Genotyping by sequencing reveals genetic relatedness and duplicates amongst local cassava (Manihot esculenta Crantz) landraces and improved genotypes in Kenya

doi:10.21203/rs.3.rs-1295398/v1

Download PDF

Research Article

Genotyping by sequencing reveals genetic relatedness and duplicates amongst local cassava (Manihot esculenta Crantz) landraces and improved genotypes in Kenya

https://doi.org/10.21203/rs.3.rs-1295398/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Future demand for cassava is expected to increase to mitigate climatic changes, sustain food security and provide raw materials for industry. To meet these demands, adoption of modern omics methods ensures reliability, precision and timely delivery of more productive and resilient varieties. Therefore the purpose of this study was to contribute towards accurate identification of cassava accessions from a mix of duplicate clones, diverse local landraces (LARs) and improved genotypes (IMGs) in farmer fields. This is vital for cassava breeding.

Results: A total of 112 germplasms sampled through a field survey in major cassava growing regions of Kenya, were genotyped using single nucleotide polymorphisms (SNPs) markers generated through genotyping-by-sequencing (GBS) approach. Of the 33672 SNPs, 88% were anchored onto chromosomes, 3% in scaffolds and 9% could not be mapped. LD pruning and identity by state matrix estimation revealed 5808 SNPs that were used for hierarchical clustering and ADMIXTURE analysis for ancestries. Considering a sub-population of 2 - 20, a 5-fold cross-validation procedure identified 14 subpopulations present in the population from which the population structure was modeled. Approximately 48% of the germplasms were classified into 17 independent clusters as identical clones or duplicates. The remaining 52% formed admixtures and hence unique or non-duplicated clones; reducing the total number of samples surveyed from 112 to 73. Of the duplicates, 10 clusters were formed from LARs, four from IMGs, and three from a mix of both LARs and IMGs. The major and minor clusters contained 8 and 2 accessions, respectively. About 71% of clusters contained accessions from the same geographical region while 29% had accessions from different regions. The results revealed genetic relationships amongst LARs and IMGs. Duplication of LARs was attributed to historical sharing or exchange of planting materials by farmers while duplicates of IMGs could be attributed to convergent evolution, selection, or sharing of common parentage. The high number of admixtures or unique clones implied minimal loss of genetic diversity. Geographical restriction of clusters adduced to the minimal movement of planting materials across the country, perhaps linked to either inefficient seed distribution system or disease-driven quarantine measures.

Conclusions: GBS was successfully used to study the genetic relatedness of cassava genetic resources and variety identification in farmer fields. This omics approach and data herein generated could be adopted by breeders and other stakeholders in designing efficient and effective cassava improvement programs which might include the development of a core set of diagnostic markers for quality assurance, disease resistance, and targeted genomic profiling in cassava.

Genotyping-by-sequencing

landraces

improved genotypes

variety identification

Cassava (Manihot esculenta Crantz) which originated around the Amazon basin (Léotard et al. 2009; Hirst 2020; Olsen 2004; Olsen and Schaal 1999) was introduced in sub-Saharan Africa (SSA) by the Portuguese traders in the 16th century (Spencer and Ezedinma 2017) and in the East Africa coast in the 18th century (Were et al. 2004). The crop is a perennial woody shrub extensively grown in the tropical and subtropical regions of the world for its edible starchy tuberous roots, which are a major food source for developing countries (New World Encyclopedia 2020; Clifton and Keogh 2016; Liu et al. 2014; Kuete 2014). The continuous rise in cassava popularity in Africa is attributed to the crop’s low input requirement, tolerance to drought stress or low water requirement, survivability in marginal soils or soils with low nutrients, and flexible harvesting window that allows the crop to be left in the soil as a food reserve (Orek et al. 2020; Rabbi et al. 2020; Shigaki 2016). These make cassava a resilient crop important for food and nutritional security in Africa, where half a billion people eat the crop daily (Amelework et al. 2021; Tize et al. 2021; Agre et al. 2018; CIAT 2019).

Despite its significance, cassava production in SSA still lags behind other parts of the world. This has largely been attributed to low investments in breeding programs and inherent genetic challenges associated with the crop. Genetic barriers such as high heterozygosity, inbreeding depression, allopolyploid, poor seed set, irregular flowering, and the polygenic and recessive nature of many desirable traits, constrain development of new or improved varieties especially via conventional breeding (Elegba et al. 2021; Ceballos et al. 2020; Makwarela and Rey 2006). These are further compounded by a mixture of diverse local landraces and improved varieties that are often cultivated by most small-scale farmers on the same piece of land. Indeed, farmers often exchange stem cuttings or planting materials with their neighbors and neighboring communities, resulting in fields with a mixture of local cassava varieties (Andersson and de Vicente 2010; Nakabonge et al. 2018). Commonly, this results in the same ethnic or local name being assigned to different cassava germplasms or the same germplasms assigned different local names. Variety naming systems in the absence of formal seed systems can be quite temporally and spatially variable, leading to inconsistencies in the names of a particular variety (Rabbi et al. 2015). All these hamper the selection of breeding lines. To overcome these limitations, molecular approaches can assist in reliable identification, characterization, and verification of genotypes or varieties and hasten selection of appropriate parental plants (OECD 2016; Otti et al. 2011; Lebot 2009), thus improve the designing and delivery of tailored breeding objectives such as high yields (Lopez-Lavalle et al. 2021).

Accurate identification of crop cultivars is crucial in assessing the impact of crop improvement research outputs and the two commonly used identification approaches; elicitation of variety names from farmer interviews and morphological plant descriptors, have inherent uncertainty levels (Rabbi et al. 2015). The major aim of variety or cultivar identification is to catalog the crop’s genetic diversity (Lopez-Lavalle et al. 2021). There are many reports on many landraces of cassava in SSA but with limited studies on the genetic relatedness between these landraces and elite or improved accessions (Turyagyenda et al. 2012). Molecular marker technologies such as RFLPs, AFLPs, SSRs, DArTs, and SNPs among others have been used to detect polymorphisms and characterize genetic variation in cassava cultivars (Lopez-Lavalle et al. 2021). Rabbi et al (2015), successfully used SNPs derived from GBS to track and identify released cassava varieties and local landraces in Ghana, West Africa. The present study, therefore, applied the GBS approach to generate SNPs that revealed genetic relatedness amongst local landraces and improved cassava genotypes sampled from various cassava growing regions in Kenya. This is a preliminary step toward the acceleration of the cassava breeding process in the country.

