Trait Model Research Articles

Validation of the Persian Version of the ICD-11 Compatible Personality Inventory for DSM-5- Brief Form Plus, Modified.

Clinical assessment increasingly emphasizes six maladaptive domains of the DSM-5 and ICD-11 trait models, including negative affectivity, detachment, antagonism/dissociality, disinhibition, psychoticism, and anankastia. The present study aimed to validate the Persian version of the ICD-11 compatible Personality Inventory for DSM-5-Brief Form Plus, Modified (PID5BF + M). Data from a mixed sample including 1,615 adults (community N = 1,476 and outpatient N = 139) were used to assess the latent structure, congruence coefficients, reliability, convergent validity, and criterion validity of the PID5BF + M. The results supported the six-factor structure of the PID5BF + M whose traits are largely congruent with those from previous studies. The scale reliabilities were acceptable, and strong associations were observed with personality disorder-type symptom counts (r ranging from .15 to .59, all p < .001). PID5BF + M scales also distinguished clinical and non-clinical samples. The present results support the validity and utility of the PID5BF + M for personality psychopathology screening in the Iranian population.

A scenario for an evolutionary selection of ageing.

Signs of ageing become apparent only late in life, after organismal development is finalized. Ageing, most notably, decreases an individual's fitness. As such, it is most commonly perceived as a non-adaptive force of evolution and considered a by-product of natural selection. Building upon the evolutionarily conserved age-related Smurf phenotype, we propose a simple mathematical life-history trait model in which an organism is characterized by two core abilities: reproduction and homeostasis. Through the simulation of this model, we observe (1) the convergence of fertility's end with the onset of senescence, (2) the relative success of ageing populations, as compared to non-ageing populations, and (3) the enhanced evolvability (i.e. the generation of genetic variability) of ageing populations. In addition, we formally demonstrate the mathematical convergence observed in (1). We thus theorize that mechanisms that link the timing of fertility and ageing have been selected and fixed over evolutionary history, which, in turn, explains why ageing populations are more evolvable and therefore more successful. Broadly speaking, our work suggests that ageing is an adaptive force of evolution.

Supplemental Material for The Cross-Cultural Big Two: A Culturally Decentered Theoretical and Measurement Model for Personality Traits

Supplemental Material for The Cross-Cultural Big Two: A Culturally Decentered Theoretical and Measurement Model for Personality Traits

The effect of very preterm birth on the Five-Factor Model of personality traits: A meta-analysis of individual participant data

There is mixed evidence on personality differences among those born very preterm or with very low birth weight (VP/VLBW). This meta-analysis of individual participant data aimed to examine differences in personality traits between VP/VLBW ( n = 568) and term-born ( n = 1,060) adults, and the role of neonatal characteristics and neurosensory impairments in childhood, which have not been previously investigated. Six studies were identified from two research consortia and a systematic search of the literature (PubMed and Scopus); studies were eligible if they included VP/VLBW and term-born adults followed from birth and assessed personality using the Five-Factor Model. Risk of bias (Newcastle-Ottawa Scale) was generally not a concern apart from the use of self-reported measures and the rate of follow-up. Using a one-stage approach, VP/VLBW scored lower on extraversion and openness and higher on neuroticism and agreeableness than term-born participants after adjusting for sex and parental education. Within the VP/VLBW group, those with bronchopulmonary dysplasia scored lower on extraversion and higher on neuroticism, with similar findings after removing participants with neurosensory impairments. Altogether, these findings suggest that a proportion of the effect of VP/VLBW birth on personality may be attributed to neonatal morbidities and altered brain development, although other confounding factors require further research.

Genomic prediction of cereal crop architectural traits using models informed by gene regulatory circuitries from maize.

