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. 2015 Nov 13;6:988. doi: 10.3389/fpls.2015.00988

Development and Utilization of InDel Markers to Identify Peanut (Arachis hypogaea) Disease Resistance

Lifeng Liu 1,2, Phat M Dang 3, Charles Y Chen 1,*
PMCID: PMC4643128  PMID: 26617627

Abstract

Peanut diseases, such as leaf spot and spotted wilt caused by Tomato Spotted Wilt Virus, can significantly reduce yield and quality. Application of marker assisted plant breeding requires the development and validation of different types of DNA molecular markers. Nearly 10,000 SSR-based molecular markers have been identified by various research groups around the world, but less than 14.5% showed polymorphism in peanut and only 6.4% have been mapped. Low levels of polymorphism limit the application of marker assisted selection (MAS) in peanut breeding programs. Insertion/deletion (InDel) markers have been reported to be more polymorphic than SSRs in some crops. The goals of this study were to identify novel InDel markers and to evaluate the potential use in peanut breeding. Forty-eight InDel markers were developed from conserved sequences of functional genes and tested in a diverse panel of 118 accessions covering six botanical types of cultivated peanut, of which 104 were from the U.S. mini-core. Results showed that 16 InDel markers were polymorphic with polymorphic information content (PIC) among InDels ranged from 0.017 to 0.660. With respect to botanical types, PICs varied from 0.176 for fastigiata var., 0.181 for hypogaea var., 0.306 for vulgaris var., 0.534 for aequatoriana var., 0.556 for peruviana var., to 0.660 for hirsuta var., implying that aequatoriana var., peruviana var., and hirsuta var. have higher genetic diversity than the other types and provide a basis for gene functional studies. Single marker analysis was conducted to associate specific marker to disease resistant traits. Five InDels from functional genes were identified to be significantly correlated to tomato spotted wilt virus (TSWV) infection and leaf spot, and these novel markers will be utilized to identify disease resistant genotype in breeding populations.

Keywords: InDel markers, cultivated peanut, genetic diversity, disease resistances

Introduction

Various types of molecular markers, such as random amplified polymorphic DNA (RAPD) (Williams et al., 1990; Burow et al., 1996; Subramanian et al., 2000); amplified fragment length polymorphism (AFLP) (Vos et al., 1995; He and Prakash, 1997); inter simple sequence repeat (ISSR) markers (Zietkiewicz et al., 1994; Raina et al., 2001) and simple sequence repeats (SSR) (Tautz, 1989; Liang et al., 2009), have been used in detecting the genetic diversity of plant germplasm resources (Cuc et al., 2008; Jiang et al., 2010; Moretzsohn et al., 2013), construction of genetic linkage maps (Varshney et al., 2009; Hong et al., 2010; Gautami et al., 2012; Nagy et al., 2012; Qin et al., 2012; Shirasawa et al., 2013), molecular marker-assisted selection (MAS) and mapping and cloning of genes/QTL (Chu et al., 2011; Ravi et al., 2011; Sujay et al., 2012) in peanut. Microsatellite or simple sequence repeat (SSR) markers have been developed using sequences derived from SSR-enriched genomic libraries and expressed sequence tags (ESTs) (Guo et al., 2009; Koilkonda et al., 2012; Wang et al., 2012; Zhang et al., 2012) and have been utilized to investigate genetic diversity for the US peanut mini-core collection (Belamkar et al., 2011; Wang et al., 2011; Chen et al., 2014), Chinese peanut mini-core collection (Jiang et al., 2010, 2014), and ICRISAT peanut mini-core collections (Ren et al., 2010; Mukri et al., 2012; Upadhyaya et al., 2012). The functional SNP markers from FAD2A/FAD2B genes have been used to screen the U.S. mini-core collection (Wang et al., 2013). Another new kind of marker called Start codon targeted polymorphism (SCoT) was also developed and showed the potential use for studying the genetic diversity and relationship in cultivated peanut (Xiong et al., 2011). Approximately 10,000 molecular markers have been identified by various research groups around the world, but only 14.5% showed polymorphism in peanut and only 6.4% were mapped (Zhao et al., 2012), mainly due to the fact that cultivated peanut possesses an extremely narrow genetic basis (Xiong et al., 2011). Low genetic diversity among cultivated peanut accessions is likely due to the single hybridization event between two ancient diploid species, likely Arachis duranensis (A genome) and Arachis ipaensis (B genome) (Burow et al., 2009; Nagy et al., 2012; Shirasawa et al., 2013). Low level of polymorphism limits the application of molecular markers in peanut breeding and genetics studies.

