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.     

Microsatellite based linkage disequilibrium analyses reveal <Emphasis Type="Italic">Saltol</Emphasis> haplotype fragmentation and identify novel QTLs for seedling stage salinity tolerance in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

A set of 84 diverse rice genotypes were assessed for seedling stage salt tolerance and their genetic diversity using 41 polymorphic SSR markers comprising of 19 Saltol QTL linked and 22 random markers. Phenotypic screening under hydroponics identified three indica landraces (Badami, Shah Pasand and Pechi Badam), two Oryza rufipogon accessions (NKSWR2 and NKSWR17) and one each of Basmati rice (Seond Basmati) and japonica cultivars (Tompha Khau) as salt tolerant, having similar tolerance as of Pokkali and FL478. Among the salt tolerant genotypes, biomass showed positive correlation with shoot fresh weight and negative association with root and shoot Na⁺ content. The results indicated repression of Na⁺ loading within the tolerant plants. Linkage disequilibrium (LD) of the Saltol linked markers was weak, suggestive of high fragmentation of Pokkali haplotype, a result of evolutionary active recombination events. Poor haplotype structure of the Saltol region, may reduce its usefulness in marker assisted breeding programmes, if the target foreground markers chosen are wide apart. LD mapping identified eight robust marker-trait associations (QTLs), of which RM10927 was found linked to root and shoot Na⁺ content and RM10871 with shoot Na⁺/K⁺ ratio. RM271 on chromosome 10, an extra Saltol marker, was found associated to root Na⁺/K⁺ ratio. This marker showed a distinct allele among O. rufipogon accessions. There were also other novel loci detected on chromosomes 2, 5 and 10 influencing salt tolerance in the tested germplasm. Although Saltol remained as the key locus, the role of other genomic regions cannot be neglected in tailoring seedling stage salt tolerance in rice.     

Genetic dissection of sorghum grain quality traits using diverse and segregating populations

Key message

Coordinated association and linkage mapping identified 25 grain quality QTLs in multiple environments, and fine mapping of the Wx locus supports the use of high-density genetic markers in linkage mapping.

Abstract

There is a wide range of end-use products made from cereal grains, and these products often demand different grain characteristics. Fortunately, cereal crop species including sorghum [Sorghum bicolor (L.) Moench] contain high phenotypic variation for traits influencing grain quality. Identifying genetic variants underlying this phenotypic variation allows plant breeders to develop genotypes with grain attributes optimized for their intended usage. Multiple sorghum mapping populations were rigorously phenotyped across two environments (SC Coastal Plain and Central TX) in 2 years for five major grain quality traits: amylose, starch, crude protein, crude fat, and gross energy. Coordinated association and linkage mapping revealed several robust QTLs that make prime targets to improve grain quality for food, feed, and fuel products. Although the amylose QTL interval spanned many megabases, the marker with greatest significance was located just 12 kb from waxy (Wx), the primary gene regulating amylose production in cereal grains. This suggests higher resolution mapping in recombinant inbred line (RIL) populations can be obtained when genotyped at a high marker density. The major QTL for crude fat content, identified in both a RIL population and grain sorghum diversity panel, encompassed the DGAT1 locus, a critical gene involved in maize lipid biosynthesis. Another QTL on chromosome 1 was consistently mapped in both RIL populations for multiple grain quality traits including starch, crude protein, and gross energy. Collectively, these genetic regions offer excellent opportunities to manipulate grain composition and set up future studies for gene validation.

