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Fusarium head blight (FHB) is a global wheat disease that devastates wheat production. Resistance to FHB spread within a wheat spike (type II resistance) and to mycotoxin accumulation in infected kernel (type III resistance) are the two main types of resistance. Of hundreds of QTL that have been reported, only a few can be used in wheat breeding because most show minor and/or inconsistent effects in different genetic backgrounds. We describe a new strategy for identifying robust and reliable meta-QTL (mQTL) that can be used for improvement of wheat FHB resistance. It involves integration of mQTL analysis with mQTL physical mapping and identification of single-copy markers and candidate genes. Using meta-analysis, we consolidated 625 original QTL from 113 publications into 118 genetic map-based mQTL (gmQTL). These gmQTL were further located on the Chinese Spring reference sequence map. Finally, 77 high-confidence mQTL (hcmQTL) were selected from the reference sequence-based mQTL (smQTL). Locus-specific single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) markers and 17 genes responsive to FHB were then identified in the hcmQTL intervals by combined analysis of transcriptomic and proteomic data. This work may lead to a comprehensive molecular breeding platform for improving wheat resistance to FHB.

Cassava is an important staple in Sub-Sahara Africa. While its production has rapidly expanded to the dry savannahs of the continent, productivity is low in this ecology due to drought by farmers, extending the growth cycle from 12 months to 18, and sometimes 24 months to ensure better harvests. Yield is a complex trait and often difficult to manipulate for genetic gain in conventional breeding. Unfortunately, the dearth of molecular tools for decades has hampered molecular breeding (MB) to improve cassava productivity. This study was conducted to explore KASpar SNPs to generate more molecular tools to enhance genetic dissection of elite African germplasm for improved cassava productivity in dry environments of Africa where molecular resources are highly limited for crop improvement. To aid molecular genetic analysis of traits, a linkage map covering 1582.8 cM with an average resolution of 3.69 cM was constructed using 505 polymorphic SNP markers distributed over 21 linkage groups. Composite interval mapping using 267 F1 progeny in initial QTL mapping identified 27 QTLs for productivity traits in the dry savannah of Nigeria. The availability of KASPar SNPs are anticipated to improve the implementation of MB for the development of high performing drought-tolerant cassava varieties in Africa.

Linum usitatissimum (flax) is an ancient crop that has now a rapidly expanding set of genomic resources. The development of molecular markers from restriction fragment length polymorphisms to simple sequence repeats and single nucleotide polymorphisms has followed the evolution of these technologies and then expanded through the advent of high-throughput sequencing technologies. The combination of markers, next-generation sequencing, and optical mapping techniques has supported the development of genetic maps with an increasing density of markers resulting in the assembly of chromosome-based pseudomolecules. These marker resources have been used to identify quantitative trait loci for numerous agronomically important traits within the cultivated flax and its wild progenitor core collections.

Black spot disease (Diplocarpon rosae) is the most important leaf disease of garden roses in warm humid areas. Although partial resistance to black spot has been shown to be moderately heritable, the responsible quantitative trait loci (QTL) remain unidentified. Because of the interspecific nature and high heterozygosity in commercial roses, as well as the relatively small research input compared to row crops, the genomic resources available for rose are limited. To effectively identify markers associated with QTL controlling black spot resistance, abundant markers across the genome and careful phenotyping are required. Fifteen inter-related diploid rose populations with black spot resistant cultivar R. wichuraiana ‘Basye’s Thornless’ in the genetic background were assessed based on the percent of total foliage covered with lesions in June, October and November of 2016 in College Station. Broad sense heritability was estimated at 0.51 which indicates black spot partial resistance is a moderately heritable trait. Genotyping-by-sequencing technology was used to generate SNP markers for linkage map construction. Previous anchor SSR markers were used to designate the linkage group number. The final consensus map used for black spot QTL detection contained 791 SNP covering 430 cM with the biggest gap being 6.6 cM on LG4. One major black spot QTL was discovered on LG3 explaining ~13% of the total phenotypic variance (equaling approximately 26% of the genetic variation) using pedigree-based analysis among all fifteen populations.

Pedigree Based Analysis (PBA) is an innovative and powerful approach for the discovery and characterisation of marker-trait associations for use in marker assisted breeding. The power of PBA comes from the simultaneous analysis of multiple segregating full sib families, the use of breeding germplasm, and the use of known genetic relationships. The use of multiple families allows representation of wide genetic diversity thus increasing probability that desired genes/alleles are presented and segregating. This also allows examination of QTL performance across different genetic backgrounds, thus providing knowledge on the general applicability of results. Finally, the use of multiple families increases the population size and thereby the statistical power. The use of breeding germplasm of ongoing breeding activities makes results directly applicable and relevant to the breeder and also reduces experimental costs since plant materials and part of the phenotypic measurements are already available. Finally, PBA provides a framework for extending the population and phenotypic data in time, thereby further strengthening statistical power. The statistical approaches and software have been validated through an integrated study on 27 families from five breeding programs from four countries [1] from which some results are presented in figures 1 &2. Current PBA software includes FlexQTLTM for QTL discovery and characterization [1], Pedimap for the graphical presentation [2], Visual-FQ, which provides structured guidance through the analyses and FQ-haplotyper which assigns alleles to haploblocks (sets of tightly linked SNP markers).

DNA-informed breeding, the integration of DNA-based genetic information into plant breeding programs, can enhance efficiency, accuracy, creativity, and pace of new cultivar development. Most genetic knowledge on key traits for plant breeding has been obtained through QTL analyses. Despite an explosion in QTL discoveries for horticulture crops, very few of those discoveries have been translated into tools for horticultural crop breeding. An example of such tools with direct application in crop genetic improvement are trait-predictive DNA tests. The translation of a promising QTL to a trait-predictive “DNA test” has five steps: (1) choose target QTL; (2) design assay to target locus; (3) assay individuals; (4) trace inheritance; and (5) disseminate DNA test details. Key information to convey to end users about a DNA test are the crop and trait(s) addressed, targeted trait locus or loci, and marker type used; trait heritability and genotypic variance explained by the DNA test; allele effects, frequencies, and germplasm distributions; and technical details for running the test. This paper provides instructions for translating promising QTLs into breeder-friendly, trait-predictive DNA tests, based on our experience with tree fruit. Our intent is to accelerate development of trait-predictive DNA tests and establish a standard framework for reporting them. As scientific understanding of genetic factors controlling breeding-relevant traits continues to expand, systematic and increased DNA test development should help bridge the chasm between academic research and breeding application.