Sample collection

A field survey was carried out in April 2018 in selected areas within major cassava growing regions of Nyanza, western, eastern, and coastal Kenya (Fig. 1). Systematic sampling was applied to identify cassava farmers or farms for cassava leaf collection (Koima et al. 2018). This involved stopping at regular pre-determined intervals (~2-5 km) allowing wide coverage of the surveyed areas between farmer fields along the major motorable roads traversing each sampling location (Mware et al. 2009). The local name of the landraces and/or names of villages and GPS coordinates where the samples were collected were recorded (Table 1). Cassava leaves were harvested and pooled from five plants per landrace or genotype. The leaves were immediately transferred to falcon tubes half-filled with silica gels to preserve their integrity prior to DNA extraction.

Sequencing cassava using DArTSeq

Cassava leaf samples were sent to Integrated Genotyping Service and Support (IGSS) platform located at the Biosciences eastern and central Africa (BecA-ILRI) Hub in Nairobi, Kenya for genotyping. DNA extraction was done using TANBead Plant extraction kit. The quality and quantity of genomic DNA were determined using NanoDrop ND-1000 (Thermo Fisher Scientific) and agarose gel electrophoresis. Libraries were constructed according to Kilian et al. (2012) DArTSeq complexity reduction method through digestion of genomic DNA using a combination of PstI and MseI restriction enzymes and ligation of barcoded adapters followed by PCR amplification of adapter-ligated fragments. Libraries were sequenced using single read sequencing runs for 77 bases. Next generation sequencing was carried out using the Illumina Hiseq2500. DArTseq markers scoring was achieved using DArTsoft14 which is an in-house marker scoring pipeline based on algorithms. Two types of DArTseq markers were scored, SilicoDArT markers and SNP markers which were both scored as binary for presence /absence (1 and 0, respectively) of the restriction fragment with the marker sequence in genomic representation of the sample. Both SilicoDArT markers and SNP markers were aligned to the reference genomes of Cassava_v61 to identify chromosome positions.

Data analysis

The quality of the SNP data was filtered using TASSEL and SNPs anchored on scaffold or missing chromosome information were discarded. TASSEL was also used to select SNPs with >0.05 minor allele frequencies (MAF) and SNPs with no more than 20% missing genotype data. For LD pruning and IBS matrix estimating, Plink 1.9 was used to select for SNP with less than 0.5 R² LD value within each 50-SNP window size i.e. considering 50 SNPs at a time, the LD between them should be less than 0.5 LD R². Two methods used for grouping the genotypes included hierarchical clustering using identity by state (IBS) matrix and a model-based maximum likelihood estimation of individual ancestries from multi-locus SNP genotype datasets using ADMIXTURE (Rabbi et al. 2015). IBS examines if two lines are identical based on the nucleotide (SNP alleles) that they share. Using the pruned SNPs from Plink, IBS matrix was calculated with the distance function of Plink (Purcell et al. 2007). The matrix was used for hierarchical clustering using the Ward2 method for distance estimation. The critical distance threshold used to declare two genotypes are identical was 0.05 based on the empirically determined evidence suggested by Rabbi et al (2015) from the distribution of distances between duplicated DNA of 64 cassava samples. A ward’s minimum variance hierarchical cluster dendrogram (Fig. 3) was then generated from IBS matrix using Analyses of Phylogenetics and Evolution (APE) package (Paradis et al. 2004) implemented within R software (R Core Team, 2020).

After filtering, LD pruning and IBS matrix were used to determine the LD threshold and select SNPs accordingly. The same set of LD-pruned SNPs used for the hierarchical clustering was also used for ADMIXTURE to identify ancestries of the collected cassava germplasms (Rabbi et al. 2015). The model-based clustering approach implemented in ADMIXTURE assumes linkage equilibrium among loci and Hardy-Weinberg equilibrium within ancestral populations (Frichot et al. 2014; Rabbi et al. 2015). Considering a sub-population of 2 - 20, a 5-fold cross-validation procedure was used to select the optimum number of sub-populations present in the population as 14. The population structure was then modeled with the optimum number of underlying sub-population groups (Fig. 5).

Cassava germplasms

Out of 112 cassava germplasms collected from five cassava growing regions (Fig. 1), 71 (~63%) were local landraces and 41 (~37%) were improved genotypes (Fig. 2). Distribution showed more landraces were cultivated in all regions except in Kitui where more improved genotypes were collected (Fig. 2). Traits or characteristics of most landraces had not been documented compared to improved genotypes that were developed for resistance or tolerance against two (CMD & CBSD) major virus diseases (Table 1). However, farmers casually interviewed during sampling attributed their preferences to local landraces for sweet or bitter tubers, early maturity, and high yield (data not shown). Improved genotypes were introduced into these regions by research institutions such as International Center for Tropical Agriculture (CIAT), International Institute of Tropical Agriculture (IITA) and Kenya Agricultural and Livestock Research Organization (KALRO) (Table 1).

Filtering and selection of SNPs and optimum population identification

A total of 33672 SNPs was identified. Out of this, 29614 SNPs (~88%) were anchored to chromosomes, 942 (~3%) were present in scaffolds, while the remaining 3116 SNPs (~9%) could not be mapped to any chromosome or scaffold. After quality filtering, 20846 SNPs were selected. LD pruning and IBS matrix estimation revealed that 5808 SNPs met the selected LD threshold criteria (Table 2). The 5-fold cross-validation procedure revealed the number of optimum populations to be 14 (Fig. 4).

Admixture analysis

Genetic relationships among genotyped cassava germplasms are shown on hierarchical clustering dendrogram (Fig. 3) while population structure depicting ancestries from admixture presented as a barplot (Fig. 5). The admixture clustering together with dendrogram topology enabled identification of clusters of genetically identical germplasms containing only landraces, only improved genotypes as well as clusters containing both landraces and improved genotypes (Table 3). A total of 54 germplasms (~48%) were grouped into 17 independent clusters (I - XVII) as identical clones or single pure lines (Table 3). They represented duplicated clones bearing different local names. Out of 17 clusters, 10 contained only landraces; four had only improved genotypes and the remaining three clusters had accessions from landraces and improved genotypes (Fig. 6). Of the 10 landrace clusters, cluster IX was the largest with eight accessions, followed by cluster XIV with five 5 accessions, cluster I and X each with four accessions, four clusters (XVII, XVI, XII, and XI) each with three accessions and two clusters (XV & VII) with two accessions each (Fig. 6). All the four clusters that contained only improved genotypes (VI, IV, III & II) had two accessions each while three clusters containing both landraces and improved genotypes (V, VIII & XIII) had three accessions each (Fig. 6).