Plant architecture is a major determinant of planting density, which enhances productivity potential for crops per unit area. Genomic prediction is well positioned to expedite genetic gain of plant architectural traits since they are typically highly heritable. Additionally, the adaptation of genomic prediction models to query predictive abilities of markers tagging certain genomic regions could shed light on the genetic architecture of these traits. Here, we leveraged transcriptional networks from a prior study that contextually described developmental progression during tassel and leaf organogenesis in maize (Zea mays) to inform genomic prediction models for architectural traits. Since these developmental processes underlie tassel branching and leaf angle, 2 important agronomic architectural traits, we tested whether genes prioritized from these networks quantitatively contribute to the genetic architecture of these traits. We used genomic prediction models to evaluate the ability of markers in the vicinity of prioritized network genes to predict breeding values of tassel branching and leaf angle traits for 2 diversity panels in maize and diversity panels from sorghum (Sorghum bicolor) and rice (Oryza sativa). Predictive abilities of markers near these prioritized network genes were similar to those using whole-genome marker sets. Notably, markers near highly connected transcription factors from core network motifs in maize yielded predictive abilities that were significantly greater than expected by chance in not only maize but also closely related sorghum. We expect that these highly connected regulators are key drivers of architectural variation that are conserved across closely related cereal crop species.

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.

The Moderating Role of the Five-Factor Model of Personality in the Relationship between Job Demands/Resources and Work Engagement: An Online Cross-Sectional Study.

When organizations or managers utilize personality assessments for their workers, it is crucial to consider not only personality profiles but also the interaction between these profiles and the psychosocial environmental factors in the workplace. The present study aimed to examine the moderating effects of the five-factor model (FFM) of personality traits on the relationship between job demands/resources and work engagement (WE). A cross-sectional online survey was conducted between November and December 2022, targeting full-time workers in Japan. Data were collected from 1500 participants (757 men and 743 women). The survey included demographic variables, job demands and resources (job control, supervisor, and coworker support), WE, and the FFM. The primary statistical analysis was hierarchical regression analysis, which tested the interactions between job demands/resources and each personality trait. Four significant interactions were found: job demands and neuroticism, control and neuroticism, control and conscientiousness, and supervisor support and extraversion. High conscientiousness was associated with higher WE when job control was abundant. Moreover, low levels of both neuroticism and extraversion were linked to higher WE. The results suggest that managers can enhance WE by aligning workplace factors with employee personality traits. These insights can be applied to organizational staffing decisions.

The Effect of Personality Traits on Criminal Behaviour

The extensive investigation into criminal careers, coupled with the growing consensus around the Five-Factor Model of personality traits, has reaffirmed the significance of personality assessments as reliable indicators of criminal behaviour. Several research studies on specialised groups have determined that agreeableness, conscientiousness, neuroticism, and extraversion are associated with criminal behaviour. This study examines the relationship between the probability of committing a crime and various personality qualities, including Agreeableness, Extraversion, Conscientiousness, Openness, Neuroticism, and demographic variables associated with crime criminologists. The data was obtained by surveying 916 ordinary people and detainees in Vietnam. For the detainees, participants must be older than 18 years old, have been convicted of the crime of aggravated murder, and have followed at least five years of education. While ordinary people also must be older than 18 years old and do not have a history of crime records. The findings show that there is a different correlation between each personality trait and the probability of committing a crime. Besides, demographic factors also play a relatively insignificant impact in predicting criminal behaviour.

Transforming estimated breeding values from observed to probability scale: how to make categorical data analyses more efficient.

