Table_8.XLSX

Spike density (SD) is an agronomically important character in wheat. In addition, an optimized spike structure is a key basis for high yields. Identification of quantitative trait loci (QTL) for SD has provided a genetic basis for constructing ideal spike morphologies in wheat. In this study, two recombinant inbred line (RIL) populations (tetraploid RIL AM and hexaploid RIL 20828/SY95-71 (2SY)) previously genotyped using the wheat55K SNP array were used to identify SD QTL. A total of 18 QTL were detected, and three were major and one was stably expressed (QSd.sau-2SY-7A.2, QSd.sau-AM-5A.2, QSd.sau-AM-7B, and QSd.sau-2SY-2D). They can explain up to 23.14, 19.97, 12.00, and 9.44% of phenotypic variation, respectively. QTL × environment and epistatic interactions for SD were further analyzed. In addition, pyramiding analysis further revealed that there were additive effects between QSd.sau-2SY-2D and QSd.sau-2SY-7A.2 in 2SY, and QSd.sau-AM-5A.2 and QSd.sau-AM-7B in AM. Pearson’s correlation between SD and other agronomic traits, and effects of major or stable QTL on yield related traits indicated SD significantly impacted spike length (SL), spikelet number per spike (SNS) and kernel length (KL). Several genes related to spike development within the physical intervals of major or stable QTL were predicted and discussed. Collectively, our research identified QTL with potential applications for modern wheat breeding and broadening the genetic basis of SD.

Spike density (SD) is an agronomically important character in wheat. In addition, an optimized spike structure is a key basis for high yields. Identification of quantitative trait loci (QTL) for SD has provided a genetic basis for constructing ideal spike morphologies in wheat. In this study, two recombinant inbred line (RIL) populations (tetraploid RIL AM and hexaploid RIL 2SY) previously genotyped using the wheat55K SNP array were used to identify SD QTL. A total of 18 QTL were detected, and three were major and one was stably expressed (QSd.sau-2SY-7A.2, QSd.sau-AM-5A.2, QSd.sau-AM-7B and QSd.sau-2SY-2D). They can explain up to 23.14%, 19.97%, 11.23%, and 9.44% of phenotypic variation, respectively. QTL × environment and epistatic interactions for SD were further analyzed. In addition, pyramiding analysis further revealed that there were additive effects between QSd.sau-2SY-2D and QSd.sau-2SY-7A.2 in 2SY, and QSd.sau-AM-5A.2 and QSd.sau-AM-7B in AM. Pearson’s correlation between SD and other agronomic traits, and effects of major or stable QTL on yield related traits indicated SD significantly impacted spike length (SL), spikelet number per spike (SNS) and kernel length (KL). Several genes related to spike development within the physical intervals of major or stable QTL were predicated and discussed. Collectively, our research identified QTL with potential applications for modern wheat breeding and broadening the genetic basis of SD.

Spike traits play a pivotal role in determining wheat yield. In this study, two recombinant inbred line (RIL) populations were employed to identify quantitative trait loci (QTLs) for spike length (SL), spikelet number per spike (SNS), and basal sterile spikelet number (BSSN). A total of 30 QTLs were identified, including 7 major-effect QTLs that exhibited stability across multiple environments. Among these, two novel QTLs were discovered: QBssn.A-3 A.3 and QBssn.AV-5B.2. Furthermore, it was found that the combination of these two QTLs with other major effect QTLs had no impact on thousand-kernel weight (TKW). For QBssn.A-3 A.3, a Kompetitive Allele-Specific PCR (KASP) marker was developed and validated based on its physical location. Additionally, candidate genes within the intervals of QBssn.A-3 A.3 and QBssn.AV-5B.2 were predicted, based on these genes primarily associated with sterile spikelet number per spike. This study provides a foundation for future map-baesd cloning of genes related to spike traits, and for the breeding of high-yield wheat varieties.

