Molecular mapping of quantitative trait loci for three kernel-related traits in maize using a double haploid population

Kernel size and kernel weight are important factors possibly involved in the determination of grain yield in maize, so identifying the genetic basis of kernel-related traits provides insights into the breeding of high-yield maize varieties. Kernel length (KL), kernel width (KW) and hundred kernel weight (HKW) were evaluated in three various planting conditions for the 240 field-grown double haploid (DH) lines derived from the single-cross hybrid Xianyu335. Variations in KL, KW and HKW were observed among DH lines, and all three traits showed a broad sense heritability of 76%. A total of 964 single nucleotide polymorphisms (SNPs) from the MaizeSNP3072 chip was utilised to create a high-density genetic map of 1546.4 cM and to identify quantitative trait loci (QTLs). Using composite interval mapping, a total of five, seven and five QTLs have been mapped for KL, KW and HKW, respectively. qkl1-2 and qkl4-1 explained 17.8% and 14.2% of the phenotypic variation in KL, respectively, and the other three QTLs contributed 3.2–4.0%. The phenotypic variation explained (PVE) of seven QTLs responsible for KW ranged from 3.3 to 9.5%. Three QTLs for HKW, qhkw1, qhkw5 and qhkw10 each explained more than 10% of the phenotypic variation, and qhkw4 and qhkw9 accounted for 3.0% and 6.0%, respectively. Due to their detection in multiple planting environments, the loci mapped here appear to be potential targets for the improvement of maize grain yield.     

QTL mapping for yield and photosynthetic related traits under different water regimes in wheat

The improvement for drought tolerance requires understanding of the genetic control of wheat (Triticum aestivum L.) reaction to drought. In this study, a set of 131 recombinant inbred lines of wheat were investigated under well-watered (WW) and drought stress (DS) environments across 2 years to map quantitative trait loci (QTLs) for yield and physiological traits. A total of 225 QTLs were detected, including 32 non-environment-specific loci that were significant in both DS and WW, one drought-specific locus and two watering-specific loci. Three consistently-expressed QTLs (QTkw-3A.2, QTss-1A, and QScn-7A.1) were identified in at least three environments and the QTkw-1D.1 was significant in DS across the 2 years. By unconditional and conditional QTL analysis, spike number per plant and kernel number per spike were more important than thousand-kernel weight for grain yield (GY) at the given genetic background. Meta-analysis identified 67 meta-QTLs that contained QTLs for at least two traits. High frequency co-location of QTLs was found among either the spike-related traits or the six physiological traits. Four photosynthesis traits (CHL, LWUE, P _N, and C _i) were co-located with GY and/or yield components on various MQTLs. The results provided QTLs that warrant further study for drought tolerance breeding and are helpful for understanding the genetic basis of drought tolerance and the genetic contribution of yield components to GY at individual QTL level in wheat.     

Mapping QTLs for physiological and biochemical traits related to grain yield under control and terminal heat stress conditions in bread wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

In order to detect genomic regions with different effects for some of the physiological and biochemical traits of wheat, four experiments were conducted at Research Farm of Agricultural and Natural Resources Research Center of Zabol in 2015–2016 and 2016–2017 growing seasons. The experiments were carried out using four alpha lattice designs with two replications under non-stress and terminal heat stress conditions. Plant materials used in this study included 167 recombinant inbred lines and their parents (‘SeriM82’ and ‘Babax’). Six traits including grain yield (GY), proline content (PRO), water soluble carbohydrates (WSC), maximum efficiency of photosystem II (Fv/Fm), cytoplasmic membrane stability (CMS) and chlorophyll content (CHL) were evaluated. Genetic linkage map consisted of 211 AFLP marker, 120 SSR marker and 144 DArT markers with 1864 cm length and 4.4 cm mean distance. QTL analysis was carried out using a mixed-model-based composite interval mapping (MCIM) method. By the combined analysis of normal phenotypic values, 27 additive QTLs and five pairs of epistatic effects were identified for studied traits, among which two additive and one epistatic QTL showed significant QTL?×?environment interactions. By the combined analysis of stress phenotypic values, a total of 26 QTLs with additive effects and 5 epistatic QTLs were detected, among which one additive and one epistatic QTL showed QTL?×?environment interactions. Six QTLs with major effects (QGY-2B, QGY-2D, QPro-5B, QWSC-4A, QFv/Fm-6A and QCMS-4B), which were common between two conditions could be useful for marker-assisted selection (MAS) in order to develop heat tolerant and high-performance wheat varieties.     

