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Construction of a genetic linkage map and QTL analysis in

bambara groundnut (Vigna subterranea (L) Verdc.)

Journal: Genome

Manuscript ID gen-2015-0153.R1

Manuscript Type: Article

Date Submitted by the Author: 17-Mar-2016

Complete List of Authors: Ahmad, Nariman; Sulaimani Univerity, Department of Crop Science, Faculty of Agriculture Redjeki, Endah ; Muhammadiyah University, Faculty of Agriculture Ho, Wai ; Crops For the Future, Breeding Biotechnology and Seed systems Aliyu, Siise; The University of Nottingham, Malaysia Campus, Bioscience; Crops For the Future, BamYIELD; CSIR-Savannah Agriculture Research

Institute , Breeding Mayes, Katie; The University of Nottingham, UK campus, (c/o Sean Mayes) Bioscience Massawe, Festo; University of Nottingham Malaysia Faculty of Science, Biosciences Kilian, Andrzej ; Diversity Array Technology Pty Ltd., Director Mayes, Sean; The University of Nottingham, UK campus, Bioscience; Crops For the Future, Breeding Biotechnology and Seed systems

Keyword: Bambara groundnut, breeding, genetic mapping, QTL analysis

https://mc06.manuscriptcentral.com/genome-pubs

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Construction of a genetic linkage map and QTL analysis in bambara

groundnut (Vigna subterranea (L.) Verdc.)

Nariman Salih Ahmad1, Endah Sri Redjeki

2, Wai Kuan Ho

3,4, Siise Aliyu

3,4,5, Katie Mayes

6,

Festo Massawe3, Andrzej Kilian

7 and Sean Mayes

4,6

1. Crop Science Department, Faculty of Agricultural Sciences, Sulaimani University,

Kurdistan- Iraq.

2. Faculty of Agriculture, Muhammadiyah University, Gresik, Indonesia.

3. University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor,

Malaysia.

4. Crops For the Future, Jalan Broga, 43500 Semenyih, Selangor, Malaysia.

5. CSIR-Savannah Agricultural Research Institute, Nyankpala N/R, Ghana.

6. University of Nottingham, Plant and Crop Sciences Division, Sutton Bonington Campus,

Loughborough, Leicestershire LE12 5RD, UK.

7. Diversity Array Technology Pty Ltd., Building 3, Level D, University of Canberra,

Kirinari St. Bruce, ACT2617, Australia.

Corresponding author email: [email protected]

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Abstract

Bambara groundnut (Vigna subterranea (L.) Verdc.) is an indigenous underutilised legume

which has the potential to improve food security in semi-arid Africa. So far, there are a lack of

reports of controlled breeding populations that could be used for variety development and

genetic studies. We reported here the construction of the first genetic linkage map of bambara

groundnut using a F3 population derived from a ‘narrow’ cross between two domesticated

landraces (Tiga Nicuru and DipC) with marked divergence in phenotypic traits. The map

consists of 238 DArT array and SSR based markers in 21 Linkage Groups (LGs) with a total

genetic distance of 608.3 cM. In addition, phenotypic traits were evaluated for a Quantitative

Trait Loci (QTL) analysis over two generations. A total of 36 significant QTLs were detected

for 19 traits. The phenotypic effect explained by a single QTL ranged from 11.6% to 49.9%.

Two stable QTLs were mapped for internode length and growth habit. The identified QTLs

could be significant for marker-assisted selection (MAS) in bambara groundnut breeding

programmes.

Key words: bambara groundnut; breeding; genetic mapping; QTL analysis

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Introduction

Agriculture is confronted with the twin challenges of population expansion (including a

fast pace of urbanisation) and climate change (extreme weather events, salinization,

desertification); for this reason, safeguarding food security still remains one of humanities

greatest challenges (Challinor et al. 2007; FAO 2010; Gilland, 2002). Humanity’s reliance on

essentially three major crops- rice [Oryza sativa (L.)], maize [Zea mays (L.)] and wheat

(Triticum spp.) for up to 70% of its calories is being recognised as a risky path for global food

security (Mayes et al. 2011). In addition, the gradual reduction in genetic diversity and/or

narrowing of the genetic base of these major crops during more recent breeding also represents

another dimension to the problem. For this reason, the idea that we might tap into the genetic

resources from ‘underutilised’ crops (also termed ‘neglected’ and ‘minor’) is gradually gaining

acceptance among the research community and agricultural policy think tanks (Jaenicke and

Höschle-Zeledon 2006, Mayes et al. 2011). One such important underutilised indigenous

African crop species which could make a positive contribution to global food security

(particularly in semi-arid Africa) is bambara groundnut [Vigna subterranea (L.) Verdc.].

Bambara groundnut belongs to the Leguminosae family (subfamily Papilionoideae) and has 11

pairs of chromosomes (2n=2x=22; Heller et al. 1995).

The contribution this crop could make to global food security has been previously reported

(Basu et al. 2007a; Massawe et al. 2005, 2007). The crop is reported to have drought tolerance

and the ability to adapt to marginal soils (Collinson et al. 1996; Mwale et al. 2007a, 2007b),

coupled with reasonable yield potential (BAMFOOD 2002). As a legume, it provides nitrogen

fixation for enhanced soil fertility within the agricultural system and nodulation is reported to

show good tolerance to soil nitrate (NO3-) (Dakora, 1998), with the seed providing balanced

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nutrition (Brough and Azam-Ali 1992; Mazahib et al 2013; Nti 2009). Despite these beneficial

traits, bambara groundnut has suffered some neglect within the research community. Hitherto,

there were no controlled cross breeding populations that could be used for variety development

and genetic analysis (Basu et al. 2007a). Landraces remain the main source of planting material

used by farmers (Basu et al. 2007a; Massawe et al. 2005, 2007). In an effort to develop

improved genotypes, key breeding objectives for bambara groundnut have been reported (Aliyu

et al. 2015; Massawe et al. 2005, 2007). A range of molecular marker systems have been

developed and applied to bambara groundnut landraces as a means of assessing breeding

systems, diversity and population origins (Massawe et al. 2002; Olukolu et al. 2012; Somta et al.

2011). The significance of molecular markers in speeding up breeding programmes through the

use of linkage maps and marker assisted selection (MAS) techniques is well established and

routinely practised in breeding programmes (Collard et al. 2005; Collard and Mackill 2008).

Comprehensive genetic maps have been constructed in legumes such as chickpea [Cicer

arietinum (L.)], pigeonpea (Cajanus cajan) and peanut [Arachis hypogaea (L.)] (Hong et al.

