Skip to main content
PMC home page
PLOS ONE logoLink to PLOS ONE
. 2016 Nov 21;11(11):e0166873. doi: 10.1371/journal.pone.0166873

Quantitative Trait Locus Analysis of Late Leaf Spot Resistance and Plant-Type-Related Traits in Cultivated Peanut (Arachis hypogaea L.) under Multi-Environments

Xiaojing Zhou 1, Youlin Xia 2, Junhua Liao 2, Kede Liu 3, Qiang Li 4, Yang Dong 1, Xiaoping Ren 1, Yuning Chen 1, Li Huang 1, Boshou Liao 1, Yong Lei 1, Liying Yan 1, Huifang Jiang 1,*
Editor: Pawan L Kulwal5
PMCID: PMC5117734  PMID: 27870916

Abstract

Late leaf spot (LLS) is one of the most serious foliar diseases affecting peanut worldwide leading to huge yield loss. To understand the genetic basis of LLS and assist breeding in the future, we conducted quantitative trait locus (QTL) analysis for LLS and three plant-type-related traits including height of main stem (HMS), length of the longest branch (LLB) and total number of branches (TNB). Significant negative correlations were observed between LLS and the plant-type-related traits in multi-environments of a RIL population from the cross Zhonghua 5 and ICGV 86699. A total of 20 QTLs were identified for LLS, of which two QTLs were identified in multi-environments and six QTLs with phenotypic variation explained (PVE) more than 10%. Ten, seven, fifteen QTLs were identified for HMS, LLB and TNB, respectively. Of these, one, one, two consensus QTLs and three, two, three major QTLs were detected for HMS, LLB and TNB, respectively. Of all 52 unconditional QTLs for LLS and plant-type-related traits, 10 QTLs were clustered in five genetic regions, of which three clusters including five robust major QTLs overlapped between LLS and one of the plant-type-related traits, providing evidence that the correlation could be genetically constrained. On the other hand, conditional mapping revealed different numbers and different extent of additive effects of QTLs for LLS conditioned on three plant-type-related traits (HMS, LLB and TNB), which improved our understanding of interrelationship between LLS and plant-type-related traits at the QTL level. Furthermore, two QTLs, qLLSB6-7 and qLLSB1 for LLS resistance, were identified residing in two clusters of NB-LRR—encoding genes. This study not only provided new favorable QTLs for fine-mapping, but also suggested that the relationship between LLS and plant-type-related traits of HMS, LLB and TNB should be considered while breeding for improved LLS resistance in peanut.

Introduction

Peanut or groundnut (Arachis hypogaea L.) is an important source of edible plant oil and protein and is cultivated in more than 100 countries. The total global production was 42.4 million tonnes and average yield was 1,653 kg ha-1 in 2014 (FAO, 2016) [1]. There is always a big gap between genetic potential of modern cultivars and their actual yield in the field. Yield losses are generally substantial when the crop is attacked by peanut diseases [2,3]. Late leaf spot (LLS) is one of the most serious foliar diseases caused by fungi [Phaeoisariopsis personata(Berk & M.A. Curtis)Arx] affecting peanut worldwide [4,5]. Peanut plants infected with this disease exhibit defoliating nature and result in over 50% yield loss [6]. Traditionally, LLS disease is controlled by fungicides, which are costly and toxic to the environment, therefore, the development of host-plant resistant cultivars has been considered as the primary strategy for controlling LLS disease.

Many important traits in agriculture are complex genetic behavior [7–9]. Genetic studies on LLS resistance suggest that resistance to this fungal disease is complex and polygenic in nature and sensitive to environments [10–15]. It has been long known that environment factors, such as temperature and humidity, are important affecting infection and development of Phaeoisariopsis personata [16–18]. In the field, a microclimate in which LLS may develop was directly influenced by plant-type-related traits. Several studies indicated associations between disease resistance and plant-type-related traits [19–21]. However, the genetic basis of LLS and relationship between LLS and plant-type-related traits in peanut are poorly understood.

QTL mapping is essential for identifying the genomic regions that control traits of interest before deploying linked markers in the breeding using marker-assisted selection (MAS) and also facilitate gene mining [22–25]. In recent years, a number of QTLs for LLS resistance have been identified in peanut. Khedikar et al. (2010) identified 11 minor QTLs for LLS using a partial linkage map comprising 56 SSR loci [14]. Sujay et al. (2012) identified 13 QTLs including five major QTLs for LLS in TAG 24 × GPBD 4 (RIL-4) population, and 15 QTLs including eight major QTLs for LLS in TG 26 × GPBD 4 (RIL-5) population, using the linkage maps with 188 and 181 loci in RIL-4 and RIL-5, respectively [15]. Based on a consensus map of these two populations, two candidate genomic regions conferring resistance to LLS were reported [15]. In the case of plant-type-related traits of height of the main stem (HMS), length of the longest branch (LLB) and total number of branches (TNB), only a few QTLs have been identified so far. Huang et al. (2014) identified three minor QTLs for HMS and two minor QTLs for TNB by using a genetic linkage map containing 470 simple sequence repeat (SSR) loci for cultivated peanut [26]. Shirasawa et al. (2012) identified three QTLs for HMS; two QTLs for LLB and one QTL for TNB with a genetic map containing 1,114 loci [27]. A major limitation of these studies was that the analyses were based on linkage maps mainly using simple sequence repeat (SSR) markers in which many regions were sparsely represented, thus it was not possible to obtain precise information about the numbers and locations of the QTLs. On the other hand, although the genetic linkage maps based on abundant single nucleotide polymorphism (SNP) markers could greatly increase marker density, there were rare cases of completeness and the maps have not been applied in QTL research in peanut yet [28,29].

Our previous study reported development of a SNP-based genetic linkage map consisting of 1,685 loci using the RIL population of Zhonghua 5 × ICGV 86699 [29]. In present study, we conducted QTL identification for resistance to LLS and the plant-type-related traits of HMS, LLB and TNB in multi-environments using this linkage map to investigate the genetic basis of LLS and the interrelationship between LLS and the plant-type-related traits.

Materials and Methods

Ethics statement

This article does not contain any studies with human participants or animals performed by any of the authors.

Plant material and field design

A RIL population including 166 lines derived from a cross between maternal parent of Zhonghua 5 and paternal parent of ICGV 86699 was employed in this study for mapping and trait analysis. Zhonghua 5 is a popular cultivar in China that is susceptible to LLS [29,30]. ICGV 86699 is a breeding variety which is highly resistant to LLS. ICGV 86699 has wild-derived source of LLS resistance and its pedigree is [Arachis batizocoi/A. duranensis//A. hypogaea cv. NC 2]—CS 29 [31]. In addition, the two parents have significant differences in HMS, LLB, TNB and other traits such as insect resistance and other disease resistances [31].

The RIL lines were grown using a randomized complete block design with two replications at two experimental stations. One is located in the experimental farm at Oil Crops Research Institute (OCRI) of Chinese Academy of Agricultural Sciences (CAAS), Wuhan (WH), Hubei, China (30°35’N, 114°33’E). The other experimental station is located at Nanchong Academy of Agricultural Sciences, Nanchong (NC), Sichuan, China (30°80’N, 106°06’E). The seeds were sown at the end of April of each experimental year. Each accession was planted in a single row, with 8–10 plants in each row and 10 cm intervals between plants within each row and 30 cm intervals between rows. The parental genotypes were also sown after every 50 rows as controls.

Phenotypic observation

The parents and 166 RIL lines were used to assess LLS resistance and plant-type-related traits of HMS, LLB and TNB. For LLS, phenotyping was carried out in NC in year 2011 (NC11), year 2012 (NC12), year 2013 (NC13), and in WH in year 2012 (WH12) and year 2013 (WH13). The traits of HMS, TNB and LLB were investigated in two consecutive years (2012 and 2013) in NC and WH. Each location and year combination was considered as individual experimental environment.

