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. 2021 Sep 5;14(3):e20141. doi: 10.1002/tpg2.20141

Genetic diversity assessment of Georgia peanut cultivars developed during ninety years of breeding

Nino Brown 1,✉, William D Branch 1, Matthew Johnson 2, Jason Wallace 2
PMCID: PMC12807293  PMID: 34482640

Abstract

The cultivated peanut(Arachis hypogaea L.) has experienced severe genetic bottlenecks over the course of its evolution and domestication. Most genetic diversity studies in peanut have focused on global genetic stocks, wild accessions, and related species, but few have focused on elite cultivars. The objective of this project was to assess the genetic diversity of 32 peanut cultivars developed by the University of Georgia breeding program since its inception in 1931. Quantifying genetic similarity (GSIM) among these cultivars will provide a better understanding of their relationships and aid in breeding for cultivar development. Genotyping‐by‐sequencing (GBS), in concert with the recently published A. hypogaea genome sequence, was used to identify a total of 27,142 single nucleotide polymorphisms (SNPs) among these cultivars. Coefficients of parentage (CoP) were calculated based on publicly available pedigree information and compared with SNP‐based GSIM estimates; the resulting correlations were low, ranging from R 2 = 0.212–0.279. Although genetic diversity is generally low in cultivated peanut, our data indicate that the genetic diversity of Georgia cultivars has actually increased since the early days of the breeding program, likely a result of the incorporation of diverse germplasm and breeding lines into the program. The results reported here provide a valuable understanding of genetic variation among elite Georgia peanut cultivars that have had a significant impact on the peanut industry within the United States.

Core Ideas

  • Genetic diversity of peanut cultivars in the Georgia breeding program increased since its inception

  • GBS is an inexpensive but detailed method of evaluating GSIM of elite peanut cultivars

  • A valuable guide for peanut breeders interested in using Georgia cultivars as crossing parents

Abbreviations

CoP

coefficient of parentage

GBS

genotyping‐by‐sequencing

GSIM

genetic similarity

PI

plant introduction

SNP

single nucleotide polymorphism

SSR

simple‐sequence repeat

tGBS

tunable genotyping‐by‐sequencing.

1. INTRODUCTION

Cultivated peanut (Arachis hypogaea L.) is valued for its highly nutritious seed, which is used primarily for food and oil production. Growers planted over 576,000 ha of peanuts in the United States during 2019 (USDA–National Agricultural Statistics Service, 2020), nearly half of that (273,000 ha) in the state of Georgia, which provides a considerable portion of row‐crop income for the state. Of the four peanut market types (Virginia, runner, Spanish, and Valencia), runner‐type peanut cultivars are the leading market type grown in the United States. This is especially so in the lower southeastern states, which have a longer growing season and produce the majority of the U.S. crop. National average peanut yields have increased significantly from 854 kg ha−1 in 1938 to 4,409 kg ha−1 in 2019. These increases can be attributed to improvements in peanut genetics, cultural practices, irrigation technologies, and chemical control of biotic factors, among others. Improvement in yield and quality as a result of genetic improvement through traditional and, to a limited degree, molecular breeding have been an important component of increasing peanut yields. The University of Georgia peanut breeding program has been an ongoing effort since 1931 (Higgins & Bailey, 1955), with the University of Georgia, University of Florida, and North Carolina State University the three oldest peanut breeding programs in the country.

Cultivated peanut has experienced severe genetic bottlenecks over the course of its evolution and domestication. Husted (1936) postulated that cultivated peanuts are the product of a single hybridization of two diploid progenitors, with recent genome sequencing efforts pointing to the polyploidization event occurring between 10,000 and 470,000 yr ago, respectively (Bertioli et al., 2016; Zhuang et al., 2019). The newly formed self‐pollinating allotetraploid would have been reproductively isolated from its progenitor species (Kochert et al., 1996). Coupled with the limited range of seed dispersal inherent to peanut, this would have resulted in an initially very narrow genetic base and limited ability for diversification. Selection pressures applied through domestication would have further limited genetic diversity of early peanut landraces.

