ABSTRACT
Sugarcane yellow leaf (SCYL), caused by the sugarcane yellow leaf virus (SCYLV) is a major disease affecting sugarcane, a leading sugar and energy crop. Despite damages caused by SCYLV, the genetic base of resistance to this virus remains largely unknown. Several methodologies have arisen to identify molecular markers associated with SCYLV resistance, which are crucial for marker-assisted selection and understanding response mechanisms to this virus. We investigated the genetic base of SCYLV resistance using dominant and codominant markers and genotypes of interest for sugarcane breeding. A sugarcane panel inoculated with SCYLV was analyzed for SCYL symptoms, and viral titer was estimated by RT-qPCR. This panel was genotyped with 662 dominant markers and 70,888 SNPs and indels with allele proportion information. We used polyploid-adapted genome-wide association analyses and machine-learning algorithms coupled with feature selection methods to establish marker-trait associations. While each approach identified unique marker sets associated with phenotypes, convergences were observed between them and demonstrated their complementarity. Lastly, we annotated these markers, identifying genes encoding emblematic participants in virus resistance mechanisms and previously unreported candidates involved in viral responses. Our approach could accelerate sugarcane breeding targeting SCYLV resistance and facilitate studies on biological processes leading to this trait.
Subject(s)
Disease Resistance/genetics , Genome, Plant , Genome-Wide Association Study , Luteoviridae/physiology , Plant Diseases/genetics , Plant Proteins/genetics , Saccharum/genetics , Chromosomes, Plant/genetics , Disease Resistance/immunology , Gene Expression Regulation, Plant , Genotype , Phylogeny , Plant Breeding , Plant Diseases/virology , Plant Leaves/genetics , Plant Leaves/growth & development , Plant Leaves/virology , Plant Proteins/metabolism , Quantitative Trait Loci , Saccharum/growth & development , Saccharum/virologyABSTRACT
O mapeamento genético é um passo necessário para entender a organização genômica e a relação entre genes e o fenótipo. Um dos principais problemas está em encontrar a ordem, o espaçamento correto dos marcadores em um mapa genético, assim como o número de indivíduos a compor uma população. Deste modo, o objetivo deste estudo foi avaliar o nível de saturação do genoma e o tamanho ideal de populações simulada duplo-haplóide para a construção de mapas de ligação mais confiáveis por meio de simulação computacional. Foram simulados genomas parentais e populações duplo-haplóide considerando marcadores moleculares do tipo dominante, espaçados de forma equidistante a 5, 10 e 20 cM. Os tamanhos das populações geradas foram de 100, 200, 300, 500, 800 e 1000 indivíduos, com dez grupos de ligação cada e 100 repetições por amostra. Procedeu-se a análise de todas as populações geradas obtendo um genoma analisado o qual foi comparado com o genoma simulado inicialmente. Observou-se que o tamanho ideal de populações duplo-haplóide para mapeamento genético foi de no mínimo 200, 500 e 1000 indivíduos para genomas saturados, medianamente saturados e com baixa saturação. Populações de mesmo tamanho tendem a produzir mapas com maior acurácia em níveis de saturação do genoma mais elevados.
Genetic mapping is a necessary step to understand the genomic organization and the relationship between genes and phenotypes. A major problem is to find the order, the correct spacing of the markers in a genetic map, and the number of individuals to compose a population. Thus, the objective of this study was to evaluate the saturation level of the genome and the optimal size of simulated double-haploid populations for the construction reliable linkage maps by means of computer simulation. Parental genomes and double-haploid populations were simulated considering dominant molecular markers, spaced equidistantly at 5, 10 and 20 cM. The sizes of the generated populations were 100, 200, 300, 500, 800 and 1000 individuals, with ten linking groups and 100 replicates per sample. It was proceeded the analysis of all generated population obtaining a genome which was compared with the first simulated genome. It was observed that the optimal size of double-haploid populations for genetic mapping has been at least 200, 500 and 1000 individuals for saturated genomes, medium unsaturated and low saturation. Populations of the same size tend to produce maps with greater accuracy in higher levels of genome saturation.
