Many morphological, behavioral, physiological, and life-history traits covary across the biological scales of individuals, populations, and species. However, the processes that cause traits to covary also change over these scales, challenging our ability to use patterns of trait covariance to infer process. Trait relationships are also widely assumed to have generic functional relationships with similar evolutionary potentials, and even though many different trait relationships are now identified, there is little appreciation that these may influence trait covariation and evolution in unique ways. We use a trait-performance-fitness framework to classify and organize trait relationships into three general classes, address which ones more likely generate trait covariation among individuals in a population, and review how selection shapes phenotypic covariation. We generate predictions about how trait covariance changes within and among populations as a result of trait relationships and in response to selection and consider how these can be tested with comparative data. Careful comparisons of covariation patterns can narrow the set of hypothesized processes that cause trait covariation when the form of the trait relationship and how it responds to selection yield clear predictions about patterns of trait covariation. We discuss the opportunities and limitations of comparative approaches to evaluate hypotheses about the evolutionary causes and consequences of trait covariation and highlight the importance of evaluating patterns within populations replicated in the same and in different selective environments. Explicit hypotheses about trait relationships are key to generating effective predictions about phenotype and its evolution using covariance data.
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http://dx.doi.org/10.1086/693482 | DOI Listing |
Plant Genome
March 2025
USDA-ARS Southeast Area, Plant Science Research, Raleigh, North Carolina, USA.
Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide polymorphic markers (SNPs) resulted in a substantial improvement in predictive ability compared to the conventional genomic prediction models. Over the course of several years, the prediction ability varied due to diverse weather conditions.
View Article and Find Full Text PDFNutrients
December 2024
Department of Food and Nutrition, Obesity/Diabetes Research Center, Hoseo University, 20 Hoseoro97bungil, BaeBang-Yup, Asan 31499, Republic of Korea.
Background: Myocardial infarction (MI) can range from mild to severe cardiovascular events and typically develops through complex interactions between genetic and lifestyle factors.
Objectives: We aimed to understand the genetic predisposition associated with MI through genetic correlation, colocalization analysis, and cells' gene expression values to develop more effective prevention and treatment strategies to reduce its burden.
Methods: A polygenic risk score (PRS) was employed to estimate the genetic risk for MI and to analyze the dietary interactions with PRS that affect MI risk in adults over 45 years ( = 58,701).
Animals (Basel)
December 2024
Albert Kázmér Faculty of Agriculture and Food Sciences, Széchenyi István University, Vár T. 2, H-9200 Mosonmagyaróvár, Hungary.
This study aimed to examine the age at first calving (AFC) in Hungarian Angus herds. This study was conducted on the basis of data from 2955 registered cows, classified into five groups (based on different Angus types), and 200 breeding bulls, which were the sires of the cows. The data were made available by the Hungarian Hereford, Angus, and Galloway Breeders' Association.
View Article and Find Full Text PDFPsychol Rep
January 2025
Department of Statistics, TU Dortmund University, Dortmund, Germany.
An important psychometric property in educational and psychological testing is differential item functioning (DIF), assessing whether different subgroups respond differently to particular items within a scale, despite having the same overall ability level. In fact, DIF occurs when respondents with the same underlying trait level have different probabilities of selecting specific response categories, depending on their subgroup membership. This study aims to demonstrate the usefulness of rating scale tree (RStree) model in detecting DIF of Likert-type scales across age and gender in social sciences.
View Article and Find Full Text PDFFront Psychol
December 2024
Faculty of Psychology, Southwest University, Chongqing, China.
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