Prior to the development of molecular markers to calculate genetic similarity (GSIM), analysis of pedigrees was the best method for estimating kinship. Knauft and Gorbet (1989) calculated coefficients of parentage (CoP) for 41 peanut cultivars released in the years prior to their study. They found that a handful of parents—‘Dixie Giant’, ‘Small White Spanish‐1’, ‘Basse’, ‘Spanish 18‐38’, ‘Jenkins Jumbo’, and ‘Small White Spanish‐2’—occurred in pedigrees of modern peanut cultivars with frequencies of 1.00, 0.92, 0.75, 0.75, 0.33, and 0.17, respectively. Shortly after the effects of corn blight in the United States, Hammons (1976) noted that the peanut industry's tendency toward growing a single cultivar had severely limited the genetic diversity of the commercial peanut crop. Additionally, as a result of the market dominance of ‘Florunner’ (Norden et al., 1969), breeders used the genotype in a large number of pedigrees, reducing genetic diversity further (Knauft & Gorbet, 1989). The foundational pedigrees of current cultivars are quite similar; however, more recent breeding efforts have increased genetic diversity because of the necessity of developing disease‐resistant cultivars (Isleib et al., 2001). Much of this increased diversity has occurred from the incorporation of plant introductions (PIs) such as PI 221057 for Sclerotinia blight resistance, PI 203396 for resistance to tomato spotted wilt virus (Isleib et al., 2001), interspecific hybrids such as TxAG‐6 (Simpson et al., 1993) for root‐knot nematode resistance, and others. Within the University of Georgia breeding program, the introgression of multiple disease resistances from PI 203396 and PI 259785, by way of cultivar ‘C‐99R’ (Gorbet & Shokes, 2002), likely added a great deal of diversity to the program. C‐99R was an impactful cultivar for peanut improvement and has been used directly in the pedigrees of several commercially relevant cultivars including some developed in Georgia (Table 1). Additional germplasm lines with interspecific sources of disease resistance have been developed in recent years that will likely increase the diversity of peanut cultivars further.

TABLE 1.

Peanut cultivars developed at the University of Georgia since the program's inception in 1931 and their associated identification information and pedigrees

Cultivar Release year PI no. Market type Pedigree Reference
‘GFA Spanish’ 1938 634993 Spanish selection from Small Spanish (Higgins & Bailey, 1955)
‘VA Bunch 67’ 1945 565438 Runner selection from Virginia Bunch (Hammons, 1970a; Higgins & Bailey, 1955)
‘SE Runner 56‐15’ 1947 565439 Runner selection from Southeastern Runner (Hammons, 1970b; Higgins & Bailey, 1955)
‘Dixie Spanish’ 1950 – Spanish selection from Col. No. 146 (Higgins and Bailey, 1955)
‘VA Bunch G2’ 1952 – Virginia selection from Virginia Bunch (Higgins and Bailey, 1955)
‘VA Runner G26’ 1952 – Virginia selection from Virginia Runner G (Higgins and Bailey, 1955)
‘Georgia 119–20’ 1954 565440 Virginia Southeastern Runner//Dixie Giant, 210–4/Virginia Runner (Hammons, 1971; Higgins & Bailey, 1955)
‘Georgia Red’ 1986 508278 Valencia Florunner (comp. line)/New Mexico Valencia A (Branch & Hammons, 1987)
‘Georgia Runner’ 1990 542960 Runner Krinkle‐leaf/PI 331334 (Branch, 1991)
‘Georgia Browne’ 1993 574450 Runner Southern Runner/Sunbelt Runner (Branch, 1994)
‘Georgia Green’ 1995 587093 Runner Southern Runner/Sunbelt Runner (Branch, 1996)
‘Georgia Bold’ 1997 601980 Runner Southern Runner/Sunbelt Runner//Sunbelt Runner (Branch, 1998)
‘Georgia Hi‐O/L’ 1999 607836 Virginia GA‐C330A/GA‐T2636M (Branch, 2000)
‘Georgia Valencia’ 2000 617040 Valencia Georgia Red/UF85I79 (Branch, 2001)
‘Georgia‐01R’ 2001 629027 Runner PI 203395/Georgia Browne (Branch, 2002)
‘Georgia‐02C’ 2002 632380 Runner Southern Runner/GA‐T2636M (Branch, 2003)
‘Georgia‐03L’ 2003 634333 Runner Georgia Browne/VA‐C 92R (Branch, 2004)
‘Georgia‐04S’ 2004 636472 Runner Georgia Browne/UF 435‐OL‐2//Georgia Browne (Branch, 2005)
‘Georgia‐05E’ 2005 641767 Virginia Georgia‐01R/GA 942010 (Branch, 2006)
‘Georgia‐06G’ 2006 644220 Runner Georgia Green/C‐99R (Branch, 2007a)
‘Georgia Greener’ 2006 644219 Runner Georgia Green/C‐99R (Branch, 2007b)
‘Georgia‐07W’ 2007 652441 Runner C‐99R/Georgia Green (Branch & Brenneman, 2008)
‘Georgia‐08V’ 2008 655573 Virginia C‐99R/Georgia Hi‐O/L (Branch, 2009)
‘Georgia‐09B’ 2009 658595 Runner Georgia Green//Georgia Green/GA 942004 (Branch, 2010)
‘Georgia‐10T’ 2010 660315 Runner Georgia‐02C/Georgia‐01R (Branch & Culbreath, 2011)
‘Georgia‐11J’ 2011 664930 Virginia Georgia‐05E/Georgia‐03L (Branch, 2012)
‘Georgia‐12Y’ 2012 667552 Runner Georgia‐09B/Georganic (Branch, 2013)
‘Georgia‐13M’ 2013 671850 Runner Georgia‐02C/Georgia‐09B (Branch, 2014)
‘Georgia‐14N’ 2014 674169 Runner Georgia‐02C//Georgia‐01R/COAN (Branch & Brenneman, 2015)
‘Georgia‐16HO’ 2016 680617 Runner Georgia‐07W/Florida‐07 (Branch, 2017)
‘Georgia‐17SP’ 2017 687138 Runner Georgia‐02C//Georgia‐01R/COAN (Branch & Brenneman, 2018)
‘Georgia‐18RU’ 2018 690012 Runner Georgia‐10T/Georgia‐09B (Branch, 2019)
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Core Ideas

