Marker Density Research Articles

Genome-Wide Association Study for Spot Blotch Resistance in Hard Winter Wheat.

Spot blotch (SB) caused by Cochliobolus sativus (anamorph: Bipolaris sorokiniana) is an economically important disease of wheat worldwide. Under a severe epidemic condition, the disease can cause yield losses up to 70%. Previous approaches like bi-parental mapping for identifying SB resistant genes/QTLs exploited only a limited portion of the available genetic diversity with a lower capacity to detect polygenic traits, and had a lower marker density. In this study, we performed genome-wide association study (GWAS) for SB resistance in hard winter wheat association mapping panel (HWWAMP) of 294 genotypes. The HWWAMP was evaluated for response to B. sorokiniana (isolate SD40), and a range of reactions was observed with 10 resistant, 38 moderately resistant, 120 moderately resistant- moderately susceptible, 111 moderately susceptible, and 15 susceptible genotypes. GWAS using 15,590 high-quality SNPs and 294 genotypes we identified six QTLs (p = <0.001) on chromosomes 2D, 3A, 4A, 4B, 5A, and 7B that collectively explained 30% of the total variation for SB resistance. Highly associated SNPs were identified for all six QTLs, QSb.sdsu-2D.1 (SNP: Kukri_c31121_1460, R2 = 4%), QSb.sdsu-3A.1 (SNP: Excalibur_c46082_440, R2 = 4%), QSb.sdsu-4A.1 (SNP: IWA8475, R2 = 5.5%), QSb.sdsu-4B.1 (SNP: Excalibur_rep_c79414_306, R2 = 4%), QSb.sdsu-5A.1 (SNP: Kukri_rep_c104877_2166, R2 = 6%), and QSb.sdsu-7B.1 (SNP: TA005844-0160, R2 = 6%). Our study not only validates three (2D, 5A, and 7B) genomic regions identified in previous studies but also provides highly associated SNP markers for marker assisted selection. In addition, we identified three novel QTLs (QSb.sdsu-3A.1, QSb.sdsu-4A.1, and QSb.sdsu-4B.1) for SB resistance in wheat. Gene annotation analysis of the candidate regions identified nine NBS-LRR and 38 other plant defense-related protein families across multiple QTLs, and these could be used for fine mapping and further characterization of SB resistance in wheat. Comparative analysis with barley indicated the SB resistance locus on wheat chromosomes 2D, 3A, 5A, and 7B identified in our study are syntenic to the previously identified SB resistance locus on chromosomes 2H, 3H, 5H, and 7H in barley. The 10 highly resistant genotypes and SNP markers identified in our study could be very useful resources for breeding of SB resistance in wheat.

Dissecting Key Adaptation Traits in the Polyploid Perennial Medicago sativa Using GBS-SNP Mapping.

Understanding key adaptation traits is crucial to developing new cultivars with broad adaptations. The main objective of this research is to understand the genetic basis of winter hardiness (WH) and fall dormancy (FD) in alfalfa and the association between the two traits. QTL analysis was conducted in a pseudo-testcross F1 population developed from two cultivars contrasting in FD (3010 with FD = 2 and CW 1010 with FD = 10). The mapping population was evaluated in three replications at two locations (Watkinsville and Blairsville, GA). FD levels showed low to moderate correlations with WH (0.22–0.57). Assessing dormancy in winter is more reliable than in the fall in southern regions with warm winters. The mapping population was genotyped using Genotyping-by-sequencing (GBS). Single dose allele SNPs (SDA) were used for constructing linkage maps. The parental map (CW 1010) consisted of 32 linkage groups spanning 2127.5 cM with 1377 markers and an average marker density of 1.5 cM/SNP. The maternal map (3010) had 32 linkage groups spanning 2788.4 cM with 1837 SDA SNPs with an average marker density of 1.5 cM/SNP. Forty-five significant (P < 0.05) QTLs for FD and 35 QTLs for WH were detected on both male and female linkage maps. More than 75% (22/28) of the dormancy QTL detected from the 3010 parent did not share genomic regions with WH QTLs and more than 70% (12/17) dormancy QTLs detected from CW 1010 parent were localized in different genomic regions than WH QTLs. These results suggest that the two traits have independent inheritance and therefore can be improved separately in breeding programs.

Effective Genomic Selection in a Narrow-Genepool Crop with Low-Density Markers: Asian Rapeseed as an Example.