The investigation of LLS in NC was under natural environment as this region belongs to the special basin terrain and is easy for the occurrence and epidemic of LLS disease. The average lowest ~ highest temperature, average day length, and average humidity of experiment periods of 2011–2013 in NC presented as following: 22.4~30.2°C/138.9h/88% (June, 2011), 24.3~31.6°C/185.4h/86% (July, 2011), 25.8~34.3°C/197.0h /83% (August, 2011); 21.6~28.5°C/129.7h/85% (June, 2012), 24.3~31.1°C/136.9h/83% (July, 2012), 25.3~33.7°C/183.0/82% (August, 2012); 23.3~32.1°C/135.5h/80% (June, 2013), 26.1~33.6°C/187.3h/79% (July, 2013), 25.6~33.5°C/191.3h/80% (August, 2013). While LLS in WH was created using artificial disease epiphytotic since the region is not conducive to the epidemic of LLS disease. Methods for isolating LLS conidia and inoculating field plants were conducted as described in Khedikar et al. (2010) [14]. The average lowest ~ highest temperature, average day length, and average humidity of experiment periods of 2012–2013 in WH were as following: 22.8~31.4°C/192h/81% (June, 2012), 26.9~34.4°C/196h/77% (July, 2012), 25.1~32.3°C/198h/69% (August, 2012); 22.5~30.8°C/200.5h/81% (June, 2013), 27.4~34.6°C/287.5h/72% (July, 2013), 26.9~35.1°C/ 281.7h/69% (August, 2013). After 120 days of sowing, severity of LLS was assessed using a 1 to 9 severity scale in both places [32].

Height of main stem, length of the longest branch and total number of branches were measured at the harvesting stage (after 130 days of sowing). The unit of measured values of HMS and LLB was centimeter (cm). TNB was counted by the number of branches. A schematic diagram of HMS and LLB was shown in S1 Fig.

Statistical analysis

The broad-sense heritability was calculated according to Hallauer and Miranda (1998) [33] as h2 = σ2g/(σ2g+ σ2ge / n + σ2e / nr), where σ2g was the genetic variance, σ2ge was the interaction variance of the genotype with environment, σ2e was the variance of residual error, n was the number of environments and r was the number of replications. The estimation of the variance components was obtained using the SAS software by general linear model (GLM) procedure [34]. Correlation coefficients between traits across environments were estimated using the PROC CORR procedure of SAS.

Unconditional QTL mapping

Detection of QTL in each environment was performed by using the composite interval mapping (CIM) program of the Windows QTL Cartographer 2.5 [35]. The forward regression method model 6 (default model) was selected to obtain covariates. The number of control markers, window size and walk space were set to 5, 10 and 2 cM, respectively. A LOD threshold of 2.5 was chosen to declare a putative QTL as significant [36]. QTLs repeatedly identified with clearly similar positions (overlapping 1-LOD confidence intervals) for the same trait in different environments were integrated into a consensus QTL [37]. QTLs detected for different traits with overlapped confidence intervals and common marker(s), or couples of overlapped QTLs with distance less than 2 cM was defined as a QTL cluster in which at least one major QTL with PVE >10% was included [37]. Average values of two replicates for each trait in a single environment were used as phenotypic data for single environment QTL analysis.

Joint QTL was analyzed by mixed linear model-based composite interval mapping (MCIM) of QTLNetwork software version 2.0 [38]. Phenotypic values of each trait in all environments were averaged for joint QTL analysis. The LOD thresholds were determined by a 1,000 permutation test at a 95% confidence level. The nomenclature of QTL was similar to that described by McCouch et al. (1997) [39]. QTL was designated with initial letter “q” followed by trait name, linkage groups and serial number (if more than one QTL of the same trait exist in the same chromosome), respectively.

Conditional QTL mapping

The phenotypic values for conditional QTL analysis was showed as y (T1|T2), where T1|T2 indicated that trait 1 conditioned on trait 2. y (T1|T2) was obtained by the mixed model approach using software QGAStation 2.0 (http://ibi.zju.edu.cn/software/qga/index.htm) [40,41]. In this study, the correlation analysis based on the phenotypic data showed significant negative correlation between LLS and plant-type-related traits. The QTL-clusters showed that the QTLs controlled different traits were mapped on the same genomic region. To further investigate the genetic interrelationship between LLS resistance and plant-type-related traits,we performed conditional QTL mapping with correlated traits as proposed by Zhao et al. (2006) [42]. Conditional phenotypic values including LLS|HMS, LLS|LLB and LLS|TNB in the four environments (NC12, NC13, WH12 and WH13) were calculated. Conditional QTL mapping was performed by the composite interval mapping method as described above for unconditional QTLs.

Results

Trait performance

The two parents, Zhonghua 5 and ICGV 86699, showed significant differences (P < 0.001) for LLS and plant-type-related traits of HMS, LLB and TNB. ICGV 86699 consistently exhibited lower disease score of LLS and higher values of the three plant-type-related traits than Zhonghua 5 (Fig 1, Table 1). The RIL individuals showed a continuous distribution for all traits across different environments (S2 Fig), which suggested a quantitative inheritance pattern for the investigated traits.

Fig 1. Phenotypes of two parental inbred lines used for QTL mapping in this study.

Fig 1

Open in a new tab

Plants were photographed at 120 days after sown on soil. Left two lines were Zhonghua 5, right two lines were ICGV 86699. The picture of top left corner showed infected leaves by late leaf spot.

Table 1. Phenotypic performance of the four traits in parents and RIL lines under multi-environments.

Trait Env. Zhonghua 5 ICGV 86699 Population
Mean SD Min Max Skew Kurt
LLS NC11 8.3 1.0 5.1 1.8 1.0 9.0 0.01 -0.81
NC12 9.0 2.7 5.3 1.9 1.3 9.0 0.41 -0.78
NC13 8.0 1.3 5.8 1.9 1.3 9.0 -0.42 -0.66
WH12 8.1 2.3 5.3 2.0 1.7 9.0 -0.06 -0.94
WH13 8.0 2.7 5.0 2.5 1.0 9.0 0.08 -0.89
HMS NC12 27.3 51.6 38.9 8.1 21.2 68.4 0.69 0.96
NC13 50.4 82.3 62.6 11.2 36.8 105.7 0.98 1.39
WH12 47.2 80.4 56.9 12.0 23.8 89.6 0.11 -0.55
WH13 42.3 72.2 59.7 13.6 35.0 97.9 0.26 -0.71
LLB NC12 46.3 70.1 53.6 9.7 35.8 90.4 0.77 0.87
NC13 56.2 94.3 73.9 10.7 48.5 106.7 0.6 0.11
WH12 42.2 91.2 65.3 13.9 35.0 102.0 0.05 -0.58
WH13 50.4 80.4 69.6 15.5 41.5 112.8 0.33 -0.61
TNB NC12 11.6 45.6 18.7 9.1 8.2 51.8 0.98 0.96
NC13 13.2 33.6 18.7 6.5 8.7 45.4 0.80 0.97
WH12 10.0 28.2 18.0 6.6 6.8 42.7 0.98 0.99
WH13 11.5 29.4 18.2 9.9 6.0 55.7 1.05 1.12
Open in a new tab

Zhonghua 5: maternal parent; ICGV86699: paternal parent.

LLS (late leaf spot disease) was assessed by 1–9 scale disease score; HMS (height of main stem) and LLB (length of the longest branch) were measured in the unit of centimeter (cm); TNB (total number of branches) was counted by the number of branches.

Env. represents environment; NC11, NC12, NC13, WH12 and WH13 represent Nanchong in 2011, 2012, 2013respectively; WH12 and WH13 represent Wuhan in 2012 and 2013, respectively.

SD: standard deviation.

ANOVA of all four traits across environments indicated that genotype (G), environment (E) and genotype-environment interactions (G×E) had significant effects on the traits. The four traits (LLS, HMS, LLB and TNB) showed high broad-sense heritability of 86.9%, 75.0%, 77.5% and 82.2%, respectively (Table 2), which suggested that genetic factors played a major role in the expression of these traits with significant environmental variation affecting these traits. To pinpoint their relationships, correlation coefficients (r) between the traits across environments were calculated (Table 3). The disease score of LLS of the RIL population exhibited significant (P < 0.05) negative correlations with three plant-type-related traits in all environmental cases, except in NC13 which had no significant phenotypic correlation between LLS and HMS. Significant positive relationships have been found between different plant-type-related traits, especially for HMS and LLB which showed the strongest correlation.

Table 2. Variance (ANOVA) Analysis and the broad-sense heritability (h2) for the four traits of RIL lines in multi-environments.

