Genome wide association mapping of agro-morphological and disease resistance traits in sugarcane

Abstract

The objectives of the study were to assess genome wide association study (GWAS) for sugarcane on a panel of 183 accessions and to evaluate the impact of population structure and family relatedness on QTL detection. The panel was genotyped with 3327 AFLP, DArT and SSR markers and phenotyped for 13 traits related to agro-morphology, sugar yield, bagasse content and disease resistances. Marker-trait associations were detected using (i) general linear models that took population structure into account with either a Q matrix from STRUCTURE software or principal components from a principal component analysis added as covariates, and (ii) mixed linear models that took into account both population structure and family relatedness estimated using a similarity matrix K* computed using Jaccard's coefficient. With general linear models analysis, test statistics were inflated in most cases, while mixed linear models analysis allowed the inflation of test statistics to be controlled in most cases. When only detections in which both population structure and family relatedness were correctly controlled were considered, only 11 markers were significantly associated with three out of the 13. Among these 11 markers, six were linked to the major resistance gene Bru1, which has already been identified. Our results confirm that the use of GWAS is feasible for sugarcane in spite of its complex polyploid genome but also underline the need to take into account family relatedness and not only population structure. The small number of significant associations detected suggests that a larger population and/or denser genotyping are required to increase the statistical power of association detection. (Résumé d'auteur

Identification of marker-trait associations for morphological descriptors and yield component traits in sugarcane

“…In case of the qualitative traits, it was observed that the MTAs were able to explain a satisfactorily higher proportion of the phenotypic trait variation as compared to that of quantitative traits. Similar finding were also reported in previous MTA studies with sugarcane (Debibakas et al 2014;Guoy et al 2015;Banerjee et al 2015). This could be due to the fact that the qualitativephenotypic traits were not quantitatively governed and hence had a reduced possibility of missing out rare variants thereby minimizing the chances of missing heritability.…”

“…Out of all the qualitative traits, the maximum six markers were identified for leaf curvature, while a single MTA was identified for few descriptors like bud cushion, bud size, growth crack, pithiness, and shape of bud (Table 5). On individual basis, a significant proportion of trait variation ranging from 16% (marker IISR_9_150 for colour of dewlap) to 40% (marker IISR_227_150 for root band width) was explained by the markers which is comparatively higher than the traits variation explained in previously reported marker-trait association studies with sugarcane (Debibakas et al 2014;Guoy et al 2015;Banerjee et al 2015). No MTA was detected for internode colour (unexposed).…”

“…In most crops, either family-based or linkage mapping has been used to tag the segregating trait(s) of interest and a number of examples of linkage mapping are available but in the past few decades, only a limited number of QTLs have been identified. Linkage disequilibrium (LD)-based mapping popularly called association mapping (AM) was initially adopted in human genetic studies but now it is being increasingly applied in crop plants including sugarcane mainly for identification of marker-trait associations (Wei et al 2006(Wei et al , 2010Debibakas et al 2014;Guoy et al 2015;Banerjee et al 2015;Singh et al 2016). In AM, the QTLs are identified by analyzing the degree of marker-trait associations that have been able to withstand segregation and reshuffling of the genetic elements in a diverse gene pool down the line of evolutionary recombination within the species (Gupta et al 2005).…”

“…In recent times, in sugarcane genomewide association mapping studies have been carried out for a number of traits (Wei et al 2006(Wei et al , 2010Debibakas et al 2014;Guoy et al 2015;Banerjee et al 2015;Singh et al 2016). However, such studies refer to a limited number of the most obvious agronomic traits that are predominantly quantitative, that resulted in identification of a multitude of small effect markers predominantly due to the complications of polyaneuploid genome.…”

Analysis of genes controlling biomass traits in the genome of sugarcane (Saccharum spp. hybrids)

“…Bundock et al (2012) conducted the solution-based hybridization (Agilent SureSelect) to capture the exome regions of sugarcane using sorghum and sugarcane coding probes, enriched the genome 10-11 fold, and detected 270,000-280,000 SNPs in each genotype of the material tested. At the moment, a great number of SNPs from a genome or haplotype can be generated by using high capacity genome sequencing instruments or high density oligonucleotide arrays (Zhu et al, 2008 Huang et al, 2010); resistance to sugarcane yellow leaf virus using 3,949 polymorphic markers (DArT and AFLP) on 189 genotypes (Debibakas et al, 2014); agro-morphological traits, sugar yield disease resistance and bagasse content using 3,327 DArT, AFLP and SSR markers on 183 genotypes (Gouy et al, 2015); and sucrose and yield contributing traits using 989 SSR markers on 108 genotypes (Banerjee et al, 2015). Employing the Affymetrix GeneChip Sugarcane Genome Array, Casu et al (2007) identified 119 transcripts associated with enzymes of cell-wall metabolism and development on sugarcane variety Q177.…”