Threshold models are often used in genetic analysis of categorical data, such as calving ease. Solutions in the liability scale are easily transformed into probabilities; therefore, estimated breeding values are published as the probability of expressing the category of main interest and are the industry's gold standard because they are easy to interpret and use for selection. However, because threshold models involve non-linear equations and probability functions, implementing such a method is complex. Challenges include long computing time and convergence issues, intensified by including genomic data. Linear models are an alternative to overcome those challenges. Estimated breeding values computed using linear models are highly correlated (≥ 0.96) with those from threshold models; however, the lack of a transformation from the observed to the probability scale limits the use of linear models. The objective of this study was to propose transformations from observed to probability scale analogous to the transformation from liability to probability scale. We assessed computing time, peak memory use, correlations between estimated breeding values, and estimated genetic trends from linear and threshold models. With 11M animals in the pedigree and almost 965k genotyped animals, linear models were 5x faster to converge than threshold models (32 vs. 145h), and peak memory use was the same (189 GB). The transformations proposed provided highly correlated probabilities from linear and threshold models. Correlations between direct (maternal) estimated breeding values from linear and threshold models and transformed to probabilities were ≥ 0.99 (0.97) for all animals in the pedigree, sires with/without progeny records, or animals with phenotypic records; therefore, estimated genetic trends were analogous, suggesting no loss of genetic progress in breeding programs that would adopt linear instead of threshold models. Furthermore, linear models reduced computing time by five-fold compared to the threshold models; this enables weekly genetic evaluations and opens the possibility of using multi-trait models for categorical traits to improve selection effectiveness.

A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses.

Natural selection often acts on multiple traits simultaneously. For example, the virus HIV-1 faces pressure to evade host immunity while also preserving replicative fitness. While past work has studied selection during HIV-1 evolution, as in other examples where selection acts on multiple traits, it is challenging to quantitatively separate different contributions to fitness. This task is made more difficult because a single mutation can affect both immune escape and replication. Here, we develop an evolutionary model that disentangles the effects of escaping CD8+ T cell-mediated immunity, which we model as a binary trait, from other contributions to fitness. After validation in simulations, we applied this model to study within-host HIV-1 evolution in a clinical data set. We observed strong selection for immune escape, sometimes greatly exceeding past estimates, especially early in infection. Conservative estimates suggest that roughly half of HIV-1 fitness gains during the first months to years of infection can be attributed to T cell escape. Our approach is not limited to HIV-1 or viruses, and could be adapted to study the evolution of quantitative traits in other contexts.

Impact of coupled input data source-resolution and aggregation on contributions of high-yielding traits to simulated wheat yield

High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets. This study investigates the implications of this practice. The SIMPLACE modeling platform was applied using field, 1 km, 25 km, and 50 km input data resolution and sources, with 1881 combinations of three traits [radiation use efficiency (RUE), light extinction coefficient (K), and fruiting efficiency (FE)] for the period 2001–2010 across Germany. Simulations at the grid level were aggregated to the administrative units, enabling the quantification of the aggregation effect. The simulated yield increased by between 1.4 and 3.1 t ha− 1 with a maximum RUE trait value, compared to a control cultivar. No significant yield improvement (< 0.4 t ha− 1) was observed with increases in K and FE alone. Utilizing field-scale input data showed the greatest yield improvement per unit increment in RUE. Resolution of water related inputs (soil characteristics and precipitation) had a notably higher impact on simulated yield than of temperature. However, it did not alter the effects of high-yielding traits on yield. Simulated yields were only slightly affected by data aggregation for the different trait combinations. Warm-dry conditions diminished the benefits of high-yielding traits, suggesting that benefits from high-yielding traits depend on environments. The current findings emphasize the critical role of input data resolution and source in quantifying a large-scale impact of high-yielding traits.

Marker weighting improves single-step genomic prediction reliabilities of udder health traits in Nordic Red and Jersey dairy cattle populations

