Editorial: Genome wide association studies and genomic selection for crop improvement in the era of Big Data

The exploitation of the genetic diversity of crops is essential for breeding purposes, as the identification of useful/beneficial alleles for target traits within plant genetic resources allows the development of new varieties capable of responding to the challenges of global agriculture (Food and Agriculture Organization of the United Nations, 2010). Whole genome re-sequencing, genome skimming, fractional genome sequencing strategies, and high-density genotyping arrays enable large-scale assessment of genetic diversity for a wide range of species, including major and “orphan” crops (D’Agostino and Tripodi, 2017; Rasheed et al., 2017). This is however of limited value unless associated with adaptation and functional improvement of crops. Recently, several advances in high-throughput phenotyping have overcome the “phenotyping bottleneck” (Walter et al., 2015; Pieruschka and Schurr, 2019; Song et al., 2021), making available robust phenotypic data points acquired following the precise characterization of the agronomic and physiological attributes of crops. More and more studies are taking advantage of these scientific advances and of data science techniques to uncover the genome-to-phenome relationship and unlock the breeding potential of plant genetic resources. Genome-wide association studies (GWAS) and genomic selection (GS) are powerful data science approaches to investigate marker-trait associations (MTAs) for the basic understanding of simple and complex adaptive and functional traits (Liu and Yan, 2019; Voss-Fels et al., 2019; Varshney et al., 2021). Both approaches accelerate the rate of genetic gain in crops and reduce the breeding cycle in a cost-effective manner. For this Research Topic we sought high-quality contributions, covering various aspects of genomics-assisted-breeding: increase in yield, improvement of nutritional content and end-use quality of crops, climate-smart agriculture, cropping systems in agriculture. We did not miss to ask for contributions on technical challenges related to the design of GWAS and GS experiments and data analysis.