Genomic selection (GS) has revolutionized breeding for quantitative traits in plants, offering potential to optimize resource allocation in breeding programs and increase genetic gain per unit of time. Modern high-density single nucleotide polymorphism (SNP) arrays comprising up to several hundred thousand markers provide a user-friendly technology to characterize the genetic constitution of whole populations and for implementing GS in breeding programs. However, GS does not build upon detailed genotype profiling facilitated by maximum marker density. With extensive genome-wide linkage disequilibrium (LD) being a common characteristic of breeding pools, fewer representative markers from available high-density genotyping platforms could be sufficient to capture the association between a genomic region and a phenotypic trait. To examine the effects of reduced marker density on genomic prediction accuracy, we collected data on three traits across 2 yr in a panel of 203 homozygous Chinese semiwinter rapeseed ( L.) inbred lines, broadly encompassing allelic variability in the Asian genepool. We investigated two approaches to selecting subsets of markers: a trait-dependent strategy based on genome-wide association study (GWAS) significance thresholds and a trait-independent method to detect representative tag SNPs. Prediction accuracies were evaluated using cross-validation with ridge-regression best linear unbiased predictions (rrBLUP). With semiwinter rapeseed as a model species, we demonstrate that low-density marker sets comprising a few hundred to a few thousand markers enable high prediction accuracies in breeding populations with strong LD comparable to those achieved with high-density arrays. Our results are valuable for facilitating routine application of cost-efficient GS in breeding programs.

PREDICTION OF PHENOTYPIC AND GENOTYPIC VALUES BY BLUP/GWS AND NEURAL NETWORKS

ABSTRACT Genome-wide selection (GWS) uses simultaneously the effect of the thousands markers covering the entire genome to predict genomic breeding values for individuals under selection. The possible benefits of GWS are the reduction of the breeding cycle, increase in gains per unit of time, and decrease of costs. However, the success of the GWS is dependent on the choice of the method to predict the effects of markers. Thus, the objective of this work was to predict genomic breeding values (GEBV) through artificial neural networks (ANN), based on the estimation of the effect of the markers, compared to the Ridge Regression-Best Linear Unbiased Predictor/Genome Wide Selection (RR-BLUP/GWS). Simulations were performed by software R to provide correlations concerning ANN and RR-BLUP/GWS. The prediction methods were evaluated using correlations between phenotypic and genotypic values and predicted GEBV. The results showed the superiority of the ANN in predicting GEBV in simulations with higher and lower marker densities, with higher levels of linkage disequilibrium and heritability.

Genome wide analysis of meiotic recombination in yeast: For a few SNPs more.

Diploid organisms undergo meiosis to produce haploid germ cells. Crossover events during meiosis promote genetic diversity and facilitate accurate chromosome segregation. The baker's yeast Saccharomyces cerevisiae is extensively used as a model for analysis of meiotic recombination. Conventional methods for measuring recombination events in S. cerevisiae have been limited by the number and density of genetic markers. Next generation sequencing (NGS)‐based analysis of hybrid yeast genomes bearing thousands of heterozygous single nucleotide polymorphism (SNP) markers has revolutionized analysis of meiotic recombination. By facilitating analysis of marker segregation in the whole genome with unprecedented resolution, this method has resulted in the generation of high‐resolution recombination maps in wild‐type and meiotic mutants. These studies have provided novel insights into the mechanism of meiotic recombination. In this review, we discuss the methodology, challenges, insights and future prospects of using NGS‐based methods for whole genome analysis of meiotic recombination. The objective is to facilitate the use of these high through‐put sequencing methods for the analysis of meiotic recombination given their power to provide significant new insights into the process. © 2018 The Authors. IUBMB Life published by Wiley Periodicals, Inc. on behalf of International Union of Biochemistry and Molecular Biology, 70(8):743–752, 2018

Genomic prediction using an iterative conditional expectation algorithm for a fast BayesC-like model.

