QTL mapping of yield components and kernel traits in wheat cultivars TAM 112 and Duster

Genotype selection identified elite lines through quantitative trait loci mapping of agronomically important traits in wheat

Wheat is one of the most important staple foods in the world. Genetic characterization of wheat agronomically important traits is crucial for yield improvement through molecular breeding. In this study, a recombinant inbred line (RIL) population was developed by crossing a local adapted high yield variety Jimai 22 (JM22) with an external variety Cunmai no.1 (CM1). A high-density genetic map containing 7,359 single nucleotide polymorphism (SNP) markers was constructed. Quantitative trait loci (QTL) mapping identified 61 QTL for eight yield-related traits under six environments (years). Among them, 17 QTL affecting spike number per plant, grain number per spike and thousand grain weight showed high predictability for theoretical yield per plant (TYP), of which, 12 QTL alleles positively contributed to TYP. Nine promising candidate genes for seven of the 12 QTL were identified including three known wheat genes and six rice orthologs. Four elite lines with TYP increased by 5.6%-15.2% were identified through genotype selection which carried 7-9 favorable alleles from JM22 and 2-3 favorable alleles from CM1 of the 12 QTL. Moreover, the linked SNPs of the 12 QTL were converted to high-throughput kompetitive allele-specific PCR (KASP) markers and validated in the population. The mapped QTL, identified promising candidate genes, developed elite lines and KASP markers are highly valuable in future genotype selection to improve wheat yield.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01496-3.

QTL mapping and candidate gene analysis reveal two major loci regulating green leaf color in non-heading Chinese cabbage

KEY MESSAGE

Two major-effect QTL GlcA07.1 and GlcA09.1 for green leaf color were fine mapped into 170.25 kb and 191.41 kb intervals on chromosomes A07 and A09, respectively, and were validated by transcriptome analysis. Non-heading Chinese cabbage (NHCC) is a leafy vegetable with a wide range of green colors. Understanding the genetic mechanism behind broad spectrum of green may facilitate the breeding of high-quality NHCC. Here, we used F2 and F7:8 recombination inbred line (RIL) population from a cross between Wutacai (dark-green) and Erqing (lime-green) to undertake the genetic analysis and quantitative trait locus (QTL) mapping in NHCC. The genetic investigation of the F2 population revealed that the variation of green leaf color was controlled by two recessive genes. Six pigments associated with green leaf color, including total chlorophyll, chlorophyll a, chlorophyll b, total carotenoids, lutein, and carotene were quantified and applied for QTL mapping in the RIL population. A total of 7 QTL were detected across the whole genome. Among them, two major-effect QTL were mapped on chromosomes A07 (GlcA07.1) and A09 (GlcA09.1) corresponding to two QTL identified in the F2 population. The QTL GlcA07.1 and GlcA09.1 were further fine mapped into 170.25 kb and 191.41 kb genomic regions, respectively. By comparing gene expression level and gene annotation, BraC07g023810 and BraC07g023970 were proposed as the best candidates for GlcA07.1, while BraC09g052220 and BraC09g052270 were suggested for GlcA09.1. Two InDel molecular markers (GlcA07.1-BcGUN4 and GlcA09.1-BcSG1) associated with BraC07gA023810 and BraC09g052220 were developed and could effectively identify leaf color in natural NHCC accessions, suggesting their potential for marker-assisted leaf color selection in NHCC breeding.

Genomics for Yield and Yield Components in Durum Wheat

In recent years, many efforts have been conducted to dissect the genetic basis of yield and yield components in durum wheat thanks to linkage mapping and genome-wide association studies. In this review, starting from the analysis of the genetic bases that regulate the expression of yield for developing new durum wheat varieties, we have highlighted how, currently, the reductionist approach, i.e., dissecting the yield into its individual components, does not seem capable of ensuring significant yield increases due to diminishing resources, land loss, and ongoing climate change. However, despite the identification of genes and/or chromosomal regions, controlling the grain yield in durum wheat is still a challenge, mainly due to the polyploidy level of this species. In the review, we underline that the next-generation sequencing (NGS) technologies coupled with improved wheat genome assembly and high-throughput genotyping platforms, as well as genome editing technology, will revolutionize plant breeding by providing a great opportunity to capture genetic variation that can be used in breeding programs. To date, genomic selection provides a valuable tool for modeling optimal allelic combinations across the whole genome that maximize the phenotypic potential of an individual under a given environment.