Limited understanding of sweet basil (Ocimum basilicum L.) genetics and genome structure has reduced efficiency of breeding strategies. This is evidenced by the rapid, worldwide dissemination of basil downy mildew (Peronospora belbahrii) in the absence of resistant cultivars. In an effort to improve available genetic resources, expressed sequence tag simple sequence repeat (EST-SSR) and single nucleotide polymorphism (SNP) markers were developed and used to genotype the MRI x SB22 F2 mapping population, which segregates for response to downy mildew. SNP markers were generated from genomic sequences derived from double digestion restriction site associated DNA sequencing (ddRADseq). Disomic segregation was observed in both SNP and EST-SSR markers providing evidence of an O. basilicum allotetraploid genome structure and allowing for subsequent analysis of the mapping population as a diploid intercross. A dense linkage map was constructed using 42 EST-SSR and 1,847 SNP markers spanning 3,030.9 cM. Multiple quantitative trait loci (QTL) model (MQM) analysis identified three QTL that explained 37-55% of phenotypic variance associated with downy mildew response across three environments. A single major QTL, dm11.1 explained 21-28% of phenotypic variance and demonstrated dominant gene action. Two minor QTL dm9.1 and dm14.1 explained 5-16% and 4-18% of phenotypic variance, respectively. Evidence is provided for an additive effect between the two minor QTL and the major QTL dm11.1 increasing downy mildew susceptibility. Results indicate that ddRADseq-facilitated SNP and SSR marker genotyping is an effective approach for mapping the sweet basil genome.

Mapping molecular QTLs has emerged as an important tool for understanding the genetic basis of cell functions. With the increasing availability of functional genomic data, it is natural to incorporate genomic annotations into QTL discovery. Discovering molecular QTLs is typically framed as a multiple hypothesis testing problem and solved using false discovery rate (FDR) control procedures. Currently, most existing statistical approaches rely on obtaining $p$-values for each candidate locus through permutation-based schemes, which are not only inconvenient for incorporating highly informative genomic annotations but also computationally inefficient. In this paper, we discuss a novel statistical approach for integrative QTL discovery based on the theoretical framework of Bayesian FDR control. We use a Bayesian hierarchical model to naturally integrate genomic annotations into molecular QTL mapping and propose an empirical Bayes-based computational procedure to approximate the necessary posterior probabilities to achieve high computational efficiency. Through theoretical arguments and simulation studies, we demonstrate that the proposed approach rigorously controls the desired type I error rate and greatly improves the power of QTL discovery when incorporating informative annotations. Finally, we demonstrate our approach by analyzing the expression-genotype data from 44 human tissues generated by the GTEx project. By integrating the simple annotation of SNP distance to transcription start sites, we discover more genes that harbor expression-associated SNPs in all 44 tissues, with an average increase of 1485 genes per tissue.

Background Dense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants. Results We introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits. Conclusions BayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2443-6) contains supplementary material, which is available to authorized users.

Common bean (Phaseolus vulgaris L.) is one of the most important grain legume crops in the world. The beans grown in north-western Himalayas possess huge diversity for seed color, shape and size but are mostly susceptible to Anthracnose disease caused by seed born fungus Colletotrichum lindemuthianum. Dozens of QTLs/genes have been already identified for this disease in common bean world-wide. However, this is the first report of gene/QTL discovery for Anthracnose using bean germplasm from north-western Himalayas of state Jammu & Kashmir, India. A core set of 96 bean lines comprising 54 indigenous local landraces from 11 hot-spots and 42 exotic lines from 10 different countries were phenotyped at two locations (SKUAST-Jammu and Bhaderwah, Jammu) for Anthracnose resistance. The core set was also genotyped with genome-wide (91) random and trait linked SSR markers. The study of marker-trait associations (MTAs) led to the identification of 10 QTLs/genes for Anthracnose resistance. Among the 10 QTLs/genes identified, two MTAs are stable (BM45 & BM211), two MTAs (PVctt1 & BM211) are major explaining more than 20% phenotypic variation for Anthracnose and one MTA (BM211) is both stable and major. Six (06) genomic regions are reported for the first time, while as four (04) genomic regions validated the already known QTL/gene regions/clusters for Anthracnose. The major, stable and validated markers reported during the present study associated with Anthracnose resistance will prove useful in common bean molecular breeding programs aimed at enhancing Anthracnose resistance of local bean landraces grown in north-western Himalayas of state Jammu and Kashmir.

The study of the interactions between genetics and nutrition has gained new impetus with the development of high-throughput genomic approaches allowing for a comprehensive approach of the variability of gene sequences or activity. The object of nutrigenetics is to identify the genetic determinants (sequence polymorphisms) of the variability of individual responses to nutrition. A quantitative trait locus (QTL) is a chromosomal region involved in the control of quantitative phenotypic traits. Based on the knowledge of the chicken genome sequence, numerous positional candidate genes can be proposed within a QTL region, and further evaluation is needed to test the link between polymorphism of those genes, their activity and the expression of the phenotype. As a proof of principle that such an approach is relevant to nutrigenetics, we identified a polymorphism in the gene encoding the enzyme -carotene 15,15'-monooxygenase 1 (BCMO1), which affected the response to dietary intake of -carotene in term of plasma, liver and duodenal concentrations of lutein. Another project aims to identify the genetic determinants of digestive ability through the development and characterization of two experimental lines of chickens divergently selected for this trait. Recently, chromosomal regions controlling this trait have been identified, and further studies of sequence polymorphisms and gene expression (functional genomics) data in extreme animals issued from a cross population are underway. Eventually, the tools of nutrigenetics will predict the potential for adaptation of genotypes to various feeds. Nutrigenomics, allowing a better understanding of the mechanisms by which nutrition affects gene activity, is expected to propose new nutritional strategies to guide phenotypes and new selection strategies to improve birds’ ability to adapt to different diets. The combination of both approaches should permit to identify genes controlling those phenotypes.

Population scale studies combining genetic information with molecular phenotypes (for example, gene expression) have become a standard to dissect the effects of genetic variants onto organismal phenotypes. These kinds of data sets require powerful, fast and versatile methods able to discover molecular Quantitative Trait Loci (molQTL). Here we propose such a solution, QTLtools, a modular framework that contains multiple new and well-established methods to prepare the data, to discover proximal and distal molQTLs and, finally, to integrate them with GWAS variants and functional annotations of the genome. We demonstrate its utility by performing a complete expression QTL study in a few easy-to-perform steps. QTLtools is open source and available at https://qtltools.github.io/qtltools/., Analysis of molecular quantitative trait loci (molQTL) can help interpret genome-wide association studies and requires efficient approaches to correct for multiple testing. This study describes a bioinformatics toolkit called QTLtool that can handle large data sets and quickly perform multiple types of molQTL analyses.

Much of the cost associated with marker discovery for marker assisted breeding (MAB) can be eliminated if a diverse, segregating population is generated, genotyped, and made available to the global breeding community. Herein, we present an example of a hybrid, wild-derived family of the octoploid strawberry that can be used by other breeding programs to economically find and tag useful genes for MAB. A pseudo test cross population between two wild species of Fragaria virginiana and F. chiloensis (FVC 11) was generated and evaluated for a set of phenotypic traits. A total of 106 individuals in the FVC 11 were genotyped for 29,251 single nucleotide polymorphisms (SNPs) utilizing a commercially available, genome-wide scanning platform (Affymetrix Axiom IStraw90(TW)). The marker trait associations were deduced using TASSEL software. The FVC 11 population segregating for daughters per mother, inflorescence number, inflorescence height, crown production, flower number, fruit size, yield, internal color, soluble solids, fruit firmness, and plant vigor. Coefficients of variations ranged from 10% for fruit firmness to 68% for daughters per mother, indicating an underlying quantitative inheritance for each trait. A total of 2,474 SNPs were found to be polymorphic in FVC 11 and strong marker trait associations were observed for vigor, daughters per mother, yield and fruit weight. These data indicate that FVC 11 can be used as a reference population for quantitative trait loci detection and subsequent MAB across different breeding programs and geographical locations.