Geographically, majority of the clusters (12 of the 17 or ∼71%) contained accessions sampled from the same region (Table 3). These included clusters II, III, IV, V, VI, VII, IX, XI, XIII, XIV, XV, and XVI. The remaining five of the 17 (∼29%) clusters (I, VIII, X, XII & XVII) had accessions sampled from different regions (Table 3). For instance cluster I, VIII and XII were from regions in closer proximity (Siaya = 0°26’N; 33°58’E, and Kakamega = 0°16’N; 34°45’E) while cluster X (Kitui = 0°10’S; 37°50’E, and Kakamega = 0°16’N; 34°45’E) and XVII (Makueni = 1°48’S; 37°37’E, and Kakamega = 0°16’N; 34°45’E) represented clustering of accessions from far regions (Table 3). Landraces from Kilifi (3°40’S; 39°45’E) located in coastal Kenya did not cluster with landraces or improved genotypes from other regions (see cluster XIII and XIV) (Table 3). Compared to other regions, Kitui (0°10’S; 37°50’E) had a majority (5) of different clusters (II, III, IV, V & VI).

The remaining 58 germplasms (~52%) were classified as admixtures and thus unique or non-duplicated clones as they did not cluster (Table 3; Fig. 6). Under this category, 31 accessions (~53%) were landraces and 27 (~47%) were improved genotypes (Fig. 6). In terms of known traits (from literature reviews), clusters containing either improved genotypes alone or a mix of improved genotypes with local landraces were described as resistant or tolerant to two major virus diseases i.e. cassava mosaic disease (CMD) and cassava brown streak disease (CBSD) compared to the majority of landrace-based clusters with no information available on their known traits (Table 1). Only clusters I and X (all landraces) had CMD and CBSD susceptible accessions. In summary, the majority of landraces clustered as identical clones or accessions compared to improved genotypes while regionally, most clusters contained accessions sampled within the same region. The unique or non clustered accessions (58) plus clustered or duplicates (17) reduced the total accessions surveyed to 73 from 112 that were originally genotyped.

Most of the sampled materials (approximately 63%) were local landraces compared to improved cassava genotypes that constituted 37%. This implied cultivation of more local cassava varieties or landraces which have been attributed to farmer preferred characteristics such as culinary attributes and cooking quality, sweet or bitter tastes, early maturity, pests and disease resistance, high yield, root storability in the ground, drought tolerance among other traits (Nakabonge et al. 2018; Bentley et al. 2017; Woyengo 2011). Farmers often hold several generations of knowledge concerning the attributes of landraces and sometimes have specific reasons why they retain particular cultivars (Ferguson et al. 2021). On the reverse, the results implied minimal adoption and cultivation of the improved varieties in Kenya, a potential drawback for the management of cassava diseases as most of the improved genotypes had been bred and introduced for resistance or tolerance to CMD and CBSD. This was corroborated by earlier studies on low dissemination, adoption, and production of improved cassava varieties in Africa, a situation that was linked to lack of involvement of farmers and end-users in designing, planning, and execution of breeding strategies and objectives (Nakabonge et al. 2018; Bechoff et al. 2018; Woyengo et al. 2014; Kamau et al. 2011). Farmer preferences and varietal attributes influence the adoption of new cassava varieties (Okuku et al. 2018; Nduwumuremyi et al. 2016; Kamau et al. 2016; Khonje et al. 2015). It is however noted that farmer preferences or attributes of the genotyped landraces and improved varieties were not assessed in the present study.

The SNPs marker data generated using GBS was successfully used to determine genetic relatedness among sampled cassava germplasms. From a total of 33672 SNPs identified, 5808 SNPs (~17%) obtained after LD pruning and IBS matrix estimation were used for hierarchical clustering and ADMIXTURE analysis to identify ancestries. This enabled the identification of germplasms that clustered together as well as unique or non-duplicated germplasms. Thus, a large number of SNPs may not be needed to achieve accurate identification of cassava varieties, whether in farmers’ fields or formal germplasms collections (Lopez-Lavalle et al. 2021; Rabbi et al. 2015; Ferguson et al. 2012). A further study could be initiated to identify these SNPs and design KASP markers for varietal identification. Knowledge of the existence of duplicates in the field is important during the collection of variability and evaluation and selection of parents for cassava improvement or breeding purposes. Similarly, genomic or SNPs markers have been used to confirm that particular cassava accessions are not identical, and others are possible duplicates (Mbanjo et al. 2021; Ferguson et al. 2012). They have also been used to track local landraces and assess the adoption of improved varieties (Rabbi et al. 2015; Assfaw et al. 2017; Turyagyenda et al. 2012). Accurate identification of crop cultivars is crucial in assessing the impact of crop improvement research outputs (Rabbi et al. 2015). Generally, the genomic approach contributes to further characterization of cassava genetic resources, an important step in improving cassava production in Kenya.

Further results from the present study showed that the majority of the duplicated clones were local landraces while geographically, most of the duplicated accessions were sampled either from the same region or from different regions of closer proximity. These redundancies were previously attributed to the historical sharing of cassava accessions or the same germplasms exchange between farmers with different genotype names (Albuquerque et al. 2019). Farmers often exchange planting materials with their neighbors or different neighboring communities, resulting in fields with a mixture of local cassava varieties (Andersson and de Vicente 2010; Nakabonge et al. 2018). Thus the same ethnic or local name could be assigned to different cassava germplasms or the same germplasms assigned different local names. Variety naming systems in the absence of formal seed systems can be quite temporally and spatially variable leading to inconsistencies in the names of a particular variety (Rabbi et al. 2015). The informal farmer-farmer seed distribution system is often inefficient, denying farmers in far flung areas access or a share of alternative planting materials.