Genomic prediction is feasible for estimating genomic breeding values because of dense genome-wide markers and credible statistical methods, such as Genomic Best Linear Unbiased Prediction (GBLUP) and various Bayesian methods. Compared with GBLUP, Bayesian methods propose more flexible assumptions for the distributions of SNP effects. However, most Bayesian methods are performed based on Markov chain Monte Carlo (MCMC) algorithms, leading to computational efficiency challenges. Hence, some fast Bayesian approaches, such as fast BayesB (fBayesB), were proposed to speed up the calculation. This study proposed another fast Bayesian method termed fast BayesC (fBayesC). The prior distribution of fBayesC assumes that a SNP with probability γ has a non-zero effect which comes from a normal density with a common variance. The simulated data from QTLMAS XII workshop and actual data on large yellow croaker were used to compare the predictive results of fBayesB, fBayesC and (MCMC-based) BayesC. The results showed that when γ was set as a small value, such as 0.01 in the simulated data or 0.001 in the actual data, fBayesB and fBayesC yielded lower prediction accuracies (abilities) than BayesC. In the actual data, fBayesC could yield very similar predictive abilities as BayesC when γ ≥ 0.01. When γ = 0.01, fBayesB could also yield similar results as fBayesC and BayesC. However, fBayesB could not yield an explicit result when γ ≥ 0.1, but a similar situation was not observed for fBayesC. Moreover, the computational speed of fBayesC was significantly faster than that of BayesC, making fBayesC a promising method for genomic prediction.

Myricetin ameliorates glucocorticoid-induced osteoporosis through the ERK signaling pathway

Myricetin has been reported to promote osteogenic differentiation and inhibit osteoclastogenesis. The aim of this study was to investigate the potential effects of myricetin on glucocorticoid-induced osteoporosis (GIOP) both in vivo and in vitro. Male Sprague-Dawley (SD) rats were given dexamethasone (DEX, 0.1 mg/kg, s.c.) once daily and myricetin (1 mg/kg or 2.5 mg/kg, i.p.) once every other day for a total of five weeks. Body weight was recorded once a week. Bone mineral density (BMD), the activities or levels of bone turnover markers, and histological changes were assessed. MC3T3-E1 cells were incubated with DEX (1 μM) and myricetin (20 μM). Osteoblast proliferation, differentiation and mineralization were evaluated. U0126 was added to evaluate the involvement of the ERK signaling pathway. The results showed that myricetin increased body weight gain and inhibited DEX-induced reduction in BMD, enhanced alkaline phosphatase (ALP) activity, and upregulated osteocalcin (OCN), bone morphogenetic protein 2 (BMP2) and runt-related transcription factor 2 (Runx2) levels, whereas reduced tartrate-resistant acid phosphatase (TRAP) activity and C-terminal telopeptide of type I collagen (CTx) level. In addition, myricetin ameliorated histological changes in the femurs. In our in vitro studies, myricetin promoted osteoblast differentiation and mineralization in DEX-treated MC3T3-E1 cells, accompanied by increases in BMP2, Runx2, ALP, OCN, collagen type I alpha 1 (COL1A1) and osteopontin (OPN) levels. The promotion effects of myricetin on osteogenic differentiation and matrix mineralization were reversed by U0126. These results suggest that myricetin may alleviate DEX-induced osteoporosis by promoting osteogenic differentiation and matrix mineralization via the ERK signaling pathway.

Blockade of adenosine A2A receptors recovers early deficits of memory and plasticity in the triple transgenic mouse model of Alzheimer's disease

Alzheimer's disease (AD) begins with a deficit of synaptic function and adenosine A2A receptors (A2AR) are mostly located in synapses controlling synaptic plasticity. The over-activation of adenosine A2A receptors (A2AR) causes memory deficits and the blockade of A2AR prevents memory damage in AD models. We now enquired if this prophylactic role of A2AR might be extended to a therapeutic potential. We used the triple transgenic model of AD (3xTg-AD) and defined that the onset of memory dysfunction occurred at 4 months of age in the absence of locomotor or emotional alterations. At the onset of memory deficits, 3xTg mice displayed a decreased density of markers of excitatory synapses (10.6 ± 3.8% decrease of vGluT1) without neuronal or glial overt damage and an increase of synaptic A2AR in the hippocampus (130 ± 22%). After the onset of memory deficits in 3xTg-AD mice, a three weeks treatment with the selective A2AR antagonist normalized the up-regulation of hippocampal A2AR and restored hippocampal-dependent reference memory, as well as the decrease of hippocampal synaptic plasticity (60.0 ± 3.7% decrease of long-term potentiation amplitude) and the decrease of global (syntaxin-I) and glutamatergic synaptic markers (vGluT1). These findings show a therapeutic-like ability of A2AR antagonists to recover synaptic and memory dysfunction in early AD.

Genomic prediction of crown rust resistance in Lolium perenne

BackgroundGenomic selection (GS) can accelerate genetic gains in breeding programmes by reducing the time it takes to complete a cycle of selection. Puccinia coronata f. sp lolli (crown rust) is one of the most widespread diseases of perennial ryegrass and can lead to reductions in yield, persistency and nutritional value. Here, we used a large perennial ryegrass population to assess the accuracy of using genome wide markers to predict crown rust resistance and to investigate the factors affecting predictive ability.ResultsUsing these data, predictive ability for crown rust resistance in the complete population reached a maximum of 0.52. Much of the predictive ability resulted from the ability of markers to capture genetic relationships among families within the training set, and reducing the marker density had little impact on predictive ability. Using permutation based variable importance measure and genome wide association studies (GWAS) to identify and rank markers enabled the identification of a small subset of SNPs that could achieve predictive abilities close to those achieved using the complete marker set.ConclusionUsing a GWAS to identify and rank markers enabled a small panel of markers to be identified that could achieve higher predictive ability than the same number of randomly selected markers, and predictive abilities close to those achieved with the entire marker set. This was particularly evident in a sub-population characterised by having on-average higher genome-wide linkage disequilibirum (LD). Higher predictive abilities with selected markers over random markers suggests they are in LD with QTL. Accuracy due to genetic relationships will decay rapidly over generations whereas accuracy due to LD will persist, which is advantageous for practical breeding applications.

Effect of mutant variants of the KRAS gene on PD-L1 expression and on the immune microenvironment and association with clinical outcome in lung adenocarcinoma patients

ObjectivesThe effect of anti-PD-1/PD-L1 inhibitors on lung adenocarcinomas (LADCs) with KRAS mutations is debatable. We examined the association between specific mutant KRAS proteins and the immune infiltrates with the outcome of patients with LADCs. Patients and methodsIn 219 LADCs harboring either wild-type (WT) or mutated KRAS gene, we quantified the density of several immune markers by immunohistochemistry followed by automated digital image analysis. Data were correlated to clinicopathological parameters and outcome of patients. ResultsTumors harboring mutant KRAS-G12 V had a significantly higher PD-L1 expression compared to other tumors (p = 0.044), while mutant KRAS-G12D tumors showed an increase in the density of CD66b+ cells (p = 0.001). High PD-L1 expression in tumor cells was associated to improved overall survival (OS) in KRAS mutant patients (p = 0.012), but not in the WT population (p = 0.385), whereas increased PD-L1 expression in immune cells correlated to poor OS of KRAS-WT patients (p = 0.025), with no difference in patients with KRAS mutations. ConclusionsKRAS mutational status can affect the immune microenvironment and survival of LADC patients in a heterogeneous way, implying that specific mutant KRAS variants expressed by the tumor should be considered when stratifying patients for immunotherapy.

An ultra-high density map allowed for mapping QTL and candidate genes controlling dry latex yield in rubber tree

Rubber tree (Hevea brasiliensis) is the most important commercial producer of high-quality natural rubber in the world. Here, we identified 571,267 SNPs and 134,184 indels, and constructed the first ultra-high density genetic linkage map in rubber tree population. This map consisted of 6940 markers, with average marker density and recombination rate 0.30 cM and 0.97 cM/Mb, respectively. Then the whole genome QTL scanning for dry latex yield (DLY) trait were performed and seventeen repQTLs were obtained, among which qDFY-10 and qDFY-18-4 could explain up to 38.3% and 33.3% phenotypic variability, respectively. Numerous highly-promising QTL candidate genes were identified and then verified significant associated with DLY trait, including some first found in rubber tree, such as thioredoxin h, plastin-like protein, calmodulin binding protein, cytochrome-c oxidase and methylglutaconyl-CoA hydratase. This ultra-high density linkage map supplies a base for important traits QTL mapping and will be useful in the improvement of the assembly of genome sequences in rubber tree. The verification of candidate QTL genes showed great importance for understanding the possible function of mapped QTLs and the comprehensive relations between phenotypes and genotypes. Accurate QTL mapping will be enhance genetic studies that can be applied in rubber tree breeding programs.

RADseq approaches and applications for forest tree genetics

As tree species vary extensively in genome size, complexity, and resource development, reduced representation methods have been increasingly employed for the generation of population genomic data. By allowing rapid marker discovery and genotyping for thousands of genomic regions in many individuals without requiring genomic resources, restriction site-associated DNA sequencing (RADseq) methods have dramatically improved our ability to bring population genomic perspectives to non-model trees. The rapid recent increase in studies of trees utilizing RADseq suggests that it is likely to become among the most common approaches for generating genome-wide data for a variety of applications. Here we provide a practical review of RADseq and its application to research areas of tree genetics. We briefly review RADseq laboratory methods and consider analytical approaches for assembly, variant calling, and bioinformatic processing. To guide considerations for study design, we use in silico analyses of eight available tree genomes to illustrate how expected marker number and density vary across laboratory approaches and genome sizes, and to consider the ability of RADseq designs to query coding regions. We review the empirical use of RADseq for different research objectives, considering its strengths and limitations. Many studies have used RADseq data to perform genome scans for selection, although limited marker density and linkage disequilibrium will often compromise its utility for such analyses. Regardless of this limitation, RADseq offers a powerful and inexpensive technique for generating genome-wide SNP data that can greatly contribute to research spanning phylogenetic and population genetic inference, linkage mapping, and quantitative genetic parameter estimation for tree genetics.

Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits

Improvement of statistical methods is crucial for realizing the potential of increasingly dense genetic markers. Bayesian methods treat all markers as random effects, exhibit an advantage on dense markers, and offer the flexibility of using different priors. In contrast, genomic best linear unbiased prediction (gBLUP) is superior in computing speed, but only superior in prediction accuracy for extremely complex traits. Currently, the existing variety in the BLUP method is insufficient for adapting to new sequencing technologies and traits with different genetic architectures. In this study, we found two ways to change the kinship derivation in the BLUP method that improve prediction accuracy while maintaining the computational advantage. First, using the settlement under progressively exclusive relationship (SUPER) algorithm, we substituted all available markers with estimated quantitative trait nucleotides (QTNs) to derive kinship. Second, we compressed individuals into groups based on kinship, and then used the groups as random effects instead of individuals. The two methods were named as SUPER BLUP (sBLUP) and compressed BLUP (cBLUP). Analyses on both simulated and real data demonstrated that these two methods offer flexibility for evaluating a variety of traits, covering a broadened realm of genetic architectures. For traits controlled by small numbers of genes, sBLUP outperforms Bayesian LASSO (least absolute shrinkage and selection operator). For traits with low heritability, cBLUP outperforms both gBLUP and Bayesian LASSO methods. We implemented these new BLUP alphabet series methods in an R package, Genome Association and Prediction Integrated Tool (GAPIT), available at http://zzlab.net/GAPIT.

Genomic selection of agronomic traits in hybrid rice using an NCII population

BackgroundHybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by several genetic and statistical factors. Here, we used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance.ResultsFrom the comparison of six GS methods, we found that predictabilities for different methods are significantly different, with genomic best linear unbiased prediction (GBLUP) and least absolute shrinkage and selection operation (LASSO) being the best, support vector machine (SVM) and partial least square (PLS) being the worst. The marker density has lower influence on predicting rice hybrid performance compared with the size of training population. Additionally, we used the 575 (115 × 5) hybrid rice as a training population to predict eight agronomic traits of all hybrids derived from 120 (115 + 5) rice varieties each mating with 3023 rice accessions from the 3000 rice genomes project (3 K RGP). Of the 362,760 potential hybrids, selection of the top 100 predicted hybrids would lead to 35.5%, 23.25%, 30.21%, 42.87%, 61.80%, 75.83%, 19.24% and 36.12% increase in grain yield per plant, thousand-grain weight, panicle number per plant, plant height, secondary branch number, grain number per panicle, panicle length and primary branch number, respectively.ConclusionsThis study evaluated the factors affecting predictabilities for hybrid prediction and demonstrated the implementation of GS to predict hybrid performance of rice. Our results suggest that GS could enable the rapid selection of superior hybrids, thus increasing the efficiency of rice hybrid breeding.

Increased experimental conditions and marker densities identified more genetic loci associated with southern and northern leaf blight resistance in maize

Southern leaf blight (SLB) and northern leaf blight (NLB) are the two major foliar diseases limiting maize production worldwide. Upon previous study with the nested association mapping (NAM) population, which consist of 5,000 recombinant inbred lines from 25 parents crossed with B73, we expanded the phenotyping environments from the United States (US) to China, and increased the marker densities from 1106 to 7386 SNPs for linkage mapping, and from 1.6 to 28.5 million markers for association mapping. We identified 49 SLB and 48 NLB resistance-related unique QTLs in linkage mapping, and multiple loci in association mapping with candidate genes involved in known plant disease-resistance pathways. Furthermore, an independent natural population with 282 diversified inbred lines were sequenced for four candidate genes selected based on their biological functions. Three of them demonstrated significant associations with disease resistance. These findings provided valuable resources for further implementations to develop varieties with superior resistance for NLB and SLB.

Genetic linkage map and QTL identification for adventitious rooting traits in red gum eucalypts.

The eucalypt species, Eucalyptus tereticornis and Eucalyptus camaldulensis, show tolerance to drought and salinity conditions, respectively, and are widely cultivated in arid and semiarid regions of tropical countries. In this study, genetic linkage map was developed for interspecific cross E. tereticornis × E. camaldulensis using pseudo-testcross strategy with simple sequence repeats (SSRs), intersimple sequence repeats (ISSRs), and sequence-related amplified polymorphism (SRAP) markers. The consensus genetic map comprised totally 283 markers with 84 SSRs, 94 ISSRs, and 105 SRAP markers on 11 linkage groups spanning 1163.4cM genetic distance. Blasting the SSR sequences against E. grandis sequences allowed an alignment of 64% and the average ratio of genetic-to-physical distance was 1.7 Mbp/cM, which strengths the evidence that high amount of synteny and colinearity exists among eucalypts genome. Blast searches also revealed that 37% of SSRs had homologies with genes, which could potentially be used in the variety of downstream applications including candidate gene polymorphism. Quantitative trait loci (QTL) analysis for adventitious rooting traits revealed six QTL for rooting percent and root length on five chromosomes with interval and composite interval mapping. All the QTL explained 12.0-14.7% of the phenotypic variance, showing the involvement of major effect QTL on adventitious rooting traits. Increasing the density of markers would facilitate the detection of more number of small-effect QTL and also underpinning the genes involved in rooting process.

Genome-Wide Linkage Mapping of Quantitative Trait Loci for Late-Season Physiological and Agronomic Traits in Spring Wheat under Irrigated Conditions

Many late-season physiological traits affect grain yield in wheat, either directly or indirectly. However, information on the genetic control of yield-related traits is still limited. In this study, we aimed to identify quantitative trait loci (QTL) for canopy temperature and chlorophyll content index during anthesis (CTa and CCIa, respectively), the mid grain-filling stage (CTg1 and CCIg1, respectively), and the late grain-filling stage (CTg2 and CCIg2, respectively) as well as for plant height (PH), thousand kernels weight (TKW), and grain yield (GY) using genome-wide linkage mapping. To this end, a double haploid population derived from a cross between two high yielding wheat cultivars, UI Platinum and SY Capstone, was phenotyped in four irrigated environments and genotyped using the wheat 90K iSelect platform and simple sequence repeats. The genotypic data were used to construct a high-density genetic map of 43 linkage groups (LGs) with a total length of 3594.0 cm and a marker density of 0.37 cm. A total of 116 QTL for all nine traits was detected on 33 LGs, spreading to all wheat chromosomes, except for Chr. 7D. Of these, six QTL (CTa.ui-4B.1, Q.CTg1.ui-5B-2.1, Q.CTg2.ui-6B.1, Q.PH.ui-6A-2.1, Q.TKW.ui-2D-1, and Q.GY.ui-6B) were consistently detected in more than three irrigated environments, called as stable QTL. Additionally, we identified 26 QTL clusters for more than two traits, of which the top four were located on Chromosomes 4A-1, 1B-1, 5B-2, and 2D-1. Overall, the stable QTL significantly related with grain yield, QTL clusters, and linked molecular markers identified in this study, may be useful in marker-assisted selection in early generation and early growth stage for grain yield improvement.

Factors affecting genomic selection revealed by empirical evidence in maize

Genomic selection (GS) as a promising molecular breeding strategy has been widely implemented and evaluated for plant breeding, because it has remarkable superiority in enhancing genetic gain, reducing breeding time and expenditure, and accelerating the breeding process. In this study the factors affecting prediction accuracy (rMG) in GS were evaluated systematically, using six agronomic traits (plant height, ear height, ear length, ear diameter, grain yield per plant and hundred-kernel weight) evaluated in one natural and two biparental populations. The factors examined included marker density, population size, heritability, statistical model, population relationships and the ratio of population size between the training and testing sets, the last being revealed by resampling individuals in different proportions from a population. Prediction accuracy continuously increased as marker density and population size increased and was positively correlated with heritability; rMG showed a slight gain when the training set increased to three times as large as the testing set. Low predictive performance between unrelated populations could be attributed to different allele frequencies, and predictive ability and prediction accuracy could be improved by including more related lines in the training population. Among the seven statistical models examined, including ridge regression best linear unbiased prediction (RR-BLUP), genomic BLUP (GBLUP), BayesA, BayesB, BayesC, Bayesian least absolute shrinkage and selection operator (Bayesian LASSO), and reproducing kernel Hilbert space (RKHS), the RKHS and additive-dominance model (Add + Dom model) showed credible ability for capturing non-additive effects, particularly for complex traits with low heritability. Empirical evidence generated in this study for GS-relevant factors will help plant breeders to develop GS-assisted breeding strategies for more efficient development of varieties.

Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants

BackgroundIncreasing marker density was proposed to have potential to improve the accuracy of genomic prediction for quantitative traits; whole-sequence data is expected to give the best accuracy of prediction, since all causal mutations that underlie a trait are expected to be included. However, in cattle and chicken, this assumption is not supported by empirical studies. Our objective was to compare the accuracy of genomic prediction of feed efficiency component traits in Duroc pigs using single nucleotide polymorphism (SNP) panels of 80K, imputed 650K, and whole-genome sequence variants using GBLUP, BayesB and BayesRC methods, with the ultimate purpose to determine the optimal method to increase genetic gain for feed efficiency in pigs.ResultsPhenotypes of average daily feed intake (ADFI), average daily gain (ADG), ultrasound backfat depth (FAT), and loin muscle depth (LMD) were available for 1363 Duroc boars from a commercial breeding program. Genotype imputation accuracies reached 92.1% from 80K to 650K and 85.6% from 650K to whole-genome sequence variants. Average accuracies across methods and marker densities of genomic prediction of ADFI, FAT, LMD and ADG were 0.40, 0.65, 0.30 and 0.15, respectively. For ADFI and FAT, BayesB outperformed GBLUP, but increasing marker density had little advantage for genomic prediction. For ADG and LMD, GBLUP outperformed BayesB, while BayesRC based on whole-genome sequence data gave the best accuracies and reached up to 0.35 for LMD and 0.25 for ADG.ConclusionsUse of genomic information was beneficial for prediction of ADFI and FAT but not for that of ADG and LMD compared to pedigree-based estimates. BayesB based on 80K SNPs gave the best genomic prediction accuracy for ADFI and FAT, while BayesRC based on whole-genome sequence data performed best for ADG and LMD. We suggest that these differences between traits in the effect of marker density and method on accuracy of genomic prediction are mainly due to the underlying genetic architecture of the traits.

Genotyping-by-sequencing application on diploid rose and a resulting high-density SNP-based consensus map

Roses, which have been cultivated for at least 5000 years, are one of the most important ornamental crops in the world. Because of the interspecific nature and high heterozygosity in commercial roses, the genetic resources available for rose are limited. To effectively identify markers associated with QTL controlling important traits, such as disease resistance, abundant markers along the genome and careful phenotyping are required. Utilizing genotyping by sequencing technology and the strawberry genome (Fragaria vesca v2.0.a1) as a reference, we generated thousands of informative single nucleotide polymorphism (SNP) markers. These SNPs along with known bridge simple sequence repeat (SSR) markers allowed us to create the first high-density integrated consensus map for diploid roses. Individual maps were first created for populations J06-20-14-3דLittle Chief” (J14-3×LC), J06-20-14-3דVineyard Song” (J14-3×VS) and “Old Blush”דRed Fairy” (OB×RF) and these maps were linked with 824 SNPs and 13 SSR bridge markers. The anchor SSR markers were used to determine the numbering of the rose linkage groups. The diploid consensus map has seven linkage groups (LGs), a total length of 892.2 cM, and an average distance of 0.25 cM between 3527 markers. By combining three individual populations, the marker density and the reliability of the marker order in the consensus map was improved over a single population map. Extensive synteny between the strawberry and diploid rose genomes was observed. This consensus map will serve as the tool for the discovery of marker–trait associations in rose breeding using pedigree-based analysis. The high level of conservation observed between the strawberry and rose genomes will help further comparative studies within the Rosaceae family and may aid in the identification of candidate genes within QTL regions.