The online version contains supplementary material available at 10.1007/s11032-022-01340-6.

Rice grain amylose contents (ACs) is a key quantitative trait influencing eating and cooking quality. Regulating the expression level of Waxy, a key gene controlling ACs, and in turn fine-tuning the grain ACs, is an ideal approach to improve grain quality of rice varieties. Based on CRISPR/Cas9 genome editing technology, we designed eight targets in the cis-regulatory region of Wxa background, screened phenotypic changes of the transgenic lines and generated eight novel Waxy alleles with altered grain ACs. Among the eight alleles, we found that a 407-bp non-homologous substitution (NHS) in the 5'UTR-intron caused by genome editing regulated Waxy expression and decreased grain ACs by 2.9%. Moreover, embedding the 407-bp NHS into the cis-regulatory region of Wxb allele can also affect gene activity. Our work suggested the effect of 5'UTR-intron on Waxy gene expression regulation, and provided a potentially useful allele in breeding that can finely adjust rice grain ACs.

The online version contains supplementary material available at 10.1007/s11032-022-01333-5.

The online version contains supplementary material available at 10.1007/s11032-022-01323-7.

The online version contains supplementary material available at 10.1007/s11032-022-01312-w.

The online version contains supplementary material available at 10.1007/s11032-022-01300-0.

The online version contains supplementary material available at 10.1007/s11032-022-01293-w.

The online version contains supplementary material available at 10.1007/s11032-022-01287-8.

Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.

The online version contains supplementary material available at 10.1007/s11032-021-01271-8.