Quantitative Trait Loci For Yield Research Articles

Genetic Architecture of Grain Yield-Related Traits in Sorghum and Maize.

Grain size, grain number per panicle, and grain weight are crucial determinants of yield-related traits in cereals. Understanding the genetic basis of grain yield-related traits has been the main research object and nodal in crop science. Sorghum and maize, as very close C4 crops with high photosynthetic rates, stress tolerance and large biomass characteristics, are extensively used to produce food, feed, and biofuels worldwide. In this review, we comprehensively summarize a large number of quantitative trait loci (QTLs) associated with grain yield in sorghum and maize. We placed great emphasis on discussing 22 fine-mapped QTLs and 30 functionally characterized genes, which greatly hinders our deep understanding at the molecular mechanism level. This review provides a general overview of the comprehensive findings on grain yield QTLs and discusses the emerging trend in molecular marker-assisted breeding with these QTLs.

Analysis of Genetic Diversity and Survey of QTLs for Grain Yield under Drought Stress in Drought Tolerant Rice Landraces Using DTY QTL-linked Markers

Genetic diversity in a set of 60 rice genotypes were analyzed using 27 random and 13 grain yield under drought (DTY) quantitative trait loci (QTLs)-linked simple sequence repeats (SSR) markers. The average gene diversity polymorphism information content were 0.53 and 0.46, respectively. The presence of DTY QTLs for grain yield under drought stress was predicted using peak marker in comparison to the positive checks. The DTY QTL qDTY12.1, amplified by the peak marker RM 28048, was found in 43.3% of genotypes, and qDTY2.2 was detected in only 6.67 % of genotypes using peak marker RM279. Two germplasm accessions, IC389895 and DT 2, possessed maximum number of DTY QTLs and absent is IC525099. Phylogenetic analysis revealed four distinct clusters within the germplasm. Overall, the evaluated genotypes presented a rich source of diversity and contain valuable DTY QTLs which can be utilized in rice genetic enhancement program for drought tolerance.

Detection of drought tolerance-related QTL in the Plainsman V./Cappelle Desprez doubled haploid wheat population

Drought stress is one of the major abiotic factors that significantly reduces wheat grain yield. Improving drought tolerance is a challenge that plant breeders are facing nowadays. In this study, our goal was to identify quantitative trait loci (QTL) in the Plainsman V./Cappelle Desprez doubled haploid (DH) population under drought induced as decreased irrigation (ds) and well-watered (ww) conditions in glasshouse. In total, 54 QTL were detected across the three years in two water regimes linked to 10 drought tolerance-related agronomic traits. Out of the detected QTL regions several have been previously reported. The QTL on chromosome 1A (wPt-744613-wPt-8016) related to thousand grain weight was detected in both ds and ww conditions, explaining the 12.7–17.4% of the phenotypic variance. QTL for grain yield was detected on chromosomes 1A, and 6B in the ds treatment. Numerous QTL was identified under both irrigation levels.

Genome-wide QTL mapping of yield and agronomic traits in two widely adapted winter wheat cultivars from multiple mega-environments.

Quantitative trait loci (QTL) analysis could help to identify suitable molecular markers for marker-assisted breeding (MAB). A mapping population of 124 F5:7recombinant inbred lines derived from the cross ‘TAM 112’/‘TAM 111’ was grown under 28 diverse environments and evaluated for grain yield, test weight, heading date, and plant height. The objective of this study was to detect QTL conferring grain yield and agronomic traits from multiple mega-environments. Through a linkage map with 5,948 single nucleotide polymorphisms (SNPs), 51 QTL were consistently identified in two or more environments or analyses. Ten QTL linked to two or more traits were also identified on chromosomes 1A, 1D, 4B, 4D, 6A, 7B, and 7D. Those QTL explained up to 13.3% of additive phenotypic variations with the additive logarithm of odds (LOD(A)) scores up to 11.2. The additive effect increased yield up to 8.16 and 6.57 g m−2 and increased test weight by 2.14 and 3.47 kg m−3 with favorable alleles from TAM 111 and TAM 112, respectively. Seven major QTL for yield and six for TW with one in common were of our interest on MAB as they explained 5% or more phenotypic variations through additive effects. This study confirmed previously identified loci and identified new QTL and the favorable alleles for improving grain yield and agronomic traits.

