In silico integration of quantitative trait loci for seed yield and yield-related traits in Brassica napus

Mapping quantitative trait loci (QTLs) is a foundation for molecular marker-assisted selection and map-based gene cloning. During the past decade, numerous QTLs for seed yield (SY) and yield-related traits in Brassica napus L. have been identified. However, integration of these results in order to compare QTLs from different mapping populations has not been undertaken, due to the lack of common molecular markers between studies. Using previously reported Brassica rapa and Brassica oleracea genome sequences, we carried out in silico integration of 1,960 QTLs associated with 13 SY and yield-related traits from 15 B. napus mapping experiments over the last decade. A total of 736 SY and yield-related QTLs were mapped onto 283 loci in the A and C genomes of B. napus. These QTLs were unevenly distributed across the 19 B. napus chromosomes, with the most on chromosome A3 and the least on chromosome C6. Our integrated QTL map identified 142 loci where the conserved QTLs were detected and 25 multifunctional loci, mostly for the traits of flowering time (FT), plant height, 1,000-seed weight, maturity time and SY. These conserved QTLs and multifunctional loci may result from pleiotropism or clustered genes. At the same time, a total of 146 genes underlying the QTLs for FT and other yield-related traits were identified by comparative mapping with the Arabidopsis genome. These results facilitate the retrieval of B. napus SY and yield-related QTLs for research communities, increase the density of targeted QTL-linked markers, validate the existence of QTLs across different populations, and advance the fine mapping of genes.     

Genetic mapping of yield traits using RIL population derived from Fuchuan Dahuasheng and ICG6375 of peanut (<Emphasis Type="Italic">Arachis hypogaea</Emphasis> L.)

The genetic architecture determinants of yield traits in peanut (Arachis hypogaea L.) are poorly understood. In the present study, an effort was made to map quantitative trait loci (QTLs) for yield traits using recombinant inbred lines (RIL). A genetic linkage map was constructed containing 609 loci, covering a total of 1557.48 cM with an average distance of 2.56 cM between adjacent markers. The present map exhibited good collinearity with the physical map of diploid species of Arachis. Ninety-two repeatable QTLs were identified for 11 traits including height of main stem, total branching number, and nine pod- and seed-related traits. Of the 92 QTLs, 15 QTLs were expressed across three environments and 65 QTLs were newly identified. Twelve QTLs for the height of main stem and the pod- and seed-related traits explaining more than 10 % of phenotypic variation showed a great potential for marker-assisted selection in improving these traits. 相似文献   

绵麦37特异位点在其衍生品种中的遗传贡献率分析

优良亲本的创制和利用能有效提高育种效率。文章对绵麦37及其衍生品种产量构成因子和抗病性进行比较鉴定, 并利用SSR标记检测高产品种绵麦367遗传背景中绵麦37的遗传贡献。结果表明：衍生品种丰产性显著提高, 穗粒数的增加是衍生品种增产的主要因素; 绵麦37及其衍生品种高抗当前国内条锈菌主要流行小种, 特别是高抗对Yr24/Yr26具有强毒性的条锈病新菌株V26; 绵麦37优良的抗病性很好地传递给了后代品种; 在绵麦367等后代品种的遗传背景中, 绵麦37的遗传贡献率达78.9%, 其中A、B、D基因组中的遗传率分别为75.0%、83.6%和74.2%, 远高于理论值50.0%; 衍生品种绵麦367与绵麦37相同染色体区段主要为2B的Xgwm374-Xbarc167-Xbarc128-Xgwm129-Xgwm388-Xbarc101, 3B的Xwmc446-Xwmc366-Xwmc533-Xbarc164- Xwmc418等区段, 这些区段包含了许多重要性状位点如穗粒数、千粒重和抗病性等。这可能是由于人工定向选择的结果, 使那些与目标性状紧密连锁的位点被高频率保留下来。     

Mapping QTL for Biomass Yield and Its Components in Rice (Oryza sativa L.)

