# Identification of Putative Molecular Markers Associated with Root Traits inCoffea canephora Pierre ex Froehner

Authors: Devaraja Achar; Mallikarjuana G. Awati; M. Udayakumar; T. G. Prasad
Journal: Molecular Biology International
 (2015)

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## Abstract

Coffea canephora exhibit poor root system and are very sensitive to drought stress that affects growth and production. Deeper root system has been largely empirical as better avoidance to soil water limitation in drought condition. The present study aimed to identify molecular markers linked to high root types in Coffea canephora using molecular markers. Contrasting parents, L1 valley with low root and S.3334 with high root type, were crossed, and 134 F1 individuals were phenotyped for root and associated physiological traits (29 traits) and genotyped with 41 of the 320 RAPD and 9 of the 55 SSR polymorphic primers. Single marker analysis was deployed for detecting the association of markers linked to root associated traits by SAS software. There were 13 putative RAPD markers associated with root traits such as root length, secondary roots, root dry weight, and root to shoot ratio, in which root length associated marker OPS1850 showed high phenotypic variance of 6.86%. Two microsatellite markers linked to root length (CPCM13400) and root to shoot ratio (CM211300). Besides, 25 markers were associated with more than one trait and few of the markers were associated with positively related physiological traits and can be used in marker assisted trait selection.


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## Body

## 1. Introduction

The world’s primary source of caffeine is the coffee “bean,” which is the seed of the coffee plant, from which coffee is brewed and therefore it is one of the most important commodities in the international agricultural trade, representing a significant source of income to several coffee growing countries. The genusCoffea belongs to Rubiaceae family which has 500 genera and over 6,000 species, in whichCoffea arabica (2n=44) andCoffea canephora (2n=22) are the two commercially cultivated species. Currently the production of arabica and robusta coffee accounts for 65% and 35%, respectively; however arabica coffee typically contains half the caffeine of the robusta variety [1, 2]. The world coffee production in 2012/2013 was 155,140 (in 1,000 60 kilogram bags), but then in 2013/2014 and 2014/2015 there is a decline in coffee production and it is predicted to be decreased. This decreased coffee production is predominantly associated with variable climatic conditions, particularly limited water stress (drought), where water shortages are responsible for the greatest crop losses around the world [3, 4]. In several coffee growing countries drought is considered to be the major environmental stress affecting coffee production, in particularCoffea robusta. Because the robusta coffee is shallow rooted, it has largely been cultivated in water constraint, experiencing the stress at various crop phenology, which impacts crop growth and development above ground [5–7].Indeed drought-adapted plants are often characterized by deep and vigorous root systems, since root associated traits play a crucial role in maintaining canopy hydraulic conductance with high carbon assimilation in drought [8, 9]. Breeding coffee plants for root traits to enhance the productivity under water stress is very much required, but little progress has been achieved due to quantitative nature and poor knowledge of the genetic control of drought tolerance. Furthermore, the quantitative nature of root traits could be either constitutive or adaptive, but difficult to phenotype. Therefore, it is not surprising that a majority of genetic research has focused on aboveground traits while the “hidden half” of the plant is much less represented in recent research [10]. With the conventional breeding methods, introgression of complex/quantitative traits is unfeasible. Molecular marker technology and genomics serve as a tool for selecting such complex traits and allow breeders to track genetic loci controlling drought resistance traits, without measuring the phenotype, thus reducing the need for extensive field testing over space and time [11–13]. With the functional genomics it was shown that the increase in the amount ofRBCS1 (Rubisco small subunit) protein could contribute to the antioxidative function of photorespiration in water-stressedCoffea canephora plants [14]. Furthermore it was also shown that drought acclimation inCoffee canephora clones probably involving the abscisic signalling pathway and nitric oxide are major molecular determinants that might explain the better efficiency in controlling stomata closure and transpiration displayed by drought-tolerant clones ofC. canephora [15].With an advent of Next Generation Sequencing (NGS) technologies,Coffea canephora is the first coffee species fully sequenced [16]. The developed crop-specific hub, the Coffee Genome Hub (CGH) (http://coffee-genome.org/), can be exploited to identify the genes/SNP markers conditioning for drought and root associated secondary traits by resequencing intra- and interspecific genetic resources of robust coffee species [17]. Therefore, it is very important to identify the genetic loci conditioning root associated traits for breeding better drought tolerance clones. With this background, our study aims at (1) developing a mapping population with contrasting parental lines for root traits and phenotyping, (2) genotyping the mapping population with RAPD and SSR markers, and (3) detecting the association of molecular markers with complex physiological and morphological traits.


## 2. Materials and Methods

### 2.1. Plant Materials

To identify the markers linked to root and associated physiological traits, F1 mapping population was developed by crossing low root type L1 valley as female parent with high root type S.3334 as male parent. After successful pollination, matured 300 coffee fruits were sown in nursery and after 48 days and 135 healthy seedlings were transplanted to carbonized rubber containers (35 kg capacity) for better establishment. This experiment was conducted at Central Coffee Research Institute, Chikmagalur District, Karnataka State, India. Institute is situated at 13°22′′ north Latitude and 75°28′′ east Longitude at an elevation of 824 to 884 meters above mean sea level.


### 2.2. Phenotypic Analysis

The moisture regime of 70% field capacity (FC) was imposed for 180 days after six months of coffee seedlings established in carbonized rubber containers. Gravimetric approach was followed to maintain 70% FC, where potted plants were daily weighed to add water which was evapotranspired [18].


### 2.3. Observations Recorded during Treatment Period

During the treatment period (180 days), cumulative water added, evaporation, evapotranspiration were recorded. After the treatment period, root traits such as root length (cm), number of secondary roots, root dry weight (g/plant), shoot dry weight (g/plant), and root to shoot ratio were recorded. Besides, morphological, gravimetric, and gas exchange parameters were also recoded [19].


### 2.4. Genomic DNA Extraction

Coffee leaves were frozen in liquid nitrogen and stored at −80°C. DNA was extracted from frozen leaves using cetyltrimethyl ammonium bromide (CTAB) method [20]. For the CTAB technique, 900 μL of CTAB extraction buffer was added to lyophilized leaf tissue in 2 mL Eppendorf tubes and then lightly vortexed. The tubes were placed in hot water bath (65°C) for 45 min and mixed with 400 μL of chloroform : isoamylalcohol (24 : 1) and centrifuged for 15 min. The aqueous layer was collected, and 800 μL of isopropanol was added to precipitate the nucleic acids. Nucleic acid pellets were washed with 400 μL of 70% ethanol, dried, and resuspended in 100 μL of Tris-EDTA buffer (10 mM Tris with pH 7.5 and 0.5 mM EDTA).


