Biparental Populations Research Articles

SEA v2.0: an R software package for mixed major genes plus polygenes inheritance analysis of quantitative traits

<p id="C3">The phenotypic values for quantitative trait from bi-parental segregation populations can be used to identify its mixed major genes plus polygenes inheritance model, which provides important information for the genetic basis of quantitative traits and crop breeding. To comprehensively summarize the research results of methodological advances, add the new functions of the software and correct its shortcomings in previous versions, an R software package SEA v2.0 with interactive graphical user interface is developed under R studio-1.4.1103 platform and R environment. In this software, there were 14 types of bi-parental segregation populations, and each type included four modules: data input, data analysis, posterior probability calculation, and distribution curve drawing. To save running time, doParallel was used to conduct parallel computing, data.table was used to quickly read and write datasets, and MASS was used to estimate the parameters in component distributions. KScorrect, kolmim, and shiny packages were used to simplify the programs. As long as users uploaded the data file with *.csv format and set the related parameters, the results could be quickly displayed. The software was validated by real data analysis of soybean podding habit and Monte Carlo simulation studies, and can be downloaded from https://cran.r-project.org/web/packages/SEA/index.html.

Development and Performance of Some Onion (Allium cepa L.) Composites and Bi-parental Populations.

Development and Performance of Some Onion (Allium cepa L.) Composites and Bi-parental Populations.

Mapping and identification of QTL in 5601T × U99-310255 RIL population using SNP genotyping: soybean seed quality traits.

Molecular markers have played and will continue to play a major role in the genetic characterization and improvement of soybeans. They have helped identify major loci for tolerance to abiotic stressors, disease resistance, herbicide resistance, soybean seed quality traits, and yield. However, most yield quantitative trait loci (QTL) are specific to a certain population, and the genetic variation found in the specific bi-parental population is not always shared in other populations. A major objective in soybean breeding is to develop high yielding cultivars. Unfortunately, soybean seed yield, as well as protein and oil content, are complex quantitative traits to characterize from the phenotypic and genotypic perspectives. The objectives of this study are to detect soybean genomic regions that increase protein content, while maintaining oil content and seed yield and to successfully identify soybean QTL associated with these seed quality traits. To achieve these objectives, data from the 138 recombinant inbred lines grown in six environments were used to perform QTL detection analyses in search of significant genomic regions affecting soybean seed protein, oil, and yield. A total of 21 QTL were successfully identified for yield, protein, oil, methionine, threonine, lodging, maturity, and meal. Knowledge of their locations and flanking markers will aid in marker assisted selection for plant breeders. This will lead to a more valuable soybean for farmers, processors, and animal nutritionists.

Development and validation of an optimized marker set for genomic selection in southern U.S. rice breeding programs.

The potential of genomic selection (GS) to increase the efficiency of breeding programs has been clearly demonstrated; however, the implementation of GS in rice (Oryza sativa L.) breeding programs has been limited. In recent years, efforts have begun to work toward implementing GS into the Louisiana State University (LSU) Agricultural Center rice breeding program. One of the first steps for successful GS implementation is to establish a suitable marker set for the target germplasm and a reliable, cost-effective genotyping platform capable of providing informative marker data with an adequate turnaround time. The objective of this study was to develop a marker set for routine GS and demonstrate its effectiveness in southern U.S. rice germplasm. The utility of the resulting marker set, the LSU500, for GS applications was demonstrated using four years of breeding data across 7,607 experimental lines and four elite biparental populations. The predictive ability of GS ranged from 0.13 to 0.78 for key traits across different market classes and yield trials. Comparisons between phenotypic selection and GS within biparental populations demonstrates similar performance of GS compared with phenotypic selection in predicting future performance. The prediction accuracies obtained with the LSU500 marker set demonstrates the utility of this marker set for cost-effective GS applications in southern U.S. rice breeding programs. The LSU500 marker set has been established through the genotyping service provider Agriplex Genomics, and in the future, it will undergo improvements to reduce the cost and increase the accuracy of GS.