InDels have been recognized as an abundant source of genetic markers that are widely spread across the genome, and there is an increasing focus on polymorphisms of the type short insertions and deletions (InDels) in genomic and breeding research (Lv et al., 2013; Yamaki et al., 2013). Short sequence and homonucleotide repeats tend to accumulate InDels due to polymerase slippage during replication and frame shift InDels in coding regions can result loss of function or non-sense mutation (Rockah-Shmuel et al., 2013). It has been reported that insertions and deletions (InDels) markers were more polymorphic than SSRs in some crops (Liu et al., 2013; Wu et al., 2014). No research of InDel marker in peanut has been reported for trait association. Therefore, it is vital to develop InDel markers in peanut and to apply these markers to associate important traits, such as disease resistance. The objectives of this research were: (1) to develop the gene-specific InDel markers; (2) to evaluate the potential use in genetic diversity study for cultivated peanut; and (3) to identify novel InDel markers that related to the disease-resistant traits.

Materials and methods

Plant materials and phenotyping of TSWV and leaf spot

One hundred and eighteen peanut accessions from the USDA peanut germplasm collection in Griffin, GA were used in the study, in which 104 accessions were selected from the US peanut mini-core collection and an additional 14 accessions were selected to represent two botanical types (hirsuta var. and aequatoriana var.) of cultivated peanut that are not present in the mini-core (Table 1). Twenty seed of each 118 Arachis hypogaea accessions were planted at Dawson, GA (31°45′ latitude, −84°30′ longitude) in 2010, 2012, and 2013 under irrigated conditions. The genotypes were planted in two-row plots 3 m long and 0.91 m between rows at a seeding rate of 3 seed m−1 in early May with three replications. Before planting, the field area was cultivated and irrigated with 15 mm of water to ensure adequate moisture for uniform seed germination. Crop management for all entries was according to best management practices for soil nutrients, herbicides, and pesticides. For evaluation of TSWV resistance, all plots of each PI were visually rated immediately prior to digging for foliar symptoms on a percentage basis, similar to the 1–10 method described by Tillman et al. (2007) where 1 = no disease and 10 = all plants severely diseased. Disease evaluations for leaf spot resistance were conducted in the field under a reduced fungicide-treatment with one application of 1.5 pt/A chlorothalonil in 2010 and no fungicide application in 2012 and 2013. Plants were rated using the Florida leaf spot scoring system during flowering, 2 weeks before harvest, and immediately prior to harvest (Chiteka et al., 1988). The data was analyzed using SAS Institute (version 9.2, 2009) with PROC GLM under the general linear model. Means were separated using Fisher's Protected LSD at p < 0.05.

Table 1.

One hundred eighteen accessions from six botanical varieties of cultivated peanuts used for disease evaluation and the InDel marker analysis.

