Genome-wide association study identifies novel genomic regions associated with yield-related traits in cassava (Manihot esculenta Crantz)

Genome-wide association study identifies novel genomic regions associated with yield-related traits in cassava (Manihot esculenta Crantz) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide association study identifies novel genomic regions associated with yield-related traits in cassava (Manihot esculenta Crantz) Bright Gyamfi Adu, Richard Akromah, Stephen Amoah, Andrzej Kilian, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8007567/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Cassava ( Manihot esculenta Crantz) is a vital calorie source in the tropics due to its adaptation to marginal agroecological conditions. Cassava’s dual role as a food security and industrial crop has stimulated extensive research into increasing yield. Genetic gain can be accelerated through investment in marker discovery and identification of genetic loci controlling important traits for use in breeding programs. Using the Diversity Array Technology genotyping-by-sequencing (DArTseq) platform, a population of 94 cassava accessions, comprising local landraces from Ghana and exotic lines from the International Institute of Tropical Agriculture (IITA), was genotyped with more than 30,000 SNP markers. A genome-wide association study (GWAS) was carried out for five yield-related traits, namely number of storage roots (NSR), mean storage root weight (MRW), root weight per plant (RW), harvest index (HI), and dry matter content (DMC) to survey the genome for the putative loci associated with these traits. A total of 55 significant marker-trait associations were detected, and haplotype analysis showed that favorable alleles at each locus had stronger genetic effects on yield-related traits, leading to the prediction of candidate genes. The identification of Manes.07G112000 (TPL-binding domain protein) and Manes.02G156800 (F-box domain) as candidate genes associated with MRW and DMC, respectively, highlights their potential roles in jasmonate signaling pathways. This connection suggests the existence of a defense-growth trade-off influencing yield traits in cassava. These findings lay a valuable foundation for the development of molecular markers to assist in breeding programs aimed at enhancing yield potential and overall productivity in cassava cultivars. Manihot esculenta Crantz single-nucleotide polymorphism (SNP) population structure genome-wide association analysis (GWAS) haplotypes Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction With the global population continuously increasing and climate changing rapidly, food insecurity poses a major challenge worldwide. Cassava ( Manihot esculenta Crantz) is a starchy root crop that is mostly vegetatively propagated and serves as a staple food for over 800 million people across tropical regions, particularly in sub-Saharan Africa, Southeast Asia, and Latin America. It is also used as an industrial raw material for starch and alcohol production (Jarvis et al. 2012 ; Parmar et al. 2017 ). Global cassava production has nearly doubled over the past two decades, from 175.8 million tons in 2000 to 333.8 million tons in 2023 partly due to the introduction of new improved varieties with superior adaptive features to thrive in marginal environments, tolerance to drought and ability to thrive in poor soils which makes it an important food security crop (Jarvis et al. 2012 ; FAO, 2025). In addition, its ability to remain in the ground post-harvest makes cassava uniquely suited to smallholder farming systems, where it contributes significantly to both food and income security (Burns et al. 2010 ). Over the past four decades, cassava breeding programs in Africa, Asia, and Latin America have developed improved varieties with resistance to biotic and abiotic stresses, as well as higher yield and starch content (Kawano, 2003 ; Okechukwu and Dixon, 2008 ). Although phenotype-based recurrent selection has achieved some success, the rate of genetic gain remains low due to several biological constraints, including asynchronous flowering, low seed set per cross, long growth cycles of 12–24 months, and low multiplication rates of planting materials (Ceballos et al. 2012 ). These challenges limit the breeding program’s ability to respond rapidly to changing human needs under volatile climatic and environmental conditions. The adoption of modern breeding tools such as marker-assisted recurrent selection and genomic selection has helped accelerate genetic gains by shortening selection cycles and increasing selection intensity, particularly in the early stages of cassava breeding (Ferguson et al. 2012 ; Ceballos et al. 2015 ; García-Ruiz et al. 2016; Bredeson et al. 2016 ). However, integration of molecular markers into cassava breeding pipelines requires investment in discovery research to identify major-effect loci as targets of selection. Advances in next-generation sequencing technologies and the completion of the cassava genome draft (Bredeson et al. 2016 ) have made it cost-effective to generate genome-wide marker data through biparental quantitative trait loci (QTL) analysis or GWAS in large natural populations (Ferguson et al. 2012 ). Combined with phenotypic data, this enables the identification and mapping of agriculturally important genes and QTLs at the whole-genome level (Varshney et al. 2014 ). GWAS has been successfully applied in cassava to map loci associated with biotic stress resistance, nutritional traits, and morphological characteristics. Previous studies identified genomic regions linked to cassava mosaic disease (CMD) resistance (Rabbi et al. 2014 ; Wolfe et al. 2016 ), cassava green mite (CGM) resistance (Ezenwaka et al. 2018 ), and root quality traits such as dry matter content and carotenoid concentration (Esuma et al. 2016 ; Ikeogu et al. 2019 ). These efforts have laid the foundation for integrating genomic tools into cassava improvement pipelines. For example, Rabbi et al. (2020) mapped genomic regions conferring resistance to CMD using African cassava germplasm through biparental QTL mapping and GWAS. Further analysis of SNPs (S12_7926132 and S12_7926163 on chromosome 12 linked to the major CMD2 locus, and S14_4626854 on chromosome 14) using Kompetitive Allele-Specific Polymerase Chain Reaction (KASP) assays showed that genotypes carrying at least one resistant allele at the CMD2 locus had significantly higher yields (Ige et al. 2021 ). Beyond disease resistance, genomic regions associated with root quality traits such as mealiness, fiber content, adhesiveness, taste, aroma, color, and firmness after boiling have been reported, revealing candidate genes linked to carbohydrate metabolism, cell adhesion, secondary cell wall formation, and proteolytic activity. For nutritional improvement, SNPs in Manes.01G124200 (phytoene synthase) have been used to select cassava lines with higher carotenoid content. While such studies have improved understanding of disease resistance and root quality traits, there is a pressing need to prioritize root and yield-related traits to guide breeding strategies for enhancing cassava productivity in Africa, where the crop forms a major part of daily calorie intake with limited export value (Otekunrin, 2024 ). Developing high-yielding cultivars is a central goal of most breeding programs; thus, elucidating the genetic architecture of yield is critical to improving this complex trait. Key yield components such as storage root weight, root number, root diameter, root length, and dry matter content directly influence total yield and are essential for developing farmer-preferred varieties with improved market value (Adjebeng-Danquah et al. 2020 ; Adu et al. 2020 ). These traits often display significant genotype-by-environment interactions and moderate heritability, which makes improvement through conventional phenotypic selection challenging (Sinclair, 1998 ; Adjebeng-Danquah et al. 2020 ). The integration of GWAS with high-throughput genotyping technologies can help dissect the genetic basis of these traits by exploiting natural variation to identify candidate genes and favorable alleles for marker-assisted breeding (MAS) (Elshire et al. 2011 ; Hamblin and Rabbi, 2014). SNPs associated with number of storage roots, storage root weight, dry matter content, and starch content have been reported using 158 cassava accessions by Zhang et al. ( 2018 ). Similarly, Mbe et al. ( 2024 ) identified 52 SNPs associated with nitrogen-use efficiency and yield-related traits, including stay-green ability, chlorophyll content, and fresh and dry root yield. Continued discovery of yield-related genes and loci will facilitate molecular breeding for cassava yield improvement. Landraces, in particular, serve as valuable reservoirs of favorable alleles for specific traits and remain an important resource for modern breeding. By integrating high-density SNP genotyping data with multi-year phenotypic evaluations, the present study aims to identify genomic regions and SNPs associated with natural variation in local and exotic cassava accessions for five root- and yield-related traits, and to suggest candidate genes for molecular breeding. These findings will promote more efficient use of molecular markers in cassava improvement. Materials and methods Plant material and field trials The panel for GWAS consisted of 94 cassava accessions with 26 breeding lines from IITA, 22 improved or released varieties from Ghana and 46 landraces were obtained through the Council for Scientific and Industrial Research (CSIR-Ghana). These accessions showed high variability in levels of branching of the main stem and color of root pulp (storage root flesh color) ranging from white to yellow (Supplementary Table S1 ). The field work study was carried out at the research field of the CSIR-Plant Genetic Resources Research Institute at Bunso in the Eastern Region of Ghana in 2019 and 2020 years. Bunso (lat. 06° 46′ N, long. 01 01′ W, 149 m above sea level) lies in the semi-deciduous forest zone of Ghana with the soil type Nta series (FAO: Gleyic Arensol) (Jones et al. 2013). The experiment was laid in a randomized complete block design with three replications. The land was ploughed and harrowed before planting. Cassava cuttings measuring about 30 cm were planted horizontally in each hole. Each accession was planted in a single row consisting of five stands with an intra and inter row spacing 1 m × 1 m to give a plot size of 5m 2 . No fertilizer was applied but weeding was done twice before harvesting (12 MAP). Trait measurement The accessions were phenotyped for five yield-related traits including number of storage roots per plant (NSR), mean storage root weight (MRW), root weight per plant (RW), harvest index (HI) and root dry matter content (DMC) as indicated in the standard cassava descriptor (Fukuda et al. 2010 ). The three middle plants were harvested and measured for the selected traits after maturity (Supplementary Table S1 ). For DMC measurement, triplicate of 100 grams of root pulp (mid-section) were dried at 80 0 C for 48 hours after which the dry weight taken and expressed as a percentage of the original fresh weight. Genomic DNA extraction and DArTseq genotyping Genomic DNA extraction and DArTseq genotyping DNA samples were extracted from the youngest fully expanded leaves of each of the 94 cassava genotypes two weeks after planting using the DArT DNA extraction protocol (Kilian et al. 2012 ). The concentration of extracted DNA was checked using the Nanodrop 2000c spectrophotometer (NanoDrop Lite, LT2878, Thermo Scientific, USA). DNA samples were diluted between 50–100 ng/µl, packaged and shipped to Diversity Array Technology corporation (Canberra, Australia) for DArTSeq genotyping. Genotyping-by-Sequencing, and SNP calling were performed for each sample using the DArTseq genotyping platform ( https://www.diversityarrays.com/technology-and-resources/dartreseq/ ). The sequences of the genomic representations were aligned to cassava reference genome v6.1, resulting in the selection of 31865 raw SNP markers, however, SNPs with over 5% missing data and/or minor allele frequency below 5% were removed. After filtering, a total of 24790 SNP markers were obtained and used for downstream analyses. Population structure analysis Population structure was estimated using the Bayesian model of the Markov chain Monte Carlo (MCMC) implemented in STRUCTURE v.2.3.4 (Pritchard et al. 2010) based on 24790 SNP points. For each run, the initial burn-in period was set to 20,000 followed by 30,000 MCMC (Markov chain Monte Carlo) replications, with no prior information on the origin of individuals. Five iterations were performed for each number of hypothetical populations (k) tested from 1 to 10. The STRUCTURE results for the assumed population (1–10) were subsequently analysed online using the STRUCTURESELECTOR (Li et al. 2018) to identify a distinct peak in the change of likelihood (ΔK) at the true value of K. Marker–Trait Association Mapping GWAS analysis was performed using a unified mixed-model approach implemented in the “ rrBLUP ” package in R version 4.5 (Endelman 2011 ). The model includes a random effect to account for population structure and relatedness, which is critical for reducing false positives, and a fixed effect for batches to ensure accurate detection of genetic associations by integrating data from two experimental datasets to mitigate batch effects (Kang et al. 2008 ). We established