Genome-wide association study and its applications in the non-model crop Sesamum indicum

Abstract

Background Sesame is a rare example of non-model and minor crop for which numerous genetic loci and candidate genes underlying features of interest have been disclosed at relatively high resolution. These progresses have been achieved thanks to the applications of the genome-wide association study (GWAS) approach. GWAS has benefited from the availability of high-quality genomes, re-sequencing data from thousands of genotypes, extensive transcriptome sequencing, development of haplotype map and web-based functional databases in sesame. Results In this paper, we reviewed the GWAS methods, the underlying statistical models and the applications for genetic discovery of important traits in sesame. A novel online database SiGeDiD (http://sigedid.ucad.sn/) has been developed to provide access to all genetic and genomic discoveries through GWAS in sesame. We also tested for the first time, applications of various new GWAS multi-locus models in sesame. Conclusions Collectively, this work portrays steps and provides guidelines for efficient GWAS implementation in sesame, a non-model crop.

Genome-wide association analysis uncovers the genetic architecture of tradeoff between flowering date and yield components in sesame

“…Likewise, a small number of associated loci for TSW were found in bi-parental sesame populations [ 3 , 60 ], which may suggest that this trait is under less complex genetic control or under the regulation of many small-effect genomic regions. The absence of significant genomic signals for TSW (as well as other traits in the current study) indicates that other approaches, such as a multi-locus GWAS model [ 61 ] or genomic prediction [ 62 ], may contribute to the understanding of the genetic basis. The positive phenotypic and genomic correlations between TSW and SYPP (0.37 and 0.44, respectively), and between SNPP and SYPP (0.86 and 0.93) on one side, and the absence of both correlations between TSW and SNPP (− 0.04 and 0.17, respectively) on the other side, open up the possibility to breed simultaneously for both traits and improve yield.…”

Identification of Chilling Accumulation-Associated Genes for Litchi Flowering by Transcriptome-Based Genome-Wide Association Studies

“…The GWAS is powerful for identifying candidate genes corresponding to natural variation of traits in field crops as well as in horticultural plants ( Su et al, 2019 ; Alseekh et al, 2021 ; Berhe et al, 2021 ). A larger population size has been used for association analysis in field crops, such as rice ( Huang et al, 2012 ), soybean ( Zhou et al, 2015 ), and maize ( Maldonado et al, 2019 ).…”

Discovering favorable genes, QTLs, and genotypes as a genetic resource for sesame (Sesamum indicum L.) improvement

“…Liang et al (2021) reported that four genomic regions were associated with five significant QTLs (qRSL1.2, qRSL1, qRSL7and qRSL12/qRSL12) based on the respective flanking markers in each drought-resistant gene QTL. Although 465 genes were identified in these four genomic regions, only 347 genes were functionally annotated; the remaining 118 genes were unknown Although more than 300 QTLs and more than 250 candidates have been identified in sesame, QTLs and important genes for chlorophyll yield, heat tolerance, waterlogging tolerance, and other traits are under investigation (Berhe et al, 2021). Different genotypes respond differently to waterlogging stress and the resistant genotypes remain more stable during waterlogging, whereas more sesame genotypes showed sensitivity to waterlogging stress (Wang et al, 2016a).…”