Quantitative trait loci mapping of yield and related traits using a high-density genetic map of maize

Improving grain yield is the ultimate goal of the maize-breeding programs. In this study, analyses of conditional and unconditional quantitative trait locus (QTL) and epistatic interactions were used to elucidate the genetic architecture of yield and its related traits. A total of 15 traits of a recombinant inbred line population, including yield per plant (YPP), seven ear-related traits, and seven kernel-related traits, were measured in six different environments. Based on the genetic linkage map constructed using 2091 bins as markers, 56 main-effect QTLs for these traits were identified. These QTLs were distributed across eight genomic regions (bin 1.06, bin 4.02/4.05/4.08, bin 5.04, bin 7.04, bin 8.08, and bin 9.04), within the marker intervals of 85.45–6260.66 kb, and the phenotypic variance explained ranging from 5.69 to 11.56 %. One gene (GRMZM2G168229) encoding SBP-box domain protein was located in the small interval of qKRN4-3 and may be involved in patterning of kernel row number. Seventeen conditional QTLs identified for YPP were conditioned on its related traits and explained 6.18–23.15 % of the phenotypic variance. Conditional mapping analysis revealed that qYPP4-1, qYPP6-1, and qYPP8-1 are partially influenced by YPP-related traits at the individual QTL level. Digenic epistatic analysis identified 12 digenic interactions involving 22 loci across the whole genome. In addition, conditional digenic epistatic analysis identified 14 digenic interactions involving 21 loci. This study provides valuable information for understanding the genetic relationship between YPP and related traits and constitutes the first step toward the cloning of the relevant genes.     

Mapping of QTLs associated with resistance to common bunt,tan spot,leaf rust,and stripe rust in a spring wheat population

Spring wheat (Triticum aestivum L.) breeding goals in western Canada include good agronomic characteristics and good end-use quality, and also moderate to elevated resistance to diseases of economic importance. In this study, we aimed to identify quantitative trait loci (QTL) associated with resistance to common bunt (Tilletia tritici and Tilletia laevis), tan spot (Pyrenophora tritici-repentis), leaf rust (Puccinia triticina), and stripe rust (Puccinia striiformis f. sp. tritici). A total of 167 recombinant inbred lines (RILs) derived from a cross between two spring wheat cultivars, ‘Attila’ and ‘CDC Go’, were evaluated for reactions to the four diseases in nurseries from three to eight environments, and genotyped with the Wheat 90K SNP array and three gene-specific markers (Ppd-D1, Vrn-A1, and Rht-B1). The RILs exhibited transgressive segregation for all four diseases, and we observed several lines either superior or inferior to the parents. Broad-sense heritability varied from 0.25 for leaf rust to 0.48 for common bunt. Using a subset of 1203 informative markers (1200 SNPs and 3 gene-specific markers) and average disease scores across all environments, we identified two QTLs (QCbt.dms-1B.2 and QCbt.dms-3A) for common bunt, and three QTLs each for tan spot (QTs.dms-2B, QTs.dms-2D, and QTs.dms-6B), leaf rust (QLr.dms-2D.1, QLr.dms-2D.2, and QLr.dms-3A), and stripe rust (QYr.dms-3A, QYr.dms-4A, and QYr.dms-5B). Each QTL individually explained between 5.9 and 18.7% of the phenotypic variation, and altogether explained from 21.5 to 26.5% of phenotypic and from 52.2 to 86.0% of the genetic variation. The resistance alleles for all QTLs except one for stripe rust (QYr.dms-5B) were from CDC Go. Some of the QTLs are novel, while others mapped close to QTLs and/or genes reported in other studies.     