Trait Source DF Sum of square Mean square F value P h2
LLS Genotype 165 4155.82 25.19 49.3 < .0001 86.86
Environment 4 180.05 45.01 88.1 < .0001
Genotype×Environment 654 2164.65 3.31 6.48 < .0001
Error 1154 589.62 0.51
HMS Genotype 165 133791.44 810.86 22.05 < .0001 74.95
Environment 3 73007.51 24335.84 661.87 < .0001
Genotype×Environment 493 100121.08 203.09 5.52 < .0001
Error 982 36106.39 36.77
LLB Genotype 165 172190.66 1043.58 17.56 < .0001 77.5
Environment 3 56379.56 18793.19 316.2 < .0001
Genotype×Environment 493 115751.2 234.79 3.95 < .0001
Error 983 58424.8 59.46
TNB Genotype 165 66507.18 403.07 18.31 < .0001 82.2
Environment 3 253.55 84.52 21.84 < .0001
Genotype×Environment 492 35278.45 71.7 3.26 < .0001
Error 974 22.01
Open in a new tab

LLS: late leaf spot disease; HMS: height of main stem; LLB: length of the longest branch; TNB: total number of branches.

DF indicates degree freedom.

h2 indicates the broad-sense heritability.

Table 3. Correlation analysis for the four traits across multi-environments.

Trait Env. LLS HMS LLB
HMS NC12 -0.22**
NC13 -0.14ns
WH12 -0.56**
WH13 -0.19*
LLB NC12 -0.31** 0.84**
NC13 -0.19* 0.9**
WH12 -0.58** 0.96**
WH13 -0.21** 0.94**
TNB NC12 -0.55** 0.24** 0.42**
NC13 -0.47** 0.23** 0.15*
WH12 -0.22** 0.21** 0.23**
WH13 -0.23** 0.24** 0.28**
Open in a new tab

Env. represents environments; NC12 and NC13 represent Nanchong in 2012 and 2013 respectively; WH12 and WH13 represent Wuhan in 2012 and 2013, respectively.

*, ** and ns indicate significance at P < 0.05, P < 0.01 and non-significant effect, respectively.

QTL analysis for LLS and plant-type-related traits

To identify QTLs for LLS, HMS, LLB and TNB, phenotypic data were analyzed together with genetic mapping data of 1,685 markers [29] (S1 Table). A total of 52 significant QTLs were identified for all four traits through single-environment QTL analysis (Table 4, S3 Fig). The QTLs were distributed on 15 chromosomes and explained 3.41% to 19.12% phenotypic variation. Six QTLs (qLLSA1-3, qLLSB6-2, qHMSA9-2, qLLBA9-2, qTNBA8 and qTNBB6-3) were identified in multiple environments. Joint QTL analysis detected 18 putative QTLs associated with the four traits (Table 4, S2 Table) and 12 of these QTLs were coincident with the results of single-environment mapping (Table 4).

Table 4. QTLs identified for LLS and plant-type-related traits through single-environment QTL mapping using high density SNP-based map.

Trait QTLa Chr.b Marker interval Range (cM) Env.c Position Additived PVEe (%) LOD
LLS qLLSA1-1 A1 Ahsnp978-Ahsnp275 30.3–33.5 NC13 32.01 0.46 5.12 3.47
qLLSA1-2 A1 Ahsnp517-Ahsnp422 38–43.4 NC13 39.11 0.48 5.63 3.89
qLLSA1-3 A1 Ahsnp1339-Ahsnp819 45.3–52 WH13 46.51 0.64 5.07 2.58
NC13 48.51 0.49 5.11 3.32
qLLSA4-1 A4 Ahsnp1681-Ahsnp1897 50.4–52.7 NC13 51.51 0.52 5.29 3.66
qLLSA5 A5 Ahsnp1180-Ahsnp78 20.6–28.1 NC13 25.81 0.38 3.64 2.56
qLLSA6-1* A6 Ahsnp629-Ahsnp1883 11.4–14.6 NC12 13.71 0.39 3.41 2.85
qLLSA6-2 A6 Ahsnp1843-Ahsnp1521 19.2–24.2 WH12 21.61 0.47 4.31 2.57
qLLSA6-3 A6 Ahsnp419-Ahsnp418 31–35.8 WH12 33.61 0.67 7.6 3.96
qLLSA8 A8 Ahsnp320-Ahsnp919 57–76.1 NC12 66.41 0.39 3.69 3.09
qLLSA10 A10 Ahsnp1559-Ahsnp809 27.5–31.1 NC11 31.01 0.69 10.26 6.37
qLLSB1* B1 GM1331-Ahsnp1805 0–8.8 WH12 4.41 0.64 8.22 5.06
qLLSB4-1* B4 Ahsnp505-Ahsnp471 11.4–32.9 NC11 22.54 0.52 5.75 3.38
qLLSB6-1* B6 Ahsnp295-AHGS1422 31.8–40.1 NC11 35.36 0.74 13.89 8.17
qLLSB6-2 B6 Ahsnp539-Ahsnp136 44.5–47 NC12 45.81 0.75 11.66 8.92
NC11 45.81 0.74 12.9 7.81
qLLSB6-3 B6 Ahsnp1070-Ahsnp1665 56–57.6 NC12 57.01 0.6 7.16 4.23
qLLSB6-4 B6 Ahsnp676-Ahsnp231 62.18–64.62 NC13 63.61 0.89 18.47 10.79
qLLSB6-5 B6 Ahsnp231-AHGS1431 64.9–67.8 WH13 65.31 0.69 6.33 2.9
qLLSB6-6 B6 AHGS1431-Ahsnp163 68.4–71.5 NC13 71.01 0.91 19.12 11.25
qLLSB6-7 B6 Ahsnp1166-Ahsnp996 73.3–78 WH13 74.51 1.03 14.18 6.85
qLLSB10 B10 Ahsnp1458-Ahsnp299 5.3–8.6 NC11 7.51 0.44 4.1 2.73
HMS qHMSA5-1 A5 Ahsnp1180-Ahsnp1133 21.6–25.7 NC13 23.71 -5.64 15.28 4.88
qHMSA5-2 A5 Ahsnp1338-Ahsnp213 33.6–38 NC13 35.61 -4.81 10.66 3.46
qHMSA9-1 A9 Ahsnp1103-Ahsnp1059 0–2.2 NC13 0.01 -3.91 10.73 4.98
qHMSA9-2* A9 Ahsnp902-Ahsnp1167 71.6–82.2 WH12 73.11 -3.08 5.39 3.06
WH13 75.11 -3.36 5.22 2.68
qHMSA10-1 A10 Ahsnp77-Ahsnp787 7.8–8.8 WH12 7.91 -3.22 5.26 2.98
qHMSA10-2 A10 Ahsnp712-GM692 14.4–19.9 WH12 17.81 -3.31 6.14 3.49
qHMSB2 B2 Ahsnp1331-Ahsnp1308 1.6–12.1 NC13 5.01 3.97 8.94 4.24
qHMSB3-1 B3 Ahsnp586-Ahsnp1194 48–52.2 WH13 50.21 -3.39 5.39 2.93
qHMSB3-2* B3 Ahsnp1236-Ahsnp1549 59.7–61.8 WH13 60.81 -4.02 7.99 4.63
qHMSB6* B6 Ahsnp227-Ahsnp295 12.1–32.3 WH13 24.11 -3.22 4.76 2.78
LLB qLLBA9-1 A9 Ahsnp1103-Ahsnp1059 0–2.4 NC13 0.01 -3.7 10.59 5.09
qLLBA9-2 A9 Ahsnp902-Ahsnp1167 72.2–82.2 WH13 77.11 -4.05 5.55 2.75
WH12 77.11 -5 11.45 5.8
qLLBB1 B1 Ahsnp966-Ahsnp1373 28.2–31.1 WH12 30.91 -3.61 5.17 3.02
qLLBB3 B3 Ahsnp1236-Ahsnp1549 59.3–61.8 WH13 60.81 -3.61 4.73 2.68
qLLBB6* B6 Ahsnp227-Ahsnp295 16.6–31.1 WH12 27.81 -2.93 4 2.55
qLLBB8-1 B8 AHGS1388-Ahsnp1644 11.5–16 NC12 14.71 -2.86 6.86 3.25
qLLBB8-2* B8 Ahsnp779-Ahsnp732 62.6–73.4 WH12 66.31 -3.25 4.28 2.71
TNB qTNBA3 A3 Ahsnp965-Ahsnp1014 35.7–39.2 WH12 36.81 -1.78 6.19 3.25
qTNBA5 A5 Ahsnp926-Ahsnp236 15.7–17.8 WH12 17.51 -1.78 5.22 2.87
qTNBA6-1 A6 Ahsnp418-AHGS0153 37.2–43.6 WH12 42.11 1.8 4.59 2.66
qTNBA6-2 A6 Ahsnp19-Ahsnp1889 51.5–57.5 WH12 55.51 1.97 6.05 2.87
qTNBA8* A8 Ahsnp1305-Ahsnp1244 23.4–41.9 NC12 32.71 -2.32 5 2.76
NC13 28.41 -1.63 5.34 3.06
NC13 40.71 -1.6 4.96 2.7
qTNBA10 A10 ARS775-Ahsnp1387 0–6.8 NC12 0.01 -2.61 6.96 3.81
qTNBB1* B1 GM1331-Ahsnp642 0–2 WH13 0.01 -2.67 5.58 3.3
qTNBB3 B3 AHGS1201-AHGS2526 12.6–25.9 WH13 17.51 3.98 5.32 3.56
qTNBB4-1 B4 Ahsnp781-Ahsnp1160 46.8–47.8 WH13 46.81 -2.88 5.76 3.4
qTNBB4-2 B4 Ahsnp207-Ahsnp232 54.7–56.6 WH13 55.61 -3.29 9.54 5.82
qTNBB4-3 B4 Ahsnp282-Ahsnp1066 64.9–68.3 WH13 67.81 -3.67 11.85 6.48
qTNBB6-1 B6 Ahsnp425-Ahsnp349 48.9–62.2 NC13 54.51 -2.32 9.8523 4.77
qTNBB6-2 B6 Ahsnp1309-Ahsnp64 62.6–65 WH13 63.61 -3.58 10.35 5.92
qTNBB6-3* B6 AHGS1431-Ahsnp690 69.5–71.5 NC13 71.01 -2.05 8.1 4.74
WH13 71.01 -4.05 13.44 7.95
qTNBB8 B8 Ahsnp1700-Ahsnp1055 10.1–14.2 WH12 10.81 -2.05 7.55 4.16
Open in a new tab