Identification of Soybean Yield QTL in Irrigated and Rain-Fed Environments

Drought is the primary abiotic stress that limits yield of soybean (Glycine max (L.) Merr.). The study aimed to identify yield-related quantitative trait loci (QTLs) in soybeans using a population of 160 F4-derived lines from ‘Hutcheson’ × PI 471938 crosses, which were cultivated under rain-fed and irrigated conditions. Seed yield was determined based on a total of nine irrigated and five rain-fed environments over two years. Twenty and twenty-seven SSR markers associated with yield (p ≤ 0.05) were identified in the irrigated and rain-fed environments, respectively. Four markers accounted for 22% of the yield variation in the irrigated environments (IR-YLD) and five markers explained 34% of the yield variation in the rain-fed environments (RF-YLD). Two independent IR-YLD and RF-YLD QTLs on chromosome (Chr) 13 (LG-F) were mapped to the Satt395-Sat_074 interval (4.2 cM) and near Sat_375 (3.0 cM), which explained 8% (LOD = 2.6) and 17% (LOD = 5.5) of the yield variation, respectively. The lines homozygous for the Hutcheson allele at the IR-YLD QTL linked to Sat_074 averaged 100 kg ha−1 higher yield than the lines homozygous for the PI 471938 allele. At two independent RF-YLD QTLs on Chr 13 and Chr 17, the lines homozygous for the PI 471938 alleles were 74 to 101 kg ha−1 higher in yield than the lines homozygous for the Hutcheson alleles. Three of the five significant SSR markers associated with RF-YLD were located in a genomic region known for canopy-wilting QTLs, in which the favorable alleles were inherited from PI 471938. The identification of yield-QTLs under the respective rain-fed and irrigated environments provides knowledge regarding differential responses of yield under different irrigation conditions, which will be helpful in developing high-yielding soybean cultivars.

Identification and Validation of QTLs for Yield and Yield Components under Long-Term Salt Stress Using IR64 CSSLs in the Genetic Background of Koshihikari and Their Backcross Progenies

Unraveling the complex genetic bases and mechanisms underlying salt tolerance is of great importance for developing salt-tolerant varieties. In this study, we evaluated 42 chromosome segment substitution lines (CSSLs) carrying chromosome segments from IR64 on the genetic background of Koshihikari under salt stress. Two CSSLs, SL2007 and SL2038, produced higher plant dry weight and grain yield than did Koshihikari under the stress condition. These CSSLs also showed lower Na+ and Cl− accumulation in the leaf and whole plant at the full heading stage, which might be related to the higher grain yield and yield components. To understand the genetic control of its grain yield and yield components, a SL2007/Koshihikari F2 population was generated for quantitative trait locus (QTL) analysis. Six QTLs for grain yield and yield-related traits were detected on chromosome 2. Using near-isogenic lines (NILs) from a SL2007/Koshihikari F5 population, qSTGY2.2 was delimited to a 2.5 Mb region and novel qSTPN2 was delimited to a 0.6 Mb region. We also detected a novel QTL, qSTGF2, for grain filling, which was considered an important contributor to grain yield under salt stress in this CSSL. Our results provide insights into mechanisms conferring grain yield under salinity stress and new genetic resources for cloning and breeding.

QTL identified for stay-green in a multi-reference nested association mapping population of wheat exhibit context dependent expression and parent-specific alleles