Identification and validation of a major QTL for kernel length in bread wheat based on two F3 biparental populations

BackgroundHigh yield and quality are essential goals of wheat (Triticum aestivum L.) breeding. Kernel length (KL), as a main component of kernel size, can indirectly change kernel weight and then affects yield. Identification and utilization of excellent loci in wheat genetic resources is of great significance for cultivating high yield and quality wheat. Genetic identification of loci for KL has been performed mainly through genome-wide association study in natural populations or QTL mapping based on genetic linkage map in high generation populations.ResultsIn this study, an F3 biparental population derived from the cross between an EMS mutant BLS1 selected from an EMS-induced wheat genotype LJ2135 (derived from the hybrid progeny of a spelt wheat (T. spelta L.) and a common wheat) mutant bank and a local breeding line 99E18 was used to rapidly identify loci controlling KL based on Bulked Segregant Analysis (BSA) and the wheat 660 K single-nucleotide polymorphism (SNP) array. The highest ratio of polymorphic SNPs was located on chromosome 4A. Linkage map analysis showed that 33 Kompetitive Allele Specific PCR markers were linked to the QTL for KL (Qkl.sicau-BLE18-4A) identified in three environments as well as the best linear unbiased prediction (BLUP) dataset. This QTL explained 10.87—19.30% of the phenotypic variation. Its effect was successfully confirmed in another F3 population with the two flanking markers KASP-AX-111536305 and KASP-AX-110174441. Compared with previous studies and given that the of BLS1 has the genetic background of spelt wheat, the major QTL was likely a new one. A few of predicted genes related to regulation of kernel development were identified in the interval of the detected QTL.ConclusionA major, novel and stable QTL (Qkl.sicau-BLE18-4A) for KL was identified and verified in two F3 biparental populations across three environments. Significant relationships among KL, kernel width (KW) and thousand kernel weight (TKW) were identified. Four predicted genes related to kernel growth regulation were detected in the interval of Qkl.sicau-BLE18-4A. Furthermore, this study laid foundation on subsequent fine mapping work and provided a possibility for breeding of elite wheat varieties.

Molecular Approaches to Overcome Self-Incompatibility in Diploid Potatoes.

There has been an increased interest in true potato seeds (TPS) as planting material because of their advantages over seed tubers. TPS produced from a tetraploid heterozygous bi-parental population produces non-uniform segregating progenies, which have had limited uniformity in yield and quality in commercial cultivation, and, thus, limited success. Inbreeding depression and self-incompatibility hamper the development of inbred lines in both tetraploid and diploid potatoes, impeding hybrid development efforts. Diploid potatoes have gametophytic self-incompatibility (SI) controlled by S-locus, harboring the male-dependent S-locus F-box (SLF/SFB) and female-dependent Stylar-RNase (S-RNase). Manipulation of these genes using biotechnological tools may lead to loss of self-incompatibility. Self-compatibility can also be achieved by the introgression of S-locus inhibitor (Sli) found in the self-compatible (SC) natural mutants of Solanum chacoense. The introgression of Sli through conventional breeding methods has gained much success. Recently, the Sli gene has been cloned from diverse SC diploid potato lines. It is expressed gametophytically and can overcome the SI in different diploid potato genotypes through conventional breeding or transgenic approaches. Interestingly, it has a 533 bp insertion in its promoter elements, a MITE transposon, making it a SC allele. Sli gene encodes an F-box protein PP2-B10, which consists of an F-box domain linked to a lectin domain. Interaction studies have revealed that the C-terminal region of Sli interacts with most of the StS-RNases, except StS-RNase 3, 9, 10, and 13, while full-length Sli cannot interact with StS-RNase 3, 9, 11, 13, and 14. Thus, Sli may play an essential role in mediating the interactions between pollen and stigma and function like SLFs to interact with and detoxify the S-RNases during pollen tube elongation to confer SC to SI lines. These advancements have opened new avenues in the diploid potato hybrid.

A GWAS identified a major QTL for resistance to Fusarium wilt (Fusarium oxysporum f. sp. vasinfectum) race 4 in a MAGIC population of Upland cotton and a meta-analysis of QTLs for Fusarium wilt resistance.