### 2.5. Polymerase Chain Reaction (PCR)

RAPD (Random Amplified Polymorphic DNA, Operon Technologies) primers were used to genotype mapping population. Polymerase chain reaction was carried out in 15μL reaction containing 1x buffer, 2 mM dNTPs, 2.5 mM MgCl2, 5 μM primer, and 1 UTaq DNA polymerase (NEB). Amplification was performed with the following thermal cycle profile: 94°C/4 min hot start denaturation, followed by 35 cycles of 94°C for 1 min, primer annealing at 38°C for 1 min, extension at 72°C for 2 min, and a final extension at 72°C for 8 min. The PCR was performed using Eppendorf thermocycler (Eppendorf, Hamburg, Germany). The PCR products were run on 1.5% agarose gel at 90 volts for 1 h 30 min and amplified fragments were documented using Hero Lab Gel Documentation system (Inkarp).SSR (simple sequence repeats) primers for polymerase chain reaction were synthesized based on the information available in coffee genome database and also from in-house developed from S.3334 coffee accession.PCR amplification was carried out with 15μL reaction mixture having 50 ng DNA, 1x PCR buffer, 100 μM dNTPs, 250 μM primers, and 1 unit Taq polymerase enzyme (NEB). Amplification was performed with the following thermal cycle profile: 95°C for 5 min, followed by 35 cycles of polymerization reaction, each consisting of denaturation at 94°C for 15 s, annealing at 60°C for 45 s, and an extension step at 72°C for 1 min. A final extension step was run for 5 min at 72°C. The PCR was performed using Eppendorf thermocycler (Eppendorf, Hamburg, Germany). The PCR products were run on 6% polyacrylamide denaturing gels. Amplified fragments were detected using a silver-staining procedure (Promega, Madison, WI, USA).


### 2.6. Study of Parental Polymorphism

The contrasting parents (L1 valley × S.3334) for root traits were screened with 320 RAPD (series from OPK1 to OPZ20) and 55 SSR markers. The polymorphic RAPD bands were visually scored for the presence (1) or absence (0) and in SSR analysis the segregating band from the female parent was scored as 3, male parent as 1, and heterozygous bands were scored as 2 in all individuals of F1 mapping population. The binary data was used for further statistical analysis. Based on the segregation of RAPD markers in the mapping population, the putative genotypic interpretation of the parents for the marker locus was made and the Chi-square test was performed (http://www.physics.csbsju.edu/stats/chi-square_form.html).


### 2.7. Association of Identified Polymorphic Markers with Physiological Traits

Single point analysis [21, 22] for detecting the association of molecular markers with complex physiological and morphological traits was done using SAS software. To find the amount of variability explained by these markers, regression (R2) values were worked out by one-way analysis of variance (ANOVA), by general linear model (GLM) procedure. In this analysis, different traits were treated as dependent variable and the various molecular markers as independent variables. A total of 30 different physiological and morphological traits were used to associate with the 85 polymorphic molecular markers [23].


## 2.1. Plant Materials

To identify the markers linked to root and associated physiological traits, F1 mapping population was developed by crossing low root type L1 valley as female parent with high root type S.3334 as male parent. After successful pollination, matured 300 coffee fruits were sown in nursery and after 48 days and 135 healthy seedlings were transplanted to carbonized rubber containers (35 kg capacity) for better establishment. This experiment was conducted at Central Coffee Research Institute, Chikmagalur District, Karnataka State, India. Institute is situated at 13°22′′ north Latitude and 75°28′′ east Longitude at an elevation of 824 to 884 meters above mean sea level.


## 2.2. Phenotypic Analysis

The moisture regime of 70% field capacity (FC) was imposed for 180 days after six months of coffee seedlings established in carbonized rubber containers. Gravimetric approach was followed to maintain 70% FC, where potted plants were daily weighed to add water which was evapotranspired [18].


## 2.3. Observations Recorded during Treatment Period

During the treatment period (180 days), cumulative water added, evaporation, evapotranspiration were recorded. After the treatment period, root traits such as root length (cm), number of secondary roots, root dry weight (g/plant), shoot dry weight (g/plant), and root to shoot ratio were recorded. Besides, morphological, gravimetric, and gas exchange parameters were also recoded [19].


## 2.4. Genomic DNA Extraction

Coffee leaves were frozen in liquid nitrogen and stored at −80°C. DNA was extracted from frozen leaves using cetyltrimethyl ammonium bromide (CTAB) method [20]. For the CTAB technique, 900 μL of CTAB extraction buffer was added to lyophilized leaf tissue in 2 mL Eppendorf tubes and then lightly vortexed. The tubes were placed in hot water bath (65°C) for 45 min and mixed with 400 μL of chloroform : isoamylalcohol (24 : 1) and centrifuged for 15 min. The aqueous layer was collected, and 800 μL of isopropanol was added to precipitate the nucleic acids. Nucleic acid pellets were washed with 400 μL of 70% ethanol, dried, and resuspended in 100 μL of Tris-EDTA buffer (10 mM Tris with pH 7.5 and 0.5 mM EDTA).


## 2.5. Polymerase Chain Reaction (PCR)

RAPD (Random Amplified Polymorphic DNA, Operon Technologies) primers were used to genotype mapping population. Polymerase chain reaction was carried out in 15μL reaction containing 1x buffer, 2 mM dNTPs, 2.5 mM MgCl2, 5 μM primer, and 1 UTaq DNA polymerase (NEB). Amplification was performed with the following thermal cycle profile: 94°C/4 min hot start denaturation, followed by 35 cycles of 94°C for 1 min, primer annealing at 38°C for 1 min, extension at 72°C for 2 min, and a final extension at 72°C for 8 min. The PCR was performed using Eppendorf thermocycler (Eppendorf, Hamburg, Germany). The PCR products were run on 1.5% agarose gel at 90 volts for 1 h 30 min and amplified fragments were documented using Hero Lab Gel Documentation system (Inkarp).SSR (simple sequence repeats) primers for polymerase chain reaction were synthesized based on the information available in coffee genome database and also from in-house developed from S.3334 coffee accession.PCR amplification was carried out with 15μL reaction mixture having 50 ng DNA, 1x PCR buffer, 100 μM dNTPs, 250 μM primers, and 1 unit Taq polymerase enzyme (NEB). Amplification was performed with the following thermal cycle profile: 95°C for 5 min, followed by 35 cycles of polymerization reaction, each consisting of denaturation at 94°C for 15 s, annealing at 60°C for 45 s, and an extension step at 72°C for 1 min. A final extension step was run for 5 min at 72°C. The PCR was performed using Eppendorf thermocycler (Eppendorf, Hamburg, Germany). The PCR products were run on 6% polyacrylamide denaturing gels. Amplified fragments were detected using a silver-staining procedure (Promega, Madison, WI, USA).