a The nomination of QTL is comprised of four parts. The first part “q” stands for QTL. The second part is the abbreviation of traits. The third part, the number stands for chromosome. The fourth part is the serial number of QTL on the same chromosome. The names followed by * were QTLs simultaneously detected by joint QTL analysis among all environments.

b Chr. indicates chromosome.

c Env. indicates environment. NC11, NC12, NC13 WH12 and WH13 represent Nanchong in 2011, Nanchong in 2012, Nanchong in 2013, Wuhan in 2012 and Wuhan 2013, respectively.

d.Additive effect of the QTL.

e The phenotypic variation explained of corresponding QTL.

Late leaf spot resistance

A total of 20 QTLs controlling LLS were identified in all investigated environments (Table 4, S3 Fig) and were mapped on chromosomes A1, A4, A5, A6, A8, A10, B1, B4, B6 and B10. Chromosome B6 had seven QTLs, the most number of QTLs of all chromosomes. Phenotypic variation explained by individual QTL ranged from 3.41% to 19.12% and by QTL together in single environment ranged from 25.58% to 62.38%. Among them, six QTLs (qLLSA10, qLLSB6-1, qLLSB6-2, qLLSB6-4, qLLSB6-6 and qLLSB6-7) with over 10% PVE were considered as major QTLs. Two QTLs (qLLSA1-3 and qLLSB6-2) were identified in multiple environments, including the major QTL qLLSB6-2 which was accounted for 11.66–12.90% PVE with LOD value 7.81–8.92. In addition, LLS resistance alleles at all QTLs were contributed by ICGV 86699.

Height of main stem

For HMS, 10 QTLs distributed on six chromosomes (A5, A9, A10, B2, B3 and B6) were identified. They individually accounted for 4.76–15.28% of the phenotypic variation and totally explained 16.79–45.61% of the HMS variation in each environment (Table 4, S3 Fig). Three major QTLs (qHMSA5-1, qHMSA5-2 and qHMSA9-1) were identified. The qHMSA9-2 was a consensus QTL detected in WH12 and WH13. The qHMSA9-2 together with two environment-specific QTLs (qHMSB3-2 and qHMSB6) was also identified by joint analysis. The alleles for increasing HMS value at nine QTL regions were contributed by ICGV 86699, while the allele for increasing trait value at one QTL region was contributed by Zhonghua 5.

Length of the longest branch

Seven QTLs influencing LLB were identified on chromosomes A9, B1, B3, B6 and B8. They individually displayed 4.00% to 11.45% PVE and totally displayed 6.86–24.90% cumulative PVE for LLB in each environment (Table 4, S3 Fig). Two major QTLs (qLLBA9-1 and qLLBA9-2) were identified. One of the major QTL qLLBA9-2 explained 5.55% and 11.45% of the phenotypic variation in the WH12 and WH13 environments, respectively. Six QTLs were environment-specific. The alleles for increasing LLB value at all QTL regions were contributed by ICGV 86699.

Total number of branches

A total of 15 QTLs for TNB were identified on chromosomes A3, A5, A6, A8, A10, B1, B3, B4, B6 and B8 (Table 4, S3 Fig). They individually displayed 4.59–13.44% PVE with cumulative PVE of 11.96 to 61.84% for TNB across environments. Two QTLs (qTNBA8 and qTNBB6-3) were identified across multi-environments, and they were also identified in correspondence with joint analysis. Three major QTLs, qTNBB4-3, qTNBB6-2 and qTNBB6-3 were identified, in which qTNBB6-3 explained 8.10% and 13.44% of the phenotypic variation in NC13 and WH13, respectively. The alleles for increasing TNB value at twelve QTL regions were contributed by ICGV 86699, while the allele for increasing the trait value at three QTL regions were contributed by Zhonghua 5.

Clusters with co-located QTLs

Five QTL clusters were identified on chromosomes A5, A9 and B6 (Table 5, S3 Fig). Among them, three clusters overlapped between LLS and one of plant-typed traits. Clusters IV and V, both located on chromosome B6, included QTL for LLS co-located with QTL for TNB. It is worth mentioning that four QTLs (qLLSB6-4 and qTNBB6-2, qLLSB6-6 and qTNBB6-3) in the two clusters were all displayed PVE >10%, in which one QTL (qTNBB6-3) was detected in multi-environments. QTL for LLS overlapped with QTL for HMS located on A5. These results indicated that obviously genetic correlation between LLS and plant-type-related traits. In addition, clusters II and III showed co-located QTLs for HMS and LLB on chromosomes A9.

Table 5. QTL clusters for four traits examined in this study.

QTL cluster No. Chr. Trait included QTL Marker interval Range (cM) Env. Pos. PVE (%) A
I A5 LLS+HMS qLLSA5 Ahsnp1180-Ahsnp78 20.6–28.1 NC13 25.81 3.64 -0.38
qHMSA5-1 Ahsnp1180-Ahsnp1133 21.6–25.7 NC13 23.71 15.28 5.64
II A9 HMS+LLB qHMSA9-1 Ahsnp1103-Ahsnp1059 0–2.2 NC13 0.01 10.73 3.91
qLLBA9-1 Ahsnp1103-Ahsnp1059 0–2.4 NC13 0.01 10.59 3.70
III A9 HMS+LLB qHMSA9-2 Ahsnp902-Ahsnp1167 71.6–82.2 WH12 73.11 5.39 3.08
WH13 75.11 5.22 3.36
qLLBA9-2 Ahsnp902-Ahsnp1167 72.2–82.2 WH12 77.11 11.45 5
WH13 77.11 5.55 4.05
IV B6 LLS+TNB qLLSB6-4 Ahsnp676-Ahsnp231 62.18–64.62 NC13 63.61 18.47 -0.89
qTNBB6-2 Ahsnp1309-Ahsnp64 62.6–65 WH13 63.61 10.35 3.58
V B6 LLS+TNB qLLSB6-6 AHGS1431-Ahsnp163 68.4–71.5 NC13 71.01 19.12 -0.91
qTNBB6-3 AHGS1431-Ahsnp690 69.5–71.5 NC13 71.01 8.1 2.05
WH13 71.01 13.44 4.05
Open in a new tab

Chr. represents chromosome.

PVE indicates the percentage of phenotypic variation explained by corresponding QTL.

A represents the additive effect.

Conditional analysis

Conditional analysis was performed to determine genetic relationship between LLS and plant-type-related traits. We identified 21 conditional QTLs for LLS distributed on chromosomes A1, A2, A3, A4, A6, B2, B3, B4 and B6 in four environments (NC12, NC13, WH12 and WH13). Of the 21 conditional QTLs, 10 were also detected as unconditional QTLs and 11 were detected as new QTLs (Table 6). Six unconditional QTLs were not recaptured under conditional analysis (Table 6).

Table 6. The unconditional and conditional QTLs for LLS in the four environments (NC12, NC13, WH12, WH13).