2010; Thudi et al. 2011; Saxena et al. 2012). The ability to develop genetic maps that could aid

MAS in bambara groundnut breeding programmes is a strategic objective.

We report here the construction of the first genetic linkage map of bambara groundnut

(using DArT array and SSR markers) from the progeny of two phenotypically contrasting

domesticated parental lines, alongside QTL analysis of important traits.

Materials and methods

Plant material and the development of the segregating population

Single plants of two landraces with contrasting features for growth habit (plant

morphology) and seed eye patterns, Tiga Nicuru and DipC (Fig. 1) were used as parents for

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controlled crossing in order to establish the mapping population. Based on our observation, Tiga

Nicuru from Mali has a bunchy plant morphology with an average petiole to internode ratio (P/I)

of 9 while DipC collected from Botswana has a semi-spreading morphology with an average P/I

ratio of 13. A total of 73 lines in an F2 population were obtained from this cross (DipC x Tiga

Nicuru) and advanced to F3.

Experimental set up and conditions

The F2 population was planted in the glasshouses at Sutton Bonington Campus, University

of Nottingham, UK (GPS: +52.8214, -1.2497) during the summer of 2003. Seeds were planted

directly into the soil beds at a planting distance of 25 x 25cm. Day-length was maintained at 12

hours with day and night temperatures of 28°C/23°C, respectively. The phenotypic evaluation of

the F3 population was conducted at the glasshouses from August 2011 to January 2012 as well

as at Bungah field, Gresik, Indonesia (GPS: -7.1608, -112.6471) from May to September 2010

with four replicates for each line. The plants were sown with 40cm spacing between and within

rows in the field.

Phenotypic data collection and analysis

The standard descriptors for bambara groundnut published by International Plant Genetic

Resources Institute (2000) were used as a reference for all data collection with a few

modifications. A total of 15 phenotypic traits were evaluated in the controlled environment

glasshouse for the F2 population and 29 traits for both controlled environment glasshouse and

Indonesian field for the F3 population (Table S1), in order to study the inheritance and

segregation pattern of the morphological traits.

Anderson Darling tests were used to test for normality of the distribution of the trait data

(Stephens, 1974). Data displaying non-normal distributions were transformed to try to obtain a

normal distribution by standard approaches (such as a Box-Cox transformation) and the

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Anderson Darling test repeated. Transformed-normal traits were analysed using Interval

Mapping analysis as implemented in MapQTL6.0. Non-normal traits were analysed using the

Kruskal-Wallis Ranks test with False Discovery Rates (FDR) to determine significance. Genstat

14th

Edition statistical software and MINITAB Release 16 were used to analyse trait variation

among parental lines (t-test for normal distribution trait and Mann-Whitney for non-normally

distributed traits; p<0.05), construct residual plots, linear regression analysis and detect

significant correlations (using Pearson’s coefficient correlation analysis) among the traits

(p<0.05) under different growth conditions.

PCR amplification and SSR marker analysis

Genomic DNA was extracted from young leaf using the Dellaporta protocol (1983). A

total of 33 polymorphic primer pairs (Table S2) were optimised using a three primer system as

reported by Schuelke (2000). The universal dye-labelled tag had a sequence of 5’-CAC GAC

GTT GTA AAA CGA C-3’. The sequences of the primers used are listed in Table S2.

Amplification was carried out with the following run profile: initial denaturation at 94°C for 3

min followed by 35 cycles of 94°C for 1 min, annealing step for 1 min and 72° C for 2 min with

a final extension at 72° C for 10 min.

The PCR products were checked on agarose gel before being loaded onto a CEQTM

8000

capillary sequencer (Beckman Coulter Inc., Fullerton, USA). CEQTM

8000 Fragments Analysis

Version 8 software was used to analyse the fragment sizes of the PCR products with manual

confirmation.

DArT array marker data scoring and analysis

The bambara groundnut DArT genotyping array was developed using 94 genotypes from

22 countries based on selecting a representation across available germplasms (Singrün and

Schenkel, 2003) by Diversity Array Technology Pty Ltd, Canberra, Australia

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(www.diversityarrays.com) using genomic representations derived from PstI/AluI and PstI/TaqI

digestion (Jaccoud et al. 2001; Stadler, 2009).

Genetic map construction and QTL analysis

The JoinMap4 software (van Ooijen, 2006) was used to construct the linkage map,

comprising both SSR and DArT markers. Markers were placed into linkage groups (LGs) using

default settings and a minimum LOD threshold of 3. For each LG, marker order and genetic

distance were inferred using the regression mapping algorithm. Marker classes at each locus

were summarised for all individuals into different genotypic classes for the F2 population with

the default expected ratios of 3:2:3 and 5:3 for SSR and DArT markers, respectively.

Segregation distortion was determined through a chi-square test for goodness-of-fit (p ≤0.05).

The QTL analysis was carried out by the rank sum test of Kruskal-Wallis mapping and

Interval Mapping using MapQTL 6 software (van Ooijen 2009), depending on trait or trait-

transformed distribution. The point detected with a maximum log-of-odds (LOD) score was

determined as the most likely position of the QTL on the map for Interval Mapping and the

confidence interval of a QTL location was calculated based on one-LOD support intervals. The

genome wide significance LOD threshold was empirically determined by performing a

permutation test of 10,000 iterations. For the non-normally distributed traits, a Kruskal-Wallis

test of Marker-QTL associations was implemented using False Discovery Rate (FDR)-control

(Benjamini and Hochberg 1995; van Ooijen and Maliepaard 2001). Overlapping QTLs within

the 1 LOD drop-off confidence intervals for the trait(s) across different environments and

generations were provisionally considered as the same gene effect.

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Results

Phenotypic trait analysis

The majority of the phenotypic traits analysed showed quantitative variation in the F2 and

F3 generations. Non-normal distribution was observed for days to emergence in F2 and F3

populations in both growing conditions (glasshouse and field), as did growth habit and eye

pattern around the hilum (only recorded in glasshouse) assessed in the F3 generation. The

segregation ratio of growth habit was consistent with the ratio of 3:2:3 (χ2

= 0.95 < 5.99; 2 df; p

= 0.05) for bunch, semi-bunch and spreading, which in turn suggested co-dominant inheritance.

The qualitative eye pattern around hilum trait observed was likely to be under the control of a

single dominant gene (χ2

= 0.06 < 3.84; 1 df; p = 0.05; assessed F3 generation, indicating F2

genotype). For the leaf area trait, a non-normal distribution was observed under controlled

environment conditions (p < 0.01) but not in the field (p = 0.51). Pod no./plant, plant height,

petiole length, internode length, peduncle length, leaf area, seed length, seed weight, 100-seed

weight, biomass dry weight, and shelling percentage are the traits found to be significantly

different (p < 0.05) between the parental DipC and Tiga Nicuru lines grown in glasshouse

(Table 2A).