  • Genetic diversity of peanut cultivars in the Georgia breeding program increased since its inception

  • GBS is an inexpensive but detailed method of evaluating GSIM of elite peanut cultivars

  • A valuable guide for peanut breeders interested in using Georgia cultivars as crossing parents

Early efforts using molecular methods to estimate genetic diversity were characterized by limited polymorphism and therefore low resolution for distinguishing genotypes. Estimates based on restriction fragment length polymorphisms revealed little diversity; indeed, differences could only be resolved at the species level (Kochert et al., 1991). Other early genetic diversity work using technologies such as DNA amplification fingerprinting, amplified fragment length polymorphisms, random amplified polymorphic DNA, and simple‐sequence repeat (SSR) markers (Ferguson et al., 2004; He & Prakash, 1997; He et al., 2003; Raina et al., 2001) were able to resolve species, subspecies, and in some cases botanical varieties but were unable to meaningfully resolve cultivars. One of the first examples of grouping cultivars based on expectations of similarity according to their pedigrees used amplified fragment length polymorphism markers (Herselman, 2003). However, once a large number of polymorphic SSR markers were available to researchers, rapid progress and a greater degree of resolution was achieved, with researchers able to resolve at the cultivar level and quantify diversity more readily, often aligning with pedigree‐based expectations (Koppolu et al., 2010; Krishna et al., 2004; Li et al., 2011; Milla‐Lewis et al., 2010a; Milla‐Lewis et al., 2010b; Moretzsohn et al., 2004; Naito et al., 2008; Ren et al., 2014; Wang et al., 2016).

Very few detailed assessments have been made of peanut cultivar development breeding programs to date. Most studies have sought to elucidate genetic relationships among diverse collections of material such as wild species, landraces, and PIs (Ferguson et al., 2004; He & Prakash, 1997; He et al., 2003; Koppolu et al., 2010; Moretzsohn, et al., 2004; Raina, et al., 2001; Zheng et al., 2018). While understanding global germplasm diversity and interrelatedness is important for the broad peanut genetics community, a more focused examination of individual breeding programs would be of greater benefit to the cultivar development community and improve breeding efficiency within and among programs (Fu, 2015).