QTL Marker interval LLS LLS|HMS LLS|LLB LLS|TNB
Env. A PVE Env. A PVE Env. A PVE Env. A PVE
qLLSA1-1 Ahsnp978-Ahsnp275 NC13 0.46 5.12 NC13 0.47 6.12 NC13 0.55 8.05
qLLSA1-2 Ahsnp517-Ahsnp422 NC13 0.48 5.63 NC13 0.5 6.91 NC13 0.63 9.99 NC13 0.48 6.24
NC12 0.46 5.67 NC12 0.4 4.57
qLLSA1-3 Ahsnp1339-Ahsnp819 NC13 0.49 5.11 NC13 0.45 4.99 NC13 0.57 7.21 NC13 0.63 9.46
WH13 0.64 5.07 NC12 0.45 4.98 NC12 0.46 5.93
qLLSA1-4 Ahsnp89-Ahsnp1913 WH13 0.8 7.7
qLLSA2 AHGS1495-Ahsnp1397 WH13 0.82 7.52
qLLSA3 Ahsnp1321-Ahsnp988 NC12 0.41 3.8 WH12 0.52 4.57
qLLSA4-1 Ahsnp1681-Ahsnp1897 NC13 0.52 5.29 NC13 0.51 6.05 NC13 0.56 7.92
qLLSA4-2 Ahsnp237-Ahsnp667 NC12 0.4 4.01
qLLSA5 Ahsnp1180-Ahsnp78 NC13 0.38 3.64
qLLSA6-1 Ahsnp629-Ahsnp1883 NC12 0.39 3.41
qLLSA6-2 Ahsnp1843-Ahsnp1521 WH12 0.47 4.31 WH12 0.49 4.5 WH12 0.4 5.2
qLLSA6-3 Ahsnp419-Ahsnp418 WH12 0.67 7.6 WH12 0.67 8 WH12 0.65 7.8 WH12 0.68 8.0
qLLSA6-4 Ahsnp1731-Ahsnp1889 NC12 0.42 4.86
qLLSA8 Ahsnp320-Ahsnp919 NC12 0.39 3.69
qLLSB1 GM1331-Ahsnp1805 WH12 0.64 8.22
qLLSB2-1 Ahsnp1281-Ahsnp1308 WH13 1.01 12.34
qLLSB2-2 Ahsnp784-Ahsnp1260 WH13 0.81 8.39
qLLSB2-3 Ahsnp784-Ahsnp1694 WH13 0.77 8.4
qLLSB3 AHGS1201-AHGS1661 WH13 0.87 8.72
qLLSB4-2 Ahsnp108-Ahsnp789 WH12 0.54 6.29
qLLSB4-3 Ahsnp239-Ahsnp929 WH12 0.57 7.26
qLLSB6-2 Ahsnp539-Ahsnp136 NC12 0.75 11.66
qLLSB6-3 Ahsnp1070-Ahsnp1665 NC12 0.6 7.16
qLLSB6-4 Ahsnp676-Ahsnp231 NC13 0.89 18.47 NC13 0.71 11.87 NC13 0.85 19.41
NC12 0.7 11.32 NC12 0.67 10.85
qLLSB6-5 Ahsnp231-AHGS1431 WH13 0.69 6.33 WH13 0.52 5.74 WH13 0.47 4.68
qLLSB6-6 AHGS1431-Ahsnp163 NC13 0.91 19.12 NC13 0.76 13.89 NC13 0.92 22.55 NC13 0.44 5.42
NC12 0.77 14.42 NC12 0.74 14.16 NC12 0.44 5.2
qLLSB6-7 Ahsnp1166-Ahsnp996 WH13 1.03 14.18 WH13 1.66 11.15 WH13 0.91 8.98
WH12 0.55 6.86 WH12 0.53 6.5
Open in a new tab

A indicates the additive effect of the QTL.

PVE: percentage of phenotypic variance explained by the QTL.

LLS|HMS: late leaf spot conditioned on height of main stem.

LLS|LLB: late leaf spot conditioned on length of the longest branch.

LLS|TNB: late leaf spot conditioned on total number of branches.

Conditional QTL mapping of LLS|HMS revealed 12 QTLs. Nine marker intervals encompassed both unconditional and conditional QTLs, of which five (qLLSA1-2, qLLSA1-3, qLLSB6-4, qLLSB6-6 and qLLSB6-7) were detected in multiple environments. Furthermore, the three QTLs (qLLSB6-4, qLLSB6-6 and qLLSB6-7) were also detected with PVE over 10%. Of the nine QTLs detected in both strategies, five conditional QTLs changed additive effects significantly and four conditional QTLs showed similar additive effects compared to the corresponding unconditional QTLs. Three new QTLs for LLS|HMS were detected and seven unconditional QTLs were not identified again.

Conditional QTL mapping of LLS|LLB identified 11 QTLs. Nine were detected with the same marker intervals as unconditional QTLs, and four of them (qLLSA1-2, qLLSB6-4, qLLSB6-6 and qLLSB6-7) were detected in multiple environments. When LLS was conditioned on LLB, qLLSB6-4 displayed 10.85% and 19.41% PVE in NC12 and NC13, respectively. The qLLSB6-6 was also identified at both NC12 and NC13 and displayed 14.16% and 22.55% PVE, respectively. These two QTLs were also detected as major QTLs under the unconditional QTL mapping. Of the nine QTLs detected in both strategies, five conditional QTLs changed additive effects significantly and the other four showed similar effects to the unconditional ones. Two extra conditional QTLs identified for LLS and seven QTLs were detected only under unconditional situation.

A total of 13 conditional QTLs of LLS|TNB were detected. Six conditional QTLs were also detected by unconditional mapping. Among them, two QTLs (qLLSA1-3 and qLLSB6-6) were detected in multiple environments, but none of them were detected with PVE more than 10%. Of the six QTLs detected in both strategies, five conditional QTLs changed additive effects obviously and one conditional QTL showed similar additive effect to their corresponding unconditional QTLs. Conditioned on TNB, however, 10 unconditional QTLs were not identified again, but seven extra QTLs were detected.

Discussion

QTL analysis has been well documented to dissect the genetic basis of important, complex traits in cultivated peanut [43–49]. However, due to the nature of low genetic diversity and polyploidy in peanut, precisely locating the QTLs controlling the traits of interest is still challenging. In this study, we conducted QTL mapping using our previously constructed genetic map with the highest number of markers (1,685 marker loci) among so far published population-specific linkage maps for tetraploid peanut [29]. Totally, we detected 20 QTLs for LLS and 32 QTLs for three plant-type-related traits. The number of QTLs was much more than the published QTL mapping in a single population of peanut [26,44], suggesting the detection power of QTL mapping was significantly improved.

QTL analysis in single environment is limited in predicting QTL positions and the stability of QTLs as well as the magnitude of genetic effects on target traits [50,51]. In addition, QTL that has obvious genetic effect could be preferentially identified in different environments. In this study, we performed QTL analysis based on phenotypic data from multiple environments. Although most of detected loci were environment-specific, six QTLs were identified in multiple environments and hence were stable in phenotypic expression. For example, a QTL for LLS resistance, namely qLLSB6-2, flanked by Ahsnp539 and Ahsnp136, was expressed with 11.66% PVE in NC12 and with 12.9% PVE in NC11. And a QTL for TBN, namely qTNBB6-3, flanked by AHGS1431 and Ahsnp690, was expressed with 8.1% PVE in NC13 and with 13.44% PVE in WH13. Due to the limitation of experimental material in the present study, the identified major QTLs and the markers linked the studied traits are needed further validation before they are deployed in MAS.

For LLS resistance, we identified 20 QTLs. Of which, six QTLs showed PVE over 10%, and other QTLs were basically significant loci with relatively lower phenotypic contributions. These results indicated that the resistance to LLS in cultivated peanut was controlled by several major QTLs and many minor QTLs, consistent with previous research results [15,46]. When comparing the QTLs for LLS in this study with previous studies (S3 Table) [14,15,46,52,53,54], six major and 10 minor QTLs for LLS in our study were shown to be novel QTLs. This could be due to different genetic backgrounds of the used materials. We also found that the physical region of qLLSA1-2 was involved in a previously identified QTL qF2LS3 [46], and qLLSA4-1 was involved in QTLLLS09 which reported by Khedikar et al. (2010) [14]. The physical region of qLLSB6-3 was overlapped with QTLR5-LLS13 in other study [15], qLLSB10 overlapped with reported QTLs of QTLLLS11 and qF2LS2 [14,46]. Therefore, these four QTLs were repeatedly detected in different populations. With respect to the QTLs for plant-type-related traits, no similar segment was found between our study and previously studies [26,27,55,56].

