Genetic Mapping and Validation of Loci for Kernel-Related Traits in Wheat (Triticum aestivum L.)

Abstract

Kernel size (KS) and kernel weight play a key role in wheat yield. Phenotypic data from six environments and a Wheat55K single-nucleotide polymorphism array–based constructed genetic linkage map from a recombinant inbred line population derived from the cross between the wheat line 20828 and the line SY95-71 were used to identify quantitative trait locus (QTL) for kernel length (KL), kernel width (KW), kernel thickness (KT), thousand-kernel weight (TKW), kernel length–width ratio (LWR), KS, and factor form density (FFD). The results showed that 65 QTLs associated with kernel traits were detected, of which the major QTLs QKL.sicau-2SY-1B, QKW.sicau-2SY-6D, QKT.sicau-2SY-2D, and QTKW.sicau-2SY-2D, QLWR.sicau-2SY-6D, QKS.sicau-2SY-1B/2D/6D, and QFFD.sicau-2SY-2D controlling KL, KW, KT, TKW, LWR, KS, and FFD, and identified in multiple environments, respectively. They were located on chromosomes 1BL, 2DL, and 6DS and formed three QTL clusters. Comparison of genetic and physical interval suggested that only QKL.sicau-2SY-1B located on chromosome 1BL was likely a novel QTL. A Kompetitive Allele Specific Polymerase chain reaction (KASP) marker, KASP-AX-109379070, closely linked to this novel QTL was developed and used to successfully confirm its effect in two different genetic populations and three variety panels consisting of 272 Chinese wheat landraces, 300 Chinese wheat cultivars most from the Yellow and Huai River Valley wheat region, and 165 Sichuan wheat cultivars. The relationships between kernel traits and other agronomic traits were detected and discussed. A few predicted genes involved in regulation of kernel growth and development were identified in the intervals of these identified major QTL. Taken together, these stable and major QTLs provide valuable information for understanding the genetic composition of kernel yield and provide the basis for molecular marker–assisted breeding.

Spike Density Quantitative Trait Loci Detection and Analysis in Tetraploid and Hexaploid Wheat Recombinant Inbred Line Populations

“…These studies revealed the potential for pleiotropism of corresponding traits and also revealed the genetic correlations among SD, SL, and SNS. In addition, higher SD may result in shorter kernels and smaller TKW, being consistent with the study reported by Qu et al (2021) . Thus, it is essential to determine relationships among different characters to accelerate breeding process.…”

“…In the CS genome, the QSd.sicau-2D.3 identified by Liu et al (2019) overlapped with QSd.sau-2SY-2D between 605.12 and 609.88 Mb on chromosome 2DL ( Figure 3A ); qSc-7A ( Fan et al, 2019 ), explaining 4.87–17.22% of variation in SD, was located between 679.70 and 679.92 Mb, and QSd.sau-2SY-7A.2 was mapped to between 678.47 and 682.29 Mb ( Figure 3B ), indicating that they may be allelic variants. In addition, comparing with the physical maps of QTL for other yield-related traits such as SNS ( Ding et al, 2021 ), KL, KW, kernel thickness (KT), TKW, kernel length–width ratio (LWR), and kernel size (KS) ( Qu et al, 2021 ), none of the physical regions of QTL for SD in the present study overlapped with these yield-related loci.…”

“…The phenotypic data of agronomic traits used in this experiment have been measured in previous studies, including spikelet number per spike (SNS), SL, PH, anthesis date (AD), productive tiller number (PTN), thousand kernel weight (TKW), kernel length (KL), kernel number per spike (KNS), kernel number per spikelet (KNL), and kernel width (KW). The agronomic traits of the 2SY populations were measured by Liu et al (2020) (PH, AD, TKW, PTN, SNS), Ding et al (2021) (SNS), Li et al (2020) (SL), and Qu et al (2021) (KL, KW). The phenotype values for SNS in 2018KB were determined by Ding et al (2021) , and the SNS data across remainder environments was determined by Liu et al (2020) .…”

Major Genomic Regions for Wheat Grain Weight as Revealed by QTL Linkage Mapping and Meta-Analysis

“…Thousand Grain Weight is a key determinant that is related to grain yield potential in wheat and is influenced by both genetic and environmental factors ( Wang L. et al, 2012 ; Avni et al, 2018 ; Duan et al, 2020 ). Compared with other yield components, TGW had more stable phenotypic variation and higher heritability ( Mir et al, 2012 ; Wang L. et al, 2012 ; Cheng et al, 2015 ; Hu et al, 2015 ; Krishnappa et al, 2017 ; Avni et al, 2018 ; Kumari et al, 2018 ; Duan et al, 2020 ; Xin et al, 2020 ; Yang et al, 2020 ; Gao et al, 2021 ; Qu et al, 2021 ). Likewise, TGW trait in the present study also showed a prominent main-effect of genotype, with a high heritability ( h 2 B = 0.77), relatively lower CV% (6.08-12.80%), and significant correlations ( r = 0.62 ** −0.92 ** ) in the performance of the RILs under eight different environments.…”

“…It is essential to identify major TGW quantitative trait loci (QTL) and further exploit elite genes in the genetic improvement of modern wheat breeding programs. In the last two decades, a large number of QTLs underlying TGW have been successfully identified by traditional bi-parental linkage mapping ( Cheng et al, 2015 ; Hu et al, 2015 ; Krishnappa et al, 2017 ; Kumari et al, 2018 ; Xin et al, 2020 ; Qu et al, 2021 ) and genome-wide association approaches ( Mir et al, 2012 ; Yang et al, 2020 ; Gao et al, 2021 ). These loci provide a great convenience for revealing the genetic basis of wheat TGW formation.…”