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Cassava Dark brown Streak Virus Infection Genome

Cassava Brown Streak Virus Infection Genome

Genome-wide prediction and association evaluation for sensitivity to cassava dark brown streak virus contamination in Cassava

Siraj Ismail Kayondo1,2, Dunia Pino Del Carpio3, Roberto Lozano3, Alfred Ozimati1,3, Marnin Wolfe3, Yona Baguma1, Vernon Gracen2,3, Offei Samuel2, Robert Kawuki1 and Jean-Luc Jannink3,4

1) National Crop Information Analysis Institute, NaCRRI, P.O. Package, 7084 Kampala, Uganda,

2) West Africa Middle for Crop Improvement, (WACCI),

3) Section of Plant breeding and Genetics, Cornell University, Ithaca, NY,

4) US Section of Agriculture – Agricultural Exploration Service (USDA-ARS)


Cassava (manihot esculenta Crantz), an integral carbohydrate origin faces unprecedented obstacle of viral diseases significantly, cassava brown streak disease (CBSD) and cassava mosaic disease (CMD). The economical elements of the crop happen to be rendered unmarketable by these viral conditions resulting into mega fiscal setbacks. The exceptional completion of the cassava genome sequence equips cassava breeders with an increase of precise selection ways of offer superior kinds with both farmer and sector preferred traits.

This document reports genomic segments affiliated to foliar and root CBSV sensitivity measured at numerous growth levels and environmental conditions.

We identified significant sole nucleotide polymorphisms (SNPs) involved to CBSV sensitivity in cassava on chromosome 4 and 11. The significantly associated areas on chromosome 4 co-localises with a Manihot glaziovii introgression from the crazy progenitors. While significant SNPs markers on chromosome 11 will be in linkage disequilibrium (LD) with a cluster of nucleotide-binding internet site leucine-rich do it again (NBS-LRR) proteins encoded by disease resistance genes in crops. Genotype by environmental interactions had been significant since SNP marker results differed across conditions and years.

Key words: Genome-large association studies (GWAS), virus sensitivity, augmented designs, de-regressed best linear unbiased Predictions (dr-BLUPs), NBS-LRR proteins, QTLs


Cassava (Manihot esculenta crantz), is a major income source and dietary calorie consumption for over a billion lives around the world specifically in Sub Saharan Africa (SSA). Edge cutting technology are rapidly turning cassava into an industrial crop especially experiencing it’s unique starch qualities hence opening new cash flow opportunities for the indegent (Pérez et al., 2011). Cassava dark brown streak virus disease (CBSD), a leading viral constraint limiting production across SSA is responsible for mega fiscal setbacks estimated at 100 US million dollars per annum at physiological maturity (ASARECA:, 2013; Ndunguru et al., 2015). Because of CBSVs, cassava yields were recorded to be eight times lower than the expected yield probable in Uganda(). Two main strains; Cassava dark brown streak virus (CBSV) and Uganda Cassava brownish streak virus (UCBSV), possess successfully colonized both lowland and highland altitudes across East Africa though newer strains happen to be being reported (Winter et al., 2010; Ndunguru et al., 2015; Alicai et al., 2016a). Furthermore to uncontrolled exchange of contaminated cassava steaks among cassava farmers across porous borders, the African whitefly (Besimia tobaci) stands out as the famous semi-persistent virus transmitter under discipline conditions (Legg et al., 2014; McQuaid et al., 2015). Upon entry, the virus exploits the plant’s transport system to traverse the susceptible cassava plant resulting into yellow chlorotic vein clearing patterns along small veins of the leaves. Prominent brown elongated lesions are produced on the stem commonly referred to as « brown streaks ». As the brown necrotic hard-corky layers will be randomly formed in the root cortex of all susceptible cassava clones.

In check out of the rapid but steadily virus evolution rates and the insufficiency of trustworthy virus diagnostic tools (Alicai et al., 2016b), breeding for resilient CBSD level of resistance emerges as a timely and economically viable option. Earlier CBSD resistance breeding initiatives contain highlighted it’s polygenic but recessive mother nature of inheritance in both intraspecific and interspecific cassava hybrids (Nichols, 1947; Hillocks and Jennings, 2003; Munga, 2008; Kulembeka, 2010). The rate of progress to genetic improvement in a normal cassava breeding pipeline has been slower because of several biology-related opportunities like; shy flowering, amount of breeding cycle, limited genetic diversity and sluggish charge of multiplication of planting components. Most of the obtainable elite cassava lines contain exhibited some level of sensitivity to CBSVs which range from slight sensitivity total susceptibility.

Therefore, a concise but then targeted exploration for potential sources of resistance using the obtainable biotechnology tools could be a promising strategy. The amazing completion of the cassava genome sequence equips cassava breeders with an increase of precise selection strategies to offer superior varieties with both farmer and industry preferred traits. A report by Bredeson et al., (2016) reports the presence of introgressions segments from the wild progenitors into the elite breeding lines produced by the Amani breeding course in Tanzania.

Hence, resistance sources to CBSD exist but may have been reshuffled over generations of recurrent selection thus not fully fixed and should be exploited.

Moving forward, a genome wide survey for existing natural variations as discussed by the noticed phenotypes for confirmed series of agronomic characteristics could facilitate identification of causal loci linked to the inheritance of a trait of curiosity. This tool, commonly referred to as genome-wide association study (GWAS) exploits the power of statistical analyses to recognize such historical recombination incidents that have occurred over time (Jannink and Walsh, 2002; Hamblin, Buckler and Jannink, 2011). Hence, GWA-studies will enhance bi-parental mapping efforts that have been widely applied in cassava breeding in the last 10 years (Ferguson et al., 2012; Ceballos et al., 2015). GWA-studies have been widely undertaken by animal, human being and plant geneticists to recognize quantitative trait loci (QTLs) in close association to many important traits. However, GWAS has been thinly utilized in cassava breeding especially in the definition of the genetic architecture of cassava mosaic disease (Wolfe et al., 2016) and beta carotene (unpublished). In this review, we exploited the lowered genotyping costs applying genotyping by sequencing (GBS) to genotype info for our association mapping panel.

The goal of the study was to recognize genomic regions closely connected with sensitivity to CBSV disease in a different regional cassava breeding panel. Excellent mapping around the identified regions would help in marker discovery in addition to identification of franking genes for CBSV sensitivity for marker assisted breeding.


Plant material

The data set comprised of field disease evaluations undertaken across five locations; Namulonge, Kamuli, Serere, Ngetta and Kasese in Uganda. Two different but carefully related GWAS panels were evaluated across environments.

Between 2012 and 2013, GWAS panel 1 contains between 308 to 429 entries which were replicated twice across three places. Each trial was built as a randomized finished block (RCB) with two-row plots of five plant life each at a spacing of 1 1 meter by 1 meter. In 2015, GWAS panel 2 comprising entries ranging from 715 to 872 clones was evaluated in three spots but contrasting sites for CBSD pressure. These entries were evaluated as one entries per blog being connected by six prevalent checks in an augmented completely randomized block design and style with 38 blocks per webpage (Federer, Nguyen and others, 2002; Federer and Crossa, 2012).

The two GWAS panels got one location in keeping; Namulonge that is regarded as the CBSD spot with the best CBSD pressure.

The data was produced from 1281 cassava clones designed through three cycles of genetic recombination with local elite lines by the National root crops breeding software at NaCRRI. These cassava clones acquired a diverse genetic history whose pedigree could possibly be traced back again to introductions from intercontinental institute for tropical agriculture (IITA), International middle for tropical Agriculture (CIAT) and Tanzania[KI1] breeding program (sup.fig1).

Phenotyping protocol for CBSV sensitivity

The key traits were CBSD severity and incidence scored at 3, 6, and 9 months after planting (MAP) for foliar and 12 MAP for root symptoms respectively. CBSD severity was measured based on a 5 point scale with a score of just one 1 implying asymptomatic circumstances and a score 5 implying over 50% leaf vain clearing under foliar symptoms. On the other hand, at 12 MAP a rating of 5 implies over 50% of root-primary being covered by a necrotic corky coating. (fig.1)

Clones were classified with a rating of 5 if pronounced vein clearing at main leaf veins were jointly displayed with brownish streaks on the stems and shoot die-back that came out as a candle-stick. Clones with 31 – 40% leaf vein clearing as well as dark brown steaks at the stems were classified under score 4. A Score of 3 was assigned to clones with 21 – 30% leaf vein clearing with emerging brownish streaks on the stems. While a rating of 2 was designated to clones that just displayed 1 – 20% leaf vein clearing without the visible dark brown streak symptoms on the stems. Vegetation classified with a score of just one 1 showed no obvious indication of leaf necrosis and brown streaks on the stems. Alternatively, root symptoms were likewise classified into 5 numerous categories predicated on a 5 – point common scale.

Two-stage genomic analyses

For the panel 1 which was crafted as a randomized complete block (RCB) we match the style: , employing the lmer function from the lme4 R package deal (Bates et al., 2015).In this version, β included a set effect for the population mean and area. The incidence matrix Zclone and the vector c represent a random result for clone and I stand for the identity matrix. The number variable, which is the row or column along which plots are arrayed, is normally nested in location-rep and is usually represented by the incidence matrix Zrange(loc.) and random results vector .Block effects were nested in ranges and integrated as random with incidence matrix Zblock(selection) and effects vector . Residuals had been fit

as random, with .

For panel 2, which followed an augmented style, we fit the style Where y was the vector of raw phenotypes, β included a fixed effect for the populace mean and site with checks included as a covariate, The incidence matrix Zclone and the vector c are exactly like over and the blocks were likewise modeled with incidence matrix and b signifies the random effect for block. The very best linear predictors (BLUPs) of the clone impact (ĉ) were extracted as de-regressed BLUPS following the formula:

Broad impression heritability was calculated applying variance pieces extracted from the two stage lmer output. SNP-structured heritability was calculated by extracting the variance components from the output attained by fitting the SNPs as a kinship covariate calculated employing the A.mat function from the rrBLUP R bundle and contained in a one step model using the emmreml function from the EMMREML R package (Akdemir and Okeke, 2015).

DNA preparation and Genotyping by sequencing (GBS)

All cassava clones included in the phenotypic data set possessed their total genomic DNA extracted from fresh tender leaves regarding to standard procedures applying the DNAeasy plant mini extraction package (Qiagen, 2012).

Genotyping-by-sequencing (GBS) (Elshire et al., 2011) libraries were constructed applying the ApeKI restriction enzyme as applied before (Hamblin & Rabbi, 2014). Marker genotypes were called applying TASSEL GBS pipeline V4 (Glaubitz et al., 2014) after aligning the reads to the Cassava v6 reference genome (Phytozome 10.3; (International Cassava Genetic Map Consortium, 2014; Prochnik et al., 2012). Variant Calling Formatting (VCF) files were produced for each and every chromosome. Markers with more than 60% missing telephone calls were taken away. Genotypes with less than 5 reads were compare and contrast template masked before imputation. Additionally, simply biallelic SNP markers had been considered for further more steps.

The marker dataset contains a complete of 173,647 SNP bi-allelic markers needed 986 individuals. This primary dataset was imputed using Beagle 4.1 (Browning and Browning, 2016). After the imputation 63,016 SNPs had an AR2 (Estimated Allelic r-squared) greater than 0.3 and were placed for research; from these, 41,530 had a minor allele frequency (MAF) greater than 0.01 in our population. Dosage files for this final dataset were made and employed for both GWAS and GS.

Structure and Genetic stratification analysis

The degree of phylogenetic relationship and degree of family group relatedness within the cassava lines was assessed using principal component research (PCA) implemented in R.

Genome-wide association analysis for CBSV sensitivity

The type binary PED data files were ready from the genotype dosage documents using PLINK version 1.07 (Rentería, Cortes and Medland, 2013; Purcell et al., 2007). Blended linear modal association examination (MLMA) implemented by GCTA variation 1.26.0 was employed to generated GWAS results (Yang et al., 2011). MLMA was implemented in a way that in every cycle of examination, the chromosome which the candidate SNPs existed acquired excluded from the GRM calculation using the modal in equation 3.

Where y is the phenotype, a is being the mean term, b getting the fixed additive effects of the applicant SNP being examined for association, x being the SNP genotype indicator adjustable and gis the accumulated aftereffect of all SNPs excluding those where in fact the candidate SNP is located making our analysis model more powerful.