Integrated analysis of leaf morphological and color traits in different populations of Chinese cabbage (<Emphasis Type="Italic">Brassica rapa</Emphasis> ssp. <Emphasis Type="Italic">pekinensis</Emphasis>)

Key message

QTLs and candidate gene markers associated with leaf morphological and color traits were identified in two immortalized populations of Brassica rapa, which will provide genetic information for marker-assisted breeding.

Abstract

Brassica rapa is an important leafy vegetable consumed worldwide and morphology is a key character for its breeding. To enhance genetic control, quantitative trait loci (QTLs) for leaf color and plant architecture were identified using two immortalized populations with replications of 2 and 4 years. Overall, 158 and 80 QTLs associated with 23 and 14 traits were detected in the DH and RIL populations, respectively. Among them, 23 common robust-QTLs belonging to 12 traits were detected in common loci over the replications. Through comparative analysis, five crucifer genetic blocks corresponding to morphology trait (R, J&U, F and E) and color trait (F, E) were identified in three major linkage groups (A2, A3 and A7). These might be key conserved genomic regions involved with the respective traits. Through synteny analysis with Arabidopsis, 64 candidate genes involved in chlorophyll biosynthesis, cell proliferation and elongation were co-localized within QTL intervals. Among them, SCO3, ABI3, FLU, HCF153, HEMB1, CAB3 were mapped within QTLs for leaf color; and CYCD3;1, CYCB2;4, AN3, ULT1 and ANT were co-localized in QTL regions for leaf size. These robust QTLs and their candidate genes provide useful information for further research into leaf architecture with crop breeding.

     

Genetic analysis of shoot fresh weight in a cross of wild (<Emphasis Type="Italic">G. soja</Emphasis>) and cultivated (<Emphasis Type="Italic">G. max</Emphasis>) soybean

Shoot fresh weight (SFW) is one of the parameters, used to estimate the total plant biomass yield in soybean. In the present study, a total of 188 F_5:8 recombinant inbred lines (RIL) derived from an interspecific cross of PI 483463 (Glycine soja) and Hutcheson (Glycine max) were investigated for SFW variation in the field for three consecutive years. The parental lines and RILs were phenotyped in the field at the R6 stage by measuring total biomass in kg/plot to identify the QTLs for SFW. Three QTLs qSFW6_1, qSFW15_1, and qSFW19_1 influencing SFW were identified on chromosome 6, 15, and 19, respectively. The QTL qSFW19_1 flanked between the markers BARC-044913-08839 and BARC-029975-06765 was the stable QTL expressed in all the three environments. The phenotypic variation explained by the QTLs across all environments ranged from 6.56 to 21.32 %. The additive effects indicated contribution of alleles from both the parents and additive × environment interaction effects affected the expression of SFW QTL. Screening of the RIL population with additional SSRs from the qSFW19_1 region delimited the QTL between the markers SSR19-1329 and BARC-29975-06765. QTL mapping using bin map detected two QTLs, qSFW19_1A and qSFW19_1B. The QTL qSFW19_1A mapped close to the Dt1 gene locus, which affects stem termination, plant height, and floral initiation in soybean. Potential candidate genes for SFW were pinpointed, and sequence variations within their sequences were detected using high-quality whole-genome resequencing data. The findings in this study could be useful for understanding genetic basis of SFW in soybean.     