A stay-green phenotype is useful for adaptation of wheat to end-of-season drought conditions. We identified quantitative trait loci (QTL) for stay-green traits, as well as for height, days to anthesis and yield, in a multi-reference nested association mapping (MR-NAM) population of wheat (Triticum aestivum L.) in two environments differing in degree of drought stress experienced post-anthesis. The MR-NAM population consisted of three inter-related nested association mapping populations developed by nesting 11 diverse adaptation donors within three common reference parents, adapted to the northern, southern and western cropping regions of Australia, respectively. The construction of the MR-NAM population enables the assessment of the effect on a trait of multiple alleles at any particular locus, in different genetic backgrounds, and facilitates concurrent QTL mapping and germplasm development. This approach enabled identification of parent-specific alleles and context dependent expression. Using a new statistical method specifically developed to identify QTL in MR-NAM populations, we identified 65 QTL for stay-green traits. Co-location was observed between (i) trait by loci associations for some of the different stay-green traits, (ii) for QTL between the two environments, and (iii) between QTL for stay-green traits, plant height and grain yield. Some QTL co-located with those identified in other studies however, others are likely novel. Genetic markers associated with QTL for stay-green can be applied in breeding to enrich populations for stay-green traits in early generations of selection, prior to field testing in yield plots, in particular for the development of wheat cultivars targeted to end-of-season drought-stressed environments. This information is important for breeders, because it facilitates identification of the sources of the most promising alleles at particular loci for specific genetic backgrounds and growing environments.

The recombination landscape and multiple QTL mapping in a Solanum tuberosum cv. \u2018Atlantic\u2019-derived F1 population

There are many challenges involved with the genetic analyses of autopolyploid species, such as the tetraploid potato, Solanum tuberosum (2n = 4x = 48). The development of new analytical methods has made it valuable to re-analyze an F1 population (n = 156) derived from a cross involving ‘Atlantic’, a widely grown chipping variety in the USA. A fully integrated genetic map with 4285 single nucleotide polymorphisms, spanning 1630 cM, was constructed with MAPpoly software. We observed that bivalent configurations were the most abundant ones (51.0~72.4% depending on parent and linkage group), though multivalent configurations were also observed (2.2~39.2%). Seven traits were evaluated over four years (2006–8 and 2014) and quantitative trait loci (QTL) mapping was carried out using QTLpoly software. Based on a multiple-QTL model approach, we detected 21 QTL for 15 out of 27 trait-year combination phenotypes. A hotspot on linkage group 5 was identified with co-located QTL for maturity, plant yield, specific gravity, and internal heat necrosis resistance evaluated over different years. Additional QTL for specific gravity and dry matter were detected with maturity-corrected phenotypes. Among the genes around QTL peaks, we found those on chromosome 5 that have been previously implicated in maturity (StCDF1) and tuber formation (POTH1). These analyses have the potential to provide insights into the biology and breeding of tetraploid potato and other autopolyploid species.

An analysis of simulated yield data for pepper shows how genotype × environment interaction in yield can be understood in terms of yield components and their QTLs

AbstractComplex traits like yield are those in which phenotypic variation can be modeled as a linear function of a set of quantitative trait loci (QTLs) with environment dependency. This environment dependency can be observed at a phenotypic level as genotype × environment interaction (GEI) for yield itself and at an underlying genetic level as QTL × environment interaction (QEI). We show how GEI in yield may follow from pleiotropic QTLs for yield components that themselves are not environment dependent. We generated synthetic yield data via a crop growth model and analyzed these data by common statistical models for GEI and QEI. The QTLs for yield were pleiotropic with those for yield components. Such pleiotropy offers a path for improvement of yield under GEI. As a model system we used sweet pepper (Capsicum annuum L.) and developed an eco‐physiological model for yield with seven genotype‐specific inputs or yield components. Synthetic yields were simulated for a back cross population of 500 lines across a factorial combination of four major environmental drivers. The yield components were given a simple QTL basis and produced credible amounts and patterns of GEI for yield. The QEI for yield could be interpreted from the expression of QTLs for yield components and the interaction of these components with the environmental drivers. We see the generation of synthetic yield data via crop growth models followed by an analysis with statistical models for GEI and QEI to quantify the contribution of yield components to GEI as a helpful step in the development of yield prediction models for complex traits across environments that can also serve as a basis for decisions on selection strategies of complex traits.

Detection of grain yield QTLs in the durum population Lahn/Cham1 tested in contrasting environments.