Code PI Number Botanical variety Origin Code PI Number Botanical variety Origin
G001 PI 152146 fastigiata Uruguay G060 PI 372305 hypogaea Nigeria
G002 PI 155107 vulgaris Uruguay G061 PI 399581 hypogaea Nigeria
G003 PI 157542 vulgaris China G062 PI 403813 vulgaris Argentina
G004 PI 158854 fastigiata China G063 PI 407667 vulgaris Thailand
G005 PI 159786 hypogaea Senegal G064 PI 429420 fastigiata Zimbabwe
G006 PI 162655 hypogaea Uruguay G065 PI 442768 hypogaea Zimbabwe
G007 PI 162857 hypogaea Sudan G066 PI 461434 hypogaea China
G008 PI 196622 hypogaea Cote D'Ivoire G067 PI 471952 hypogaea Zimbabwe
G009 PI 196635 hypogaea Madagascar G068 PI 471954 fastigiata Zimbabwe
G010 PI 200441 fastigiata Japan G069 PI 476432 hypogaea Nigeria
G011 PI 240560 hypogaea South Africa G070 PI 476636 hypogaea Nigeria
G012 PI 259617 fastigiata Cuba G071 PI 478819 vulgaris India
G013 PI 259658 hypogaea Cuba G072 PI 478850 fastigiata Uganda
G014 PI 259836 fastigiata Malawi G073 PI 481795 hypogaea Zambezia
G015 PI 259851 hypogaea Malawi G074 PI 482120 hypogaea Zimbabwe
G016 PI 262038 fastigiata Brazil G075 PI 482189 fastigiata Zimbabwe
G017 PI 268586 hypogaea Zambia G076 PI 494795 hypogaea Zambia
G018 PI 268696 hypogaea South Africa G077 PI 496401 hypogaea Burkina
G019 PI 268755 hypogaea Zambia G078 PI 496448 hypogaea Burkina
G020 PI 268806 hypogaea Zambia G079 PI 502040 fastigiata Peru
G021 PI 268868 hypogaea Sudan G080 PI 502111 peruviana Peru
G022 PI 268996 hypogaea Zambia G081 PI 502120 peruviana Peru
G023 PI 270786 hypogaea Zambia G082 PI 504614 hypogaea Colombia
G024 PI 270905 hypogaea Zambia G083 PI 475863 fastigiata Bolivia
G025 PI 270907 hypogaea Zambia G084 PI 475918 fastigiata Bolivia
G026 PI 270998 vulgaris Zambia G085 PI 476025 fastigiata Peru
G027 PI 271019 vulgaris Zambia G086 PI 493329 fastigiata Argentina
G028 PI 274193 hypogaea Bolivia G087 PI 493356 fastigiata Argentina
G029 PI 288146 vulgaris India G088 PI 493547 fastigiata Argentina
G030 PI 290536 hypogaea India G089 PI 493581 fastigiata Argentina
G031 PI 290560 vulgaris India G090 PI 493631 fastigiata Argentina
G032 PI 290566 fastigiata India G091 PI 493693 fastigiata Argentina
G033 PI 290594 hypogaea India G092 PI 493717 fastigiata Argentina
G034 PI 290620 fastigiata Argentina G093 PI 493729 fastigiata Argentina
G035 PI 292950 hypogaea South Africa G094 PI 493880 fastigiata Argentina
G036 PI 295250 hypogaea Israel G095 PI 493938 fastigiata Argentina
G037 PI 295309 hypogaea Israel G096 PI 497517 fastigiata Brazil
G038 PI 295730 fastigiata India G097 PI 497639 fastigiata Ecuador
G039 PI 296550 hypogaea Israel G098 PI 497318 hypogaea Bolivia
G040 PI 296558 hypogaea Israel G099 PI 497395 hypogaea Bolivia
G041 PI 298854 hypogaea South Africa G100 PI 494018 vulgaris Argentina
G042 PI 313129 fastigiata Taiwan G101 PI 494034 vulgaris Argentina
G043 PI 319768 hypogaea Israel G102 PI 288210 vulgaris India
G044 PI 323268 hypogaea Pakistan G103 PI 371521 hypogaea Israel
G045 PI 325943 hypogaea Venezuela G104 PI 461427 hypogaea China
G046 PI 331297 hypogaea Argentina G105 PI 576613 hirsuta Mexico
G047 PI 331314 hypogaea Argentina G106 Grif 14051 aequatoriana Guatemala
G048 PI 337293 hypogaea Brazil G107 PI 576634 hirsuta Mexico
G049 PI 337399 hypogaea Morocco G108 PI 648241 hirsuta Ecuador
G050 PI 337406 fastigiata Paraguay G109 PI 648250 aequatoriana Ecuador
G051 PI 338338 peruviana Venezuela G110 PI 576616 hirsuta Mexico
G052 PI 339960 fastigiata Argentina G111 PI 648249 aequatoriana Ecuador
G053 PI 343384 hypogaea Israel G112 PI 648242 aequatoriana Ecuador
G054 PI 343398 fastigiata Israel G113 PI 648245 aequatoriana Ecuador
G055 PI 355268 hypogaea Mexico G114 Grif 12579 aequatoriana Ecuador
G056 PI 355271 hypogaea Mexico G115 PI 576614 hirsuta Mexico
G057 PI 356004 fastigiata Argentina G116 Grif 12545 aequatoriana Ecuador
G058 PI 370331 hypogaea Israel G117 PI 576636 hirsuta Mexico
G059 PI 372271 hypogaea Unknown G118 PI 576637 hirsuta Mexico
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Identification of InDels and primer design

Publically available peanut expressed sequence tags (ESTs) derived from various tissues, developmental stages, and under different biotic and abiotic stresses (Feng et al., 2012) were utilized to identify potential InDel markers. Sequences were downloaded and alignment was performed by Sequencher v5.1 (Gene Codes, Ann Arbor, MI). Individual clusters or contigs were visually observed to identify potential InDels and selected contigs were reassembled using “large gap” criteria for assembly algorithm, resulting in the identification of 48 InDels. Primers were designed using Primer Express 3.0 (Applied Biosystems, Foster City, CA) for the sizes of 150–500 bp. Potential plant gene function was identified through BLASTx (NCBI) and comparison of the sequences according to conserved sequences of functional genes. The procedure of identification of peanut EST InDels, primer design and marker scoring was illustrated by flowchart (Figure 1).

Figure 1.

Figure 1

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Flowchart showing identification of peanut EST InDels, primer design, and marker scoring.

