Psycho-lexically based Openness to Experience

We investigated whether NEO-PI-R Openness to Experience (Costa & McCrae, 1992) and its six facets could be identified in the natural trait lexicon. To represent the NEO-PI-R Openness, a list of 113 items was selected from a lexically derived trait list developed for the eight-factor trait model of De Raad and Barelds (2008). We used ratings from two samples. The first (N=271) filled out the lexical Openness scales, the NEO-PI-R Openness scales, and scales measuring the eight-factor model. From the second sample (N=1,466), ratings were used to analyze the lexical Openness scales. Correlations between the eight-factor scales and the two sets of Openness scales indicated that Openness scales are fairly covered by the eight factors, except for the Ideas and Values facets of the NEO-PI-R. The lexical Openness scales correlated well with the NEO-PI-R Openness scales. Openness to Experience and its six facets were identified in the natural trait lexicon, but exploratory factor analyses did not support the six-facet structure of the NEO-PI-R Openness, neither did they lead to a similar six-facet structure across samples. Moreover, it did not consistently support a proposed two-facet structure emphasizing internal openness (fantasy, aesthetics) and external openness (ideas, change).

Traits, States, and Stories

Chapter 1 provides an overview of the Big Five trait model combined with two additional layers of personality expression: states and stories. The author explains that personality starts with traits, simple compounds that are captured in language with words like shy, belligerent, outgoing, ambitious, and friendly. By sifting and simplifying, or what is called factor analysis, all such adjectives reduce to five dimensions, the so-called Big Five. These dimensions (the dimensions are the traits) reveal the why behind creativity as well as the how, the ways in which creativity functions. The Big Five traits are neuroticism, extraversion, conscientiousness, agreeableness, and openness. Writer Truman Capote is used as an illustration of how traits, states, and stories are related to the personality of the artist.

Analyzing Stability and Change in Dyadic Attachment: The Multi-Rater Latent State-Trait Model With Autoregressive Effects

Previous research suggests that parental attachment is stable throughout emerging adulthood. However, the relationships between the mutual attachments in the dyads of emerging adults and their parents are still unclear. Our study examines the stability and change in dyadic attachment. We asked 574 emerging adults and 463 parents at four occasions over 1 year about their mutual attachments. We used a latent state-trait model with autoregressive effects to estimate the time consistency of the attachments. Attachment was very stable, and earlier measurement occasions could explain more than 60% of the reliable variance. Changes of attachment over time showed an accumulation of situational effects for emerging adults but not for their parents. We estimated the correlations of the mutual attachments over time using a novel multi-rater latent state-trait model with autoregressive effects. This model showed that the mutual attachments of parents and emerging adults were moderately to highly correlated. Our model allows to separate the stable attachment from the changing attachment. The correlations between the mutual attachments were higher for the stable elements of attachment than for the changing elements of attachment. Emerging adults and their parents share a stable mutual attachment, but they do not share the changes in their respective attachments.

The ICD-11 Personality Disorder Trait Model Fits the Kurdish Population Better Than the DSM-5 Trait Model

The ICD-11 Classification of Personality Disorders and the DSM-5 Alternative Model of Personality Disorders (AMPD) operate with trait domains that contribute to the individual expression of personality disturbance (i.e., negative affectivity, detachment, dissociality, disinhibition, anankastia, and psychoticism). To date, these trait frameworks have not been investigated sufficiently in Middle Eastern cultures. Thus, the present study explored the structure of the ICD-11 and AMPD personality disorder (PD) trait domains in a large mixed sample from the Kurdistan zone of Iran. The ICD-11 and AMPD trait domains were operationalized using empirically supported algorithms for the Personality Inventory for DSM-5 (PID-5). The PID-5 was administered to a large mixed sample (N = 3,196) composed of 2,678 community and 518 clinical participants. Structural validity was investigated using Exploratory Factor Analysis (EFA), whereas differential construct validity was explored by comparing clinical and community scores. Model fit and the expected factor structure were deemed appropriate for the ICD-11 trait model, but less adequate for the DSM-5 trait model (i.e., disinhibition did not emerge as a separate factor). All domain and facet scores showed significant differences between clinical and community subsamples with moderate to large effects, mostly for disinhibition and dissociality/antagonism while least for anankastia. The findings of the present study may suggest that the ICD-11 trait model is more cross-culturally fitting than the DSM-5 AMPD trait model, at least with respect to a large mixed sample from the region of Kurdistan. Accordingly, there is evidence for using PID-5 data for WHO ICD-11 purposes in this part of the World.