With the recent publication of the cultivated peanut genome, it is now possible to use next‐generation sequencing to genotype and identify a large number of reliable SNPs at relatively low cost. Genotyping‐by‐sequencing (GBS) is one such method that uses restriction enzymes to focus sequencing near restriction sites, thus increasing the number of reads per sequenced site compared with random sequencing. A refinement of the technique, tunable GBS (tGBS), uses an additional selective step that increases the number of on‐target reads, reducing the amount of missing data per marker and allowing for accurate genotyping of homozygous and heterozygous loci alike (Ott et al., 2017). It should be noted that one of the primary concerns with GBS is the relatively high rate of missing data; however, this can be overcome with sample replication, albeit at a higher price. Recently, an extensive genetic diversity study using tGBS‐based SNPs evaluated 320 peanut accessions from around the world and identified 37,128 polymorphic SNPs (Zheng et al., 2018). However, even among this set of presumably highly diverse genotypes and a large number of SNPs, the calculated genetic distance was surprisingly low.

Genetic diversity and interrelationships within a breeding program should be well understood in order to make crosses with the highest potential for genetic gain. Assessing these relationships within a breeding program is beneficial internally as well as for external users of the germplasm. The objective of this project was to assess the genetic diversity of cultivars released by the University of Georgia over the course of the institution's peanut breeding effort dating back to 1931 (Higgins & Bailey, 1955). Quantifying GSIMs among these cultivars will provide a better understanding of their kinship and aid in breeding decisions for future cultivar development.

2. METHODS

2.1. Plant material

A total of 32 cultivars, all released by the University of Georgia peanut breeding program since 1938, were chosen for this study (Table 1). Two of these cultivars are Spanish market types (A. hypogaea L. subsp. fastigiata Waldron var. vulgaris Harz), two are Valencia types [A. hypogaea L. subsp. fastigiata Waldron var. fastigiata (Waldron) Krapov. & W. C. Greg.], seven are Virginia types, and 21 are runner market types (A. hypogaea L. subsp. hypogaea var. hypogaea). These 32 cultivars represent a range of phenotypes for growth habit, maturity, testa (seed coat) color, seed characteristics, oleic acid chemistry, and disease resistance, among other important traits. For the purpose of brevity, when discussing cultivars in this trial (Table 1), the words southeastern (SE), and Virginia (VA) are abbreviated in cultivar names.

Seed increase plots were grown in 2018 at the University of Georgia's Gibbs Research Farm at Tifton, GA. Plots consisted of two rows spaced 0.8 m apart on beds 1.8 m wide and 6.1 m long. Seeding rate at planting was 19.7 seeds m−1. Leaf samples were collected from 12 individual plants per cultivar from each of the 32 cultivars for 384 total samples. Multiple individual plant samples were used to quantify variation both within and among cultivars. Leaf samples consisted of a single unfolded leaf from each plant that was lyophilized shortly after collection. Tissue samples were shipped to Data2Bio (Ames, IA) for DNA extraction and sequencing. DNA extraction was carried out using Qiagen's BioSPrint and MagAttract 96 DNA Plant Core Kit, with quantification achieved using PicoGreen on an Eppendorf PlateReader.

2.2. DNA analyses

Tunable GBS was carried out using restriction enzyme Bsp 1286I following the method outlined in Ott et al. (2017). The resulting library was sequenced on five lanes of an Illumina HiSeq X, yielding a total of 1.7 × 109 reads, with an average of 3.6 × 106 reads per sample. After demultiplexing, reads were quality trimmed to remove low‐quality sequence data. Low‐quality sequence was defined as having PHRED quality score ≤15, which corresponds to an error rate of ≤3%. The trimming parameters were entered into trimming software Lucy (Chou & Holmes, 2001; Li & Chou, 2004). Quality‐trimmed sequences were aligned to the recently published A. hypogaea genome sequence (Bertioli et al., 2019) hosted on the PeanutBase website (Sudhansu et al., 2016) using GSNAP (Wu & Nacu, 2010). Sequence reads with a single, unique alignment to the A. hypogaea genome, and at most a single polymorphic site compared with the published genome sequence, were used for analysis.