The standard single-step genomic prediction model assumes that all SNP markers explain an equal amount of genetic variance, which, however, may not be true. This is because SNPs are located in or near different genes with different functions. Therefore, it seems logical to consider SNP marker-specific weights when predicting genomic breeding values. We hypothesized that allowing differences in the amount of genetic variance explained by each SNP marker will improve prediction reliability and response to selection. To investigate this hypothesis, we first developed multi-trait standard single-step genomic models based on the current multi-trait random regression evaluation models for udder health traits of the Nordic Red (RDC) and Jersey (JER) dairy cattle populations. The models included 4 clinical mastitis (CM) traits, 3 test-day somatic cell score (SCS) traits, and the conformation traits fore udder attachment and udder depth. In the second step, we investigated the effect of applying different SNP marker weighting scenarios in the single-step genomic prediction models, for which a single-step SNP best linear unbiased prediction model was applied. We investigated the prediction reliability of the different models by forward prediction, where the last 4 years of the data were removed to estimate breeding values for validation candidates. In addition, genetic trends of the pedigree-based estimated breeding values (PEBV) and genomic enhanced breeding values (GEBV) were examined. The data sets for RDC and JER included 6.9 and 1.2 million animals of which 5.6 and 0.9 million cows had records, respectively. The number of genotyped animals was 125,789 and 64,777 for RDC and JER, respectively. Cows had repeated SCS observations but only single observations for all other traits and breeding values for all traits were modeled by one covariance function. This required modeling 12 eigenvalue breeding value coefficients for each cow and developing SNP marker weights for the principal components rather than for the biological traits. We investigated 3 SNP marker weighting scenarios: 1) a nonlinear method similar to BayesA, 2) using the classical formula 2pqû2 that accounts for allele heterozygosity, and 3) applying a mean SNP weight calculated by 2pqû2 for every 20 adjacent SNP markers. Bias, dispersion, and prediction reliability were calculated using PEBV or GEBV from the evaluation based on the full data set on those using the reduced data set. We found that the recent favorable genetic trend in CM and SCS has been accelerated since the introduction of genomic selection. The study also shows that a significant increase in prediction reliability, i.e., 0.74 vs. 0.48 for RDC and 0.72 vs. 0.41 for JER cows for CM, can be achieved with a standard single-step genomic prediction model compared with a pedigree-based prediction model. Almost all scenarios with SNP marker weighting further improved the prediction reliability between 0.5% and 12.7%. The highest improvement was achieved by weighing the SNP markers based on the 2pqû2 formula.

Comparison of predictive ability of single-trait and multitrait genomic selection models for body growth traits in Maiwa yaks

Yaks are grazed extensively on the Qinghai–Tibet Plateau, which has a long history of semi-domestication. The predicted weight of yaks over consecutive years helps make strategic decisions when selecting yak calves for breeding. To achieve more accurate predictions of genomic estimated breeding values, we used a dataset comprising the genotype and weight records of 396 Maiwa yaks collected from 2015 to 2020. We compared the predictive accuracy of the genome best linear unbiased prediction (GBLUP) model with that of six other models. Based on the GBLUP model, we applied two prediction strategies. In the first strategy, the year was treated as a fixed effect in the GBLUP model, and the kinship from all individuals and the markers were treated as random effects. In the second strategy, all individuals were divided into six age groups, with GBLUP performed for each group, and the phenotypes of the closest age groups were treated as fixed effects. Although the GBLUP model provided better prediction accuracy than other single-trait models, most of the predictive capacity was derived from the best linear unbiased estimation. Additionally, incorporating the phenotype of the closest age group as a factor in multitrait prediction enhanced the accuracy of the model. Our findings provide a robust and credible strategy for predicting continuous economic traits in the presence of strong correlations.

Modeling maladaptive personality traits with unipolar item response theory: The case of Callousness

Most IRT applications in personality assume that the measured trait is a bipolar dimension, normally distributed in the population. These assumptions, however, could be questionable for maladaptive, (quasi) pathological traits that still fall in the normal range. This study focuses on one such trait, Callousness, and uses two different instruments and samples to determine whether there is a basis for modeling it as a unipolar trait instead of a bipolar one. More specifically, the following community samples were used, recruited in several Spanish high schools: a) 719 adolescents (13-19 years old, 55.8% girls), b) 681 adolescents (13-19 years old, 44.9% girls). Callousness was assessed with the Inventory of Callous-unemotional traits and Antisocial behavior in the first sample and with the Inventory of Callous Unemotional traits in the second sample. We compared the outcomes of fitting the Graded-Response model (a bipolar-trait model) and the Log-Logistic model (a unipolar trait model) in these community samples and found that they differed considerably at the scoring level. In terms of accuracy, the conditional reliability functions had opposite patterns: it was maximum at high levels in the Graded-Response model and at low levels in the Log-Logistic model. In terms of validity, the models showed different results regarding the prediction of indirect aggressiveness and non-planning impulsiveness.