## 2.6. Study of Parental Polymorphism

The contrasting parents (L1 valley × S.3334) for root traits were screened with 320 RAPD (series from OPK1 to OPZ20) and 55 SSR markers. The polymorphic RAPD bands were visually scored for the presence (1) or absence (0) and in SSR analysis the segregating band from the female parent was scored as 3, male parent as 1, and heterozygous bands were scored as 2 in all individuals of F1 mapping population. The binary data was used for further statistical analysis. Based on the segregation of RAPD markers in the mapping population, the putative genotypic interpretation of the parents for the marker locus was made and the Chi-square test was performed (http://www.physics.csbsju.edu/stats/chi-square_form.html).


## 2.7. Association of Identified Polymorphic Markers with Physiological Traits

Single point analysis [21, 22] for detecting the association of molecular markers with complex physiological and morphological traits was done using SAS software. To find the amount of variability explained by these markers, regression (R2) values were worked out by one-way analysis of variance (ANOVA), by general linear model (GLM) procedure. In this analysis, different traits were treated as dependent variable and the various molecular markers as independent variables. A total of 30 different physiological and morphological traits were used to associate with the 85 polymorphic molecular markers [23].


## 3. Results

### 3.1. Phenotyping Root Length, Secondary Roots, Root Dry Weight, and Physiological Traits

At the end of experiment period about 180 days water stress, pots were completely saturated with water and then, in next day, all plants were carefully detached with water force to avoid root loss. The data on root length, secondary roots, and root biomass data of parental lines (Table1) and F1 mapping population were recorded (Table 2). There was significant genetic variability observed in root length, ranging from 41.5 cm to 83.0 cm with a mean of 57.08 cm. Similar trend was followed in dry weight, ranging from 2.57 g to 34.69 g with a mean of 13.81 g/plant, and root to shoot ratio varies from 2.57 to 4.41 with mean ratio of 3.77. It was observed that F1s root traits were distributed between the values of the respective parental lines without much considerable transgressive segregation (Figure 1). The quantitative nature of these traits showed continuous variation, confirming the metric nature of the traits.Table 1
Genotypic variation of parental accessions S.3334 (high root type) and L1 valley (low root type).
S.3334
L1 valley
1
Root length
45
30
2
Secondary roots
40
15
3
Root dry weight
31.81
4.95
4
Root to shoot ratio
0.41
0.36
5
Total dry matter
106.77
15.49
6
Cumulative water transpired
28.86
31.15
7
water use efficiency
3.72
3.62
8
Net assimilation rate
13.75
9.09
9
Mean transpiration rate
3.72
2.23
10
Pn/gs
78.39
43.78
11
Pn/E
2.99
1.84
12
Ci/gs
3863.5
3399.4Table 2
Range and mean values of the morphophysiological parameters in 134 F1 mapping population ofCoffea canephora (L1 valley × S.3334).
Serial number
Parameters
Range
Mean
STDEV
Root traits
1
Root length (cm2)
41.5–83.0
57.08
7.29
2
Number of secondary roots
2–60
26.9
12.61
3
Root dry weight (g/plant)
2.57–34.69
13.77
6.6
4
Root : shoot ratio
0.23–0.39
0.27
0.03
Morphological traits
5
Plant height (cm2)
13.5–105.3
72.6
15.69
6
Number of nodes
11.0–42.0
24.08
6.88
7
Number of leaves
2–70
34.02
13.42
8
Stem girth (mm)
5.88–13.65
10.28
1.79
9
Internodal length (cm)
1.9–12.5
8.54
2
10
Specific leaf dry weight (mg/dm2)
1.41–2.64
1.8
0.22
11
Final leaf area (cm2)
1136.92–8190.61
4491.19
1710.17
12
Specific leaf area (cm)
378.99–710.97
562.44
65.34
13
Total leaf dry weight (g/plant)
5.78–48.59
26.02
10.43
14
Stem dry weight (g/plant)
4.81–52.62
24.33
10.4
15
Shoot dry weight (g/plant)
10.59–96.64
50.35
20.63
16
Total dry matter (g/plant)
11.6–121.43
57.93
24.77
Gravimetric traits
17
Leaf area duration (dm2 days)
1065.89–7992.77
4372.52
1658.04
18
Net assimilation rate
8.9–19.38
13.07
1.71
19
Cumulative water transpiration (lt/plant)
2.5–23.5
10.81
4.62
20
Water use efficiency (g/kg)
3.86–6.84
5.4
0.58
21
Mean transpiration rate (mL/dm2 days)
1.5–3.52
2.44
0.38
22
Delta 13c (mill)
19.75–26.53
21.8
1.11
Gas exchange parameters
23
Photosynthesis (Pn). (mmol/m2/S)
5.45–27.27
16.9
3.91
24
Transpiration rate (E) (mg/m2/s)
0.71–7.47
3.38
1.34
25
Conductance (gs) (mmol/m2/s)
0.05–1
0.35
0.17
26
Ci/gs
288.76–3484.25
859.3
405.1
27
Ci
186.5–296.67
247.98
24.88
28
A/gs
24.06–93.26
54.42
15.52
29
A/E
2.63–14.07
5.52
1.76Genetic variability of root system and quantitative traits of mapping population of L1 valley × S.334.Morphological traits such as plant height, number of nodes and leaves, stem girth, internodal length, leaf area, shoot dry weight, and total dry matter were recorded. Nonetheless, all these traits showed significant and continuous variation (Table2). Besides, genetic variability in the mapping population in net photosynthesis (Pn), stomatal conductance (gs), transpiration rate (E), intrinsic WUE (Pn/gs), and mesophyll efficiency (Ci/gs) was observed at single leaf level.Significant variations were observed in water use efficiency (WUE) and associated physiological traits like cumulative water transpired (CWT), net assimilation rate (NAR), mean transpiration rate (MTR), leaf area duration (LAD), and Δ13C in the population. The WUE ranges from 3.86 to 6.84 g/kg with an average of 5.40 g/kg and considerable transgressive segregation was observed. The variation in CWT was observed between 2.50 and 23.50 kg/plant. The average transpiration rate was 10.81 L/plant in the population. The NAR and MTR also varied with mean values of 13.07 mg/dm2 and 2.44 mL/dm2 of leaf area, respectively. Similarly, the functional leaf area (LAD) varied between 1065.89 and 7992.77 dm2/day with mean leaf area duration of 4376.57 dm2/day. Δ13C also varied between 19.75 and 26.53‰ with a mean of 21.80‰ (Table 2). The variation in WUE, CWT, Δ13C, and so forth, showed that traits are metric nature and mapping population could be utilized for the identification of marker linked to quantitative traits.


