Skip to content

Advertisement

  • Research article
  • Open Access

Co-localization of major quantitative trait loci for pod size and weight to a 3.7 cM interval on chromosome A05 in cultivated peanut (Arachis hypogaea L.)

  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 2,
  • 2,
  • 3,
  • 4,
  • 1 and
  • 1Email author
BMC Genomics201718:58

https://doi.org/10.1186/s12864-016-3456-x

  • Received: 16 September 2016
  • Accepted: 22 December 2016
  • Published:

Abstract

Background

Cultivated peanut (Arachis hypogaea L.), an important source of edible oil and protein, is widely grown in tropical and subtropical areas of the world. Genetic improvement of yield-related traits is essential for improving yield potential of new peanut varieties. Genomics-assisted breeding (GAB) can accelerate the process of genetic improvement but requires linked markers for the traits of interest. In this context, we developed a recombinant inbred line (RIL) mapping population (Yuanza 9102 × Xuzhou 68-4) with 195 individuals and used to map quantitative trait loci (QTLs) associated with three important pod features, namely pod length, pod width and hundred-pod weight.

Results

QTL analysis using the phenotyping data generated across four environments in two locations and genotyping data on 743 mapped loci identified 15 QTLs for pod length, 11 QTLs for pod width and 16 QTLs for hundred-pod weight. The phenotypic variation explained (PVE) ranged from 3.68 to 27.84%. Thirteen QTLs were consistently detected in at least two environments and three QTLs (qPLA05.7, qPLA09.3 and qHPWA05.6) were detected in all four environments indicating their consistent and stable expression. Three major QTLs, detected in at least three environments, were found to be co-localized to a 3.7 cM interval on chromosome A05, and they were qPLA05.7 for pod length (16.89–27.84% PVE), qPWA05.5 for pod width (13.73–14.12% PVE), and qHPWA05.6 for hundred-pod weight (13.75–26.82% PVE). This 3.7 cM linkage interval corresponds to ~2.47 Mb genomic region of the pseudomolecule A05 of A. duranensis, including 114 annotated genes related to catalytic activity and metabolic process.

Conclusions

This study identified three major consistent and stable QTLs for pod size and weight which were co-localized in a 3.7 cM interval on chromosome A05. These QTL regions not only offer further investigation for gene discovery and development of functional markers but also provide opportunity for deployment of these QTLs in GAB for improving yield in peanut.

Keywords

  • Peanut
  • QTL
  • Pod length
  • Pod width
  • Hundred-pod weight
  • Yield

Background

Cultivated peanut or groundnut (Arachis hypogaea L.) is an allotetraploid (2n = 4x = 40) legume crop and is widely grown worldwide in >100 countries with global annual production of 42.32 million tonnes (FAOSTAT, 2014). Peanut is an important oil crop and has a key role in human nutrition [1]. Improving yield has been one of the major objectives in peanut breeding programs, which is directly influenced by pod-related traits (PRTs) [24]. Quantitative traits, including PRTs, show complex interaction with environment leading to varied productivity under different environments. In order to select a promising line for varietal release, breeders need to assess its potential in multiple environments to check its stable performance to achieve higher adoption in the farmers’ field. In a breeding program, it is very difficult and expensive to screen large number of lines across multiple environments for yield assessment. Genomics-assisted breeding (GAB) has potential to accelerate the process of achieving higher genetic gain in less time and with minimum resources using molecular markers [4, 5]. In order to deploy GAB, linked markers for PRTs is essential for developing high yielding peanut varieties.

Quantitative trait locus (QTL) mapping using bi-parental population has been widely conducted successfully to identify the genomic regions associated with quantitative traits in several crop plants [6, 7] including peanut. In recent years, QTLs associated with economically important traits such as disease resistance [8, 9], drought tolerance [10, 11], seed and oil quality [12, 13], agronomic and yield traits [14, 15] were identified in peanut crop. Molecular markers tightly linked to QTLs after validation can be further deployed in GAB [5, 16]. For example, one major QTL for rust resistance was introgressed from resistant cultivar ‘GPBD 4’ into three early maturing elite varieties through marker-assisted backcrossing (MABC) [17].

Limited efforts were made in identifying QTLs controlling PRTs in peanut which did not provided significant results deployable in breeding program. For example, Selvaraj et al. [4] identified two SSR markers, PM375 and Seq8D09, linked with pod length using bulked segregant analysis. Similarly, Shirasawa et al. [18] identified three QTLs for pod length and two for pod width in an F2 population while Fonceka et al. [19] mapped three QTLs for pod length, six for pod width and two for hundred-pod weight in an advanced backcross population. More recently, Huang et al. [15] detected one QTL for pod length, two QTLs for pod width and three QTLs for hundred-pod weight in an F2:3 population. In addition to above, Chen et al. [3] detected 22 QTLs for pod length and width in two F2:3 populations. However, quantitative traits are highly influenced by environments and QTLs identified at one specific location may not be valid for another location with varied environmental conditions [14]. Majority of the studies identified QTLs in segregating populations and not in fixed population such as RIL population.

The RIL population can be repeatedly used for generation of phenotyping data in multiple environments which is a key factor in doing genetic dissection of complex and quantitative traits, thereby helping in precise identification of consistent and stable QTLs. The variety Yuanza 9102 is small-podded with low pod weight while the variety Xuzhou 68-4 has large pods and higher pod weight. In this study, a RIL population was developed from the cross between Yuanza 9102 and Xuzhou 68-4 and used to identify QTLs controlling yield-related traits such as pod length (PL), pod width (PW), and hundred-pod weight (HPW) across four environments.

Methods

Plant materials

A recombinant inbred line (RIL) population in F5 generation was developed from a cross between Yuanza 9102 and Xuzhou 68-4 using single seed decent method to construct a dense genetic linkage map and conducting QTL analysis for pod features. The female parent, Yuanza 9102, belongs to A. hypogaea subsp. hypogaea var. vulgaris and is derived from interspecific hybridization between the cultivated peanut Baisha1016 and wild species A. chacoense. The male parent, Xuzhou 68-4, belongs to A. hypogaea subsp. hypogaea var. hypogaea and has significantly larger pods than the female parent, Yuanza 9102. A total of 195 recombinant inbred lines (RILs) were used in the present study for generating genotyping and phenotyping data followed by genetic map construction and QTL analysis.

