Immunity Traits in Pigs: Substantial Genetic Variation and Limited Covariation

BACKGROUND: Increasing robustness via improvement of resistance to pathogens is a major selection objective in livestock breeding. As resistance traits are difficult or impossible to measure directly, potential indirect criteria are measures of immune traits (ITs). Our underlying hypothesis is that levels of ITs with no focus on specific pathogens define an individual's immunocompetence and thus predict response to pathogens in general. Since variation in ITs depends on genetic, environmental and probably epigenetic factors, our aim was to estimate the relative importance of genetics. In this report, we present a large genetic survey of innate and adaptive ITs in pig families bred in the same environment. METHODOLOGY/PRINCIPAL FINDINGS: Fifty four ITs were studied on 443 Large White pigs vaccinated against Mycoplasma hyopneumoniae and analyzed by combining a principal component analysis (PCA) and genetic parameter estimation. ITs include specific and non specific antibodies, seric inflammatory proteins, cell subsets by hemogram and flow cytometry, ex vivo production of cytokines (IFNα, TNFα, IL6, IL8, IL12, IFNγ, IL2, IL4, IL10), phagocytosis and lymphocyte proliferation. While six ITs had heritabilities that were weak or not significantly different from zero, 18 and 30 ITs had moderate (0.1<h2≤0.4) or high (h2>0.4) heritability values, respectively. Phenotypic and genetic correlations between ITs were weak except for a few traits that mostly include cell subsets. PCA revealed no cluster of innate or adaptive ITs. CONCLUSIONS/SIGNIFICANCE: Our results demonstrate that variation in many innate and adaptive ITs is genetically controlled in swine, as already reported for a smaller number of traits by other laboratories. A limited redundancy of the traits was also observed confirming the high degree of complementarity between innate and adaptive ITs. Our data provide a genetic framework for choosing ITs to be included as selection criteria in multitrait selection programmes that aim to improve both production and health traits

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Attachment Features and Functions in Adult Romantic Relationships

The present research examined the development of attachment bonds in adult romantic relationships using a cross-sectional internet survey (Study 1) and a longitudinal study (Study 2). Results suggested that attachment features and functions emerge in a specific sequence that begins with proximity-seeking, followed by safe haven, and finally secure base. Our cross-sectional data indicated that people who had been in relationships for longer were more likely to use their partners for attachment functions. However, in our longitudinal study, after controlling for relationship length and age, there was relatively little change in attachment features and functions over time. The data also indicated that adult attachment bonds might develop more quickly than has been previously assumed

The Experiences in Close Relationships—Relationship Structures Questionnaire: A Method for Assessing Attachment Orientations Across Relationships

Most research on adult attachment is based on the assumption that working models are relatively general and trait-like. Recent research, however, suggests that people develop attachment representations that are relationship-specific, leading people to hold distinct working models in different relationships. The authors report a measure, the Relationship Structures questionnaire of the Experiences in Close Relationships— Revised (ECR-RS; R. C. Fraley, N. G. Waller, & K. A. Brennan, 2000), that is designed to assess attachment dimensions in multiple contexts. Based on a sample of over 21,000 individuals studied online, it is shown that ECR-RS scores are reliable and have a structure similar to those produced by other measures. In Study 2 (N=388), it is shown that relationship-specific measures of attachment generally predict intra- and interpersonal outcomes better than broader attachment measures but that broader measures predict personality traits better than relationship-specific measures. Moreover, it is demonstrated that differentiation in working models is not related to psychological outcomes independently of mean levels of security