population parameters previously determined (P3D) as FALSE to allow the model to reestimate variance components for each marker, providing a more accurate estimation of marker-trait associations. Significant SNPs linked to traits were identified, with peaks surpassing a threshold of -log10 (p-value) ≥ 4. Manhattan plots for association mapping were visualized using the “ qqman ” package in R (Turner 2018 ). Haplotype analysis and candidate gene identification Haplotype and candidate gene analysis were performed using significant SNPs selected from the GWAS results. SNPs with the strongest association with the target signal were used to perform the haplotype analysis and the phenotypes of the accessions with different haplotypes were compared. For the candidate gene analysis, mapping of these selected SNP markers onto genes was done using the SNP location and gene description from the M.esculenta_ 305_v6.1.gene.gff3 file of the cassava reference genome available in Phytozome_v14 (Goodstein et al. 2012 ), and the intersect function from bedtools (Quinlan and Hall 2010 ). Gene ontology annotation was carried out on the plant Ensembl website ( https://plants.ensembl.org/index.html ) and UniProtKB tool ( https://www.uniprot.org/ ). Statistical analysis All phenotypic data were subjected to various statistical analyses using R Statistical Software v4.5. The differences in the different years for each trait were estimated from the significance of the mean square for years from the analysis of variance (ANOVA) while the effect of alleles at significant SNPs was assessed by comparing phenotypic data for the haplotype groups (p < 0.05). Using the “ lme4 ” package (De Boeck et al. 2011 ), we considered the genotype as a random effect to obtain the variance components of all the traits while considering years as environment (Piepho 1998 ). A linear mixed model was used to obtain the best linear unbiased predictions (BLUPs) for each genotype. The genetic source of phenotypic variance was indicated as broad sense heritability (H²) according to Falconer and Mackay ( 1996 ). $$\:{\text{H}}^{2}=\frac{{{\sigma\:}}^{2}g}{{{{\sigma\:}}^{2}\text{g}+{\sigma\:}}^{2}y+{{\sigma\:}}^{2}gy+{{\sigma\:}}^{2}e}$$ Where σ 2 g​ is genotype variance, σ 2 y is variance across years, σ 2 gy is the genotype by year interaction variance and σ 2 e​ is error variance. Pearson’s correlation analysis between different traits was performed using the “ corrplot ” package (Wei et al. 2017 ) while data were visualized in R using the “ ggplot2 ” package (Wickham 2016 ). Results Phenotypic variations and relationships among measured traits Understanding the variation that exists in traits and the underlying structure of the population is necessary for GWAS and other trait-marker association studies. Here we characterize five yield-related traits in cassava; number of storage root per plant (NSR), mean storage root weight (MRW), storage root weight per plant (RW), harvest index (HI) and storage root dry matter content (DMC). Summary statistics showed significant variation in these traits in the association panel based on the average performance of each genotype, which is useful for deciphering their genetic architectures (Table 1 and Fig. 1 A). Except for DMC, all measured exhibited large coefficient of variation (> 30) an indication of broad phenotypic variability within the association panel. The data showed significant genetic variance and environmental variance for all traits except NSR, while genotype by environment interaction was significant for RW (Table 1 ). Analysis of the phenotypic classes of the panel showed that all measured traits followed a normal distribution though DMC were slightly skewed towards the tail (Fig. 1 A) Broad sense (H 2 ) estimates ranged from 59.2% (RW) to 76.1% (DMC) suggesting considerable potential for improvement of the phenotypes across all traits through targeted selection (Table 1 ). Pearson’s correlation (r 2 ) between traits estimates showed that all traits had positive correlations with RW, with that of NSR and MRW exceeding 0.60 (p < 0.001) (Fig. 1 B). MRW was also significantly positively correlated with HI and DMC making it an important trait to improve field and economic yield of cassava. Table 1 Summary statistics and heritability for yield-related traits assessed among the cassava genotypes Traits min max mean std CV MSg MSgy σ 2 g σ 2 p H 2 NSR 1 13 5.52 1.99 35.94 7.30 *** 2.69ns 1.140 1.812 0.628 MRW (kg) 0.133 1.53 0.54 0.21 39.22 0.09 *** 0.03ns 0.015 0.023 0.656 RW (kg) 0.21 8.11 3.05 1.50 49.19 4.56 *** 1.86 ** 0.674 1.140 0.592 HI (%) 7.22 80.9 46.10 13.44 31.09 0.06 *** 0.02ns 0.010 0.014 0.714 DMC (%) 19.91 44.35 37.02 4.34 15.06 58.36 *** 12.94ns 11.102 14.589 0.761 NSR = number of storage roots, MRW = mean storage root weight, RW = root weight per plant, HI = harvest index, and DMC = storage root dry matter content min = minimum, max = maximum, Std = standard deviation, CV = coefficient of variation, MSg = mean square of genotypes, MSgy = mean square of genotype x year interaction, σ 2 g= genotypic variance, σ 2 p= phenotypic variance, H 2 = broad sense heritability. Distribution of SNP markers and population structure analysis A total of 31865 SNPs distributed across the 18 chromosomes were generated through DArT sequencing platform ranging from 1251 on chromosome12 to 3697 on chromosome 1 (Fig. 2 A). Considering the exclusion of SNPs with less than 95% call rate and/or less than 5% minor allele frequency (MAF), 24790 SNP markers that passed the quality test were used to estimate the genetic structure of the cassava population using the Bayesian clustering model implemented in the computer software STRUCTURE and GWAS. Population structure analysis is required in genome-wide association to avoid false-positive associations. The population stratification inferred by assuming admixture model-based clustering method indicated the presence of four (K = 4) subgroups (Fig. 2 B). Though 65% of the accessions showed admixture (with ≥ 1% of ancestry from any of the subgroups) composition, assignment of accessions into subgroups ranged from 10 in subgroup2 (green) comprising of IITA lines to 36 in subgroup4 (orange) which were mainly landraces from Ghana. The expected heterozygosity was used to compute the diversity between individuals in each subgroup. Subgroup3 (blue-black) and subgroup4 (orange) showed a similar average genetic diversity (0.35) between individuals within each group defined by the expected heterozygosity indicating the diverse nature of the groups (Supplementary Table S1 ). Again, the highest difference was observed between subgroups occurred between subgroup1 and subgroup2 (Supplementary Table S1 ) indicating large genetic base of the study population and present opportunity to detect both favorable and unfavorable alleles which enhances the power of GWAS to detect true associations. Marker-trait association mapping and haplotype analysis To identify genomic regions and potential SNP markers associated with variation in the five yield related traits in cassava, GWAS analysis for the traits was performed. To mitigate the batch effect that could arise from the two experimental sets, a unified mixed-model approach was employed for GWAS. The resulting Manhattan plot displayed multiple peaks across various chromosomes for all the traits except for RW (Fig. 3 ). A total of 55 significant SNPs associated with yield-related traits were detected at a threshold of − log(P) = 4 (Supplementary Table S1 ) comprising of 20, 5, 1, 12, and 17 for NSR, MRW, RW, HI and DMC respectively. Genome-wide association study for variation in NSR revealed only genomic regions on chromosome 5 (1.66–5.45 Mb) to be significantly associated with the trait (Fig. 3 A, Table 2 ). A total of 20 markers were significantly associated with NSR on chromosome 5. First (SNP_2747056, p = 8.73E-08) and second (SNP_2749412, 2.31E-07) peak SNPs were only 2.3 Kb apart and averagely explained about 21% (R 2 ) of the trait variation. We observed in both planting seasons that, accessions homozygote for the top SNP allele (SNP_2747056, TT) had higher number of storage roots per plant (> 9) followed by the heterozygote (GT) and the least in the lines homozygote for reference allele (GG) (Fig. 3 F). For MRW, five significant SNPs localized between 24.02 to 24.58 Mb on Chromosome 7 were identified to be significantly associated with the variation in the traits (Fig. 3 B, Table 2 ). The topmost SNP (SNP_24033698, p = 1.91E-05) occurred in the same gene ( Manes.07G112000 ) as the third one (SNP_24026637, 5.42E-05). The mean storage root weight of the genotype’s homozygote for the top SNP allele (SNP_24033698, CC) or the heterozygotes (TC) was > 33% higher than those harboring the two of the reference alleles (TT) (Fig. 3 G). A single SNP tagged as SNP_5559383 (p = 1.91E-07) around 5.55 Mb of chromosome 14 was found to be associated with RW (Fig. 3 C, Table 2 ). The SNP explained 19% of the total phenotypic variance in the root weight among the lines. Contrast to NSR and MRW, individual’s homozygote for the reference allele (SNP_5559383, CC) possessed higher root weight per plant (> 3.3 kg) relative to the alternative alleles (< 2.6 kg for CT or TT) (Fig. 3 H). Association analysis for harvest index identified significant SNPs from different genomic regions (Fig. 3 D, Table 2 ). A total of 12 significant marker traits associations were detected for HI. The top three SNPs on chromosomes 12, 14 and 16 explained on average 16.7% of the total variation in the trait (Table 2 ). The HI of the accession’s homozygote for the top SNP (SNP_21013081, CC) was 18% higher than those without (Fig. 3 I). GWAS for variation in dry matter content of the storage roots uncovered several regions scattered across 6 chromosomes. A total of 17 SNPs were significantly associated with the trait; however, the first two significant SNPs (SNP_11795901 and SNP_493861) occurred on chromosome 2 (Fig. 3 E, Table 2 ). Cassava lines homozygote for the SNP allele (SNP_11795901, TT) was on average 13% higher in dry matter content than those with homozygote reference allele (GG) (Fig. 3 J). Table 2 Summary statistics of top significant SNPs associated with yield-related traits Traits SNP_ID Chr Position Allele (Ref > SNP) p-value MAF R 2 NSR SNP_2747056 5 2747056 G > T 8.73E-08 0.08 0.21 NSR SNP_2749412 5 2749412 C > T 2.31E-07 0.08 0.20 NSR SNP_2373946 5 2373946 G > T 2.31E-07 0.08 0.18 NSR SNP_2814227 5 2814227 G > A 3.07E-07 0.06 0.15 MRW SNP_24033698 7 24033698 T > C 1.91E-05 0.53 0.19 MRW SNP_24218174 7 24218174 A > T 3.21E-05 0.33 0.18 MRW SNP_24026637 7 24026637 A > G 5.43E-05 0.28 0.17 MRW SNP_24587896 7 24587896 A > G 5.61E-05 0.47 0.17 MRW SNP_24069335 7 24069335 T > C 7.95E-05 0.28 0.14 RW SNP_5559383 14 5559383 C > T 1.91E-07 0.23 0.19 HI SNP_21013081 12 21013081 T > C 5.75E-06 0.69 0.18 HI SNP_22383767 14 22383767 T > G 7.22E-06 0.06 0.16 HI SNP_5919578 16 5919578 G > C 9.29E-06 0.05 0.14 DMC SNP_11795901 2 11795901 G > T 2.23E-07 0.47 0.17 DMC SNP_493861 2 493861 T > C 4.87E-07 0.11 0.14 DMC SNP_3087896 12 3087896 A > G 9.39E-07 0.59 0.14 DMC SNP_26380158 11 26380158 A > G 1.78E-06 0.17 0.13 Chr = chromosome number, MAF = minor allele frequency, R 2 = proportion of phenotypic variation explained by SNPs. The alleles in bold letters correspond to the favorable alleles. Candidate gene identification Genomic regions localized by the top significant SNPs were explored to identify putative candidate genes (Table 3 ) using the M.esculenta _305_v6.1.gene.gff3 file of the cassava reference genome available on phytozome. For NSR, the top two SNPs (SNP_2747056 and SNP_2749412) occurred in the exons of the same gene ( Manes.05G037900 ) annotated as malate dehydrogenase making it the potential candidate for NSR. Similarly, the first (SNP_24033698) and third (SNP_24026637) top SNPs associated with MRW co-localized in the 5’ untranslated region (UTR) and second exon respectively of Manes.07G112000 encoding the TPL-binding domain in jasmonate signaling (Table 3 , Fig. 4 A). Haplotypes generated from the combination of the alleles from two SNPs (SNP_24033698 and SNP_24026637) localized within Manes.07G112000 revealed the haplotypes had significantly different MRW across the two separate sets of experiments. Cultivars carrying haplotype E and F (with one or two favorable alleles at each SNP point) showed significantly high MRW relative to others (Fig. 4 ). The candidate genes Manes.14G068300 and Manes.14G068200 encoding ubiquitin-conjugating enzyme E2 protein and protein kinase domain-containing protein (Pkinase), were found closest to the only significant SNP (SNP_5559383) identified for RW. The SNPs; SNP_21013081 localized in the exon of Manes.12G101200 (expressed protein with a Myb domain) while SNP_5919578 occurred in the intron of Manes.16G042900 (SF7 complex intermediate associated protein), hence, were suggested as candidate genes for HI (Table 3 ). For DMC, candidate genes for the top three SNPs are reported. SNP_11795901 occurred 9.3Kb upstream of Manes.02G156800 encoding F-box domain protein, SNP_493861 localized in the exon of Manes.02G004000 encoding SF2-N6-adenosine-methyltransferase while SNP_26380158 was the closest to Manes.11G153600 annotated as PLATZ transcription factor family protein (Table 3 ). (A) Exon structure of Manes.07G112000 and DNA polymorphisms in the gene resulting in six different haplotypes. The two lines indicate the position of the two SNPs (SNP_24026637 and SNP_24033698) within the gene. The