The SNPs were filtered using TASSEL V5.2.29 (Bradbury et al., 2007) and custom Python scripts to remove low‐quality calls with >50% missing data, minor allele frequency <1%, or >10% heterozygosity from the raw SNP data set (the heterozygosity filter removes potentially false SNPs because of misalignment of reads). Filtering reduced total missing data to ∼30% for all samples. Individual plant data were compared within cultivars to identify off‐types such as outcrosses or seed mixtures; plants that had very different GSIM values or did not cluster with the rest of their cultivar in the dendrogram were designated as off‐types. Of the 384 samples submitted for tGBS, 363 were successfully genotyped; of those, 29 were designated as potential off‐types and excluded from further analysis.

An unrooted phylogenetic tree was generated using the UPGMA method in TASSEL V5.2.29 and visualized with Archeopteryx, an improved version of phyloXML (Han & Zmasek, 2009). Principle component analysis was conducted with TASSEL using multidimensional scaling of the genetic marker data and visualized using JMP V.14 (SAS Institute Inc.). The SNP density plot was generated using CMplot and marker positional data. Coefficient of parentage, a pedigree‐based estimate of genetic relatedness and probability of identity by descent, was calculated using winKIN, an updated version of KIN (Tinker & Mather, 1993) obtained from the author. Assumptions used in computing CoP followed those described by St. Martin (1982) and Tinker and Mather (1993); briefly, that (a) each progeny received half of its genes equally from each parent, (b) ancestors and parents were homozygous and homogeneous, (c) directional selection during selfing generations had limited effect on overall random inheritance at the majority of loci, and (d) ancestors or breeding lines for which pedigree information was unavailable were considered unrelated to each other.

3. RESULTS

After filtering low‐quality SNP calls, a total of 37,036 SNPs were identified, over 98% of which (36,453) aligned to the 20 chromosomes of the recently published A. hypogaea genome (Bertioli et al., 2019), while the others aligned to unassigned scaffolds. Only 27,142 of the identified SNPs were polymorphic among this set of cultivars and were subsequently used to characterize genetic diversity. Distribution of SNPs averaged 1,823 SNPs per chromosome with average distance between polymorphic loci of 101.3 kb. The minimum number of SNPs identified was 394 SNPs on chromosome 8 (A08, the smallest) with average distance of 168.9 kb between loci. The highest number of polymorphic loci were identified on chromosome 9 (A09) with 3,416 SNPs, averaging 50.5 kb between markers (Figure 1). Plants with poor sequencing results (missing data ≥90%), GSIM values outside of normal ranges, or widely different according to the UPGMA phylogenetic tree were excluded from analysis.

FIGURE 1.

FIGURE 1

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Single nucleotide polymorphisms (SNP) identified using tunable genotyping‐by‐sequencing on a panel of 32 peanut cultivars released by the University of Georgia. Horizontal grey bars represent the 20 chromosomes of the cultivated peanut (Arachis hypogaea L.) genome. Colors at bottom right represent the density and distribution of the >27,000 SNPs identified among these 32 cultivars

3.1. tGBS‐based genetic similarity

A limited amount of variation was seen among individual plants of the same cultivar. Average within‐cultivar GSIM values ranged from 94.6 to 99.9% (Table 2; Figure 2a), indicating a high level of genetic purity of the samples. Relative to the total number of SNP loci, there were few loci polymorphic within cultivars, averaging 572 loci per cultivar (2.1% of total SNPs) (Table 2). Because of the high level of uniformity, we used the most common allele at each locus to create a single composite genotype for each cultivar.

TABLE 2.

Average within‐cultivar genetic similarity values of Georgia peanut cultivars, number (n) of individual plants successfully genotyped per cultivar, and number of within‐cultivar polymorphic loci

