One of the main requirements in breeding activities is the exploitation of crop genetic diversity.
Whole genome re-sequencing, sequence capture and target-enrichment methods, fractional genome sequencing strategies and high-density genotyping arrays allow genomic variants like single nucleotide polymorphism (SNP) markers to be identified and scored at unprecedented level. As a consequence, sequence-based information is increasingly used to perform large-scale diversity studies for a wide range of species, including both major and "orphan" crops. This large genetic diversity assessment is however of limited value unless it is associated with adaptation and functional crop improvement.
Recently, a number of breakthroughs in high-throughput phenotyping technologies have overcome the “phenotyping bottleneck”, making robust phenotypic data points available to precisely characterize agronomic and physiological attributes for functional crop improvement. Capitalizing on these technological advancements, it is indisputable that breeding programs are at a unique point where techniques in data science will help to uncover the genome-to-phenome relationship, and to accelerate breeding potential. Among these data science techniques, genome-wide association studies (GWAS) and genomic selection (GS) are powerful tools to investigate trait-allele associations. Both methods are based on: (i) a high-density marker catalogue from genome-wide assessment of the diversity among individuals in a population and (ii) accurate phenotypic data. Combined these, aim to facilitate the selection of superior genotypes and reduce the breeding cycle in a cost-effective manner.
1) Genomics-assisted-breeding for climate-smart agriculture.
2) Predictive analysis with more than one type of genomic variants as predictors, predictive analysis that include physiological, functional or annotation knowledge and/or assessment of algorithmic predictability on response to selection in changing environment.
3) Genomics-assisted-breeding for improving nutrition content and end-use quality, for the purpose to enhance the health benefits (e.g., increase the levels of bioactive compounds) of crops.
4) Genomics-assisted-breeding for high yielding crop varieties.
5) Genomics-assisted breeding that considers agriculture as a system; this includes studies that investigate biotic interaction between crop-cover crops, crop-rotation crops and/or crop-soil/field management; studies on predictive analysis that incorporate cropping system are also encouraged.
6) Technical aspects in designing GWAS and GS experiments.
7) Technical challenges in GWAS and GS data analysis and use of simulations.
In this Research Topic, we would like to consider submissions of high-quality Original Research (or Brief Research Report) and Review (or Mini Review) articles on topics related to genome-wide association studies and/or genomic selection in crops.
Comparative transcriptomic analyses or descriptive studies will not be considered for review unless they are extended to provide meaningful insights into gene/protein function and/or the biology of plants.
One of the main requirements in breeding activities is the exploitation of crop genetic diversity.
Whole genome re-sequencing, sequence capture and target-enrichment methods, fractional genome sequencing strategies and high-density genotyping arrays allow genomic variants like single nucleotide polymorphism (SNP) markers to be identified and scored at unprecedented level. As a consequence, sequence-based information is increasingly used to perform large-scale diversity studies for a wide range of species, including both major and "orphan" crops. This large genetic diversity assessment is however of limited value unless it is associated with adaptation and functional crop improvement.
Recently, a number of breakthroughs in high-throughput phenotyping technologies have overcome the “phenotyping bottleneck”, making robust phenotypic data points available to precisely characterize agronomic and physiological attributes for functional crop improvement. Capitalizing on these technological advancements, it is indisputable that breeding programs are at a unique point where techniques in data science will help to uncover the genome-to-phenome relationship, and to accelerate breeding potential. Among these data science techniques, genome-wide association studies (GWAS) and genomic selection (GS) are powerful tools to investigate trait-allele associations. Both methods are based on: (i) a high-density marker catalogue from genome-wide assessment of the diversity among individuals in a population and (ii) accurate phenotypic data. Combined these, aim to facilitate the selection of superior genotypes and reduce the breeding cycle in a cost-effective manner.
1) Genomics-assisted-breeding for climate-smart agriculture.
2) Predictive analysis with more than one type of genomic variants as predictors, predictive analysis that include physiological, functional or annotation knowledge and/or assessment of algorithmic predictability on response to selection in changing environment.
3) Genomics-assisted-breeding for improving nutrition content and end-use quality, for the purpose to enhance the health benefits (e.g., increase the levels of bioactive compounds) of crops.
4) Genomics-assisted-breeding for high yielding crop varieties.
5) Genomics-assisted breeding that considers agriculture as a system; this includes studies that investigate biotic interaction between crop-cover crops, crop-rotation crops and/or crop-soil/field management; studies on predictive analysis that incorporate cropping system are also encouraged.
6) Technical aspects in designing GWAS and GS experiments.
7) Technical challenges in GWAS and GS data analysis and use of simulations.
In this Research Topic, we would like to consider submissions of high-quality Original Research (or Brief Research Report) and Review (or Mini Review) articles on topics related to genome-wide association studies and/or genomic selection in crops.
Comparative transcriptomic analyses or descriptive studies will not be considered for review unless they are extended to provide meaningful insights into gene/protein function and/or the biology of plants.