Case Report: Pathological Personality Traits Through the Lens of the ICD-11 Trait Qualifiers and the DSM-5 Section III Trait Model: Two Patients Illustrating the Clinical Utility of a Combined View

We report on two individuals presenting for treatment as part of everyday clinical practice, comparing their pathological personality traits through the lens of the ICD-11 trait qualifiers and the DSM-5 Section III personality trait model. We compare higher order pathological personality domains and lower order pathological personality trait facets of patient M (diagnosed with borderline personality traits according to DSM-5 Section II), and patient L (diagnosed with obsessive-compulsive personality traits according to DSM-5 Section II) with normative data and with each other. Findings highlight the clinical utility of a ICD-11/DSM-5 combined view, including: (1) the Disinhibition/Anankastia personality domain distinction as advocated in the ICD-11 model, (2) the Psychoticism personality domain as conceptualized in the DSM-5 Section III personality trait model, as well as (3) the use of lower order personality trait facets within each higher order personality domain.

Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy

Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low (ρg ≤ 0.33), moderate (0.34 ≤ ρg ≤ 0.66), and high magnitude (ρg ≥ 0.67) were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials.

Genetic parameters for worm resistance in Santa Inês sheep using the Bayesian animal model

Objective: The objective of this study was to estimate the genetic parameters for worm resistance (WR) and associated characteristics, using the linear-threshold animal model via Bayesian inference in single- and multiple-trait analyses.Methods: Data were collected from a herd of Santa Inês breed sheep. All information was collected with animals submitted to natural contamination conditions. All data (number of eggs per gram of feces [FEC], Famacha score [FS], body condition score [BCS], and hematocrit [HCT]) were collected on the same day. The animals were weighed individually on the day after collection (after 12-h fasting). The WR trait was defined by the multivariate cluster analysis, using the FEC, HCT, BCS, and FS of material collected from naturally infected sheep of the Santa Inês breed. The variance components and genetic parameters for the WR, FEC, HCT, BCS, and FS traits were estimated using the Bayesian inference under the linear and threshold animal model.Results: A low magnitude was obtained for repeatability of worm-related traits. The mean values estimated for heritability were of low-to-high (0.05 to 0.88) magnitude. The FEC, HCT, BCS, FS, and body weight traits showed higher heritability (although low magnitude) in the multiple-trait model due to increased information about traits. All WR characters showed a significant genetic correlation, and heritability estimates ranged from low (0.44; single-trait model) to high (0.88; multiple-trait model).Conclusion: Therefore, we suggest that FS be included as a criterion of ovine genetic selection for endoparasite resistance using the trait defined by multivariate cluster analysis, as it will provide greater genetic gains when compared to any single trait. In addition, its measurement is easy and inexpensive, exhibiting greater heritability and repeatability and a high genetic correlation with the trait of resistance to worms.

A single-step genome wide association study on Body Size Traits using imputation-based whole-genome sequence data in Yorkshire pigs

Background: The body shape of pig is the most direct production index of pig, which can fully reflect the growth status of pig and is closely related to some important economic traits. In this study, genome-wide association study on seven body size traits, the body length (BL), height (BH), chest circumference (CC), abdominal circumference (AC), cannon bone circumference (CBC), rump width (RW) and chest width (CW) were conducted in Yorkshire pigs. Methods: Illumina Porcine 80K SNP chip were used to genotype 589 of 5,572 Yorkshire pigs with body size records, and then the chip data was imputed to sequencing data. After quality control of imputed sequencing data, 784,267 SNPs were obtained, and the averaged linkage disequilibrium (r2) was 0.191. We used the single-trait model and the two-trait model to conduct single-step genome wide association study (ssGWAS) on seven body size traits.Results: A total of 198 significant SNPS were finally identified according to the P value and the contribution to the genetic variance of individual SNP. 11 candidate genes (CDH13, SIL2, CDC14A, TMRPSS15, TRAPPC9, CTNND2, KDM6B, CHD3, MUC13, MAPK4 and HMGA1) were found to be associated with body size traits in pigs, KDM6B and CHD3 jointly affect AC and CC, and MUC13 jointly affect RW and CW. These genes are involved in the regulation of bone growth and development as well as the absorption of nutrients and are associated with obesity. HMGA1 is proposed as strong candidate gene for body size traits because of its important function and high consistency with other studies regarding the regulation of body size traits. Our results could provide valuable information for pig breeding based on molecular breeding.

Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.