Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning.

Inferring trait networks from a large volume of genetically correlated diverse phenotypes such as yield, architecture, and disease resistance can provide information on the manner in which complex phenotypes are interrelated. However, studies on statistical methods tailored to multidimensional phenotypes are limited, whereas numerous methods are available for evaluating the massive number of genetic markers. Factor analysis operates at the level of latent variables predicted to generate observed responses.

Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice.

Elucidating genotype-by-environment interactions and partitioning its contribution to phenotypic variation remains a challenge for plant scientists. We propose a framework that utilizes genome-wide markers to model genotype-specific shoot growth trajectories as a function of time and soil water availability. A rice diversity panel was phenotyped daily for 21 d using an automated, high-throughput image-based, phenotyping platform that enabled estimation of daily shoot biomass and soil water content.

Divergent phenotypic response of rice accessions to transient heat stress during early seed development.

Increasing global surface temperatures is posing a major food security challenge. Part of the solution to address this problem is to improve crop heat resilience, especially during grain development, along with agronomic decisions such as shift in planting time and increasing crop diversification. Rice is a major food crop consumed by more than 3 billion people.

Network-based feature selection reveals substructures of gene modules responding to salt stress in rice.

Rice, an important food resource, is highly sensitive to salt stress, which is directly related to food security. Although many studies have identified physiological mechanisms that confer tolerance to the osmotic effects of salinity, the link between rice genotype and salt tolerance is not very clear yet. Association of gene co-expression network and rice phenotypic data under stress has penitential to identify stress-responsive genes, but there is no standard method to associate stress phenotype with gene co-expression network.

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping.

The accessibility of high-throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, has provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species.

ShinyAIM: Shiny-based application of interactive Manhattan plots for longitudinal genome-wide association studies.

Owning to advancements in sensor-based, non-destructive phenotyping platforms, researchers are increasingly collecting data with higher temporal resolution. These phenotypes collected over several time points are cataloged as longitudinal traits and used for genome-wide association studies (GWAS). Longitudinal GWAS typically yield a large number of output files, posing a significant challenge to data interpretation and visualization.

Exogenous prostaglandin F2 alpha stimulates utero-ovarian release of prostaglandin F2 alpha in sheep: a possible component of the luteolytic mechanism of action of exogenous prostaglandin F2 alpha.

Exogenous prostaglandin F2 alpha (PGF2 alpha) is luteolytic in sheep, but its mechanism of action is not completely understood. We hypothesized that exogenous PGF2 alpha stimulates the uterine and(or) ovarian secretion of PGF2 alpha and that, when intramuscular doses of PGF2 alpha are minimal, the utero-ovarian unit is a component of the luteolytic mechanism of action of exogenous PGF2 alpha. Thus, this study was conducted to determine whether exogenous PGF2 alpha stimulates the utero-ovarian release of PGF2 alpha.