Ferguson et al (2021) reported that individual cassava landraces were not widely distributed across Tanzania with limited farmer-to-farmer diffusion with implications for seed systems. Indeed, smallholder farmers recycle stem cuttings of traditional landrace cultivars (Nweke et al. 2002) and there is a flow of seed within and outside the villages, with little introduction of new cultivars (Mtunguja et al. 2014). The absence of an effective seed distribution system (Kyamanywa et al. 2011) has limited farmers’ access to planting materials from improved genotypes. Additionally, elicitation of cassava variety names from farmer interviews during surveys and/or use of morphological plant descriptors have had inherent uncertainty levels (Rabbi et al. 2015). Morphological descriptors are also greatly influenced by the environment and show continuous variation and high plasticity, with most of them only scorable at maturity (Ndung’u et al. 2014). Restrictions of clusters to the same geographical areas where accessions were sampled could also be attributed to quarantine measures that restricted the movement of planting materials in order to stop the spread of virus diseases such as CMD and CBSD.

Similarities in cassava accessions can also arise due to convergent evolution, selection, or sharing of common parentage (Ndung’u et al. 2014). This was probably the case in Kitui region where the majority of duplicates were improved genotypes that had shared the same parents during their breeding for resistance to cassava brown streak disease (Koima and Orek 2018). Crops gradually lose their genetic variability through domestication and breeding, resulting in more uniform cultivars and reducing their recombination rates (Rufo et al. 2019). This could perhaps be used to explain clusters that included both improved genotypes and local landraces. It is however noted that no recent evidence has shown loss of genetic variation from genetic drift during the introduction of cassava to Africa (Ferguson et al. 2019). The relatively low levels of diversity reported in the previous study were only observed in IITA breeders’ germplasms and may represent rather a genetic bottleneck (Ferguson et al. 2019). For future breeding programs involving hybridization or selection, de Oliveira et al (2015) recommended the introduction of new genetic variability into commercial cultivars to avoid low genetic variation and to improve the quality of cassava roots. The unique or non-duplicated landraces and improved genotypes in the present study represented a more expanded cassava genetic pool from which variability can be derived for future breeding purposes. It might also be important to build the core collection of the 73 unique genotypes studied in this study for further efficient conservation and cassava breeding. High genetic diversity drives better crop adaptation to emerging environmental cues.

Molecular markers have an important role to play as farmers frequently give different names to the same cultivar or landraces, making identification difficult, particularly as cassava varieties are not easy to distinguish morphologically (Mbanjo et al. 2021). This enables the correct assessment of adoption rates, which in turn, influences breeding priorities and agricultural policies (Kretzschmar et al. 2018). Knowledge on the extent of genetic diversity among cassava landraces and improved genotypes in Kenya using GBS-derived SNP markers may promote their conservation and/or efficient selection and utilization as parental lines for breeding for biotic and abiotic stress tolerance. Although local landraces may be low-yielding, they may have high genetic diversity that could promote gene flow through hybridization (Turyagyenda et al. 2012), enabling crop improvement and adaptability of species to changing climatic conditions, new pests, and diseases (Prempeh et al. 2020).

Acknowledgements:

The authors thank Dr. Nasser Yao and Dr. Oluwaseyi ShorinolaandMartina Kyallo for their valuable comments, inputs, corrections and technical assistance during development of the manuscript

Author contribution:

OC conceptualized the research; KM and OC carried out data collection and methodology; OS and OC carried out data curation and analysis: NY and KM helped with funding acquisition and resources; OC prepared the original draft and editing; KM, OS and NY provided supervision and project administration; all authors read and approved the fnal manuscript. Where OC = Orek Charles; KM = Kyallo Martina; OS = Oluwaseyi Shorinola; NY = Nasser Yao.

Funding:

This research was funded by the BecA-ILRI Hub through the Africa Biosciences Challenge Fund (ABCF) program. The ABCF Program is funded by the Australian Department for Foreign Affairs and Trade (DFAT) through the Biosciences eastern and central Africa - The Commonwealth Scientific and Industrial Research Organization (BecA-CSIRO) partnership; the Syngenta Foundation for Sustainable Agriculture (SFSA); the Bill and Melinda Gates Foundation (BMGF); the UK Department for International Development (DFID) and the Swedish International Development Cooperation Agency (Sida).

Availability of data and materials:

All data are provided in the manuscript. To access materials researched on, please contact the corresponding author.

Code availability:

Not applicable.

Author Declarations:

Ethics approval and consent to participate:

Not applicable.

Consent for publication:

Not applicable.

Confict of interest:

The authors declare no competing interests

Agre AP, Bhattacharjee R, Rabbi IY, et al (2018) Classification of elite cassava varieties (Manihot esculenta Crantz) cultivated in Benin Republic using farmers’ knowledge, morphological traits and simple sequence repeat markers. Genet Res. Crop Evol 65:513–525.
Albuquerque HYG, de Oliveira EJ, Brito AC, et al (2019) Identification of duplicates in cassava germplasm banks based on single nucleotide polymorphisms (SNPs). Sci. agric. (Piracicaba, Braz.) 76 (4) https://doi.org/10.1590/1678-992X-2017-0389
Amelework AB, Bairu MW, Obakeng M, Venter SL, Laing M (2021) Adoption and Promotion of Resilient Crops for Climate Risk Mitigation and Import Substitution: A Case Analysis of Cassava for South African Agriculture. Front. in Sust. Food Syt 5:105
Andersson MS, de Vicente MC (2010) Cassava, manioc, yuca”, Chapter 6, in: Andersson, M.S. and M.C. de Vicente (eds.), Gene Flow Between Crops and Their Wild Relatives, Johns Hopkins University Press, Baltimore, Maryland, 125-146.
Assfaw WT, Girma TG, Abdoulaye T, Rabbi IY, Olanrewaju A, Bentley J, Manyong V (2017) The cassava monitoring survey in Nigeria final report. IITA, Ibadan, Nigeria. ISBN 978-978-8444-81-7.66
Bechoff A, Tomlins K, Fliedel G, Becerra Lopez-lavalle LA, Westby A, Hershey C, et al (2018) Cassava traits and end-user preference: Relating traits to consumer liking, sensory perception, and genetics. Crit. Rev. Food Sci. Nutr. 58:547–567.
Bentley J, Olanrewaju A, Madu T, Olaosebikan O, Abdoulaye T, Wossen V, et al (2017) Cassava farmers’ preferences for varieties and seed dissemination system in Nigeria: Gender and regional perspectives. IITA Monograph, IITA, Ibadan, Nigeria.
CIAT (2019) Cassava: https://ciat.cgiar.org/what-we-do/breeding-better-crops/rooting-for-cassava/
Ceballos H, Rojanaridpiched C, Phumichai C, Becerra LA, et al (2020) Excellence in Cassava Breeding: Perspectives for the Future. Crop Breed Genet Genom.2 (2):e200008. https://doi.org/10.20900/cbgg20200008
Clifton P, Keogh J (2016) Starch: Encyclopedia of Food and Health, pp. 146-151
de Oliveira EJ, Alves F, Alves SL, de Oliveira V, da Silva S (2015) Genetic variation of traits related to quality of cassava roots using affinity propagation algorithm. Genetics & Plant Breeding, Sci. Agric.72 (1) https://doi.org/10.1590/0103-9016-2014-0043
Elegba W, McCallum EJ, Wilhelm G, Vanderschuren H (2021) Genetic transformation and regeneration of a farmer-preferred cassava cultivar from Ghana. Fron. in Plant Sci. 12:909
Ferguson ME, Tumwegamire S, Chidzanga C, Shah T, Mtunda K, Kulembeka H, et al (2021) Collection, genotyping and virus elimination of cassava landraces from Tanzania and documentation of farmer knowledge. PLoS ONE 16(8): e0255326. https://doi.org/10.1371/journal.pone.0255326
Ferguson ME, Shah T, Kulakow P, Ceballos H (2019) A global overview of cassava genetic diversity. PloS one, 14(11), e0224763. https://doi.org/10.1371/journal.pone.0224763
Ferguson ME, Hearne SJ, Close TJ, et al (2012) Identification, validation and high-throughput genotyping of transcribed gene SNPs in cassava. Theor. Appl. Genet. 124:685–695 https://doi.org/10.1007/s00122-011-1739-9
Frichot E, Mathieu F, Trouillon T, Bouchard G, Francois O (2014) Fast and efficient estimation of individual ancestry coefficients. Genetics, 196(4):973–83
Hirst KK (2020) The History and Domestication of Cassava." ThoughtCo, Aug. 28, 2020,thoughtco.com/cassava-manioc-domestication-170321.
Kamau J, Melis R, Laing M, Derera J, Shanahan P, Ngugi K, Migwa Y (2016) Farmers’ Perceptions of Production Constraints and Preferences in Cassava Grown in Semi-Arid Areas of Kenya. Int.J.Curr.Microbiol.App.Sci, Vol. 5(3): 844-859.
Kamau J, Melis R, Laing M, Derera J, Shanahan P, Ngugi ECK (2011) Farmers’ participatory selection for early bulking cassava genotypes in semi-arid Eastern Kenya. Journal of Plant Breeding and Crop Science 3(3):44-52
Khonje M, Mkandawire P, Manda J, Alene DA (2015) Analysis of adoption and impacts of improved cassava varieties in Zambia. International Conference of Agricultural Economics, 1 – 28
Kilian A, Wenzl P, Huttner E, Carling J, Xia L, Blois H, et al. (2012) Diversity arrays technology: a generic genome profiling technology on open platforms. Methods Mol. Biol. 88: 67–89. https://doi.10.1007/978-1-61779-870-2_5
Koima IN, Orek CO (2018) Response to Cassava Brown Streak Disease infections in local and improved cassava genotypes under field and greenhouse assays in lower eastern Kenya. Int. J. of Path. Res. 1(3):1-14 https://doi.org/10.9734/ijpr/2018/v1i329616
Koima IN, Orek CO, Nguluu SN (2018) Distribution of Cassava Mosaic and Cassava Brown Streak Diseases in agro-ecological zones of lower Eastern Kenya. IJISRT, 3(1):391 – 399.
Kretzschmar T, Mbanjo EG N, Magalit GA, Dwiyanti MS, Habib MA, Diaz MG, et al (2018) DNA fingerprinting at farm level maps rice biodiversity across Bangladesh and reveals regional varietal preferences. Sci. Rep. 8:14920.
Kuete V (2014) Physical, Hematological and Histopathological Signs of Toxicity Induced by African Medicinal Plants. Toxicological Survey of African Medicinal Plants, 635 – 657
Kyamanywa S, Kashaija IN, Getu E, Amata R, Senkesha N, Kullaya A (2011) Enhancing Food Security through Improved Seed Systems of Appropriate Varieties of Cassava, Potato and Sweetpotato Resilient to Climate Change in Eastern Africa. Nairobi, Kenya, ILRI.
Lebot V (2009) Tropical Root and Tuber Crops: Cassava, Sweet Potato, Yams, and Aroids, Crop Production Science in Horticulture Series, Volume 17, CABI, Wallingford, United Kingdom.