Includes Supplementary Figures S1 to S4, Tables S1 to S4, and a summary of the independent experiments used to identify the "Lactation Gene Set". (DOCX 1332Â kb)

Pedigree Based Analysis (PBA) is an innovative and powerful approach for the discovery and characterisation of marker-trait associations for use in marker assisted breeding. The power of PBA comes from the simultaneous analysis of multiple segregating full sib families, the use of breeding germplasm, and the use of known genetic relationships. The use of multiple families allows representation of wide genetic diversity thus increasing probability that desired genes/alleles are presented and segregating. This also allows examination of QTL performance across different genetic backgrounds, thus providing knowledge on the general applicability of results. Finally, the use of multiple families increases the population size and thereby the statistical power. The use of breeding germplasm of ongoing breeding activities makes results directly applicable and relevant to the breeder and also reduces experimental costs since plant materials and part of the phenotypic measurements are already available. Finally, PBA provides a framework for extending the population and phenotypic data in time, thereby further strengthening statistical power. The statistical approaches and software have been validated through an integrated study on 27 families from five breeding programs from four countries [1] from which some results are presented in figures 1 &2. Current PBA software includes FlexQTLTM for QTL discovery and characterization [1], Pedimap for the graphical presentation [2], Visual-FQ, which provides structured guidance through the analyses and FQ-haplotyper which assigns alleles to haploblocks (sets of tightly linked SNP markers).

Pedigree Based Analysis (PBA) is an innovative and powerful approach for the discovery and characterisation of marker-trait associations for use in marker assisted breeding. The power of PBA comes from the simultaneous analysis of multiple segregating full sib families, the use of breeding germplasm, and the use of known genetic relationships. The use of multiple families allows representation of wide genetic diversity thus increasing probability that desired genes/alleles are presented and segregating. This also allows examination of QTL performance across different genetic backgrounds, thus providing knowledge on the general applicability of results. Finally, the use of multiple families increases the population size and thereby the statistical power. The use of breeding germplasm of ongoing breeding activities makes results directly applicable and relevant to the breeder and also reduces experimental costs since plant materials and part of the phenotypic measurements are already available. Finally, PBA provides a framework for extending the population and phenotypic data in time, thereby further strengthening statistical power. The statistical approaches and software have been validated through an integrated study on 27 families from five breeding programs from four countries [1] from which some results are presented in figures 1 &2. Current PBA software includes FlexQTLTM for QTL discovery and characterization [1], Pedimap for the graphical presentation [2], Visual-FQ, which provides structured guidance through the analyses and FQ-haplotyper which assigns alleles to haploblocks (sets of tightly linked SNP markers).

Mouse genetic resources include inbred strains, recombinant inbred lines, chromosome substitution strains, heterogeneous stocks, and the Collaborative Cross (CC). These resources were generated through various breeding designs that potentially produce different genetic architectures, including the level of diversity represented, the spatial distribution of the variation, and the allele frequencies within the resource. By combining sequencing data for 16 inbred strains and the recorded history of related strains, the architecture of genetic variation in mouse resources was determined. The most commonly used resources harbor only a fraction of the genetic diversity of Mus musculus, which is not uniformly distributed thus resulting in many blind spots. Only resources that include wild-derived inbred strains from subspecies other than M. m. domesticus have no blind spots and a uniform distribution of the variation. Unlike other resources that are primarily suited for gene discovery, the CC is the only resource that can support genome-wide network analysis, which is the foundation of systems genetics. The CC captures significantly more genetic diversity with no blind spots and has a more uniform distribution of the variation than all other resources. Furthermore, the distribution of allele frequencies in the CC resembles that seen in natural populations like humans in which many variants are found at low frequencies and only a minority of variants are common. We conclude that the CC represents a dramatic improvement over existing genetic resources for mammalian systems biology applications.

Thousands of quantitative trait loci (QTL) have been identified for a wide range of economically important phenotypes in pigs. Recently, QTL analyses have begun to use high-density single nucleotide polymorphism (SNP) panels and applications have extended beyond experimental intercrosses to outbred populations by exploiting long-range linkage disequilibrium that results in higher resolution QTL mapping. Relevant phenotypes generally fall under categories of growth and body composition, carcass and meat quality, reproduction, and disease resistance. A few expression QTL (eQTL) studies have been performed that integrate transcriptional profiles with genotype data by considering expression levels as response variables in QTL analyses for identifying genes controlling important trait phenotypes. Rapidly evolving genomics technologies, including RNAseq, provide tremendous opportunities for QTL and eQTL discovery. In this review, we discuss recent progress in pig QTL and eQTL discovery, including approaches for allele-specific expression, and implications of these discoveries for pig breeding and genetics.

Background Arabidopsis thaliana is a useful model organism for deciphering the genetic determinants of seed size; however the small size of its seeds makes measurements difficult. Bulk seed weights are often used as an indicator of average seed size, but details of individual seed is obscured. Analysis of seed images is possible but issues arise from variations in seed pigmentation and shadowing making analysis laborious. We therefore investigated the use of a consumer level scanner to facilitate seed size measurements in conjunction with open source image-processing software. Results By using the transmitted light from the slide scanning function of a flatbed scanner and particle analysis of the resulting images, we have developed a method for the rapid and high throughput analysis of seed size and seed size distribution. The technical variation due to the approach was negligible enabling us to identify aspects of maternal plant growth that contribute to biological variation in seed size. By controlling for these factors, differences in seed size caused by altered parental genome dosage and mutation were easily detected. The method has high reproducibility and sensitivity, such that a mutant with a 10% reduction in seed size was identified in a screen of endosperm-expressed genes. Our study also generated average seed size data for 91 Arabidopsis accessions and identified a number of quantitative trait loci from two recombinant inbred line populations, generated from Cape Verde Islands and Burren accessions crossed with Columbia. Conclusions This study describes a sensitive, high-throughput approach for measuring seed size and seed size distribution. The method provides a low cost and robust solution that can be easily implemented into the workflow of studies relating to various aspects of seed development.