DNA extraction and PCR

Genomic DNA extraction from dry seeds was performed following the method of Dang and Chen (2013). A Nano-Drop 2000c spectrophotometer (Nano Drop Technologies, USA) was used to evaluate the quality and concentration of all DNA. DNA samples were diluted to 20 ng/μL and PCR conditions were applied: 94°C for 1 min, 30 cycles of 30 s at 94°C, 50°C for 1.0 min, 72°C for 1.5 min, and 1 cycle at 72°C for 10 min. PCR products and DNA molecular weight marker (Promega, Madison, WI) were separated on a 1.2% TAE-agarose gel and image was captured on a Gel Logic 200 Imaging System (Kodak, Rochester, NY).

Data analysis

Polymorphism Information Content (PIC) based on allelic frequencies among 118 genotypes was calculated for each InDel marker using the following formula: PIC = 1-∑xi2 where xi is the relative frequency of the ith allele of the SSR loci. Clustering analyses were performed using SAS (SAS 9.3; SAS Institute, 2009) to calculate the genetic similarity matrices, and a neighbor-joining (NJ) algorithm (Saitou and Nei, 1987) was used to construct a phylogram from a distance matrix using the MEGA4 software (Tamura et al., 2007). Single marker analysis (SMA) method was used for trait-marker analysis (Jansen and Stam, 1994). It was carried out by PROC GLM of SAS (SAS 9.3; SAS Institute, 2009) with the following linear model: Yiklm = u + Ei + Mk + F(M)kl + E x F(M)ikl + eiklm, where Yiklm is each observed phenotype, u is the population mean, Ei is the effect of year (i = 1, 2), Mk is the effect of marker genotype (k = 1, 2), F(M) kl is the effect of PIs within marker genotype (l = 1, …, 118), E x F(M)ikl is the interaction between the effect of year and the effect of PIs within marker genotype, and eiklm is residual error. Threshold for declaring a marker significant was chosen to be marker-wise p < 0.0001, which is approximately equal to an experiment-wise p < 0.05 in this study based on 16 polymorphic markers.

Results

Polymorphic information of the InDel markers and genetic diversity of the different botanical types based on InDel markers

Forty-eight primer-pairs of InDel markers were designed from coding and non-coding regions of the 48 functional genes (Table 2). All 48 primer-pairs generated PCR bands, of which 16 were polymorphic, with different sizes from 200 to 470 bp (Figure 2). The polymorphic information content (PIC) values of each primer ranged from 0.0169 of InDel-03 to 0.5960 of InDel-18 with an average of 0.1349 (Table 3). The distributions of 16 polymorphic InDel markers among the six botanical types were quite different. More polymorphic markers were detected in the botanical types of hirsuta var., aequatoriana var., hypogaea var., and fastigiata var. than the other two types of peruviana var. and vulgaris var. (12, 9, 9, 7, vs. 2, 2) (Table 3). The least polymorphic marker was InDel-03 which only showed in hirsuta var., while InDel-16 and InDel-18 showed polymorphism in five of six botanical types. In respect to the different botanical types, PICs varied from 0.176 for fastigiata var., 0.181 for hypogaea var., 0.306 for vulgaris var., 0.534 for aequatoriana var., 0.556 for peruviana var., to 0.660 for hirsuta var., which implied that hirsuta var., peruviana var., and aequatoriana var. have higher genetic diversity than the other types (Table 4).

Table 2.

The sequence and annotations of the 48 InDel markers that were developed and used in this study.