BackgroundYield-related traits including thousand grain weight (TGW), grain number per spike (GNS), grain width (GW), grain length (GL), plant height (PH), spike length (SL), and spikelet number per spike (SNS) are greatly associated with grain yield of wheat (Triticum aestivum L.). To detect quantitative trait loci (QTL) associated with them, 193 recombinant inbred lines derived from two elite winter wheat varieties Chuanmai42 and Chuanmai39 were employed to perform QTL mapping in six/eight environments.ResultsA total of 30 QTLs on chromosomes 1A, 1B, 1D, 2A, 2B, 2D, 3A, 4A, 5A, 5B, 6A, 6D, 7A, 7B and 7D were identified. Among them, six major QTLs QTgw.cib-6A.1, QTgw.cib-6A.2, QGw.cib-6A, QGl.cib-3A, QGl.cib-6A, and QSl.cib-2D explaining 5.96-23.75% of the phenotypic variance were detected in multi-environments and showed strong and stable effects on corresponding traits. Three QTL clusters on chromosomes 2D and 6A containing 10 QTLs were also detected, which showed significant pleiotropic effects on multiple traits. Additionally, three Kompetitive Allele Specific PCR (KASP) markers linked with five of these major QTLs were developed. Candidate genes of QTgw.cib-6A.1/QGl.cib-6A and QGl.cib-3A were analyzed based on the spatiotemporal expression patterns, gene annotation, and orthologous search.ConclusionsSix major QTLs for TGW, GL, GW and SL were detected. Three KASP markers linked with five of these major QTLs were developed. These QTLs and KASP markers will be useful for elucidating the genetic architecture of grain yield and developing new wheat varieties with high and stable yield in wheat.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 13 major effect QTLs that contributed more than 10% toward phenotypic variation (PV) and 9 stable QTLs that could be repeatedly mapped in different generations. Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosomes D1 and D11. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18–21.45% of PV and qFS-A11-1 in F2:3, F2:4, and F2:5 with 6.05–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, while Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

BackgroundWheat (Triticum aestivum L.) is an important cereal crop. Increasing grain yield for wheat is always a priority. Due to the complex genome of hexaploid wheat with 21 chromosomes, it is difficult to identify underlying genes by traditional genetic approach. The combination of genetics and omics analysis has displayed the powerful capability to identify candidate genes for major quantitative trait loci (QTLs), but such studies have rarely been carried out in wheat. In this study, candidate genes related to yield were predicted by a combined use of linkage mapping and weighted gene co-expression network analysis (WGCNA) in a recombinant inbred line population.ResultsQTL mapping was performed for plant height (PH), spike length (SL) and seed traits. A total of 68 QTLs were identified for them, among which, 12 QTLs were stably identified across different environments. Using RNA sequencing, we scanned the 99,168 genes expression patterns of the whole spike for the recombinant inbred line population. By the combined use of QTL mapping and WGCNA, 29, 47, 20, 26, 54, 46 and 22 candidate genes were predicted for PH, SL, kernel length (KL), kernel width, thousand kernel weight, seed dormancy, and seed vigor, respectively. Candidate genes for different traits had distinct preferences. The known PH regulation genes Rht-B and Rht-D, and the known seed dormancy regulation genes TaMFT can be selected as candidate gene. Moreover, further experiment revealed that there was a SL regulatory QTL located in an interval of about 7 Mbp on chromosome 7A, named TaSL1, which also involved in the regulation of KL.ConclusionsA combination of QTL mapping and WGCNA was applied to predicted wheat candidate genes for PH, SL and seed traits. This strategy will facilitate the identification of candidate genes for related QTLs in wheat. In addition, the QTL TaSL1 that had multi-effect regulation of KL and SL was identified, which can be used for wheat improvement. These results provided valuable molecular marker and gene information for fine mapping and cloning of the yield-related trait loci in the future.