QTLs for heading date and plant height under multiple environments in rice

Both heading date and plant height are important traits related to grain yield in rice. In this study, a recombinant inbred lines (RILs) population was used to map quantitative trait loci (QTLs) for both traits under 3 long-day (LD) environments and 1 short-day (SD) environment. A total of eight QTLs for heading date and three QTLs for plant height were detected by composite interval mapping under LD conditions. Additional one QTL for heading date and three QTLs for plant height were identified by Two-QTL model under LD conditions. Among them, major QTLs qHd7.1, qHd7.2 and qHd8 for heading date, and qPh1 and qPh7.1 for plant height were commonly detected. qHd7.1 and qHd7.2 were mapped to small regions of less than 1 cM. Genome position comparison of previously cloned genes with QTLs detected in this study revealed that qHd5 and qPh3.1 were two novel QTLs. The alleles of these QTLs increasing trait values were dispersed in both parents, which well explained the transgressive segregation observed in this population. In addition, the interaction between qHd7.1 and qHd8 was detected under all LD conditions. Multiple-QTL model analysis revealed that all QTLs and their interactions explained over 80% of heading date variation and 50% of plant height variation. Two heading date QTLs were detected under SD condition. Of them, qHd10 were commonly identified under LD condition. The difference in QTL detection between LD and SD conditions indicated most heading date QTLs are sensitive to photoperiod. These findings will benefit breeding design for heading date and plant height in rice.     

QTL mapping of root traits in phosphorus-deficient soils reveals important genomic regions for improving NDVI and grain yield in barley

Key message

Major QTLs for root rhizosheath size are not correlated with grain yield or yield response to phosphorus. Important QTLs were found to improve phosphorus efficiency.

Abstract

Root traits are important for phosphorus (P) acquisition, but they are often difficult to characterize and their breeding values are seldom assessed under field conditions. This has shed doubts on using seedling-based criteria of root traits to select and breed for P efficiency. Eight root traits were assessed under controlled conditions in a barley doubled-haploid population in soils differing in P levels. The population was also phenotyped for grain yield, normalized difference vegetation index (NDVI), grain P uptake and P utilization efficiency at maturity (PutE_GY) under field conditions. Several quantitative traits loci (QTLs) from the root screening and the field trials were co-incident. QTLs for root rhizosheath size and root diameter explained the highest phenotypic variation in comparison to QTLs for other root traits. Shared QTLs were found between root diameter and grain yield, and total root length and PutE_GY. A common major QTL for rhizosheath size and NDVI was mapped to the HvMATE gene marker on chromosome 4H. Collocations between major QTLs for NDVI and grain yield were detected on chromosomes 6H and 7H. When results from BIP and MET were combined, QTLs detected for grain yield were also those QTLs found for NDVI. QTLs qGY5H, qGY6H and qGY7Hb on 7H were robust QTLs in improving P efficiency. A selection of multiple loci may be needed to optimize the breeding outcomes due to the QTL x Environment interaction. We suggest that rhizosheath size alone is not a reliable trait to predict P efficiency or grain yield.

     

A genetic analysis of the quality of northern-style Chinese steamed bread

Dissecting the genetic basis for the traits of northern-style Chinese steamed bread (NCSB) is of great significance for wheat quality breeding. Quantitative trait loci (QTLs) for the processing quality of NCSB were studied using a recombinant inbred line (RIL) consisting of 173 lines derived from a “Shannong01–35 × Gaocheng9411” cross. Twenty-four putative additive QTLs were detected on chromosomes 1A, 1B, 1D, 3A, 3B, 4A, 4B, 5B, 6B, and 7B. Of these QTLs, QTex1A.1-27, QHei5B.5-488, and QGum4B.4-17 had the highest contribution and accounted for 9.33, 10.9, and 12.0% of the phenotypic variations, respectively. Several co-located QTLs with additive effects were detected on chromosomes 1A, 1D, 4B, and 5B. Two clusters (RFL_CONTIG2160_524-WSNP_CAP12_C2438_1180601 and EX_C101685_705-RAC875_C27536_611) for height, total score, and texture and for chewiness, gumminess, and hardness were detected on chromosomes 1A and 4B, respectively. Two QTLs for chewiness and hardness (QCh1D-4, QHa1D-4) with additive effects were detected; these alleles could be good targets for improving the processing quality of steamed bread from common wheat (Triticum aestivum L.). In addition, QTLs for wheat flour quality and the associated correlations with NCSB were simultaneously analyzed. Negative correlations were detected between chewiness and the wet/dry gluten content (WGC/DGC) or protein content. Two QTLs (QCh4B.4-17 and QPr4B.4-17) and three QTLs (QCh4B.4-13, QWG4B.4-13, and QDG4B.4-13) clustered in the same chromosomal region. The detected QTL clusters should be further investigated during wheat breeding and could be used by breeders to improve wheat quality and especially the processing quality of NCSB.     