Pearson’s correlation coefficient analysis revealed biomass dry weight to be associated

with a number of vegetative and yield related traits including leaf no./plant, plant height, petiole

length and 100-seed weight in both populations under all growing conditions (Table S3, S4 and

S5). Interestingly, the relatively strong correlation between plant spread and biomass dry weight

(r > 0.7; p < 0.001) explained as much as 55 to 65% of the trait variation in glasshouse and field

conditions. Among the evaluated traits, seed weight and pod weight recorded the highest

positive correlation with biomass dry weight (r = 0.9, p < 0.001) in F2 and F3 populations under

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the controlled environment, which also accounted for a high percentage of trait variation (r2

=

91.3% and 89.7%). However, this correlation was not significant under field conditions,

suggesting environmental influence on these traits. A strong association (r ≥ 0.9) was observed

between pod weight and seed weight traits, as might be expected, accounting for 79.6% of

variance in the field trial.

Construction of the linkage map

A total of 33 SSR markers were used to score all 73 lines of the F3 population with an

average level of residual heterozygosity of 24.9%, which was consistent with this being an F3

population. Of the 7,680 fragments immobilised on the bambara groundnut DArT array, 236

(3.1%) were identified as polymorphic markers. As a result, a total of 269 polymorphic loci

were used to construct the genetic linkage map. A slightly lower level of distorted segregation

was found in SSR markers (24.1%; 7) than in the DArT markers (32.9%; 76) and distorted

regions were distributed across a number of linkage groups. The final map consisted of 29 co-

dominant SSR markers and 209 DArT dominant markers assigned to 21 LGs, covering a total of

608.3 cM with a density of 2.6 marker per cM (although notice the clustering on LG1; Fig. 2 &

Table 1). LG10 was the longest group, consisting of 23 markers covering 76.4 cM of the map.

QTL mapping

Genome-wide significance thresholds ranged from 2.5 to 3.1 depending on the trait, trial

and generation. Among the traits being examined, 36 significant QTLs were identified by the

QTL analysis, present on a total of 8 linkage groups (Fig. 2, Table 2A and 2B). Specifically, the

following QTLs associated with important phenotypic traits related to yield potential in bambara

groundnut are worth emphasising, particularly those found to be significant different (p < 0.05)

between parental lines; pod no./plant, seed length, seed weight and 100-seed weight. (1) Seed

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weight and pod weight: the significant QTL (LG1 33.0 cM) associated with seed weight (LOD:

2.7) and pod weight (LOD: 2.6) in the F3 under glasshouse conditions was also detected as an

indicative QTL associated with seed no./plant (LOD: 2.3), explaining 17.8% and 17.0% of the

phenotypic variation in the respective significant traits. The same genomic region was also

observed to have an indicative QTL for pod no./plant (LOD: 2.3) and a significant QTL for node

no./stem trait (LOD: 3.3). (2) 100-seed weight: one significant QTL on LG7 (10.5 cM) was

found to be associated with 100-seed weight under controlled environment conditions in the F3

population.

Additionally, the following QTLs associated with phenotypic traits related to

‘domestication syndrome’ in bambara groundnut are worth highlighting. Despite both DipC and

Tiga Nicuru being domesticated lines, they differed significantly (p < 0.05) in terms of plant

height, petiole length, internode length, peduncle length, leaf area, biomass (dry weight) and

shelling percentage, with the earlier traits contributing to bunchy and semi-spreading growth

habits. (1) Plant spread: two significant QTLs adjacent to each other on LG4 were found to be

associated with this phenotypic trait in the F2 (33.5 cM; LOD 3.2) and F3 (0.0 cM; LOD 3.9)

under glasshouse conditions. (2) Growth habit: non-parametric mapping of the trait in the F3

generation in the glasshouse and the field detected a very strong association (p < 0.0005) on

LG4 0.0 cM, which was also identified as a significant QTL for plant spread (LOD: 3.9) and

double seeded pods/plant (LOD: 3.3). Another QTL on LG18 5.1 cM was detected to be linked

to the growth habit trait (p > 0.01) under field conditions, which was also an indicative QTL for

pod no./plant trait (LOD: 2.4). (3) Internode length: Data analysis for both glasshouse and the

field detected a major QTL for internode length which mapped on LG4 3.0 cM, scoring high

LOD values of 7.9 and 7.1, respectively, accounting for as much as 43.5% and 40.9% of total

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phenotypic variation. In addition, it was also identified to be strongly linked to the growth habit

trait (p < 0.0001) in the field F3 population.

Discussion

Population polymorphism, phenotypic variability and genetic mapping

In this study, polymorphic DArT Array and SSR markers were utilised to construct an

initial genetic linkage map of bambara groundnut using an F3 population from an intraspecific

cross. To the best of our knowledge, this is the first linkage map of bambara groundnut between

genotypes drawn from domesticated landraces. The two parental genotypes originating from

divergent agro-ecological backgrounds [DipC from Botswana (Southern Africa) and Tiga

Nicuru from Mali (West Africa)] have marked phenotypic differences in terms of plant

morphology and phenology (Fig 1). Furthermore, a previous diversity analysis using co-

dominant SSR and dominant DArT array markers revealed that the two parental landraces that

the specific genotypes were drawn from belong to different genetic clusters (Molosiwa et al.

2015; Stadler, 2009). A polymorphism level of 36.3% (among 124 within-species SSR markers

tested) was recorded between the parental materials, DipC and Tiga Nicuru. This is higher than

the 19% polymorphism reported by Somta et al. (2011) in their genetic diversity study of

bambara groundnut. Worthy of note is the fact that while Somta et al. (2011) used a relatively

large number of accessions (240) from diverse geographical backgrounds. Out of the 188 SSR

markers tested polymorphic in bambara groundnut accessions in their study, only 10 were

derived from within the species with the remaining from adzuki bean, cowpea and mung bean.

This could probably have accounted for the lower levels of polymorphism recorded in their

report, despite a far larger number of accessions being analysed. The level of polymorphism

observed for the DArT array markers was significantly lower (3.6%), although in line with other

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reports. For example, the polymorphism rate detected by DArT array markers ranged from

0.65% to 9.38% in six intra-specific chickpea crosses with higher rates observed (3.98% to

17.21%) in five inter-specific crosses (Thudi et al. 2011). Nevertheless, the bambara groundnut

DArT array markers had a good average polymorphic information content (PIC) value of 0.32

(Stadler 2009).

Together with the polymorphic DArT markers, the generated map consisted of 21 LGs

with a total of 608.3 cM in length. Nevertheless, the high marker-marker linkage (238 out of 269

markers in groups of at least two markers) at 89% of all markers might suggest a more

comprehensive coverage. This could be due to the parental dissimilarity which has suppressed

recombination or the developed markers clustering into particular regions of the plant genome.

In terms of the observed clustering of DArT markers such as on LG01, there could be a few

possibilities. This may indicate the presence of gene-rich regions, potentially as a reflection of

hypomethylated regions of the restriction enzyme sites, which is consistent with the

observations found in the genetic maps of other species such as chickpea and rapeseed (Raman

et al. 2013; Thudi et al. 2011). Mapping of DArT array markers to the Eucalyptus reference

genomes using the unique sequence tag of each marker has suggested that PstI-based DArT

markers are predominant at the low copy gene-rich regions (Petroli et al. 2012). Another

explanation for clustering could be the localised proliferation of a repetitive sequence on LG01

in one of the parents, but not the other (Stadler, 2009). Alternatively, the introgression of a

segment of distantly related genome into one of the parents might lead to high DArT array

marker polymorphism and clustering. In an analysis of a wheat cross between two parents

differing for the 1B-1R translocation from Rye, it was noted that very high levels of clustered

polymorphism existed in the region of the introgression (S. Mayes pers. comm, data not

published). The addition of further markers could improve the current map by reducing the

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number of linkage groups to 11 to correspond to the number of chromosomes in the bambara

groundnut genome. A larger population size is required to determine the marker order with a

greater confidence, which is one limitation of the current study. However, it is worth noting that

this is one of the first populations developed for bambara groundnut. It has been suggested that

uniformly distributed loci every 10cM over the entire genome is effective in MAS and QTL

identification (Stuber et al. 1999). Against this backdrop, the current map is suitable for MAS

and QTL analysis.

QTL for yield determinant components and ‘domestication syndrome’ in bambara

groundnut

This is also the first report of a QTL analysis for phenotypic traits in bambara groundnut. A total

of 36 significant QTLs were revealed to be associated with 19 out of 29 assessed traits. The

majority of these were located on LG1, LG4 and LG12 with the QTL detected from interval

mapping analysis explaining between 11.6 to 49.9% of the phenotypic variation of the evaluated

traits. While our data suggested a strong environmental component in most of the traits, the QTL

for internode length (3.0 cM LG4; LOD 7.9 and 7.1) and growth habit (0.0 cM LG4; p <

0.0005) were stably expressed in F3 populations evaluated in both controlled environment and

field. Previous phenotypic evaluation (analysis of variance and/or principal component analysis)

has reported a high level of variation in internode length among landrace populations (Aliyu and

Massawe 2013; Molosiwa et al. 2002). Consistent with this, the stable QTL detected in this

study explained a high proportion of phenotypic variation (more than 40%) from the F3

generation grown under controlled environment glasshouse and field conditions. This

observation of relatively strong genetic effects in the current study could be further supporting

evidence for the previous report that internode length is under the control of a single dominant

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gene for domestication and that this locus still retains variation in domesticated germplasms

(Basu et al. 2007c). It is worth noting that, although relatively weak, significant associations of

this trait with seed weight and pod weight (r = 0.3 – 0.4, p < 0.05; Table S4 and S5) were

observed in the current study under both glasshouse and field conditions which could be useful

in MAS. The regression analysis (Fig. 3) has suggested that seed weight and pod weight

accounted for 11.1% and 7.6%, respectively of the variation in internode length of F3 population

grown in glasshouse. Furthermore, this same QTL was identified in the F3 field trial to be

significantly associated with growth habit. As expected, a strong negative correlation was

observed between internode length and growth habit (r = -0.7; p < 0.001). Therefore the

progenies which inherit this QTL allele are likely to exhibit a transition from bunch to semi-

bunch growth habit. This is a favourable agronomic trait particularly in small scale and/or

subsistence mixed farming systems typical of agro-ecologies where bambara groundnut is

cultivated. Generally, the transformation from spreading type to semi-bunch/bunch type of

growth habit in bambara groundnut has been reported as one of the key ‘domestication

syndrome’ changes of the crop (Aliyu and Massawe 2013; Basu et al. 2007b). We postulate that

QTL identified in this study to be associated with internode length could be one of the key

genetic loci selected for by farmers (shorter internode length) during the course of domestication

of this crop, which has progressively resulted in a higher P/I ratio (7-9 for semi-bunch, and > 9

for complete bunchy) of landraces. This could possibly explain the observation that 47% and 8%

of the landraces in IITA accessions were semi-bunch and spreading types respectively (Goli et

al. 1995). Overall, it can be observed that a number of genes controlling vegetative growth are

co-localised from 0.0 to 33.5 cM on LG4.

It is possible that certain genetic loci exhibit pleiotropic effects. Specifically, one genetic

locus on 33.0 cM of LG1 is worth noting for a potential pleiotropic effect. This is a major QTL

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for biomass dry weight, node no./plant, pod weight and seed weight and an indicative QTL for

pod no. and seed no./plant. More than one QTL have been identified for different traits mapped

to/near the equivalent position in soybean (Wenxin et al. 2008; Zhang et al. 2004). However, we

do not rule out the possibility of having multiple tightly linked genes clustered together at this

locus in the current analysis. Further investigation using larger populations would lead to a

better understanding of the nature of this QTL and whether it is really a number of linked QTL.

While this locus could be significant for MAS related to yield potential, its environmental

dependence (detected only under glasshouse conditions) is a potentially limiting factor. For

MAS related to yield potential, we would recommend using it in conjunction with other QTLs.

Conclusion

A total of 33 polymorphic species-specific SSR markers and 236 DArT array based

markers were used to construct an initial linkage map allowing QTL analysis of important

phenotypic/agronomic traits. To the best of our knowledge, this is the first report in the literature

of genetic mapping between domesticated landraces and QTL analysis in this underutilised

species. The map comprises of 29 SSR and 209 DArT array markers grouped into 21 linkage

groups. In total, 36 significant QTL were detected using interval mapping and non-parametric

mapping. Most of the QTL detected were clustered on LG1, LG4 and LG12. Specifically, QTL

linked to important traits related to ‘domestication syndrome’ and yield potential in bambara

groundnut have been identified. The current map could be useful in a breeding improvement

programme of this underutilised crop and could allow MAS strategies to be deployed.

Acknowledgements: The authors gratefully acknowledge the EU BAMLINK project for

providing the funding for this project and MOHESR-IRAQ for awarding a scholarship to the

first author.

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Table 1. Distribution of SSR and DArT markers among 21 LGS in the map constructed from a

F3 population of DipC x Tiga Nicuru cross.

LG Length (cM) No. of

markers

Type of marker

SSR DArT

1 72.6 46 3 43

2 36.6 11 0 11

3 38.1 15 0 15

4 33.5 13 3 10

5 74.2 28 5 23

6 20.9 13 0 13

7 13.3 12 2 10

8 16.8 5 2 3

9 1.6 3 0 3

10 76.4 23 4 19

11 15.3 14 1 13

12 47.5 10 1 9

13 18.0 9 1 8

14 3.1 9 1 8

15 42.9 7 1 6

16 8.0 5 1 4

17 23.5 4 2 2

18 34.4 4 1 3

19 30.2 3 1 2

20 0.0 2 0 2

21 1.4 2 0 2

Total 608.3 238 29 209

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Table 2A. QTL analysis from interval mapping

Traits Population/condition LG Position (cM) Nearest marker Interval mapping

LOD PTa PVE

b Additive

Terminal leaflet length (TLL) F2 CE 8 0.0 Bam2coL63 2.3

2.6 11.6 -0.6

F3 CE 8 0.0 Bam2coL63 3.1 19.7 -0.4

Terminal leaflet width (TLW) F2 CE 5 74.2 bgPt-595387 2.6 2.5 15.3 0.3

F3 CE 3 19.7 bgPt-600935 3.2 2.6 20.4 0.2

Plant spread (PS) F2 CE 4 33.5 bgPabg-597624 3.2

2.7 18.0 5.5

F3 CE 4 0.0 BN6b 3.9 24.6 3.7

Node no./stem (NN) F3 CE

1 33.0 bgPabg-596774 3.3 2.6

21.1 -1.1

4 11.2 bgPt-600898 2.7 17.9 1.1

F3 field 3 30.2 bgPabg-595707 2.8 2.7 18.4 1.0

Internode length (IL)* F3 CE 4 3.0 bgPabg-596988 7.9 2.6 43.5 0.7

F3 field 4 3.0 bgPabg-596988 7.1 2.7 40.9 0.3

Double seeded pods/plant (DPN) F2 CE 4 33.5 bgPabg-597624 3.3 2.8 19.2 0.7

F3 CE 4 0.0 BN6b 3.3 2.9 21.7 0.5

Peduncle length (PEL)* F3 CE 4 1.0 bgPt-423527 9.7 2.7 49.9 0.9

Pod weight (PWE) F3 CE 1 33.0 bgPabg-596774 2.6 2.5 17.0 -6.8

Pod length (PLE) F3 CE 12 15.0 - 4.6 2.7 28.0 0.8

F3 field 11 0.0 bgPabg-595822 3.0 2.5 19.9 0.1

Pod width (PWD) F3 CE 12 20.0 - 5.7 2.4 32.7 0.5

Pod length of double seeded (DPL)* F3 CE 1 0.0 bgPabg-597086 3.8

2.7 24.5 -1.5

12 10.5 - 3.3 21.7 1.6

Pod width of double seeded (DPW) F3 CE 12 17.0 - 4.0 2.8 24.0 0.5

Seed weight (SWT)* F3 CE 1 33.0 bgPabg-596774 2.6 2.6 17.8 -0.5

Biomass dry weight (BDW)* F3 CE 1 33.0 bgPabg-596774 3.5 3.0 22.4 -11.6

F3 field 1 28.9 bgPt-602039 2.9 2.9 17.6 -1.8

Shelling % (SH)* F2 CE 12 47.5 bgPt-595486 4.8 2.6 26.3 -4.0

F3 CE 7 13.3 bgPabg-594335 3.0 2.9 19.4 3.4

100-seed weight (HSW)* F3 CE 7 10.5 bgPt-601852 2.6 2.5 17.3 4.3

*: significantly different between parental lines; CE: controlled environment; a: permutation 10,000 times test;

b: percentage of total

phenotypic variation explained by the QTL

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Table 2B: QTL analysis from Kruskal Wallis analysis (FDR-corrected, Q < 0.05)

Traits Population/condition LG Position (cM) Locus Kruskal-Wallis analysis

K Siga

Days to emergence (DE)

F2 CE 5 74.2 bgPt-595387 7.8 ***

17 23.5 PRIMER16 12.3 ****

F3 field 1 9.3 bgPabg-423556 7.3 ***

13 9.0 bgPt-598091 6.9 ***

Growth habit (GH)

F3 CE 4 0.0 BN6b 23.4 *******

10 70.1 bgPabg-596205 8.3 ****

F3 field

4 0.0 BN6b 17.6 ******

4 3.0 bgPabg-596988 18.1 *******

14 0.0 bgPt-597832 7.4 ***

18 5.1 PRIMER10 9.7 ***

Eye pattern around hilum (EP) F3 CE 12 22.5 bgPabg-594999 29.7 *******

18 0.0 bgPabg-594261 9.3 ****

CE: controlled environment; a: Significance: ***: 0.01, ****: 0.005, *****: 0.001, ******: 0.0005, *******: 0.0001

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List of figures

Figure 1. DipC and Tiga Nicuru with contrasting growth habit and seed eye pattern were used

as parental lines to develop this segregating bi-parental population. (A) DipC plant and (B) seed;

(C) Tiga Nicuru plant and (D) seed.

Figure 2. The genetic linkage map of bambara groundnut consisting of 21 linkage groups from

the cross of DipC x Tiga Nicuru. The position of 29 SSR and 209 DArT markers are given in

centimorgan to the left of the linkage groups and the name of markers to the right with asterisk

(*) indicating markers with segregating distortion. The position of the maximum LOD value of a

particular QTL is written at the top of QTL pointer with the growth condition of the derived

QTL was indicated in black four pointed star for F2 progeny, by black rectangle for glasshouse

experiment and white rectangle for field F3 data. QTL confidence intervals (1 LOD drop-off) are

represented by plain lines (LOD score ≥ GW threshold). QTL detected with Kruskal-Wallis

analysis (KW) are discontinuous with no confidence interval. The LOD plots of internode length

(IL; LG4 F3 population grown at field) and pod width (PWD; LG12 F3 population grown in

glasshouse) are given as examples.

Figure 3. Regression plots of internode length vs (A) seed weight and (B) pod weight observed

from F3 segregating population grown under controlled environments.

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A C

B D

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B.

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Table S1: Phenotypic traits evaluated during the field and controlled environment.

No Trait Unit Description

1 Days to emergence d Number of days from sowing to the

appearance of first true leaf on the soil

surface.