We estimated variance pieces using restricted maximum likelihood (REML). Sub human population stratification was corrected for by taking GRM as a random impact term in the model during analysis. A more conservative Bonferroni correction method was used to repair genome-vast significance threshold at P≤10-7 as a means of correcting for experimental-wise error.

Manhattan and Quantile – Quantile plots for all your traits were constructed employing R package qqman program applied in R (Turner, 2014).

Genomic prediction models

GBLUP. In this prediction unit the GEBVs are attained by assuming , where is the additive genetic variance, and K is the symmetric genomic understood relation matrix based on GBS SNP marker dosages. The genomic relationship matrix used was produced using the function A.mat in the R package deal rrBLUP(Endelman, 2011) and follows the method of VanRaden (2008), method two. GBLUP predictions had been made with the function emmreml in the R package deal EMMREML (Akdemir and Okeke, 2015).

RKHS. Unlike GBLUP for RKHS we use a Gaussian kernel function: , where Kij is the measured romantic relationship between two persons, dij is their euclidean genetic distance based on marker dosages and θ is definitely a tuning (« bandwidth ») parameter that determines the charge of decay of correlation among people. This function is normally nonlinear therefore the kernels used for RKHS can take non-additive and also additive genetic variation. To match a multiple-kernel model with six covariance matrices we applied the emmremlMultiKernel function in the EMMREML offer, with the following bandwidth parameters: 0.0000005, 0.00005, 0.0005, 0.005, 0.01, 0.05 (Multi-kernel RKHS) and allowed REML to find optimum weights for every single kernel.For the « optimal kernel RKHS » we utilized the kernel weights assigned by emmremlMultiKernel in the first step to construct a single kernel that is the weighted average of the original six. We then utilized this « optimal kernel » in single-kernel predictions.

Bayesian maker regressions.We tested four Bayesian prediction models: BayesCpi (Habier et al., 2011), the Bayesian LASSO (BL; Park and Casella, 2008), BayesA, and BayesB (Meuwissen, Hayes and Goddard, 2001). The Bayesian products we tested allow for choice genetic architectures by differential shrinkage of marker results. We performed Bayesian predictions with the R program BGLR (Pérez and De Los Campos, 2014)

Random Forest. Random forest (RF) is a machine learning method used for regression and classification (Strobl et al. 2009, Breiman 2001). Random forest regression with marker info has been shown to capture epistatic effects and has been successfully utilized for prediction (Sakar et al 2015, Heslot et al 2012, Charmet et al 2014, Spindel et al 2015, Breiman, et al 2001, Michaelson et al 2010, Motsinger-Reif et al 2008). We implemented RF applying the randomForest program in R (Liaw and Wiener 2002) with the parameter, ntree established to 500 and the number of variables sampled at each split (mtry) add up to 300.

Multikernel GBLUP

We used a multikernel approach by fitting three kernels constructed with SNPs with MAF> 0.01 from chromosomes 4,11 and the SNPs from the other chromosomes. We determined chromosomes 4 and 11 because they contained QTLs for foliar intensity 3 and 6 MAP. Multikernel GBLUP predictions were made with the function emmremlMultiKernel in the R package deal EMMREML (Akdemir and Okeke, 2015)

Introgression Segment Detection

To identify the genome segments how many pages in 1000 words inside our germplasm, we adopted the strategy of Bredeson et al. (2015). We employed the M. glaziovii diagnostic markers determined Supplementary Dataset 2 of Bredeson et al. (2015). These ancestry diagnostic (AI) SNPs were discovered as being fixed for distinct alleles in a sample of two pure M. esculenta (Albert and CM33064) and two clean M. glaziovii (GLA XXX-8 and M. glaziovii(S)).

Out of 173,647 SNP in our imputed dataset, 12,502 matched published AI SNPs. For these AI SNPs, we divided each chromosome into non-overlapping house windows of 20 SNP. Within each window, for every single individual, we calculated the proportion of genotypes that were homozygous (G/G) or heterozygous (G/E) for M. glaziovii allele and the proportion that were homozygous for the M. esculenta allele (E/E). We designated G/G, G/E or E/E ancestry to each windowpane, for each individual only when the proportion of the most common genotype in that screen was at least twice the proportion of the next most prevalent genotype. We assigned home windows a « No Call » position otherwise.