InDels Primer Sequence from 5′ to 3′ Contig Annotation bp difference Location
Indel-001- F AATTCGAGGGTGCTGAAATG [0016] Metallothionein, type 2 6 bp 3′ non-coding
Indel-001-R TCAAGGATGCAGCAAGACAC
Indel-002_F GCTCAACCGGTTCCAGAATA [0023] Allergen II 5 bp 3′ non-coding
Indel-002_R AGGCAATGCCATAAAAGCAC
Indel-003_F GGCCCATGACAAAAGGACTA [0031] Peroxidase 6 bp 3′ non-coding
Indel-003_R GAACTGTGACTGCCACGCAC
Indel-004_F GCCTGTAACTGCCTCAAAGC [0038] LTP 18 bp 3′ non-coding
Indel-004_R CATACAAAGACTACAAGAGGARAGG
Indel-005_F CAAGCCAGGCTATTGACTCC [0041] Isoprene synthase 3 bp Coding
Indel-005_R TCGTGAAATGACCATCATTG
Indel-006_F AGCTTAACGGCATCCTCTCA [0055] Glyceraldehyde-3-phosphate dehydrogenase 10 bp 3′ non-coding
Indel-006_R GCTTAACAAGTGTAGTGGTAATAGTAG
Indel-007_F ACCGTGCTGTGACAAATTCA [0047] Hyoscyamine-6-dioxygenase 22 bp 3′ non-coding
Indel-007_R GCACCTCTACATGAAGGTGAAC
Indel-008_F ACGTCTGACCCATGAAATCC [0061] Catalase 30 bp 3′ non-coding
Indel-008_R CGTACACGCGGACAGATTTAG
Indel-009_F GCCTTATCAACYCTTTCACCCTC [0057] Gibberellin 2-oxidase 15 bp 5′ coding
Indel-009_R AGCGGCAAGGAGAAGAATTT
Indel-010_F AGAGCATTAAGGAGAAAGCTGC [0100] LEA 4 3 bp Coding
Indel-010_R ATGTTGTCCGGTTGTGGAAT
Indel-011_F CTGCAAATTCGACAAGAGCA [0059] Cysteine proteinase 5 bp 3′ non-coding
Indel-011_R GCAGAACATTTCACAGCATACATG
Indel-012_F CACATAGTGGGGCCTGATCT [0113] 1-Cys peroxiredoxin 3 bp 3′ non-coding
Indel-012_R AACCATATTTAGATTTGTGAGATAGC
Indel-013_F CCACCCCCAGAGTACATCAC [0110] Vacuolar processing enzyme 69 bp Coding
Indel-013_R GATGGATGCAGGATCGAAGC
Indel-014_F GGCACAGAGCAAAGTGAACA [0115] F-box protein 3 bp Coding
Indel-014_R TTCTCAGAACCCCACAAAGG
Indel-015_F AGAGAAGCTGTGGGATGACG [0276] Auxin repressed protein 2 bp 3′ non-coding
Indel-015_R CCACAGACCAAACAAGCAGA
Indel-016_F TCCTCATCAGGAACTGGGATA [0160] Alkaline alpha galactosidase 19 bp 3′ non-coding
Indel-016_R TGCAGCAATAGGACTTCTGG
Indel-017_F GTGGAGGAGTGTACGGAGGA [0137] Drought induced protein 7 bp 3′ non-coding
Indel-017_R CACACAAGAATGAAAGTGTAAAACC
Indel-018_F AGCTGGAAAGCAAGAGCAAG [0177] Arachin Ahy-3 12 bp Coding
Indel-018_R GCTGTTTGCGTTCATGTTGT
Indel-019_F CACCGACAACCTAGGCGTAT [0285] Lipid binding protein 26 bp 3′ non-coding
Indel-019_R GAGCAATAGTGACCTTGCATTG
Indel-020_F CATTTTCAAACATTACACTCACTCATC [0294] Plant lipid transfer protein 5 bp 3′ non-coding
Indel-020_R CAACACATGCAATGCAACAA
Indel-021_F CCGATTCCTTCAGATAGCAC [0296] 40S ribosomal protein 2 bp 3′ non-coding
Indel-021_R GAGAAAATTGAAATTCAACTTCATC
Indel-022_F GCGGTGAAATCAACTCATCA [0315] Cell wall N rich protein 6 bp Coding
Indel-022_R CTTTGTTGAAGCCACCGTTG
Indel-023_F CATCCGACATGTTACAATACTGAG [0326] bZip Transcription factor 26 bp 3′ non-coding
Indel-023_R CCATTGATAGAGTGATTACAATTTCTC
Indel-024_F GTTGTGTTGATCCTTTCATTCGG [0421] Glutamate binding 12 bp 5′ non-coding
Indel-024_R AGACGGTGATGGAGGATACG
Indel-025_F GACTCCATAATCGGAATCCAAG [0495] Vesicle membrane protein 18 bp 5′ non-coding
Indel-025_R GCTTGAGCGCTGGAAGTAAC
Indel-026_F TCGGCTTACTCTCCCCTGAAC [0500] Plastic protein 3 bp Coding
Indel-026_R GTCAATCTCGCACCCAAATC
Indel-027_F GGCTATTGCAGGTGGAACAC [0518] Wound induced protein 3 bp Coding
Indel-027_R GACCCCACGTGCTCAAATAC
Indel-028_F ACCAATGCATGTGGATCATGC [0534] Lipid binding protein 3 bp 5′ non-coding
Indel-028_R GCAGTGCACAAACAAAGTGC
Indel-029_F TTCCTTTGCTTTCCACCATT [1556] Protease inhibitor 5 bp 3′ non-coding
Indel-029_R GCATGATGAGGATTAAAAGATGATAG
Indel-030_F TTGAAGGCAGAGGAGGTAGC [0522] Remorin 11 bp 3′ non-coding
Indel-030_R GAAAGGAACATTGAACTAAATTTTGC
Indel-031_F CGTCATATCCATCACCACCA [0581] Proline rich cell wall protein 12 bp Coding
Indel-031_R GGAGGAGTCATGCCACAAGT
Indel-032_F AGGAGCAACCGGACACATAC [0628] Electron transporter/metal ion 7 bp 3′ non-coding
Indel-032_R TGCACCTCATCAACCTCTCA
Indel-033_F CCTTTAGGCCCAAGGATTTC [3275] Salt tolerance protein 3 bp Coding
Indel-033_R TGCCTCTAAGTCCCTTCTTATTG
Indel-034_F TGCAGCACGTAAGGATCAAG [0898] Unknown 3 bp 3′ non-coding
Indel-034_R TTTGTAACGCAACCTTGCAC
Indel-035_F CGTGGGAGGGACAGAGATTA [1457] Arginine/serine splicing factor 3 bp 3′ non-coding
Indel-035_R AGATCGTCCATCACGGCTAC
Indel-036_F ATTGGCTTGTGAAGCATTCC [2962] ATARLA, GTP binding 3 bp 3′ non-coding
Indel-036_R CAGCTACATCAACAATGACATGA
Indel-037_F CACCCCAAGTTTGGAAAATG [3189] Unknown 7 bp 3′ non-coding
Indel-037_R CACTTGATTGCAAGCTTGTACAAAT
Indel-038_F TGAAGTCAGTGACAGTGGTGAA [3291] Glycine dehydrogenase 1 bp 3′ non-coding
Indel-038_R GCAGTCAAAGCACAAGACAAG
Indel-039_F ACTTCCAATTCCCAGCACAG [3482] Unknown 6 bp 5′ non-coding
Indel-039_R CCCAATGAAAGCTTGAAGGA
Indel-040_F CTTAATAATTTGGATGAAGGATCATC [3624] Unknown 6 bp 5′ non-coding
Indel-040_R CGGTGGTTCCAAAAAGAAGA
Indel-041_F AAGCTGCTGAGAGGGAAAGAC [3694] Unknown 18 bp 5′ non-coding
Indel-041_R GCCCACACATGCATAGACAG
Indel-042_F GGGATTGAGCATGAACGATT [3863] Dihydroxy-acid dehydratase 2 bp 3′ non-coding
Indel-042_R GATAACAAATGGGGGCAAGA
Indel-043_F GATATAGCACCAGCAGCATAGTTTC [1258] Unknown 9 bp 3′ non-coding
Indel-043_R TTTTCAGTCAAATGATGGAAGC
Indel-044_F TTGAGGCCCTAAGAATGAGC [2367] Cyclin-dependent protein kinase 12 bp 3′ non-coding
Indel-044_R TTTTTGTCCTCATGAAGAACTACG
Indel-045_F GAGGAGGCCAAGAAGGAGTT [3274] Frutose-bisphosphate aldolase 2 bp 3′ non-coding
Indel-045_R TGGCTCCTAACTTATGGCAAA
Indel-046_F TGAACTCGAGCGAACATCAC [1585] Ran GTPase binding 24 bp Coding
Indel-046_R TTTGTGCTTTGGCACCATTA
Indel-047_F GCGCCTTTCTTTCACAACTC [1596] YABBy-like transcription factor 18 bp 5′ non-coding
Indel-047_R AACAAAGCTGTTCGGAAGGA
Indel-048_F CTCCACATTCTTATCCTCAGATCTG [3076] Omega-3 fatty acid desaturase 9 bp Coding
Indel-048_R CTCATTGACCTCCATGGATCC
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Figure 2.