Optimal spike characteristics are critical in improving the sink capacity and yield potential of wheat even in harsh environments. However, the genetic basis of their response to nitrogen deficiency is still unclear. In this study, quantitative trait loci (QTL) for six spike-related traits, including heading date (HD), spike length (SL), spikelet number (SN), spike compactness (SC), fertile spikelet number (FSN), and sterile spikelet number (SSN), were detected under two different nitrogen (N) supplies, based on a high-density genetic linkage map constructed by PCR markers, DArTs, and Affymetrix Wheat 660 K SNP chips. A total of 157 traditional QTLand 54 conditional loci were detected by inclusive composite interval mapping, among which three completely low N-stress induced QTL for SN and FSN (qSn-1A.1, qFsn-1B, and qFsn-7D) were found to maintain the desired spikelet fertility and kernel numbers even under N deficiency through pyramiding elite alleles. Twenty-eight stable QTL showing significant differencet in QTL detection model were found and seven genomic regions (R2D, R4A, R4B, R5A, R7A, R7B, and R7D) clustered by these stable QTL were highlighted. Among them, the effect of R4B on controlling spike characteristics might be contributed from Rht-B1. R7A harboring three major stable QTL (qSn-7A.2, qSc-7A, and qFsn-7A.3) might be one of the valuable candidate regions for further genetic improvement. In addition, the R7A was found to show syntenic with R7B, indicating the possibly exsting homoeologous candidate genes in both regions. The SNP markers involved with the above highlighted regions will eventually facilitate positional cloning or marker-assisted selection for the optimal spike characteristics under various N input conditions.

Plant height (PH) is an important agronomic trait that is closely related to soybean yield and quality. However, it is a complex quantitative trait governed by multiple genes and is influenced by environment. Unraveling the genetic mechanism involved in PH, and developing soybean cultivars with desirable PH is an imperative goal for soybean breeding. In this regard, the present study used high-density linkage maps of two related recombinant inbred line (RIL) populations viz., MT and ZM evaluated in three different environments to detect additive and epistatic effect quantitative trait loci (QTLs) as well as their interaction with environments for PH in Chinese summer planting soybean. A total of eight and 12 QTLs were detected by combining the composite interval mapping (CIM) and mixed-model based composite interval mapping (MCIM) methods in MT and ZM populations, respectively. Among these QTLs, nine QTLs viz., QPH-2, qPH-6-2MT, QPH-6, qPH-9-1ZM, qPH-10-1ZM, qPH-13-1ZM, qPH-16-1MT, QPH-17 and QPH-19 were consistently identified in multiple environments or populations, hence were regarded as stable QTLs. Furthermore, Out of these QTLs, three QTLs viz., qPH-4-2ZM, qPH-15-1MT and QPH-17 were novel. In particular, QPH-17 could detect in both populations, which was also considered as a stable and major QTL in Chinese summer planting soybean. Moreover, eleven QTLs revealed significant additive effects in both populations, and out of them only six showed additive by environment interaction effects, and the environment-independent QTLs showed higher additive effects. Finally, six digenic epistatic QTLs pairs were identified and only four additive effect QTLs viz., qPH-6-2MT, qPH-19-1MT/QPH-19, qPH-5-1ZM and qPH-17-1ZM showed epistatic effects. These results indicate that environment and epistatic interaction effects have significant influence in determining genetic basis of PH in soybean. These results would not only increase our understanding of the genetic control of plant height in summer planting soybean but also provide support for implementing marker assisted selection (MAS) in developing cultivars with ideal plant height as well as gene cloning to elucidate the mechanisms of plant height.

The study was undertaken to identify the quantitative trait loci (QTLs) governing yield and its related traits using a recombinant inbred line (RIL) population derived from the popular rice hybrid, KRH-2 (IR58025A/KMR3R). A genetic map spanning 294.2 cM was constructed with 126 simple sequence repeats (SSR) loci uniformly distributed across the rice genome. QTL analysis using phenotyping and genotyping information identified a total of 22 QTLs. Of these, five major effect QTLs were identified for the following traits: total grain yield/plant (qYLD3-1), panicle weight (qPW3-1), plant height (qPH12-1), flag leaf width (qFLW4-1) and panicle length (qPL3-1), explaining 20.23–22.76% of the phenotypic variance with LOD scores range of 6.5–10.59. Few genomic regions controlling several traits (QTL hotspot) were identified on chromosome 3 for total grain yield/plant (qYLD3-1) and panicle length (qPL3-1). Significant epistatic interactions were also observed for total grain yield per plant (YLD) and panicle length (PL). While most of these QTLs were observed to be co-localized with the previously reported QTL regions, a novel, major QTL associated with panicle length (qPL3-1) was also identified. SNP genotyping of selected high and low yielding RILs and their QTL mapping with 1,082 SNPs validated most of the QTLs identified through SSR genotyping. This facilitated the identification of novel major effect QTLs with much better resolution and precision. In-silico analysis of novel QTLs revealed the biological functions of the putative candidate gene (s) associated with selected traits. Most of the high-yielding RILs possessing the major yield related QTLs were identified to be complete restorers, indicating their possible utilization in development of superior rice hybrids.