We also used this approach on six whole-genome sequenced samples from the cassava HapMap II (Punna et al. under Analysis). These included both « pure cassava » and M. glaziovii(S) from Bredeson et al. (2015), plus yet another M. glaziovii, and two samples labeled Namikonga. Because these samples came from a different origin from the majority of our samples, we could actually find only 11,686 SNPs that matched both sites in the others of our study sample and the list of ancestry informative sites for research.

Linkage disequilibrium plots

LD ratings were calculated for every SNP in chromosome 4 with a screen of 1Mb applying the GCTA Software (Yang et al., 2011). Briefly, LD rating for a given marker is the sum of R2 altered between the index marker and all markers within a specified windowpane. The adjusted R2 is an unbiased measure of LD:

where « n » is the population size and R2 may be the standard estimator of the squared Pearson’s correlation (Bulik-Sullivan et al 2015).

We calculated the LD between that marker and various other markers in a windowpane of 2Mb (1Mb upstream and 1Mb downstream) For the most notable significant SNP struck in chromosome 11 for the 6MAP GWAS derive from panel 1 and panel 2. The LD was evaluated using squared Pearson’s correlation coefficient (r2) as calculated with the −r2 -ld-snp commands in the software PLINK version 1.9.

Candidate gene identification

To identify applicant genes for CBSD intensity in leaves and CBSD root necrosis we employed the GCTA mlma GWAS end result obtained for every single trait. We filtered the SNP markers predicated on -log10 (P-value)> Bonf, being these values greater than the Bonferroni threshold (~ 5.9). The resulting SNP markers were assigned onto genes using the SNP area and gene explanation from the Mesculenta_305_v6.1.gene.gff3 available in Phytozome 11 (ref) for Manihot esculenta v6.1 employing the intersect function from bedtools (ref).


Phenotypic assessment of cassava for sensitivity to cassava dark brown streak virus infection

Most clones showed varied responses to CBSV infection spanning from super susceptibility that represented candle-like die-back of

the shoot to tolerance (Fig.). Foliar phenotyping clumped these plant responses into five significant classes based on a 1 to 5 scale. The broad good sense heritability of the studied traits ranged 0.17 to 0.72 for both GWAS panels (Table 1). Evaluation of the phenotypic data showed incredibly significant GxE interactions (P<0.001) consequently justifying the relevance of sole environment data analysis.

Genetic correlations and heritability estimates

We found modest heritability estimates for CBSV sensitivity for foliar phenotypes at 3, 6 and 9 MAP along with root phenotypes under five conditions (fig..) . Genetic correlation on the traits assayed had been performed and exposed that ranged from average to high confident correlations among characteristics studied.

Assessment of linkage disequilibrium

Genome-wide association mapping frequently explores the advantage of existence of several historic recombination events as time passes to associate noticed phenotypic variation with genome.

Detection of applicant QTLs for CBSV sensitivity in cassava

To efficiently a run GWAS, we utilized SNP data to examine the degree of genetic interrelatedness and sub-population structure of the cassava clones. A principal component analysis (PCA) to account for structure showed no distinct clusters implying that the picked clones weren’t highly structured (Fig.1). Therefore, we didn’t include PCs in our GWAS linear modal examination.

The Bonferroni suggestive threshold (α = 0.05) was used to identify loci linked to CBSV sensitivity on both chromosome 4 and 11 that experienced clear peak indicators at the different phases of phenotyping (Fig. 2). The observed P-values at first aligned well with the predicted P-values but later differed substantially due the large introgression block on chromosome 4 presumably from the wild progenitors of cassava traceable from the AMANI breeding program (Jennings, 1959).

The significant signals on chromosome 11 included loci with good association with CBSV sensitivity in a 2 Mb region that annotated well with several candidate genes.

Genome-large prediction for CBSV sensitivity in cassava

We did genome vast prediction for CBSV sensitivity predicated on the identified SNPs with the best effects found on both chromosome 4 and 11 as a way to capture most of the genetic variation. We explored different genomic prediction model methods; GBLUP, RR-BLUP, B-LASSO, random forest, BayesA, BayesB, and BayesC.



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[KI1]Supplementary documents attached describing the pedigree