<Emphasis Type="Italic">qNCLB7.02</Emphasis>, a novel QTL for resistance to northern corn leaf blight in maize

Northern corn leaf blight (NCLB), which is caused by the hemibiotrophic fungal pathogen Setosphaeria turcica, is a devastating foliar disease that results in considerable maize yield losses. In the present study, quantitative trait locus (QTL) analysis was conducted across two environments using an ultra-high-density bin map constructed using recombinant inbred lines (RILs) derived from a cross between Ye478 and Qi319. A total of 11 QTLs, located on chromosomes 1, 4, 5, 6, 7, 8, 9, and 10, were detected that confer resistance to physiological race 0 of NCLB. Each QTL could explain 3.53–15.29% of the total phenotypic variation in disease resistance after artificial inoculation in two environments. Among these QTL, qNCLB7.02, which is located on chromosome 7, had the largest effect, accounting for 10.11 and 15.29% of the phenotypic variation in resistance in two field trials and BLUP. The common confidence interval (CI) for qNCLB7.02 was 1.4 Mb, according to the B73 RefGen_v3 sequence. The resistance effect of qNCLB7.02 was validated in 2016 by using chromosome segment substitution lines (CSSLs) derived from Qi319 as the donor in the genetic background of Ye478. The type 6 CSSL, which harbors introgressed qNCLB7.02, was found to be significantly associated with resistance to NCLB by linked marker bnlg1808 and exhibited greater resistance than the other CSSLs that did not carry this QTL (P?=?0.0008). The combination of linkage mapping in RILs and validation in CSSLs is a powerful approach for the dissection of QTL for disease resistance in maize.     

Fine mapping a major QTL <Emphasis Type="Italic">qFCC7</Emphasis><Subscript><Emphasis Type="Italic">L</Emphasis></Subscript> for chlorophyll content in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) cv. PA64s

Chlorophyll (Chl) content is an important agronomic trait directly affecting the photosynthetic rate. Using a high-density genetic map of 132 recombinant inbred lines (RILs) derived from the cross between 93-11 and PA64s, we detected the quantitative trait loci (QTLs) for Chl content of the top three leaves under two nitrogen (N) conditions at two developmental stages. A total of 32 main-effect QTLs located on chromosomes 1, 4, 5, 6, 7, 8, and 12 were identified, and these QTLs individually accounted for 6.0–20.8?% of the total phenotypic variation. A major QTL qFCC7 _L affecting the Chl content under low N condition was identified, and its positive allele came from PA64s. This QTL might be associated with the ability to tolerate low-N stress in rice. The chromosomal segment substitution line (CSSL) with the corresponding segment from PA64s had a higher SPAD value and photosynthetic rate than 93-11 and showed a lower specific leaf area (SLA). We performed a fine-mapping using a BC₄F₂ population via marker-assisted backcross and finally mapped this QTL to a 124.5 kb interval on the long arm of chromosome 7. Candidate gene analysis showed that there were sequence variations and expression differences in the predicted candidate gene between the two parents. These results suggest that the QTL qFCC7 _L may be useful for breeding the rice varieties with higher photosynthetic rate and grain yield.     

Chromosomes A07 and A05 associated with stable and major QTLs for pod weight and size in cultivated peanut (<Emphasis Type="Italic">Arachis hypogaea</Emphasis> L.)