A major QTL conferring resistance to Fusarium wilt race 4 in a narrow region of chromosome D02 was identified in a MAGIC population of 550 RILs of Upland cotton. Numerous studies have been conducted to investigate the genetic basis of Fusarium wilt (FW, caused by Fusarium oxysporum f. sp. vasinfectum, FOV) resistance using bi-parental and association mapping populations in cotton. In this study, a multi-parent advanced generation inter-cross (MAGIC) population of 550 recombinant inbred lines (RILs), together with their 11 Upland cotton (Gossypium hirsutum) parents, was used to identify QTLs for FOV race 4 (FOV4) resistance. Among the parents, Acala Ultima, M-240 RNR, and Stoneville 474 were the most resistant, while Deltapine Acala 90, Coker 315, and Stoneville 825 were the most susceptible. Twenty-two MAGIC lines were consistently resistant to FOV4. Through a genome-wide association study (GWAS) based on 473,516 polymorphic SNPs, a major FOV4 resistance QTL within a narrow region on chromosomes D02 was detected, allowing identification of 14 candidate genes. Additionally, a meta-analysis of 133 published FW resistance QTLs showed a D subgenome and individual chromosome bias and no correlation between homeologous chromosome pairs. This study represents the first GWAS study using a largest genetic population and the most comprehensive meta-analysis for FW resistance in cotton. The results illustrated that 550 lines were not enough for high resolution mapping to pinpoint a candidate gene, and experimental errors in phenotyping cotton for FW resistance further compromised the accuracy and precision in QTL localization and identification of candidate genes. This study identified FOV4-resistant parents and MAGIC lines, and the first major QTL for FOV4 resistancein Upland cotton, providing useful information for developing FOV4-resistant cultivars and further genomic studies towards identification of causal genes for FOV4 resistance in cotton.

Genomic prediction in doubled haploid maize (Zea mays) populations under water stress at flowering and well‐watered conditions using high‐density single‐nucleotide polymorphisms

AbstractThe most important applications of genomic selection (GS) in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same cross. A total of 300 hybrids were generated using doubled haploid lines crossed to single known tester. The test hybrids and checks were evaluated for drought tolerance, grain yield and yield attributes under well‐watered (WW) and water stress at flowering (WSF) conditions during rabi 2018 at Hyderabad and Aurangabad locations. The study was further deep dived and practiced GS using 3352 single‐nucleotide polymorphism (SNP) markers. An extension of the genomic best linear unbiassed predictor that incorporates genotype × environment (GE) interaction was used to predict genotypic values; cross‐validation methods were applied to quantify prediction accuracy. Our results showed that the highest cross‐validation prediction accuracy for grain yield was 0.47 under WSF condition in TPS3, whereas under WW condition, prediction accuracy was 0.44 in TPS2, which is statistically on par with WSF condition. Among the secondary traits, the peak GS accuracies recorded for the traits anthesis silking interval (0.52) and ears per plant (0.48) under WSF. Under both the water regimes, anthesis silking interval and plant height recorded higher prediction accuracy when compared with grain yield. Hence, GS could be practiced for anthesis silking interval and ears per plant under stress condition in maize. Further while optimizing the population size, it was revealed from the study that increasing size of the population increases GS accuracy and TPS2 considered as optimum size of population for GS prediction.

Evaluation of Genomic Selection Methods for Wheat Quality Traits in Biparental Populations Indicates Inclination towards Parsimonious Solutions

Breeding for end-use quality traits is often challenging since their assessment requires larger quantities of grain and flour samples, which are usually not available early in the breeding process. Using the mixograph as a fast and effective method of evaluating dough quality together with genomic selection (GS) can help in pre-selecting high-performing progenies earlier in the breeding process and achieve a higher gain per unit of time and cost. In the present study, the potential of GS to predict seven end-use quality traits, including mixograph traits, in two biparental wheat populations was investigated. Field trials with both populations were conducted at two locations in Croatia (Osijek and Slavonski Brod) over three years. Results showed that the size of the training population (TP) plays an important role in achieving higher prediction accuracies, while marker density is not a major limitation. Additionally, results of the present study did not support the optimization of TP based on phenotypic variance as a tool to increase prediction accuracy. The performance of eight prediction models was compared and among them elastic net showed the lowest prediction accuracy for all traits. Bayesian models provided slightly higher prediction accuracy than the ridge regression best linear unbiased prediction (RR-BLUP) model, which is negligible considering the time required to perform an analysis. Although RR-BLUP was not the best performing model in all cases, no advantage of using any other model studied here was observed. Furthermore, strong differences between environments in terms of the prediction accuracy achieved were observed, suggesting that environments that are less predictive should be removed from the dataset used to train the prediction model. The prediction accuracies obtained in this study support implementation of GS in wheat breeding for end-use quality, including some mixograph traits.

Variation in the Content and Composition of Tocols in a Wheat Population.

Wheat is a well-known source of B vitamins but also contains significant amounts of vitamin E and related tocols, which have a number of positive health benefits. However, there are no reports on increasing the tocol content of wheat. A prerequisite for increasing the tocol content is the identification of variation in its amount within wheat and related cereals. We therefore determined the tocol content and composition in the grain of 230 recombinant inbred lines (RILs) of a diverse biparental wheat population (Mv Toborzó/Tommi), showing variation in the total content from 13.69 to 45.18 μg/g d.m. The total content also showed transgressive segregation in the population. The effect of the genotype on the variance components of tocols was studied, and the broad-sense heritability was calculated to be 0.71. The lines were also grouped based on their tocol content and analyzed for their chemical composition and breadmaking quality. The high heritability value and the wide variation found in the total amount indicate that increasing the content of tocols is a possible breeding strategy.