Nucleotide-binding—leucine-rich repeat (NB-LRR)-encoding genes usually confer resistance against pests and diseases. Bertioli et al. (2016) [57] identified 345 and 397 of these genes in the A. duranensis and A. ipaensis genotypes, which serve as proxies for the A and B subgenomes of peanut, respectively. We therefore compared the physical locations of QTLs for LLS resistance in our study with the NB-LRR—encoding genes and found five QTLs for LLS resistance were associated with these genes (S4 Table). A major QTL qLLSB6-7 for resistance to LLS identified on B6 of ICGV 86699 [31] resides in a cluster of six NB-LRR—encoding genes covering 3.9 Mb. Another QTL qLLSB1 was identified on a segment that covering 8.9 Mb of chromosome B1 which contains five NB-LRR—encoding genes. The genes harbored on these genome segments provide good disease resistance and warrant further investigation.

In the RIL population of Zhonghua 5 × ICGV 86699, the LLS resistance alleles at 20 QTL regions were contributed by ICGV 86699. This level of resistance to LLS has never been identified in “pure” cultivated peanut and in order to widen primary genetic pool, wild species were used [58–62]. From the pedigree of ICGV 86699, we knew that the resistance to LLS was introgressed from wild species of A. duranensis and A. batizocoi [31]. Due to that genome fragments from wild peanut were transferred into cultivated peanuts, this population therefore had high genetic variation and the map had so many polymorphic markers [29]. In the previous studies of QTL mapping for resistance to LLS, GPBD 4 was usually used as a resistant parent [14,15,53]. The source of resistance of GPBD 4 can be traced back to wild species of A. cardenasii [63], which is different from this study.

QTL cluster is an important central concept in genetical genomics [64] and associative traits tend to share regions with QTLs [65–67]. Several studies in plants have mapped QTLs for disease resistances which overlap with those for plant-type-related traits [20,21,68,69]. In an overview of QTL distribution in this study, 10 QTLs including eight major QTLs were clustered in five genetic regions on chromosomes A5, A9 and B6. The genomic regions of QTL cluster IV and V harbored co-localized QTLs for LLS and TNB while cluster I harbored co-localized QTL for LLS and HMS. These results provided evidence that correlation of LLS and plant-type-related traits could be genetically constrained. In peanut, Shoba et al. (2013) [50] found that markers PM 384, pPGPseq5D5, PM 3, PMc 588 and PM 343 associated with number of branches and LLS severity score through single marker analysis, also suggesting genetic correlation existed between LLS and plant type. The genetic regions with clustered robust QTLs of this study are worth of further investigation due to the importance of genetic control of LLS as well as plant type.

The conditional analysis provides an efficient method for dissecting the genetic interrelationship between traits at individual QTL level [9]. Comparing the QTLs of LLS analyzed by unconditional and conditional mappings, the following four possible implications existed in this study: (1) the QTLs, such as qLLSB6-2 and qLLSA5, were detected by unconditional mapping and not be recaptured when LLS was conditioned on HMS, LLB or TNB, suggesting that these QTLs for LLS were completely depended on the plant-type-related traits; (2) the loci, such as qLLSA2 and qLLSB2-1, were identified by conditional, but not unconditional mapping, indicating that such QTLs were completely suppressed by conditional plant-type-related traits; (3) the QTLs, such as qLLSA6-3, identified by two mapping methods had similar effects, indicating that the QTLs for LLS were independent of conditional traits; (4) the QTLs, such as qLLSB6-4 and qLLSB6-6, identified by both unconditional and conditional mapping methods showed significantly changed effects, indicating that these QTLs for LLS were partially influenced by plant-type-related traits. Of the 16 unconditional QTLs for LLS, seven, seven and 10 QTLs were entirely determined by HMS, LLB and TNB respectively; five QTLs each were partial contributions from HMS, LLB and TNB, respectively; four, four and one QTLs were independent of HMS, LLB and TNB, respectively. These results suggested that TNB contributed the strongest influence on LLS.

This study improved our understanding of genetic basis of LLS and provided several QTLs for LLS, HMS, LLB and TNB. From the QTL cluster and conditional analysis, we found that interrelationship existed between LLS and plant-type-related traits. However, challenging questions remain such as how to fast-expanding the population to meet the needs of fine-mapping in peanut. Further validation of major QTLs and markers lined the studied traits will be proceeded before applied in MAS. Besides, support from other omics researches, like transcriptomics, should also be considered to promote genetic analysis and the isolation of favorable alleles based on this study.

Supporting Information

S1 Fig. The schematic diagram of HMS and LLB investigated in this study.

HMS and LLB were measured in the unit of centimeter (cm). HMS: height of main stem; LLB: length of the longest branch.

(EPS)

Click here for additional data file. (2.4MB, eps)
S2 Fig. Frequency distribution of RIL populations for LLS, HMS, LLB and TNB traits in multi-environments.

(EPS)

Click here for additional data file. (13.6MB, eps)
S3 Fig. Distribution of identified QTLs for LLS, HMS, LLB and TNB on genetic linkage maps.

Scale bars on the left side describe map distance in centimorgans. Markers were shown on left side of the linkage groups. The QTLs for LLS, HMS, LLB and TNB were shown by boxes filled with red, green, blue and purple, respectively. The regions for QTL clusters were designed as yellow box on chromosome bars. Each cluster includes at least one major QTL. LLS: late leaf spot disease; HMS: height of main stem; LLB: length of the longest branch; TNB: total number of branches.

(PDF)

Click here for additional data file. (10.6MB, pdf)
S1 Table. The marker sequences of the linkage map used for QTL mapping.

(XLSX)

Click here for additional data file. (121.5KB, xlsx)
S2 Table. QTLs of LLS and plant-type-related traits identified only in joint analysis.

(XLSX)

Click here for additional data file. (10.9KB, xlsx)
S3 Table. The identified QTLs (or nearest marker) for LLS (or LS) of peanut in previous and this studies.

(XLSX)

Click here for additional data file. (17.5KB, xlsx)
S4 Table

(A) Information of QTLs for LLS resistance in this study in which genomic regions harbored NB-LRR—encoding genes. Physical locations of each marker on the genome were determined by searching SNP markers via blast and SSR markers using ePCR to align to AA and BB genome sequence. (B) Genes with NBS domains in the QTL covering segments of pseudomolecule.

(XLSX)

Click here for additional data file. (18.7KB, xlsx)

Acknowledgments

We thank Dr. Jiaqing Shi and Jinying Yu in the Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan, China for valuable discussions.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work was supported by the National Natural Science Foundation of China (grants no. 31301362, 31471534 and 31271764) and the National Program for Crop Germplasm Protection of China (grant no. 2005DKA21002-13).