     

QTL mapping of potato chip color and tuber traits within an autotetraploid family

Cultivated potato (Solanum tuberosum L.) is a highly heterozygous autotetraploid crop species, and this creates challenges for traditional line development and molecular breeding. Recent availability of a single-nucleotide polymorphism (SNP) array with 8303 features and software packages for linkage and association mapping in autotetraploid species present new opportunities for the identification of genomic regions that contribute to high-value traits in cultivated potato. A biparental tetraploid potato population was evaluated across three field seasons and storage trials in order to identify quantitative trait loci (QTL) for multiple tuber traits including fried chip color after 5.5–7.2 °C storage. Tetra-allelic dosage information was used to construct a genetic linkage map that covered 1041 cM and contained 2095 SNP markers with a median marker interval of 0.4 cM. A total of 41 QTL were identified for flower color, tuber yield, tuber number per plant, tuber weight, tuber size, and chip color after various storage regimes. Moderate effect QTL for chip color at 3 months were identified that co-localized with candidate genes vacuolar invertase (VInv), invertase inhibitor (INH2), and apoplastic invertase (Inv _ap -b). A separate QTL for chip color after 6 months of storage was identified in the short arm of chromosome 2, and this locus may contribute to variation in senescent sweetening resistance. QTL for tuber weight, length, and width co-localized with a known QTL for plant maturity on chromosome 5. Genome-wide association mapping using a polyploid model detected the tuber size QTL and identified a number of candidate SNPs, but was unable to detect markers significantly associated with chip color.     

Genetic analysis of genetic basis of a physiological disorder “straighthead” in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Straighthead is a physiological disorder in rice that causes yield losses and is a serious threat to rice production worldwide. Identification of QTL conferring resistance will help develop resistant cultivars for straighthead control. We conducted linkage mapping to identify QTL involved with straighthead. The study was based on a F₂ population developed from a cross between ‘Zhe733(resistant)/R312(susceptible)’. Using phenotypic data of F₂ plants and their F_2:3 families, two major QTL, qSTH-2 and qSTH-8, were identified using bulked segregant analysis, explaining 11.1 and 28.1 % of the phenotypic variation on chromosome 2 and 8, respectively. The qSTH-2 for straighthead resistance was identified by linkage mapping. qSTH-2 was situated near a QTL “AsS” responsible for arsenic accumulation. Straighthead is frequently observed on land where As has accumulated. The result suggests a kind of internal connection between qSTH-2 and AsS. Additionally, the QTL qSTH-8 was located close to HD5 related with heading date. The close location may be associated with the observation of early heading among straighthead resistant varieties. These findings should be useful for further genetic study of straighthead.     

A novel QTL <Emphasis Type="Italic">qSPP2.2</Emphasis> controlling spikelet per panicle identified from <Emphasis Type="Italic">Oryza longistaminata</Emphasis> (A. Chev. et Roehr.), mapped and transferred to <Emphasis Type="Italic">Oryza sativa</Emphasis> (L.)

Increasing the rice productivity from the current 10 to 12 tons/ha to meet the demand of estimated 8.8 billion people in 2035 is posing a major challenge. Wild relatives of rice contain some novel genes which can help in improving rice yield. Spikelet per panicle (SPP) is a valuable trait for determining yield potential in rice. In this study, a major QTL for increasing SPP has been identified, mapped, and transferred from African wild rice O. longistaminata to O. sativa (L.). The QTL was mapped on the long arm of chromosome 2 in a 167.1 kb region flanked by SSR markers RM13743 and RM13750, which are 1.0 cM apart, and is designated as qSPP2.2. The QTL explained up to 30% of phenotypic variance in different generations/seasons and showed positive additive effect of allele contributed by O. longistaminata. In addition, O. longistaminata allele in qSPP2.2 contributed to increase in grains per panicle, but decrease in the tillers per plant. The 167.1 kb region contains 23 predicted genes. Based on the functional annotation, three genes, LOC_Os02g44860, LOC_Os02g44990, and LOC_Os02g45010, were selected as putative candidates for characterization. Sequence analysis of the three genes revealed functional variations between the parental lines for LOC_Os02g44990 and a variation in 5′UTR for LOC_Os02g45010 which will help further to identify putative candidate gene(s). This is the first yield component QTL to be identified, mapped, and transferred from O. longistaminata.     