Léotard G, Duputié A, Kjellberg F, Douzery E, Debain C, Granville JJ, Mckey D (2009) Phylogeography and the origin of cassava: New insights from the northern rim of the Amazonian basin. Molecular phylogenetics and evolution, 53
Liu Q, Liu J, Zhang P, He S (2014) Root and tuber crops, in: Encyclopedia of Agriculture and Food Systems, https://doi.org/10.1016/B978-0-444-52512-3.00151-0
Lopez-Lavalle LAB, Bohorquez-Chaux A, Zhang X (2021) Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer’s Fields: An Update from 1990 to 2020. IntechOpen, 1-30
Makwarela M, Rey C (2006) Cassava Biotechnology, a southern African Perspective. Biotechnology and Molecular Biology Review, 1(1):2-11
Mbanjo EGN, Rabbi IY, Ferguson ME, Kayondo SI, Hwa EN, Tripathi L, Kulakow P, Egesi C (2021) Technological Innovations for Improving Cassava Production in Sub-Saharan Africa. Frontiers in Genetics, 11:1829 https://doi.org/10.3389/fgene.2020.623736
Mtunguja MK, Laswai HS, Muzanila YC, Ndunguru J (2014) Farmers knowledge on selection and conservation of cassava (Manihot esculenta) genetic resources in Tanzania. J. Biol. Agric. Healthcare 4:120–129.
Mware B, Narla R, Amata R, Olubayo F, Songa J, Kyamanyua S, Ateka EM (2009) Efficiency of cassava brown streak virus transmission by two whitefly species in coastal Kenya. J. Gen. Mol. Virol. 1: 40-45.
Nakabonge G, Samukoya C, Baguma Y (2018) Local varieties of cassava: Conservation, Cultivation and use in Uganda. Environ. Dev. Sust. 20: 2427–2445.
New World Encyclopedia (2020) Cassava: Retrieved on Sept. 6, 2021: https://www.newworldencyclopedia.org/p/index.php?title=Cassava&oldid=1030031.
Ndung’u JN, Wachira FN, Kinyua MG, Lelgut DK, Njau P, Okwaro H, Obiero H (2014) Genetic diversity study of Kenyan cassava germplasm using simple sequence repeats. African J. of Biot.13 (8):926-935
Nduwumuremyi A, Melis R, Shanahan P, Asiimwe T (2016) Participatory appraisal of preferred traits, production constraints and postharvest challenges for cassava farmers in Rwanda. Food Sec. 8:375–388 https://doi.org/10.1007/s12571-016-0556-z
Nweke FI, Spencer DSC, Lynam JK (2002) The Cassava Transformation: Africa’s Best-Kept Secret, Michigan State University Press; 2002.
OECD (2016) Cassava (Manihot esculenta Crantz), in Safety Assessment of Transgenic Organisms in the Environment, 6: OECD Consensus Documents, OECD Publishing, and Paris. https://doi.org/10.1787/9789264253421-6-en
Olsen KM (2004) SNPs, SSRs and inferences on cassava’s origin. Plant Mol. Biol.56:517–526
Olsen KM, Schaal B (1999) Evidence on the origin of cassava: Phylogeography of cassava Manihot esculenta Crantz. Proc. Natl. Acad. Sci. USA, 96:5586–5591.
Okuku IO, Nyikal RA, Otieno DJ (2018) An assessment of the effect of varietal attributes on the adoption of improved cassava in HomaBay County, Kenya. MSc. Thesis, University of Nairobi
Orek C, Gruissem W, Ferguson M, Vanderschuren H (2020) Morpho-physiological and molecular evaluation of drought tolerance in cassava (Manihot esculenta Crantz). Field Crops Research, 255, http://doi.org/10.1016/j.fcr.2020.107861
Otti G, Fakoya A, Andrew I, Gedil M (2011) Development of genomic tools for verification of hybrids and selfed progenies in cassava (Manihot esculenta Crantz). African J. of Bio. 10(76): 17400-17408
Paradis E, Claude J, Strimmer K (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics, 20(2):289–90.
Prempeh WNA, Manu-Aduening JA, Quain MD, Asante IK, Offei SK, Danquah EY (2020)
Assessment of genetic diversity among cassava landraces using single nucleotide polymorphic markers. Afr. J. Biot, Vol. 19(6):383-391
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. https://doi.org/10.1086/519795
Rabbi IY, Kayondo SI, Bauchet G, Yusuf M, Aghogho CI, Ogunpaimo K, et al (2020) Genome-wide association analysis reveals new insights into the genetic architecture of defensive, agro-morphological and quality-related traits in cassava. Plant Mol Biol. https://doi.org/10.1007/s11103-020-01038-3
Rabbi IY, Kulakow PA, Manu-Aduening JA, Dankyi AA, Asibuo JY, Parkes EY, et al (2015) Tracking crop varieties using genotyping-by-sequencing markers: a case study using cassava (Manihot esculenta Crantz). BMC Genetics 6:115 https://doi.org/10.1186/s12863-015-0273-1
Rufo R, Alvaro F, Royo C, Soriano JM (2019) From landraces to improved cultivars: Assessment of genetic diversity and population structure of Mediterranean wheat using SNP markers. PLoS ONE 14(7): e0219867 https://doi.org/10.1371/journal.pone.0219867
Shigaki T (2016) Cassava: The Nature and Uses. Encyclopedia of Food and Health, 687-693
Spencer D, Ezedinma C (2017) Cassava cultivation in sub-Saharan Africa. https://doi.org/10.19103/AS.2016.0014.06
Tize I, Fotso AK, Nukenine EN, Masso C, Ngome FA, Suh C, Lendzemo VW, Nchoutnji I, et al (2021) New cassava germplasm for food and nutritional security in Central Africa. Sci Rep 11:7394
Turyagyenda LF, Kizito EB, Ferguson ME, Baguma Y, Harvey JW, Gibson P, et al (2012) Genetic diversity among farmer-preferred cassava landraces in Uganda. Afr. Crop Sci. J. 20: 15–30.
Were HK, Winter S, Maiss E (2004) Viruses infecting cassava in Kenya. Plant Disease, 88:17-22
Woyengo WV, Melis R, Shanahan P, Odongo OM (2014) Participatory evaluation methods of cassava varieties preferred in mild-altitude tropical climate conditions of western Kenya. African J. of Agric. Res. 9(17):1326-1333.
Woyengo WV (2011) Cassava breeding through complementary conventional and participatory approaches in western Kenya. Doctoral thesis, University of KwaZulu-Natal, South Africa

