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That root system architecture (RSA) has an essential role in nitrogen acquisition is expected in maize, but the genetic relationship between RSA and nitrogen use efficiency (NUE) traits remains to be elucidated. Here, the genetic basis of RSA and NUE traits was investigated in maize using a recombination inbred line population that was derived from two lines contrasted for both traits. Under high-nitrogen and low-nitrogen conditions, 10 NUE- and 9 RSA-related traits were evaluated in four field environments and three hydroponic experiments, respectively. In contrast to nitrogen utilization efficiency (NutE), nitrogen uptake efficiency (NupE) had significant phenotypic correlations with RSA, particularly the traits of seminal roots (r = 0.15–0.31) and crown roots (r = 0.15–0.18). A total of 331 quantitative trait loci (QTLs) were detected, including 184 and 147 QTLs for NUE- and RSA-related traits, respectively. These QTLs were assigned into 64 distinct QTL clusters, and ∼70% of QTLs for nitrogen-efficiency (NUE, NupE, and NutE) coincided in clusters with those for RSA. Five important QTLs clusters at the chromosomal regions bin1.04, 2.04, 3.04, 3.05/3.06, and 6.07/6.08 were found in which QTLs for both traits had favourable effects from alleles coming from the large-rooted and high-NupE parent. Introgression of these QTL clusters in the advanced backcross-derived lines conferred mean increases in grain yield of ∼14.8% for the line per se and ∼15.9% in the testcross. These results reveal a significant genetic relationship between RSA and NUE traits, and uncover the most promising genomic regions for marker-assisted selection of RSA to improve NUE in maize.

Summary Cotton is widely cultivated globally because it provides natural fibre for the textile industry and human use. To identify quantitative trait loci (QTLs)/genes associated with fibre quality and yield, a recombinant inbred line (RIL) population was developed in upland cotton. A consensus map covering the whole genome was constructed with three types of markers (8295 markers, 5197.17 centimorgans (cM)). Six fibre yield and quality traits were evaluated in 17 environments, and 983 QTLs were identified, 198 of which were stable and mainly distributed on chromosomes 4, 6, 7, 13, 21 and 25. Thirty‐seven QTL clusters were identified, in which 92.8% of paired traits with significant medium or high positive correlations had the same QTL additive effect directions, and all of the paired traits with significant medium or high negative correlations had opposite additive effect directions. In total, 1297 genes were discovered in the QTL clusters, 414 of which were expressed in two RNA‐Seq data sets. Many genes were discovered, 23 of which were promising candidates. Six important QTL clusters that included both fibre quality and yield traits were identified with opposite additive effect directions, and those on chromosome 13 (qClu‐chr13‐2) could increase fibre quality but reduce yield; this result was validated in a natural population using three markers. These data could provide information about the genetic basis of cotton fibre quality and yield and help cotton breeders to improve fibre quality and yield simultaneously.

The seed weight of soybean [ (L.) Merr.] is one of the major traits that determine soybean yield and is closely related to seed size. However, the genetic basis of the synergistic regulation of traits related to soybean yield is unclear. To understand the molecular genetic basis for the formation of soybean yield traits, the present study focused on QTLs mapping for seed size and weight traits in different environments and target genes mining. A total of 85 QTLs associated with seed size and weight traits were identified using a recombinant inbred line (RIL) population developed from Guizao1×B13 (GB13). We also detected 18 environmentally stable QTLs. Of these, was a novel QTL with a stable main effect associated with seed length. It was detected in all environments, three of which explained more than 10% of phenotypic variance (PV), with a maximum of 15.91%. In addition, was a novel QTL with a stable main effect associated with seed width, which was identified in four environments. And the amount of phenotypic variance explained (PVE) varied from 9.22 to 21.93%. Five QTL clusters associated with both seed size and seed weight were summarized by QTL cluster identification. Fifteen candidate genes that may be involved in regulating soybean seed size and weight were also screened based on gene function annotation and GO enrichment analysis. The results provide a biologically basic reference for understanding the formation of soybean seed size and weight traits.