2 Days to flowering d Recorded from seedling emergence to the

appearance of the first flower(s)

3 Plant height cm Measured from the ground level to the tip

of the highest point recorded at 10 weeks

after planting

4 Petiole length mm Measured from the stem node to the

junction of the three leaflets at the longest

stem at the fourth node at 10 weeks after

planting

5 Flower no./plant - Counted each 2-3 days from the first day of

flowering for the duration of study.

6 Terminal leaflet length mm Length of median leaflet at the fourth node

recorded at 10 weeks after planting

7 Terminal leaflet width mm Width of median leaflet at the fourth node

recorded at 10 weeks after planting

8 Leaf area m2 Estimated based on the central leaflet

length and width using the method of

Cornelissen et al. (2002) in the following

equation:

Aplant = 0.86 * Leaf number [0.91 * 3

(0.95 * Length * Width * π /4)]

Where leaf number = leaf number/plant;

length and width being mean length and

width of the terminal leaflet of five

leaves/plant, and π = 3.1416.

9 Plant spread cm Widest point between two opposite points

of the plant canopy recorded at 10 weeks

after planting

10 Stem no./plant - Recorded at harvest

11 Node no./stem - Average node number on three stems/plant,

recorded at harvest.

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12 Internode length mm Length of fourth internode of the longest

stem, recorded at 10 weeks after planting

13 Growth habit (ratio of

petiole/internode)

- Bunch: P/I > 9; Semi-bunch: P/I =7-9;

spreading: P/I < 7)

14 Pod no./plant - Counted at harvest. Number of pods with

more than one seed was also determined.

15 Double seeded pods/plant - Total number of double seeded pods per

plant counted after harvesting

16 Peduncle length mm

17 Pod weight/plant g Weight of dried pods (at 12% moisture

content) was recorded after maintaining the

harvest pods for three weeks at 37oC

18 Pod length mm Digital Vernier Caliper (model no. OD-

15GP, serial no. 211810, Mitutoyo UK

Ltd.) was used to measure the greatest

length and width of five dried pods

containing one seed.

19 Pod width mm Digital Vernier Caliper was used to

measure the greatest length and width of

four dried seeds (at 12% moisture content).

20 Double seeded pod length mm Digital Vernier Caliper (model no. OD-



length of five dried pods containing two

seed.

21 Double seeded pod width mm Digital Vernier Caliper (model no. OD-



width of five dried pods containing two

seed.

22 Seed length mm Digital Vernier Caliper was used to

measure the greatest length of four dried

seeds (at 12% moisture content).

23 Seed width mm Digital Vernier Caliper was used to

measure the greatest width of four dried

seeds (at 12% moisture content).

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24 Seed no./plant - Total number of seeds per plant counted

after harvesting

25 Seed weight/plant g Average width of 5 seeds from plant

26 Biomass dry weight g Weight of above ground biomass of

harvested plants

27 Shelling % Measured as an average of all pods/plant,

based on the weight of matured dried seeds

compared to the weight of dried pods.

28 100-seed weight g Recorded after harvest at 12% moisture

content

29 Eye pattern around hilum - 0 = No eye pattern; 1 = Butterfly; 2 =

Triangular; 3 = Mottled; 4 = Thick dotted

lines; 5 = Circular; 6 = Thin lines

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Table S2: SSR primer sequences and parental allelic scoring

Name Forward sequence (5ʹ-3ʹ) Reverse sequence (3ʹ-5ʹ) Optimal TA (oC)

Fragment length (bp)

Parent 1 Parent 2

PRIMER2 CGTGGATACCCATACCGTCT TAAGTCCATTTTGTCCGATTGA 51 171 173

PRIMER7 GTAGGCCCAACACCACAGTT GGAGGTTGATCGATGGAAAA 54 210 212

PRIMER10 TCAGTGCTTCAACCATCAGC GACCAAACCATTGCCAAACT 54 260 234

PRIMER15 AGGAGCAGAAGCTGAAGCAG CCAATGCTTTTGAACCAACA 58 238 212

PRIMER16 CCGGAACAGAAAACAACAAC CGTCGATGACAAAGAGCTTG 55 189 187

PRIMER19 AGGCAAAAACGTTTCAGTTC TTCATGAAGGTTGAGTTTGTCA 57 273 235

PRIMER26 CGCTCATTTTAACCAGACCTC CAAACAAACCAACGGAATGA 55 183 185

PRIMER32 TTCACCTGAACCCCTTAACC AGGCTTCACTCACGGGTATG 55 247 251

PRIMER37 CCGATGGACGGGTAGATATG GCAACCCTCTTTTTCTGCAC 60 258 260

PRIMER38 TCACACTTGCAATGGTGCTT TCGTTGTTTCTCTTTTCATTGC 57 194 191

PRIMER43 CTTGATGCTACCGAGAGAGAG AGGCTCCAACAATGCGATAG 55 199 205

PRIMER45 CGTGGATACCCATACCGTCT AAGTCCATTTTGTCCGATTGA 52 171 173

PRIMER48 TACCTGCATTCGGGACAGTT TTCACTCTTTCTTGATCACATGC 60 238 230

PRIMER65 GGACGTGAATCGATGGAGAT TCCTTCCCCCTTCTCTGATT 55 172 176

PRIMER66 CGTTAGATCTGAGACGCCATT CATCCATCACCTGTCACCAG 60 225 213

PRIMER85 TTTCCAGATTGGATCGTTGA TGTCTTCACACCGGAATTTG 58 248 252

PRIMER88 TGTGGTTGTGCTCCTTCTCA GGGAAGAAGAGTGAAGTTGGAA 62 233 239

PRIMER95 AAGTCCATTTTGTCCGATTGA CGTGGATACCCATACCGTCT 58 168 170

PRIMER98 TTTTGTCACTGTTTGCCACAA AGATTTATATCTGGATGAGAGAGAGAG 57 264 294

PRIMER103 AAATTCAAAGGCCTGGAAAAA TTTTTGAGTTCTGCGAGCAA 57 210 220

GH-19-B2-D9 ATCAAAATCAAGCAAATGAGA ACCTTTTACGCTCATTTTAACCAG 50 236 238

BamcoL17 AACCTGAGAGAAGCGCGTAGAGAA GGCTCCCTTCTAAGCAGCAGAACT 58 162 166

Bam2coL33 ATGTTCCTTCGTCCTTTTCTCAGC AAAACAATCTCTGCCCCAAAAAGA 54 253 255

Bam2coL63 AAAATCTCACTCGGATGGCATGTG TGGAATCACCTGATAGTAGTGTATTGG 55 293 295

Bam2coL80 GAGTCCAATAACTGCTCCCGTTTG ACGGCAAGCCCTAACTCTTCATTT 58 220 224

mBam3co7 GGGTTAGTGATAATAAATGGGTGTG GTCATAGGAAAGGACCAGTTTCTC 59 267 275

mBam3co33 TGTGTCTGTTTGTGGGGATATGTA TTATCCCGGTCCTAATTCATCTTA 58 295 319

AG81 ATTTTCCAACTCGAATTGACC TCATCAATCTCGACAAAGAATG 52 202 190

BN 6b CACTACCCTGTTCTTCATCCGT CATTGCACGTCATAGAATTTGG 53 146 150

BN 145 GGCACTGGTAGCAACGAAA CGTGGACGTAACAACACAACAC 50 150 154

BN 259 CGATTGCACGTCATAGAATTTG GTTCCAGACACTACCCTCGTTC 50 159 163

D.24269 AGGTTCATGATCGTAGATGTGGAT ACGATATCATACTGACATGTTTCATAC 60 246 238

D.35497 ACTTTTAGCTCTTGTCAGGAAACG TCTTTCTACTTTTCTCTGGCTGGT 55 168 202

Page 38 of 41


Genome

Draft

Table S3: Correlation analysis of recorded traits in the F2 population grown in glasshouses with p-values. Traits 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Days to emergence 1

Leaf no./plant 2 -0.107

0.366

Days to flowering 3 0.609 -0.199

0.000 0.091

Plant height 4 0.275 0.575 0.217

0.018 0.000 0.065

Petiole length 5 0.125 0.673 0.200 0.749

0.301 0.000 .097 0.000

Terminal leaflet length 6 -0.029 0.183 -0.192 0.213 0.312

0.810 0.129 0.104 0.071 0.009

Terminal leaflet width 7 -0.064 0.495 -0.219 0.350 0.447 0.752

0.593 0.000 0.063 0.002 0.000 0.000

Plant spread 8 0.055 0.635 0.049 0.615 0.832 0.547 0.590

0.641 0.000 0.681 0.000 0.000 0.000 0.000

Pod no./plant 9 0.079 0.530 0.133 0.545 0.768 0.388 0.456 0.728

0.507 0.000 0.263 0.000 0.000 0.001 0.000 0.000

Double seeded

pods./plant 10

0.032 0.407 0.099 0.329 0.508 0.328 0.332 0.653 0.615

0.790 0.000 0.412 0.005 0.000 0.005 0.005 0.000 0.000

Pod weight/plant 11 0.126 0.632 0.198 0.655 0.836 0.276 0.410 0.800 0.909 0.702

0.288 0.000 0.094 0.000 0.000 0.018 0.000 0.000 0.000 0.000

Seed weight 12 0.166 0.648 0.228 0.677 0.840 0.264 0.396 0.788 0.888 0.690 0.990

0.159 0.000 0.053 0.000 0.000 0.024 0.001 0.000 0.000 0.000 0.000

Biomass dry weight 13 0.114 0.636 0.203 0.665 0.854 0.273 0.411 0.807 0.918 0.666 0.987 0.973

0.336 0.000 0.083 0.000 0.000 0.019 0.000 0.000 0.000 0.000 0.000 0.000

Shelling% 14 0.304 -.001 0.294 0.117 0.107 -0.147 -0.165 -0.062 -0.081 -0.074 0.064 0.156 0.025

0.009 0.994 0.011 0.323 0.379 0.216 0.162 0.604 0.497 0.542 0.588 0.186 0.832

100-seed weight 15 0.306 0.445 0.296 0.481 0.456 -0.004 0.176 0.370 0.156 0.234 0.447 0.507 0.418 0.628

0.009 0.000 0.011 0.000 0.000 0.975 0.137 0.001 0.188 0.050 0.000 0.000 0.000 0.000

Page 39 of 41


Genome

Draft

Table S4: Correlation analysis of recorded traits in the F3 population grown in glasshouses with p-values.