BACKGROUND: Within the last few years a large amount of genomic information has become available in cattle. Densities of genomic information vary from a few thousand variants up to whole genome sequence information. In order to combine genomic information from different sources and infer genotypes for a common set of variants, genotype imputation is required. RESULTS: In this study we evaluated the accuracy of imputation from high density chips to whole genome sequence data in Brown Swiss cattle. Using four popular imputation programs (Beagle, FImpute, Impute2, Minimac) and various compositions of reference panels, the accuracy of the imputed sequence variant genotypes was high and differences between the programs and scenarios were small. We imputed sequence variant genotypes for more than 1600 Brown Swiss bulls and performed genome-wide association studies for milk fat percentage at two stages of lactation. We found one and three quantitative trait loci for early and late lactation fat content, respectively. Known causal variants that were imputed from the sequenced reference panel were among the most significantly associated variants of the genome-wide association study. CONCLUSIONS: Our study demonstrates that whole-genome sequence information can be imputed at high accuracy in cattle populations. Using imputed sequence variant genotypes in genome-wide association studies may facilitate causal variant detection., BMC Genomics, 18, ISSN:1471-2164

Background The detection of quantitative trait loci has accelerated with recent developments in genomics. The introduction of genomic selection in combination with sequencing efforts has made a large amount of genotypic data available. Functional traits such as fertility and calving traits have been included in routine genomic estimation of breeding values making large quantities of phenotypic data available for these traits. This data was used to investigate the genetics underlying fertility and calving traits and to identify potentially causative genomic regions and variants. We performed genome-wide association studies for 13 functional traits related to female fertility as well as for direct and maternal calving ease based on imputed whole-genome sequences. Deregressed breeding values from ~1000–5000 bulls per trait were used to test for associations with approximately 10 million imputed sequence SNPs. Results We identified a QTL on BTA17 associated with non-return rate at 56 days and with interval from first to last insemination. We found two significantly associated non-synonymous SNPs within this QTL region. Two more QTL for fertility traits were identified on BTA25 and 29. A single QTL was identified for maternal calving traits on BTA13 whereas three QTL on BTA19, 21 and 25 were identified for direct calving traits. The QTL on BTA19 co-localizes with the reported BH2 haplotype. The QTL on BTA25 is concordant for fertility and calving traits and co-localizes with a QTL previously reported to influence stature and related traits in Brown Swiss dairy cattle. Conclusion The detection of QTL and their causative variants remains challenging. Combining comprehensive phenotypic data with imputed whole genome sequences seems promising. We present a QTL on BTA17 for female fertility in dairy cattle with two significantly associated non-synonymous SNPs, along with five additional QTL for fertility traits and calving traits. For all of these we fine mapped the regions and suggest candidate genes and candidate variants. Electronic supplementary material The online version of this article (10.1186/s12864-017-4308-z) contains supplementary material, which is available to authorized users.

Quantitative trait locus (QTL) analyses of multiomic molecular traits, such as gene transcription (eQTL), DNA methylation (mQTL) and histone modification (haQTL), have been widely used to infer the functional effects of genome variants. However, the QTL discovery is largely restricted by the limited study sample size, which demands higher threshold of minor allele frequency and then causes heavy missing molecular trait–variant associations. This happens prominently in single-cell level molecular QTL studies because of sample availability and cost. It is urgent to propose a method to solve this problem in order to enhance discoveries of current molecular QTL studies with small sample size. In this study, we presented an efficient computational framework called xQTLImp to impute missing molecular QTL associations. In the local-region imputation, xQTLImp uses multivariate Gaussian model to impute the missing associations by leveraging known association statistics of variants and the linkage disequilibrium (LD) around. In the genome-wide imputation, novel procedures are implemented to improve efficiency, including dynamically constructing a reused LD buffer, adopting multiple heuristic strategies and parallel computing. Experiments on various multiomic bulk and single-cell sequencing-based QTL datasets have demonstrated high imputation accuracy and novel QTL discovery ability of xQTLImp. Finally, a C++ software package is freely available at https://github.com/stormlovetao/QTLIMP.

Over the last decade, multiparental populations have become a mainstay of genetics research in diploid species. Our goal was to extend this paradigm to autotetraploids by developing software for quantitative trait locus (QTL) mapping in connected F1 populations derived from a set of shared parents. For QTL discovery, phenotypes are regressed on the dosage of parental haplotypes to estimate additive effects. Statistical properties of the model were explored by simulating half-diallel diploid and tetraploid populations with different population sizes and numbers of parents. Across scenarios, the number of progeny per parental haplotype (pph) largely determined the statistical power for QTL detection and accuracy of the estimated haplotype effects. Multiallelic QTL with heritability 0.2 were detected with 90% probability at 25 pph and genome-wide significance level 0.05, and the additive haplotype effects were estimated with over 90% accuracy. Following QTL discovery, the software enables a comparison of models with multiple QTL and nonadditive effects. To illustrate, we analyzed potato tuber shape in a half-diallel population with three tetraploid parents. A well-known QTL on chromosome 10 was detected, for which the inclusion of digenic dominance lowered the Deviance Information Criterion (DIC) by 17 points compared to the additive model. The final model also contained a minor QTL on chromosome 1, but higher-order dominance and epistatic effects were excluded based on the DIC. In terms of practical impacts, the software is already being used to select offspring based on the effect and dosage of particular haplotypes in breeding programs.

As the threat of climate change rises, breeders and scientists are facing new challenges in cultivar development. Many traits are becoming even more important in the changing environment, in particular, heat stress, changes in flowering behavior and unexpected pathogen infections. New cultivars will have to be equipped with adequate biotic resistances, abiotic tolerances and flowering requirements, which are largely quantitatively inherited, complex traits. Quantitative trait loci analysis is still the method of choice for identifying significant regions associated with complex traits. With up-and-coming, complementary technology, QTL discovery and introgressions of multi-gene traits are becoming a possibility in breeding cultivars in the changing environment. Herein, we discuss recent advancements in protocols for assessing these complex traits, and the associated QTLs to incorporate into innovative breeding strategies. Certain emphasis is made on woody perennial crops, as they present a greater challenge due to their longer life cycles compared to annual crops. New technologies are surging in, and are demonstrating new variations and augmentations to QTL discovery and introgression of multi-genes, such as fast-track breeding, and genomic selection.

The identification of quantitative trait loci (QTL) for plant metabolites requires the quantitation of these metabolites across a large range of progeny. We developed a rapid metabolic profiling method using both untargeted and targeted direct infusion tandem mass spectrometry (DIMSMS) with a linear ion trap mass spectrometer yielding sufficient precision and accuracy for the quantification of a large number of metabolites in a high-throughput environment. The untargeted DIMSMS method uses top-down data-dependent fragmentation yielding MS(2) and MS(3) spectra. We have developed software tools to assess the structural homogeneity of the MS(2) and MS(3) spectra hence their utility for phenotyping and genetical metabolomics. In addition we used a targeted DIMS(MS) method for rapid quantitation of specific compounds. This method was compared with targeted LC/MS/MS methods for these compounds. The DIMSMS methods showed sufficient precision and accuracy for QTL discovery. We phenotyped 200 individual Lolium perenne genotypes from a mapping population harvested in two consecutive years. Computational and statistical analyses identified 246 nominal m/z bins with sufficient precision and homogeneity for QTL discovery. Comparison of the data for specific metabolites obtained by DIMSMS with the results from targeted LC/MS/MS analysis showed that quantitation by this metabolic profiling method is reasonably accurate. Of the top 100 MS(1) bins, 22 ions gave one or more reproducible QTL across the 2 years.