Durum wheat (Triticum turgidum L. var durum) is tetraploid wheat (AABB); it is the main source of semolina and other pasta products. Grain yield in wheat is quantitatively inherited and influenced by the environment. The genetic map construction constitutes the essential step in identifying quantitative trait loci (QTLs) linked to complex traits, such as grain yield. The study aimed to construct a genetic linkage map of two parents that are widely grown durum cultivars (Lahn and Cham1) in the Mediterranean basin, which is characterized by varying climate changes. The genetic linkage map of Lahn/Cham1 population consisted of 112 recombinant inbred lines (RILs) and was used to determine QTLs linked to the grain yield in 11 contrasting environments (favorable, cold, dry, and hot). Simple sequence repeat (SSR) molecular markers were used to construct an anchor map, which was later enriched with single nucleotide polymorphisms (SNPs). The map was constructed with 247 SSRs and enriched with 1425 SNPs. The map covered 6122.22 cM. One hundred and twenty-six QTLs were detected on different chromosomes. Chromosomes 2A and 4B harbored the most significant grain yield QTLs. Furthermore, by comparison with several wheat mapping populations, all the A and B chromosomes of Lahn/Cham1 QTLs contributed to grain yield. The results showed that the detected QTLs can be used as a potential candidate for marker-assisted selection in durum breeding programs.

Mapping additive and epistatic QTLs for forage quality and yield in soybean [Glycine max (L.) Merri.] in two environments

Soybean plants have high protein content and can be used as a supplementary source of high-protein feed. To map quantitative trait loci (QTL) for the content of crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF) and dry weight of plant (DWP) in R2 stage of soybean, two recombinant inbred lines, RIL3613 and RIL6013, containing 134 and 156 RILs, derived from the cross of Dongnong L13 × Heihe 36 and Dongnong L13 × Henong 60, were planted for two consecutive years. Based on a simple sequence repeat (SSR) linkage map, QTLs of CP, NDF, ADF and DWP were mapped by interval mapping (IM) and inclusive composite interval mapping method (ICIM) using additive effect, epistatic effect and environmental interaction model. The variance components of genotype, environment and genotype × environment (G × E) interaction for quality and yield traits in the two RIL populations were significant under multiple environmental conditions. Eighteen additive effect QTLs on 10 of 20 soybean chromosomes explained 7.02%–15.67% and 2.13%–11.42% of the phenotypic variation in RIL3613 and RIL6013, respectively. Three epistatic QTL pairs related to CP and six ones for DWP were identified. Eight additive effect QTLs for CP, eight ones for NDF, three ones for ADF, and another three ones for DWP were identified by genotype × environment interaction analysis. One epistatic QTL for CP, 11 epistatic QTL pairs for ADF and 38 ones for NDF were identified. These results can provide better understanding of the genetic basis of soybean feed quality and yield.

Interéaction of submergence tolerance and drought yield QTLs (Sub1 and qDTYs) enhances morpho‐physiological traits and survival of rice (Oryza sativa L.) under submergence

AbstractClimate change has caused increasing incidences of the extreme flooding around the world, which has impacted rice production, especially in rainfed ecosystems. Breeding for submergence tolerant rice varieties has been conducted to mitigate the adverse effects and help farmers to reduce yield loss. The present study was carried out to introgress the submergence tolerance QTL (Sub1) from IR64‐Sub1 into drought‐tolerant lines; UKM5 and UKM91 possessing the drought yield quantitative trait loci (qDTYs), viz. qDTY12.1 and qDTY3.1, and to evaluate the effects of the combination of these QTLs in improving the morpho‐physiological traits and survival under submergence. UKM5 and UKM91 were selected as the recipient of the Sub1 locus because of the intermediate submergence tolerance they possessed even though they only have qDTY/s. Therefore, the introgression of Sub1 into these lines was hypothesised to enhance survival. Submergence stress was given for 14 days to BC1F4 lines from the two breeding populations. Lines with better survival than the tolerant check, IR64‐Sub1 were selected and evaluated under 18 days of submergence stress. Generally, lines with Sub1 and qDTYs from UKM5*/IR64‐Sub1 and UKM91*/IR64‐Sub1 populations had higher survival rate (SR) of 90–100%, lower shoot elongation percentage (EP) and a smaller percentage of chlorophyll content change (CCC) than IR64‐Sub1. The selected lines also showed a low percentage of non‐structural carbohydrate change (NSCC) which related to the ability to recover after submergence. In both populations, the interaction of qDTY3.1 with Sub1 proved to give the best improvement on EP and CCC. This experiment provides novel findings; that is, the combinations of Sub1 + qDTY12.1 + qDTY3.1 showed high survival rate in the population of UKM5*/IR64‐Sub1, while qDTY3.1 improved SR of the lines from UKM91*/IR64‐Sub1 population to 100%. These results proved that the action of Sub1, qDTY/s and their interaction differ in contrasting population or background. The QTL combinations showed a consistent effect in both submergence experiments and proved the effectiveness of Sub1 and qDTYs combinations in enhancing the morpho‐physiological traits and survival.