Trait 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Days to emergence 1

Flower

no./plant 2

-

0.158

0.121

leaf no./plant 3

-

0.158

0.212

0.553

0.000

Days to

flowering 4

0.442

0.000

-

0.478

0.000

-

0.391

0.001

Plant height 5

-

0.230

0.068

0.435

0.000

0.410

0.001

-

0.426

0.000

Petiole length 6

-

0.390

0.001

0.442

0.000

0.299

0.017

-

0.477

0.000

0.859

0.000

Leaf area 7

-

0.101

0.426

0.624

0.000

0.873

0.000

-

0.454

0.000

0.456

0.000

0.372

0.002

Terminal leaflet length 8

-

0.128

0.313

0.445

0.000

0.133

0.293

-

0.489

0.000

0.341

0.006

0.379

0.002

0.485

0.000

Terminal

leaflet width 9

-

0.045

0.721

0.344

0.005

0.291

0.020

-

0.308

0.013

0.333

0.007

0.335

0.007

0.645

0.000

0.563

0.000

Plant spread 10

-

0.112

0.379

0.576

0.000

0.385

0.002

-

0.382

0.002

0.254

0.043

0.330

0.008

0.525

0.000

0.449

0.000

0.447

0.000

Stem

no./plant 11

-

0.349

0.005

0.352

0.004

0.443

0.000

-

0.381

0.002

0.583

0.000

0.474

0.000

0.416

0.001

0.212

0.093

0.270

0.031

0.041

0.750

Node

no./stem 12

0.103

0.420

0.568

0.000

0.620

0.000

0.297

0.017

0.142

0.264

0.108

0.394

0.600

0.000

0.257

0.041

0.188

0.138

0.612

0.000

-

0.117

0.358

Internode

length 13

-

0.007

0.958

0.395

0.001

-

0.047

0.713

-

0.321

0.010

0.051

0.691

0.231

0.066

0.139

0.273

0.401

0.001

0.290

0.020

0.722

0.000

-

0.132

0.297

0.343

0.006

Pod no./plant 14

-

0.121

0.342

0.684

0.000

0.763

0.000

-

0.385

0.002

0.448

0.000

0.382

0.002

0.772

0.000

0.331

0.008

0.411

0.001

0.660

0.000

0.262

0.036

0.720

0.000

0.254

0.043

Double

seeded

pods/plant 15

0.219

0.082

0.185

0.143

0.346

0.005

-

0.325

0.009

0.347

0.005

0.379

0.002

0.410

0.001

0.238

0.058

0.309

0.013

0.641

0.000

-

0.057

0.653

0.433

0.000

0.408

0.001

0.554

0.000

Peduncle

length 16

-

0.049

0.703

0.370

0.003

0.065

0.609

-

0.315

0.011

0.035

0.784

0.160

0.208

0.211

0.095

0.394

0.001

0.243

0.053

0.750

0.000

-

0.172

0.173

0.472

0.000

0.799

0.000

0.268

0.032

0.533

0.000

Pod weight/plant 17

-

0.160

0.206

0.604

0.000

0.619

0.000

-

0.418

0.001

0.514

0.000

0.456

0.000

0.728

0.000

0.431

0.000

0.535

0.000

0.701

0.000

0.180

0.156

0.657

0.000

0.287

0.021

0.875

0.000

0.691

0.000

0.414

0.001

Pod length 18

-

0.173

0.171

0.233

0.064

0.134

0.292

-

0.189

0.134

0.463

0.000

0.468

0.000

0.303

0.015

0.493

0.000

0.416

0.001

0.405

0.001

0.291

0.020

0.086

0.500

0.158

0.213

0.171

0.176

0.294

0.018

0.342

0.006

0.438

0.000

Pod width 29

-

0.176

0.164

0.215

0.089

0.121

0.342

-

0.258

0.040

0.465

0.000

0.411

0.001

0.301

0.016

0.466

0.000

0.442

0.000

0.350

0.005

0.327

0.008

0.005

0.968

0.134

0.290

0.106

0.405

0.219

0.083

0.303

0.449

0.387

0.002

0.903

0.000

Pod length of

double seeded 20

-0.178

0.166

0.139

0.281

0.071

0.583

-0.051

0.694

0.361

0.004

0.366

0.003

0.214

0.096

0.356

0.005

0.298

0.018

0.424

0.001

0.104

0.423

0.155

0.299

0.261

0.040

0.217

0.090

0.481

0.000

0.449

0.000

0.453

0.000

0.782

0.000

0.621

0.000

Pod width of

double seeded 21

-

0.032

0.802

0.102

0.428

0.136

0.290

0.033

0.800

0.319

0.011

0.296

0.019

0.325

0.010

0.355

0.005

0.449

0.000

0.320

0.011

0.194

0.131

0.040

0.758

0.033

0.801

0.147

0.253

0.278

0.029

0.264

0.038

0.423

0.001

0.840

0.000

0.839

0.000

0.661

0.000

Seed length 22

-

0.189

0.135

0.223

0.076

0.056

0.660

-

0.237

0.060

0.486

0.000

0.485

0.000

0.261

0.037

0.475

0.000

0.441

0.000

0.237

0.059

0.318

0.011

-

0.052

0.681

0.082

0.520

0.086

0.498

0.222

0.078

0.236

0.061

0.399

0.001

0.803

0.000

0.737

0.000

0.644

0.000

0.723

0.000

Page 40 of 41


Genome

Draft

Seed width 23

-

0.389

0.001

0.294

0.018

0.051

0.689

-

0.366

0.003

0.432

0.000

0.513

0.000

0.269

0.031

0.536

0.000

0.499

0.000

0.312

0.012

0.326

0.009

0.017

0.894

0.193

0.126

0.114

0.371

0.241

0.055

0.325

0.009

0.423

0.001

0.775

0.000

0.750

0.000

0.601

0.000

0.734

0.000

0.884

0.000

Seed no./plant 24

-

0.145

0.252

0.623

0.000

0.703

0.000

-

0.419

0.001

0.437

0.000

0.405

0.001

0.725

0.000

0.343

0.005

0.416

0.001

0.718

0.000

0.182

0.149

0.725

0.000

0.332

0.007

0.969

0.000

0.698

0.000

0.362

0.003

0.910

0.000

0.187

0.139

0.109

0.391

0.264

0.038

0.153

0.234

0.098

0.441

0.146

0.250

Seed

weight/plant 25

-0.136

0.285

0.645

0.000

0.642

0.000

-0.446

0.000

0.485

0.000

0.438

0.000

0.745

0.000

0.448

0.000

0.523

0.000

0.728

0.000

0.149

0.240

0.713

0.000

0.342

0.006

0.881

0.000

0.680

0.000

0.449

0.000

0.981

0.000

0.382

0.002

0.326

0.008

0.389

0.002

0.342

0.007

0.374

0.002

0.400

0.001

0.920

0.000

Biomass dry

weight 26

-

0.099

0.436

0.671

0.000

0.667

0.000

-

0.414

0.001

0.445

0.000

0.394

0.001

0.774

0.000

0.441

0.000

0.520

0.000

0.754

0.000

0.137

0.279

0.777

0.000

0.371

0.003

0.886

0.000

0.644

0.000

0.486

0.000

0.948

0.000

0.363

0.003

0.310

0.013

0.382

0.002

0.308

0.015

0.298

0.017

0.323

0.009

0.9-3

0.000

0.956

0.000

Shelling % 27

0.052

0.683

0.303

0.015

0.232

0.065

-

0.134

0.291

-

0.112

0.380

0.030

0.812

0.200

0.114

0.064

0.614

-

0.029

0.821

0.184

0.146

-

0.104

0.412

0.333

0.007

0.279

0.025

0.174

0.169

0.049

0.699

0.129

0.309

0.066

0.604

-

0.298

0.017

-

0.334

0.007

-

0.282

0.026

-

0.392

0.002

-

0.104

0.412

-

0.125

0.324

0.190

0.133

0.234

0.063

0.167

0.187

Growth habit 38

0.083

0.512

-

0.245

0.051

0.051

0.689

0.364

0.003

0.003

0.978

-

0.140

0.270

-

0.115

0.366

-

0.427

0.000

-

0.246

0.050

-

0.615

0.000

0.049

0.702

0.299

0.017

-

0.793

0.000

-

0.165

0.191

-0349

0.005

-

0.726

0.000

-

0.216

0.086

-

0.314

0.011

-

0.245

0.051

-

0.405

0.001

0.155

0.299

0.158

0.214

-

0.253

0.044

-

0.233

0.064

-

0.243

0.053

-

0.294

0.018

-

0.100

0.430

100-seed weight 29

-

0.060

0.636

0.349

0.005

0.129

0.311

-

0.256

0.041

0.349

0.005

0.317

0.011

0.351

0.004

0.444

0.000

0.457

0.000

0.287

0.022

0.109

0.390

0.200

0.113

0.175

0.166

0.189

0.135

0.224

0.076

0.305

0.014

0.524

0.000

0.549

0.000

0.564

0.000

0.385

0.002

0.503

0.000

0.800

0.000

0.726

0.000

0.206

0.102

0.550

0.000

0.474

0.000

0.242

0.054

-0.113

0.375

Page 41 of 41


Genome

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Draft...Draft 4 nutrition (Brough and Azam-Ali 1992; Mazahib et al 2013; Nti 2009). Despite these...

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