The reinvention of potato, from a tetraploid clonal crop into a diploid seed-based hybrid crop, requires insight in the mutational load, recombination landscape, and the genetic basis of fertility. Genomics-based breeding and QTL discovery rely on efficient genotyping strategies such as skim sequencing, to gather genotypic information. The application of skim sequencing to full-sib population of non-inbred parents remains challenging. Here, we report on an R implementation of the OutcrossSeq pipeline for diploids. We applied this pipeline to a large diploid skim sequenced potato population. We used the resulting bin-markers for the construction of high-density parent specific linkage maps, highlighting variation in parental recombination rate and structural variations. We subsequently explored transmission ratio distortion and non-independent assortment of alleles, indicative of large-effect deleterious mutations. Finally, we identified QTLs for seedling tuber yield in pots and pollen shed. This study showcases the range of genetic analyses, from marker inference, identification of transmission ratio distortion, and linkage map construction to QTL mapping, resulting in new insights that contribute to breeding diploid potato.

WOS: 000335066300001, PubMed ID: 24581289, Background: Breeders in the allo-octoploid strawberry currently make little use of molecular marker tools. As a first step of a QTL discovery project on fruit quality traits and resistance to soil-borne pathogens such as Phytophthora cactorum and Verticillium we built a genome-wide SSR linkage map for the cross Holiday x Korona. We used the previously published MADCE method to obtain full haplotype information for both of the parental cultivars, facilitating in-depth studies on their genomic organisation. Results: The linkage map incorporates 508 segregating loci and represents each of the 28 chromosome pairs of octoploid strawberry, spanning an estimated length of 2050 cM. The sub-genomes are denoted according to their sequence divergence from F. vesca as revealed by marker performance. The map revealed high overall synteny between the sub-genomes, but also revealed two large inversions on LG2C and LG2D, of which the latter was confirmed using a separate mapping population. We discovered interesting breeding features within the parental cultivars by in-depth analysis of our haplotype data. The linkage map-derived homozygosity level of Holiday was similar to the pedigree-derived inbreeding level (33% and 29%, respectively). For Korona we found that the observed homozygosity level was over three times higher than expected from the pedigree (13% versus 3.6%). This could indicate selection pressure on genes that have favourable effects in homozygous states. The level of kinship between Holiday and Korona derived from our linkage map was 2.5 times higher than the pedigree-derived value. This large difference could be evidence of selection pressure enacted by strawberry breeders towards specific haplotypes. Conclusion: The obtained SSR linkage map provides a good base for QTL discovery. It also provides the first biologically relevant basis for the discernment and notation of sub-genomes. For the first time, we revealed genomic rearrangements that were verified in a separate mapping population. We believe that haplotype information will become increasingly important in identifying marker-trait relationships and regions that are under selection pressure within breeding material. Our attempt at providing a biological basis for the discernment of sub-genomes warrants follow-up studies to streamline the naming of the sub-genomes among different octoploid strawberry maps., Technological Top Institute Green Genetics [1C004RP]; Socrates-Erasmus Student Exchange Programme, The Socrates-Erasmus Student Exchange Programme is acknowledged for the fellowship awarded to Hulya Yilmaz Temel. Tom Davis, University of New Hampshire, is acknowledged for sharing sequence information for the design of the primers TDFM1, -2, and -6, and Paul Arens, Wageningen UR Plant Breeding is acknowledged for the design of these specific primers. Funding for this research was provided through the Technological Top Institute Green Genetics, project number 1C004RP.

The reinvention of potato, from a tetraploid clonal crop into a diploid seed-based hybrid crop, requires insight in the mutational load, recombination landscape and the genetic basis of fertility. Genomics based breeding and QTL discovery relies on efficient genotyping strategies such as skim-sequencing, to gather genotypic information. The application of skim-sequencing to full-sib population of non-inbred parents remains challenging. Here, we report on a R implementation of the OutcrossSeq pipeline for diploids and applied it to a large diploid skim-sequenced potato population. We used the resulting bin-markers for the construction of high-density parent specific linkage maps, highlighting variation in parental recombination rate and structural variations. We subsequently explored transmission ratio distortion (TRD) including epistatic ones, indicative of large effect deleterious mutations. Finally, we identified QTLs for seedling tuber yield in pots and pollen production. This study showcases the range of genetic analyses, from marker inference, TRD identification and linkage map construction to QTL mapping, resulting in new insights that contribute to breeding diploid potato.

Quantitative genetics seeks to understand the role of allelic variation in trait differences. Introgression lines (ILs) contain a single genetic locus introgressed into another genetic background, and are one of the most powerful quantitative trait locus (QTL) mapping designs. However, albeit useful for QTL discovery, this homogenous background confounds genetic interactions. Here, we created a novel IL population, complementary to a previously created population, which enables identification of genetic interactions in ILs. The novel ILCB4856panel was made by crossing divergent strains of the model nematodeCaenorhabditis elegans(N2 and CB4856). The ILCB4856panel comprises a population of 145 strains with sequencing confirmed N2 introgressions in a CB4856 background from which a core set of 87 strains covering the entire genome was selected. We present three experiments demonstrating the power of the complementary IL panels. First, we performed QTL mapping identifying new regions associated with lifespan. Second, the existence of opposite-effect loci regulating heat-stress survival is demonstrated. Third, by combining ILN2and ILCB4856strains, an interacting expression QTL was uncovered. In conclusion, the complementary IL panels are a unique and ready-to-use resource to identify, resolve, and refine complex trait architectures inC. elegans.

Future demands for increased productivity and resilience to abiotic/biotic stresses of major crops require new technologies of breeding by design (BBD) built on massive information from functional and population genomics research. A novel strategy of breeding by selective introgression (BBSI) has been proposed and practiced for simultaneous improvement, genetic dissection and allele mining of complex traits to realize BBD. BBSI has three phases: a) developing large numbers of trait-specific introgression lines (ILs) using backcross breeding in elite genetic backgrounds as the material platform of BBD; b) efficiently identifying genes or quantitative trait loci (QTL) and mining desirable alleles affecting different target traits from diverse donors as the information platform of BBD; and c) developing superior cultivars by BBD using designed QTL pyramiding or marker-assisted recurrent selection. Phase (a) has been implemented massively in rice by many Chinese research institutions and IRRI, resulting in the development of many new green super rice cultivars plus large numbers of ILs in 30 + elite genetic backgrounds. Phase (b) has been demonstrated in a series of proof-of-concept studies of high-efficiency genetic dissection of rice yield and tolerance to abiotic stresses using ILs and DNA markers. Phase (c) has also been implemented by designed QTL pyramiding, resulting in a prototype of BBD in several successful cases. The BBSI strategy can be easily extended for simultaneous trait improvement, efficient gene and QTL discovery and allele mining of complex traits using advanced breeding lines from crosses between a common “backbone” parent and a set of elite parents in conventional pedigree breeding programs. BBSI can be relatively easily adopted by breeding programs with small budgets, but the BBSI-based BBD strategy can be fully and more efficiently implemented by large seed companies with sufficient capacity.