### 3.2. Association of Root with Other Physiological Traits

The plants with the higher root biomass showed higher total biomass and correlation between these two traits is highly significant (r=0.968) (Figure 2). It signifies the importance of root system in determining the total dry matter by absorbing the water from sub-soil layers from its better root system compared to parental lines. Subsequently strong positive significant correlations were noticed between root weight and total transpiration (r=0.940), CWT and MTR (r=0.524), and total dry matter and CWT (r=0.970) (Figure 2), suggesting that maintaining canopy hydraulic conductance with high carbon assimilation is one of the drought tolerance mechanisms.Relationship between the RDW/TDM, RDW/CWT, TDM/CWT, CWT/MTR, CWT/MTR, and WUE/Δ13C in the mapping population ofC. canephora (L1 valley × S.334).
(a)
(b)
(c)
(d)
(e)
(f)


### 3.3. Relationship between WUE with Other Physiological Traits

Significant inverse relationship between WUE and Δ13C (r=-0.413) was observed (Figure 2). It suggests that Δ13C could be a strong surrogate measure for WUE even in mapping population. The relationship between CWT and WUE was poor, suggesting stomatal control of WUE in mapping population and, similarly, between root weight and WUE. However, significant negative relationship between MTR and WUE was observed (r=-0.552) in mapping population (data not shown). This signifies the genetic nature of the coffee plants for its heritable conductance type. It suggests that stomatal factors regulate the WUE in these plants rather than photosynthetic efficiency (mesophyll factors).


### 3.4. Identification of Polymorphic RAPD and Microsatellite Primers in Parental Lines

The contrasting parental lines L1 valley (low root type) and S.3334 (high root type) were genotyped with 320 RAPD primers, of which 41 polymorphic primers generated 70 polymorphic loci (Table3). Since RAPD is dominant marker, the PCR amplified DNA fragments were scored as dominant (AA/Aa), whereas absence of bands was scored as recessive (aa). We have analyzed our data within the framework of these assumptions.Table 3
Polymorphic RAPD primers and their sequences used to study the segregation of   markers in the mapping population.
Polymorphic bands
Primer name
Sequence
Primer name
Sequence
Primer name
Sequence
Primer name
Sequence
1
OPK18
CCTAGTCGAG
OPN6
GAGACGCACA
OPQ5
CCGCGTCTTG
OPV5
TCCGAGAGGG
OPK19
CACAGGCGGA
OPO1
GGCACGTAAG
OPS1
CTACTGCGCT
OPW9
GTGACCGAGT
OPK20
GTGTCGCGAG
OPO18
CTCGCTATCC
OPV11
CTCGACAGAG
OPY20
AGCCGTGGAA
OPM5
GGGAACGTGT
OPO4
AAGTCCGCTC
OPV14
AGATCCCGCC
OPZ11
CTCAGTCGCA
OPN19
GTCCGTACTG
OPP2
TCGGCACGCA
OPV2
AGTCACTCCC
2
OPK10
GTGCSSCGTG
OPL18
ACCACCCACC
OPO16
TCGGCGGTTC
OPT7
GTCCATGCCA
OPK11
AATGCCCCAG
OPM11
GTCCACTGTG
OPP5
CCCCGGTAAC
OPU1
ACGGACGTCA
OPL1
GGCATGACCT
OPM17
TCAGTCCGGG
OPQ20
TCGCCCAGTC
OPW17
GTCCTGGGTT
OPL12
GGGCGGTACT
OPO10
TCAGAGCGCC
OPR4
CCCGTAGCAC
OPZ3
CAGCACCGCA
3
OPL19
GAGTGGTGAC
OPM2
ACAACGCCTC
OPP11
AACGCGTCGG
OPP9
GTGGTCCGCA
OPZ10
CCGACAAACC
4
OPK17
CCCAGCTGTGAmong them more than 85% of the primers produced single polymorphic locus and 15% of the primers yielded more than three polymorphic loci.All the RAPD markers in the F1 generation ofC. canephora segregated in ratios that were not consistent with Mendelian inheritance. The Chi-square tests were performed for each marker to determine segregation distortion from the expected allele frequency ratio of 1 : 1. At a significance level of P=0.05, about 62% of the marker loci were in agreement with the expected ratios and 38% of the marker loci were not following the Mendelian inheritance (Table 4). It is expected that RAPD bands of maternal DNA origin would show non-Mendelian inheritance.Table 4
Chi-square tests (χ2) for RAPD and SSR markers in the Coffea canephora mapping population.
Parents
F1 progenies
Marker system
Total polymorphic markers
Marker loci notfollowing expected ratio
Marker loci followingexpected ratio
Number of significant markers
P
=
0.95
P
=
0.05
P
=
0.001
L1 valley  × S.3334
134
RAPD
70
28
42 (1 : 1)
32
3
7
SSR
9
9
0 (3 : 1)
—
—
—Besides RAPD marker system, 55 SSR markers were also used for genotyping; however, only nine primers showed polymorphism (Table5), but did not follow the Mendelian segregation. The low level of DNA polymorphism between the two parental accessions, coupled with the large number of common bands, implies parental lines could be less diverse.Table 5
Polymorphic microsatellite primers and their sequences used to study the segregation of   markers in the mapping population.
Serial number
Primer
Sequence
Tm (°C)
Size range
1
CM11
F: GCTGCCAGAAAAATGTTGCAGTG
58
270–338
R: CTGCCTCGTAAAAGCTTGCGTTG
2
CM13
F: GCTATGCAGCTTGTTCGCAATCC
59
350–411
R: CCAGCTATATCAGGAGCAGAACC
3
CM180
F: CATGTGTAATACATTCAACAGTGA
60
300–350
R: GCAATAGTGGTTGTCATCCTT
4
CM32
F: AACTCTCCATTCCCGCATTC
60
180–200
R: CTGGGTTTTCTGTGTTCTCG
5
CM46
F: CAGCTAGTGTGAAGGGAAAC
55
300–350
R: GTTATCATGGTCTTACACG
6
CM20
F: CTTGTTTGAGTCTGTCGCTG
60
250–300
R: TTTCCCTCCCAATGTCTGTA
7
CM171
F: TTCCCCCATCTTTTTCTTTC
60
250–300
R: TTGTATACGGCTCGTCAGGT
8
CM166
F: AAGAGGTGCCTATCACCGTC
60
230–260
R: CGAGGTATCAAAAAGCACCT
9
CM211
F: TCATGCCAAATATGAGTGGA
60
300–350
R: GAGATGGCAAAGGCTGTTC


### 3.5. Single Marker Analysis for Root and Associated Physiological Traits

Complex physiological traits have been described by a small number of major QTL [24], but it is intricate to find useful QTL for a particular trait, as their individual contribution is smaller. Therefore under such condition Single Marker Analysis (SMA) is generally a good choice when the goal is simply a detection of marker locus linked to a trait. However, estimation of its position and its effects requires further complex analysis such as marker regression [25] or interval analysis [26].In our study Single Marker ANOVA is used to identify markers showing significant association with 29 morphophysiological traits (SAS software withP≤0.05).In the single marker analysis, simple regression model was examined to study the association between marker loci (independent) and trait score (dependent variable) and also computed the percent phenotypic variance explained by each marker. Seventy polymorphic marker loci were employed for simple regression analysis; however, it was discovered that 37 RAPD and 5 SSR markers explained their association with morphophysiological traits.