Within‐cultivar polymorphic loci
Entry Cultivar n Genetic similarity, mean (SD) Min. Max. Count
1 GFA Spanish 8 0.9994 (0.0005) 0.9983 0.9999 96
2 VA Bunch 67 12 0.9999 (0.0001) 0.9996 1.0000 18
3 SE Runner 56‐15 12 0.9922 (0.0067) 0.9842 1.0000 471
4 Dixie Spanish 6 0.9993 (0.0008) 0.9978 1.0000 83
5 VA Bunch G2 11 0.9997 (0.0002) 0.9991 1.0000 26
6 VA Runner G26 11 0.9999 (0.0001) 0.9995 1.0000 10
7 Georgia 119–20 8 0.9995 (0.0003) 0.9991 1.0000 41
8 Georgia Red 10 0.9988 (0.0010) 0.9969 1.0000 162
9 Georgia Runner 10 0.9998 (0.0002) 0.9991 1.0000 25
10 Georgia Browne 9 0.9968 (0.0031) 0.9912 1.0000 206
11 Georgia Green 12 0.9988 (0.0007) 0.9974 1.0000 116
12 Georgia Bold 12 0.9926 (0.0053) 0.9857 1.0000 536
13 Georgia Hi‐O/L 9 0.9891 (0.0156) 0.9524 1.0000 109
14 Georgia Valencia 9 0.9460 (0.0730) 0.8013 0.9999 4,535
15 Georgia‐01R 10 0.9820 (0.0150) 0.9495 1.0000 1,140
16 Georgia‐02C 12 0.9964 (0.0030) 0.9923 1.0000 289
17 Georgia‐03L 10 0.9976 (0.0012) 0.9949 1.0000 131
18 Georgia‐04S 12 0.9934 (0.0076) 0.9818 1.0000 571
19 Georgia‐05E 12 0.9966 (0.0034) 0.9900 1.0000 337
20 Georgia‐06G 10 0.9960 (0.0029) 0.9920 1.0000 253
21 Georgia Greener 6 0.9900 (0.0090) 0.9758 0.9998 924
22 Georgia‐07W 10 0.9815 (0.0160) 0.9472 1.0000 1,510
23 Georgia‐08V 12 0.9773 (0.0210) 0.9408 0.9997 2,110
24 Georgia‐09B 11 0.9967 (0.0041) 0.9900 0.9999 354
25 Georgia‐10T 9 0.9931 (0.0069) 0.9752 0.9998 676
26 Georgia‐11J 12 0.9996 (0.0002) 0.9990 1.0000 45
27 Georgia‐12Y 12 0.9997 (0.0003) 0.9990 1.0000 33
28 Georgia‐13M 12 0.9899 (0.0072) 0.9754 1.0000 871
29 Georgia‐14N 10 0.9912 (0.0117) 0.9633 0.9995 815
30 Georgia‐16HO 12 0.9949 (0.0050) 0.9875 0.9999 369
31 Georgia‐17SP 12 0.9897 (0.0073) 0.9696 1.0000 765
32 Georgia‐18RU 11 0.9896 (0.0065) 0.9809 0.9998 710
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FIGURE 2.

FIGURE 2

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Genetic similarity distributions. (a) Distribution of pairwise comparisons for individual plants within each of 32 peanut cultivars released by the University of Georgia. (b) Distribution of pairwise comparisons among the 32 peanut cultivars released by the University of Georgia

Entries from A. hypogaea, ssp. fastigiata (Spanish and Valencia types) formed a distinct outgroup from the ssp. hypogaea (runner and Virginia types) cultivars (Figures 3 & 4). Within the fastigiata group, the two Spanish types (var. vulgaris), ‘Dixie Spanish’ and ‘GFA Spanish’ (C.1, Figure 3), formed a distinct and distant group from the two Valencia market type (var. fastigiata) cultivars, Georgia Red and Georgia Valencia (C.2, Figure 3). Each pairwise comparison of GSIM for the cultivars in this study can be found in Figure 5.

FIGURE 3.

FIGURE 3

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Unrooted phylogenetic tree of 32 peanut (Arachis hypogaea L.) cultivars released by the University of Georgia since 1931. Cluster C.1 represents Spanish market type cultivars (ssp. fastigiata var. vulgaris) (red), C.2 represents Valencia market type cultivars (ssp. fastigiata var. fastigiata) (green), and the remaining cultivars, including those from clusters C.3–C.6, represent runner‐ and Virginia‐type cultivars (ssp. hypogaea var. hypogaea) (blue and noncircled). Clusters C.1 and C.3 contain cultivars released prior to 1980

FIGURE 4.

FIGURE 4

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Pedigree‐based genetic similarity estimate (coefficient of parentage [CoP]) compared with tunable genotyping‐by‐sequencing‐based genetic similarity. (left) All comparisons. (right) Only A. hypogaea, ssp. hypogaea cultivars where CoP > 0

FIGURE 5.

