Quantitative trait loci underlying root yield and starch content in anF₁derived cassava population (Manihot esculentaCrantz)

Abstract

SUMMARYCassava (Manihot esculenta Crantz) root yield measured as fresh weight (hereafter root yield) is declining in much of Asia and Africa. The current study aimed to identify quantitative trait loci (QTL) underlying both root and starch fresh weights in F1 cassava. Eight QTL were associated with root yield, underlying 12·9–40·0% of the phenotypic variation (PVE). Nine QTL were associated with starch content, underlying 11·3–27·3% of the PVE. Quantitative trait loci were identified from four different environments that encompassed two locations and 3 years. Consistent QTL for root yield, YLD5_R11 and YLD8_L09 on linkage group (LG) 16, were detected across years and locations. Quantitative trait loci for starch content, ST3_R09, ST6_R10 and ST7_R11 on LG 11, were found across 3 years. Co-localization of QTL for both traits with positive correlation was detected between YLD3_R10 and ST5_R10 on LG 9. Candidate genes within the QTL that were consistent across multiple environments were identified based on cassava genome sequences. Genes predicted to encode for glycosyl hydrolases, uridine 5’-diphospho-(UDP)-glucuronosyl transferases and UDP-glucosyl transferases were found among the 44 genes located within the region containing the QTL controlling starch content. Sixteen genes predicted to encode proteins that were possibly associated with root yield were identified. The QTL controlling root yield and starch content in the current study will be useful for molecular breeding of cassava through marker-assisted selection. The identification of candidate genes underlying both traits will be useful both as markers and for gene expression studies.

Genome-Wide Association Studies of 11 Agronomic Traits in Cassava (Manihot esculenta Crantz)

“…Furthermore, a new cassava haplotype map (HapMapII) was constructed, which contained 25.9 million SNPs and 1.9 million insertions/deletions, at the same time, population genetic analysis and deleterious mutations in asexual reproduction were studied in this paper ( Ramu et al, 2017 ; Ramu et al, unpublished). These genetic maps with high-density molecular markers are utilized in QTL mapping of several crucial traits, such as starch-related ( Pootakham et al, 2014 ; Sraphet et al, 2016 ), disease-resistant ( Kulakow et al, 2013 ; Rabbi et al, 2014 ; Boonchanawiwat et al, 2016 ), cold resistance-related ( Zou et al, 2017 ), drought-related ( Wang et al, 2017 ), and yield-related traits ( Balyejusa et al, 2007 ; Sraphet et al, 2016 ). All these genetic maps are based on parent genetic groups, and they exhibit disadvantages, such as low resolution and limited availability.…”

Identification of Quantitative Trait Loci (QTLs) Conferring Dry Matter Content and Starch Content in Cassava (<i>Manihot esculenta</i> Crantz)

“…SMA using simple linear regression identified seven marker alleles, which were found to be strongly associated with DMC viz., [25,29]. QTLs controlling the root yield traits were studied by Okogbenin and Fregene [42] and Okogbenin et al [44], root productivity and plant architecture [43] plant and first branch height, which were associated with root yield was reported by Boonchanawiwat et al, [6] and root yield and starch content [51]. In SMA, the association of single marker with that particular trait was revealed but the exact QTL positions, its effects and gene interaction were undetermined.…”

Technological Innovations for Improving Cassava Production in Sub-Saharan Africa

“…Among these, six QTLs were location-specific, and three QTLs were detected across three environments. The percentage of phenotypic variance explained by the QTLs ranged from 11.3 to 27.3% of the phenotypic variation ( Sraphet et al, 2017 ; Table 2 ). Using GWAS, 10 SNP associated with waxy phenotypes were identified on chromosome 2 that co-located in genic regions that included five known genes and five genes of unknown function ( do Carmo et al, 2020 ; Table 3 ).…”

“…Eight QTLs associated with fresh root yield were identified on seven linkage groups (LG1, 2, 6, 9,12,13, and 16) from a bi-parental mapping population, of which two QTLs on linkage group 16 were found across two environments. These QTLs explained 12.9 to 40% of phenotypic variation ( Sraphet et al, 2017 ; Table 2 ). Zhang et al (2018) reported 36 loci related to 11 agronomic traits, including leaf characteristics, morphological characteristics, yield components, and root quality that were identified by GWAS analyses.…”