Figure 2

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The fragments amplified by InDel-016 (above) and Indel-042 (bottom). The sequences (5′–3′) of Indel-016 primer are TCCTCATCAGGAACTGGGATA(F) and TGCAGCAATAGGACTTCTGG(R). For Indel-042 primer, the sequences (5′–3′) are GGGATTGAGCATGAACGATT(F) and GATAACAAATGGGGGCAAGA(R). 1-PI 152146; 2-PI 155107; 3-PI 157542; 4-PI 158854; 5-PI 159786; 6-PI 162655; 7-PI 162857; 8-PI 196622; 9-PI 196635; 10-PI 200441; 11-PI 240560; 12-PI 259617; 13-PI 259658; 14-PI 259836; 15-PI 259851; 16-PI 262038; 17-PI 268586.

Table 3.

Polymorphic information of 16 InDel markers among six botanical types of cultivated peanut.

Markers Distribution of polymorphic InDels marker PCR product PIC
Fastigiata hypogaea vulgaris peruviana hirsuta aequatoriana
InDel-03 √ 440 0.0169
InDel-04 √ √ √ 310 0.0830
InDel-05 √ √ √ 420 0.0666
InDel-07 √ 430 0.0169
InDel-011 √ 470 0.0169
InDel-016 √ √ √ √ √ 320 0.5288
InDel-017 √ √ √ √ 320 0.1151
InDel-018 √ √ √ √ √ 470 0.5960
InDel-020 √ √ 390 0.0336
InDel-029 √ √ 300 0.0336
InDel-030 √ √ √ 240 0.0502
InDel-032 √ √ 400 0.2232
InDel-033 √ √ 300 0.0336
InDel-039 √ √ 200 0.0666
InDel-042 √ √ 250 0.1467
InDel-046 √ √ √ 300 0.1310
Total 7 9 2 2 12 9
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Table 4.

