The advances in “omics” technologies have enabled unprecedented progress in agricultural and biological sciences. The synergy of high-performance computing, high throughput omics approaches, and high dimensional phenotyping data with high spatial and temporal resolution have demonstrated the capacity to enhance our understanding of biological mechanisms but also to provide powerful insights into dissecting the genetic basis of complex traits with agricultural and economical importance.
Genome-wide association study (GWAS) has become a useful approach to identify mutations that underlie diseases and complex traits and has provided important insights in exploring genetic profiles. However, it is less suitable for quantitative traits influenced by a large number of genes with small effects. In addition, false discoveries are a major concern and can be partially attributed to population structure. Genomic selection holds the promise to overcome the limitations by using whole-genome information to predict the genetic merits of phenotypes. It has been a powerful tool for predicting the breeding values of candidates for selection in breeding populations. One of the challenges of genomic prediction of breeding values with large-p-with-small-n regressions is to develop robust and efficient approaches that accurately predict phenotypic traits as functions of genotypic and environmental inputs. In addition, the integration of multi-omics data in phenotypic prediction would offer the opportunity to understand the flow of information that underlies the phenotypic traits.
This Research Topic serves as a forum for the development of innovative methodology and novel application of existing methods for agricultural genome-, phenome- and transcriptome-wide association and prediction studies. Scientific problems include algorithms, modelling, computing and testing. Thus, comprehensive literature reviews regarding methodology, computation, and testing are highly desired. Insightful opinions and perspectives will benefit the research community in agriculture. One of the goals is to provide the agricultural community with new and efficient analytical approaches to make the best use of the availability of high throughput and high dimensional data for accelerating gene discovery, trait dissection, and facilitating the plant and animal breeding to keep pace with a changing environment and meet the needs of the ever-growing global population and changing lifestyles.
This Research Topic welcomes, but is not limited to, the following subtopics:
• Development of innovative statistical and machine learning methodologies for GWAS, Phenome-wide association studies (PheWAS), transcriptome-wide association studies (TWAS), genomic prediction and selection
• Novel application of the existing statistical methodology for GWAS, PheWAS, TWAS, genomic prediction and selection
• GWAS, genomic prediction and selection in the context of multi-environments and multi-traits
• Reviews of statistical methodology, resources, and significant findings in agricultural multi-omics studies
The advances in “omics” technologies have enabled unprecedented progress in agricultural and biological sciences. The synergy of high-performance computing, high throughput omics approaches, and high dimensional phenotyping data with high spatial and temporal resolution have demonstrated the capacity to enhance our understanding of biological mechanisms but also to provide powerful insights into dissecting the genetic basis of complex traits with agricultural and economical importance.
Genome-wide association study (GWAS) has become a useful approach to identify mutations that underlie diseases and complex traits and has provided important insights in exploring genetic profiles. However, it is less suitable for quantitative traits influenced by a large number of genes with small effects. In addition, false discoveries are a major concern and can be partially attributed to population structure. Genomic selection holds the promise to overcome the limitations by using whole-genome information to predict the genetic merits of phenotypes. It has been a powerful tool for predicting the breeding values of candidates for selection in breeding populations. One of the challenges of genomic prediction of breeding values with large-p-with-small-n regressions is to develop robust and efficient approaches that accurately predict phenotypic traits as functions of genotypic and environmental inputs. In addition, the integration of multi-omics data in phenotypic prediction would offer the opportunity to understand the flow of information that underlies the phenotypic traits.
This Research Topic serves as a forum for the development of innovative methodology and novel application of existing methods for agricultural genome-, phenome- and transcriptome-wide association and prediction studies. Scientific problems include algorithms, modelling, computing and testing. Thus, comprehensive literature reviews regarding methodology, computation, and testing are highly desired. Insightful opinions and perspectives will benefit the research community in agriculture. One of the goals is to provide the agricultural community with new and efficient analytical approaches to make the best use of the availability of high throughput and high dimensional data for accelerating gene discovery, trait dissection, and facilitating the plant and animal breeding to keep pace with a changing environment and meet the needs of the ever-growing global population and changing lifestyles.
This Research Topic welcomes, but is not limited to, the following subtopics:
• Development of innovative statistical and machine learning methodologies for GWAS, Phenome-wide association studies (PheWAS), transcriptome-wide association studies (TWAS), genomic prediction and selection
• Novel application of the existing statistical methodology for GWAS, PheWAS, TWAS, genomic prediction and selection
• GWAS, genomic prediction and selection in the context of multi-environments and multi-traits
• Reviews of statistical methodology, resources, and significant findings in agricultural multi-omics studies
