Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network

Abstract

In wheat breeding, improved quality traits, including grain quality and dough rheological properties, have long been a critical goal. To understand the genetic basis of key quality traits of wheat, two single-locus and five multi-locus GWAS models were performed for six grain quality traits and three dough rheological properties based on 19, 254 SNPs in 267 bread wheat accessions. As a result, 299 quantitative trait nucleotides (QTNs) within 105 regions were identified to be associated with these quality traits in four environments. Of which, 40 core QTN regions were stably detected in at least three environments, 19 of which were novel. Compared with the previous studies, these novel QTN regions explained smaller phenotypic variation, which verified the advantages of the multi-locus GWAS models in detecting important small effect QTNs associated with complex traits. After characterization of the function and expression in-depth, 67 core candidate genes involved in protein/sugar synthesis, histone modification and the regulation of transcription factor were observed to be associated with the formation of grain quality, which showed that multi-level regulations influenced wheat grain quality. Finally, a preliminary network of gene regulation that may affect wheat quality formation was inferred. This study verified the power and reliability of multi-locus GWAS methods in wheat quality trait research, and increased the understanding of wheat quality formation mechanisms. The detected QTN regions and candidate genes in this study could be further used for gene cloning and marker-assisted selection in highquality breeding of bread wheat.

Large-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat

“…To verify the contribution of candidate genes to yield and yield-related traits, 94 wheat accessions containing 3 foreign materials and 91 accessions from 3 major winter production regions in China were planted in eld during three winter cropping seasons (October to early June of 2016-2017, 2017-2018 and 2018-2019)(Table S7), on the experimental farm of the Institute of Water Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling, Shaanxi, China (34°7'N, 108°4'E). The detailed eld trials were as described in Yang et al 2020.…”

“…With the advent of DNA sequencing technology, high-throughput genotyping based on SNP array or Next-generation sequencing (NGS) provides convenience for genome-wide association studies (GWAS) of complex quantitative traits. This association analysis method based on natural population has been applied to QTL and gene mapping of rice, barley, wheat and other crops (Wang et al 2015;Fan et al 2016;Yang et al 2020), and has also achieved very good results in QTL mapping for wheat yield and yield-related traits (Edae et al 2014;Sun et al 2017;Sukumaran et al 2015). In addition, several important QTLs identi ed by GWAS have been further con rmed by linkage mapping studies Wu et al 2020).…”

Amino Acid Transporter (AAT) Gene Family in Foxtail Millet (Setaria Italica L.): Widespread Family Expansion, Functional Differentiation, Roles in Quality Formation and Response to Abiotic Stresses

“…The AAP genes are closed related to grain development and quality formation in many species. AtAAP1 in Arabidopsis and OsAAP6 in rice are directly related to grain protein content [7,14], multiple AAT genes were located in or near the QTL regions associated with quality traits in wheat by genome-wide association analysis (GWAS) [27]. The RNA-seq analysis of the foxtail millet grains at development stages revealed that 70% AAPs (14/20), 67% CATs (8/12), 63% BATs (5/8), 83% ATLa (5/6) and 75% AUXs (3/4) were highly expressed during grain development, including SiAAP9 and SiAAP8.…”

“…In rice, OsAAP1 affects plant growth and grain yield by modulating the redistribution of neutral amino acids [26], and the quantitative trait locus qPC1 (QTL) modulation of the rice GPC is linked to the expression of OsAAP6 [7]. In addition, several wheat TaAAP genes have been con rmed to co-localize with QTL of grain protein content [27].…”

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

“…Previous linkage mapping and genome-wide association studies in SWW have shown that a large number of small effect QTLs control most end-use quality traits in addition to the already fixed genes (Carter et al 2012;Jernigan et al 2018). Similarly, 299 small effects QTLs were identified using multi-locus genome-wide association studies for nine end-use quality traits in hard wheat (Yang et al 2020). Kristensen et al (2018) were unable to identify significant QTLs for Zeleny sedimentation, grain protein content, test weight, thousand kernel weight, and falling number in wheat and suggested genomic selection as the best alternative for predicting quantitative traits.…”