Number of alleles, PIC of different botanical types based on the InDel markers.

Botanical type No. of accessions Alleles PIC
fastigiata 34 7 0.1763
hypogaea 55 9 0.1809
vulgaris 12 2 0.3056
peruviana 3 2 0.5556
hirsuta 7 12 0.6597
aequatoriana 7 9 0.5341
Total 118 16 0.1457
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The genetic relationships revealed by InDel markers among 6 botanical varieties

A neighbor-joining (NJ) algorithm method assigned the 118 accessions into four major basic groups and some small clusters. Cluster 1 consists of 51 accessions from G101 to G004 (Figure 3). This is a complex cluster, in which var. fastigiata; var. vulgaris; var. hypogaea var. peruviana were included. Cluster 2 has all 20 var. hypogaea accessions (from G005 to G103) plus two var. fastigiata G038 and G083. In cluster 3, eight of 10 accessions are var. hypogaea (G008 to G059). Cluster 4 contains 12 var. fastigiata accessions, 4 var. hypogaea accessions (G024, G060, G073, and G074), and 2 var. vulgaris accessions (G002 and G031). The rest of 15 accessions formed small clusters. They are mainly var. aequatoriana lines and var. hirsuta lines and have longest genetic distances to other 4 botanical varieties. The results from this analysis are consistent with the PIC values among different botanical varieties.

Figure 3.

Figure 3

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Dengrogram of 118 accessions of six botanical varieties of cultivated peanuts based on 16 polymorphism Indel makers with a neighbor-joining (NJ) algorithm method. Inline graphic - var. fastigiata, Inline graphic - var. vulgaris, Inline graphic -var. hypogaea, Inline graphic - var. aequatoriana, Inline graphic - var. hirsuta, Inline graphic - var. peruviana.

Marker–trait correlation

Five markers, InDel-016, InDel-018, InDel-032, InDel-042, and InDel-046, were identified by single marker analysis to be significantly correlated to tomato spotted wilt virus (TSWV) and leaf spot resistance. Among them, three markers (InDel-032, InDel-042, and InDel-046) were associated to both TSWV and leaf spot resistance, but InDel-018 and 046 were only for leaf spot (Table 4). These markers were designed from conserved sequences of functional genes that were associated with alkaline alpha galactosidase, arachin Ahy-3, electron transporter/metal ion, dihydroxy-acid dehydratase, and ran GTPase binding, respectively. InDel-018 and InDel-046 were from the coding region, while InDel-016, InDel-032, and InDel-042 were from non-coding region (Table 2).

In general, the accessions carrying the alleles of the markers had a low leaf spot rate or low percentages of TSWV incidents (Table 5). For example, 43 accessions with InDel-018 alleles had an average of 2.9 leaf spot rate while 75 accessions without the alleles had an average of 4.1 (Table 5). Similar results were observed for TSWV, in which the accessions carrying the alleles of InDel-032 showed a low disease incident (10.7%) compared to the accessions that are lacking of the alleles (46.1%) (Table 5).

Table 5.

Significance (P-value) of associations between the InDel makers and the targeted traits.

Marker Leaf spot TSWV
P-value Mean of rate Number of lines Genotype P-value Mean of rate Number of lines Genotype
InDel-016 0.0099 3.9 81 + − − − −
3.1 37 − − − −
InDel-018 < 0.0001 4.1 75 + − − − −
2.9 43 − − − −
InDel-032 < 0.0001 4.1 104 + < 0.0001 46.1% 104 +
0.28 14 − 10.7% 14 −
InDel-042 < 0.0001 4.0 109 + < 0.0001 44.5% 109 +
0 9 − 11.1% 9 −
InDel-046 < 0.0001 3.9 110 + 0.0053 43.5% 110 +
0.7 8 − 20% 8 −
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Discussion

Difference in genetic pattern or polymorphism is a main criterion to evaluate the potential functionality of DNA molecular markers. In the present study, the polymorphism of the InDel markers was 33.3%, which was higher than some markers that have been previously reported as to RAPD marker (6.6%) by Subramanian et al. (2000); AFLP marker (3.6%) by He and Prakash (1997); EST-SSR marker (10.4%) by Liang et al. (2009); SSR marker (14.5%) by Zhao et al. (2012) but was lower than Start Codon Targeted polymorphism (SCoT) marker (38.2%) as reported by Xiong et al. (2011) (Table 6). Among the reports, the numbers of accessions evaluated were much less than the 118 accessions used in this study. In general, the larger the number of accessions with diverse genetic background the higher the accuracy of estimated polymorphism associated with a particular trait. Therefore, our reported polymorphism for the InDel markers in this study can be useful in peanut breeding programs.

