A genome‐wide association study for partial resistance to southern corn rust in tropical maize

Abstract

Southern corn rust (SCR) is a fungal disease found on corn in several countries worldwide. In Brazil, the disease can result in productivity losses of 65%, especially in areas with a history of the disease. In this study, the genetic architecture and identification of genomic regions associated with SCR resistance was investigated by performing a genome‐wide association study. Genotyping‐by‐sequencing was performed to carry out the association between single nucleotide polymorphism (SNP) markers and phenotypic data from two environments on a panel of 164 maize inbred lines. Eight SNPs were identified as significant for SCR resistance. These SNPs were colocalized with QTL regions, some of which underlie candidate resistance genes with functions that play an important role in the stress response during pathogen recognition. These candidate genes, involved in plant defense pathways, could be associated with partial resistance to SCR and provide a partial comprehensive insight into the genetic architecture of this trait. After validation of the SNPs, they will be useful for marker–assisted selection and for a better understanding of maize resistance to SCR.

A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize

“…These findings agree with a previous study by Kaur and Kaur (2013), who reported that the hierarchical algorithm provides better results and higher quality than KM. Additionally, the results of this study were consistent with the findings of previous research, indicating that dent corn and popcorn lines from Brazilian germplasms are grouped into two genetically differentiated clusters (Coan et al, 2018;Camacho et al, 2019;Maldonado et al, 2019;Senhorinho et al, 2019). In this regard, the results of this study showed that the DeepAEbased data preprocessing had better accuracy values than those achieved by PCA.…”

Southern corn rust caused by Puccinia polysora Underw: a review

“…In addition, Deng et al (2020) reported a major QTL named Ascr4.01 on chromosome 4, which can explain 48% to 65% of the total phenotypic variation, based on a recombinant inbred line derived from a cross between CIMBL83 and Lx9801. QTLs with minor effects or partial resistance to SCR have been detected on all maize chromosomes, which provide more valuable source for SCR resistance (Brewbaker et al 2011;Wanlayaporn et al 2013;Zhou et al 2018;de Souza Camacho et al 2019;Chen et al 2019;Deng et al 2019Deng et al , 2020Lu et al 2020).…”

Quantitative trait loci (QTL) mapping of resistance to coffee berry disease (Colletotrichum kahawae Waller Bridge) in Coffea arabica L. variety Rume Sudan

“…Evaluation of population structure on the relatedness of the 106 F 2 genotypes and their parents using the PCA revealed that a total of 20 F 2 genotypes were weakly related with the rest of the genotypes and were therefore excluded from QTL analysis. The analysis of population structure reduces type I error during association mapping (Camacho et al, 2019). The mapping population was confirmed as a suitable population for genetic mapping of a dominant gene-based and Chi-Square test based on 3:1 Mendelian ratio of segregation (Gimase et al, 2019).…”

“…This type of enzyme was chosen due to its partial sensitivity to DNA methylation, thus avoiding repetitive element regions and frequency of DNA cutting (Elshire et al, 2011;Brito et al, 2017). After digestion, the DNA was ligated to adapters and then combined into pools of 96 samples and amplified with primers compatible with the adapter sequences (Camacho et al, 2019). After polymerase chain reaction (PCR), the pooled products were purified and quantified for sequencing on the Illumina HiSeq 2500 flow cell (Illumina platform) (Elshire et al, 2011;Akohoue et al, 2020).…”