### 3.6. Markers Linked to Root Traits

The single marker analysis revealed four markers such as OPS1850, OPK11780, OPY201200, OPZ101350, and CM13400 were significantly associated with root length (P=0.05) and explained phenotypic variance of 4.41%, 2.95%, 3.56%, 5.12%, and 5.12%, respectively. A number of secondary roots linked to three markers such as OPO16450 (4.40%), OPK111000 (5.51%), and OPV14550 (5.82%) were significantly (P=0.05) associated. Similarly six markers such as OPK111000 (3.26%), OPP91030 (3.5%), OPM11600 (3.75%), OPL19900 (5.61%), OPL19600 (4.11%), OPR4600 (3.30%), and CM211300 (R2=4.96%) revealed significant (P=0.05) association with the root to shoot ratio (Table 6). This implies the root associated traits are polygenic controlling many genes. However, root dry weight is associated with only one maker OPL11400 (4.86%).Table 6
RAPD markers linked to root traits in the mapping population ofC. canephora (L1 valley × S.3334).
Trait
Marker
R
2 (%)
P
<
0.05
Root length
OPY201200
2.95
0.0449
OPZ101350
3.56
0.0286
OPK11780
4.41
0.0178
OPS1850
6.86
0.0001
CPCM13400
5.12
0.0105
Number of secondary roots
OPO16450
4.4
0.0179
OPK111000
5.51
0.0059
OPV14550
5.82
0.0042
Root dry weight
OPL11450
4.76
0.0073
Root to shoot ratio
OPK111000
3.26
0.038
OPR4600
3.3
0.0367
OPP91030
3.5
0.0288
OPM11600
3.75
0.0267
OPL19600
4.11
0.0223
OPL19900
5.61
0.0062
CM211300
4.96
0.0105


### 3.7. Markers Linked to Morphophysiological Traits

Besides root traits, polygenic inheritance traits such as morphological, gravimetric, and gas exchange at single leaf level traits were also deployed for marker trait association. Single marker analysis revealed more than three markers linked to each trait and 25 markers are colocalized with more than one trait and 17 markers are associated with single trait (Table7). The marker OPP51800 is coassociated with shoot dry weight and leaf area duration (LAD), and these two traits are positively correlated (R2=0.93). Four markers (OPL11450, OPL191650, OPM171400, and OPK20350) linked to NAR and two markers OPR4600 and OPL122000 are significantly (P=0.01) associated with cumulative water transpired (CWT).Table 7
List of RAPD and SSR markers overlapped with more than one trait.
Markers
Traits
R
2 (%)(P<0.05)
Markers
Traits
R
2%  (P<0.05)
OPK111000
Internodal length
4.74
OPR4600   OPR4750
CWT
5.08
Root to shoot ratio
3.26
RSR
3.30
Number of secondary roots
5.51
Number of leaves
5.65
Stem dry weight
3.44
gs
2.93
OPK181200
Specific leaf area
4.70
OPV14550
Pn
9.30
Plant height
4.51
Number of secondary Roots
5.82
OPK192000
Photosynthetic rate (Pn)
5.55
OPY201200
WUE
3.10
WUE
4.43
Plant height
4.23
K20350
NAR
4.82
OPZ111500
Number of leaves
3.99
WUE
4.45
Root length
2.95
Ci
4.32
Pn
9.23
OPL11400
Plant height
5.77
OPZ101350   OPZ101030
MTR
8.21
Internodal length
3.81
13ΔC
4.12
NAR
4.27
Plant height
3.03
Shoot dry weight
4.00
Root length
3.56
Root dry weight
4.76
WUE
3.02
Pn
2.61
Ci/gs
5.09
OPL122000
Plant height
3.68
Pn
7.56
CWT
4.74
MTR
4.38
OPL121650
13ΔC
3.61
OPZ10720
gs
2.96
pn
3.07
gs
3.26
OPL181000
Number of nodes
7.78
Pn
3.14
LAD
3.82
Internodal length
3.58
OPL19900
SLW
3.33
CPCM13450
Root length
5.12
RSR
3.60
Ci/gs
4.83
OPM11600
A/gs
3.85
CM171300    CM166260
Stem girth
4.54
RSR
4.11
A/gs
3.45
MTR
2.94
No. of nodes
3.93
OPO101400
Ci
3.13
SLA
7.19
E
3.18
E
4.18
OPP51800
LAD
3.82
CM211300
SLW
2.83
Internodal length
5.82
RSR
4.96
Shoot dry weight
2.71
E
4.23
OPP91030
Ci/gs
6.12
SLW
3.33
RSR
3.50The13ΔC is the surrogate method to quantify the water use efficiency and it was demonstrated that there is inverse relationship between these two traits. Of the six associated markers, OPZ101350 was found to be associated with 13ΔC and WUE with phenotypic variance of 4.12% and 3.02%, respectively. This marker locus could be negative additive effect since traits are negatively related. The mesophyll efficiency (Ci/gs), intrinsic WUE (A/gs), and photosynthetic rate (Pn) are, respectively, associated with CM13400 (4.83%), CM171210 (3.45%), and CM171190 (3.72%) (Table 7). Whereas positively related traits are associated with the same marker such as OPP51800 (LAD and shoot dry weight), OPZ101030 (MTR and gs), and OPK20350 (Ci and NAR) alleles contribution from S.3334. The markers linked to other gas exchange parameters were given in Table 7, which are contributed from S.3334.


