Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. However, there are still limits in current GWAS statistics. For example, (1) almost all the existing methods do not estimate additive and dominance effects in quantitative trait nucleotide (QTN) detection; (2) the methods for detecting QTN-by-environment interaction (QEI) are not straightforward and do not estimate additive and dominance effects as well as additive-by-environment and dominance-by-environment interaction effects, leading to unreliable results; and (3) no or too simple polygenic background controls have been employed in QTN-by-QTN interaction (QQI) detection. As a result, few studies of QEI and QQI for complex traits have been reported based on multiple-environment experiments. Recently, new statistical tools, including 3VmrMLM, have been developed to address these needs in GWAS. In 3VmrMLM, all the trait-associated effects, including QTN, QEI and QQI related effects, are compressed into a single effect-related vector, while all the polygenic backgrounds are compressed into a single polygenic effect matrix. These compressed parameters can be accurately and efficiently estimated through a unified mixed model analysis. To further validate these new GWAS methods, particularly 3VmrMLM, they should be rigorously tested in real data of various plants and a wide range of other species.
Recent advancement in GWAS methodologies has facilitated more robust and efficient genetic analysis of complex traits in plants under multiple environment settings to better address QEI and QQI effects. The purpose of this Research Topic includes: (1) review the current advances in GWAS methods and their applied studies, (2) test new GWAS methods and compare them with existing methods via both Monte Carlo simulation and analysis of real data in plants, and (3) apply these new methods to mine novel candidate genes for further decoding the gene function and molecular mechanism which contribute to complex traits in plants.
We welcome the submission of Review and Original Research Articles on the below subjects but not limited to:
• Reviews and perspectives of genome-wide association studies in plants
• Methodological development and comparison of new statistical tools with existing methods in genome-wide association studies
• Applications of new methods on the genetic dissection of complex traits in plants
• Identification of novel main-effect genes, and gene-by-environment and gene-by-gene interactions of complex traits in plants using new methods
• Biological function and molecular mechanism of novel genes mined by new GWAS methods
Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. However, there are still limits in current GWAS statistics. For example, (1) almost all the existing methods do not estimate additive and dominance effects in quantitative trait nucleotide (QTN) detection; (2) the methods for detecting QTN-by-environment interaction (QEI) are not straightforward and do not estimate additive and dominance effects as well as additive-by-environment and dominance-by-environment interaction effects, leading to unreliable results; and (3) no or too simple polygenic background controls have been employed in QTN-by-QTN interaction (QQI) detection. As a result, few studies of QEI and QQI for complex traits have been reported based on multiple-environment experiments. Recently, new statistical tools, including 3VmrMLM, have been developed to address these needs in GWAS. In 3VmrMLM, all the trait-associated effects, including QTN, QEI and QQI related effects, are compressed into a single effect-related vector, while all the polygenic backgrounds are compressed into a single polygenic effect matrix. These compressed parameters can be accurately and efficiently estimated through a unified mixed model analysis. To further validate these new GWAS methods, particularly 3VmrMLM, they should be rigorously tested in real data of various plants and a wide range of other species.
Recent advancement in GWAS methodologies has facilitated more robust and efficient genetic analysis of complex traits in plants under multiple environment settings to better address QEI and QQI effects. The purpose of this Research Topic includes: (1) review the current advances in GWAS methods and their applied studies, (2) test new GWAS methods and compare them with existing methods via both Monte Carlo simulation and analysis of real data in plants, and (3) apply these new methods to mine novel candidate genes for further decoding the gene function and molecular mechanism which contribute to complex traits in plants.
We welcome the submission of Review and Original Research Articles on the below subjects but not limited to:
• Reviews and perspectives of genome-wide association studies in plants
• Methodological development and comparison of new statistical tools with existing methods in genome-wide association studies
• Applications of new methods on the genetic dissection of complex traits in plants
• Identification of novel main-effect genes, and gene-by-environment and gene-by-gene interactions of complex traits in plants using new methods
• Biological function and molecular mechanism of novel genes mined by new GWAS methods