References

  • 1.FAO. FAO statistical database. FAO, Rome, Italy: Available: http://faostat.fao.org/. 2016. Accessed 23 October 2014. [Google Scholar]
  • 2.Subrahmanyam P, Williams JH, McDonald D, Gibbons RW. The influence of foliar diseases and their control by selective fungicides on a range of groundnut (Arachis hypogaea L.) genotypes. Annals of Applied Biology. 1984; 104: 467–476. [Google Scholar]
  • 3.Nigam SN, PrasadaRao RDVJ, Bhatnagar-Mathur P, Sharma KK. Genetic management of virus diseases in peanut. Plant Breed. 2012; 36:293–356. [Google Scholar]
  • 4.McDonald D, Subrahmanyam P, Gibbons RW, Smith DH. Early and late leaf spot of groundnut. Information Bulletin no.21. Patancheru, A.P. 502 324, India: International Crops Research Institute for the Semi-Arid Tropics. 24 pp; 1985.
  • 5.Kokalis-Burette N, Porter DM, Rodríguez-Kábana R, Smith DH, Subrahmanyam P. Compendium of peanut diseases. American Phytopathology Society, St. Paul, MN. USA, pp. 94; 1997. [Google Scholar]
  • 6.Waliyar F. Evaluation of yield losses due to groundnut leaf diseases in West Africa In: Nduguru BJ, Waliyar F and Ntare BR eds. Summary proc. of the second ICRISAT regional groundnut meeting for West Africa. ICRISAT Sahelian Centre, Niamey, Niger, pp.32–33. 1991. [Google Scholar]
  • 7.Wu R, Lin M. Functional mapping: how to map and study the genetic architecture of dynamic complex traits. Nat Rev Gent. 2006; 7: 229–237. [DOI] [PubMed] [Google Scholar]
  • 8.Zhu J. Analysis of conditional genetic effects and variance components in developmental genetics. Genetics. 1995; 141: 1633–1639. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Zhang Z, Liu Z, Hu Y, Li W, Fu Z, Ding D, et al. QTL analysis of kernel-related rraits in maize using an immortalized F2 population. PLoS ONE, 2014; 9(2): e89645 10.1371/journal.pone.0089645 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Nevill DJ. Inheritance of resistance to Cercosporidium personatum in groundnuts: a genetic model and its implications for selection. Oleagineux. 1982; 37: 355–362. [Google Scholar]
  • 11.Green CC, Wynne JC. Diallel and generation means analyses for the components of resistance to Cercospora arachidicola in peanut. Theor Appl Genet. 1986; 73:228–235. 10.1007/BF00289279 [DOI] [PubMed] [Google Scholar]
  • 12.Motagi. Genetic analysis of resistance to late leaf spot and rust vis-à-vis productivity in groundnut (Arachis hypogaea L.). Dissertation, University of Agriculture Sciences, Dharwad, India; 2001. [Google Scholar]
  • 13.Dwivedi SL, Pande S, Rao JN, Nigam SN. Components of resistance to late leaf spot and rust among interspecific derivatives and their significance in a foliar disease resistance breeding in groundnut (Arachis hypogaea L.). Euphytica. 2002; 125:81–88. [Google Scholar]
  • 14.Khedikar YP, Gowda MVC, Sarvamangala C, Patgar KV, Upadhyaya HD, Varshney RK. A QTL study on late leaf spot and rust revealed one major QTL for molecular breeding for rust resistance in groundnut (Arachis hypogaea L.). Theor Appl Genet. 2010; 121:971–984. 10.1007/s00122-010-1366-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Sujay V, Gowda MVC, Pandey MK, Bhat RS, Khedikar YP, Nadaf HL, et al. Quantitative trait locus analysis and construction of consensus genetic map for foliar disease resistance based on two recombinant inbred line populations in cultivated groundnut (Arachis hypogaea L.). Mol Breeding. 2012; 30:773–788. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Butler DR, Wadia KDR, Reddy RK. Effects of humidity, leaf wetness, temperature and light on conidial production by Phaeoisariopsis personata on groundnut. Plant Pathology.1995; 44(4):662–674. [Google Scholar]
  • 17.Butler DR, Reddy RK, Wadia KDR. Single plant chambers to control humidity for pathological studies. Plant pathology.1995b; 44:1–9. [Google Scholar]
  • 18.Nath BC,Sarma BK,Vaish SS, Chand R,Pal C. Temperature-growth relationship of Phaeoisariopsis personata. Asian Journal of Bio Science. 2013; 8:32–35. [Google Scholar]
  • 19.Yi H, Richards EJ. Phenotypic instability of Arabidopsis alleles affecting a disease Resistance gene cluster. BMC Plant Biology 2008, 8:36 10.1186/1471-2229-8-36 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Li Z, Pinson SRM, Marchetti MA, Stansel JW, Park WD. Characterization of quantitative trait loci (QTLs) in cultivated rice contributing to field resistance to sheath blight (Rhizoctonia solani). Theor Appl Genet. 1995; 91:382–388. 10.1007/BF00220903 [DOI] [PubMed] [Google Scholar]
  • 21.Sato H, Ideta O, Audo I, Kunihiro Y, Hirabayashi H, Iwano M, et al. Mapping QTLs for sheath blight resistance in the rice line WSS2. Breeding Sci. 2004; 54 (3): 265–271. [Google Scholar]
  • 22.Buerstmayr H, Steiner B, Hartl L, Griesser M, Angerer N, Lengauer D, et al. Molecular mapping of QTL for Fusarium head blight resistance in spring wheat. II. Resistance to fungal penetration and spread. Theor Appl Genet. 2003; 107:503–508. 10.1007/s00122-003-1272-6 [DOI] [PubMed] [Google Scholar]
  • 23.Ruge-Wehling B, Linz A, Habekus A, Wehling P. Mapping of Rym16Hb, the second soil-borne virus-resistance gene introgressed from Hordeum bulbosum. Theor Appl Genet. 2006; 113:867–873. 10.1007/s00122-006-0345-8 [DOI] [PubMed] [Google Scholar]
  • 24.Scheurer KS, Friedt W, Huth W, Waugh R, Ordon F. QTL analysis of tolerance to a German strain of BYDV-PAV in barley (Hordeum vulgare L.). Theor Appl Genet. 2001; 103:1074–1083. [Google Scholar]
  • 25.Su Y, Liu Y, Li Z, Fang Z, Yang L, Zhuang M, et al. QTL analysis of head splitting resistance in cabbage (Brassica oleracea L. var. capitata) using SSR and InDel makers based on whole-genome re-sequencing. PLoS ONE. 2015; 10(9): e0138073 10.1371/journal.pone.0138073 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Huang L, He H, Chen W, Ren X, Chen Y, Zhou X, et al. Quantitative trait locus analysis of agronomic and quality-related traits in cultivated peanut (Arachis hypogaea L.). Theor Appl Genet. 2015; 128:1103–1115. 10.1007/s00122-015-2493-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.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 Biology. 2012; 12:80 10.1186/1471-2229-12-80 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Nagy ED, Guo Y, Tang S, Bowers JE, Okashah RA, Taylor CA, et al. A high-density genetic map of Arachis duranensis, a diploid ancestor of cultivated peanut. BMC Genomics. 2012; 13:469 10.1186/1471-2164-13-469 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Zhou X, Xia Y, Ren X, Chen Y, Huang L, Huang S, 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; 15:351 10.1186/1471-2164-15-351 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Xia Y, Jing Y, Mao J, Ren X, Qi Y, Liao J, et al. Segregation analysis of resistance to late leaf spot in peanut. Chinese Journal of Oil Crop Science. 2015; 37(2):134–140. [Google Scholar]
  • 31.Reddy LJ, Nigam SN, Moss JP, Singh AK, Subrahmanyam P, Mcdonald D, et al. Registration of ICGV 86699 Peanut Germplasm Line with Multiple Disease and Insect Resistance. Crop Sci. 1996; 36:821. [Google Scholar]
  • 32.Subbarao PV, Subrahmanyam P, Reddy PM. A modified nine point disease scale for assessment of rust and late leaf spot of groundnut. In: Second international congress of French phytopathological society. 28–30 November 1990, Montpellier, France, pp. 25, 1990.
  • 33.Hallauer AR, Miranda JB. Quantitative genetics in maize breeding, 2nd edn Iowa State Univ Press, Ames; 1998. [Google Scholar]
  • 34.Sas Institute. SAS/STAT User’s Guide, Version 8. SAS Institute, Cary, NC; 2000. [Google Scholar]
  • 35.Wang S, Basten C, Zeng Z. Windows QTL cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC; 2007. [Google Scholar]
  • 36.Lander ES, Botstein D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics. 1989; 121(1):185–199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Liu Y, Wang L, Sun C, Zhang Z, Zheng Y, Qiu F. Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments. Theor Appl Genet. 2014; 127:1019–1037. 10.1007/s00122-014-2276-0 [DOI] [PubMed] [Google Scholar]
  • 38.Yang J, Zhu J, Williams RW. Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics. 2007; 23:1527–1536. 10.1093/bioinformatics/btm143 [DOI] [PubMed] [Google Scholar]
  • 39.McCouch SR, Cho YG, Yano M, Paul E, Blinstrub M. Report on QTL nomenclature. Rice Genet Newlett. 1997; 14:11–13. [Google Scholar]
  • 40.Zhu J. Mixed model approaches for estimating genetic variances and covariances. J Biomath. 1992; 7: 1–11. [Google Scholar]
  • 41.Chen GB, Zhu J. Software for the classical quantitative genetics. Institute of Bioinformatics, Zhejiang University, Hangzhou, China; 2003. Available: http://ibi.zju.edu.cn/software/qga/index.htm [Google Scholar]
  • 42.Zhao JY, Becker HC, Zhang DQ, Zhang YF, Ecke W. Conditional QTL mapping of oil content in rapeseed with respect to protein content and traits related to plant development and grain yield. Theor Appl Genet. 2006; 113: 33–38. 10.1007/s00122-006-0267-5 [DOI] [PubMed] [Google Scholar]
  • 43.Sarvamangala C, Gowda MVC, Varshney RK. Identification of quantitative trait loci for protein content, oil content and oil quality for groundnut (Arachis hypogaea L.) Field Crops Research. 2011; 122: 49–59. [Google Scholar]
  • 44.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 Genetics. 2016; 17:25 10.1186/s12863-016-0337-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.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 Breeding. 2012; 30:757–772. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Wang H, Pandey MK, Qiao L, Qin H, Culbreath AK, He G, et al. Genetic mapping and quantitative trait loci analysis for disease resistance using F2 and F5 generation-based genetic maps derived from ‘Tifrunner’ × ‘GT-C20’ in peanut. Plant Genome. 2013; 6(3): 10.3835/plantgenome2013.05.0018 [DOI] [Google Scholar]
  • 47.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; 122:1119–1132. 10.1007/s00122-010-1517-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Varshney RK, Bertioli DJ, Moretzsohn MC, Vadez V, Krishnamurthy L, Aruna R, et al. The first SSR-based genetic linkage map for cultivated groundnut (Arachis hypogaea L.). Theor Appl Genet. 2009; 118:729–739. 10.1007/s00122-008-0933-x [DOI] [PubMed] [Google Scholar]
  • 49.Wang ML, Khera P, Pandey MK, Wang H, Qiao L, Feng S, et al. Genetic mapping of QTLs controlling fatty acids provided insights into the genetic control of fatty acid synthesis pathway in peanut (Arachis hypogaea L.). PLoS ONE. 2015; 10(4): e0119454 10.1371/journal.pone.0119454 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Malosetti M, Ribaut JM, Vargas M, Crossa J, van Eeuwijk FA. A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.). Euphytica. 2008; 161:241–257. [Google Scholar]
  • 51.Messmer R, Fracheboud Y, Bänziger M, Vargas M, Stamp P, Ribaut J-M. Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits. Theorl Appl Genet. 2009; 119:913–930 [DOI] [PubMed] [Google Scholar]
  • 52.Shoba D, Manivannan N, Vindhiyavarman P, Nigam SN. Identification of quatitative trait loci (QTL) for late leaf spot disease resistance in groundnut (Arachis hypogaea L.). Legume Res. 2013; 36 (5): 467–472. [Google Scholar]
  • 53.Kolekar RM, Sujay V, Shirasawa K, Sukruth M, Khedikar YP, Gowdan MVC. QTL mapping for late leaf spot and rust resistance using an improved genetic map and extensive phenotypic data on a recombinant inbred line population in peanut (Arachis hypogaea L.). Euphytica. 2016; 209:147–156. [Google Scholar]
  • 54.Leal-Bertioli SCM, AC VF José, Alves-Freitas DMT, Moretzsohn MC, Guimarães PM, Nielen S, et al. Identification of candidate genome regions controlling disease resistance in Arachis. BMC Plant Biology. 2009; 9:112 10.1186/1471-2229-9-112 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Fonceka D, Tossim H-A, Rivallan R, Vignes H, Lacut E, de Bellis F, et al. Construction of chromosome segment substitution lines in peanut (Arachis hypogaea L.) using a wild synthetic and QTL mapping for plant morphology. PLoS ONE. 2012; 7(11): e48642 10.1371/journal.pone.0048642 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Fonceka D, Tossim H-A, 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 Biology. 2012; 12:26 10.1186/1471-2229-12-26 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Bertioli DJ, Cannon SB, Froenicke L, Huang G, Farmer AD, Cannon EKS, et al. The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid ancestors of cultivated peanut. Nature Genetics. 2016; 48:438–446. 10.1038/ng.3517 [DOI] [PubMed] [Google Scholar]
  • 58.Burow MD, Simpson CE, Starr JL, Paterson AH. Transmission genetics of chromatin from a synthetic amphidiploid to cultivated peanut (Arachis hypogaea L.): broadening the gene pool of a monophyletic polyploid species. Genetics. 2001; 159: 823–837. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Burow MD, Starr JL, Simpson CE, Paterson AH. Identification of RAPD markers in peanut (Arachis hypogaea) associated with root-knot nematode resistance derived from A. cardenasii. Mol Breed. 1996; 2: 307–319. [Google Scholar]
  • 60.Choi K, Burow MD, Church G, Burow G, Paterson AH, Simpson CE, et al. Genetics and mechanism of resistance to Meloidogyne arenaria in peanut germplasm. J. Nematol. 1999; 31: 283–290. [PMC free article] [PubMed] [Google Scholar]
  • 61.Simpson CE, Starr JL. Registration of ‘COAN’ peanut. Crop Sci. 2001; 41: 918. [Google Scholar]
  • 62.Kochert G, Stalker HT, Gimenes M, Galgaro L, Lopes CR, Moore K. RFLP and cytological evidence on the origin and evolution of allotetraploid domesticated peanut, Arachis hypogaea (Leguminosae). Am J Bot. 1996; 83: 1282–1291. [Google Scholar]
  • 63.Gowda MVC, Motagi BN, Naidu GK, Diddimani SB, Sheshagiri R. GPBD 4: A Spanish bunch groundnut genotype resistant to rust and late leaf spot. Int Arachis Newsl. 2002; 22:29–32. [Google Scholar]
  • 64.Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, et al. Genetics of gene expression surveyed in maize, mouse and man. Nature. 2003; 422:297–302. 10.1038/nature01434 [DOI] [PubMed] [Google Scholar]
  • 65.Austin D, Lee M. Comparative mapping in F2:3 and F6:7 generations of quantitative trait loci for grain yield and yield components in maize. Theor Appl Genet. 1996; 92:817–826. 10.1007/BF00221893 [DOI] [PubMed] [Google Scholar]
  • 66.Austin DF, Lee M. Detection of quantitative trait loci for grain yield and yield components in maize across generations in stress and nonstress environments. Crop Sci. 1998; 38:1296–1308. [Google Scholar]
  • 67.Li Y, Niu S, Dong Y, Cui D, Wang Y, Liu Y, et al. Identification of trait-improving quantitative trait loci for grain yield components from a dent corn inbred line in an advanced backcross BC2F2 population and comparison with its F2:3 population in popcorn. Theor Appl Genet. 2007; 115:129–140. 10.1007/s00122-007-0549-6 [DOI] [PubMed] [Google Scholar]
  • 68.Zou JH, Pan XB, Chen ZX, Xu JY, Lu JF, Zhai WX, et al. Mapping quantitative trait loci controlling sheath blight resistance in two rice cultivars (Oryza sativa L.). Theo Appl Genet. 2000; 101: 569–575. [Google Scholar]
  • 69.Kunihiro Y, Qian Q, Sato H, Teng S, Zeng D-L, Fujimoto K, et al. QTL analysis of sheath blight resistance in rice (Oryza sativa L.). Acta Genetica Sinica. 2002; 29(1):50–55. [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. The schematic diagram of HMS and LLB investigated in this study.