Who benefits from virtual collaboration? The interplay of team member expertness and Big Five personality traits

This research applies and integrates transactive memory systems (TMS) theory and the Big Five personality traits model to investigate the performance dynamics of dyadic teams engaged in virtual collaborative problem-solving (CPS). Specifically, this study examines how the personal attributes of team members, including their expertness and Big Five personality traits (extraversion, agreeableness, openness, conscientiousness, and neuroticism), as well as the resultant diversity in expertness and Big Five personality traits within teams, influence both team-level and individual-level performance gain from virtual collaboration. Studying 377 dyadic teams composed of 754 individuals working on an online collaborative intellective task, this research found that dyads with high expertness diversity had greater performance gain from virtual collaboration than dyads with low expertness diversity. Further, dyads, where both members scored low on agreeableness, showed the most significant improvement in team performance. At the individual level, a team member who had a low expertness level but was paired with a high-expertness teammate demonstrated the greatest performance gain from virtual collaboration. The integration of TMS theory and the Big Five personality traits model provides a richer and more nuanced understanding of how individual attributes and team dynamics contribute to successful virtual CPS outcomes.

Genomic prediction for potato (Solanum tuberosum) quality traits improved through image analysis.

Potato (Solanum tuberosum L.) is the most widely grown vegetable in the world. Consumers and processors evaluate potatoes based on quality traits such as shape and skin color, making these traits important targets for breeders. Achieving and evaluating genetic gain is facilitated by precise and accurate trait measures. Historically, quality traits have been measured using visual rating scales, which are subject to human error and necessarily lump individuals with distinct characteristics into categories. Image analysis offers a method of generating quantitative measures of quality traits. In this study, we use TubAR, an image-analysis R package, to generate quantitative measures of shape and skin color traits for use in genomic prediction. We developed and compared different genomic models based on additive and additive plus non-additive relationship matrices for two aspects of skin color, redness, and lightness, and two aspects of shape, roundness, and length-to-width ratio for fresh market red and yellow potatoes grown in Minnesota between 2020 and 2022. Similarly, we used the much larger chipping potato population grown during the same time to develop a multi-trait selection index including roundness, specific gravity, and yield. Traits ranged in heritability with shape traits falling between 0.23 and 0.85, and color traits falling between 0.34 and 0.91. Genetic effects were primarily additive with color traits showing the strongest effect (0.47), while shape traits varied based on market class. Modeling non-additive effects did not significantly improve prediction models for quality traits. The combination of image analysis and genomic prediction presents a promising avenue for improving potato quality traits.

Integrating hot topics and implementation of treatable traits in asthma.

People with asthma experience many different problems related to their illness. The number and type of problems differs between patients. This results in asthma being a complex and heterogeneous disorder which mandates a personalised approach to management. These features pose very significant challenges for the effective implementation of evidence-based management. Treatable Traits is a model of care that has been specifically designed to address these issues. Traits are identified in the pulmonary, extra-pulmonary (comorbidity) and risk factor/behavioural domains. Traits are clinically relevant, recognisable with validated trait identification markers, and treatable using evidence-based therapies. The clinician and patient agree on a personalised management plan that addresses the relevant traits, and trials show superiority of this approach with significant improvements in asthma control and quality of life. A number of tools have now been developed to assist the clinician in the implementation of this approach. The success of the treatable traits model of care is now being realised in other disease areas.

Genetic variation in CCDC93 is associated with elevated central systolic blood pressure, impaired arterial relaxation, and mitochondrial dysfunction.