FIGURE 5

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Genetic similarity (GSIM) matrix for 32 peanut cultivars released by the University of Georgia based on >27,000 polymorphic single nucleotide polymorphisms (SNPs) identified using tunable genotyping‐by‐sequencing. Cultivars are grouped by clusters revealed in Figure 4. Average GSIM across subspecies and compared with entries within subspecies hypogaea are summarized at bottom

The ssp. hypogaea var. hypogaea group, containing 28 of the 32 entries, was a relatively diverse group. The most distinct ssp. hypogaea cultivar was ‘Georgia Hi‐O/L’, a Virginia market type with 76.8% GSIM vs. all other entries and 79.7% vs. entries within ssp. hypogaea.

Cultivars released prior to 1986, which includes ‘SE Runner 56‐15’, ‘VA Bunch 67’, ‘VA Bunch G2’, ‘VA Runner G26’, and ‘Georgia 119‐20’, clustered closely together (C.3, Figure 3). Genetic similarity within this group was high, ranging from 97.4 to 98.9%. These obsolete cultivars formed a distinct outgroup from the bulk of the ssp. hypogaea entries, exhibiting only 85.2–85.5% average GSIM with other members of the ssp. hypogaea.

The four modern Virginia types, ‘Georgia‐Hi‐O/L’, ‘Georgia‐05E’, ‘Georgia‐08V’, and ‘Georgia‐11J’, stand apart from the other ssp. hypogaea cultivars in that they each form an individual branch from the main group and do not cluster closely with the runner types or each other (Figure 3). The level of differentiation seen here points to the distinctiveness of these Virginia market‐type cultivars compared with the other ssp. hypogaea cultivars, with average GSIM estimates ranging from 79.7 to 85.3%.

Three large clusters were formed by the remaining cultivars, all of which were runner types (C.4, C.5, C.6 in Figure 3). ‘Georgia Runner’, ‘Georgia‐02C’, ‘Georgia‐04S’, ‘Georgia‐14N’, and ‘Georgia‐17SP’ form cluster C.4. Genetic similarity within this cluster ranged 82.3 to 93.6%. Average GSIM of runner types within C.4 to other cultivars of the ssp. hypogaea group ranged from 83.3 to 84.6%. The second cluster of modern runners, C.5, includes ‘Georgia‐01R’, ‘Georgia‐06G’, ‘Georgia Greener’, ‘Georgia‐07W’, ‘Georgia‐10T’, ‘Georgia‐12Y’, ‘Georgia‐13M’, and ‘Georgia‐16HO’. Genetic similarity within the C.5 group of cultivars ranged from 85.7 to 97.8%. Average GSIM of these cultivars with other cultivars from the ssp. hypogaea group was 83.6–87.8%. The final group of runners, C.6, included ‘Georgia Browne’, ‘Georgia Green’, ‘Georgia Bold’, ‘Georgia‐03L’, ‘Georgia‐09B’, and ‘Georgia‐18RU’, with average GSIM within this subset of lines ranging from 87.0 to 97.6%. Average GSIM of these cultivars with the other cultivars of the ssp. hypogaea group was 86.7–88.1%.

3.2. Pedigree‐based genetic similarity

Coefficients of parentage were calculated for each pairwise comparison of cultivars (Supplemental Figure S1) using publicly available pedigree information (Table 1). Members of the ssp. fastigiata had CoP ranging from 0.000 to 0.500 within the group and average CoP of 0.0–0.125 with the cultivars from ssp. hypogaea in the study. Average CoP of cultivars from C.3 of the ssp. hypogaea group ranged from 0.0 to 1.0, C.4 ranged from 0.000 to 0.602, C.5 ranged from 0.080 to 0.625, and C.6 ranged from 0.266 to 0.750. Values of CoP were regressed on the corresponding tGBS‐based measurement of GSIM. Although the correlation was statistically significant (p < .0001), CoP did not explain a large portion of the variation seen in GSIM (R 2 = 0.212) (Figure 4). Because some pedigrees are unable to trace lineages back to a common ancestor, CoP values often reach zero, which is biologically unreasonable for two genotypes of the same species. Removing pairwise comparisons where CoP = 0 did not improve correlation with GSIM (R 2 = 0.195). Further focusing only on the ssp. hypogaea cultivars with CoP > 0 slightly improved the correlation, though it was still weak (R 2 = 0.279).