The identification of quantitative trait loci (QTL) for plant metabolites requires the quantitation of these metabolites across a large range of progeny. We developed a rapid metabolic profiling method using both untargeted and targeted direct infusion tandem mass spectrometry (DIMSMS) with a linear ion trap mass spectrometer yielding sufficient precision and accuracy for the quantification of a large number of metabolites in a high-throughput environment. The untargeted DIMSMS method uses top-down data-dependent fragmentation yielding MS2 and MS3 spectra. We have developed software tools to assess the structural homogeneity of the MS2 and MS3 spectra hence their utility for phenotyping and genetical metabolomics. In addition we used a targeted DIMS(MS) method for rapid quantitation of specific compounds. This method was compared with targeted LC/MS/MS methods for these compounds. The DIMSMS methods showed sufficient precision and accuracy for QTL discovery. We phenotyped 200 individual Lolium perenne genotypes from a mapping population harvested in two consecutive years. Computational and statistical analyses identified 246 nominal m/z bins with sufficient precision and homogeneity for QTL discovery. Comparison of the data for specific metabolites obtained by DIMSMS with the results from targeted LC/MS/MS analysis showed that quantitation by this metabolic profiling method is reasonably accurate. Of the top 100 MS1 bins, 22 ions gave one or more reproducible QTL across the 2 years. Copyright © 2009 John Wiley & Sons, Ltd.

Additional file 2: Figure S1. Manhattan plot of p values from single-trait GWAS results of heat tolerance milk (A), fat (B), protein (C) yield slope traits for the Holstein cow discovery set (N = 20,623). The dashed line represents p-value cut-off = 0.001. Figure S2. QQ-plot for heat tolerance milk (HTMYslope), fat (HTFYslope), and protein (HTPYslope) from GWAS of the Holstein cow discovery set (N = 20,623). Figure S3. Manhattan plot of p values from single-trait GWAS results of heat tolerance milk (A), fat (B), protein (C) yield slope traits for combined set of Holstein and Jersey cow discovery set (N = 25,766). The dashed line represents p-value cut-off = 0.001. Figure S4. QQ-plot for heat tolerance milk (HTMYslope), fat (HTFYslope), and protein (HTPYslope) from GWAS using a combined set of Holsteins + Jersey cows (N = 25,766). Figure S5. Minor allele frequency (MAF) distribution of the 50k SNP data and the selected ���top SNPs��� (most significant) from the imputed-whole genome sequence variants. Figure S6. Accuracy of genomic predictions in Holsteins (A; N = 1223), Jersey (B; N = 6338), and Holstein���Jersey crossbreds (C; N = 790) validation cows for milk (MYint), fat (FYint) and protein (PYint) yield intercept traits from different SNP sets based on the BayesR and BayesRC methods: (a) standard 50k SNP array (50k; colored grey) (b) 50k + top SNPs selected from single-trait GWAS (colored blue) and multi-trait meta-analysis (colored orange) at a less stringent cut-off threshold of [��� log10(p-value) ��� 2] and a more stringent p-value of [��� log10(p-value) ��� 3]. The top SNPs were selected from GWAS of Holstein cows (N = 20,623). Vertical lines represent standard errors calculated from three (Holsteins) and two (Jersey) random validation subsets. Figure S7. Bias of genomic predictions in Holsteins (A; N = 1223), Jersey (B; N = 6338), and Holstein���Jersey crossbreds (C; N = 790) validation cows for milk (MYint), fat (FYint), and protein (PYint) yield intercept traits from different SNP sets based on the BayesR and BayesRC methods. Figure S8. Bias of genomic predictions in Holsteins (A; N = 1223), Jersey (B; N = 6338), and Holstein-Jersey crossbreds (C; N = 790) validation cows for heat tolerance milk (HTMYslope), fat (HTFYslope), and protein (HTPYslope) yield slope traits from different SNP sets based on the BayesR and BayesRC methods. Figure S9. QTL discovery using the single-breed (Holstein cows; left) and the across-breed (Holsteins + Jersey cows; right) discovery set.

Cracked sweet cherry (Prunus avium L.) fruit are unmarketable and lead to higher labor costs, and sub-optimal fruit firmness decreases storability and consumer satisfaction. Trait improvement through breeding for increased cracking tolerance and fruit firmness is an attractive alternative to the variable results from management practices. DNA-informed breeding increases effectiveness, yet upstream and translational research, such as quantitative trait locus (QTL) discovery and characterization and DNA test development, respectively, are needed. The objectives of this work were to identify and characterize QTLs associated with fruit cracking or firmness, and develop DNA-based tests from these QTLs for use in DNA-informed breeding. Fruit cracking incidence and firmness were evaluated for two years in 2012 (half of seedlings; firmness only) or 2019 and 2020 for 259 unselected seedlings representing 22 important breeding parents. Phenotypic data, in conjunction with single nucleotide polymorphism (SNP) genotypic data from the RosBREED cherry 6K SNP array, were used in QTL analyses performed via Pedigree-Based Analysis using FlexQTL��� software, supplemented by a genome-wide association study using BLINK software. Haplotype analysis was conducted at QTLs to characterize SNP haplotypes and estimate phenotypic effects, and DNA-based tests were developed and/or evaluated. Four QTLs (two per trait) were consistent across years and/or analysis methods. The two fruit cracking QTLs combined explained 19���27% of the overall cracking phenotypic variance, while the two fruit firmness QTLs combined explained 21���31% of the overall firmness phenotypic variance. SNP haplotypes at these QTLs were successfully tracked to ancestral origins. For each QTL, pairwise SNP haplotype comparisons revealed different (p = 0.05) associated phenotypic levels between at least two haplotypes, which were considered functional SNP haplotypes. Primary SNPs successfully distinguished at least one of the contrasting functional SNP haplotypes at each QTL, and were successfully converted (array SNPs) into DNA tests. A previously developed SSR-based DNA-test was successfully evaluated and distinguished seven unique SSR haplotypes. The four stable QTLs detected here were in genomic regions aligned with previously reported QTLs, although two were reported as only transient. The DNA tests reported here can be implemented in breeding operations to genetically improve fruit cracking tolerance and firmness.

Background Self-incompatibility (SI) is a physiological mechanism that many flowering plants employ to prevent self-fertilization and maintain heterozygosity. In the grass family this is known to be controlled by a two locus (S-Z) system; however, the SI system is intrinsically leaky. Modifier genes of both the S and Z loci and a further locus, T, are known to override SI leading to self-fertilization and self-seed production. This has implications for the ecological and evolutionary success as well as the commercial breeding of grasses. Here we report a study where the genetic control of self-compatibility (SC) was determined from the results of self-pollinating an F2 population of perennial ryegrass from two independently derived inbred lines produced by single-seed descent. Methods In vitro self-pollinations of 73 fertile plants were analysed. A genetic association analysis was made with a panel of 1863 single-nucleotide polymorphism (SNP) markers, generated through genotype-by-sequencing methodology. Markers were placed on a recombination map of seven linkage groups (LGs) created using Joinmap v.5. The seed set on self- and open-pollinated inflorescences was determined on 143 plants, including the 73 plants analysed for self-pollination response. Key Results Self-pollinations revealed a bimodal distribution of percentage SC with peaks at 50 and 100 %. A single quantitative trait locus (QTL) was identified with peak association for marker 6S14665z17875_11873 that mapped to LG 6. Peak position was associated with maximum marker segregation distortion. The self-compatible plants were equally fecund after self- and open pollination. Conclusions This is the first report in the Poaceae family of an SC locus located on LG 6. This new SC QTL discovery, as well as indicating the complex nature of the pollen–stigma recognition process and its evolutionary significance, provides an additional source of SC for breeding perennial ryegrass.