Genome-wide association studies for methane emission and ruminal volatile fatty acids using Holstein cattle sequence data

BackgroundMethane emission by ruminants has contributed considerably to the global warming and understanding the genomic architecture of methane production may help livestock producers to reduce the methane emission from the livestock production system. The goal of our study was to identify genomic regions affecting the predicted methane emission (PME) from volatile fatty acids (VFAs) indicators and VFA traits using imputed whole-genome sequence data in Iranian Holstein cattle.ResultsBased on the significant-association threshold (p < 5 × 10− 8), 33 single nucleotide polymorphisms (SNPs) were detected for PME per kg milk (n = 2), PME per kg fat (n = 14), and valeric acid (n = 17). Besides, 69 genes were identified for valeric acid (n = 18), PME per kg milk (n = 4) and PME per kg fat (n = 47) that were located within 1 Mb of significant SNPs. Based on the gene ontology (GO) term analysis, six promising candidate genes were significantly clustered in organelle organization (GO:0004984, p = 3.9 × 10− 2) for valeric acid, and 17 candidate genes significantly clustered in olfactory receptors activity (GO:0004984, p = 4 × 10− 10) for PME traits. Annotation results revealed 31 quantitative trait loci (QTLs) for milk yield and its components, body weight, and residual feed intake within 1 Mb of significant SNPs.ConclusionsOur results identified 33 SNPs associated with PME and valeric acid traits, as well as 17 olfactory receptors activity genes for PME traits related to feed intake and preference. Identified SNPs were close to 31 QTLs for milk yield and its components, body weight, and residual feed intake traits. In addition, these traits had high correlations with PME trait. Overall, our findings suggest that marker-assisted and genomic selection could be used to improve the difficult and expensive-to-measure phenotypes such as PME. Moreover, prediction of methane emission by VFA indicators could be useful for increasing the size of reference population required in genome-wide association studies and genomic selection.

Identification of QTLs for grain yield and other traits in tropical maize under Striga infestation.

Striga is an important biotic factor limiting maize production in sub-Saharan Africa and can cause yield losses as high as 100%. Marker-assisted selection (MAS) approaches hold a great potential for improving Striga resistance but requires identification and use of markers associated with Striga resistance for adequate genetic gains from selection. However, there is no report on the discovery of quantitative trait loci (QTL) for resistance to Striga in maize under artificial field infestation. In the present study, 198 BC1S1 families obtained from a cross involving TZEEI 29 (Striga resistant inbred line) and TZEEI 23 (Striga susceptible inbred line) plus the two parental lines were screened under artificial Striga-infested conditions at two Striga-endemic locations in Nigeria in 2018, to identify QTL associated with Striga resistance indicator traits, including grain yield, ears per plant, Striga damage and number of emerged Striga plants. Genetic map was constructed using 1,386 DArTseq markers distributed across the 10 maize chromosomes, covering 2076 cM of the total genome with a mean spacing of 0.11 cM between the markers. Using composite interval mapping (CIM), fourteen QTL were identified for key Striga resistance/tolerance indicator traits: 3 QTL for grain yield, 4 for ears per plant and 7 for Striga damage at 10 weeks after planting (WAP), across environments. Putative candidate genes which encode major transcription factor families WRKY, bHLH, AP2-EREBPs, MYB, and bZIP involved in plant defense signaling were detected for Striga resistance/tolerance indicator traits. The QTL detected in the present study would be useful for rapid transfer of Striga resistance/tolerance genes into Striga susceptible but high yielding maize genotypes using MAS approaches after validation. Further studies on validation of the QTL in different genetic backgrounds and in different environments would help verify their reproducibility and effective use in breeding for Striga resistance/tolerance.