HMS and LLB were measured in the unit of centimeter (cm). HMS: height of main stem; LLB: length of the longest branch.

(EPS)

Click here for additional data file. (2.4MB, eps)
S2 Fig. Frequency distribution of RIL populations for LLS, HMS, LLB and TNB traits in multi-environments.

(EPS)

Click here for additional data file. (13.6MB, eps)
S3 Fig. Distribution of identified QTLs for LLS, HMS, LLB and TNB on genetic linkage maps.

Scale bars on the left side describe map distance in centimorgans. Markers were shown on left side of the linkage groups. The QTLs for LLS, HMS, LLB and TNB were shown by boxes filled with red, green, blue and purple, respectively. The regions for QTL clusters were designed as yellow box on chromosome bars. Each cluster includes at least one major QTL. LLS: late leaf spot disease; HMS: height of main stem; LLB: length of the longest branch; TNB: total number of branches.

(PDF)

Click here for additional data file. (10.6MB, pdf)
S1 Table. The marker sequences of the linkage map used for QTL mapping.

(XLSX)

Click here for additional data file. (121.5KB, xlsx)
S2 Table. QTLs of LLS and plant-type-related traits identified only in joint analysis.

(XLSX)

Click here for additional data file. (10.9KB, xlsx)
S3 Table. The identified QTLs (or nearest marker) for LLS (or LS) of peanut in previous and this studies.

(XLSX)

Click here for additional data file. (17.5KB, xlsx)
S4 Table

(A) Information of QTLs for LLS resistance in this study in which genomic regions harbored NB-LRR—encoding genes. Physical locations of each marker on the genome were determined by searching SNP markers via blast and SSR markers using ePCR to align to AA and BB genome sequence. (B) Genes with NBS domains in the QTL covering segments of pseudomolecule.

(XLSX)

Click here for additional data file. (18.7KB, xlsx)

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES