Genetic analysis of resistance to bean leaf crumple virus identifies a candidate LRR-RLK gene.

Bean leaf crumple virus (BLCrV) is a novel begomovirus (family Geminiviridae, genus Begomovirus) infecting common bean (Phaseolus vulgaris L.), threatening bean production in Latin America. Genetic resistance is required to ensure yield stability and reduce the use of insecticides, yet the available resistance sources are limited. In this study, three common bean populations containing a total of 558 genotypes were evaluated in different yield and BLCrV resistance trials under natural infection in the field. A genome-wide association study identified the locus BLC7.1 on chromosome Pv07 at 3.31 Mbp, explaining 8 to 16% of the phenotypic variation for BLCrV resistance. In comparison, whole-genome regression models explained 51 to 78% of the variation and identified the same region on Pv07 to confer resistance. The most significantly associated markers were located within the gene model Phvul.007G040400, which encodes a leucine-rich repeat receptor-like kinase subfamily III member and is likely to be involved in the innate immune response against the virus. The allelic diversity within this gene revealed five different haplotype groups, one of which was significantly associated with BLCrV resistance. As the same genome region was previously reported to be associated with resistance against other geminiviruses affecting common bean, our study highlights the role of previous breeding efforts for virus resistance in the accumulation of positive alleles against newly emerging viruses. In addition, we provide novel diagnostic single-nucleotide polymorphism markers for marker-assisted selection to exploit BLC7.1 for breeding against geminivirus diseases in one of the most important food crops worldwide.

Association mapping of a locus that confers southern stem canker resistance in soybean and SNP marker development.

BACKGROUND: Southern stem canker (SSC), caused by Diaporthe aspalathi (E. Jansen, Castl. & Crous), is an important soybean disease that has been responsible for severe losses in the past. The main strategy for controlling this fungus involves the introgression of resistance genes. Thus far, five main loci have been associated with resistance to SSC. However, there is a lack of information about useful allelic variation at these loci. In this work, a genome-wide association study (GWAS) was performed to identify allelic variation associated with resistance against Diaporthe aspalathi and to provide molecular markers that will be useful in breeding programs. RESULTS: We characterized the response to SSC infection in a panel of 295 accessions from different regions of the world, including important Brazilian elite cultivars. Using a GBS approach, the panel was genotyped, and we identified marker loci associated with Diaporthe aspalathi resistance through GWAS. We identified 19 SNPs associated with southern stem canker resistance, all on chromosome 14. The peak SNP showed an extremely high degree of association (p-value = 6.35E-27) and explained a large amount of the observed phenotypic variance (R2 = 70%). This strongly suggests that a single major gene is responsible for resistance to D. aspalathi in most of the lines constituting this panel. In resequenced soybean materials, we identified other SNPs in the region identified through GWAS in the same LD block that clearly differentiate resistant and susceptible accessions. The peak SNP was selected and used to develop a cost-effective molecular marker assay, which was validated in a subset of the initial panel. In an accuracy test, this SNP assay demonstrated 98% selection efficiency. CONCLUSIONS: Our results suggest relevance of this locus to SSC resistance in soybean cultivars and accessions from different countries, and the SNP marker assay developed in this study can be directly applied in MAS studies in breeding programs to select materials that are resistant against this pathogen and support its introgression.

Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding.

Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding.

Correlation between gene expression profiles in muscle and live weight in dzhalginsky merino sheep / Correlación entre los perfiles de expresión génica en músculo y peso vivo de ovejas Dzhalginsky Merino / Correlação entre perfis de expressão gênica no músculo e peso vivo de ovinos Dzhalginsky Merino

Summary Background: marker assisted selection methods of sheep require the identification of genes that positively and negatively affect meat quality. Genes with high expression levels could have the greatest impact on growth and structure of muscle fibers. Objective: this study evaluated the expression of genes in the loin muscle of Dzhalginsky Merino sheep. Methods: reverse transcription-quantitative real-time PCR (RT-qPCR) was used to investigate the expression of 48 genes in the loin muscle of Dzhalginsky Merino sheep bred in Russia. Results: genes GAPDH, PYGM, CAST, ATP5G1, CAPN3, SOD1, VEGFA, CALM2, YWHAZ, ASIP,MYOD1, CAPN1, GHR, OXTR, BEGAIN, SLC2A3, and SS18L2 showed the highest expression. The group of genes with a medium level of expression included ATOX1, BAMBI, TLR6, IGF2, FOS, FST, GGTA2P, C-MET, FGF5, ACVR2A, CAPN2, GH, DGAT1, and IGF1. Low levels of expression were identified for genes ABCG2, SPP2, PYGL, PPARG2, TGFB1, CXCR4, MSTN, CYP2J, LEPR, CDKN1A, IGFBP4, and SERT. Trace expression was detected in genes SST, TSHR, GDF9, FGF7 and BMP15. Significant correlation between expression level and live weight was observed for most of the investigated genes. Conclusion: our results demonstrate the feasibility of using these newly identified candidate genes as genetic markers in sheep.

Resumen Antecedentes: los métodos de selección asistida de ovejas a través de marcadores requieren la identificación de los genes que afectan positiva o negativamente la calidad de la carne. Los genes con niveles más altos de expresión podrían tener mayor impacto en el crecimiento y estructura de las fibras musculares. Objetivo: evaluar la expresión de los genes en el músculo del lomo de carneros de la raza Dzhalginsky Merino. Métodos: se utilizó RT-PCR cuantitativa en tiempo real (RT-qPCR) para investigar la expresión de 48 genes en el músculo del lomo de ovejos raza Merino Dzhalginsky criados en Rusia. Resultados: los genes GAPDH, PYGM, CAST, ATP5G1, CAPN3, SOD1, VEGFa, CALM2, YWHAZ, ASIP, MYOD1, CAPN1, GHR, OXTR, BEGAIN,SLC2A3 y SS18L2 mostraron la más alta expresión. El grupo de genes con un nivel medio de expresión incluyó ATOX1, BAMBI, TLR6, IGF2, FOS, FST, GGTA2P, C-MET, FGF5, ACVR2A, CAPN2, GH, DGAT1 y IGF1. Se identificaron bajos niveles de expresión en los genes ABCG2, SPP2, PYGL, PPARG2, TGFB1, CXCR4, NTCR, CYP2J, LEPR, CDKN1A, IGFBP4 y SERT. Expresión traza fue detectada en los genes SST, TSHR, GDF9, FGF7 y BMP15. Para la mayoría de los genes investigados hubo una correlación significativa entre el nivel de expresión y el peso vivo de los carneros. Conclusión: los resultados demuestran la factibilidad del uso de estos genes candidatos identificados recientemente como marcadores genéticos en ovejas.

Resumo Antecedentes: métodos que utilizam marcadores de seleção assistida em ovelhas exigem a identificação de novos genes que afetam a qualidade da carne. Genes com maiores níveis de expressão podem ter maior impacto sobre o crescimento e a estrutura das fibras músculares. Objetivo: avaliar a expressão de genes no músculo lombar de ovinos da raça Merino Dzhalginsky. Métodos: foi utilizada a reação em cadeia da polimerase-transcriptase reversa e em tempo real (RT-qPCR) para investigar a expressão de 48 genes em músculo do lombo da raça de ovinos Merino Dzhalginsky, que foram criados na Rússia. Resultados: os genes GAPDH, PYGM, CAST, ATP5G1, CAPN3, SOD1, VEGFA, CALM2, YWHAZ, ASIP, MYOD1, CAPN1, GHR,OXTR, BEGAIN, SLC2A3 e SS18L2 apresentaram a maior expressão. O grupo de genes com um nível médio de expressão incluíram ATOX1, BAMBI, TLR6, IGF2, FOS, FST, GGTA2P, C-MET, FGF5, ACVR2A, CAPN2,GH, DGAT1 e IGF1. Foram identificados baixos níveis de expressão para os genes ABCG2, SPP2, PYGL, PPARG2, TGFB1, CXCR4, MSTN, CYP2J, LEPR, CDKN1A, IGFBP4 e SERT. Foi detectado rastreamento de expressão nos genes SST, TSHR, GDF9, FGF7 e BMP15. Para a maioria dos genes investigados, houve uma correlação significativa entre o nível de expressão e o peso vivo dos carneiros Dzhalginsky Merino. Conclusão: os resultados demonstram a viabilidade do uso desses genes candidatos recentemente identificados como marcadores genéticos no desenvolvimento de novas raças de ovinos.