Genetic Dissection and Identification of Candidate Genes for Salinity Tolerance Using Axiom®CicerSNP Array in Chickpea.

Globally, chickpea production is severely affected by salinity stress. Understanding the genetic basis for salinity tolerance is important to develop salinity tolerant chickpeas. A recombinant inbred line (RIL) population developed using parental lines ICCV 10 (salt-tolerant) and DCP 92-3 (salt-sensitive) was screened under field conditions to collect information on agronomy, yield components, and stress tolerance indices. Genotyping data generated using Axiom®CicerSNP array was used to construct a linkage map comprising 1856 SNP markers spanning a distance of 1106.3 cM across eight chickpea chromosomes. Extensive analysis of the phenotyping and genotyping data identified 28 quantitative trait loci (QTLs) explaining up to 28.40% of the phenotypic variance in the population. We identified QTL clusters on CaLG03 and CaLG06, each harboring major QTLs for yield and yield component traits under salinity stress. The main-effect QTLs identified in these two clusters were associated with key genes such as calcium-dependent protein kinases, histidine kinases, cation proton antiporter, and WRKY and MYB transcription factors, which are known to impart salinity stress tolerance in crop plants. Molecular markers/genes associated with these major QTLs, after validation, will be useful to undertake marker-assisted breeding for developing better varieties with salinity tolerance.

Quantitative trait locus mapping of yield and plant height in autotetraploid alfalfa (Medicago sativa L.)

Alfalfa (Medicago sativa L.) is the most widely grown forage legume crop worldwide. Yield and plant height are important agronomic traits influenced by genetic and environmental factors. The objective of this study was to identify quantitative trait loci (QTL) and molecular markers associated with alfalfa yield and plant height. To understand the genetic basis of these traits, a full-sib F1 population composed of 392 individuals was developed by crossing a low-yielding precocious alfalfa genotype (male parent) with a high-yielding late-maturing alfalfa cultivar (female parent). The linkage maps were constructed with 3818 single-nucleotide polymorphism (SNP) markers on 64 linkage groups. QTL for yield and plant height were mapped using phenotypic data for three years. Sixteen QTL associated with yield and plant height were identified on chromosomes 1 to 8. Six QTL explained more than 10% of phenotypic variation, representing major loci controlling yield and plant height. One locus on chromosome 1 controlling yield traits had not been identified in previous studies. Three QTL co-located with other QTL (qyield-1 and qheight-7, qheight-5 and qyield-4, qheight-6, and qyield-6). With further validation, the markers closely linked with these QTL may be used for marker-assisted selection in breeding new alfalfa varieties with high yield.

Detecting CSSLs and yield QTLs with additive, epistatic and QTL\xd7environment interaction effects from Oryza sativa \xd7 O. nivara IRGC81832 cross

Chromosome segment substitution lines (CSSLs) are useful tools for precise mapping of quantitative trait loci (QTLs) and the evaluation of gene action and interaction in inter-specific crosses. In this study, a set of 90 back cross lines at BC2F8 generation derived from Swarna x Oryza nivara IRGC81832 was evaluated for yield traits under irrigated conditions in wet seasons of 3 consecutive years. We identified a set of 70 chromosome segment substitution lines, using genotyping data from 140 SSR markers covering 94.4% of O. nivara genome. Among these, 23 CSSLs were significantly different for 7 traits. 22 QTLs were detected for 11 traits with 6.51 to 46.77% phenotypic variation in 90 BILs. Three pleiotropic genomic regions associated with yield traits were mapped on chromosomes 1, 8 and 11. The marker interval RM206-RM144 at chromosome 11 was recurrently detected for various yield traits. Ten QTLs were identified consistently in the three consecutive years of testing. Seventeen pairs of significant epistatic QTLs (E-QTLs) were detected for days to flowering, days to maturity and plant height. Chromosome segments from O. nivara contributed trait enhancing alleles. The significantly improved lines and the stable QTLs identified in this study are valuable resource for gene discovery and yield improvement.

The genetic associations between yield component traits and bacterial leaf blight resistance in rice (O. sativa L.)

Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae (Xoo) is highly destructive to high-yielding susceptible rice cultivars. In severe epidemics, yield losses up to 75% have been reported. Considerable information on BLB resistant genes is available; however, genetic overlaps between BLB resistant and yield related traits largely remain unclear. Using two sets of backcross introgression inbred lines and one set of recombinant inbred lines, genetic associations between bacterial leaf blight resistance and yield component traits in three Xoo races were analyzed using Single Nucleotide polymorphism (SNP) markers. Fifteen quantitative trait loci conferring BLB resistant and 112 QTLs for yield and yield related traits were detected. Among 15 QTLs conferring bacteria leaf blight resistance, 14 QTLs overlapped with 39 QTLs for yield and yield related traits. The total resistance variation explained by additive QTLs (R2) ranged between 5-76% and epistasis QTLs ranged between 1-26%. Quantitative trait loci (QTLs) with the highest additive effects for BLB resistance were detected at positions os04-17305294 (on chr.4), os05-07310209 (on chr.5), os09-15240130 (on chr.9), os11-28421947 (on chr.11) and os12-25291547(on chr.12). The BLB resistance QTL at position os05-07310209 (on chr.5) overlapped with QTLs for 1000-grain weight, grain number per panicle, days to heading, grain length width ratio and grain yield per plant. The BLB resistance QTL at position os09-15240130 (on chr.9) overlapped with QTLs for grain yield per plant, panicle number, grain width and 1000-grain weight. The BLB resistance QTL at position os12-25291547-(on chr.12) overlapped with QTLs for 1000-grain weight, panicle number, grain yield per plant, filled grain number and grain width. Eleven percent of the 717 plants in this study were resistant to at least two Xoo races and had higher grain yield compared to both recurrent parents. Overlapping regions especially with already cloned genes and epistasis detected in this study offer opportunity to develop rice varieties which combine high yield and resistance to bacteria leaf blight.

Mapping of QTLs Associated with Yield and Yield Related Traits in Durum Wheat (Triticum durum Desf.) Under Irrigated and Drought Conditions.

Global durum wheat consumption (Triticum durum Desf.) is ahead of its production. One reason for this is abiotic stress, e.g., drought. Breeding for resistance to drought is complicated by the lack of fast, reproducible screening techniques and the inability to routinely create defined and repeatable water stress conditions. Here, we report the first analysis of dissection of yield and yield-related traits in durum wheat in Pakistan, seeking to elucidate the genetic components of yield and agronomic traits. Analysis of several traits revealed a total of 221 (160 with logarithm of odds (LOD) > 2 ≤ 3 and 61 with LOD > 3) quantitative trait loci (QTLs) distributed on all fourteen durum wheat chromosomes, of which 109 (78 with LOD > 2 ≤ 3 and 31 with LOD > 3) were observed in 2016-17 (S1) and 112 (82 with LOD > 2 ≤ 3 and 30 with LOD > 3) were observed in 2017-18 (S2). Allelic profiles of yield QTLs on chromosome 2A and 7B indicate that allele A of Xgwm895 and allele B of Xbarc276 can enhance the Yd up to 6.16% in control and 5.27% under drought. Moreover, if combined, a yield gain of up to 11% would be possible.

A QTL atlas for grain yield and its component traits in maize (Zea mays)

AbstractGrain yield and its component traits are essential targets in maize breeding. These traits are genetically complex and controlled by a large number of quantitative trait loci (QTL). The aim of this study was to compile reported QTL and major genes for grain yield and its component traits in a QTL atlas, as a valuable resource for the maize community. To this end, 1,177 QTL related to maize yield were collected from 56 studies published between 1992 and 2018. These QTL were projected to genetic map “IBM2 2008 Neighbors”, which led to the identification of 135 meta‐QTL. Some genomic regions appear to be hotspots for yield‐related meta‐QTL, often affecting more than one of the investigated traits. Moreover, we catalogued 20 major maize loci associated with yield and identified 65 maize homologs of 21 rice yield‐related genes. Interestingly, we found that a significant proportion of them are located in meta‐QTL regions. Collectively, this study provides a reference for QTL fine‐mapping and gene cloning, as well as for molecular marker‐assisted breeding of yield‐related traits in maize.