Key message

Co-localized intervals and candidate genes were identified for major and stable QTLs controlling pod weight and size on chromosomes A07 and A05 in an RIL population across four environments.

Abstract

Cultivated peanut (Arachis hypogaea L.) is an important legume crops grown in > 100 countries. Hundred-pod weight (HPW) is an important yield trait in peanut, but its underlying genetic mechanism was not well studied. In this study, a mapping population (Xuhua 13 × Zhonghua 6) with 187 recombinant inbred lines (RILs) was developed to map quantitative trait loci (QTLs) for HPW together with pod length (PL) and pod width (PW) by both unconditional and conditional QTL analyses. A genetic map covering 1756.48 cM was constructed with 817 markers. Additive effects, epistatic interactions, and genotype-by-environment interactions were analyzed using the phenotyping data generated across four environments. Twelve additive QTLs were identified on chromosomes A05, A07, and A08 by unconditional analysis, and five of them (qPLA07, qPLA05.1, qPWA07, qHPWA07.1, and qHPWA05.2) showed major and stable expressions in all environments. Conditional QTL mapping found that PL had stronger influences on HPW than PW. Notably, qHPWA07.1, qPLA07, and qPWA07 that explained 17.93–43.63% of the phenotypic variations of the three traits were co-localized in a 5 cM interval (1.48 Mb in physical map) on chromosome A07 with 147 candidate genes related to catalytic activity and metabolic process. In addition, qHPWA05.2 and qPLA05.1 were co-localized with minor QTL qPWA05.2 to a 1.3 cM genetic interval (280 kb in physical map) on chromosome A05 with 12 candidate genes. This study provides a comprehensive characterization of the genetic components controlling pod weight and size as well as candidate QTLs and genes for improving pod yield in future peanut breeding.

     

Combining drought and submergence tolerance in rice: marker-assisted breeding and QTL combination effects

TDK1 is a popular rice variety from the Lao PDR. Originally developed for irrigated conditions, this variety suffers a high decline in yield under drought conditions. Studies have identified three quantitative trait loci (QTLs) for grain yield under drought conditions, qDTY _3.1 , qDTY _6.1 , and qDTY _6.2 , that show a high effect in the background of this variety. We report here the pyramiding of these three QTLs with SUB1 that provides 2–3 weeks of tolerance to complete submergence, with the aim to develop drought- and submergence-tolerant near-isogenic lines (NILs) of TDK1. We used a tandem approach that combined marker-assisted backcross breeding with phenotypic selection to develop NILs with high yield under drought stress and non-stress conditions and preferred grain quality. The effect of different QTL combinations on yield and yield-related traits under drought stress and non-stress conditions is also reported. Our results show qDTY _3.1 to be the largest and most consistent QTL affecting yield under drought conditions, followed by qDTY _6.1 and qDTY _6.2 , respectively. QTL class analysis also showed that lines with a combination of qDTY _3.1 and qDTY _6.1 consistently showed a higher tolerance to drought than those in which one of these QTLs was missing. In countries such as Lao PDR, where large areas under rice cultivation suffer vegetative-stage submergence and reproductive-stage drought, these lines could ensure yield stability. These lines can also serve as valuable genetic material to be used for further breeding of high-yielding, drought- and submergence-tolerant varieties in local breeding programs.     

Genetic Dissection of Wheat Kernel Hardness Using Conditional QTL Mapping of Kernel Size and Protein-Related Traits

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.     