Background Improving fiber quality and yield are the primary research objectives in cotton breeding for enhancing the economic viability and sustainability of Upland cotton production. Identifying the quantitative trait loci (QTL) for fiber quality and yield traits using the high-density SNP-based genetic maps allows for bridging genomics with cotton breeding through marker assisted and genomic selection. In this study, a recombinant inbred line (RIL) population, derived from cross between two parental accessions, which represent broad allele diversity in Upland cotton, was used to construct high-density SNP-based linkage maps and to map the QTLs controlling important cotton traits. Results Molecular genetic mapping using RIL population produced a genetic map of 3129 SNPs, mapped at a density of 1.41 cM. Genetic maps of the individual chromosomes showed good collinearity with the sequence based physical map. A total of 106 QTLs were identified which included 59 QTLs for six fiber quality traits, 38 QTLs for four yield traits and 9 QTLs for two morphological traits. Sub-genome wide, 57 QTLs were mapped in A sub-genome and 49 were mapped in D sub-genome. More than 75% of the QTLs with favorable alleles were contributed by the parental accession NC05AZ06. Forty-six mapped QTLs each explained more than 10% of the phenotypic variation. Further, we identified 21 QTL clusters where 12 QTL clusters were mapped in the A sub-genome and 9 were mapped in the D sub-genome. Candidate gene analyses of the 11 stable QTL harboring genomic regions identified 19 putative genes which had functional role in cotton fiber development. Conclusion We constructed a high-density genetic map of SNPs in Upland cotton. Collinearity between genetic and physical maps indicated no major structural changes in the genetic mapping populations. Most traits showed high broad-sense heritability. One hundred and six QTLs were identified for the fiber quality, yield and morphological traits. Majority of the QTLs with favorable alleles were contributed by improved parental accession. More than 70% of the mapped QTLs shared the similar map position with previously reported QTLs which suggest the genetic relatedness of Upland cotton germplasm. Identification of QTL clusters could explain the correlation among some fiber quality traits in cotton. Stable and major QTLs and QTL clusters of traits identified in the current study could be the targets for map-based cloning and marker assisted selection (MAS) in cotton breeding. The genomic region on D12 containing the major stable QTLs for micronaire, fiber strength and lint percentage could be potential targets for MAS and gene cloning of fiber quality traits in cotton.

Wheat ( Triticum aestivum L.) is one of the most important cereal crops for ensuring food security worldwide. Identification of major quantitative trait loci (QTL) for spike-related traits is important for improvement of yield potential in wheat breeding. In this study, by using the wheat 55K single nucleotide polymorphism (SNP) array and diversity array technology (DArT), two recombinant inbred line populations derived from crosses avocet/chilero and avocet/huites were used to map QTL for kernel number per spike (KNS), total spikelet number per spike (TSS), fertile spikelet number per spike (FSS), and spike compactness (SC). Forty-two QTLs were identified on chromosomes 2A (4), 2B (3), 3A (2), 3B (7), 5A (11), 6A (4), 6B, and 7A (10), explaining 3.13–21.80% of the phenotypic variances. Twelve QTLs were detected in multi-environments on chromosomes 2A, 3B (2), 5A (4), 6A (3), 6B, and 7A, while four QTL clusters were detected on chromosomes 3A, 3B, 5A, and 7A. Two stable and new QTL clusters, QKns/Tss/Fss/SC.haust-5A and QKns/Tss/Fss.haust-7A , were detected in the physical intervals of 547.49–590.46 Mb and 511.54–516.15 Mb, accounting for 7.53–14.78% and 7.01–20.66% of the phenotypic variances, respectively. High-confidence annotated genes for QKns/Tss/Fss/SC.haust-5A and QKns/Tss/Fss.haust-7A were more highly expressed in spike development . The results provide new QTL and molecular markers for marker-assisted breeding in wheat.

As the major determinant for nutrient uptake, root system architecture (RSA) has a massive impact on nitrogen use efficiency (NUE). However, little is known the molecular control of RSA as related to NUE in rapeseed. Here, a rapeseed recombinant inbred line population (BnaZNRIL) was used to investigate root morphology (RM, an important component for RSA) and NUE-related traits under high-nitrogen (HN) and low-nitrogen (LN) conditions by hydroponics. Data analysis suggested that RM-related traits, particularly root size had significantly phenotypic correlations with plant dry biomass and N uptake irrespective of N levels, but no or little correlation with N utilization efficiency (NUtE), providing the potential to identify QTLs with pleiotropy or specificity for RM- and NUE-related traits. A total of 129 QTLs (including 23 stable QTLs, which were repeatedly detected at least two environments or different N levels) were identified and 83 of them were integrated into 22 pleiotropic QTL clusters. Five RM-NUE, ten RM-specific and three NUE-specific QTL clusters with same directions of additive-effect implied two NUE-improving approaches (RM-based and N utilization-based directly) and provided valuable genomic regions for NUE improvement in rapeseed. Importantly, all of four major QTLs and most of stable QTLs (20 out of 23) detected here were related to RM traits under HN and/or LN levels, suggested that regulating RM to improve NUE would be more feasible than regulating N efficiency directly. These results provided the promising genomic regions for marker-assisted selection on RM-based NUE improvement in rapeseed.

In the present study, four large-scale field trials using two doubled haploid wheat populations were conducted in different environments for two years. Grain protein content (GPC) and 21 other yield-related traits were investigated. A total of 227 QTL were mapped on 18 chromosomes, which formed 35 QTL clusters. The potential candidate genes underlying the QTL clusters were suggested. Furthermore, adding to the significant correlations between yield and its related traits, correlation variations were clearly shown within the QTL clusters. The QTL clusters with consistently positive correlations were suggested to be directly utilized in wheat breeding, including 1B.2, 2A.2, 2B (4.9–16.5 Mb), 2B.3, 3B (68.9–214.5 Mb), 4A.2, 4B.2, 4D, 5A.1, 5A.2, 5B.1, and 5D. The QTL clusters with negative alignments between traits may also have potential value for yield or GPC improvement in specific environments, including 1A.1, 2B.1, 1B.3, 5A.3, 5B.2 (612.1–613.6 Mb), 7A.1, 7A.2, 7B.1, and 7B.2. One GPC QTL (5B.2: 671.3–672.9 Mb) contributed by cultivar Spitfire was positively associated with nitrogen use efficiency or grain protein yield and is highly recommended for breeding use. Another GPC QTL without negatively pleiotropic effects on 2A (50.0–56.3 Mb), 2D, 4D, and 6B is suggested for quality wheat breeding.