Table 1

Cassava landraces and genotypes sampled during field surveys from different cassava growing regions of Kenya.

Local ID	Code	Region / GPS	Origin / Attributes	Local ID	Code	Region / GPS	Origin / Attributes	Local ID	Code	Region / GPS	Origin / Attributes
Shavirotsi	KK1	0°16’N;34°45’E	Landrace / no information	Nya-Yenga	SYA8	0°26’N;33°58’E	Landrace / no information	Kitwa_II	MK4	1°48’S;37°37’E	Landrace / no information
Bwichina	KK2	"	Landrace / no information	Nya-Gang	SYA9	"	Landrace / no information	Kitwa_III	MK5	"	Landrace / no information
Lunyalala	KK3	"	Landrace / no information	Nyal-Kada	SYA10	"	Landrace / no information	Masokani_I	MK6	"	Landrace / no information
Shanina	KK4	"	Landrace / no information	Nya-Udai	SYA11	"	Landrace / CMD susceptible	Masokani_II	MK7	"	Landrace / no information
Mukulusu	KK5	"	Landrace / no information	AdhiamboLera	SYA12	"	Landrace / CMD susceptible	Kaliluni	MK8	"	Landrace / no information
Itenyi	KK6	"	Landrace / no information	Nya-Bungoma	SYA13	"	Landrace / no information	Muvila	MK9	"	Landrace / no information
Shisembe	KK7	"	Landrace / no information	Lady Gay	SYA14	"	Improved genotype	TC14	MK10	"	Cuba / CBSD & CMD resistant
Inzakula	KK8	"	Landrace / no information	Kiboko297	SEK1	0°10’S;37°50’E	KALRO / CBSD resistant	TC4-Katune	MK11	"	Cuba / CBSD & CMD resistant
Shitaho	KK9	"	Landrace / no information	Thika272	SEK2	"	KALRO / CBSD resistant	99/0056	MK12	"	IITA / Improved genotype
Lugala	KK10	"	Landrace / no information	Thika273	SEK3	"	KALRO / CBSD resistant	Kalimbini_I	MK13	"	Landrace / no information
Lugusisti	KK11	"	Landrace / no information	Kiboko275	SEK4	"	KALRO / CBSD resistant	Kalimbini_II	MK14	"	Landrace / no information
Banasa	KK12	"	Landrace / no information	Kiboko274	SEK5	"	KALRO / CBSD resistant	Kalimbini_III	MK15	"	Landrace / no information
Isambe	KK13	"	Landrace / no information	Thika280	SEK6	"	KALRO / CBSD resistant	Kalimbini_IV	MK16	"	Landrace / no information
Isulu	KK14	"	Landrace / no information	Kiboko300	SEK7	"	KALRO / CBSD resistant	Katsuhanzala	MK17	"	KALRO / Improved genotype
Ikholi	KK15	"	Landrace / no information	Kiboko271	SEK8	"	KALRO / CBSD resistant	Kasukari (990127)	MK18	"	KALRO / Improved genotype
Ingotse	KK16	"	Landrace / no information	Thika279	SEK9	"	KALRO / CBSD resistant	Kitivo	MK19	"	Landrace / no information
Shikoti	KK17	"	Landrace / no information	Thika289	SEK10	"	KALRO / CBSD resistant	Kimutwa	MK20	"	Landrace / no information
Shipalo	KK18	"	Landrace / no information	Kiboko295	SEK11	"	KALRO / CBSD resistant	Mumbuni	MK21	"	Landrace / no information
Shamiloli	KK19	"	Landrace / no information	Kiboko277	SEK12	"	KALRO / CBSD resistant	Halu	KF1	3°40’S;39°45’E	Landrace / no information
Madioli	KK20	"	Landrace / no information	Kiboko276	SEK13	"	KALRO / CBSD resistant	Kibandameno	KF2	"	Landrace / CMD susceptible
Shiswa	KK21	"	Landrace / no information	Thika278	SEK14	"	KALRO / CBSD resistant	Agriculture	KF3	"	IITA / improved genotype
MM96/1871	KK22	"	IITA / CMD resistant	Kiboko281	SEK15	"	KALRO / CBSD resistant	Tajirika/KME-0802	KF4	"	Landrace / CMD resistant
MM97/0293	KK23	"	KALRO / CMD resistant	Thika5	SEK16	"	Landrace / CMD resistant	Kaleso	KF5	"	Landrace / CMD resistant
Magana	KK24	"	Landrace / CBSD resistant	Serere	SEK17	"	CIAT / CBSD susceptible	Soyosoyo	KF6	"	Landrace / no information
CK9	KK25	"	Landrace / no information	Kiboko9	SEK18	"	KALRO / CBSD resistant	Sokoke_I	KF7	"	Landrace / no information
Matuja	KK26	"	Landrace / CMD susceptible	Kiboko10	SEK19	"	KALRO / CBSD resistant	Sokoke_II	KF8	"	Landrace / no information
Fumbachai	KK27	"	Landrace / no information	Kiboko11	SEK20	"	KALRO / CBSD resistant	Kakanjuni_I	KF9	"	Landrace / no information
MM98/1313-HS	KK28	"	KALRO / Improved	Kiboko159	SEK21	"	KALRO / CBSD resistant	Kakanjuni_II	KF10	"	Landrace / no information
MH95/0183	KK29	"	IITA / CMD resistant	Kiboko257	SEK22	"	KALRO / CBSD resistant	Kakanjuni_III	KF11	"	Landrace / no information
MM08/2206	KK30	"	IITA / Improved genotype	Kiboko258	SEK23	"	KALRO / CBSD resistant	Mkongo_I	KF12	"	Landrace / no information
MM96/0686	KK31	"	KALRO / CMD resistant	Kiboko259	SEK24	"	KALRO / CBSD resistant	Mkongo_II	KF13	"	Landrace / no information
Aruaro	SYA1	0°26’N;33°58’E	Landrace / no information	Kiboko267	SEK25	"	KALRO / CBSD resistant	Cha-Vyango_I	KF14	"	Landrace / no information
Othigo-Diep	SYA2	"	Landrace / no information	Kiboko268	SEK26	"	KALRO / CBSD resistant	Cha-Vyango_II	KF15	"	Landrace / no information
Nyakatanegi_I	SYA3	"	Landrace / no information	Kiboko269	SEK27	"	KALRO / CBSD resistant	Chumani	KF16	"	Landrace / no information
Nyakatanegi_II	SYA4	"	Landrace / no information	Kiboko270	SEK28	"	KALRO / CBSD resistant	Matano-Manne	KF17	"	Landrace / no information
Nya-Uyoma	SYA5	"	Landrace / no information	Kasioni	MK1	1°48’S;37°37’E	Landrace / no information	KALRO	KF18	"	KALRO / Improved genotype
Kamis	SYA6	"	Landrace / CMD susceptible	Kisimba	MK2	"	Landrace / no information
Nya-Uganda	SYA7	"	Landrace / CMD susceptible	Kitwa_I	MK3	"	Landrace / no information
CMD = cassava mosaic disease; CBSD = cassava brown streak disease; KALRO = Kenya Agricultural & Livestock Research Organization; IITA = International Institute of Tropical Agriculture; CIAT = International center for tropical agriculture; KG = Kakamega (0°16’N;34°45’E) SYA = Siaya (0°26’N;33°58’E );SEK = SEKU / Kitui (0°10’S;37°50’E); MK = Makueni (1°48’S;37°37’E); KF = Kilifi (3°40’S;39°45’E). Information on germplasm attributes were sourced from several literature reviews

Table 2

The distribution of the SNPs across the cassava genome

##	Chromosome	No_of_SNPs
## 1	01	495
## 2	02	431
## 3	03	396
## 4	04	367
## 5	05	335
## 6	06	416
## 7	07	254
## 8	08	307
## 9	09	258
## 10	10	363
## 11	11	392
## 12	12	262
## 13	13	215
## 14	14	315
## 15	15	350
## 16	16	249
## 17	17	199
## 18	18	204
Total		5,808