Field trials for generating phenotyping data

Phenotyping data was generated on the RIL population for four environments i.e., three environments at Wuhan (WH), China (F5 generation during 2013, F6 generation during 2014 and F7 generation during 2015) while single environment at Xiangyang (XY), China (F7 generation during 2015). These experiments were designated as WH2013, WH2014, WH2015 and XY2015, respectively. Each environment was a field trial conducted at a location in a year in this paper. The random block design (RBD) with three replications was adopted for generating phenotyping data during all the four environments. Each RIL was planted in a 2.5 m long single-row and row-to-row space was 33 cm. There were 12 plants in each row with plant-to-plant distance of 20 cm. Of these 12 plants, 8 plants in the middle of each row were harvested for trait measurement. Three important pod related traits (PRTs), pod length (PL), pod width (PW) and hundred-pod weight (HPW), were measured three times for each replication according to previously described standard procedures [15, 20]. To reduce the influence of environmental factors, the mean trait value in each trial was used in analysis.

Statistical analysis of phenotyping data

Statistical analysis for the phenotypic data of PRTs was conducted using IBM SPSS Statistics Version 22 software [21]. The Shapiro-Wilk (w) statistic was used to test the null hypothesis that the phenotypic data were normally distributed. The univariate variance analyses were performed using standard GLM method and variance components were estimated using restricted maximum likelihood (REML) method. The broad-sense heritability for each trait across the four environment trials was calculated based on the estimated variance components with the following formula: \( {H}^2={\upsigma}_g^2/\left({\upsigma}_g^2+{\upsigma}_{g\times e}^2+{\upsigma}_e^2\right) \) based on plot mean and \( {H}^2={\upsigma}_g^2/\left({\upsigma}_g^2+{\upsigma}_{g\times e}^2/r+{\upsigma}_e^2/rn\right) \) based on entry mean, where \( {\upsigma}_g^2 \) is the genotypic variance component among RILs, \( {\upsigma}_{g\times e}^2 \) is the RILs × environment interaction variance component, \( {\upsigma}_e^2 \) is the residual (error) variance component, and r is the number of environment trials, n is the number of replications in each field experiment [22]. Correlation coefficients between each pair of the three traits were also calculated using IBM SPSS Statistics Version 22 software [21].

Genotyping of mapping population

A total of 8,112 SSR markers from either published reports [18, 2338] or newly developed SSR markers (unpublished) from the genome sequences of diploid ancestors [1] were used to screen the polymorphism between parental genotypes of the RIL population. Polymorphic markers were used to genotype complete RIL population along with parental genotypes. Genomic DNA was extracted from young leaves collected from RILs in F5 generation using a modified CTAB method [39]. The integrity and quality of the DNA was evaluated on a 1% agarose gel by comparison with uncut lambda DNA. PCR amplification was conducted in a 10 μl volume, containing 20 ng DNA template, 0.5 μM each primer, 1× PCR buffer, 1 mM MgCl2, 0.2 mM dNTP and 0.5 U Taq polymerase. PCR was performed with a Bio-Rad T100 Thermal Cycler using the standard PCR program with little modification i.e., 95 °C for 4 min; 35 cycles of 94 °C for 55 s, 55–58 °C (varies for each primer pair) for 45 s, and 72 °C for 1 min; and a final extension step of 72 °C for 10 min. The PCR products were separated on a 6% polyacrylamide gel and visualized by silver staining [40].

Construction of genetic linkage map

Pearson’s Chi square test was used to assess the goodness of fit to the expected segregation ratio 15:2:15 for co-dominant marker or 17:15 for dominant marker (P < 0.05). A genetic linkage map was constructed using the JoinMap 4.0 [41] with a maximum recombinant frequency of 0.4. The recombination ratio was converted to genetic distance by the Kosambi mapping function [42]. The linkage groups (LGs) were designated as chromosome A01-A10 and B01-B10 based on the common markers as a previously published integrated consensus map [43]. This consensus map was constructed based on 16 genetic linkage maps [43] and used as reference in other publications [3, 15, 44]. The graphical presentation of genetic linkage map was generated with the MapChart 2.3 software [45].

QTL analysis

Genome-wide QTL mapping was performed using the mean value of each trait in each environment. QTL analysis was conducted using the composite interval mapping (CIM) method [46] in the Windows QTL Cartographer 2.5 software [47]. The standard CIM model (model 6) and forward regression method were selected. The number of control markers, window size and walk speed were 5, 10 and 2 cM, respectively. The threshold of LOD for declaring the presence of a QTL was determined by 1000 permutation tests. When separated by a minimum distance of 20 cM, two peaks on one chromosome were considered as two different QTLs [10]. Otherwise, the higher peak was chosen to more closely approximate the position of the QTL. If QTLs for the same trait detected in different environments had overlapping 2-LOD support intervals, they were considered to be the same QTL and also been designated as consistent QTLs. Similarly, if the same QTL appeared in both the locations (Wuhan and Xiangyang), such QTLs were refereed as stable QTLs. QTLs were designated with an initial letter ‘q’ followed by the trait name and the LG corresponding chromosome, similar to the previously described nomenclature [48]. After the linkage group, a number was added if more than one QTL was detected for the same trait and linkage group. For example, if two QTLs for pod length were detected on chromosome A05, they were named as qPLA05.1 and qPLA05.2, respectively. If QTLs for different traits had overlapping 2-LOD support intervals, they were clustered in specific co-localized chromosomal regions. Genome sequences and annotations of the diploid ancestors of cultivated peanut were downloaded from PeanutBase [1]. Molecular markers were positioned on the chromosomal pseudomolecules using BLAST and ePCR (electronic PCR) with high similarity parameters (taking the top hits only, with placement by BLAST (\( \mathrm{e}\;\mathrm{value}<1\times {10}^{-10} \)) given preference over ePCR where both were available) [1].

Results

Phenotypic variation of Pod Related Traits (PRTs)

Significant differences were found between the two parents for various PRTs across four environments i.e., WH2013, WH2014, WH2015 and XY2015 (Table 1). Large phenotypic variations for the PRTs were observed among RILs in all the four environments, showing continuous distributions with transgressive segregation (Table 1, Fig. 1). The normality test indicated that the phenotypic data were normally distributed for PRTs, except pod weight (PW) in WH2013 trial, pod length (PL) in WH2015 trial and hundred-pod weight (HPW) in XY2015 trial (Table 1, Fig. 1). Variance analysis for the PRTs across the four trials showed significant differences among RILs, environments and RILs × environment interactions (Table 2). The values of broad sense heritability were estimated to be 0.70 for pod length, 0.51 for pod weight and 0.66 for hundred-pod weight based on plot mean while these estimates were much higher based on entry mean, such as 0.92 for pod length, 0.83 for pod weight and 0.90 for hundred-pod weight. Correlation analysis indicated that the three PRTs had significant positive association between each other (Table 3) and therefore positive relationship with potential yield.
Table 1

Descriptive statistical analysis of phenotypes of pod-related traits in the RIL population

Env

Trait

P1

P2

Range

Mean

SD

Skew

Kurt

w(Sig)

WH2013

PL(cm)

3.07

3.59

2.57–3.91

3.16

0.27

0.24

−0.25

0.99(0.304)

PW(cm)

1.49

1.71

1.25–1.85

1.52

0.12

0.47

−0.23

0.97(0.003)

HPW(g)

178.31

222.26

126.6–281.52

195.84

28.46

0.19

0.01

0.99(0.891)

WH2014

PL(cm)

2.90

3.70

2.62–3.97

3.26

0.27

0.09

−0.46

0.99(0.582)

PW(cm)

1.58

1.87

1.35–2.07

1.68

0.14

0.13

−0.31

0.99(0.635)

HPW(g)

190.06

273.19

155.23–297.5

218.45

30.30

0.23

−0.59

0.99(0.066)

WH2015

PL(cm)

2.87

3.55

2.78–4.18

3.28

0.26

0.52

0.33

0.98(0.009)

PW(cm)

1.60

1.78

1.38–2.04

1.69

0.12

0.12

−0.02

0.99(0.926)

HPW(g)

179.90

267.33

153–306.94

215.30

28.55

0.22

−0.10

0.99(0.410)

XY2015

PL(cm)

3.61

4.07

2.89–4.61

3.63

0.31

0.32

0.35

0.99(0.289)

PW(cm)

1.49

1.73

1.32–1.95

1.66

0.11

0.14

0.00

0.99(0.641)

HPW(g)

195.63

248.84

172.13–354.7

239.75

32.84

0.63

0.23

0.97(0.000)

Env Environment, P1 female parent Yuanza 9102, P2 male parent Xuzhou 68-4, SD standard deviation, Skew Skewness, Kurt Kurtosis, w Shariro-Wilk statistic value, Sig Significance, WH Wuhan, XY Xiangyang, PL Pod length, PW pod width, HPW hundred-pod weight

Fig. 1
Fig. 1

Phenotype distribution of pod length, pod width and hundred-pod weight. The y-axis represented density, while the x-axis represented values of each trait. The normal distribution curve in each graph represented the expected density. WH Wuhan, XY Xiangyang, PL Pod lentth, PW pod width, HPW hundred-pod weight. PL2013WH means pod length in Wuhan 2013, etc

Table 2

Analysis of variance for pod-related traits in the RIL population across four environment trials

Trait

Variables

df

Mean square

F-value

P-value

PL

RILs

194

0.498

49.257

<0.001

Evironments

3

16.382

1620.365

<0.001

RILs × Evironments

572

0.040

3.933

<0.001

Error

768

0.010

  

PW

RILs

194

0.077

26.852

<0.001

Evironments

3

3.323

808.951

<0.001

RILs × Evironments

572

0.013

4.499

<0.001

Error

768

0.003

  

HPW

RILs

194

5470.988

70.586

<0.001

Evironments

3

125432.506

1618.314

<0.001

RILs × Evironments

572

567.502

7.322

<0.001

Error

768

77.508

  

PL Pod length, PW pod width, HPW hundred-pod weight

Table 3

Correlation analysis for pod-related traits in the RIL population

Env

Trait

PL

PW

HPW

WH2013

PL

1

  

PW

0.725a

1

 

HPW

0.738a

0.669a

1

WH2014

PL

1

  

PW

0.736a

1

 

HPW

0.789a

0.910a

1

WH2015

PL

1

  

PW

0.610a

1

 

HPW

0.814a

0.805a

1

XY2015

PL

1

  

PW

0.489a

1

 

HPW

0.674a

0.635a

1

aCorrelation is significant at the 0.01 level

WH Wuhan, XY Xiangyang, PL pod length, PW pod width, HPW hundred-pod weight

Molecular marker polymorphisms and genetic map construction

Out of 8,112 SSR markers screened on the parental genotypes of the RIL population, 729 markers showed polymorphisms in the parents as well as in the RIL population (Additional file 1: Table S1). Among them, one marker AHGS0729 amplified three genetic loci and 12 markers amplified two loci, while the remaining 716 markers amplified a single locus. Among these 743 genetic loci, 660 loci were co-dominant and 83 loci were dominant. The Chi square analysis identified 356 loci (47.91%) with segregation distortion. A genetic linkage map containing 743 loci was constructed spanning 1,232.57 cM with an average inter-marker distance of 1.66 cM (Table 4, Additional file 2: Table S2). All the 743 loci were assigned to 22 LGs whose length varied from 9.47 cM to 119.48 cM and number of mapped loci ranged from 3 to 97 marker loci (Table 4, Additional file 3: Figure S1). Based on 292 common markers which were also included in a previously published integrated consensus map [43], 19 of the 22 LGs were assigned to 17 chromosomes of the A and B subgenomes (Table 4, Additional file 4: Figure S2). Chromosome A01 was found to be divided into two LGs (LG01 and LG02) due to insufficient linkage between them. Similarly, chromosome A08 was divided into LG08 and LG09.
Table 4

Description of the genetic linkage map constructed in this study

LGs

Chrom

Length(cM)

Locia

Common locib

LG01

A01

86.62

61

23

LG02

A01

39.84

6

4

LG03

A03

42.98

10

6

LG04

A04

20.47

6

2

LG05

A05

99.27

97

24

LG06

A06

55.05

26

7

LG07

A07

46.26

38

19

LG08

A08

50.86

23

12

LG09

A08

15.43

3

1

LG10

A09

86.68

56

24

LG11

A10

13.78

3

2

LG12

B01

67.91

64

33

LG13

B02

80.57

77

32

LG14

B03

72.29

7

4

LG15

B04

119.48

71

34

LG16

B05

83.97

81

33

LG17

B09

42.56

7

3

LG18

B10

63.95

85

29

LG19

-

106.19

11

0

LG20

-

16.12

5

0

LG21

-

9.47

3

0

LG22

-

12.82

3

0

Total

-

1,232.57

743

292

aNumber of loci in each linkage group

bNumber of common markers which were contained in a previously published integrated consensus linkage map [43]

Detection of QTLs for Pod Related Traits (PRTs)

QTL analysis using phenotyping and genotyping data identified a total of 65 QTLs with 3.68 to 27.84% phenotypic variation explained (PVE) associated with the PRTs in the four environments (Fig. 2, Additional file 5: Table S3). For pod length, six QTLs were detected in WH2013 trial (5.45–16.89% PVE), seven QTLs in WH2014 trial (5.27–27.84% PVE), seven QTLs in WH2015 trial (9.33–23.91% PVE), and seven QTLs in XY2015 trial (3.68–25.68% PVE). For pod width, two QTLs were detected in WH2013 trial (5.88–8.90% PVE), seven QTLs in WH2014 trial (5.26–14.03% PVE), five QTLs in WH2015 trial (6.42–13.73% PVE), and three QTLs in XY2015 trial (5.40–14.12% PVE). For hundred-pod weight, six QTLs were detected in WH2013 trial (4.81–21.74% PVE), seven QTLs in WH2014 trial (5.72–21.29% PVE), five QTLs in WH2015 trial (4.12–26.82%), and three QTLs in XY2015 trial (6.52–13.75% PVE).
Fig. 2
Fig. 2

QTLs distribution of pod-related traits in the genetic map. Loci with “#” were common markers which also included in a previously published integrated consensus linkage map [43]. WH Wuhan, XY Xiangyang, PL Pod length, PW pod width, HPW hundred-pod weight. PL2013WH means QTL for pod length detected in Wuhan 2013, etc. The co-localized regions of QTLs for different traits were highlighted in blue or red color on the chromosome bars

As shown in Fig. 2, some QTLs detected in different environments for the same trait had overlapping 2-LOD support intervals, and they were considered to be one QTL which could be repeatedly detected. Therefore, the 65 loci detected in four environment trials were designated as 15 QTLs for pod length, 11 QTLs for pod width, and 16 QTLs for hundred-pod number (Additional file 5: Table S3).

For pod length, the 15 QTLs were identified on chromosomes A05, A09, B04 and B05. Two QTLs, qPLA05.7 and qPLA09.3, were found to be consistent and stable as they were detected in all four environments. Flanked by the marker A05A1430 and A05A1601 on chromosome A05, the QTL for pod length, qPLA05.7, explained 16.89, 17.84, 23.91 and 25.68% of the phenotypic variance in WH2013, WH2014, WH2015 and XY2015 environments, respectively. Similarly, another QTL for pod length, qPLA09.3, flanked by AGGS1606 and AGGS2134 on chromosome A09 explained 5.45, 17.76, 14.47 and 12.41% of the phenotypic variance in four environments, respectively. Further, two additional QTLs for pod length, qPLA09.4 (AGGS2134 - AGGS2492) and qPLA09.5 (AGGS1137 - AGGS1925), were mapped on chromosome A09 in three environments (WH2014, WH2015 and XY2015) with 12.05–16.91% and 11.33–16.86% PVE, respectively.

For pod width, of the 11 QTLs identified on five chromosomes (A05, A06, A09, B04 and B05), two QTLs, qPWA05.5 and qPWB05, were consistent and stable in expression as they were detected in three environments (WH2014, WH2015 and XY2015). The first QTL qPWA05.5 (A05A1344 - A05A1562) had showed 13.73–14.12% PVE while the second QTL qPWB05 (AHGA152207 - AHGS1228) showed 5.88–7.30% PVE in WH2014, WH2015 and XY2015 trials.

Similarly for hundred-pod weight, the 16 QTLs were identified on chromosomes A05, A06, A09, B04, B05 and B10. A major QTL on chromosome A05, designated as qHPWA05.6, was detected in all four environments and hence consistent and stable. Interestingly, it was flanked by the same markers (A05A1430 - A05A1601) as the major QTL qPLA05.7 for pod length and had shown 21.74, 21.29, 26.82 and 13.75% PVE in WH2013, WH2014, WH2015 and XY2015, respectively.

A co-localized region of stable and major QTLs for PRTs on chromosome A05

A total of 11 chromosomal regions harbored QTLs for different traits where multiple QTLs were mapped (Fig. 2). This phenomenon was not unexpected given the strong positive correlations among the three traits (Table 3), indicating the existing of pleiotropic effects of single gene or tight linkage. A co-localized QTL interval close to the end region of chromosome A05 was significantly more dominant than others. It was located at 80.4–84.1 cM map position on chromosome A05 and covering around 3.7 cM in length with flanking markers A05A1344 and A05A1601 (Figs. 2 and 3). This region harbored the major QTLs for pod length (qPLA05.7), pod width (qPWA05.5), and hundred-pod weight (qHPWA05.6) (Fig. 3, Table 5). Each QTL was detected at least in three environments and hence more consistent and stable in expression.
Fig. 3
Fig. 3

Genetic and physical maps of the dominant and co-localized interval on chromosome A05. The legend for QTLs was the same as that in Fig. 2. Gene density indicated gene numbers per 50 kb interval

Table 5

QTLs harbored in the dominant and co-localized interval on chromosome A05

Trait

QTL

Marker interval

Location & year

LOD

Additive

PVE(%)

PL

qPLA05.7

A05A1430-A05A1601

Wuhan 2013

11.1

0.1154

16.89

Wuhan 2014

22.7

0.1490

27.84

Wuhan 2015

19.4

0.1297

23.91

Xiangyang 2015

18.6

0.1595

25.68

PW

qPWA05.5

A05A1344-A05A1562

Wuhan 2014

9.9

0.0541

14.03

Wuhan 2015

9.0

0.0538

13.73

Xiangyang 2015

8.0

0.0441

14.12

HPW

qHPWA05.6

A05A1430-A05A1601

Wuhan 2013

13.0

13.6342

21.74

Wuhan 2014

14.6

14.6633

21.29

Wuhan 2015

16.3

15.6463

26.82

Xiangyang 2015

7.5

12.8723

13.75

PL pod length, PW pod width, HPW hundred-pod weight, PVE phenotypic variation explained

BLAST searching and ePCR of eight markers mapped in this region could be traced to the pseudomolecule A05 of A subgenome (A. duranensis V14167) [1] (Fig. 3). The corresponding position of 3.7 cM on the genetic map was about 2.47 Mb in the physical map i.e., 98,478,303 bp to 100,945,376 bp containing 114 putative genes [1]. Fifteen novel genes encoded unknown proteins, while the other 99 genes had reported homologs (Additional file 6: Table S4). Protein Aradu.H6FXW might have a function in the regulation of cell proliferation. Eight genes might encode transcription factors, including homeobox transcription factor (Aradu.5W2E7, Aradu.WUY99 and Aradu.S4P4Y), transcription factor IIS (Aradu.C2I2I and Aradu.TWC9B), MYB transcription factor (Aradu.BVL00) and others (Aradu.C989A and Aradu.PSF4U). As shown in Additional file 6: Table S4, 56 of the 99 genes were assigned at least one GO term. Among the various biological processes, metabolic process (41.1%) and cellular process (21.4%) were most highly represented (Additional file 7: Figure S3). The genes involved in other important biological processes such as biological regulation, cellular component organization, establishment of localization, pigmentation and response to stimulus, were alos identified through GO annotations. Similarly, binding (57.1%) and catalytic activity (46.4%) were most represented among the various molecular functions, and cell (26.8%) and cell part (26.8%) were most represented among the cellular components (Additional file 7: Figure S3). Enrichment analysis indicated that 30 of the 56 genes were enriched in 48 GO terms using all gene models of A subgenome assembly (A. duranensis V14167) as reference (P < 0.05), including carbon-oxygen lyase activity, pectate lyase activity and other catalytic activities (Additional file 8: Table S5). Through KEGG analyses, a total of 11 genes encoding oxidase, dehydrogenase, lyase, synthase, dehydratase and lactoperoxidase were assigned to 16 biological pathways, including amino acid metabolism, carbohydrate metabolism, energy metabolism, metabolism of cofactors and vitamins and biosynthesis of antibiotics and other secondary metabolites (Additional file 9: Table S6).

Discussion

The broad-sense heritability estimated in this study was relatively high for pod length, pod width and hundred-pod weight, indicating that genetic factors play a major role in determination of these traits, although influenced by environment. In this study, a RIL population was used to construct a dense genetic linkage map and conducting QTL analysis for pod features. A genetic linkage map is a prerequisite to efficiently identify molecular markers associated with quantitative traits. Because of a lack of polymorphism at the DNA level, the first SSR-based genetic linkage map for peanut only had 135 SSR loci. In this study, a genetic linkage map containing 743 loci was constructed using JoinMap 4.0 [41]. It is an user-friendly and widely used commercial software in the scientific community [49], although it was outperformed by some recent tools [49, 50] at speed or manipulation of noisy data. The constructed linkage map covered a total length of 1,232.57 cM an average inter-marker distance of 1.66 cM. The loci number and density of our map were relatively higher than that of recent studies [3, 10, 15, 51], except for the integrated consensus map [43]. Using this linkage map, fifteen QTLs were identified for pod length, 11 QTLs for pod width and 16 QTLs for hundred-pod weight (Additional file 5: Table S3) in the RIL population across four environments. The LOD values of these QTLs ranged from 3.2 to 22.7 and were higher than the threshold of LOD for declaring the presence of a QTL which was determined by 1000 permutation tests. All the linked markers identified for pod related traits after validation can be deployed in breeding for marker-based selection to improve yield in peanut.

QTLs for pod related traits with stable performance

Besides identification of QTLs, it is very important to assess their stable performance across varied environments. A similar study conducted by Chen et al. [3] detected six QTLs for pod length and eight QTLs for pod width in a F2:3 populations in two environments, but none of them were detected in both environments. Despite the significant G × E interactions (P < 0.001) present in the four trials conducted in this study, three major QTLs (qHPWA05.6 for hundred-pod weight, qPLA05.7 and qPLA09.3 for pod length) have shown stable performance across four environments and two locations. In addition, four QTLs for pod length, four QTLs for pod width and two QTLs for HPW were detected in two or three trials. Such QTLs with stable performance for pod related traits have been identified for the first time in peanut and will be very useful for further fine mapping of the QTL region and development of diagnostic markers to use in breeding.

The present study reports 15 QTLs for pod length mapped on chromosomes A05, A09, B04 and B05, the three QTLs (qPLB04.1, qPLB04.2 and qPLB05) were not detected in earlier studies, hence, novel QTLs. The chromosomes A05 and A09 might harbor important genes for pod length as seven QTLs from this study and six QTLs from earlier studies [3, 18] were mapped on A05, and five QTLs from this study and six QTLs from previous studies [3, 4, 18, 19] were identified on A09. Of these QTLs, two QTLs, qPLA05.7 and qPLA09.3, identified in this study had stable expression across environments.

Similarly, of the 11 QTLs identified for pod width in this study on chromosomes A05, A06, A09, B04 and B05, QTLs identified on chromosomes A06 (qPWA06.1, qPWA06.2, qPWA06.3) and B04 (qPWB04) were novel QTLs. Chromosome A05 seems very important and might harbor important genes for pod width, as five QTLs from present study and six QTLs from previous studies [3, 15] were mapped on this chromosome. The QTL qPWA05.5 explained the largest phenotypic variations and consistently expressed across environments. The two QTLs reported by previous study conducted by Chen et al. [3] and one QTL, qPWA09, identified in this study were mapped on the chromosome A09. Similarly, two QTLs identified by Fonceka et al. [19] and one QTL, qPWB05, detected in this study were mapped on the chromosome B05.

The 16 QTLs detected in the present study for hundred-pod weight were mapped on the chromosomes A05, A06, A09, B04, B05 and B10, while the five QTLs reported in previous studies [15, 19] were located on chromosomes A07, B02, B03 and B05. Therefore, the 16 QTLs identified in the present study were novel in nature. Of these QTLs, the QTL qHPWA05.6 was the most consistent and stable one. The above results suggested that these pod-related traits are quantitative in nature controlled by multiple genomic regions and their effects were often affected by the environment.

Co-localized region on chromosome A05 play a major role in controlling pod related traits

The present study identified a co-localized genomic region on A05 harboring QTLs for pod related traits. This region harbored one important QTL for each pod related traits i.e., qPLA05.7 for pod length, qPWA05.5 for pod width, and qHPWA05.6 for hundred-pod weight. This region also provided a significant level of contribution to phenotypic variation explained by these QTLs i.e., 16.89–27.84% PVE for pod length and 13.75–26.82% PVE for hundred-pod weight across all the four environments, and 13.73–14.12% PVE for pod width in three of the four environments. The above results indicate importance of this co-localized region for improving pod related traits through GAB. Further, this important genomic region also provides opportunity for fine mapping and development of diagnostic markers for use in improving these traits.

In addition to above mentioned further possible studies, the recently completed genome sequences of the diploid ancestors of cultivated peanut [1] provides a physical map of the highest resolution and allows the possibility to examine the co-localized region at the end of chromosome A05. The 3.7 cM genetic map distance was corresponding to the 2.47 Mb physical map region which houses 114 candidate genes. Thirteen percent of these genes are novel genes with unknown function and seems to be an enrichment of genes involved in catalytic activity and metabolic process. Eight genes were transcription factors and protein Aradu.H6FXW seems to have a function in the regulation of cell proliferation. The application of the genome sequences of wild peanut provided us an overview of candidate genes in the chromosome region of interest; however, these genes remain candidates until shown to be causally associated with the phenotypic variations in further studies.

Conclusions

The present study identified 15 QTLs for pod length, 11 QTLs for pod width and 16 QTLs for hundred-pod weight using a RIL population across four environments in two locations. Multiple stable and major QTLs for pod related traits were co-located at the end of chromosome A05. These QTLs needs further investigation to fine map and develop diagnostic markers for these traits to use them in routine breeding program using GAB in peanut.

Abbreviations

CIM: 

Composite interval mapping

HPW: 

Hundred-pod weight

LGs: 

Linkage groups

PL: 

Pod length

PRTs: 

Pod-related traits

PVE: 

Phenotypic variance explained

PW: 

Pod width

QTLs: 

Quantitative trait loci

RILs: 

Recombinant inbred lines

WH: 

Wuhan

XY: 

Xiangyang

Declarations

Acknowledgements

Not applicable.

Funding

This study was supported by the National Natural Science Foundations of China (31271764, 31371662, 31471534 and 31461143022), the China Agriculture Research System (CARS-14), and the Crop Germplasm Resources Project of China (NB2012-2130135-28). The work reported in this article was undertaken as a part of the CGIAR Research Program on Grain Legumes. ICRISAT is a member of the CGIAR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials

The datasets supporting the conclusions of this article are included within the article and its additional files.

Author’ contributions

HL XZ YC YL BL BG FL and HJ conceived and designed the experiments. XR and HJ developed the RIL population. XR XZ YC YL FL and XJ conducted field trials and data collections. ZL ZX XL WC and LH performed genotyping. HL and HJ constructed the genetic linkage map and performed QTL analysis. HL BL BG MKP RKV and HJ interpreted the results. HL prepared the first draft of the manuscript and HL BL BG MKP RKV and HJ contributed to the final editing of manuscript. All authors contributed in the interpretation of results and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan, 430062, China
(2)
International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India
(3)
Crop Protection and Management Research Unit, USDA-ARS, Tifton, GA 31793, USA
(4)
Xiangyang Academy of Agricultural Sciences, Xiangyang, 461057, China

References

  1. Bertioli DJ, Cannon SB, Froenicke L, Huang G, Farmer AD, Cannon EK, et al. The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid ancestors of cultivated peanut. Nat Genet. 2016;4:438–46.View ArticleGoogle Scholar
  2. Gomes RLF, Lopes ÂCDA. Correlations and path analysis in peanut. Crop Breed Appl Biotehnol. 2005;1:105–12.View ArticleGoogle Scholar
  3. Chen W, Jiao Y, Cheng L, Huang L, Liao B, Tang M, et al. Quantitative trait locus analysis for pod- and kernel-related traits in the cultivated peanut (Arachis hypogaea L.). BMC Genet. 2016;1:25.View ArticleGoogle Scholar
  4. Selvaraj MG, Narayana M, Schubert AM, Ayers JL, Baring MR, Burow MD. Identification of QTLs for pod and kernel traits in cultivated peanut by bulked segregant analysis. Electron J Biotechnol. 2009;2:10.Google Scholar
  5. Janila P, Variath MT, Pandey MK, Desmae H, Motagi BN, Okori P, et al. Genomic tools in groundnut breeding program: status and perspectives. Front Plant Sci. 2016;7:289.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Gondo T, Sato S, Okumura K, Tabata S, Akashi R, Isobe S. Quantitative trait locus analysis of multiple agronomic traits in the model legume Lotus japonicus. Genome. 2007;7:627–37.Google Scholar
  7. Zhang G, Zhou W. Genetic analyses of agronomic and seed quality traits of synthetic oilseed Brassica napus produced from interspecific hybridization of B. campestris and B. oleracea. J Genet. 2006;1:45–51.View ArticleGoogle Scholar
  8. Shoha D, Manivannan N, Vindhiyavarman P, Nigam SN. Identification of quantitative trait loci (Qtl) for Late leaf spot disease resistance in groundnut (Arachis Hypogaea L.). Legum Res. 2013;5:467–72.Google Scholar
  9. Leal-Bertioli SC, Moretzsohn MC, Roberts PA, Ballen-Taborda C, Borba TC, Valdisser PA, et al. Genetic mapping of resistance to Meloidogyne arenaria in Arachis stenosperma: a new source of nematode resistance for peanut. G3. 2015;2:377–90.Google Scholar
  10. Ravi K, Vadez V, Isobe S, Mir RR, Guo Y, Nigam SN, et al. Identification of several small main-effect QTLs and a large number of epistatic QTLs for drought tolerance related traits in groundnut (Arachis hypogaea L.). Theor Appl Genet. 2011;6:1119–32.View ArticleGoogle Scholar
  11. Gautami B, Pandey MK, Vadez V, Nigam SN, Ratnakumar P, Krishnamurthy L, et al. Quantitative trait locus analysis and construction of consensus genetic map for drought tolerance traits based on three recombinant inbred line populations in cultivated groundnut (Arachis hypogaea L.). Mol Breed. 2012;2:757–72.View ArticleGoogle Scholar
  12. Pandey MK, Wang ML, Qiao L, Feng S, Khera P, Wang H, et al. Identification of QTLs associated with oil content and mapping FAD2 genes and their relative contribution to oil quality in peanut (Arachis hypogaea L.). BMC Genet. 2014;15:133.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Mondal S, Phadke RR, Badigannavar AM. Genetic variability for total phenolics, flavonoids and antioxidant activity of testaless seeds of a peanut recombinant inbred line population and identification of their controlling QTLs. Euphytica. 2015;2:311–21.View ArticleGoogle Scholar
  14. Faye I, Pandey MK, Hamidou F, Rathore A, Ndoye O, Vadez V, et al. Identification of quantitative trait loci for yield and yield related traits in groundnut (Arachis hypogaea L.) under different water regimes in Niger and Senegal. Euphytica. 2015;3:631–47.View ArticleGoogle Scholar
  15. Huang L, He HY, Chen WG, Ren XP, Chen YN, Zhou XJ, et al. Quantitative trait locus analysis of agronomic and quality-related traits in cultivated peanut (Arachis hypogaea L.). Theor Appl Genet. 2015;6:1103–15.View ArticleGoogle Scholar
  16. Sukruth M, Paratwagh SA, Sujay V, Kumari V, Gowda MVC, Nadaf HL, et al. Validation of markers linked to late leaf spot and rust resistance, and selection of superior genotypes among diverse recombinant inbred lines and backcross lines in peanut (Arachis hypogaea L.). Euphytica. 2015;2:343–51.View ArticleGoogle Scholar
  17. Varshney RK, Pandey MK, Janila P, Nigam SN, Sudini H, Gowda MV, et al. Marker-assisted introgression of a QTL region to improve rust resistance in three elite and popular varieties of peanut (Arachis hypogaea L.). Theor Appl Genet. 2014;8:1771–81.View ArticleGoogle Scholar
  18. Shirasawa K, Koilkonda P, Aoki K, Hirakawa H, Tabata S, Watanabe M, et al. In silico polymorphism analysis for the development of simple sequence repeat and transposon markers and construction of linkage map in cultivated peanut. BMC Plant Biol. 2012;12:80.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Fonceka D, Tossim HA, Rivallan R, Vignes H, Faye I, Ndoye O, et al. Fostered and left behind alleles in peanut: interspecific QTL mapping reveals footprints of domestication and useful natural variation for breeding. BMC Plant Biol. 2012;12:26.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Jiang H, Duan N, Ren X. Descriptors and data standard for peanut (Arachis spp.). Beijing: China Agriculture Press; 2006.Google Scholar
  21. IBM Corp. Statistical Package for Social Sciences (IBM SPSS) 22.0 version. Armonk: IBM United States; 2013. Accessed.Google Scholar
  22. Holl JB, Nyquist WE. Estimating and interpreting heritability for plant breeding: an update. In: Janick J, editor. Plant breeding reviews, vol. 22. 2010. p. 9–112.Google Scholar
  23. Wang H, Penmetsa RV, Yuan M, Gong L, Zhao Y, Guo B, et al. Development and characterization of BAC-end sequence derived SSRs, and their incorporation into a new higher density genetic map for cultivated peanut (Arachis hypogaea L.). BMC Plant Biol. 2012;1:1–11.Google Scholar
  24. Macedo SE, Moretzsohn MC, Leal-Bertioli SC, Alves DM, Gouvea EG, Azevedo VC, et al. Development and characterization of highly polymorphic long TC repeat microsatellite markers for genetic analysis of peanut. BMC Res Notes. 2012;5:86.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Koilkonda P, Sato S, Tabata S, Shirasawa K, Hirakawa H, Sakai H, et al. Large-scale development of expressed sequence tag-derived simple sequence repeat markers and diversity analysis in Arachis spp. Mol Breed. 2012;1:125–38.View ArticleGoogle Scholar
  26. Nagy ED, Chu Y, Guo Y, Khanal S, Tang S, Li Y, et al. Recombination is suppressed in an alien introgression in peanut harboring Rma, a dominant root-knot nematode resistance gene. Mol Breed. 2010;2:357–70.View ArticleGoogle Scholar
  27. Moretzsohn MC, Barbosa AV, Alves-Freitas DM, Teixeira C, Leal-Bertioli SC, Guimaraes PM, et al. A linkage map for the B-genome of Arachis (Fabaceae) and its synteny to the A-genome. BMC Plant Biol. 2009;9:40.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Leal-Bertioli SC, Jose AC, Alves-Freitas DM, Moretzsohn MC, Guimaraes PM, Nielen S, et al. Identification of candidate genome regions controlling disease resistance in Arachis. BMC Plant Biol. 2009;9:112.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Guo B, Chen X, Hong Y, Liang X, Dang P, Brenneman T, et al. Analysis of gene expression profiles in leaf tissues of cultivated peanuts and development of EST-SSR markers and gene discovery. Int J Plant Genomics. 2009;2009:14.View ArticleGoogle Scholar
  30. Naito Y, Suzuki S, Iwata Y, Kuboyama T. Genetic diversity and relationship analysis of peanut germplasm using SSR markers. Breed Sci. 2008;3:293–300.View ArticleGoogle Scholar
  31. Cuc LM, Mace ES, Crouch JH, Quang VD, Long TD, Varshney RK. Isolation and characterization of novel microsatellite markers and their application for diversity assessment in cultivated groundnut (Arachis hypogaea). BMC Plant Biol. 2008;1:1–11.Google Scholar
  32. Gimenes MA, Hoshino AA, Barbosa AV, Palmieri DA, Lopes CR. Characterization and transferability of microsatellite markers of the cultivated peanut (Arachis hypogaea). BMC Plant Biol. 2007;7:9.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Moretzsohn MC, Leoi L, Proite K, Guimaraes PM, Leal-Bertioli SC, Gimenes MA, et al. A microsatellite-based, gene-rich linkage map for the AA genome of Arachis (Fabaceae). Theor Appl Genet. 2005;6:1060–71.View ArticleGoogle Scholar
  34. Ferguson ME, Burow MD, Schulze SR, Bramel PJ, Paterson AH, Kresovich S, et al. Microsatellite identification and characterization in peanut ( A. hypogaea L.). Theor Appl Genet. 2004;6:1064–70.View ArticleGoogle Scholar
  35. He G, Meng R, Newman M, Gao G, Pittman RN, Prakash C. Microsatellites as DNA markers in cultivated peanut (Arachis hypogaea L.). BMC Plant Biol. 2003;1:1–6.View ArticleGoogle Scholar
  36. Hopkins MS, Casa AM, Wang T, Mitchell SE, Dean RE, Kochert GD, et al. Discovery and characterization of polymorphic Simple Sequence Repeats (SSRs) in peanut. Crop Sci. 1999;4:1243–7.View ArticleGoogle Scholar
  37. Huang L, Wu B, Zhao J, Li H, Chen W, Zheng Y, et al. Characterization and transferable utility of microsatellite markers in the wild and cultivated Arachis species. PLoS One. 2016;5:15.Google Scholar
  38. Zhou X, Dong Y, Zhao J, Huang L, Ren X, Chen Y, et al. Genomic survey sequencing for development and validation of single-locus SSR markers in peanut (Arachis hypogaea L.). BMC Genomics. 2016;1:420.View ArticleGoogle Scholar
  39. Doyle J. Isolation of plant DNA from fresh tissue. Focus. 1990;12:13–5.Google Scholar
  40. Fountain JC, Qin H, Chen C, Dang P, Wang ML, Guo B. A note on development of a low-cost and high-throughput SSR-based genotyping method in peanut (Arachis hypogaea L.). Peanut Science. 2011;2:122–7.View ArticleGoogle Scholar
  41. JoinMap 4. Software for the calculation of genetic linkage maps in experimental populations. Wageningen: Kyazma B.V; 2006. Accessed.Google Scholar
  42. Kosambi DD. The estimation of map distances from recombination values. Ann Hum Genet. 2011;1:172–5.Google Scholar
  43. Shirasawa K, Bertioli DJ, Varshney RK, Moretzsohn MC, Leal-Bertioli SC, Thudi M, et al. Integrated consensus map of cultivated peanut and wild relatives reveals structures of the A and B genomes of Arachis and divergence of the legume genomes. DNA Res. 2013;2:173–84.View ArticleGoogle Scholar
  44. Zhou XJ, Xia YL, Ren XP, Chen YL, Huang L, Huang SM, et al. Construction of a SNP-based genetic linkage map in cultivated peanut based on large scale marker development using next-generation double-digest restriction-site-associated DNA sequencing (ddRADseq). BMC Genomics. 2014;15:14.View ArticleGoogle Scholar
  45. Voorrips RE. MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered. 2002;1:77–8.View ArticleGoogle Scholar
  46. Zeng ZB. Precision mapping of quantitative trait loci. Genetics. 1994;4:1457–68.Google Scholar
  47. Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC. 2012. http://statgen.ncsu.edu/qtlcart/WQTLCart.htm. Accessed 1 Dec 2015.Google Scholar
  48. Udall JA, Quijada PA, Lambert B, Osborn TC. Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 2. Identification of alleles from unadapted germplasm. Theor Appl Genet. 2006;4:597–609.View ArticleGoogle Scholar
  49. Wu Y, Bhat PR, Close TJ, Lonardi S. Efficient and accurate construction of genetic linkage maps from the minimum spanning tree of a graph. PLoS Genet. 2008;10:11.Google Scholar
  50. Preedy KF, Hackett CA. A rapid marker ordering approach for high-density genetic linkage maps in experimental autotetraploid populations using multidimensional scaling. Theor Appl Genet. 2016;11:2117–32.View ArticleGoogle Scholar
  51. Qin H, Feng S, Chen C, Guo Y, Knapp S, Culbreath A, et al. An integrated genetic linkage map of cultivated peanut (Arachis hypogaea L.) constructed from two RIL populations. Theor Appl Genet. 2012;4:653–64.View ArticleGoogle Scholar

Copyright

Advertisement