Addicive effects, additive by additive epistatic effects, and their environmental interactions of QTLs are important genetic components of quantitative traits. Genetic architecture underlying rice biomass yield and its two component traits (straw yield and grain yield) were analyzed for a population of 125 DH lines from an inter-subspecific cross of IR64/Azucena. The mixed-model based composite interval mapping approach (MCIM) was used to detect QTLs, There were 12 QTLs detected with additive main effects, 27 QTLs involved in digenic interaction with aa and/or aae effects, and 18 QTLs affected by environments with ae and/or aae effects. It was revealed that epistatic effects and QE interaction effects existed on biomass yield and its component traits in rice. In addition, the genetic basis of relationships among these traits were investigated. Four QTLs and one pair of epistatic QTLs were detected to be responsible for the positive correlation between biomass yield and straw yield. Three QTLs might be responsible for the negative correlation between straw yield and grain yield. This result could partially explain the genetic basis of correlation among the three traits, and provide useful information for genetic improvement of these traits by marker-assisted selection.     

A haplotype inference algorithm for trios based on deterministic sampling

Background

The three-dimensional shape of grain, measured as grain length, width, and thickness (GL, GW, and GT), is one of the most important components of grain appearance in rice. Determining the genetic basis of variations in grain shape could facilitate efficient improvements in grain appearance. In this study, an F_7:8 recombinant inbred line population (RIL) derived from a cross between indica and japonica cultivars (Nanyangzhan and Chuan7) contrasting in grain size was used for quantitative trait locus (QTL) mapping. A genetic linkage map was constructed with 164 simple sequence repeat (SSR) markers. The major aim of this study was to detect a QTL for grain shape and to fine map a minor QTL, qGL7.

Results

Four QTLs for GL were detected on chromosomes 3 and 7, and 10 QTLs for GW and 9 QTLs for GT were identified on chromosomes 2, 3, 5, 7, 9 and 10, respectively. A total of 28 QTLs were identified, of which several are reported for the first time; four major QTLs and six minor QTLs for grain shape were also commonly detected in both years. The minor QTL, qGL7, exhibited pleiotropic effects on GL, GW, GT, 1000-grain weight (TGW), and spikelets per panicle (SPP) and was further validated in a near isogenic F₂ population (NIL-F₂). Finally, qGL7 was narrowed down to an interval between InDel marker RID711 and SSR marker RM6389, covering a 258-kb region in the Nipponbare genome, and cosegregated with InDel markers RID710 and RID76.

Conclusion

Materials with very different phenotypes were used to develop mapping populations to detect QTLs because of their complex genetic background. Progeny tests proved that the minor QTL, qGL7, could display a single mendelian characteristic. Therefore, we suggested that minor QTLs for traits with high heritability could be isolated using a map-based cloning strategy in a large NIL-F₂ population. In addition, combinations of different QTLs produced diverse grain shapes, which provide the ability to breed more varieties of rice to satisfy consumer preferences.     

Yield-related QTLs and Their Applications in Rice Genetic Improvement

Grain yield is one of the most important indexes in rice breeding,which is governed by quantitative trait loci (QTLs).Different map-ping populations have been used to explore the QTLs controlling yield related traits.Primary populations such as F 2 and recombi-nant inbred line populations have been widely used to discover QTLs in rice genome-wide,with hundreds of yield-related QTLs detected.Advanced populations such as near isogenic lines (NILs) are efficient to further fine-map and clone target QTLs.NILs for primarily identified QTLs have been proposed and confirmed to be the ideal population for map-based cloning.To date,20 QTLs directly affecting rice grain yield and its components have been cloned with NIL-F 2 populations,and 14 new grain yield QTLs have been validated in the NILs.The molecular mechanisms of a continuously increasing number of genes are being unveiled,which aids in the understanding of the formation of grain yield.Favorable alleles for rice breeding have been ’mined’ from natural cultivars and wild rice by association analysis of known functional genes with target trait performance.Reasonable combination of favorable alleles has the potential to increase grain yield via use of functional marker assisted selection.     

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.     

QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice

Photosynthesis of carbohydrate is the primary source of grain yield in rice (Oryza sativa L.). It is important to genetically analyze the morphological and the physiological characteristics of functional leaves, especially flag leaf, in rice improvement. In this study, a recombinant inbred population derived from a cross between an indica (O. sativa L. ssp. indica) cultivar and a japonica (O. sativa L. ssp. japonica) cultivar was employed to map quantitative traits loci (QTLs) for the morphological (i.e., leaf length, width, and area) and physiological (i.e., leaf color rating and stay-green) characteristics of flag leaf and their relationships with yield and yield traits in 2003 and 2004. A total of 17 QTLs for morphological traits (flag leaf length, width, and area), 6 QTLs for degree of greenness and 14 QTLs for stay-green-related traits (retention-degrees of greenness, relative retention of greenness, and retention of the green area) were resolved, and 10 QTLs were commonly detected in both the years. Correlation analysis revealed that flag leaf area increased grain yield by increasing spikelet number per panicle. However, the physiological traits including degree of greenness and stay-green traits were not or negatively correlated to grain yield and yield traits, which may arise from the negative relation between degree of greenness and flag leaf size and the partial sterility occurred in a fraction of the lines in this population. The region RM255-RM349 on chromosome 4 controlled the three leaf morphological traits simultaneously and explained a large part of variation, which was very useful for genetic improvement of grain yield. The region RM422-RM565 on chromosome 3 was associated with the three stay-green traits simultaneously, and the use of this region in genetic improvement of grain yield needs to be assessed by constructing near-isogenic lines.     

Identification of candidate genes for key fibre‐related QTLs and derivation of favourable alleles in Gossypium hirsutum recombinant inbred lines with G. barbadense introgressions

Fine mapping QTLs and identifying candidate genes for cotton fibre‐quality and yield traits would be beneficial to cotton breeding. Here, we constructed a high‐density genetic map by specific‐locus amplified fragment sequencing (SLAF‐seq) to identify QTLs associated with fibre‐quality and yield traits using 239 recombinant inbred lines (RILs), which was developed from LMY22 (a high‐yield Gossypium hirsutumL. cultivar) × LY343 (a superior fibre‐quality germplasm with G. barbadenseL. introgressions). The genetic map spanned 3426.57 cM, including 3556 SLAF‐based SNPs and 199 SSR marker loci. A total of 104 QTLs, including 67 QTLs for fibre quality and 37 QTLs for yield traits, were identified with phenotypic data collected from 7 environments. Among these, 66 QTLs were co‐located in 19 QTL clusters on 12 chromosomes, and 24 QTLs were detected in three or more environments and determined to be stable. We also investigated the genomic components of LY343 and their contributions to fibre‐related traits by deep sequencing the whole genome of LY343, and we found that genomic components from G. hirsutum races (which entered LY343 via its G. barbadense parent) contributed more favourable alleles than those from G. barbadense. We further identified six putative candidate genes for stable QTLs, including Gh_A03G1147 (GhPEL6), Gh_D07G1598 (GhCSLC6) and Gh_D13G1921 (GhTBL5) for fibre‐length QTLs and Gh_D03G0919 (GhCOBL4), Gh_D09G1659 (GhMYB4) and Gh_D09G1690 (GhMYB85) for lint‐percentage QTLs. Our results provide comprehensive insight into the genetic basis of the formation of fibre‐related traits and would be helpful for cloning fibre‐development‐related genes as well as for marker‐assisted genetic improvement in cotton.     

Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum

The identification of quantitative trait loci (QTLs) affecting agronomically important traits enable to understand their underlying genetic mechanisms and genetic basis of their complex interactions. The aim of the present study was to detect QTLs for 12 agronomic traits related to staygreen, plant early development, grain yield and its components, and some growth characters by analyzing replicated phenotypic datasets from three crop seasons, using the population of 168 F₇ RILs of the cross 296B × IS18551. In addition, we report mapping of a subset of genic-microsatellite markers. A linkage map was constructed with 152 marker loci comprising 149 microsatellites (100 genomic- and 49 genic-microsatellites) and three morphological markers. QTL analysis was performed by using MQM approach. Forty-nine QTLs were detected, across environments or in individual environments, with 1–9 QTLs for each trait. Individual QTL accounted for 5.2–50.4% of phenotypic variance. Several genomic regions affected multiple traits, suggesting the phenomenon of pleiotropy or tight linkage. Stable QTLs were identified for studied traits across different environments, and genetic backgrounds by comparing the QTLs in the study with previously reported QTLs in sorghum. Of the 49 mapped genic-markers, 18 were detected associating either closely or exactly as the QTL positions of agronomic traits. EST marker Dsenhsbm19, coding for a key regulator (EIL-1) of ethylene biosynthesis, was identified co-located with the QTLs for plant early development and staygreen trait, a probable candidate gene for these traits. Similarly, such exact co-locations between EST markers and QTLs were observed in four other instances. Collectively, the QTLs/markers identified in the study are likely candidates for improving the sorghum performance through MAS and map-based gene isolations.     

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.     

Molecular diversity, structure and domestication of grasses

Map-based cloning has been considered problematic for isolating quantitative trait loci (QTLs) due to the confounding phenotypic effects of environment and other QTLs. However, five recent studies, all in plants, have succeeded in cloning QTLs using map-based methods. We review the important features of these studies and evaluate the prospects for broader application of the techniques. Successful map-based cloning requires that QTLs represent single genes that can be isolated in near-isogenic lines, and that genotypes can be unambiguously inferred by progeny testing. In plants or animals for which map-based cloning of genes with discrete phenotypes is feasible, the modified procedures required for QTLs should not be limiting in most cases. The choice between map-based cloning and alternative methods will depend on details of the species and traits being studied.     

Mapping QTLs for Plant Phenology and Production Traits Using Indica Rice (Oryza sativa L.) Lines Adapted to Rainfed Environment

Drought is a major abiotic stress limiting rice production and yield stability in rainfed ecosystems. Identifying quantitative trait loci (QTL) for rice yield and yield components under water limited environments will help to develop drought resilient cultivars using marker assisted breeding (MAB) strategy. A total of 232 recombinant inbred lines of IR62266/Norungan were used to map QTLs for plant phenology and production traits under rainfed condition in target population of environments. A total of 79 QTLs for plant phenology and production traits with phenotypic variation ranging from 4.4 to 72.8% were detected under non-stress and drought stress conditions across two locations. Consistent QTLs for phenology and production traits were detected across experiments and water regimes. The QTL region, RM204-RM197-RM217 on chromosome 6 was linked to days to 50% flowering and grain yield per plant under both rainfed and irrigated conditions. The same genomic region, RM585-RM204-RM197 was also linked to harvest index under rainfed condition with positive alleles from Norungan, a local landrace. QTLs for plant production and drought resistance traits co-located near RM585-RM204-RM197-RM217 region on chromosome 6 in several rice genotypes. Thus with further fine mapping, this region may be useful as a candidate QTL for MAB, map-based cloning of genes and functional genomics studies for rainfed rice improvement.     

Meta-analysis of quantitative trait loci for grain yield and component traits under reproductive-stage drought stress in an upland rice population

A recombinant inbred population developed from a cross between high-yielding lowland rice (Oryza sativa L.) subspecies indica cv. IR64 and upland tropical rice subspecies japonica cv. Cabacu was used to identify quantitative trait loci (QTLs) for grain yield (GY) and component traits under reproductive-stage drought stress. One hundred fifty-four lines were grown in field trials in Indonesia under aerobic conditions by giving surface irrigation to field capacity every 4 days. Water stress was imposed for a period of 15 days during pre-flowering by withholding irrigation at 65 days after seeding. Leaf rolling was scored at the end of the stress period and eight agronomic traits were evaluated after recovery. The population was also evaluated for root pulling force, and a total of 201 single nucleotide polymorphism markers were used to construct the molecular genetic linkage map and QTL mapping. A QTL for GY under drought stress was identified in a region close to the sd1 locus on chromosome 1. QTL meta-analysis across diverse populations showed that this QTL was conserved across genetic backgrounds and co-localized with QTLs for leaf rolling and osmotic adjustment (OA). A QTL for percent seed set and grains per panicle under drought stress was identified on chromosome 8 in the same region as a QTL for OA previously identified in three different populations.     

QTLs for agronomic traits in the Mediterranean environment identified in recombinant inbred lines of the cross 'Arta' × <Emphasis Type="Italic">H. spontaneum</Emphasis> 41-1

A genetic linkage map has been developed for recombinant inbred lines (RILs) of the cross 'Arta' × Hordeum spontaneum 41-1. One hundred and ninety four RILs, randomly chosen from a population of 494 RILs, were mapped with 189 markers including one morphological trait (btr = brittle rachis locus). The linkage map extended to 890 cM. Agronomic traits such as grain yield, biological yield, days to heading, plant height, cold tolerance and others were evaluated at the ICARDA research stations Tel Hadya and Breda during the years 1996–97 and 1997–98. QTLs for agronomic traits related to drought resistance were localized. For the most-important character 'plant height under drought stress', QTLs on 2H, 3H and 7H were detected. The 'plant height' QTLs, specially the one on 3H, showed pleiotropic effects on traits such as days to heading, grain yield and biological yield. QTLs were also identified for other traits associated with adaptation to the Mediterranean environment such as cold tolerance, days to heading and tiller number. The identification of QTLs for agronomic traits is a first step to analyze and to dissect complex characters such as adaptation to drought tolerance.Comunicated by R. Hagemann     

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.

     

Efficient construction of high-density linkage map and its application to QTL analysis in barley

Using a High Efficiency Genome Scanning (HEGS) system and recombinant inbred (RI) lines derived from the cross of Russia 6 and H.E.S. 4, a high-density genetic map was constructed in barley. The resulting 1,595.7-cM map encompassed 1,172 loci distributed on the seven linkage groups comprising 1,134 AFLP, 34 SSR, three STS and vrs1 (kernel row type) loci. Including PCR reactions, gel electrophoresis and data processing, 6 months of work by a single person was sufficient for the whole mapping procedure under a reasonable cost. To make an appraisal of the resolution of genetic analysis for the 95 RI lines based on the constructed linkage map, we measured three agronomic traits: plant height, spike exsertion length and 1,000-kernel weight, and the analyzed quantitative trait loci (QTLs) associated with these traits. The results were compared on the number of detected QTLs and their effects between a high-density map and a skeleton map constructed by selected AFLP and anchor markers. The composite interval mapping on the high-density map detected more QTLs than the other analyses. Closely linked markers with QTLs on the high-density map could be powerful tools for marker-assisted selection in barley breeding programs and further genetic analyses including an advanced backcross analysis or a map-based cloning of QTL. Electronic Supplementary Material Supplementary material is available in the online version of this article at Communicated by J.S. Heslop-Harrison     

Toward understanding genetic mechanisms of complex traits in rice

Rice is the primary carbohydrate staple cereal feeding the world population. Many genes, known as quantitative trait loci (QTLs), con-trol most of the agronomically important traits in rice. The identification of QTLs controlling agricultural traits is vital to increase yield and meet the needs of the increasing human population, but the progress met with challenges due to complex QTL inheritance. To date,many QTLs have been detected in rice, including those responsible for yield and grain quality; salt, drought and submergence tolerance;disease and insect resistance; and nutrient utilization efficiency. Map-based cloning techniques have enabled scientists to successfully fine map and clone approximately seventeen QTLs for several traits. Additional in-depth functional analyses and characterizations of these genes will provide valuable assistance in rice molecular breeding.     

Molecular-marker-facilitated studies of morphological traits in maize. II: Determination of QTLs for grain yield and yield components

Genetic factors controlling quantitative inheritance of grain yield and its components have not previously been investigated by using replicated lines of an elite maize (Zea mays L.) population. The present study was conducted to identify quantitative trait loci (QTLs) associated with grain yield and grain-yield components by using restriction fragment length polymorphism (RFLP) markers. A population of 150 random F₂₃ lines was derived from the single cross of inbreds Mo17 and H99, which are considered to belong to the Lancaster heterotic group. Trait values were measured in a replicated trial near Ames, Iowa, in 1989. QTLs were located on a linkage map constructed with one morphological and 103 RFLP loci. QTLs were found for grain yield and all yield components. Partial dominance to overdominance was the primary mode of gene action. Only one QTL, accounting for 35% of the phenotypic variation, was identified for grain yield. Two to six QTLs were identified for the other traits. Several regions with pleiotropic or linked effects on several of the yield components were detected.