Table 3

Classification of cassava accessions into clusters based on underlying sub-population groups derived from Figure 5

Local ID	Type	Class	Cluster #	Region / GPS	Local ID	Type	Class	Cluster#	Region / GPS
Matuja Othigo-Diep Aruaro Nya-Udai	LAR " " "	All Identical	I	0°16’N;34°45’E	Shavirotsi	LAR	Unique	NCL	0°16’N;34°45’E
				0°26’N;33°58’E	Bwichina	LAR	"	"	"
				"	Lunyalala	LAR	"	"	"
				"	Mukulusu	LAR	"	"	"
Kiboko276 Kiboko297	IMG "	All Identical	II	0°10’S;37°50’E	Shisembe	LAR	"	"	"
Kiboko276 Kiboko297	IMG "	All Identical	II	"	Shitaho	LAR	"	"	"
Kiboko274 Thika278	IMG "	All Identical	III	0°10’S;37°50’E	Lugusisti	LAR	"	"	"
Kiboko274 Thika278	IMG "	All Identical	III	"	Banasa	LAR	"	"	"
Kiboko271 Thika289	IMG "	All Identical	IV	0°10’S;37°50’E	Ingotse	LAR	"	"	"
Kiboko271 Thika289	IMG "	All Identical	IV	"	Shiswa	LAR	"	"	"
Kiboko300 Thika273 Thika5	IMG " LAR	All Identical	V	0°10’S;37°50’E	MM96/1871	IMG	"	"	"
				"	MM97/0293	IMG	"	"	"
				"	Magana	LAR	"	"	"
Kiboko281 Thika280	IMG "	All Identical	VI	0°10’S;37°50’E	CK9	LAR	"	"	"
Kiboko281 Thika280	IMG "	All Identical	VI	"	MM98/1313-HS	IMG	"	"	"
Itenyi Inzakula	LAR "	All Identical	VII	0°16’N;34°45’E	MM08/2206	IMG	"	"	"
Itenyi Inzakula	LAR "	All Identical	VII	"	MM96/0686	IMG	"	"	"
Lady Gay Shanina MH95/0183	LAR " IMG	All Identical	VIII	0°26’N;33°58’E	Nyakatanegi-II	LAR	"	"	0°26’N;33°58’E
				0°16’N;34°45’E	Nya-Uyoma	LAR	"	"	"
				"	Kamis	LAR	"	"	"
Kalimbini-I Kasioni Kitwa-II Kitwa-III Kitivo Kitwa-I Kimutwa Mumbuni	LAR " " " " " " "	All Identical	IX	1°48’S;37°37’E	Nya-Uganda	LAR	"	"	"
				"	AdhiamboLera	LAR	"	"	"
				"	Nya-Bungoma	LAR	"	"	"
				"	Thika272	IMG	"	"	0°10’S;37°50’E
				"	Kiboko275	IMG	"	"	"
				"	Thika279	IMG	"	"	"
				"	Kiboko295	IMG	"	"	"
				"	Kiboko277	IMG	"	"	"
Serere Madioli Shikoti Ikholi	LAR " " "	All Identical	X	0°10’S;37°50’E	Kiboko9	IMG	"	"	"
				0°16’N;34°45’E	Kiboko10	IMG	"	"	"
				"	Kiboko11	IMG	"	"	"
				"	Kiboko159	IMG	"	"	"
Lugala Shamiloli Shipalo	LAR " "	All Identical	XI	0°16’N;34°45’E	Kiboko257	IMG	"	"	"
				"	Kiboko258	IMG	"	"	"
				"	Kiboko259	IMG	"	"	"
Fumbachai Isambe Nyal-Kada	LAR " "	All Identical	XII	0°16’N;34°45’E	Kiboko267	IMG	"	"	"
				"	Kiboko268	IMG	"	"	"
				0°26’N;33°58’E	Kiboko269	IMG	"	"	"
KALRO Matano-Manne Kakanjuni-II	IMG LAR "	All Identical	XIII	3°40’S;39°45’E	Kiboko270	IMG	"	"	"
				"	Masokani-I	LAR	"	"	1°48’S;37°37’E
				"	Masokani-II	LAR	"	"	"
Tajirika Kaleso Cha-Vyango-II Sokoke-I Chumani	LAR " " " "	All Identical	XIV	3°40’S;39°45’E	Muvila	IMG	"	"	"
				"	TC14	IMG	"	"	"
				"	TC4-Katune	IMG	"	"	"
				"	99/0056	IMG	"	"	"
				"	Kalimbini-II	LAR	"	"	"
Kalimbini-III Kalimbini-IV	LAR "	All Identical	XV	1°48’S;37°37’E	Katsuhanzala	IMG	"	"	"
Kalimbini-III Kalimbini-IV	LAR "	All Identical	XV	"	Kasukari (99/0127)	IMG	"	"	"
Nya-Gang Nya-Yenga Nyakatanegi-I	LAR " "	All Identical	XVI	0°26’N;33°58’E	Halu	LAR	"	"	3°40’S;39°45’E
				"	Kibandameno	LAR	"	"	"
				"	Agriculture	LAR	"	"	"
Kaliluni Kisimba Isulu	LAR " "	All Identical	XVII	1°48’S;37°37’E	Soyosoyo	LAR	"	"	"
				"	Sokoke-II	LAR	"	"	"
				0°16’N;34°45’E	Kakanjuni-I	LAR	"	"	"
					Kakanjuni-III	LAR	"	"	"
					Mkongo-I	LAR	"	"	"
					Mkongo-II	LAR	"	"	"
					Cha-Vyango-I	LAR	"	"	"
LAR = Landrace; IMG = Improved Genotype; Unique = non-duplicated clone; NCL = Non-clustered landraces / improved genotypes

Download PDF

Version 1

posted

You are reading this latest preprint version

Genotyping by sequencing reveals genetic relatedness and duplicates amongst local cassava (Manihot esculenta Crantz) landraces and improved genotypes in Kenya

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Sample collection

Sequencing cassava using DArTSeq

Data analysis

Results

Cassava germplasms

Filtering and selection of SNPs and optimum population identification

Admixture analysis

Discussion

Conclusion

Declarations

Acknowledgements:

Author contribution:

Funding:

Availability of data and materials:

Code availability:

Author Declarations:

Ethics approval and consent to participate:

Consent for publication:

Confict of interest:

References

Tables

Status:

Version 1