At least 75% of the world’s grain production comes from the three most important cereal crops: rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays). However, abiotic stressors such as heavy metal toxicity, salinity, low temperatures, and drought are all significant hazards to the growth and development of these grains. Quantitative trait locus (QTL) discovery and mapping have enhanced agricultural production and output by enabling plant breeders to better comprehend abiotic stress tolerance processes in cereals. Molecular markers and stable QTL are important for molecular breeding and candidate gene discovery, which may be utilized in transgenic or molecular introgression. Researchers can now study synteny between rice, maize, and wheat to gain a better understanding of the relationships between the QTL or genes that are important for a particular stress adaptation and phenotypic improvement in these cereals from analyzing reports on QTL and candidate genes. An overview of constitutive QTL, adaptive QTL, and significant stable multi-environment and multi-trait QTL is provided in this article as a solid framework for use and knowledge in genetic enhancement. Several QTL, such as DRO1 and Saltol, and other significant success cases are discussed in this review. We have highlighted techniques and advancements for abiotic stress tolerance breeding programs in cereals, the challenges encountered in introgressing beneficial QTL using traditional breeding techniques such as mutation breeding and marker-assisted selection (MAS), and the in roads made by new breeding methods such as genome-wide association studies (GWASs), the clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system, and meta-QTL (MQTL) analysis. A combination of these conventional and modern breeding approaches can be used to apply the QTL and candidate gene information in genetic improvement of cereals against abiotic stresses.

Livestock plays a central role in sustaining human livelihood in South Asia. There are numerous and distinct livestock species in South Asian countries. Several of them have experienced genetic development in recent years due to the application of genomic technologies and effective breeding programs. This review discusses genomic studies on cattle, buffalo, sheep, goat, pig, horse, camel, yak, mithun, and poultry. The frontiers covered in this review are genetic diversity, admixture studies, selection signature research, QTL discovery, genome-wide association studies (GWAS), and genomic selection. The review concludes with recommendations for South Asian livestock systems to increasingly leverage genomic technologies, based on the lessons learned from the numerous case studies. This paper aims to present a comprehensive analysis of the dichotomy in the South Asian livestock sector and argues that a realistic approach to genomics in livestock can ensure long-term genetic advancements.

Background Common bacterial blight (CBB), incited by Xanthomonas axonopodis pv. phaseoli (Xap), is a major yield-limiting factor of common bean (Phaseolus vulgaris L.) production around the world. Host resistance is practically the most effective and environmentally-sound approach to control CBB. Unlike conventional QTL discovery strategies, in which bi-parental populations (F2, RIL, or DH) need to be developed, association mapping-based strategies can use plant breeding populations to synchronize QTL discovery and cultivar development. Results A population of 469 dry bean lines of different market classes representing plant materials routinely developed in a bean breeding program were used. Of them, 395 lines were evaluated for CBB resistance at 14 and 21 DAI (Days After Inoculation) in the summer of 2009 in an artificially inoculated CBB nursery in south-western Ontario. All lines were genotyped using 132 SNPs (Single Nucleotide Polymorphisms) evenly distributed across the genome. Of the 132 SNPs, 26 SNPs had more than 20% missing data, 12 SNPs were monomorphic, and 17 SNPs had a MAF (Minor Allelic Frequency) of less than 0.20, therefore only 75 SNPs were used for association study, based on one SNP per locus. The best possible population structure was to assign 36% and 64% of the lines into Andean and Mesoamerican subgroups, respectively. Kinship analysis also revealed complex familial relationships among all lines, which corresponds with the known pedigree history. MLM (Mixed Linear Model) analysis, including population structure and kinship, was used to discover marker-trait associations. Eighteen and 22 markers were significantly associated with CBB rating at 14 and 21 DAI, respectively. Fourteen markers were significant for both dates and the markers UBC420, SU91, g321, g471, and g796 were highly significant (p ≤ 0.001). Furthermore, 12 significant SNP markers were co-localized with or close to the CBB-QTLs identified previously in bi-parental QTL mapping studies. Conclusions This study demonstrated that association mapping using a reasonable number of markers, distributed across the genome and with application of plant materials that are routinely developed in a plant breeding program can detect significant QTLs for traits of interest.

Parents and 318 F8 recombinant inbred lines (RILs) derived from the cross, TAG 24 × ICGV 86031 were evaluated for peanut bud necrosis disease (PBND) resistance and agronomic traits under natural infestation of thrips at a disease hotspot location for 2 years. Significant genotype, environment and genotype × environment interaction effects suggested role of environment in development and spread of the disease. Quantitative trait loci (QTL) analysis using QTL Cartographer identified a total of 14 QTL for six traits of which five QTL were for disease incidence. One quantitative trait locus q60DI located on LG_AhII was identified using both QTL Cartographer and QTL Network. Another QTL q90DI was detected with a high PVE of 12.57 using QTL Cartographer. A total of nine significant additive × additive (AA) interactions were detected for PBND disease incidence and yield traits with two and seven interactions displaying effects in favour of the parental and recombinant genotype combinations, respectively. This is the first attempt on QTL discovery associated with PBND resistance in peanut. Superior RILs identified in the study can be recycled or released as variety following further evaluations.

A deep understanding of the genetic control of drought tolerance and iron deficiency tolerance is essential to hasten the process of developing improved varieties with higher tolerance through genomics-assisted breeding. In this context, an improved genetic map with 1205 loci was developed spanning 2598.3 cM with an average 2.2 cM distance between loci in the recombinant inbred line (TAG 24 x ICGV 86031) population using high-density 58K single nucleotide polymorphism (SNP) "Axiom_Arachis" array. Quantitative trait locus (QTL) analysis was performed using extensive phenotyping data generated for 20 drought tolerance- and two iron deficiency tolerance-related traits from eight seasons (2004-2015) at two locations in India, one in Niger, and one in Senegal. The genome-wide QTL discovery analysis identified 19 major main-effect QTLs with 10.0-33.9% phenotypic variation explained (PVE) for drought tolerance- and iron deficiency tolerance- related traits. Major main-effect QTLs were detected for haulm weight (20.1% PVE), SCMR (soil plant analytical development (SPAD) chlorophyll meter reading, 22.4% PVE), and visual chlorosis rate (33.9% PVE). Several important candidate genes encoding glycosyl hydrolases; malate dehydrogenases; microtubule-associated proteins; and transcription factors such as MADS-box, basic helix-loop-helix (bHLH), NAM, ATAF, and CUC (NAC), and myeloblastosis (MYB) were identified underlying these QTL regions. The putative function of these genes indicated their possible involvement in plant growth, development of seed and pod, and photosynthesis under drought or iron deficiency conditions in groundnut. These genomic regions and candidate genes, after validation, may be useful to develop molecular markers for deploying genomics-assisted breeding for enhancing groundnut yield under drought stress and iron-deficient soil conditions.

