 students from the University of Basra, utilized a 35-item scale validated through rigorous psychometric testing. The results indicated that the average narcissism score was significantly higher than the hypothesized mean. Male students exhibited higher levels of narcissism compared to female students, and humanities students scored higher than their scientific counterparts. Novelty: This study provides new insights into the prevalence and variance of narcissistic traits among university students, highlighting significant differences related to gender and academic specialization. Implications: The findings underscore the need for university programs to address narcissistic traits and their impact on student behavior and academic performance. Future research should investigate the longitudinal effects of narcissism and evaluate interventions aimed at reducing these traits. Highlights: Gender differences: males show higher narcissism. Humanities students exhibit more narcissistic traits. Calls for targeted university interventions. Keywords: narcissistic personality, university students, gender differences, academic specialization, psychometric scale

Within-population variation in an invasive fish' sociability when associating with conspecifics or heterospecifics.

Behavioural traits are key to promote invasion success because they are easier to adjust to changing environmental conditions than morphological or life history traits. Often, research has overlooked variance in behavioural traits within populations or has assumed it to be mere noise. However, a recent focus towards individual variation of behaviour of successful invaders has revealed new and more profound insights into the invasion process. Behavioural variation within a population could lead to more successful invasions, as they include individuals with diverse behaviours, which ensures at least some individuals could be able to cope with changing conditions. The aim of this research was to examine if invasive guppies (Poecilia reticulata) present within-population differences in their sociability (time spent associating with a shoal) when interacting with conspecifics or heterospecifics. Guppies presented significant differences in their individual tendencies to associate with conspecific or heterospecific shoals. There were among-individual differences in the time spent shoaling with conspecifics versus heterospecifics, where most individuals did not differ in their sociability with conspecifics or heterospecifics, and only 22% of individuals presented a higher tendency to associate with conspecifics. Our results are the first to show individual differences in fish' tendencies to associate with heterospecifics among individuals of the same population and rearing conditions. Given that associations with heterospecific natives have been found to be as beneficial as associations with conspecifics for invaders, our results contribute to the understanding of mechanisms behind heterospecific sociability between natives and invaders.

Endurance training and physiological variables: effects on sub-elite volleyball players

Volleyball is perceived as a physically demanding sport that involves rapid changes in speed, deceleration, and abrupt alterations in movement direction. It has been emphasized that physiological attributes such as VO2max, PIF, PEF, and FVC play a crucial role in the athletic performance of individuals participating in dynamic sports, aiding them in effectively managing critical situations. Hence, the aim of this research was to evaluate the impact of endurance training on physiological parameters among volleyball athletes. A randomized controlled trial was carried out with 24 male players (mean age 21,58; SD±2,46 years). The assessment of physiological characteristics involved the utilization of a dry spirometer for VO2max measurement and the Multistage Fitness Test for assessing PIF, PEF, and FVC. A two-way Anova analysis was executed to ascertain any disparities in physiological parameters from pre-test to post-test. The results indicated notable variances in physiological traits (VO2max, PIF, PEF, and FVC) among volleyball participants following the implementation of the Tabata intervention regimen. These metrics are instrumental in enhancing physiological capabilities to attain superior athletic performances. When designing training regimens, it is imperative to consider physiological functional elements aligned with the specific demands of the sport. Key words. VO2max; physiology; sport; exercise; Tabata training.

Environment found to explain the largest variance in physical and compositional traits in malting barley grain.

Starch is the most abundant constituent (dry weight) in the barley endosperm, followed by protein. Variability of compositional and potentially related physical traits due to genotype and environment can have important implications for the malting and brewing industry. This was the first study to assess the effects of genotype, environment, and their interaction (G × E) on endosperm texture, protein content, and starch traits corresponding to granule size, gelatinization, content, and composition, using a multi-environment variety trial in California, USA. Overall, environment explained the largest variance for all traits (ranging from 23.2% to 76.5%), except the endosperm texture traits wherein the G × E term explained the largest variance (45.0-86.5%). Our unique method to quantify the proportion of fine and coarse milled barley particles using laser diffraction showed a binomial distribution of endosperm texture. The number of small starch granules varied significantly (P-value < 0.05) across genotypes and environments. We observed negative correlations between total protein content and each of enthalpy (-0.70), total starch content (-0.54), and difference between offset and onset gelatinization temperature (-0.52). Furthermore, amylose to amylopectin ratio was positively correlated to volume of small starch granules (0.36). Our findings indicate that environment played a larger role in influencing the majority of starch-related physical and compositional traits. In contrast, variance in endosperm texture was largely explained by G × E. Maltsters would benefit from accounting for environmental contributions in addition to solely genotype when making sourcing decisions, especially with regards to total protein, total starch, enthalpy, and difference between offset and onset gelatinization temperature. © 2024 Society of Chemical Industry.

The core tendencies underlying prosocial behavior: Testinga person-situation framework.

According to a recently proposed theoretical framework, different personality traits should explain pro-social behavior in different situations. We empirically tested the key proposition of this framework that each of four "coretendencies" (i.e., the shared variance of related traits) specifically predicts pro-social behavior in the presence of a different situational affordance. We used a large-scale dataset (N = 2479) including measures of various personality traits and six incentivized economic games assessing pro-social behavior in different social situations. Using bifactor modeling, we extracted four latent core tendencies and tested their predictive validity for pro-social behavior. We found mixed support for the theoretically derived, preregistered hypotheses. The core tendency of beliefs about others' pro-sociality predicted pro-social behavior in both games involving dependence under uncertainty, as expected. Unconditional concern for others' welfare predicted pro-social behavior in only one of two games providing a possibility for exploitation. For conditional concern for others' welfare and self-regulation, in turn, evidence relating them to pro-social behavior in the presence of a possibility for reciprocity and temporal conflict was relatively weak. Different features of social situations may activate different personality traits to influence pro-social behavior, but more research is needed to fully understand these person-situation interactions.