Exploring Machine Learning Algorithms to Unveil Genomic Regions Associated With Resistance to Southern Root-Knot Nematode in Soybeans.

Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.

Development of first linkage map for Silphium integrifolium (Asteraceae) enables identification of sporophytic self-incompatibility locus.

Silphium integrifolium (Asteraceae) has been identified as a candidate for domestication as a perennial oilseed crop and is assumed to have sporophytic self-incompatibility system-the genetic basis of which is not well understood in the Asteraceae. To address this gap, we sought to map the genomic location of the self-recognition locus (S-locus) in this species. We used a biparental population and genotyping-by-sequencing to create the first genetic linkage map for this species, which contained 198 SNP markers and resolved into the correct number of linkage groups. Then we developed a novel crossing scheme and set of analysis methods in order to infer S-locus genotypes for a subset of these individuals, allowing us to map the trait. Finally, we evaluated potential genes of interest using synteny analysis with the annual sunflower (Helianthus annuus) and lettuce (Lactuca sativa) genomes. Our results confirm that S. integrifolium does indeed have a sporophytic self-incompatibility system. Our method is effective and efficient, allowed us to map the S. integrifolium S-locus using fewer resources than existing methods, and could be readily applied to other species.

Outliers and their distribution in breeding populations

AbstractOutliers with highly superior performance are valuable in plant breeding, but their distribution in populations has not been well‐studied. My objectives were to determine (a) if outliers behave in a predictable manner; (b) if they are distributed according to a normal distribution as is assumed for quantitative traits; and (c) which parental characteristics are indicative of the best chances of getting progeny with extreme performance. All possible biparental populations were made among 15 simulated barley (Hordeum vulgare L.) parental lines in BreedingGames software. Ten million lines were simulated within each cross for a total of 1.05 billion lines. Within each biparental population, recombinant inbreds in the top 1.0, 0.1, and 0.01% tails had a continuous distribution, indicating that outliers behave in a predictable manner but are rare in practice only because the population sizes used in breeding are small. Having a finite number of loci led to slight kurtosis, which caused a minor upward bias when the usefulness criterion was applied to the extreme upper tails. The midparent value was an excellent indicator of which biparental crosses had high upper‐tail means, to the extent that modeling the genetic variance within each biparental population had little added benefit. In the simulations, selection for protein concentration and Fusarium (F. graminearum) head blight resistance decreased the gains for yield and changed the biparental population that led to the highest yield gains. Results indicated that having one very large breeding population in a select‐only scheme is inferior to two or more select‐and‐recombine cycles with smaller populations.

Association mapping reveals a reciprocal virulence/avirulence locus within diverse US Pyrenophora teres f. maculata isolates

BackgroundSpot form net blotch (SFNB) caused by the necrotrophic fungal pathogen Pyrenophora teres f. maculata (Ptm) is an economically important disease of barley that also infects wheat. Using genetic analysis to characterize loci in Ptm genomes associated with virulence or avirulence is an important step to identify pathogen effectors that determine compatible (virulent) or incompatible (avirulent) interactions with cereal hosts. Association mapping (AM) is a powerful tool for detecting virulence loci utilizing phenotyping and genotyping data generated for natural populations of plant pathogenic fungi.ResultsRestriction-site associated DNA genotyping-by-sequencing (RAD-GBS) was used to generate 4,836 single nucleotide polymorphism (SNP) markers for a natural population of 103 Ptm isolates collected from Idaho, Montana and North Dakota. Association mapping analyses were performed utilizing the genotyping and infection type data generated for each isolate when challenged on barley seedlings of thirty SFNB differential barley lines. A total of 39 marker trait associations (MTAs) were detected across the 20 barley lines corresponding to 30 quantitative trait loci (QTL); 26 novel QTL and four that were previously mapped in Ptm biparental populations. These results using diverse US isolates and barley lines showed numerous barley-Ptm genetic interactions with seven of the 30 Ptm virulence/avirulence loci falling on chromosome 3, suggesting that it is a reservoir of diverse virulence effectors. One of the loci exhibited reciprocal virulence/avirulence with one haplotype predominantly present in isolates collected from Idaho increasing virulence on barley line MXB468 and the alternative haplotype predominantly present in isolates collected from North Dakota and Montana increasing virulence on barley line CI9819.ConclusionsAssociation mapping provided novel insight into the host pathogen genetic interactions occurring in the barley-Ptm pathosystem. The analysis suggests that chromosome 3 of Ptm serves as an effector reservoir in concordance with previous reports for Pyrenophora teres f. teres, the causal agent of the closely related disease net form net blotch. Additionally, these analyses identified the first reported case of a reciprocal pathogen virulence locus. However, further investigation of the pathosystem is required to determine if multiple genes or alleles of the same gene are responsible for this genetic phenomenon.