Background and aimsAtherosclerosis is a lipid-driven inflammatory disease presumably initiated by endothelial activation. Low vascular shear stress is known for its ability to activate endothelial cells. Differential DNA methylation (DNAm) is a relatively unexplored player in atherosclerotic disease development and endothelial dysfunction. Literature search revealed that expression of 11 genes have been found to be associated with differential DNAm due to low shear stress in endothelial cells. We hypothesized a causal relationship between DNAm of shear stress associated genes in human carotid plaque and increased risk of cardiovascular disease.MethodsUsing Mendelian randomisation (MR) analysis, we explored the potential causal role of DNAm of shear stress associated genes on cardiovascular disease risk. We used genetic and DNAm data of 442 carotid endarterectomy derived advanced plaques from the Athero-Express Biobank Study for quantitative trait loci (QTL) discovery and performed MR analysis using these QTLs and GWAS summary statistics of coronary artery disease (CAD) and ischemic stroke (IS).ResultsWe discovered 9 methylation QTLs in plaque for differentially methylated shear stress associated genes. We found no significant effect of shear stress gene promotor methylation and increased risk of CAD and IS.ConclusionsDifferential methylation of shear stress associated genes in advanced atherosclerotic plaques in unlikely to increase cardiovascular risk.Highlights-Plaque-derived DNA methylation in shear stress associated genes shows no significant effect on cardiovascular disease-Genetic variants in shear stress associated genes affect DNA methylation in human carotid plaque-Human validation of atherosclerotic associated genes in murine models

Durum wheat is an important crop for the human diet and its consumption is gaining popularity. In order to ensure that durum wheat production maintains the pace with the increase in demand, it is necessary to raise productivity by approximately 1.5% per year. To deliver this level of annual genetic gain the incorporation of molecular strategies has been proposed as a key solution. Here, four RILs populations were used to conduct QTL discovery for grain yield (GY) and 1,000 kernel weight (TKW). A total of 576 individuals were sown at three locations in Morocco and one in Lebanon. These individuals were genotyped by sequencing with 3,202 high-confidence polymorphic markers, to derive a consensus genetic map of 2,705.7 cM, which was used to impute any missing data. Six QTLs were found to be associated with GY and independent from flowering time on chromosomes 2B, 4A, 5B, 7A and 7B, explaining a phenotypic variation (PV) ranging from 4.3 to 13.4%. The same populations were used to train genomic prediction models incorporating the relationship matrix, the genotype by environment interaction, and marker by environment interaction, to reveal significant advantages for models incorporating the marker effect. Using training populations (TP) in full sibs relationships with the validation population (VP) was shown to be the only effective strategy, with accuracies reaching 0.35–0.47 for GY. Reducing the number of markers to 10% of the whole set, and the TP size to 20% resulted in non-significant changes in accuracies. The QTLs identified were also incorporated in the models as fixed effects, showing significant accuracy gain for all four populations. Our results confirm that the prediction accuracy depends considerably on the relatedness between TP and VP, but not on the number of markers and size of TP used. Furthermore, feeding the model with information on markers associated with QTLs increased the overall accuracy.

Background: Fruit quality traits have a significant effect on consumer acceptance and subsequently on peach (Prunus persica (L.) Batsch) consumption. Determining the genetic bases of key fruit quality traits is essential for industry to improve fruit quality and increase consumption. A Bayesian approach embedded in the FlexQTL software increases the accuracy of QTL mapping and the probability of identifying new and validating known QTLs across a wide range of genetic backgrounds.Results: Phenotypic data of seven F1 low to medium chill full-sib families were collected over two years at two locations and genotyped using the 9K SNP Illumina array. One major QTL for fruit blush was found on linkage group 4 (LG4) at 40–46 cM that explained from 20 to 32% of the total phenotypic variance and showed three QTL alleles of different effects. For SSC, one QTL was mapped on LG5 at 60-72cM and explained from 17 to 39% of the phenotypic variance. A major QTL for TA that co-localized with the major locus for low-acid fruit (D-locus) was mapped at the proximal end of LG5 and explained 35 to 80% of the phenotypic variance. The new QTL for TA on the distal end of LG5 explained 14 to 22% of the phenotypic variance. This QTL co-localized with the QTL for SSC and affected TA only when the first QTL is homozygous for high acidity (epistasis). Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles.Conclusions: A multi-family-based QTL discovery approach enhanced the ability to discover a new TA QTL and validated other QTLs which were reported in previous studies. Identified predictive SNPs and their original sources will facilitate the selection of parents and/or seedlings that have desired haplotype alleles. Our findings will help peach breeders develop new predictive, DNA-based molecular marker tests for routine use in marker-assisted breeding (MAB).

The concentration degree of floret flowering date (CDFFD) is an important selection index for earliness breeding of peanut, while the genetic basis of CDFFD-related traits in peanut is poorly understood. The aim of this study was to develop a high-density genetic linkage map of cultivated peanut by combining SLAF-seq (specific locus amplified fragment sequencing) with SSR (simple sequence repeat) techniques and to identify QTL (quantitative trait loci) controlling CDFFD. A RIL population derived from a cross between Silihong and Jinonghei 3 was sequenced by the HiSeq 2500 platform. A total of 1262.02 M high-quality paired-end reads were obtained, and 1,328,966 SNPs (single nucleotide polymorphisms) were developed from parents and progenies, of which 754,499 SNPs were successfully encoded and genotyping. The resulting map consisted of 3326 genetic markers (2996 SNPs and 330 SSRs) distributed in 20 linkage groups (LGs), spanning 1822.83 cM with an average distance of 0.55 cM between adjacent markers. These assigned SNP markers had average sequencing depths of 16.94-, 15.66-, and 20.94-fold in the male parent, female parent, and their progenies, respectively. Based on phenotyping in 4 environments, 15 significant QTL for the CDFFD were identified. And one major QTL for days to accumulation of 25 flowers (DA25F), qDA25F6.2, located on chromosome 6 was obtained, with a phenotypic variance explanation (PVE) of 12.49%. This map exhibited higher resolution and accuracy, which will facilitate more QTL discovery for interested agronomic and quality traits in cultivated peanut.

The production of superior rice genotypes requires that a combination of desirable traits be introduced into the background of a high-yielding, commercially successful variety. This requires that multiple tolerance QTLs be combined by pyramiding. Single nucleotide polymorphism or SNP markers have become popular because the ease and efficiency of their use make them amenable to the type of pyramiding needed. The low cost of sequencing technologies has in turn made SNP discovery very easy and information on millions of these are available in public databases. SNP markers are being used for both genetic mapping and QTL analysis in populations derived from two contrasting parents. SNPs are also the marker of choice in the analysis of diverse germplasms for genome-wide association studies, or GWAS. For breeding purposes, rapid SNP detection is being done by the use of fluorescent tags, by methods referred to as Taqman and KASP. Efficient software for SNP discovery, mapping, QTL discovery, and GWAS has made breeding applications easier. The production of mapping populations with diverse sources for the trait of interest is also making identification of the correct allele easier for eventual incorporation into breeding lines.