Quantitative traits are explained by both genetics and environmental factors. Understanding heredity, or the proportion of phenotypic variance explained by genetics and the interaction between genetics and environments, is a central topic of quantitative genetic research. There are various important application fields such as ecology, and animal & plant breeding. For example, plant breeders are always interested in knowing the phenotype performance of a plant in multiple locations and years (i.e. with different climate and soil conditions). Hence, in many genome-wide association or genomic selection studies, data are collected from multiple environments in order to reveal whether a gene has a constant effect on a quantitative trait, or whether there is G*E interaction- a gene may only be coupled with a specific environment.
Two major research questions of analyzing multiple environments genomic data sets are: (1) how to efficiently model and estimate ultra high dimensional G*E interaction effect, and (2) how to properly incorporate the spatial and temporal information as well as environmental data into the statistical models. Linear mixed models have served as a mainstream approach have been proposed to analyze multiple environment genomic data sets, for this purpose. There is also an increasing trend of applying some machine learning methods such as kernel methods, Gaussian processes and deep learning in this area.
This Research Topic aims to collect Original Research, Brief Research Reports, Methods, Reviews, and Mini-Review articles including (while not limited to) the following topic areas:
• Novel analytical approaches or software packages for conducting gene mapping or phenotype prediction using multiple environmental data.
• Case studies in this research area.
Quantitative traits are explained by both genetics and environmental factors. Understanding heredity, or the proportion of phenotypic variance explained by genetics and the interaction between genetics and environments, is a central topic of quantitative genetic research. There are various important application fields such as ecology, and animal & plant breeding. For example, plant breeders are always interested in knowing the phenotype performance of a plant in multiple locations and years (i.e. with different climate and soil conditions). Hence, in many genome-wide association or genomic selection studies, data are collected from multiple environments in order to reveal whether a gene has a constant effect on a quantitative trait, or whether there is G*E interaction- a gene may only be coupled with a specific environment.
Two major research questions of analyzing multiple environments genomic data sets are: (1) how to efficiently model and estimate ultra high dimensional G*E interaction effect, and (2) how to properly incorporate the spatial and temporal information as well as environmental data into the statistical models. Linear mixed models have served as a mainstream approach have been proposed to analyze multiple environment genomic data sets, for this purpose. There is also an increasing trend of applying some machine learning methods such as kernel methods, Gaussian processes and deep learning in this area.
This Research Topic aims to collect Original Research, Brief Research Reports, Methods, Reviews, and Mini-Review articles including (while not limited to) the following topic areas:
• Novel analytical approaches or software packages for conducting gene mapping or phenotype prediction using multiple environmental data.
• Case studies in this research area.