haplotypes indicate nucleotide or SNP combinations. Boxplots for mean storage root weight of the haplotypes for the (B) 2019 and (C) 2020 planting seasons are shown. In the boxplots, the central lines denote the average. Significant levels were determined using a least significant difference test and the different lowercase letters above the boxplots represent significant differences (P ≤ 0.05). Table 3 List of potential candidate genes identified in the vicinity of the GWAS hits for the traits Traits Chr Position Candidate gene Localization of SNP Annotation NSR 5 2747056 Manes.05G037900 exon Malate dehydrogenase NSR 5 2749412 Manes.05G037900 exon Malate dehydrogenase NSR 5 2373946 Manes.05G032800 exon SNF2 family N-terminal domain protein NSR 5 2814227 Manes.05G039500 downstream (2.9Kb) COP9 signalosome complex subunit MRW 7 24033698 Manes.07G112000 exon TPL-binding domain in jasmonate signalling MRW 7 24218174 Manes.07G113800 upstream (6Kb) SEL-1-like protein MRW 7 24026637 Manes.07G112000 5'UTR TPL-binding domain in jasmonate signalling MRW 7 24069335 Manes.07G112400 exon GTP-binding protein SEC4, Ras family GTP-binding proteins RW 14 5559383 Manes.14G068300 upstream (0.1Kb) Ubiquitin-conjugating enzyme e2 RW 14 5559383 Manes.14G068200 upstream (2.9Kb) Protein kinase domain (Pkinase) // Leucine rich repeat (LRR_8) RW 14 5559383 Manes.14G067800 downstream (29.8Kb) Alpha, alpha-trehalose-phosphate synthase (UDP-forming) HI 12 21013081 Manes.12G101200 exon Uncharacterized Myb domain HI 14 22383767 Manes.14G166100 upstream (18Kb) ATP binding / protein kinase-related HI 16 5919578 Manes.16G042900 intron SF7 complex intermediate associated protein DMC 2 11795901 Manes.02G156800 upstream (9.3Kb) F-box domain protein DMC 2 493861 Manes.02G004000 exon SF2 - N6-adenosine-methyltransferase DMC 12 3087896 Manes.12G037300 downstream (0.8Kb) Thioredoxin DMC 11 26380158 Manes.11G153600 downstream (10.7Kb) PLATZ transcription factor family protein Chr = chromosome number, UTR = untranslated region. Discussion Root yield and dry matter content of cassava are important traits in breeding for subsistence and commercial use; therefore, understanding their genetic architecture and underlying genomic regions influencing variations in these traits could accelerate genetic improvement. The population showed large phenotypic variation within all traits, and the magnitude of broad-sense heritability estimates for the traits were comparable to previously reported estimates value (Adjebeng-Danquah et al. 2020 ; Adu et al. 2020 ) indicating the presence of substantial genetic component of these traits to allow for selection. Root weight (RW), which defines actual fresh yield per plant is a component of number of storage roots (NSR) and the individual root weight (MRW). The significant positive correlation between MRW and both RW and DMC makes MRW a significant trait to simultaneously improve both RW and DMC hence selection for high MRW could potentially improve both traits (Tumuhimbise et al. 2015 ; Adjebeng-Danquah et al. 2016 , Adu et al. 2020 ). Knowledge of the structure underlying the population for association analysis could eliminate the effect of false positives associations (Yu et al. 2006). Estimation of population structure revealed that most of the accessions were in admixture state with their genetic composition coming from the different subgroups (K = 4). More importantly, each subgroup consists of accessions from different sources/origins indicating broad genetic base of the study population and its suitability for association analysis. GWAS provides insights into the genetic basis for complex traits where it highlights signals of associations between SNPs and phenotypic traits in diverse population (Bangarwa et al. 2020 ). NSR and MRW which are directly related to root yield, were highly associated with SNPs on chr5 and chr7 respectively, similar to other reports (Zhang et al. 2018 ). The variation in the haplotype’s performance and more importantly the localization of the top significant SNPs within the exonic regions of candidate genes could serve as important steps towards the development of functional markers (Uchendu et al. 2021 ). Though none of the SNP markers showed pleiotropic effect (a single SNP significantly influencing more than one trait), loci with favorable alleles influencing individual traits mark the beginning of marker discovery for the trait. The most notable candidate genes identified in this study were found in the vicinity occupied by SNP_2747056 and SNP_2749412 on chromosome 5 for NSR as well as SNP_24033698 and SNP_24026637 on chromosome 7 for MRW. SNP_2747056 and SNP_2749412 for NSR co-located with Manes.05G037900 a malate dehydrogenase (MDH) protein involved in the interconversion of oxaloacetate and malate a critical step in tricarboxylic acid (TCA) cycle and photosynthesis directly implicating it in energy production and carbon flux distribution in plants (Scheibe, 2004 ; Tomaz et al. 2010 ; Baird et al. 2024 ; Martinez-Vaz et al. 2024 ). MDH enzymes are localized in chloroplasts, mitochondria, peroxisomes, and the cytosol of plants (Gietl 1992 ). In mitochondria, MDH enzymes are reported to participate in the tricarboxylic acid (Krebs) cycle (Gietl 1992 ), provision of NAD + for glycine oxidation (Journet et al. 1981 ) and provision of CO 2 for carbon fixation in the bundle sheath cells of some C 4 plants (Hatch and Osmond 1976 ). Mutations in mitochondrial MDH affected photorespiration efficiency, leading to growth retardation and reduced ATP production in Arabidopsis. Knocked out lines ( mmdh1 and mmdh2 ) in Arabidopsis thaliana were sensitive to MDH activity with impaired root growth, delayed development, and reduced respiration rates (Tomaz et al. 2010 ). In tomato, the expression of the antisense fragment of mMDH revealed low root dry weight and low respiration in the roots (van der Merwe et al. 2009 ). This suggests a distinct impact of MDH disruption on roots development (Tesfaye et al. 2001 ; Tomaz et al. 2010 ) which serves as storage organ of carbon in cassava. In addition, the MDH enzyme was recently reported to be associated with the LIKE SEX FOUR 1-malate dehydrogenase complex involved in starch degradation which included glucan phosphatase and β-amylase (Liu et al. 2024 ) suggesting sideline roles for MDH in starch metabolism that demand further analysis. These putative gene has not been well studied in cassava and further investigation will be needed to explore their potential roles in cassava in relation to root development and carbon partition. For MRW, SNP_24026637 and SNP_24033698 on chromosome7 were localized in the 5’UTR and exon region respectively of Manes.07G112000 gene encoding a TOPLESS (TPL) binding domain involved in jasmonate signaling. Manes.07G112000 (TPL-binding domain protein) has a C-terminal zinc binding domain from the NINJA (Novel Interactor of JAZ) protein which interacts with the TIFY domain of JAZ1 (Jasmonate Zim-domain protein 1). TOPLESS (TPL) is a transcriptional co-repressor that plays a key role in jasmonate (JA) signaling by suppressing the expression of JA-responsive genes under non-stress conditions to promote growth where NINJA (Novel Interactor of JAZ) acts as a bridge, connecting JAZ to TPL to form a JAZ–NINJA–TPL Repressor Complex (Long et al. 2006 ; Pauwels et al. 2010 ; Acosta et al. 2013 ). Several evidence suggests JA-signaling regulates defense-growth trade-off (Noir et al. 2013 , Attaran et al. 2014 , Major et al. 2017 ; Guo et al. 2018 ) suggesting JA-signaling as a regulatory hub mediating metabolic reprogramming in response to changing environmental conditions. The role of JA-signaling in the regulation of carbohydrates were evident in poplar tree leaves (Babst et al. 2005 ), tobacco (Hanik et al. 2010), cabbage leaves (Tytgat et al. 2013 ) and tobacco plants (Wang et al. 2014 ) with low starch concentration as result of impaired JA signaling. In Arabidopsis, mutants with non-functional TPL showed stunted growth, short roots, and enhanced defense gene expression, due to uncontrolled JA signaling (Pauwels et al. 2010 ; Acosta et al. 2013 ). These independent findings suggest the role of the JAZ-NINJA-TPL complex and JA signaling mediated modulation of carbohydrate metabolism in the regulation of plant growth and induction of defense responses. Herein, cassava accession harboring the two SNPs (SNP_24033698 and SNP_24026637; haplotype E) localized in Manes.07G112000 (TPL-binding domain) had bigger tubers (high MRW) compared to those with reference alleles or without the SNPs (Fig. 4 ) making it ideal for marker development. The potential role of Manes.07G112000 (TPL-binding domain) in jasmonate signaling coupled with the reported effect of the hormone in the regulation of carbon in other crops through resource allocation (growth-defense) makes it a potential gene for further studies in cassava in relation to yield. DMC and HI on the other hand were associated with SNPs on different chromosomes revealing the complexity of the traits (Rabbi et al. 2022 ). Chromosomes14 was associated with both RW and HI suggesting that these traits could be coinherited. Previous studies linked stem diameter and dry mass content to Chromosme14 (Zhang et al 2018 ). Candidates have been suggested for DMC, including Manes.02G156800 , annotated as F-box domain (F-box) protein 9.3Kb upstream of SNP_11795901 and Manes.02G004000 (N6-adenosine-methyltransferase) containing SNP_493861, both on chromosome 2. The gene Manes.02G004000 which encodes N6-adenosine-methyltransferase harbored the SNP_493861 in the seventh exon was reported to be involved in mRNA modifications via methylation. Methylation of adenosine residues in mRNA is reported to influence embryonic development in Arabidopsis (Zhong et al. 2008 ; Bodi et al. 2012 ). Recently, Arabidopsis thaliana plants lacking mRNA adenosine methylase were reported to exhibit heightened sensitivity to drought implicating it in stress response (Ganguly et al. 2024). Manes.02G156800 has F-box domain found in Arabidopsis thaliana protein as ATTENUATED FAR-RED RESPONSE (AT2G24540.1, AtAFR) also knowns as SKP1-interacting partner29 (AtSKIP29) a component of SCF (SKP1/ASK-cullin-F-box protein) E3 ubiquitin ligase complexes involved in the ubiquitination and subsequent proteasomal degradation of target proteins thereby regulating a wide range of physiological processes including hormone signaling, development, and stress responses (Guo et al. 2003; Thines et al. 2007 ). More importantly, the SCF complex through its interaction with COI1 (CORONATINE INSENSITIVE1) targets JAZ repressors following jasmonate perception to regulate growth and defense trade-offs (Xu et al. 2002 ; Thines et al. 2007 ). However, these putative genes have not been well studied in cassava and would need further investigation in relation to dry matter accumulation in the roots of cassava. These findings suggest some genetic basis of yield-related traits in cassava. Through GWAS, we identified significant SNPs for the studied traits and promising genes for the MRW and DMC to be associated with jasmonate signaling which could modulate carbohydrate profiles in plant growth-defense conflicts. These SNPs and genes could be potential targets for breeders in marker-assisted breeding to improve yield in cassava after further validation. Conclusion The SNPs and candidate genes identified for yield-related traits provide valuable insights into the genetic basis of cassava yield improvement and offer potential markers for MAS breeding of high-yielding cultivars. The studies identified significant SNPs associated with natural variation in traits in the cassava population. The haplotype analysis highlighted Manes.05G037900 on chromosome 5, Manes.07G112000 on chromosome 7 and Manes.02G156800 on chromosome 2 as potential target for enhancing number of storage roots, mean storage root weight and dry matter content in cassava respectively through marker-assisted and genomic selection. Cassava is a relatively hardy and drought-tolerant crop, whether the disruption of Manes.07G112000 reported to be involved in plant defense through jasmonate signaling, will enhance yield via a growth-defense tradeoff or through novel biological pathways remains to be exploited. Further validation of these associations and candidate genes will be essential to accelerate cassava genetic improvement. Declarations Author contributions AGB, RA, SA conceived and designed the project, AGB, RA, SA, RAA, AY, and JAD collected the cassava landraces and contributed to the field evaluation of the accessions. AGB, RA, AK and DA contributed to the data analysis, AGB wrote the draft manuscript and DA, RA, FIM and TF revised the paper. All authors read and approved of the final manuscript. Funding The authors gratefully acknowledge the Alliance for a Green Revolution in Africa, through the Improved Masters in Cultivar Development Programme (IMCDA; 2014 PASS-012), Faculty of Agriculture, Kwame Nkrumah University of Science and Technology, Ghana for providing funds for this study. AGB is supported by JSPS as a foreign post-doctoral fellow. Data availability The datasets supporting the conclusions of this article are provided within the article and its additional files. Declarations Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest. References Acosta IF, Gasperini D, Chételat A, Stolz S, Santuari L, Farmer EE (2013) Role of NINJA in root jasmonate signaling. 