Genomic regions controlling components of resistance for pea rust caused by Uromyces fabae (Pers.) de-Bary

Pea rust caused by Uromyces fabae (Pers.) de-Bary is an important disease in subtropical regions of the world. The use of partial resistance or slow rusting is an important strategy for developing varieties having durable rust resistance. A mapping population of 136 F_6:7 Recombinant Inbred Lines (RILs) derived from the cross HUVP 1?×?FC 1 was evaluated for disease severity percent (DS%) and three components of slow rusting, number of aecial pustules per leaf (AP), leaf area covered by sporulating pustules (LASP) and number of aecial cups per leaf (TNAC) during crop seasons 2006–07 and 2007–08 in polyhouse and field experiments. The components were governed by four quantitative trait loci, two major (Qruf on LGVII, Qruf2 on LGI), and two minor QTLs (Qruf1 on LG VII and Qruf3 on LGVI). This confirmed the positions of one each of the major (Qruf) and minor (Qruf1) QTLs and also detected two new QTLs Qruf2 and Qruf3. The new major QTL Qruf2 (phenotypic variance 21.3 to 29.6 %) appeared to be the most important component-specific QTL and played key role in deciding disease resistance. The minor QTL Qruf3 appeared environment-specific and contributed by the susceptible parent.     

Identification and pyramiding of QTLs for cold tolerance at the bud bursting and the seedling stages by use of single segment substitution lines in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Cold stress is one of the main constraints in rice production, and damage from cold can occur at different developmental stages in rice. Understanding the genetic basis of cold tolerance is the key for breeding cold-tolerant variety. In this study, we used single segment substitution lines (SSSLs) derived from a cross between cold-tolerant japonica variety “Nan-yang-zhan” and a popular indica variety “Hua-jing-xian 74” to detect and pyramid QTLs for cold tolerance at the bud bursting and the seedling stages. Evaluation of cold tolerance of these SSSLs and their recurrent parent helped identify two cold-tolerant QTLs (qCTBB-5 and qCTBB-6) at the bud bursting stage and two cold-tolerant QTLs (qCTS-6 and qCTS-12) at the seedling stage. The SSSLs carrying these QTLs showed stronger cold tolerance than their recurrent parent HJX74 did in three independent experiments. The qCTBB-6 and qCTS-6 were mapped to the same chromosomal region. QTL pyramiding was performed by intercrossing of SSSLs carrying the respective QTLs for cold tolerance at the bud bursting stage and the seedling stage and marker-assisted selection (MAS). The selected pyramiding line SC1-1 with different cold-tolerant QTLs showed cumulative effects on cold tolerance. Our results suggest that different genes (QTLs) control cold tolerance at bud bursting and seedling stages, and pyramiding of stable expression QTLs for cold tolerance at different developmental stages through MAS is a good strategy to prevent cold damage in rice.     

Locating QTLs controlling overwintering seedling rate in perennial glutinous rice 89-1 (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

A new cold tolerant germplasm resource named glutinous rice 89-1 (Gr89-1, Oryza sativa L.) can overwinter using axillary buds, with these buds being ratooned the following year. The overwintering seedling rate (OSR) is an important factor for evaluating cold tolerance. Many quantitative trait loci (QTLs) controlling cold tolerance at different growth stages in rice have been identified, with some of these QTLs being successfully cloned. However, no QTLs conferring to the OSR trait have been located in the perennial O. sativa L. To identify QTLs associated with OSR and to evaluate cold tolerance. 286 F₁₂ recombinant inbred lines (RILs) derived from a cross between the cold tolerant variety Gr89-1 and cold sensitive variety Shuhui527 (SH527) were used. A total of 198 polymorphic simple sequence repeat (SSR) markers that were distributed uniformly on 12 chromosomes were used to construct the linkage map. The gene ontology (GO) annotation of the major QTL was performed through the rice genome annotation project system. Three main-effect QTLs (qOSR2, qOSR3, and qOSR8) were detected and mapped on chromosomes 2, 3, and 8, respectively. These QTLs were located in the interval of RM14208 (35,160,202 base pairs (bp))–RM208 (35,520,147 bp), RM218 (8,375,236 bp)–RM232 (9,755,778 bp), and RM5891 (24,626,930 bp)–RM23608 (25,355,519 bp), and explained 19.6%, 9.3%, and 11.8% of the phenotypic variations, respectively. The qOSR2 QTL displayed the largest effect, with a logarithm of odds score (LOD) of 5.5. A total of 47 candidate genes on the qOSR2 locus were associated with 219 GO terms. Among these candidate genes, 11 were related to cell membrane, 7 were associated with cold stress, and 3 were involved in response to stress and biotic stimulus. OsPIP1;3 was the only one candidate gene related to stress, biotic stimulus, cold stress, and encoding a cell membrane protein. After QTL mapping, a total of three main-effect QTLs—qOSR2, qOSR3, and qOSR8—were detected on chromosomes 2, 3, and 8, respectively. Among these, qOSR2 explained the highest phenotypic variance. All the QTLs elite traits come from the cold resistance parent Gr89-1. OsPIP1;3 might be a candidate gene of qOSR2.     

Quantitative trait locus (QTL) analysis of fruit-quality traits for mandarin breeding in Japan

The improvement of fruit quality is an important objective in citrus breeding. Using an F₁ segregating population from a cross between citrus cultivars ‘Harehime’ (‘E647’—‘Kiyomi’ [Citrus unshiu Marcow. ‘Miyagawa Wase’ × Citrus sinensis (L.) Osbeck ‘Trovita’] × ‘Osceola’—a cultivar of clementine [Citrus clementina hort. ex Tanaka] × ‘Orland’ [Citrus paradisi Macfad. ‘Duncan’ × Citrus tangerina hort. ex Tanaka] × ‘Miyagawa Wase’) and ‘Yoshida’ ponkan (Citrus reticulata Blanco ‘Yoshida’), a SNP-based genetic linkage map was constructed and quantitative trait locus (QTL) mapping of four fruit-quality traits (fruit weight, sugar content, peel puffing, and water rot) was performed. The constructed genetic linkage map of ‘Harehime’ consisted of 442 single nucleotide polymorphisms (SNPs) on 9 linkage groups (LGs) and covered 635.8 cM of the genome, while that of ‘Yoshida’ ponkan consisted of 332 SNPs on 9 LGs and covered 892.9 cM of its genome. We identified four QTLs associated with fruit weight, one QTL associated with sugar content, three QTLs associated with peel puffing, and one QTL associated with water rot. For these QTL regions, we estimated the haplotypes of the crossed parents and verified the founding cultivars that these QTLs were originated from and their inheritance in descendant cultivars using pedigree information. QTLs identified in this study provide useful information for marker-assisted breeding of citrus in Japan.     

Temperature-dependent QTLs in <Emphasis Type="Italic">indica</Emphasis> alleles for improving grain quality in rice: increased prominence of QTLs responsible for reduced chalkiness under high-temperature conditions

Quantitative trait loci (QTLs) for the apparent quality of brown rice under high temperatures during ripening were analyzed using chromosomal segment substitution lines. Segments from the indica cultivar Habataki were substituted into a japonica cultivar with a Sasanishiki background. We found the following two QTLs for increasing grain quality in the Habataki allele on chromosome 3: (1) qTW3-2, located near the marker RM14702, decreased the percentage of total white immature (TWI) grains, and (2) qRG3-2, located near RM3766, increased the percentage of regular grains. The effects of these two QTLs were more obvious under high-temperature ripening conditions; hence, these loci are considered QTLs not only for reducing TWI grains but also for increasing high-temperature tolerance. Additionally, we found two QTLs, i.e., qTW3-1 and qRG3-1, responsible for reduced grain quality near RM14314 on chromosome 3. Although the QTL for narrow grains in the Habataki allele qNG3 was genetically linked to qTW3-2, the effect was only slightly significant, and the length/width ratio of qNG3-carrying grains was within the range observed in widely grown japonica cultivars. Incorporating the Habataki region, including qRG3-2 and qTW3-2 but not qTW3-1 and qRG3-1, in addition to previously reported grain quality QTLs in breeding japonica cultivars will improve high-temperature tolerance and grain quality.