Kernel hardness (KH) is one of the primary quality parameters for common wheat (Triticum aestivum L.) and has a major impact on milling, flour quality, and end-product properties. In addition to Puroindoline (Pin) mutations and differences in Pin expression, other factors, such as kernel size and protein-related traits, play noticeable roles in determining hardness, but at the quantitative trait locus (QTL) level, the influence of these factors remains unclear. In this study, genetic relationships between KH and kernel size traits and between KH and protein-related traits were demonstrated by unconditional and conditional mapping using a wheat 90K genotyping assay with a segregating population of 173 recombinant inbred lines in four environments. Eight additive QTL for KH were detected using unconditional QTL mapping analysis; these QTL were primarily distributed on chromosomes 4B, 5A, 5B, and 6D, with phenotypic variation that ranged from 0.2 to 17.7%. In addition, one pair of epistatic QTL (QKH3B.4-65/QKH4B.6-2) was identified by unconditional mapping, and this pair accounted for 1.6% of the phenotypic variation. Through conditional mapping, after excluding the influences of kernel size and protein-related traits, 14 QTL were discovered and accounted for 0.6–18.5% of the phenotypic variation. Of them, the stable QTL QKH4B.4-17 made the largest contribution, which was partially contributed by the kernel length (KL), kernel thickness (KT), and dry gluten content (DGC). Furthermore, QKH4B.4-17 was crucially contributed by the kernel width (KW), kernel diameter (KD), kernel protein content (KPC), and wet gluten content (WGC) and was independent of the sedimentation volume (SV) and gluten index (GI). Another major QTL, QKH5B.10-63, was independent of the KW and KT; partly due to the variations in KL, KD, DGC, and WGC; and conclusively contributed by the KPC, SV, and GI. Seven additional QTL were only detected in the conditional analysis and were crucially contributed by kernel size or protein-related traits. These results demonstrated that kernel size and protein-related traits play significant roles in determining KH. The present study increases the understanding of the relationships between KH and kernel size and between KH and protein-related traits at the QTL level.

Silique length (SL) and seed weight (SW) are two important yield-related traits controlled by quantitative trait loci (QTL) in oilseed rape (Brassica napus L.). The genetic bases underlying these two traits are largely unknown at present. In this study, we conducted QTL analyses for SL and SW using 186 recombinant inbred lines (RILs) derived from a cross between S1, an EMS mutant with extremely long siliques and large seeds, and S2, an inbred line with regular silique length and seed size. RILs were grown in Wuhan in the 2008/09 (SS09) and 2009/10 (SS10) growing seasons, and mean SL and SW for each line were investigated. Ten non-redundant QTL were identified for SL. Of these, a major QTL, cqSLA9, consistently explained as much as 53.4% of SL variation across environments. The others are minor QTL and individually explained less than 10% of the SL variation. Nine non-redundant QTL were identified for SW. Of which, one major QTL, cqSWA9, explained as much as 28.2% of the total SW variation in the SS09 and SS10 environments. In addition, three additive by additive interactions with small effects were detected for SL, and no interactions were detected for SW. Interestingly, the two major QTL, cqSLA9 for SL and cqSWA9 for SW colocalized in the same chromosomal region and were integrated into a unique QTL, uqA9. The S1 allele at this locus increases both SL and SW, suggesting that uqA9 has pleiotropic effects on both SL and SW. The existence and effect of uqA9 was confirmed in genetically different RILs derived from the cross between S1 and No2127, a resynthesized DH line having regular silique length and seed size. Individuals in one residual heterozygous line for cqSLA9 showed significant difference in silique length. The results in this study revealed that silique length in the S1 mutant is mainly controlled by the cqSLA9 locus, which will be suitable for fine mapping and marker-assisted selection in rapeseed breeding for high yield.