## 3.1. Phenotyping Root Length, Secondary Roots, Root Dry Weight, and Physiological Traits

At the end of experiment period about 180 days water stress, pots were completely saturated with water and then, in next day, all plants were carefully detached with water force to avoid root loss. The data on root length, secondary roots, and root biomass data of parental lines (Table1) and F1 mapping population were recorded (Table 2). There was significant genetic variability observed in root length, ranging from 41.5 cm to 83.0 cm with a mean of 57.08 cm. Similar trend was followed in dry weight, ranging from 2.57 g to 34.69 g with a mean of 13.81 g/plant, and root to shoot ratio varies from 2.57 to 4.41 with mean ratio of 3.77. It was observed that F1s root traits were distributed between the values of the respective parental lines without much considerable transgressive segregation (Figure 1). The quantitative nature of these traits showed continuous variation, confirming the metric nature of the traits.Table 1
Genotypic variation of parental accessions S.3334 (high root type) and L1 valley (low root type).
S.3334
L1 valley
1
Root length
45
30
2
Secondary roots
40
15
3
Root dry weight
31.81
4.95
4
Root to shoot ratio
0.41
0.36
5
Total dry matter
106.77
15.49
6
Cumulative water transpired
28.86
31.15
7
water use efficiency
3.72
3.62
8
Net assimilation rate
13.75
9.09
9
Mean transpiration rate
3.72
2.23
10
Pn/gs
78.39
43.78
11
Pn/E
2.99
1.84
12
Ci/gs
3863.5
3399.4Table 2
Range and mean values of the morphophysiological parameters in 134 F1 mapping population ofCoffea canephora (L1 valley × S.3334).
Serial number
Parameters
Range
Mean
STDEV
Root traits
1
Root length (cm2)
41.5–83.0
57.08
7.29
2
Number of secondary roots
2–60
26.9
12.61
3
Root dry weight (g/plant)
2.57–34.69
13.77
6.6
4
Root : shoot ratio
0.23–0.39
0.27
0.03
Morphological traits
5
Plant height (cm2)
13.5–105.3
72.6
15.69
6
Number of nodes
11.0–42.0
24.08
6.88
7
Number of leaves
2–70
34.02
13.42
8
Stem girth (mm)
5.88–13.65
10.28
1.79
9
Internodal length (cm)
1.9–12.5
8.54
2
10
Specific leaf dry weight (mg/dm2)
1.41–2.64
1.8
0.22
11
Final leaf area (cm2)
1136.92–8190.61
4491.19
1710.17
12
Specific leaf area (cm)
378.99–710.97
562.44
65.34
13
Total leaf dry weight (g/plant)
5.78–48.59
26.02
10.43
14
Stem dry weight (g/plant)
4.81–52.62
24.33
10.4
15
Shoot dry weight (g/plant)
10.59–96.64
50.35
20.63
16
Total dry matter (g/plant)
11.6–121.43
57.93
24.77
Gravimetric traits
17
Leaf area duration (dm2 days)
1065.89–7992.77
4372.52
1658.04
18
Net assimilation rate
8.9–19.38
13.07
1.71
19
Cumulative water transpiration (lt/plant)
2.5–23.5
10.81
4.62
20
Water use efficiency (g/kg)
3.86–6.84
5.4
0.58
21
Mean transpiration rate (mL/dm2 days)
1.5–3.52
2.44
0.38
22
Delta 13c (mill)
19.75–26.53
21.8
1.11
Gas exchange parameters
23
Photosynthesis (Pn). (mmol/m2/S)
5.45–27.27
16.9
3.91
24
Transpiration rate (E) (mg/m2/s)
0.71–7.47
3.38
1.34
25
Conductance (gs) (mmol/m2/s)
0.05–1
0.35
0.17
26
Ci/gs
288.76–3484.25
859.3
405.1
27
Ci
186.5–296.67
247.98
24.88
28
A/gs
24.06–93.26
54.42
15.52
29
A/E
2.63–14.07
5.52
1.76Genetic variability of root system and quantitative traits of mapping population of L1 valley × S.334.Morphological traits such as plant height, number of nodes and leaves, stem girth, internodal length, leaf area, shoot dry weight, and total dry matter were recorded. Nonetheless, all these traits showed significant and continuous variation (Table2). Besides, genetic variability in the mapping population in net photosynthesis (Pn), stomatal conductance (gs), transpiration rate (E), intrinsic WUE (Pn/gs), and mesophyll efficiency (Ci/gs) was observed at single leaf level.Significant variations were observed in water use efficiency (WUE) and associated physiological traits like cumulative water transpired (CWT), net assimilation rate (NAR), mean transpiration rate (MTR), leaf area duration (LAD), and Δ13C in the population. The WUE ranges from 3.86 to 6.84 g/kg with an average of 5.40 g/kg and considerable transgressive segregation was observed. The variation in CWT was observed between 2.50 and 23.50 kg/plant. The average transpiration rate was 10.81 L/plant in the population. The NAR and MTR also varied with mean values of 13.07 mg/dm2 and 2.44 mL/dm2 of leaf area, respectively. Similarly, the functional leaf area (LAD) varied between 1065.89 and 7992.77 dm2/day with mean leaf area duration of 4376.57 dm2/day. Δ13C also varied between 19.75 and 26.53‰ with a mean of 21.80‰ (Table 2). The variation in WUE, CWT, Δ13C, and so forth, showed that traits are metric nature and mapping population could be utilized for the identification of marker linked to quantitative traits.


## 3.2. Association of Root with Other Physiological Traits

The plants with the higher root biomass showed higher total biomass and correlation between these two traits is highly significant (r=0.968) (Figure 2). It signifies the importance of root system in determining the total dry matter by absorbing the water from sub-soil layers from its better root system compared to parental lines. Subsequently strong positive significant correlations were noticed between root weight and total transpiration (r=0.940), CWT and MTR (r=0.524), and total dry matter and CWT (r=0.970) (Figure 2), suggesting that maintaining canopy hydraulic conductance with high carbon assimilation is one of the drought tolerance mechanisms.Relationship between the RDW/TDM, RDW/CWT, TDM/CWT, CWT/MTR, CWT/MTR, and WUE/Δ13C in the mapping population ofC. canephora (L1 valley × S.334).
(a)
(b)
(c)
(d)
(e)
(f)


## 3.3. Relationship between WUE with Other Physiological Traits

Significant inverse relationship between WUE and Δ13C (r=-0.413) was observed (Figure 2). It suggests that Δ13C could be a strong surrogate measure for WUE even in mapping population. The relationship between CWT and WUE was poor, suggesting stomatal control of WUE in mapping population and, similarly, between root weight and WUE. However, significant negative relationship between MTR and WUE was observed (r=-0.552) in mapping population (data not shown). This signifies the genetic nature of the coffee plants for its heritable conductance type. It suggests that stomatal factors regulate the WUE in these plants rather than photosynthetic efficiency (mesophyll factors).


## 3.4. Identification of Polymorphic RAPD and Microsatellite Primers in Parental Lines

The contrasting parental lines L1 valley (low root type) and S.3334 (high root type) were genotyped with 320 RAPD primers, of which 41 polymorphic primers generated 70 polymorphic loci (Table3). Since RAPD is dominant marker, the PCR amplified DNA fragments were scored as dominant (AA/Aa), whereas absence of bands was scored as recessive (aa). We have analyzed our data within the framework of these assumptions.Table 3
Polymorphic RAPD primers and their sequences used to study the segregation of   markers in the mapping population.
Polymorphic bands
Primer name
Sequence
Primer name
Sequence
Primer name
Sequence
Primer name
Sequence
1
OPK18
CCTAGTCGAG
OPN6
GAGACGCACA
OPQ5
CCGCGTCTTG
OPV5
TCCGAGAGGG
OPK19
CACAGGCGGA
OPO1
GGCACGTAAG
OPS1
CTACTGCGCT
OPW9
GTGACCGAGT
OPK20
GTGTCGCGAG
OPO18
CTCGCTATCC
OPV11
CTCGACAGAG
OPY20
AGCCGTGGAA
OPM5
GGGAACGTGT
OPO4
AAGTCCGCTC
OPV14
AGATCCCGCC
OPZ11
CTCAGTCGCA
OPN19
GTCCGTACTG
OPP2
TCGGCACGCA
OPV2
AGTCACTCCC
2
OPK10
GTGCSSCGTG
OPL18
ACCACCCACC
OPO16
TCGGCGGTTC
OPT7
GTCCATGCCA
OPK11
AATGCCCCAG
OPM11
GTCCACTGTG
OPP5
CCCCGGTAAC
OPU1
ACGGACGTCA
OPL1
GGCATGACCT
OPM17
TCAGTCCGGG
OPQ20
TCGCCCAGTC
OPW17
GTCCTGGGTT
OPL12
GGGCGGTACT
OPO10
TCAGAGCGCC
OPR4
CCCGTAGCAC
OPZ3
CAGCACCGCA
3
OPL19
GAGTGGTGAC
OPM2
ACAACGCCTC
OPP11
AACGCGTCGG
OPP9
GTGGTCCGCA
OPZ10
CCGACAAACC
4
OPK17
CCCAGCTGTGAmong them more than 85% of the primers produced single polymorphic locus and 15% of the primers yielded more than three polymorphic loci.All the RAPD markers in the F1 generation ofC. canephora segregated in ratios that were not consistent with Mendelian inheritance. The Chi-square tests were performed for each marker to determine segregation distortion from the expected allele frequency ratio of 1 : 1. At a significance level of P=0.05, about 62% of the marker loci were in agreement with the expected ratios and 38% of the marker loci were not following the Mendelian inheritance (Table 4). It is expected that RAPD bands of maternal DNA origin would show non-Mendelian inheritance.Table 4
Chi-square tests (χ2) for RAPD and SSR markers in the Coffea canephora mapping population.
Parents
F1 progenies
Marker system
Total polymorphic markers
Marker loci notfollowing expected ratio
Marker loci followingexpected ratio
Number of significant markers
P
=
0.95
P
=
0.05
P
=
0.001
L1 valley  × S.3334
134
RAPD
70
28
42 (1 : 1)
32
3
7
SSR
9
9
0 (3 : 1)
—
—
—Besides RAPD marker system, 55 SSR markers were also used for genotyping; however, only nine primers showed polymorphism (Table5), but did not follow the Mendelian segregation. The low level of DNA polymorphism between the two parental accessions, coupled with the large number of common bands, implies parental lines could be less diverse.Table 5
Polymorphic microsatellite primers and their sequences used to study the segregation of   markers in the mapping population.
Serial number
Primer
Sequence
Tm (°C)
Size range
1
CM11
F: GCTGCCAGAAAAATGTTGCAGTG
58
270–338
R: CTGCCTCGTAAAAGCTTGCGTTG
2
CM13
F: GCTATGCAGCTTGTTCGCAATCC
59
350–411
R: CCAGCTATATCAGGAGCAGAACC
3
CM180
F: CATGTGTAATACATTCAACAGTGA
60
300–350
R: GCAATAGTGGTTGTCATCCTT
4
CM32
F: AACTCTCCATTCCCGCATTC
60
180–200
R: CTGGGTTTTCTGTGTTCTCG
5
CM46
F: CAGCTAGTGTGAAGGGAAAC
55
300–350
R: GTTATCATGGTCTTACACG
6
CM20
F: CTTGTTTGAGTCTGTCGCTG
60
250–300
R: TTTCCCTCCCAATGTCTGTA
7
CM171
F: TTCCCCCATCTTTTTCTTTC
60
250–300
R: TTGTATACGGCTCGTCAGGT
8
CM166
F: AAGAGGTGCCTATCACCGTC
60
230–260
R: CGAGGTATCAAAAAGCACCT
9
CM211
F: TCATGCCAAATATGAGTGGA
60
300–350
R: GAGATGGCAAAGGCTGTTC


## 3.5. Single Marker Analysis for Root and Associated Physiological Traits

Complex physiological traits have been described by a small number of major QTL [24], but it is intricate to find useful QTL for a particular trait, as their individual contribution is smaller. Therefore under such condition Single Marker Analysis (SMA) is generally a good choice when the goal is simply a detection of marker locus linked to a trait. However, estimation of its position and its effects requires further complex analysis such as marker regression [25] or interval analysis [26].In our study Single Marker ANOVA is used to identify markers showing significant association with 29 morphophysiological traits (SAS software withP≤0.05).In the single marker analysis, simple regression model was examined to study the association between marker loci (independent) and trait score (dependent variable) and also computed the percent phenotypic variance explained by each marker. Seventy polymorphic marker loci were employed for simple regression analysis; however, it was discovered that 37 RAPD and 5 SSR markers explained their association with morphophysiological traits.


## 3.6. Markers Linked to Root Traits

The single marker analysis revealed four markers such as OPS1850, OPK11780, OPY201200, OPZ101350, and CM13400 were significantly associated with root length (P=0.05) and explained phenotypic variance of 4.41%, 2.95%, 3.56%, 5.12%, and 5.12%, respectively. A number of secondary roots linked to three markers such as OPO16450 (4.40%), OPK111000 (5.51%), and OPV14550 (5.82%) were significantly (P=0.05) associated. Similarly six markers such as OPK111000 (3.26%), OPP91030 (3.5%), OPM11600 (3.75%), OPL19900 (5.61%), OPL19600 (4.11%), OPR4600 (3.30%), and CM211300 (R2=4.96%) revealed significant (P=0.05) association with the root to shoot ratio (Table 6). This implies the root associated traits are polygenic controlling many genes. However, root dry weight is associated with only one maker OPL11400 (4.86%).Table 6
RAPD markers linked to root traits in the mapping population ofC. canephora (L1 valley × S.3334).
Trait
Marker
R
2 (%)
P
<
0.05
Root length
OPY201200
2.95
0.0449
OPZ101350
3.56
0.0286
OPK11780
4.41
0.0178
OPS1850
6.86
0.0001
CPCM13400
5.12
0.0105
Number of secondary roots
OPO16450
4.4
0.0179
OPK111000
5.51
0.0059
OPV14550
5.82
0.0042
Root dry weight
OPL11450
4.76
0.0073
Root to shoot ratio
OPK111000
3.26
0.038
OPR4600
3.3
0.0367
OPP91030
3.5
0.0288
OPM11600
3.75
0.0267
OPL19600
4.11
0.0223
OPL19900
5.61
0.0062
CM211300
4.96
0.0105


## 3.7. Markers Linked to Morphophysiological Traits

Besides root traits, polygenic inheritance traits such as morphological, gravimetric, and gas exchange at single leaf level traits were also deployed for marker trait association. Single marker analysis revealed more than three markers linked to each trait and 25 markers are colocalized with more than one trait and 17 markers are associated with single trait (Table7). The marker OPP51800 is coassociated with shoot dry weight and leaf area duration (LAD), and these two traits are positively correlated (R2=0.93). Four markers (OPL11450, OPL191650, OPM171400, and OPK20350) linked to NAR and two markers OPR4600 and OPL122000 are significantly (P=0.01) associated with cumulative water transpired (CWT).Table 7
List of RAPD and SSR markers overlapped with more than one trait.
Markers
Traits
R
2 (%)(P<0.05)
Markers
Traits
R
2%  (P<0.05)
OPK111000
Internodal length
4.74
OPR4600   OPR4750
CWT
5.08
Root to shoot ratio
3.26
RSR
3.30
Number of secondary roots
5.51
Number of leaves
5.65
Stem dry weight
3.44
gs
2.93
OPK181200
Specific leaf area
4.70
OPV14550
Pn
9.30
Plant height
4.51
Number of secondary Roots
5.82
OPK192000
Photosynthetic rate (Pn)
5.55
OPY201200
WUE
3.10
WUE
4.43
Plant height
4.23
K20350
NAR
4.82
OPZ111500
Number of leaves
3.99
WUE
4.45
Root length
2.95
Ci
4.32
Pn
9.23
OPL11400
Plant height
5.77
OPZ101350   OPZ101030
MTR
8.21
Internodal length
3.81
13ΔC
4.12
NAR
4.27
Plant height
3.03
Shoot dry weight
4.00
Root length
3.56
Root dry weight
4.76
WUE
3.02
Pn
2.61
Ci/gs
5.09
OPL122000
Plant height
3.68
Pn
7.56
CWT
4.74
MTR
4.38
OPL121650
13ΔC
3.61
OPZ10720
gs
2.96
pn
3.07
gs
3.26
OPL181000
Number of nodes
7.78
Pn
3.14
LAD
3.82
Internodal length
3.58
OPL19900
SLW
3.33
CPCM13450
Root length
5.12
RSR
3.60
Ci/gs
4.83
OPM11600
A/gs
3.85
CM171300    CM166260
Stem girth
4.54
RSR
4.11
A/gs
3.45
MTR
2.94
No. of nodes
3.93
OPO101400
Ci
3.13
SLA
7.19
E
3.18
E
4.18
OPP51800
LAD
3.82
CM211300
SLW
2.83
Internodal length
5.82
RSR
4.96
Shoot dry weight
2.71
E
4.23
OPP91030
Ci/gs
6.12
SLW
3.33
RSR
3.50The13ΔC is the surrogate method to quantify the water use efficiency and it was demonstrated that there is inverse relationship between these two traits. Of the six associated markers, OPZ101350 was found to be associated with 13ΔC and WUE with phenotypic variance of 4.12% and 3.02%, respectively. This marker locus could be negative additive effect since traits are negatively related. The mesophyll efficiency (Ci/gs), intrinsic WUE (A/gs), and photosynthetic rate (Pn) are, respectively, associated with CM13400 (4.83%), CM171210 (3.45%), and CM171190 (3.72%) (Table 7). Whereas positively related traits are associated with the same marker such as OPP51800 (LAD and shoot dry weight), OPZ101030 (MTR and gs), and OPK20350 (Ci and NAR) alleles contribution from S.3334. The markers linked to other gas exchange parameters were given in Table 7, which are contributed from S.3334.


## 4. Discussion

We developed a F1 mapping population by crossing contrasting root traits S.3334 high root type as male and L1 valley low root type as female to identify makers linked to root traits and water use efficient types. However, in annual crops, F2, recombinant inbred lines (RILs), near isogenic lines (NILs), and doubled haploids (DH) mapping population will be used to associate markers with the trait of our interest. However, in perennials the germplasm lines and the cultivated accessions are often highly heterozygous; consequently F1 population developed from heterozygous parents was used in this study and showed considerable phenotypic and genetic variations.One of the objectives of studying the mapping population was to score the variability in roots and other physiological traits under drought and thus mapping population was gown up in 70% FC of water stress to study the heritability of root traits. Indeed root traits are highly complex genetic mechanism, controlled by multi genes and highly heritable in water non limiting environment [27]. The results demonstrated significant variability was observed in root dry weight (RDW), total dry matter (TDM), root to shoot ratio (RSR), cumulative water transpired (CWT) with considerable transgressive segregation (Figure 1), and quantitative inheritance of drought tolerance [28]. However, significant positive correlation between RDW/TDM, RDW/CW, and TDM/CWT indicates that under drought increased root growth maintains the shoot hydraulic conductance as adoptive strategy [29, 30]. The decreased mean transpiration (MTR) and increased water use efficiency (WUE) could be due to decreased stomatal aperture through chemical signals such as ABA [31]. However, some of the clones showed that increased MTR with increased CWT would favor the stomatal conductance and net photosynthesis. Such clones are better for breeding drought tolerance. Inverse relationship between Δ13C and WUE in robusta coffee accessions was observed (Figure 2), thus measuring Δ13C which can be a surrogate method to identify best WUE types clones in breeding [19, 32]. Breeding for such quantitative traits is highly complex cumbersome and thus by determining the allele of a DNA marker, plants that possess particular genes or quantitative trait loci (QTLs) are better than their phenotype. With this background effort was made to identify the markers linked to root and associated physiological traits inCoffea canephora.Despite the efforts, no successful linkage map could be obtained, partly due to the inadequate number of polymorphic markers generated from this study. The output of the association analysis indicates that, out of 29 traits, only 24 traits were linked to 85 markers. Even markers linked to traits explaining the phenotypic variation were not more than 10%. In the present study, phenotypic variation explanation for all the traits ranges from 2.61 to 9.86%. This suggests that the successful application of molecular markers in trait mapping greatly requires more polymorphic markers, while successful mapping attempts could still be carried out in the absence of a linkage map as done in chickpea [33]. Although construction of linkage map was done inC. arabica [34] andC. canephora [35] predominantly using AFLP and RAPD markers, association with the traits was not attempted. Similarly the first molecular linkage map generated using pseudo testcross strategy forCoffea canephora (CxR × Kagnalla) has the largest number of mapped SSRs (71 genomic, including nine EST-SSRs) [36], but trait association was not correlated.Most of the markers explained phenotypic variance between 3 and 4%; however, only two markers, that is, OPZ111500 and OPV14500, were significantly (P<0.01) linked to photosynthetic rate (Pn), accounting the variation of about 9.23% and 9.30%, respectively (Table 7). The root traits such as root length, number of secondary roots, root dry weight, and root: shoot ratio, revealed significant association (P<0.05) with markers and the variability ranges from 2.95 to 6.86% (Table 6). This suggests the trait variability explanation is not sufficient to answer the probabilities of occurrence of these markers due to limited polymorphic markers.In the present study, codominant SSR markers were also used for the marker-trait association study. The SSR markers identified herein are significantly associated with morphological and physiological traits, but these markers were explaining the trait in the range between 2.83% and 7.19%. This study has provided more detailed information of root and associated physiological traits relationships under drought and identification of markers linked to such traits with the limited polymorphic markers by employing Single Marker Analysis (SMA). QTL maps could be used to for long-term, drought breeding. Further studies are needed to confirm the estimation of QTL positions and effects and to validate markers prior to routine marker assisted selection for drought tolerance in coffee.



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*Source: 532386-2015-03-03.xml*