Constructing high-density genetic maps for polypoid sugarcane (Saccharum spp.) and identifying quantitative trait loci controlling brown rust resistance

Authors: YANG, XIPING - University Of Florida; Sood, Sushma; GLYNN, NEIL - Syngenta; Islam, Md; COMSTOCK, JACK - Former ARS Employee; WANG, JIANPING - University Of Florida

Submitted to: Molecular Breeding
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/22/2017
Publication Date: 9/2/2017
Citation: Yang, X., Sood, S.G., Glynn, N., Islam, M.S., Comstock, J.C., Wang, J. 2017. Constructing high-density genetic maps for polypoid sugarcane (Saccharum spp.) and identifying quantitative trait loci controlling brown rust resistance. Molecular Breeding. 37:116. doi:10.1007/s11032-017-0716-7.
DOI: https://doi.org/10.1007/s11032-017-0716-7

Interpretive Summary: Sugarcane (Saccharum spp.) is an important economic crop for producing edible sugar and bioethanol. Brown rust had long been a major disease impacting sugarcane production world widely. Resistance resource and markers linked to the resistance are valuable tools for disease resistance improvement. An F1 segregating population derived from a cross between two hybrid sugarcane clones, brown rust susceptible CP95-1039 and brown rust resistant CP88-1762, were genotyped using genotyping by sequencing approach and also phenotyped in a replicated field trial. Single nucleotide polymorphism (SNP) and presence/absence markers were called with seven different pipelines to maximize reliable marker identification. High density maps were constructed for both parental clones with a total map length of 5,718.2cM, and a marker density of one marker per 2.0cM for CP95-10392, and a total map length of 5,782.0cM, and one marker per 2.2cM for CP88-1762. Among the seven SNP callers, Tassel and GATK performed better than other callers in single dose (SD) SNPs detection and contribution to genetic maps. Two major quantitative trait loci (QTL) controlling brown rust resistance were identified, which can explain 24 and 30% of the phenotypic variation, respectively. The genetic maps generated here will improve our understanding of sugarcane complex genome structure and discovery of underlying sequence variations controlling agronomic traits. The putative QTL controlling brown rust resistance can effectively be utilized in sugarcane breeding programs to expedite the selection process of brown rust resistance after validation.

Technical Abstract: Sugarcane (Saccharum spp.) is an important economic crop for producing edible sugar and bioethanol. Brown rust had long been a major disease impacting sugarcane production world widely. Resistance resource and markers linked to the resistance are valuable tools for disease resistance improvement. An F1 segregating population derived from a cross between two hybrid sugarcane clones, brown rust susceptible CP95-1039 and brown rust resistant CP88-1762, were genotyped using genotyping by sequencing approach and also phenotyped in a replicated field trial. Single nucleotide polymorphism (SNP) and presence/absence markers were called with seven different pipelines to maximize reliable marker identification. High density maps were constructed for both parental clones with a total map length of 5,718.2cM, and a marker density of one marker per 2.0cM for CP95-10392, and a total map length of 5,782.0cM, and one marker per 2.2cM for CP88-1762. Among the seven SNP callers, Tassel and GATK performed better than other callers in single dose (SD) SNPs detection and contribution to genetic maps. Two major quantitative trait loci (QTL) controlling brown rust resistance were identified, which can explain 24 and 30% of the phenotypic variation, respectively. The genetic maps generated here will improve our understanding of sugarcane complex genome structure and discovery of underlying sequence variations controlling agronomic traits. The putative QTL controlling brown rust resistance can effectively be utilized in sugarcane breeding programs to expedite the selection process of brown rust resistance after validation.