Seed vigor is an important parameter of seed quality, and identification of seed vigor related genes can provide an important basis for highly efficient molecular breeding in wheat. In the present study, a doubled haploid (DH) population with 174 lines derived from a cross between Yangmai16 and Zhongmai 895 was used to evaluate 10 seed vigor related traits in Luoyang during the 2018-2019 cropping season and in Mengjin and Luoning Counties during 2019-2020 cropping season for three environments. Quantitative trait locus (QTL) mapping of 10 seed vigor related traits in the DH population resulted in the discovery/identification of 28 QTLs on chromosomes 2B, 3D, 4B, 4D, 5A, 5B, 6A, 6B, 6D, 7A and 7D, explaining 3.6-23.7% of the phenotypic variances. Among them, one QTL cluster for shoot length, root length and vigor index was mapped between AX-89421921 and Rht-D1_SNP on chromosome 4D in the physical intervals of 18.78-19.29 Mb (0.51 Mb), explaining 9.2-20.5% of the phenotypic variances. Another QTL for these traits was identified at the physical position 185.74 Mb on chromosome 5B, which was flanked by AX-111465230 and AX-109519938 and accounted for 8.0-13.3% of the phenotypic variances. Two QTLs for shoot length, shoot fresh weight and shoot dry weight were identified in the marker intervals of AX-109384026-AX-111120402 and AX-111651800-AX-94443918 on chromosomes 6A and 6B, explaining 8.2-11.7% and 3.6-10.3% of the phenotypic variance, respectively; both alleles for increasing phenotypic values were derived from Yangmai 16. We also developed the KASP markers for the QTL cluster QVI.haust-4D.1/QSL.haust-4D/QRL.haust-4D , and validated in an international panel of 135 wheat accessions. The germplasm, genes and KASP markers were developed for breeders to improve wheat varieties with seed vigor related traits.

Plant height (PH), branch initiation height (BIH), and stem diameter (SD) are three stem-related traits that play crucial roles in plant architecture and lodging resistance. Herein, we show one doubled haploid (DH) population obtained from a cross between Y689 (one - derived intertribal introgression) and Westar ( . cultivar) that these traits were significantly positively correlated with one another and with flowering time (FT). Based on a high-density SNP map, a total of 102 additive quantitative trait loci (QTL) were identified across six environments. Seventy-two consensus QTL and 49 unique QTL were identified using a two-round strategy of QTL meta-analysis. Notably, a total of 19 major QTL, including 11 novel ones, were detected for these traits, which comprised two QTL clusters on chromosomes A02 and A07. Conditional QTL mapping was performed to preliminarily evaluate the genetic basis (pleiotropy or tight linkage) of the co-localized QTL. In addition, QTL by environment interactions (QEI) mapping was performed to verify the additive QTL and estimate the QEI effect. In the genomic regions of all major QTL, orthologs of the genes involved in phytohormone biosynthesis, phytohormone signaling, flower development, and cell differentiation in were proposed as candidate genes. Of these, , an ortholog of , was suggested as a candidate gene for PH, SD, and FT; and , an ortholog of , was associated with PH, BIH and FT. These results provide useful information for further genetic studies on stem-related traits and plant growth adaptation.

The recombinant inbred lines of inter-specific cross, Gossypium hirsutum cv. DS-28 × G. barbadense cv. SBYF-425 was evaluated in three consecutive rainy seasons of 2017–18 (F13), 2018–19 (F14) and 2019–20 (F15) in an augmented design. The preponderance of huge continuous variability for both productivity and fiber quality traits was recorded. The principal component analysis revealed that the mapping population was well suited for mapping of productivity and fiber quality traits. On the basis the Z-scores for skewness and kurtosis, 178 RILs with normal distribution were selected for genetic linkage mapping. A high-density saturated linkage map was constructed using SNP arrays of CottonSNP63K, an Illumina’s infinium array and CottonSNP50K, CSIR-National Botanical Research Institute’s Axiom array with a total spanned length of 2402.65 cM, an average marker density of 1.54 and with map coverage of 96.99% of the reference genome. The developed genetic map of inter specific cross of Indian cotton varieties is a highly saturated in terms of coverage and highly comparable to the published maps. In QTL analysis, altogether 99 QTLs were identified for productivity and fiber quality traits. Among those, eight were stable and 38 were major QTLs. Cluster 1, 4 and 6 respectively on chromosome AD_chr.03, AD_chr.14 and AD_chr.18 were the biggest QTL clusters each with four QTLs and cluster 4 and 6 were QTL hotspots for fiber quality traits.