Table 6.

Comparisons of the polymorphism of various molecular markers developed in the previous reports.

Marker No. of markers tested Polymorphic markers Polymorphism rate (%) No. of accessions tested No. of botanical types References
RADP 408 27 6.6 70 4 Subramanian et al., 2000
AFLP 111 4 3.6 6 3 He and Prakash, 1997
EST-SSR 251 26 10.4 22 4 Liang et al., 2009
SSR 9274 1343 14.5 8 Var. Zhao et al., 2012
ScoT 157 60 38.2 20 4 Xiong et al., 2011
InDel 48 16 33.3 118 6 Present study
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Germplasm resources provide fundamental materials for peanut genetic improvement, and the study of genetic diversity on cultivated peanut will enhance the utilization of peanut genetic resources. Genetic diversity of six botanical types of cultivated peanuts has been extensively investigated using molecular markers. Based on SSR markers, Jiang et al. (2010) demonstrated that the accessions of fastigiata and hypogaea were more diversified than other botanical types. The genetic diversity of 72 accessions of the U.S. mini core was estimated using 67 SSR primer pairs and the results indicated that the PIC of SSR markers ranged from 0.063 to 0.918 and the gene diversity ranged from 0.027 to 0.50 (Kottapalli et al., 2007). In the present study, PICs varied from 0.176 for fastigiata var. to 0.660 for hirsuta var., and hirsuta var., peruviana var., and aequatoriana var. have higher genetic diversity than the other types, indicating that, like other molecular markers, InDel markers can be used for evaluation of genetic diversity for peanuts. Cluster analysis showed that hirsuta var. and aequatoriana var. have longest genetic distances from the other four types, indicating that hirsuta var. and aequatoriana var. have higher genetic diversity than the other types.

Unlike the QTL that using biparental RIL (Recombinant Inbred Lines) mapping populations to link markers with target traits, the identified marker trait association in present cannot validated in different backgrounds, but in our another apparel association mapping study we have extensively evaluated leaf spot and TSWV resistances for the U.S. mini-core collection and mapped three SSR markers named “pPGPseq2D12B,” “pPGSseq19B1,” and “TC04F12,” to be associated both with leaf spot and TSWV resistances. The marker “TC20B05” can explain 15% phenotypical variation of leaf spot resistance.

Regarding application of MAS in peanut, there are only two molecular markers currently being utilized in breeding programs: nematode resistance and high oleic seed chemistry. Chu et al. (2011) demonstrated that a tremendous reduction in the amount of time (at least 3-fold) for plant selection was achieved with MAS to pyramid nematode resistance with high oleic trait in peanut. This recent success is only possible due to the initial discovery of the genetic markers and the development of breeding lines. For example, the identification of high oleic marker was achieved by utilizing different genes in fatty acid biosynthesis for high oleic chemistry in other oil seed crops enabling a straightforward characterization in peanut and discovery of similar functional mutations in breeding populations (Jung et al., 2000; Lopez et al., 2002). Nematode resistance was introgressed from wild species (Simpson and Starr, 2001), and resistant plants were selected based on the availability of molecular markers at the time (Nagy et al., 2010). High Oleic trait resulted from the expression of two recessive genes (Lopez et al., 2001) while nematode resistance was determined to result from the expression of two dominant genes (Garcia et al., 1996). For other traits such as disease resistance or drought tolerance, complex interaction between genetic and environment poses daunting challenge to breeders to select resistant plants. Since InDel markers were developed from sequences of functional genes, they will lay the groundwork for the identification of genes related to superior agronomic traits, provide information on population genetic variations, and identify homologous genes for functional studies. Since InDel markers were found to be associated with leaf spot and TSWV resistance with a higher level of DNA polymorphism compared to other molecular markers, they provide a very useful type of molecular marker to identify other agronomical important traits in peanut.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We are indebted to Brian Gamble and Larry Wells for devoted assistance with management of field experiment research plots at the Wiregrass Research and Extension Center, Auburn University, Headland, Alabama. The contributions and assistances of Sam Hilton, Joseph Powell, Kathy Gray, Lori Riles, Dan Todd, Robin Barfield, Staci Ingram, and Bill Edwards from the USDA-ARS National Peanut Research Laboratory are gratefully acknowledged. The author, LL was sponsored by Grant of 948 project (2013-Z65) and The Excellent Going Abroad Experts Training Program in Hebei, China to conduct this research in Auburn University.

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