Large-effect Quantitative Trait Loci Research Articles

Back to Acid Soil Fields: The Citrate Transporter SbMATE Is a Major Asset for Sustainable Grain Yield for Sorghum Cultivated on Acid Soils

Aluminum (Al) toxicity damages plant roots and limits crop production on acid soils, which comprise up to 50% of the world’s arable lands. A major Al tolerance locus on chromosome 3, AltSB, controls aluminum tolerance in sorghum [Sorghum bicolor (L.) Moench] via SbMATE, an Al-activated plasma membrane transporter that mediates Al exclusion from sensitive regions in the root apex. As is the case with other known Al tolerance genes, SbMATE was cloned based on studies conducted under controlled environmental conditions, in nutrient solution. Therefore, its impact on grain yield on acid soils remains undetermined. To determine the real world impact of SbMATE, multi-trait quantitative trait loci (QTL) mapping in hydroponics, and, in the field, revealed a large-effect QTL colocalized with the Al tolerance locus AltSB, where SbMATE lies, conferring a 0.6 ton ha–1 grain yield increase on acid soils. A second QTL for Al tolerance in hydroponics, where the positive allele was also donated by the Al tolerant parent, SC283, was found on chromosome 9, indicating the presence of distinct Al tolerance genes in the sorghum genome, or genes acting in the SbMATE pathway leading to Al-activated citrate release. There was no yield penalty for AltSB, consistent with the highly localized Al regulated SbMATE expression in the root tip, and Al-dependent transport activity. A female effect of 0.5 ton ha–1 independently demonstrated the effectiveness of AltSB in hybrids. Al tolerance conferred by AltSB is thus an indispensable asset for sorghum production and food security on acid soils, many of which are located in developing countries.

Genetic basis of maize kernel starch content revealed by high-density single nucleotide polymorphism markers in a recombinant inbred line population.

BackgroundStarch from maize kernels has diverse applications in human and animal diets and in industry and manufacturing. To meet the demands of these applications, starch quantity and quality need improvement, which requires a clear understanding of the functional mechanisms involved in starch biosynthesis and accumulation. In this study, a recombinant inbred line (RIL) population was developed from a cross between inbred lines CI7 and K22. The RIL population, along with both parents, was grown in three environments, and then genotyped using the MaizeSNP50 BeadChip and phenotyped to dissect the genetic architecture of starch content in maize kernels.ResultsBased on the genetic linkage map constructed using 2,386 bins as markers, six quantitative trait loci (QTLs) for starch content in maize kernels were detected in the CI7/K22 RIL population. Each QTL accounted for 4.7 % (qSTA9-1) to 10.6 % (qSTA4-1) of the starch variation. The QTL interval was further reduced using the bin-map method, with the physical distance of a single bin at the QTL peak ranging from 81.7 kb to 2.2 Mb. Based on the functional annotations and prior knowledge of the genes in the top bin, seven genes were considered as potential candidate genes for the identified QTLs. Three of the genes encode enzymes in non-starch metabolism but may indirectly affect starch biosynthesis, and four genes may act as regulators of starch biosynthesis.ConclusionsA few large-effect QTLs, together with a certain number of minor-effect QTLs, mainly contribute to the genetic architecture of kernel starch content in our maize biparental linkage population. All of the identified QTLs, especially the large-effect QTL, qSTA4-1, with a small QTL interval, will be useful for improving the maize kernel starch content through molecular breeding.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-015-0675-2) contains supplementary material, which is available to authorized users.

Improving accuracy of genomic prediction by genetic architecture based priors in a Bayesian model.

BackgroundIn recent years, with the development of high-throughput sequencing technology and the commercial availability of genotyping bead chips, more attention is being directed towards the utilization of abundant genetic markers in animal and plant breeding programs, human disease risk prediction and personal medicine. Several useful approaches to accomplish genomic prediction have been developed and used widely, but still have room for improvement to gain more accuracy. In this study, an improved Bayesian approach, termed BayesBπ, which differs from the original BayesB in priors assigning, is proposed. An effective method for calculating the locus-specific π by converting p-values from association between SNPs and traits’ phenotypes is given and systemically validated using a German Holstein dairy cattle population. Furthermore, the new method is applied to a loblolly pine (Pinus taeda) dataset.ResultsCompared with the original BayesB, BayesBπ can improve the accuracy of genomic prediction up to 7.62 % for milk fat percentage, a trait which shows a large effect of quantitative trait loci (QTL). For milk yield, which is controlled by small to moderate effect genes, the accuracy of genomic prediction can be improved up to 4.94 %. For somatic cell score, of which no large effect QTL has been reported, GBLUP performs better than Bayesian methods. BayesBπ outperforms BayesCπ in 10 out of 12 scenarios in the dairy cattle population, especially in small to moderate population sizes where accuracy of BayesCπ are dramatically low. Results of the loblolly pine dataset show that BayesBπ outperforms BayesB in 14 out of 17 traits and BayesCπ in 8 out of 17 traits, respectively.ConclusionsFor traits controlled by large effect genes, BayesBπ can improve the accuracy of genomic prediction and unbiasedness of BayesB in moderate size populations. Knowledge of traits’ genetic architectures can be integrated into practices of genomic prediction by assigning locus-specific priors to markers, which will help Bayesian approaches perform better in variable selection and marker effects shrinkage.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0278-9) contains supplementary material, which is available to authorized users.

Alleles versus mutations: Understanding the evolution of genetic architecture requires a molecular perspective on allelic origins.

Perspectives on the role of large-effect quantitative trait loci (QTL) in the evolution of complex traits have shifted back and forth over the past few decades. Different sets of studies have produced contradictory insights on the evolution of genetic architecture. I argue that much of the confusion results from a failure to distinguish mutational and allelic effects, a limitation of using the Fisherian model of adaptive evolution as the lens through which the evolution of adaptive variation is examined. A molecular-based perspective reveals that allelic differences can involve the cumulative effects of many mutations plus intragenic recombination, a model that is supported by extensive empirical evidence. I discuss how different selection regimes could produce very different architectures of allelic effects under a molecular-based model, which may explain conflicting insights on genetic architecture from studies of variation within populations versus between divergently selected populations. I address shortcomings of genome-wide association study (GWAS) practices in light of more suitable models of allelic evolution, and suggest alternate GWAS strategies to generate more valid inferences about genetic architecture. Finally, I discuss how adopting more suitable models of allelic evolution could help redirect research on complex trait evolution toward addressing more meaningful questions in evolutionary biology.

Environmental and genetic interactions reveal FLOWERING LOCUS C as a modulator of the natural variation for the plasticity of flowering in Arabidopsis.

The timing of flowering initiation depends strongly on the environment, a property termed as the plasticity of flowering. Such plasticity determines the adaptive potential of plants because it provides phenotypic buffer against environmental changes, and its natural variation contributes to evolutionary adaptation. We addressed the genetic mechanisms of the natural variation for this plasticity in Arabidopsis thaliana by analysing a population of recombinant inbred lines derived from Don-0 and Ler accessions collected from distinct climates. Quantitative trait locus (QTL) mapping in four environmental conditions differing in photoperiod, vernalization treatment and ambient temperature detected the folllowing: (i) FLOWERING LOCUS C (FLC) as a large effect QTL affecting flowering time differentially in all environments; (ii) numerous QTL displaying smaller effects specifically in some conditions; and (iii) significant genetic interactions between FLC and other loci. Hence, the variation for the plasticity of flowering is determined by a combination of environmentally sensitive and specific QTL, and epistasis. Analysis of FLC from Don identified a new and more active allele likely caused by a cis-regulatory deletion covering the non-coding RNA COLDAIR. Further characterization of four FLC natural alleles showed different environmental and genetic interactions. Thus, FLC appears as a major modulator of the natural variation for the plasticity of flowering to multiple environmental factors.

First insights into the genotype–phenotype map of phenotypic stability in rye

Improving phenotypic stability of crops is pivotal for coping with the detrimental impacts of climate change. The goal of this study was to gain first insights into the genetic architecture of phenotypic stability in cereals. To this end, we determined grain yield, thousand kernel weight, test weight, falling number, and both protein and soluble pentosan content for two large bi-parental rye populations connected through one common parent and grown in multi-environmental field trials involving more than 15 000 yield plots. Based on these extensive phenotypic data, we calculated parameters for static and dynamic phenotypic stability of the different traits and applied linkage mapping using whole-genome molecular marker profiles. While we observed an absence of large-effect quantitative trait loci (QTLs) underlying yield stability, large and stable QTLs were found for phenotypic stability of test weight, soluble pentosan content, and falling number. Applying genome-wide selection, which in contrast to marker-assisted selection also takes into account loci with small-effect sizes, considerably increased the accuracy of prediction of phenotypic stability for all traits by exploiting both genetic relatedness and linkage between single-nucleotide polymorphisms and QTLs. We conclude that breeding for crop phenotypic stability can be improved in related populations using genomic selection approaches established upon extensive phenotypic data.

Detection of quantitative trait loci for mineral content of Nelore longissimus dorsi muscle.

BackgroundBeef cattle require dietary minerals for optimal health, production and reproduction. Concentrations of minerals in tissues are at least partly genetically determined. Mapping genomic regions that affect the mineral content of bovine longissimus dorsi muscle can contribute to the identification of genes that control mineral balance, transportation, absorption and excretion and that could be associated to metabolic disorders.MethodsWe applied a genome-wide association strategy and genotyped 373 Nelore steers from 34 half-sib families with the Illumina BovineHD BeadChip. Genome-wide association analysis was performed for mineral content of longissimus dorsi muscle using a Bayesian approach implemented in the GenSel software.ResultsMuscle mineral content in Bos indicus cattle was moderately heritable, with estimates ranging from 0.29 to 0.36. Our results suggest that variation in mineral content is influenced by numerous small-effect QTL (quantitative trait loci) but a large-effect QTL that explained 6.5% of the additive genetic variance in iron content was detected at 72 Mb on bovine chromosome 12. Most of the candidate genes present in the QTL regions for mineral content were involved in signal transduction, signaling pathways via integral (also called intrinsic) membrane proteins, transcription regulation or metal ion binding.ConclusionsThis study identified QTL and candidate genes that affect the mineral content of skeletal muscle. Our findings provide the first step towards understanding the molecular basis of mineral balance in bovine muscle and can also serve as a basis for the study of mineral balance in other organisms.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-014-0083-3) contains supplementary material, which is available to authorized users.

Genetic variants in root architecture-related genes in a Glycine soja accession, a potential resource to improve cultivated soybean.

BackgroundRoot system architecture is important for water acquisition and nutrient acquisition for all crops. In soybean breeding programs, wild soybean alleles have been used successfully to enhance yield and seed composition traits, but have never been investigated to improve root system architecture. Therefore, in this study, high-density single-feature polymorphic markers and simple sequence repeats were used to map quantitative trait loci (QTLs) governing root system architecture in an inter-specific soybean mapping population developed from a cross between Glycine max and Glycine soja.ResultsWild and cultivated soybean both contributed alleles towards significant additive large effect QTLs on chromosome 6 and 7 for a longer total root length and root distribution, respectively. Epistatic effect QTLs were also identified for taproot length, average diameter, and root distribution. These root traits will influence the water and nutrient uptake in soybean. Two cell division-related genes (D type cyclin and auxin efflux carrier protein) with insertion/deletion variations might contribute to the shorter root phenotypes observed in G. soja compared with cultivated soybean. Based on the location of the QTLs and sequence information from a second G. soja accession, three genes (slow anion channel associated 1 like, Auxin responsive NEDD8-activating complex and peroxidase), each with a non-synonymous single nucleotide polymorphism mutation were identified, which may also contribute to changes in root architecture in the cultivated soybean. In addition, Apoptosis inhibitor 5-like on chromosome 7 and slow anion channel associated 1-like on chromosome 15 had epistatic interactions for taproot length QTLs in soybean.ConclusionRare alleles from a G. soja accession are expected to enhance our understanding of the genetic components involved in root architecture traits, and could be combined to improve root system and drought adaptation in soybean.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1334-6) contains supplementary material, which is available to authorized users.

Genetic mapping of the Cre8 locus for resistance against cereal cyst nematode (Heterodera avenae Woll.) in wheat

The cereal cyst nematode (CCN, Heterodera avenae Woll.) resistance locus Cre8 on the long arm of chromosome 6B (6BL) of wheat (Triticum aestivum L.) is effective in lowering the nematode population in soil. Identification of reliable and high-throughput molecular markers linked to the Cre8 locus is important for the successful deployment of Cre8-derived resistance in wheat breeding programs. Here, we report the addition of over 600 markers to improve an existing linkage map for the Trident/Molineux wheat population. With the improved map, Cre8 was mapped as a large-effect quantitative trait locus (QTL) near the distal end of 6BL. This QTL explained up to 35 % of the phenotypic variation in CCN resistance. New marker assays were developed for DNA polymorphisms in the Cre8 region. Seven molecular markers closely linked with Cre8 (at 0.9, 2.2 or 5.9 cM from the estimated QTL position) were found to be diagnostic across a panel of wheat cultivars and are recommended for use in marker-assisted selection of the Cre8 resistance locus in wheat breeding. Prospects for the isolation of the causal gene are discussed.

Joint-multiple family linkage analysis predicts within-family variation better than single-family analysis of the maize nested association mapping population

Quantitative trait locus (QTL) mapping has been used to dissect the genetic architecture of complex traits and predict phenotypes for marker-assisted selection. Many QTL mapping studies in plants have been limited to one biparental family population. Joint analysis of multiple biparental families offers an alternative approach to QTL mapping with a wider scope of inference. Joint-multiple population analysis should have higher power to detect QTL shared among multiple families, but may have lower power to detect rare QTL. We compared prediction ability of single-family and joint-family QTL analysis methods with fivefold cross-validation for 6 diverse traits using the maize nested association mapping population, which comprises 25 biparental recombinant inbred families. Joint-family QTL analysis had higher mean prediction abilities than single-family QTL analysis for all traits at most significance thresholds, and was always better at more stringent significance thresholds. Most robust QTL (detected in >50% of data samples) were restricted to one family and were often not detected at high frequency by joint-family analysis, implying substantial genetic heterogeneity among families for complex traits in maize. The superior predictive ability of joint-family QTL models despite important genetic differences among families suggests that joint-family models capture sufficient smaller effect QTL that are shared across families to compensate for missing some rare large-effect QTL.

Large effect QTL explain natural phenotypic variation for the developmental timing of vegetative phase change in maize (Zea mays L.).

Natural variation for the timing of vegetative phase change in maize is controlled by several large effect loci, one corresponding to Glossy15 , a gene known for regulating juvenile tissue traits. Vegetative phase change is an intrinsic component of developmental programs in plants. Juvenile and adult vegetative tissues in grasses differ dramatically in their anatomical and biochemical composition affecting the utility of specific genotypes as animal feed and biofuel feedstock. The molecular network controlling the process of developmental transition is incompletely characterized. In this study, we used scoring for juvenile and adult epicuticular wax as an entry point to discover quantitative trait loci (QTL) controlling phenotypic variation for the developmental timing of juvenile to adult transition in maize. We scored the last leaf with juvenile wax on 25 recombinant inbred line families of the B73 reference Nested Association Mapping (NAM) population and the intermated B73×Mo17 (IBM) population across multiple seasons. A total of 13 unique QTL were identified through genome-wide association analysis across the NAM populations, three of which have large effects. A QTL located on chromosome nine had the most significant SNPs within Glossy15, a gene controlling expression of juvenile leaf traits. The second large effect QTL is located on chromosome two. The most significant SNP in this QTL is located adjacent to a homolog of the Arabidopsis transcription factor, enhanced downy mildew-2, which has been shown to promote the transition from juvenile to adult vegetative phase. Overall, these results show that several major QTL and potential candidate genes underlie the extensive natural variation for this developmental trait.

Genetic Improvement of Iron Toxicity Tolerance in Rice-Progress, Challenges and Prospects in West Africa

:In sub-Saharan Africa, the demand for higher rice production continues to grow rapidly. Although there is a huge potential for increasing rice production through expansion of the rice cultivation area in wetlands, iron (Fe) toxicity tends to occur and consequently results in low rice yield. Development and deployment of varieties tolerant to Fe toxicity is one of the practical options to overcome this constraint. Several tolerant varieties have been developed through conventional breeding but progress in breeding has been generally slow mainly due to large genotype × environment interaction and field heterogeneity, which make rice selection ineffective. In addition, there are no valid managed-stress screening protocols which are highly efficient and that can predict rice performance in the diverse target environments of West Africa. Many O. glaberrima accessions have superior tolerance, but only a few of them have been utilized in breeding programs. The known quantitative trait loci (QTLs) related to Fe toxicity, have not been used for marker-assisted selection (MAS), as they gave small effects with a large confidence interval. Accelerating rice breeding efficiency for tolerance to Fe toxicity requires establishment of reliable screening protocols, use of O. glaberrima accessions as donors, identification of large-effect QTLs and MAS using such QTLs. This paper reviews the past and current efforts in West Africa to develop new varieties with superior tolerance to Fe toxicity.

Quantitative genetic analysis indicates natural selection on leaf phenotypes across wild tomato species (Solanum sect. Lycopersicon; Solanaceae).

Adaptive evolution requires both raw genetic material and an accessible path of high fitness from one fitness peak to another. In this study, we used an introgression line (IL) population to map quantitative trait loci (QTL) for leaf traits thought to be associated with adaptation to precipitation in wild tomatoes (Solanum sect. Lycopersicon; Solanaceae). A QTL sign test showed that several traits likely evolved under directional natural selection. Leaf traits correlated across species do not share a common genetic basis, consistent with a scenario in which selection maintains trait covariation unconstrained by pleiotropy or linkage disequilibrium. Two large effect QTL for stomatal distribution colocalized with key genes in the stomatal development pathway, suggesting promising candidates for the molecular bases of adaptation in these species. Furthermore, macroevolutionary transitions between vastly different stomatal distributions may not be constrained when such large-effect mutations are available. Finally, genetic correlations between stomatal traits measured in this study and data on carbon isotope discrimination from the same ILs support a functional hypothesis that the distribution of stomata affects the resistance to CO2 diffusion inside the leaf, a trait implicated in climatic adaptation in wild tomatoes. Along with evidence from previous comparative and experimental studies, this analysis indicates that leaf traits are an important component of climatic niche adaptation in wild tomatoes and demonstrates that some trait transitions between species could have involved few, large-effect genetic changes, allowing rapid responses to new environmental conditions.

Generalized linear model for mapping discrete trait loci implemented with LASSO algorithm.

Generalized estimating equation (GEE) algorithm under a heterogeneous residual variance model is an extension of the iteratively reweighted least squares (IRLS) method for continuous traits to discrete traits. In contrast to mixture model-based expectation–maximization (EM) algorithm, the GEE algorithm can well detect quantitative trait locus (QTL), especially large effect QTLs located in large marker intervals in the manner of high computing speed. Based on a single QTL model, however, the GEE algorithm has very limited statistical power to detect multiple QTLs because of ignoring other linked QTLs. In this study, the fast least absolute shrinkage and selection operator (LASSO) is derived for generalized linear model (GLM) with all possible link functions. Under a heterogeneous residual variance model, the LASSO for GLM is used to iteratively estimate the non-zero genetic effects of those loci over entire genome. The iteratively reweighted LASSO is therefore extended to mapping QTL for discrete traits, such as ordinal, binary, and Poisson traits. The simulated and real data analyses are conducted to demonstrate the efficiency of the proposed method to simultaneously identify multiple QTLs for binary and Poisson traits as examples.

Breeding high-yielding drought-tolerant rice: genetic variations and conventional and molecular approaches.

The increased occurrence and severity of drought stress have led to a high yield decline in rice in recent years in drought-affected areas. Drought research at the International Rice Research Institute (IRRI) over the past decade has concentrated on direct selection for grain yield under drought. This approach has led to the successful development and release of 17 high-yielding drought-tolerant rice varieties in South Asia, Southeast Asia, and Africa. In addition to this, 14 quantitative trait loci (QTLs) showing a large effect against high-yielding drought-susceptible popular varieties were identified using grain yield as a selection criterion. Six of these (qDTY 1.1 , qDTY 2.2 , qDTY 3.1 , qDTY 3.2 , qDTY 6.1 , and qDTY 12.1 ) showed an effect against two or more high-yielding genetic backgrounds in both the lowland and upland ecosystem, indicating their usefulness in increasing the grain yield of rice under drought. The yield of popular rice varieties IR64 and Vandana has been successfully improved through a well-planned marker-assisted backcross breeding approach, and QTL introgression in several other popular varieties is in progress. The identification of large-effect QTLs for grain yield under drought and the higher yield increase under drought obtained through the use of these QTLs (which has not been reported in other cereals) indicate that rice, because of its continuous cultivation in two diverse ecosystems (upland, drought tolerant, and lowland, drought susceptible), has benefited from the existence of larger genetic variability than in other cereals. This can be successfully exploited using marker-assisted breeding.

Genetic loci associated with high grain zinc concentration and pleiotropic effect on kernel weight in wheat (Triticum aestivum L.)

Zinc deficiency in humans is recognized as a widespread public health problem worldwide, but can be combated via genetic biofortification through breeding high zinc containing wheat varieties. CIMMYT (International Maize and Wheat Improvement Center, Int.) is engaged in enhancing, among others, the grain zinc concentration (GZnC) of high-yielding wheat germplasm under the HarvestPlus initiative of the Consultative Group on International Agricultural Research consortium. In the present study, we determined GZnC in a recombinant inbred line population from the cross between PBW343 and Kenya Swara in replicated trials grown in Zn-enriched field. An integrated genetic map with 1,133 loci (diversity arrays technology and simple sequence repeats markers) covering all 21 wheat chromosomes was constructed and used for quantitative trait loci (QTL) analysis. Two novel QTL of large effect were stably detected for increasing GZnC on chromosomes 2Bc (centromeric region) and 3AL (long arm). The two QTL individually explained about 10–15 % of the total phenotypic variation. The 2Bc QTL from PBW343 have pleiotropic effect and can increase thousand-kernel weight at significant level. The QTL and the closely linked markers identified will make selection for this difficult trait feasible in breeding program.

Large-effect pleiotropic or closely linked QTL segregate within and across ten US cattle breeds.

BackgroundThe availability of high-density SNP assays including the BovineSNP50 (50 K) enables the identification of novel quantitative trait loci (QTL) and improvement of the resolution of the locations of previously mapped QTL. We performed a series of genome-wide association studies (GWAS) using 50 K genotypes scored in 18,274 animals from 10 US beef cattle breeds with observations for twelve body weights, calving ease and carcass traits.ResultsA total of 159 large-effects QTL (defined as 1-Mb genome windows explaining more than 1% of additive genetic variance) were identified. In general, more QTL were identified in analyses with bigger sample sizes. Four large-effect pleiotropic or closely linked QTLs located on BTA6 at 37–42 Mb (primarily at 38 Mb), on BTA7 at 93 Mb, on BTA14 at 23–26 Mb (primarily at 25 Mb) and on BTA20 at 4 Mb were identified in more than one breed. Several breed-specific large-effect pleiotropic or closely linked QTL were also identified. Some identified QTL regions harbor genes known to have large effects on a variety of traits in cattle such as PLAG1 and MSTN and others harbor promising candidate genes including NCAPG, ARRDC3, ERGIC1, SH3PXD2B, HMGA2, MSRB3, LEMD3, TIGAR, SEPT7, and KIRREL3. Gene ontology analysis revealed that genes involved in ossification and in adipose tissue development were over-represented in the identified pleiotropic QTL. Also, the MAPK signaling pathway was identified as a common pathway affected by the genes located near the pleiotropic QTL.ConclusionsThis largest GWAS ever performed in beef cattle, led us to discover several novel across-breed and breed-specific large-effect pleiotropic QTL that cumulatively account for a significant percentage of additive genetic variance (e.g. more than a third of additive genetic variance of birth and mature weights; and calving ease direct in Hereford). These results will improve our understanding of the biology of growth and body composition in cattle.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-442) contains supplementary material, which is available to authorized users.

Complementary epistasis involving Sr12 explains adult plant resistance to stem rust in Thatcher wheat (Triticum aestivum L.).

Quantitative trait loci conferring adult plant resistance to Ug99 stem rust in Thatcher wheat display complementary gene action suggesting multiple quantitative trait loci are needed for effective resistance. Adult plant resistance (APR) in wheat (Triticum aestivum L.) to stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is desirable because this resistance can be Pgt race non-specific. Resistance derived from cultivar Thatcher can confer high levels of APR to the virulent Pgt race TTKSK (Ug99) when combined with stem rust resistance gene Sr57 (Lr34). To identify the loci conferring APR in Thatcher, we evaluated 160 RILs derived from Thatcher crossed to susceptible cultivar McNeal for field stem rust reaction in Kenya for two seasons and in St. Paul for one season. All RILs and parents were susceptible as seedlings to race TTKSK. However, adult plant stem rust severities in Kenya varied from 5 to 80 %. Composite interval mapping identified four quantitative trait loci (QTL). Three QTL were inherited from Thatcher and one, Sr57, was inherited from McNeal. The markers closest to the QTL peaks were used in an ANOVA to determine the additive and epistatic effects. A QTL on 3BS was detected in all three environments and explained 27-35 % of the variation. The peak of this QTL was at the same location as the Sr12 seedling resistance gene effective to race SCCSC. Epistatic interactions were significant between Sr12 and QTL on chromosome arms 1AL and 2BS. Though Sr12 cosegregated with the largest effect QTL, lines with Sr12 were not always resistant. The data suggest that Sr12 or a linked gene, though not effective to race TTKSK alone, confers APR when combined with other resistance loci.

New quantitative trait loci for enhancing adaptation to salinity in rice from Hasawi, a Saudi landrace into three African cultivars at the reproductive stage

Salinity is a major constraint affecting rice productivity in rainfed and irrigated agro-ecosystems. Understanding salinity effects on rice production at the reproductive stage could improve adaptation for this trait. Identifying quantitative trait loci (QTLs) controlling adaptation to salinity may also accelerate breeding rice germplasm for environments prone to this stress. We used the salt tolerant landrace ‘Hasawi’ as a donor parent to generate three F2 offspring (consisting each of 500 individuals) with three African cultivars (‘NERICA-L-19’, ‘Sahel 108’ and ‘BG90-2’) used as recipient parents (RP). The F2s and F2:3s were evaluated for grain yield and other traits in saline fields. Salinity caused reduction in all measured traits across the F2-derived offspring, e.g. grain yield reduced between 65 and 73 %, but some offspring had twice the RP’s grain yield. QTL analysis revealed 75 QTLs for different traits in all 3 genetic backgrounds (GBs): 24 of them were common among all the 3 GBs while 31 were noted in 2 GBs, and 17 in one GB. ‘Hasawi’ contributed on average 49 % alleles to these QTLs. Two yield and yield related QTLs (qGY11 and qTN11) common in all 3 GBs were mapped on the same chromosomal segment suggesting these QTLs might be stable across different GBs. Four other QTLs were strongly associated with salinity tolerance with peak marker RM419, representing a potential candidate for MAS due to high LOD score and relatively large effect QTLs.

Identification of large-effect QTL for kernel row number has potential for maize yield improvement

The kernel row number (KRN) per ear is an important component of maize (Zea mays L.) yield. In this study, a line with six kernel rows, MT-6, was used to investigate the genetic basis of KRN by quantitative trait locus (QTL) mapping. MT-6 was derived from a maize inbred line Mo17 and a teosinte entry X26-4 (Zea mays ssp. mexicana), with 23 % of its genome being homologous to X26-4. An MT-6/B73 F2 segregating population consisting of 266 individuals was genotyped using 192 molecular markers spread evenly across the genome. The same F2 population, together with its F2:3 population, was phenotyped for KRN in three environments. Five individual QTL for KRN, including three substantially consistent major QTL detected in all environments, were identified on chromosomes 1, 2, 3, 4, and 5, respectively. These QTL accounted for 39.5–65.0 % of the KRN variation in these populations. Additionally, one pair of epistatic interaction between two loci with additive effects was detected and accounted for about 3 % of KRN variation. These results demonstrate that a few major QTL could substantially affect the evolution of maize KRNs and therefore provide valuable information for our understanding of the mechanism of KRN and the improvement in maize grain yield by molecular breeding.