Combined GWAS and QTL mapping revealed candidate genes and SNP network controlling recovery and tolerance traits associated with drought tolerance in seedling winter wheat

To date, very little research on drought tolerance has been conducted at the seedling stage in winter wheat. In this study, two types of traits, namely tolerance and recovery traits, associated with drought tolerance were scored in biparental mapping population (BPP) and association mapping population (A-set). The results of this study revealed no or weak significant correlation between the two types of traits. Based on GWAS and QTL mapping analyses, all QTLs associated with recovery traits were completely different from those associated with tolerance traits except one QTL in each population that was found to be associated with one tolerance trait and one recovery trait. The analysis of SNP and gene networks confirmed the results of combined GWAS and QTL mapping. One SNP marker located on the 2B chromosome (S2B_26494801) was found to be associated with recovery traits in both populations. The results of this study provided new information on understanding and improving drought tolerance in winter wheat.

Meta-QTL analysis associated with bacterial stalk rot resistance in maize (Zea mays L.) segregating populations

Bacterial stalk rot (BSR) caused by Dickeya zeae is one of the important diseases of maize that significantly affects maize yield performance. Resistance to D. zeae is influenced by high humidity and temperature. Affected tissues are described as soft, mushy, and emitting a foul odor. Yield losses can reach approximately 98.8% of the grower‟s potential. Quantitative trait locus (QTL) mapping experiments using seven biparental populations were conducted at Syngenta Philippines, Inc., from 2014–2020 to locate consistent QTL and markers involved in BSR resistance. The QTL detected in NMM033, NMM073, NMM089, NMM090, NMM091, NMX003, and NMX001 populations were used to estimate the numbers and positions of consensus QTL with BioMercator V4.2.3 software. Meta-analysis for BSR resistance was conducted by considering all QTL for BSR resistance traits identified in 2014–2020. Among the 49 distinct markers on chromosomes (chrs) 1 to 10, eight most significant loci were detected, i.e., MSRQTL1-1, MSRQTL2-1, MSRQTL3-1, MSRQTL3-2, MSRQTL5-1, MSRQTL5-2, MSRQTL6-1, and MSRQTL10-1. Meta-QTL were identified in chrs 1, 5, and 10 in four populations; in chr 2 in three populations; and in chrs 3 and 6 in two populations evaluated in this study. The regions identified in chrs 1, 2, 3, 5, 6, and 10 with high QTL colocalization across biparental populations were considered as important QTL for BSR resistance traits. Further implementation through fine-mapping is recommended for marker development. The impact of this discovery would strengthen downstream applications in marker-assisted backcrossing and is not only limited to maize BSR resistance but also to other native traits of different crops.

Effects of marker density and training set size on the genomic selection accuracy for predicting sheathblight resistance in rice (Oryza sativa L.)

The success of genomic selection mainly depends on the extent of linkage disequilibrium between markers and quantitative trait loci, size of training set, heritability of the trait etc. The extent of linkage disequilibrium depends on the genetic structure of the population and marker density. This study was conducted to determine the effects of marker density and size of training set on prediction accuracy using 1545 recombinant inbred lines derived from eleven bi-parental rice populations. All RILs were genotyped with 6564 SNPs and screened in two hot spot locations to assess reaction against sheath blight.Bayesian B model was used to train the statistical model for calculation of marker effects and genomic estimated breeding values. To evaluate the genomic prediction accuracy, various levels of training set size (300, 500, 700, 900 and 1200 lines) and marker density (500, 800, 1100, 1400, 1700, 2000, 4000 and 6000 markers) were considered. In our study, the prediction accuracy increased with increase in training set size, however, average prediction accuracy of 0.717 was obtained for the training set comprising of 900 lines before reaching plateau with marginal increase in prediction accuracy with higher training set sizes. The predictive ability increased dramatically with more SNPs included in the analysis until 2000 markers with average prediction accuracy of 0.681, no significant improvement beyond this was observed. The results indicate that training set with approximately 900 lines and 2000 uniformly distributed SNP markers with good amount of polymorphism across populations would be enough to reach achievable accuracy to predict sheath blight resistance in rice.

Analysis of the Efficiency of Genomic Selection Models for Predicting Sheath Blight Resistance in Rice (Oryza sativa L.,)

The research was undertaken during June-October 2020 at Seethanagaram and Draksharam villages of East Godavari district, Andhra Pradesh, India with an objective to evaluate efficiency of genomic selection models involving 1545 recombinant inbred lines (RILs) derived from eleven bi-parental populations in Rice. During June-October 2020, the F7 RILs were screened in two hot spot locations. The genotyping was done with Infinium platform having 6564 SNP markers. Five models were used rrBLUP, BayesA, BayesB, BayesCPi and GBLUP to train the statistical model for calculation of marker effects and genomic estimated breeding values (GEBVs). The prediction accuracy (data fit) of training set across models ranged 0.63–0.72, lowest and highest prediction accuracies were observed with rrBLUP and GBLUP models respectively. Tenfold cross validation with different approaches, the average prediction accuracy ranged from 0.60 (rrBLUP, BayesA, BayesB and BayesCPi) –0.72 (GBLUP). BayesB and GBLUP models exhibit higher prediction accuracies compared to other models studied. The predictive ability increased dramatically with more SNPs included in analysis until 2000 markers with average prediction accuracy of 0.681, no significant improvement beyond this was observed. The results are lucrative, all in all, high prediction accuracies observed in this study suggest genomic selection as a very promising strategy while breeding for sheath blight resistance in rice to increase genetic gain.

Ethylene Production Affects Blueberry Fruit Texture and Storability.

Ethylene, produced endogenously by plants and their organs, can induce a wide array of physiological responses even at very low concentrations. Nevertheless, the role of ethylene in regulating blueberry (Vaccinium spp.) ripening and storability is still unclear although an increase in ethylene production has been observed in several studies during blueberry ripening. To overcome this issue, we evaluated the endogenous ethylene production of a Vaccinium germplasm selection at different fruit ripening stages and after cold storage, considering also textural modifications. Ethylene and texture were further assessed also on a bi-parental full-sib population of 124 accessions obtained by the crossing between “Draper” and “Biloxi”, two cultivars characterized by a different chilling requirement and storability performances. Our results were compared with an extensive literature research, carried out to collect all accessible information on published works related to Vaccinium ethylene production and sensitivity. Results of this study illustrate a likely role of ethylene in regulating blueberry shelf life. However, a generalisation valid for all Vaccinium species is not attainable because of the high variability in ethylene production between genotypes, which is strictly genotype-specific. These differences in ethylene production are related with blueberry fruit storage performances based on textural alterations. Specifically, blueberry accessions characterized by the highest ethylene production had a more severe texture decay during storage. Our results support the possibility of tailoring ad hoc preharvest and postharvest strategies to extend blueberry shelf life and quality according with the endogenous ethylene production level of each cultivar.

Genomic Analysis and Delineation of the Tan Spot Susceptibility Locus Tsc1 in Wheat.

The necrotrophic fungal pathogen Pyrenophora tritici-repentis (Ptr) causes the foliar disease tan spot in both bread wheat and durum wheat. Wheat lines carrying the tan spot susceptibility gene Tsc1 are sensitive to the Ptr-produced necrotrophic effector (NE) Ptr ToxC. A compatible interaction results in leaf chlorosis, reducing yield by decreasing the photosynthetic area of leaves. Developing genetically resistant cultivars will effectively reduce disease incidence. Toward that goal, the production of chlorosis in response to inoculation with Ptr ToxC-producing isolates was mapped in two low-resolution biparental populations derived from LMPG-6 × PI 626573 (LP) and Louise × Penawawa (LouPen). In total, 58 genetic markers were developed and mapped, delineating the Tsc1 candidate gene region to a 1.4 centiMorgan (cM) genetic interval spanning 184 kb on the short arm of chromosome 1A. A total of nine candidate genes were identified in the Chinese Spring reference genome, seven with protein domains characteristic of resistance genes. Mapping of the chlorotic phenotype, development of genetic markers, both for genetic mapping and marker-assisted selection (MAS), and the identification of Tsc1 candidate genes provide a foundation for map-based cloning of Tsc1.