Assessment of genetic diversity of <Emphasis Type="Italic">Saltol</Emphasis> QTL among the rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) genotypes

Eight Saltol quantitative trait locus (QTL) linked simple sequence repeat (SSR) markers of rice (Oryza sativa L.) were used to study the polymorphism of this QTL in 142 diverse rice genotypes that comprised salt tolerant as well as sensitive genotypes. The SSR profiles of the eight markers generated 99 alleles including 20rare alleles and 16 null alleles. RM8094 showed the highest number (13) of alleles followed by RM3412 (12), RM562 (11), RM493 (9) and RM1287 (8) while as, RM10764 and RM10745 showed the lowest number (6) of alleles. Based on the highest number of alleles and PIC value (0.991), we identified RM8094 as suitable marker for discerning salt tolerant genotypes from the sensitive ones. Based upon the haplotype analysis using FL478 as a reference (salt tolerant genotypes containing Saltol QTL), we short listed 68 rice genotypes that may have at least one allele of FL478 haplotype. Further study may confirm that some of these genotypes might have Saltol QTL and can be used as alternative donors in salt tolerant rice breeding programmes.     

Development of an integrated linkage map of einkorn wheat and its application for QTL mapping and genome sequence anchoring

Key message

An integrated genetic map was constructed for einkorn wheat A genome and provided valuable information for QTL mapping and genome sequence anchoring.

Abstract

Wheat is one of the most widely grown food grain crops in the world. The construction of a genetic map is a key step to organize biologically or agronomically important traits along the chromosomes. In the present study, an integrated linkage map of einkorn wheat was developed using 109 recombinant inbred lines (RILs) derived from an inter sub-specific cross, KT1-1 (T. monococcum ssp. boeoticum) × KT3-5 (T. monococcum ssp. monococcum). The map contains 926 molecular markers assigned to seven linkage groups, and covers 1,377 cM with an average marker interval of 1.5 cM. A quantitative trait locus (QTL) analysis of five agronomic traits identified 16 stable QTL on all seven chromosomes, except 6A. The total phenotypic variance explained by these stable QTL using multiple regressions varied across environments from 8.8 to 87.1 % for days to heading, 24.4–63.0 % for spike length, 48.2–79.6 % for spikelet number per spike, 13.1–48.1 % for plant architecture, and 12.2–26.5 % for plant height, revealing that much of the RIL phenotypic variation had been genetically dissected. Co-localizations of closely linked QTL for different traits were frequently observed, especially on 3A and 7A. The QTL on 3A, 5A and 7A were closely associated with Eps-A ^m 3, Vrn1 and Vrn3 loci, respectively. Furthermore, this genetic map facilitated the anchoring of 237 T. urartu scaffolds onto seven chromosomes with a physical length of 26.15 Mb. This map and the QTL data provide valuable genetic information to dissect important agronomic and developmental traits in diploid wheat and contribute to the genetic ordering of the genome assembly.

     

Expression of sorghum gene <Emphasis Type="Italic">SbSGL</Emphasis> enhances grain length and weight in rice

Grain weight is a major determining factor of rice (Oryza sativa L.) yield and the comprehensive embodiment of grain length, width, and thickness. Here, we describe the molecular and functional characterization of SbSGL (Sorghum bicolor L. stress tolerance and grain length), a sorghum gene that encodes a putative member of the DUF1645 protein family of unknown function. Expression of SbSGL in rice promoted cell division and grain filling, which affected an array of traits of rice, including grain length, grain weight, and seed setting rate. Expression of SbSGL also affected the expression of genes related to the plant cell cycle and grain size.     

QTL mapping and comparative genome analysis of agronomic traits including grain yield in winter rye

Key message

Genetic diversity in elite rye germplasm as well as F _2:3 testcross design enables fast QTL mapping to approach genes controlling grain yield, grain weight, tiller number and heading date in rye hybrids.

Abstract

Winter rye (Secale cereale L.) is a multipurpose cereal crop closely related to wheat, which offers the opportunity for a sustainable production of food and feed and which continues to emerge as a renewable energy source for the production of bioethanol and biomethane. Rye contributes to increase agricultural crop species diversity particularly in Central and Eastern Europe. In contrast to other small grain cereals, knowledge on the genetic architecture of complex inherited, agronomic important traits is yet limited for the outbreeding rye. We have performed a QTL analysis based on a F_2:3 design and testcross performance of 258 experimental hybrids in multi-environmental field trials. A genetic linkage map covering 964.9 cM based on SSR, conserved-orthologous set (COS), and mixed-phase dominant DArT markers allowed to describe 22 QTL with significant effects for grain yield, heading date, tiller number, and thousand grain weight across seven environments. Using rye COS markers, orthologous segments for these traits have been identified in the rice genome, which carry cloned and functionally characterized rice genes. The initial genome scan described here together with the existing knowledge on candidate genes provides the basis for subsequent analyses of the genetic and molecular mechanisms underlying agronomic important traits in rye.

     

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.     

QTL mapping of panicle blast resistance in japonica landrace heikezijing and its application in rice breeding

The rice blast caused by Magnaporthe oryzae is one of the most devastating diseases worldwide, and the panicle blast could result in more loss of yield in rice production. However, the quantitative trait loci (QTLs) and genes related to panicle-blast resistance have not been well studied due to the time-consuming screening methodology involved and variation in symptoms. The QTLs for panicle blast resistance have been mapped in a population of 162 RILs (recombination inbreeding lines), derived from a cross between a highly blast-resistant rice landrace, Heikezijing, and a susceptible variety, Suyunuo. Two QTLs for panicle-blast resistance, qPbh-11–1 and qPbh-7-1, were identified, which were distributed on chromosomes 11 and 7. The QTL qPbh-11–1 was stably detected in three independent experiments, at Nanjing in 2013 and 2014 and at Hainan in 2014, located between the region of RM27187 and RM27381 on the distal end of chromosome 11 far from the reported resistant loci Pb1 and qPbm11 for panicle blast. The QTL qPbh-7-1 was detected only at Nanjing in 2013 and located between the region of M18 and RM3555 on chromosome 7. With marker-assisted selection (MAS) three introgression lines with the major panicle blast-resistance QTL qPbh-11–1 were developed from a recurrent parent Nanjing 44 (NJ44) and the panicle resistance of introgression lines was improved 46.36–55.47 % more than NJ44. Based on the results provided, Heikezijing appears to be a valuable source for panicle blast resistance.     

The causal deletions in the second exon of <Emphasis Type="Italic">An-3</Emphasis> closely associated with awn development and rice yield

Awn is one of important traits during rice domestication. To understand the development of rice awn and the roles it played in rice domestication, we preliminary mapped a major QTL An-3 for awn development using chromosome segment substitution line CSSL138 developed by introgressed genomic fragments of long-awned Guangxi common wild rice (GXCWR, Oryza rufipogon Griff.) into genetic background of short-awned indica cultivar 93–11. An-3 was then fine mapped to a 7-kb region of chromosome 8. An epidermal patterning factor-like protein gene was identified as the single candidate gene corresponding to this QTL. An-3 was showed to be an allele of RAE2 and GAD1, and negatively regulated 1000-grains weight, grain length, and length–width ratio. Comparing with the coding sequences of An-3 from CSSL138, a 2- and 4-bp frame-shift deletions in the second exon were identified in 93–11 and Nipponbare, respectively. Taken together, our results provide valuable natural variation in the alleles of An-3 between common wild rice and cultivated rice, which will be helpful in clarifying the mechanism of awn development and promoting the application of an-3 in genetic improvement of rice yield traits.     

Analysis of the genetic architecture of maize ear and grain morphological traits by combined linkage and association mapping

Key message

Using combined linkage and association mapping, 26 stable QTL and six stable SNPs were detected across multiple environments for eight ear and grain morphological traits in maize. One QTL, PKS2, might play an important role in maize yield improvement.

Abstract

In the present study, one bi-parental population and an association panel were used to identify quantitative trait loci (QTL) for eight ear and grain morphological traits. A total of 108 QTL related to these traits were detected across four environments using an ultra-high density bin map constructed using recombinant inbred lines (RILs) derived from a cross between Ye478 and Qi319, and 26 QTL were identified in more than two environments. Furthermore, 64 single nucleotide polymorphisms (SNPs) were found to be significantly associated with the eight ear and grain morphological traits (?log₁₀(P)?>?4) in an association panel of 240 maize inbred lines. Combining the two mapping populations, a total of 17 pleiotropic QTL/SNPs (pQTL/SNPs) were associated with various traits across multiple environments. PKS2, a stable locus influencing kernel shape identified on chromosome 2 in a genome-wide association study (GWAS), was within the QTL confidence interval defined by the RILs. The candidate region harbored a short 13-Kb LD block encompassing four SNPs (SYN11386, PHM14783.16, SYN11392, and SYN11378). In the association panel, 13 lines derived from the hybrid PI78599 possessed the same allele as Qi319 at the PHM14783.16 (GG) locus, with an average value of 0.21 for KS, significantly lower than that of the 34 lines derived from Ye478 that carried a different allele (0.25, P?<?0.05). Therefore, further fine mapping of PKS2 will provide valuable information for understanding the genetic components of grain yield and improving molecular marker-assisted selection (MAS) in maize.

     

Genetic mapping and confirmation of quantitative trait loci for grain chalkiness in rice

Grain chalkiness is a highly undesirable trait affecting rice grain quality and milled rice yield. In order to clarify the genetic basis of chalkiness, a recombinant inbred line population (RIL) derived from a cross between Beilu130 (a japonica cultivar with high chalkiness) and Jin23B (an indica cultivar with low chalkiness) was developed for quantitative trait locus (QTL) mapping. A total of 10 QTLs for white belly rate (WBR) and white core rate (WCR) were detected on eight different chromosomes over 2 years. Two QTLs for WBR (qWBR2 and qWBR5) showed similar chromosomal locations with GW2 and qSW5/GW5, which have been cloned previously to control the grain width and should be the right candidate genes. Three novel minor QTLs controlling WCR, qWCR1, qWCR3, and qWCR4 were further validated in near isogenic F₂ populations (NIL-F₂) and explained 26.1, 18.3, and 21.1% of the phenotypic variation, respectively. These QTLs could be targets for map-based cloning of the candidate genes to elucidate the molecular mechanism of chalkiness and for marker-assisted selection (MAS) in rice grain quality improvement.     

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 Locus (QTL) meta-analysis and comparative genomics for candidate gene prediction in perennial ryegrass (<Emphasis Type="Italic">Lolium perenne</Emphasis> L.)

Background

In crop species, QTL analysis is commonly used for identification of factors contributing to variation of agronomically important traits. As an important pasture species, a large number of QTLs have been reported for perennial ryegrass based on analysis of biparental mapping populations. Further characterisation of those QTLs is, however, essential for utilisation in varietal improvement programs.

Results

A bibliographic survey of perennial ryegrass trait-dissection studies identified a total of 560 QTLs from previously published papers, of which 189, 270 and 101 were classified as morphology-, physiology- and resistance/tolerance-related loci, respectively. The collected dataset permitted a subsequent meta-QTL study and implementation of a cross-species candidate gene identification approach. A meta-QTL analysis based on use of the BioMercator software was performed to identify two consensus regions for pathogen resistance traits. Genes that are candidates for causal polymorphism underpinning perennial ryegrass QTLs were identified through in silico comparative mapping using rice databases, and 7 genes were assigned to the p150/112 reference map. Markers linked to the Lp DGL1, Lp Ph1 and Lp PIPK1 genes were located close to plant size, leaf extension time and heading date-related QTLs, respectively, suggesting that these genes may be functionally associated with important agronomic traits in perennial ryegrass.

Conclusions

Functional markers are valuable for QTL meta-analysis and comparative genomics. Enrichment of such genetic markers may permit further detailed characterisation of QTLs. The outcomes of QTL meta-analysis and comparative genomics studies may be useful for accelerated development of novel perennial ryegrass cultivars with desirable traits.

     

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.     

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.