Identification of major QTL for seed number per pod on chromosome A05 of tetraploid peanut (Arachis hypogaea L.)

Breeding for fruit quality traits is complex due to the polygenic (quantitative) nature of the genetic control of these traits. Therefore, to improve the speed and efficiency of genotype selection, attention in recent years has focused on the identification of quantitative trait loci (QTLs) and molecular markers associated with these QTLs. However, despite the huge potential of molecular markers in breeding programmes, their implementation in practice has been limited by the lack of information on the stability of QTLs across different environments and within different genetic backgrounds. Here, we present the results from a comprehensive analysis of the inheritance of fruit quality traits within a population derived from a cross between the apple cultivars ‘Telamon’ and ‘Braeburn’ over two successive seasons. A total of 74 different QTLs were identified for all the major fruit physiological traits including fruit height, diameter, weight and stiffness, flesh firmness, rate of flesh browning, acidity, the oBrix content and harvest date. Seventeen of these QTLs were ‘major’ QTLs, accounting for over 20% of the observed population variance of the trait. However, only one third (26) of the identified QTLs were stable over both harvest years, and of these year-stable QTLs only one was a major QTL. A direct comparison with published QTL results obtained using other populations (King et al., Theor Appl Genet 102:1227–1235, 2001; Liebhard et al., Plant Mol Biol 52:511–526, 2003) is difficult because the linkage maps do not share a sufficient number of common markers and due to differences in the trait evaluation protocols. Nonetheless, our results suggest that for the six fruit quality traits which were measured in all populations, nine out of a total of 45 QTLs were common or stable across all population × environments combinations. These results are discussed in the framework of the development and application of molecular markers for fruit quality trait improvement.

Grain weight, one of the three major components of rice yield, is largely determined by grain size, which is controlled by quantitative trait loci (QTLs). In a previous study, we identified qGS5 as a major QTL for grain width. Here, we report our identification of two more major grain-size QTLs (qGL3 and qGW2a) by using a recombinant inbred line (RIL) population from a cross of two indica varieties, ‘Zhenshan 97’ and ‘SLG’. To investigate the contribution of the three grain-size QTLs to final grain weight, we developed near-isogenic lines (NILs) NIL-qGL3, NIL-qGW2a, and NIL-qGS5 and used these to build the combined QTLs–NIL in the genetic background of ‘Zhenshan 97’ by marker-assisted selection and conventional backcrossing, respectively. A BCF2 population of 957 individuals was developed from the combined QTLs-NIL for further study of the genetic control of grain size. The QTL analysis revealed that qGW2a and qGL3 played more important roles in grain weight gain than qGS5. All three QTLs showed additive effects with respect to grain weight, with no interaction. These results clearly indicate that pyramiding of major grain-size QTLs is a useful approach for improving rice yield.

Lodging tolerance (LT) is an important trait for high yield and combine-harvesting efficiency in soybean [Glycine max (L.) Merr.]. Many previous studies have investigated quantitative trait loci (QTLs) for lodging score (LS) in soybean. Most of the investigated QTLs were located in the proximal region of maturity or growth habit loci. The aim of this study was to identify genetic factors for LT not associated with maturity or growth habit. QTL analysis was performed using a recombinant inbred line (RIL) population derived from a cross between ‘Toyoharuka’ (TH), a lodging-tolerant cultivar, and ‘Toyomusume’ (TM). The genotypes of TH and TM were estimated as both e1e2E3E4 and dt1. The average LS over 4 years was used for QTL analysis, identifying a major and stable QTL, qLS19-1, on chromosome 19. The LS of the near-isogenic line (NIL) with the TH allele at Sat_099, the nearest marker to qLS19-1, was significantly lower than the NIL with the TM allele at that position. The TH allele at Sat_099 rarely had a negative influence on seed yield or other agronomic traits in both NILs and the TM-backcrossed lines. Our results suggest that marker-assisted selection for qLS19-1 is effective for improving LT in breeding programs.