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Adu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABSElEQVRIie3QsWqDQBjA8S9cMcuJHU9S7Ct8VjAtlOZVFCFTKIUMzWDkpmYRugYa8g5dMiuCLj5ARiWQKUPHDrb0bCRtEkvXQv0P54fy484DaGr6k1kSAIIFhITZy+jbB1Y9yU+EtiVHn6a71/gLEStVqNmRH2rIYcpkYK5Gd+PbHqEmk+djUNpRRigUmjp1Anh1od3dJyzddPUUkyEl0vBSXSSg+n0UBI0O61stPwZyxffNcmCqHGPbJ+R5qS9iwCUFIr+jPWcDBJkDwWBPnH8RQGbPYuiVROzySVpvxwS3xBXk5IKF3BWwIk+CkJpd9HR9r3MMbD8Sl8zjgLK0j+EMDEP111Z0FrPDf9ESZ5HzwrMnj1G4KlxPUyZRnm1A01jihPnGvXYObqwq2g20XLcnObXKgTlYS7yjQaRs5U09aWpqavo/fQAWzm28mNuhxAAAAABJRU5ErkJggg==","orcid":"","institution":"The University of Tokyo","correspondingAuthor":true,"prefix":"","firstName":"Bright","middleName":"Gyamfi","lastName":"Adu","suffix":""},{"id":551633050,"identity":"16503ada-a238-4bd3-8c4a-e965634e4e4d","order_by":1,"name":"Richard Akromah","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Akromah","suffix":""},{"id":551633051,"identity":"df5a6833-4d01-4d3a-a61e-1cf88a23da73","order_by":2,"name":"Stephen Amoah","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Amoah","suffix":""},{"id":551633052,"identity":"512afc68-2e60-4daa-8e47-f5b83147e4fb","order_by":3,"name":"Andrzej Kilian","email":"","orcid":"","institution":"Diversity Array Technology","correspondingAuthor":false,"prefix":"","firstName":"Andrzej","middleName":"","lastName":"Kilian","suffix":""},{"id":551633053,"identity":"fe05869b-9d7d-404c-8da2-8a5c5c534903","order_by":4,"name":"Richard Adu Amoah","email":"","orcid":"","institution":"Council for Scientific and Industrial Research-Plant Genetics Resources Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"Adu","lastName":"Amoah","suffix":""},{"id":551633054,"identity":"8d0dfcbd-5077-4072-a4f5-30575af72327","order_by":5,"name":"Joseph Adjebeng-Danquah","email":"","orcid":"","institution":"Council for Scientific and Industrial Research -Savanna Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Adjebeng-Danquah","suffix":""},{"id":551633055,"identity":"30f17dde-a0d6-479a-a8a7-a9984244931d","order_by":6,"name":"Alex Yeboah","email":"","orcid":"","institution":"Council for Scientific and Industrial Research -Savanna Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Yeboah","suffix":""},{"id":551633056,"identity":"8b96b081-b4d5-4402-815a-d49f28a7d9e5","order_by":7,"name":"Daniel Nyadanu","email":"","orcid":"","institution":"Cocoa Research Institute of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Nyadanu","suffix":""},{"id":551633057,"identity":"40cb8b64-cafd-44c0-8086-de76c8768537","order_by":8,"name":"Fakhrul Islam Monshi","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Fakhrul","middleName":"Islam","lastName":"Monshi","suffix":""},{"id":551633058,"identity":"bca41451-4eab-4b44-9d3b-79b160bfb958","order_by":9,"name":"Toru Fujiwara","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Toru","middleName":"","lastName":"Fujiwara","suffix":""}],"badges":[],"createdAt":"2025-11-01 19:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8007567/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8007567/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97142171,"identity":"bc29d543-01ec-4bf5-bbb7-91ad61f11210","added_by":"auto","created_at":"2025-12-01 10:07:23","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3924579,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8007567/v1/1f8e6fea5cb04827399734a7.docx"},{"id":97134258,"identity":"1d1f9be1-be15-483c-89b5-810d55398e02","added_by":"auto","created_at":"2025-12-01 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09:18:49","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48470,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8007567/v1/97c135e27427c5f9f864f711.png"},{"id":97134271,"identity":"6c18cdc8-1fbe-4d88-8bf4-edfb18cdf008","added_by":"auto","created_at":"2025-12-01 09:18:49","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":205995,"visible":true,"origin":"","legend":"","description":"","filename":"316303aa741c4f96ae5814099b2051b11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8007567/v1/a3aeaf36ed387fa88908a33f.xml"},{"id":97142645,"identity":"e3fbd106-9945-46b3-b8e3-bfb92582beaa","added_by":"auto","created_at":"2025-12-01 10:07:47","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":216188,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8007567/v1/ac286fc681cc6aa1d7753d7f.html"},{"id":97142955,"identity":"e27da5f8-da60-4575-b50a-1d3658b0fef3","added_by":"auto","created_at":"2025-12-01 10:08:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":146248,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of traits and relationship among the traits based on best linear unbiased predictions (BLUPs). (A) Density distribution of variation in five traits in cassava germplasm, (B) correlation coefficients among traits. *, **, *** indicate significance at 0.05, 0.01 and 0.001. NSR= number of storage roots, MRW= mean storage root weight, RW= root weight per plant, HI= harvest index, and DMC= storage root dry matter content.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8007567/v1/5f0f27eaa6f9d7062928583d.jpg"},{"id":97134256,"identity":"4000a4a9-c636-4470-88a0-5fa68a4ac183","added_by":"auto","created_at":"2025-12-01 09:18:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218644,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of SNP genotyping data and population structure based on SNPs. (A) Genome-wide single nucleotide polymorphism (SNP) distributions on 18 chromosomes of cassava. The horizontal axis displays the chromosome length in base pairs; the vertical axis on the left displays the number of SNPs (inserted bar indicates the number of SNP within 1 Mb window). (B) Individual ancestry estimated by ADMIXTURE analysis (K = 4). The colored sections within each bar indicate membership of the genotypes in the different subpopulations (subgroup1=blue, subgroup2=green, subgroup3=blue-black, subgroup4=orange).\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8007567/v1/42aa2535922642890f7c1e42.jpg"},{"id":97142747,"identity":"6a0b848b-dc46-48d3-9e96-a6e13dc23a34","added_by":"auto","created_at":"2025-12-01 10:07:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":368077,"visible":true,"origin":"","legend":"\u003cp\u003eGenome-wide association and haplotype analysis for the five traits based on the topmost SNP. Manhattan plots of -log10(P-value) showing chromosomal positions of SNP markers for (A) number of storage root, NSR, (B) mean storage root weight, MRW, (C) root weight, RW, (D) harvest index, HI, and (E) dry matter content, DMC. The dashed horizontal red line indicates the genome-wide significance threshold. Allelic effect on (F) NSR, (G) MRW, (H) RW, (I) HI and (J) DMC based on the highest significant SNP associated with the trait across the two years. For the haplotypes, from left to right is homozygote reference, heterozygote and homozygote SNP. In the boxplots, the central lines denote the average. Significant levels were determined using a least significant difference test and the different lowercase letters above the boxplots represent significant differences (P≤0.05).\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8007567/v1/df96c6ae4d98d4c4e8409344.jpg"},{"id":97134257,"identity":"ba0cbc9a-c2d9-45de-8fa6-b6e43a6a87b2","added_by":"auto","created_at":"2025-12-01 09:18:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":138388,"visible":true,"origin":"","legend":"\u003cp\u003eGene structure and haplotype analysis for the mean storage root weight (MRW) based on the two topmost SNPs.\u003c/p\u003e\n\u003cp\u003e(A) Exon structure of \u003cem\u003eManes.07G112000\u003c/em\u003eand DNA polymorphisms in the gene resulting in six different haplotypes. The two lines indicate the position of the two SNPs (SNP_24026637 and SNP_24033698) within the gene. The haplotypes indicate nucleotide or SNP combinations. Boxplots for mean storage root weight of the haplotypes for the (B) 2019 and (C) 2020 planting seasons are shown. In the boxplots, the central lines denote the average. Significant levels were determined using a least significant difference test and the different lowercase letters above the boxplots represent significant differences (P≤0.05).\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8007567/v1/6bdc786ed487aedfcc31ba70.jpg"},{"id":97366867,"identity":"f23e4e39-ad19-42cb-8810-71fa4237dba9","added_by":"auto","created_at":"2025-12-03 16:11:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1936853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8007567/v1/0c9c1632-5627-4196-a2d0-d0fdd051b89a.pdf"},{"id":97134274,"identity":"84408906-dc87-474b-bf68-614f6371050d","added_by":"auto","created_at":"2025-12-01 09:18:49","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12097379,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8007567/v1/4a150f8f4cf27b6f0e5f2389.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide association study identifies novel genomic regions associated with yield-related traits in cassava (Manihot esculenta Crantz)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the global population continuously increasing and climate changing rapidly, food insecurity poses a major challenge worldwide. Cassava (\u003cem\u003eManihot esculenta\u003c/em\u003e Crantz) is a starchy root crop that is mostly vegetatively propagated and serves as a staple food for over 800\u0026nbsp;million people across tropical regions, particularly in sub-Saharan Africa, Southeast Asia, and Latin America. It is also used as an industrial raw material for starch and alcohol production (Jarvis et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Parmar et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Global cassava production has nearly doubled over the past two decades, from 175.8\u0026nbsp;million tons in 2000 to 333.8\u0026nbsp;million tons in 2023 partly due to the introduction of new improved varieties with superior adaptive features to thrive in marginal environments, tolerance to drought and ability to thrive in poor soils which makes it an important food security crop (Jarvis et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; FAO, 2025). In addition, its ability to remain in the ground post-harvest makes cassava uniquely suited to smallholder farming systems, where it contributes significantly to both food and income security (Burns et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOver the past four decades, cassava breeding programs in Africa, Asia, and Latin America have developed improved varieties with resistance to biotic and abiotic stresses, as well as higher yield and starch content (Kawano, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Okechukwu and Dixon, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Although phenotype-based recurrent selection has achieved some success, the rate of genetic gain remains low due to several biological constraints, including asynchronous flowering, low seed set per cross, long growth cycles of 12\u0026ndash;24 months, and low multiplication rates of planting materials (Ceballos et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These challenges limit the breeding program\u0026rsquo;s ability to respond rapidly to changing human needs under volatile climatic and environmental conditions. The adoption of modern breeding tools such as marker-assisted recurrent selection and genomic selection has helped accelerate genetic gains by shortening selection cycles and increasing selection intensity, particularly in the early stages of cassava breeding (Ferguson et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ceballos et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Garc\u0026iacute;a-Ruiz et al. 2016; Bredeson et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, integration of molecular markers into cassava breeding pipelines requires investment in discovery research to identify major-effect loci as targets of selection.\u003c/p\u003e\u003cp\u003eAdvances in next-generation sequencing technologies and the completion of the cassava genome draft (Bredeson et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) have made it cost-effective to generate genome-wide marker data through biparental quantitative trait loci (QTL) analysis or GWAS in large natural populations (Ferguson et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Combined with phenotypic data, this enables the identification and mapping of agriculturally important genes and QTLs at the whole-genome level (Varshney et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). GWAS has been successfully applied in cassava to map loci associated with biotic stress resistance, nutritional traits, and morphological characteristics. Previous studies identified genomic regions linked to cassava mosaic disease (CMD) resistance (Rabbi et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wolfe et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), cassava green mite (CGM) resistance (Ezenwaka et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and root quality traits such as dry matter content and carotenoid concentration (Esuma et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ikeogu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These efforts have laid the foundation for integrating genomic tools into cassava improvement pipelines. For example, Rabbi et al. (2020) mapped genomic regions conferring resistance to CMD using African cassava germplasm through biparental QTL mapping and GWAS. Further analysis of SNPs (S12_7926132 and S12_7926163 on chromosome 12 linked to the major CMD2 locus, and S14_4626854 on chromosome 14) using Kompetitive Allele-Specific Polymerase Chain Reaction (KASP) assays showed that genotypes carrying at least one resistant allele at the CMD2 locus had significantly higher yields (Ige et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Beyond disease resistance, genomic regions associated with root quality traits such as mealiness, fiber content, adhesiveness, taste, aroma, color, and firmness after boiling have been reported, revealing candidate genes linked to carbohydrate metabolism, cell adhesion, secondary cell wall formation, and proteolytic activity. For nutritional improvement, SNPs in \u003cem\u003eManes.01G124200\u003c/em\u003e (phytoene synthase) have been used to select cassava lines with higher carotenoid content.