Genetic studies of blood pressure (BP) traits to date have been performed on conventional measures by brachial cuff sphygmomanometer for systolic BP (SBP) and diastolic BP, integrating several physiologic occurrences. Genetic associations with central SBP (cSBP) have not been well-studied. Genetic discovery studies of BP have been most often performed in European-ancestry samples. Here, we investigated genetic associations with cSBP in a Chinese population and functionally validated the impact of a novel associated coiled-coil domain containing 93 (CCDC93) gene on BP regulation. An exome-wide association study (EWAS) was performed using a mixed linear model of non-invasive cSBP and peripheral BP traits in a Han Chinese population (N = 5,954) from Beijing, China genotyped with a customized Illumina ExomeChip array. We identified four SNP-trait associations with three SNPs, including two novel associations (rs2165468-SBP and rs33975708-cSBP). rs33975708 is a coding variant in the CCDC93 gene, c.535C>T, p.Arg179Cys (MAF = 0.15%), and was associated with increased cSBP (β = 29.3 mmHg, P = 1.23x10-7). CRISPR/Cas9 genome editing was used to model the effect of Ccdc93 loss in mice. Homozygous Ccdc93 deletion was lethal prior to day 10.5 of embryonic development. Ccdc93+/- heterozygous mice were viable and morphologically normal, with 1.3-fold lower aortic Ccdc93 protein expression (P = 0.0041) and elevated SBP as compared to littermate Ccdc93+/+ controls (110±8 mmHg vs 125±10 mmHg, P = 0.016). Wire myography of Ccdc93+/- aortae showed impaired acetylcholine-induced relaxation and enhanced phenylephrine-induced contraction. RNA-Seq transcriptome analysis of Ccdc93+/- mouse thoracic aortae identified significantly enriched pathways altered in fatty acid metabolism and mitochondrial metabolism. Plasma free fatty acid levels were elevated in Ccdc93+/- mice (96±7mM vs 124±13mM, P = 0.0031) and aortic mitochondrial dysfunction was observed through aberrant Parkin and Nix protein expression. Together, our genetic and functional studies support a novel role of CCDC93 in the regulation of BP through its effects on vascular mitochondrial function and endothelial function.

Towards a better understanding of auxin response factors for improving cereal crops

The auxin response factor (ARF) is the key component of phytohormone auxin signal pathway and is crucial in regulating a plurality of functions throughout plant life cycle. Although ARFs’ structure and function have been well studied in Arabidopsis, such knowledge is far from being sufficient for cereal crops, especially wheat, rice and maize. This review is based on a comprehensive retrospection into the studies on ARFs in the three cereal crops, consisting of four parts: (1) characterization of the domains of 23, 25 and 33 ARF family members in wheat, rice and maize, respectively; (2) revision of nomenclatures for previously reported ARFs to the family numbers based on sequence alignment, and summary of ARFs’ functions including the regulation of agronomic traits and response to biotic/abiotic stresses; (3) highlight of general regulatory models for fundamental physiological and reproductive traits from miRNA-ARFs, IAA-ARF-LBD, IAA-ARF-Auxin response gene, and IAA-ARF-ERF (4) prospects to promising future ARF research for anticipated agronomic traits. Hopefully, the review will facilitate the understanding of ARF functions in the three cereal crops and promote its application in molecular breeding for achieving optimal plant architecture, higher yield, and wide adaptability.

Invariance of comparisons: Separation of item and person parameters beyond Rasch models

The Rasch model is the most prominent member of the class of latent trait models that are in common use. The main reason is that it can be considered as a measurement model that allows to separate person and item parameters, a feature that is referred to as invariance of comparisons or specific objectivity. It is shown that the property is not an exclusive trait of Rasch type models but is also found in alternative latent trait models. It is distinguished between separability in the theoretical measurement model and empirical separability with empirical separability meaning that parameters can be estimated without reference to the other group of parameters. A new type of pairwise estimator with this property is proposed that can be used also in alternative models. Separability is considered in binary models as well as in polytomous models.