\u003c/p\u003e\u003cp\u003eWhile such studies have improved understanding of disease resistance and root quality traits, there is a pressing need to prioritize root and yield-related traits to guide breeding strategies for enhancing cassava productivity in Africa, where the crop forms a major part of daily calorie intake with limited export value (Otekunrin, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Developing high-yielding cultivars is a central goal of most breeding programs; thus, elucidating the genetic architecture of yield is critical to improving this complex trait. Key yield components such as storage root weight, root number, root diameter, root length, and dry matter content directly influence total yield and are essential for developing farmer-preferred varieties with improved market value (Adjebeng-Danquah et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Adu et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These traits often display significant genotype-by-environment interactions and moderate heritability, which makes improvement through conventional phenotypic selection challenging (Sinclair, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Adjebeng-Danquah et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The integration of GWAS with high-throughput genotyping technologies can help dissect the genetic basis of these traits by exploiting natural variation to identify candidate genes and favorable alleles for marker-assisted breeding (MAS) (Elshire et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hamblin and Rabbi, 2014).\u003c/p\u003e\u003cp\u003eSNPs associated with number of storage roots, storage root weight, dry matter content, and starch content have been reported using 158 cassava accessions by Zhang et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, Mbe et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) identified 52 SNPs associated with nitrogen-use efficiency and yield-related traits, including stay-green ability, chlorophyll content, and fresh and dry root yield. Continued discovery of yield-related genes and loci will facilitate molecular breeding for cassava yield improvement. Landraces, in particular, serve as valuable reservoirs of favorable alleles for specific traits and remain an important resource for modern breeding. By integrating high-density SNP genotyping data with multi-year phenotypic evaluations, the present study aims to identify genomic regions and SNPs associated with natural variation in local and exotic cassava accessions for five root- and yield-related traits, and to suggest candidate genes for molecular breeding. These findings will promote more efficient use of molecular markers in cassava improvement.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant material and field trials\u003c/h2\u003e\u003cp\u003eThe panel for GWAS consisted of 94 cassava accessions with 26 breeding lines from IITA, 22 improved or released varieties from Ghana and 46 landraces were obtained through the Council for Scientific and Industrial Research (CSIR-Ghana). These accessions showed high variability in levels of branching of the main stem and color of root pulp (storage root flesh color) ranging from white to yellow (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The field work study was carried out at the research field of the CSIR-Plant Genetic Resources Research Institute at Bunso in the Eastern Region of Ghana in 2019 and 2020 years. Bunso (lat. 06\u0026deg; 46\u0026prime; N, long. 01 01\u0026prime; W, 149 m above sea level) lies in the semi-deciduous forest zone of Ghana with the soil type Nta series (FAO: Gleyic Arensol) (Jones et al. 2013). The experiment was laid in a randomized complete block design with three replications. The land was ploughed and harrowed before planting. Cassava cuttings measuring about 30 cm were planted horizontally in each hole. Each accession was planted in a single row consisting of five stands with an intra and inter row spacing 1 m \u0026times; 1 m to give a plot size of 5m\u003csup\u003e2\u003c/sup\u003e. No fertilizer was applied but weeding was done twice before harvesting (12 MAP).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTrait measurement\u003c/h3\u003e\n\u003cp\u003eThe accessions were phenotyped for five yield-related traits including number of storage roots per plant (NSR), mean storage root weight (MRW), root weight per plant (RW), harvest index (HI) and root dry matter content (DMC) as indicated in the standard cassava descriptor (Fukuda et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The three middle plants were harvested and measured for the selected traits after maturity (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For DMC measurement, triplicate of 100 grams of root pulp (mid-section) were dried at 80 \u003csup\u003e0\u003c/sup\u003eC for 48 hours after which the dry weight taken and expressed as a percentage of the original fresh weight.\u003c/p\u003e\n\u003ch3\u003eGenomic DNA extraction and DArTseq genotyping\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eGenomic DNA extraction and DArTseq genotyping\u003c/div\u003e\u003cp\u003eDNA samples were extracted from the youngest fully expanded leaves of each of the 94 cassava genotypes two weeks after planting using the DArT DNA extraction protocol (Kilian et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The concentration of extracted DNA was checked using the Nanodrop 2000c spectrophotometer (NanoDrop Lite, LT2878, Thermo Scientific, USA). DNA samples were diluted between 50\u0026ndash;100 ng/\u0026micro;l, packaged and shipped to Diversity Array Technology corporation (Canberra, Australia) for DArTSeq genotyping. Genotyping-by-Sequencing, and SNP calling were performed for each sample using the DArTseq genotyping platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.diversityarrays.com/technology-and-resources/dartreseq/\u003c/span\u003e\u003cspan address=\"https://www.diversityarrays.com/technology-and-resources/dartreseq/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The sequences of the genomic representations were aligned to cassava reference genome v6.1, resulting in the selection of 31865 raw SNP markers, however, SNPs with over 5% missing data and/or minor allele frequency below 5% were removed. After filtering, a total of 24790 SNP markers were obtained and used for downstream analyses.\u003c/p\u003e\n\u003ch3\u003ePopulation structure analysis\u003c/h3\u003e\n\u003cp\u003ePopulation structure was estimated using the Bayesian model of the Markov chain Monte Carlo (MCMC) implemented in STRUCTURE v.2.3.4 (Pritchard et al. 2010) based on 24790 SNP points. For each run, the initial burn-in period was set to 20,000 followed by 30,000 MCMC (Markov chain Monte Carlo) replications, with no prior information on the origin of individuals. Five iterations were performed for each number of hypothetical populations (k) tested from 1 to 10. The STRUCTURE results for the assumed population (1\u0026ndash;10) were subsequently analysed online using the STRUCTURESELECTOR (Li et al. 2018) to identify a distinct peak in the change of likelihood (ΔK) at the true value of K.\u003c/p\u003e\n\u003ch3\u003eMarker–Trait Association Mapping\u003c/h3\u003e\n\u003cp\u003eGWAS analysis was performed using a unified mixed-model approach implemented in the \u0026ldquo;\u003cem\u003errBLUP\u003c/em\u003e\u0026rdquo; package in R version 4.5 (Endelman \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The model includes a random effect to account for population structure and relatedness, which is critical for reducing false positives, and a fixed effect for batches to ensure accurate detection of genetic associations by integrating data from two experimental datasets to mitigate batch effects (Kang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). We established population parameters previously determined (P3D) as FALSE to allow the model to reestimate variance components for each marker, providing a more accurate estimation of marker-trait associations. Significant SNPs linked to traits were identified, with peaks surpassing a threshold of -log10 (p-value)\u0026thinsp;\u0026ge;\u0026thinsp;4. Manhattan plots for association mapping were visualized using the \u0026ldquo;\u003cem\u003eqqman\u003c/em\u003e\u0026rdquo; package in R (Turner \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eHaplotype analysis and candidate gene identification\u003c/h2\u003e\u003cp\u003eHaplotype and candidate gene analysis were performed using significant SNPs selected from the GWAS results. SNPs with the strongest association with the target signal were used to perform the haplotype analysis and the phenotypes of the accessions with different haplotypes were compared. For the candidate gene analysis, mapping of these selected SNP markers onto genes was done using the SNP location and gene description from the \u003cem\u003eM.esculenta_\u003c/em\u003e305_v6.1.gene.gff3 file of the cassava reference genome available in Phytozome_v14 (Goodstein et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and the intersect function from bedtools (Quinlan and Hall \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Gene ontology annotation was carried out on the plant Ensembl website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plants.ensembl.org/index.html\u003c/span\u003e\u003cspan address=\"https://plants.ensembl.org/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and UniProtKB tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll phenotypic data were subjected to various statistical analyses using R Statistical Software v4.5. The differences in the different years for each trait were estimated from the significance of the mean square for years from the analysis of variance (ANOVA) while the effect of alleles at significant SNPs was assessed by comparing phenotypic data for the haplotype groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Using the \u0026ldquo;\u003cem\u003elme4\u003c/em\u003e\u0026rdquo; package (De Boeck et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), we considered the genotype as a random effect to obtain the variance components of all the traits while considering years as environment (Piepho \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA linear mixed model was used to obtain the best linear unbiased predictions (BLUPs) for each genotype. The genetic source of phenotypic variance was indicated as broad sense heritability (H\u0026sup2;) according to Falconer and Mackay (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\text{H}}^{2}=\\frac{{{\\sigma\\:}}^{2}g}{{{{\\sigma\\:}}^{2}\\text{g}+{\\sigma\\:}}^{2}y+{{\\sigma\\:}}^{2}gy+{{\\sigma\\:}}^{2}e}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere σ\u003csup\u003e2\u003c/sup\u003eg​ is genotype variance, σ\u003csup\u003e2\u003c/sup\u003ey is variance across years, σ\u003csup\u003e2\u003c/sup\u003egy is the genotype by year interaction variance and σ\u003csup\u003e2\u003c/sup\u003ee​ is error variance.\u003c/p\u003e\u003cp\u003ePearson\u0026rsquo;s correlation analysis between different traits was performed using the \u0026ldquo;\u003cem\u003ecorrplot\u003c/em\u003e\u0026rdquo; package (Wei et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) while data were visualized in R using the \u0026ldquo;\u003cem\u003eggplot2\u003c/em\u003e\u0026rdquo; package (Wickham \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePhenotypic variations and relationships among measured traits\u003c/h2\u003e\u003cp\u003eUnderstanding the variation that exists in traits and the underlying structure of the population is necessary for GWAS and other trait-marker association studies. Here we characterize five yield-related traits in cassava; number of storage root per plant (NSR), mean storage root weight (MRW), storage root weight per plant (RW), harvest index (HI) and storage root dry matter content (DMC). Summary statistics showed significant variation in these traits in the association panel based on the average performance of each genotype, which is useful for deciphering their genetic architectures (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Except for DMC, all measured exhibited large coefficient of variation (\u0026gt;\u0026thinsp;30) an indication of broad phenotypic variability within the association panel. The data showed significant genetic variance and environmental variance for all traits except NSR, while genotype by environment interaction was significant for RW (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Analysis of the phenotypic classes of the panel showed that all measured traits followed a normal distribution though DMC were slightly skewed towards the tail (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA)\u003c/p\u003e\u003cp\u003eBroad sense (H\u003csup\u003e2\u003c/sup\u003e) estimates ranged from 59.2% (RW) to 76.1% (DMC) suggesting considerable potential for improvement of the phenotypes across all traits through targeted selection (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Pearson\u0026rsquo;s correlation (r\u003csup\u003e2\u003c/sup\u003e) between traits estimates showed that all traits had positive correlations with RW, with that of NSR and MRW exceeding 0.60 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). MRW was also significantly positively correlated with HI and DMC making it an important trait to improve field and economic yield of cassava.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary statistics and heritability for yield-related traits assessed among the cassava genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003emin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003estd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMSg\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMSgy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eσ\u003csup\u003e2\u003c/sup\u003eg\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eσ\u003csup\u003e2\u003c/sup\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e7.30\u003c/em\u003e\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e2.69ns\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRW (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.09\u003c/em\u003e\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e0.03ns\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e4.56\u003c/em\u003e\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e1.86\u003c/em\u003e\u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.06\u003c/em\u003e\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e0.02ns\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDMC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e58.36\u003c/em\u003e\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e12.94ns\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e14.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.761\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNSR\u0026thinsp;=\u0026thinsp;number of storage roots, MRW\u0026thinsp;=\u0026thinsp;mean storage root weight, RW\u0026thinsp;=\u0026thinsp;root weight per plant, HI\u0026thinsp;=\u0026thinsp;harvest index, and DMC\u0026thinsp;=\u0026thinsp;storage root dry matter content min\u0026thinsp;=\u0026thinsp;minimum, max\u0026thinsp;=\u0026thinsp;maximum, Std\u0026thinsp;=\u0026thinsp;standard deviation, CV\u0026thinsp;=\u0026thinsp;coefficient of variation, MSg\u0026thinsp;=\u0026thinsp;mean square of genotypes, MSgy\u0026thinsp;=\u0026thinsp;mean square of genotype x year interaction, σ\u003csup\u003e2\u003c/sup\u003eg= genotypic variance, σ\u003csup\u003e2\u003c/sup\u003ep= phenotypic variance, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;broad sense heritability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDistribution of SNP markers and population structure analysis\u003c/h2\u003e\u003cp\u003eA total of 31865 SNPs distributed across the 18 chromosomes were generated through DArT sequencing platform ranging from 1251 on chromosome12 to 3697 on chromosome 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Considering the exclusion of SNPs with less than 95% call rate and/or less than 5% minor allele frequency (MAF), 24790 SNP markers that passed the quality test were used to estimate the genetic structure of the cassava population using the Bayesian clustering model implemented in the computer software STRUCTURE and GWAS. Population structure analysis is required in genome-wide association to avoid false-positive associations. The population stratification inferred by assuming admixture model-based clustering method indicated the presence of four (K\u0026thinsp;=\u0026thinsp;4) subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Though 65% of the accessions showed admixture (with \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;1% of ancestry from any of the subgroups) composition, assignment of accessions into subgroups ranged from 10 in subgroup2 (green) comprising of IITA lines to 36 in subgroup4 (orange) which were mainly landraces from Ghana. The expected heterozygosity was used to compute the diversity between individuals in each subgroup. Subgroup3 (blue-black) and subgroup4 (orange) showed a similar average genetic diversity (0.35) between individuals within each group defined by the expected heterozygosity indicating the diverse nature of the groups (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Again, the highest difference was observed between subgroups occurred between subgroup1 and subgroup2 (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) indicating large genetic base of the study population and present opportunity to detect both favorable and unfavorable alleles which enhances the power of GWAS to detect true associations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMarker-trait association mapping and haplotype analysis\u003c/h2\u003e\u003cp\u003eTo identify genomic regions and potential SNP markers associated with variation in the five yield related traits in cassava, GWAS analysis for the traits was performed. To mitigate the batch effect that could arise from the two experimental sets, a unified mixed-model approach was employed for GWAS. The resulting Manhattan plot displayed multiple peaks across various chromosomes for all the traits except for RW (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A total of 55 significant SNPs associated with yield-related traits were detected at a threshold of \u0026minus;\u0026thinsp;log(P)\u0026thinsp;=\u0026thinsp;4 (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) comprising of 20, 5, 1, 12, and 17 for NSR, MRW, RW, HI and DMC respectively.\u003c/p\u003e\u003cp\u003eGenome-wide association study for variation in NSR revealed only genomic regions on chromosome 5 (1.66\u0026ndash;5.45 Mb) to be significantly associated with the trait (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A total of 20 markers were significantly associated with NSR on chromosome 5. First (SNP_2747056, p\u0026thinsp;=\u0026thinsp;8.73E-08) and second (SNP_2749412, 2.31E-07) peak SNPs were only 2.3 Kb apart and averagely explained about 21% (R\u003csup\u003e2\u003c/sup\u003e) of the trait variation. We observed in both planting seasons that, accessions homozygote for the top SNP allele (SNP_2747056, TT) had higher number of storage roots per plant (\u0026gt;\u0026thinsp;9) followed by the heterozygote (GT) and the least in the lines homozygote for reference allele (GG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eFor MRW, five significant SNPs localized between 24.02 to 24.58 Mb on Chromosome 7 were identified to be significantly associated with the variation in the traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The topmost SNP (SNP_24033698, p\u0026thinsp;=\u0026thinsp;1.91E-05) occurred in the same gene (\u003cem\u003eManes.07G112000\u003c/em\u003e) as the third one (SNP_24026637, 5.42E-05). The mean storage root weight of the genotype\u0026rsquo;s homozygote for the top SNP allele (SNP_24033698, CC) or the heterozygotes (TC) was \u0026gt;\u0026thinsp;33% higher than those harboring the two of the reference alleles (TT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eA single SNP tagged as SNP_5559383 (p\u0026thinsp;=\u0026thinsp;1.91E-07) around 5.55 Mb of chromosome 14 was found to be associated with RW (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The SNP explained 19% of the total phenotypic variance in the root weight among the lines. Contrast to NSR and MRW, individual\u0026rsquo;s homozygote for the reference allele (SNP_5559383, CC) possessed higher root weight per plant (\u0026gt;\u0026thinsp;3.3 kg) relative to the alternative alleles (\u0026lt;\u0026thinsp;2.6 kg for CT or TT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003eAssociation analysis for harvest index identified significant SNPs from different genomic regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A total of 12 significant marker traits associations were detected for HI. The top three SNPs on chromosomes 12, 14 and 16 explained on average 16.7% of the total variation in the trait (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The HI of the accession\u0026rsquo;s homozygote for the top SNP (SNP_21013081, CC) was 18% higher than those without (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI).\u003c/p\u003e\u003cp\u003eGWAS for variation in dry matter content of the storage roots uncovered several regions scattered across 6 chromosomes. A total of 17 SNPs were significantly associated with the trait; however, the first two significant SNPs (SNP_11795901 and SNP_493861) occurred on chromosome 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Cassava lines homozygote for the SNP allele (SNP_11795901, TT) was on average 13% higher in dry matter content than those with homozygote reference allele (GG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary statistics of top significant SNPs associated with yield-related traits\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePosition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAllele\u003c/p\u003e\u003cp\u003e(Ref\u0026thinsp;\u0026gt;\u0026thinsp;SNP)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_2747056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2747056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.73E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_2749412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2749412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.31E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_2373946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2373946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.31E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_2814227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2814227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.07E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_24033698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24033698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.91E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_24218174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24218174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.21E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_24026637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24026637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.43E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_24587896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24587896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.61E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_24069335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24069335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.95E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_5559383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5559383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.91E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_21013081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21013081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.75E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_22383767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22383767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.22E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_5919578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5919578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.29E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDMC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_11795901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11795901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.23E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDMC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_493861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e493861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.87E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDMC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_3087896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3087896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.39E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDMC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP_26380158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26380158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.78E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eChr\u0026thinsp;=\u0026thinsp;chromosome number, MAF\u0026thinsp;=\u0026thinsp;minor allele frequency, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;proportion of phenotypic variation explained by SNPs. The alleles in bold letters correspond to the favorable alleles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCandidate gene identification\u003c/h2\u003e\u003cp\u003eGenomic regions localized by the top significant SNPs were explored to identify putative candidate genes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) using the \u003cem\u003eM.esculenta\u003c/em\u003e_305_v6.1.gene.gff3 file of the cassava reference genome available on phytozome.\u003c/p\u003e\u003cp\u003eFor NSR, the top two SNPs (SNP_2747056 and SNP_2749412) occurred in the exons of the same gene (\u003cem\u003eManes.05G037900\u003c/em\u003e) annotated as malate dehydrogenase making it the potential candidate for NSR. Similarly, the first (SNP_24033698) and third (SNP_24026637) top SNPs associated with MRW co-localized in the 5\u0026rsquo; untranslated region (UTR) and second exon respectively of \u003cem\u003eManes.07G112000\u003c/em\u003e encoding the TPL-binding domain in jasmonate signaling (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Haplotypes generated from the combination of the alleles from two SNPs (SNP_24033698 and SNP_24026637) localized within \u003cem\u003eManes.07G112000\u003c/em\u003e revealed the haplotypes had significantly different MRW across the two separate sets of experiments. Cultivars carrying haplotype E and F (with one or two favorable alleles at each SNP point) showed significantly high MRW relative to others (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The candidate genes \u003cem\u003eManes.14G068300\u003c/em\u003e and \u003cem\u003eManes.14G068200\u003c/em\u003e encoding ubiquitin-conjugating enzyme E2 protein and protein kinase domain-containing protein (Pkinase), were found closest to the only significant SNP (SNP_5559383) identified for RW. The SNPs; SNP_21013081 localized in the exon of \u003cem\u003eManes.12G101200\u003c/em\u003e (expressed protein with a Myb domain) while SNP_5919578 occurred in the intron of \u003cem\u003eManes.16G042900\u003c/em\u003e (SF7 complex intermediate associated protein), hence, were suggested as candidate genes for HI (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For DMC, candidate genes for the top three SNPs are reported. SNP_11795901 occurred 9.3Kb upstream of \u003cem\u003eManes.02G156800\u003c/em\u003e encoding F-box domain protein, SNP_493861 localized in the exon of \u003cem\u003eManes.02G004000\u003c/em\u003e encoding SF2-N6-adenosine-methyltransferase while SNP_26380158 was the closest to \u003cem\u003eManes.11G153600\u003c/em\u003e annotated as PLATZ transcription factor family protein (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(A) Exon structure of \u003cem\u003eManes.07G112000\u003c/em\u003e and DNA polymorphisms in the gene resulting in six different haplotypes. The two lines indicate the position of the two SNPs (SNP_24026637 and SNP_24033698) within the gene. The haplotypes indicate nucleotide or SNP combinations. Boxplots for mean storage root weight of the haplotypes for the (B) 2019 and (C) 2020 planting seasons are shown. In the boxplots, the central lines denote the average. Significant levels were determined using a least significant difference test and the different lowercase letters above the boxplots represent significant differences (P\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of potential candidate genes identified in the vicinity of the GWAS hits for the traits\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePosition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCandidate gene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLocalization of SNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnnotation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2747056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.05G037900\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eexon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMalate dehydrogenase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2749412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.05G037900\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eexon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMalate dehydrogenase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2373946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.05G032800\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eexon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSNF2 family N-terminal domain protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2814227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.05G039500\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edownstream (2.9Kb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCOP9 signalosome complex subunit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24033698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.07G112000\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eexon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTPL-binding domain in jasmonate signalling\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24218174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.07G113800\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eupstream (6Kb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSEL-1-like protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24026637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.07G112000\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5'UTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTPL-binding domain in jasmonate signalling\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24069335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.07G112400\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eexon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGTP-binding protein SEC4, Ras family GTP-binding proteins\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5559383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.14G068300\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eupstream (0.1Kb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUbiquitin-conjugating enzyme e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5559383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.14G068200\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eupstream (2.9Kb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProtein kinase domain (Pkinase) // Leucine rich repeat (LRR_8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5559383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.14G067800\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edownstream (29.8Kb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAlpha, alpha-trehalose-phosphate synthase (UDP-forming)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21013081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.12G101200\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eexon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUncharacterized Myb domain\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22383767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.14G166100\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eupstream (18Kb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eATP binding / protein kinase-related\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5919578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.16G042900\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eintron\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSF7 complex intermediate associated protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDMC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11795901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.02G156800\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eupstream (9.3Kb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eF-box domain protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDMC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e493861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.02G004000\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eexon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSF2 - N6-adenosine-methyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDMC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3087896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.12G037300\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edownstream (0.8Kb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThioredoxin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDMC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26380158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eManes.11G153600\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edownstream\u003c/p\u003e\u003cp\u003e(10.7Kb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePLATZ transcription factor family protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eChr\u0026thinsp;=\u0026thinsp;chromosome number, UTR\u0026thinsp;=\u0026thinsp;untranslated region.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRoot yield and dry matter content of cassava are important traits in breeding for subsistence and commercial use; therefore, understanding their genetic architecture and underlying genomic regions influencing variations in these traits could accelerate genetic improvement. The population showed large phenotypic variation within all traits, and the magnitude of broad-sense heritability estimates for the traits were comparable to previously reported estimates value (Adjebeng-Danquah et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Adu et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) indicating the presence of substantial genetic component of these traits to allow for selection. Root weight (RW), which defines actual fresh yield per plant is a component of number of storage roots (NSR) and the individual root weight (MRW). The significant positive correlation between MRW and both RW and DMC makes MRW a significant trait to simultaneously improve both RW and DMC hence selection for high MRW could potentially improve both traits (Tumuhimbise et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Adjebeng-Danquah et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Adu et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Knowledge of the structure underlying the population for association analysis could eliminate the effect of false positives associations (Yu et al. 2006). Estimation of population structure revealed that most of the accessions were in admixture state with their genetic composition coming from the different subgroups (K\u0026thinsp;=\u0026thinsp;4). More importantly, each subgroup consists of accessions from different sources/origins indicating broad genetic base of the study population and its suitability for association analysis. GWAS provides insights into the genetic basis for complex traits where it highlights signals of associations between SNPs and phenotypic traits in diverse population (Bangarwa et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). NSR and MRW which are directly related to root yield, were highly associated with SNPs on chr5 and chr7 respectively, similar to other reports (Zhang et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The variation in the haplotype\u0026rsquo;s performance and more importantly the localization of the top significant SNPs within the exonic regions of candidate genes could serve as important steps towards the development of functional markers (Uchendu et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Though none of the SNP markers showed pleiotropic effect (a single SNP significantly influencing more than one trait), loci with favorable alleles influencing individual traits mark the beginning of marker discovery for the trait.\u003c/p\u003e\u003cp\u003eThe most notable candidate genes identified in this study were found in the vicinity occupied by SNP_2747056 and SNP_2749412 on chromosome 5 for NSR as well as SNP_24033698 and SNP_24026637 on chromosome 7 for MRW.\u003c/p\u003e\u003cp\u003eSNP_2747056 and SNP_2749412 for NSR co-located with \u003cem\u003eManes.05G037900\u003c/em\u003e a malate dehydrogenase (MDH) protein involved in the interconversion of oxaloacetate and malate a critical step in tricarboxylic acid (TCA) cycle and photosynthesis directly implicating it in energy production and carbon flux distribution in plants (Scheibe, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Tomaz et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Baird et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Martinez-Vaz et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). MDH enzymes are localized in chloroplasts, mitochondria, peroxisomes, and the cytosol of plants (Gietl \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). In mitochondria, MDH enzymes are reported to participate in the tricarboxylic acid (Krebs) cycle (Gietl \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), provision of NAD\u003csup\u003e+\u003c/sup\u003e for glycine oxidation (Journet et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) and provision of CO\u003csub\u003e2\u003c/sub\u003e for carbon fixation in the bundle sheath cells of some C\u003csub\u003e4\u003c/sub\u003e plants (Hatch and Osmond \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Mutations in mitochondrial MDH affected photorespiration efficiency, leading to growth retardation and reduced ATP production in Arabidopsis. Knocked out lines (\u003cem\u003emmdh1\u003c/em\u003e and \u003cem\u003emmdh2\u003c/em\u003e) in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e were sensitive to MDH activity with impaired root growth, delayed development, and reduced respiration rates (Tomaz et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In tomato, the expression of the antisense fragment of mMDH revealed low root dry weight and low respiration in the roots (van der Merwe et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This suggests a distinct impact of MDH disruption on roots development (Tesfaye et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Tomaz et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) which serves as storage organ of carbon in cassava. In addition, the MDH enzyme was recently reported to be associated with the LIKE SEX FOUR 1-malate dehydrogenase complex involved in starch degradation which included glucan phosphatase and β-amylase (Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) suggesting sideline roles for MDH in starch metabolism that demand further analysis. These putative gene has not been well studied in cassava and further investigation will be needed to explore their potential roles in cassava in relation to root development and carbon partition.\u003c/p\u003e\u003cp\u003eFor MRW, SNP_24026637 and SNP_24033698 on chromosome7 were localized in the 5\u0026rsquo;UTR and exon region respectively of \u003cem\u003eManes.07G112000\u003c/em\u003e gene encoding a TOPLESS (TPL) binding domain involved in jasmonate signaling. \u003cem\u003eManes.07G112000\u003c/em\u003e (TPL-binding domain protein) has a C-terminal zinc binding domain from the NINJA (Novel Interactor of JAZ) protein which interacts with the TIFY domain of JAZ1 (Jasmonate Zim-domain protein 1). TOPLESS (TPL) is a transcriptional co-repressor that plays a key role in jasmonate (JA) signaling by suppressing the expression of JA-responsive genes under non-stress conditions to promote growth where NINJA (Novel Interactor of JAZ) acts as a bridge, connecting JAZ to TPL to form a JAZ\u0026ndash;NINJA\u0026ndash;TPL Repressor Complex (Long et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pauwels et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Acosta et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Several evidence suggests JA-signaling regulates defense-growth trade-off (Noir et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Attaran et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Major et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggesting JA-signaling as a regulatory hub mediating metabolic reprogramming in response to changing environmental conditions. The role of JA-signaling in the regulation of carbohydrates were evident in poplar tree leaves (Babst et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), tobacco (Hanik et al. 2010), cabbage leaves (Tytgat et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and tobacco plants (Wang et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) with low starch concentration as result of impaired JA signaling. In Arabidopsis, mutants with non-functional \u003cem\u003eTPL\u003c/em\u003e showed stunted growth, short roots, and enhanced defense gene expression, due to uncontrolled JA signaling (Pauwels et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Acosta et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These independent findings suggest the role of the JAZ-NINJA-TPL complex and JA signaling mediated modulation of carbohydrate metabolism in the regulation of plant growth and induction of defense responses. Herein, cassava accession harboring the two SNPs (SNP_24033698 and SNP_24026637; haplotype E) localized in \u003cem\u003eManes.07G112000\u003c/em\u003e (TPL-binding domain) had bigger tubers (high MRW) compared to those with reference alleles or without the SNPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) making it ideal for marker development. The potential role of \u003cem\u003eManes.07G112000\u003c/em\u003e (TPL-binding domain) in jasmonate signaling coupled with the reported effect of the hormone in the regulation of carbon in other crops through resource allocation (growth-defense) makes it a potential gene for further studies in cassava in relation to yield.\u003c/p\u003e\u003cp\u003eDMC and HI on the other hand were associated with SNPs on different chromosomes revealing the complexity of the traits (Rabbi et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Chromosomes14 was associated with both RW and HI suggesting that these traits could be coinherited. Previous studies linked stem diameter and dry mass content to Chromosme14 (Zhang et al \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Candidates have been suggested for DMC, including \u003cem\u003eManes.02G156800\u003c/em\u003e, annotated as F-box domain (F-box) protein 9.3Kb upstream of SNP_11795901 and \u003cem\u003eManes.02G004000\u003c/em\u003e (N6-adenosine-methyltransferase) containing SNP_493861, both on chromosome 2. The gene \u003cem\u003eManes.02G004000\u003c/em\u003e which encodes N6-adenosine-methyltransferase harbored the SNP_493861 in the seventh exon was reported to be involved in mRNA modifications via methylation. Methylation of adenosine residues in mRNA is reported to influence embryonic development in Arabidopsis (Zhong et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bodi et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Recently, \u003cem\u003eArabidopsis thaliana\u003c/em\u003e plants lacking mRNA adenosine methylase were reported to exhibit heightened sensitivity to drought implicating it in stress response (Ganguly et al. 2024). \u003cem\u003eManes.02G156800\u003c/em\u003e has F-box domain found in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e protein as ATTENUATED FAR-RED RESPONSE (AT2G24540.1, AtAFR) also knowns as SKP1-interacting partner29 (AtSKIP29) a component of SCF (SKP1/ASK-cullin-F-box protein) E3 ubiquitin ligase complexes involved in the ubiquitination and subsequent proteasomal degradation of target proteins thereby regulating a wide range of physiological processes including hormone signaling, development, and stress responses (Guo et al. 2003; Thines et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). More importantly, the SCF complex through its interaction with \u003cem\u003eCOI1\u003c/em\u003e (CORONATINE INSENSITIVE1) targets JAZ repressors following jasmonate perception to regulate growth and defense trade-offs (Xu et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Thines et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, these putative genes have not been well studied in cassava and would need further investigation in relation to dry matter accumulation in the roots of cassava.\u003c/p\u003e\u003cp\u003eThese findings suggest some genetic basis of yield-related traits in cassava. Through GWAS, we identified significant SNPs for the studied traits and promising genes for the MRW and DMC to be associated with jasmonate signaling which could modulate carbohydrate profiles in plant growth-defense conflicts. These SNPs and genes could be potential targets for breeders in marker-assisted breeding to improve yield in cassava after further validation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe SNPs and candidate genes identified for yield-related traits provide valuable insights into the genetic basis of cassava yield improvement and offer potential markers for MAS breeding of high-yielding cultivars. The studies identified significant SNPs associated with natural variation in traits in the cassava population. The haplotype analysis highlighted \u003cem\u003eManes.05G037900\u003c/em\u003e on chromosome 5, \u003cem\u003eManes.07G112000\u003c/em\u003e on chromosome 7 and \u003cem\u003eManes.02G156800\u003c/em\u003e on chromosome 2 as potential target for enhancing number of storage roots, mean storage root weight and dry matter content in cassava respectively through marker-assisted and genomic selection. Cassava is a relatively hardy and drought-tolerant crop, whether the disruption of \u003cem\u003eManes.07G112000\u003c/em\u003e reported to be involved in plant defense through jasmonate signaling, will enhance yield via a growth-defense tradeoff or through novel biological pathways remains to be exploited. Further validation of these associations and candidate genes will be essential to accelerate cassava genetic improvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAGB, RA, SA conceived and designed the project, AGB, RA, SA, RAA, AY, and JAD collected the cassava landraces and contributed to the field evaluation of the accessions. AGB, RA, AK and DA contributed to the data analysis, AGB wrote the draft manuscript and DA, RA, FIM and TF revised the paper. All authors read and approved of the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Alliance for a Green Revolution in Africa, through the Improved Masters in Cultivar Development Programme (IMCDA; 2014 PASS-012), Faculty of Agriculture, Kwame Nkrumah University of Science and Technology, Ghana for providing funds for this study. \u0026nbsp; AGB is supported by JSPS as a foreign post-doctoral fellow.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are provided within the article and its additional files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations Conflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcosta IF, Gasperini D, Ch\u0026eacute;telat A, Stolz S, Santuari L, Farmer EE (2013) Role of NINJA in root jasmonate signaling. 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Plant Cell 20(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1105/tpc.108.058883\u003c/span\u003e\u003cspan address=\"10.1105/tpc.108.058883\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-breeding","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molb","sideBox":"Learn more about [Molecular Breeding](https://www.springer.com/journal/11032)","snPcode":"11032","submissionUrl":"https://submission.nature.com/new-submission/11032/3","title":"Molecular Breeding","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Manihot esculenta Crantz, single-nucleotide polymorphism (SNP), population structure, genome-wide association analysis (GWAS), haplotypes","lastPublishedDoi":"10.21203/rs.3.rs-8007567/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8007567/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCassava (\u003cem\u003eManihot esculenta\u003c/em\u003e Crantz) is a vital calorie source in the tropics due to its adaptation to marginal agroecological conditions. Cassava\u0026rsquo;s dual role as a food security and industrial crop has stimulated extensive research into increasing yield. Genetic gain can be accelerated through investment in marker discovery and identification of genetic loci controlling important traits for use in breeding programs. Using the Diversity Array Technology genotyping-by-sequencing (DArTseq) platform, a population of 94 cassava accessions, comprising local landraces from Ghana and exotic lines from the International Institute of Tropical Agriculture (IITA), was genotyped with more than 30,000 SNP markers. A genome-wide association study (GWAS) was carried out for five yield-related traits, namely number of storage roots (NSR), mean storage root weight (MRW), root weight per plant (RW), harvest index (HI), and dry matter content (DMC) to survey the genome for the putative loci associated with these traits. A total of 55 significant marker-trait associations were detected, and haplotype analysis showed that favorable alleles at each locus had stronger genetic effects on yield-related traits, leading to the prediction of candidate genes. The identification of \u003cem\u003eManes.07G112000\u003c/em\u003e (TPL-binding domain protein) and \u003cem\u003eManes.02G156800\u003c/em\u003e (F-box domain) as candidate genes associated with MRW and DMC, respectively, highlights their potential roles in jasmonate signaling pathways. This connection suggests the existence of a defense-growth trade-off influencing yield traits in cassava. These findings lay a valuable foundation for the development of molecular markers to assist in breeding programs aimed at enhancing yield potential and overall productivity in cassava cultivars.\u003c/p\u003e","manuscriptTitle":"Genome-wide association study identifies novel genomic regions associated with yield-related traits in cassava (Manihot esculenta Crantz)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 09:18:44","doi":"10.21203/rs.3.rs-8007567/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-16T11:58:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T03:06:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186890989740169690279140652727938328282","date":"2026-02-19T19:55:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126670665462285916842533600605177212610","date":"2026-02-18T14:41:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-15T04:11:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250334902515450089073268702032473422155","date":"2026-01-11T09:23:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T08:52:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-06T05:47:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-03T07:25:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Breeding","date":"2025-11-01T18:55:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-breeding","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molb","sideBox":"Learn more about [Molecular Breeding](https://www.springer.com/journal/11032)","snPcode":"11032","submissionUrl":"https://submission.nature.com/new-submission/11032/3","title":"Molecular Breeding","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2b862962-c57b-4312-afce-9fc44854c221","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T12:11:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 09:18:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8007567","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8007567","identity":"rs-8007567","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}