4. DISCUSSION

Pairwise comparison of GSIM for each entry (Figure 2b) was generally <95%, indicating that tGBS was successful at differentiating among closely related cultivars and could potentially be used for identifying plants of unknown origin. The Georgia Valencia samples were the most variable, with 94.6% GSIM within the cultivar and 4,535 polymorphic loci. This relatively high level of variability could have been due to outcrossing, seed mixture, or higher nascent variability within the cultivar at the time of release. However, no phenotypic variation has been noted within this cultivar, and without sampling additional seed sources, it is difficult to define the cause of this variability.

The genetic relationships revealed by this tGBS‐based assessment were mostly predictable in that cultivars with pedigrees indicating a high level of relatedness tended to group closely together. For instance, Georgia‐09B is a backcross progeny of Georgia Green (Table 1), so it is not surprising that these two cultivars are genetically similar (GSIM = 97.3%, CoP = 0.750) (Figure 5; Supplemental Figure S!). Georgia Bold, Georgia Browne, and Georgia Green were derived from crosses involving ‘Southern Runner’ and ‘Sunbelt Runner’ and so also showed high levels of similarity to each other (GSIM = 92.1–97.6%, CoP = 0.643).

The three cultivars, Georgia‐06G, its full‐sib Georgia Greener, and its reciprocal cross sib Georgia‐07W all have CoP = 0.500 (Supplemental Figure S1), group closely together in the dendrogram (Figure 3), and share a high level of GSIM (95.5–96.9%) (Figure 5). While these cultivars cluster closely with each other, they do not cluster closely with their common parent Georgia Green, having GSIM of only 91.5–93.2% (Figure 5). The other parent in these crosses was C‐99R, which was not included in this diversity panel.

Cultivars resulting from single crosses often resembled one parent more than the other rather than an even representation of both parents. For instance, Georgia‐10T is more similar to Georgia‐01R (GSIM = 95.8%) than Georgia‐02C (GSIM = 80.2%), and Georgia‐18RU is more similar to parent Georgia‐09B (GSIM = 95.0%) than Georgia‐10T (GSIM = 89.1%). Georgia‐02C, resulting from a cross between Southern Runner and GA‐T2636M (an unreleased, irradiated version of Georgia Runner), showed a high level of GSIM with Georgia Runner (93.6%). This suggests that selection favored alleles from one parent over the other, which violates two of the assumptions mentioned earlier regarding CoP estimates based on pedigrees (St. Martin, 1982; Tinker & Mather, 1993). It is also possible that some of these cultivars could have been the product of unintentional self‐pollination; however, it is unlikely given that F1 individual plants and segregating populations were evaluated for hybridity based on phenotypic characteristics.

Some relationships elucidated by this work were unexpected based upon pedigree information. For example, Georgia‐04S is the result of crossing Georgia Browne with high‐oleic breeding line UF 435‐OL‐2 followed by a backcross to Georgia Browne (Table 1). Interestingly, Georgia‐04S was more similar to Georgia‐02C and Georgia Runner than Georgia Browne, its recurrent parent. Neither Georgia‐02C or Georgia Runner appear in the pedigree of Georgia‐04S. It is possible that selection for high‐oleic acid concentration could have resulted in an outsized influence of the UF 435‐OL‐2 genome in the segregating population. Alternatively, it is possible that an outcross or seed mixture occurred somewhere in development that lead to the superior plant from which Georgia‐04S was ultimately derived. Unfortunately, because UF 435‐OL‐2 was not included in this dataset, it is difficult to explain the genetic distance of Georgia‐04S and recurrent parent Georgia Browne. Georgia‐12Y and Georgia‐13M are half‐sibs that share parentage only with Georgia‐09B but are surprisingly similar to each other (GSIM = 92.2%). Another interesting result was seen in Georgia‐13M, which resulted from a cross between Georgia‐02C and Georgia‐09B but does not group with either parent (Figure 3). Georgia‐09B is represented in C.6 and Georgia‐02C in C.4, whereas Georgia‐13M is found in C.5. Full sibs Georgia‐14N and Georgia‐17SP resulted from the cross Georgia‐02C//Georgia‐01R/‘COAN’ (Simpson & Starr, 2001). These two full sibs resemble parent Georgia‐02C (GSIM = 85.4 and 88.2%, respectively); however, they show a clear differentiation from the entries within C.4, likely a result of the influence of parent COAN, which is a cultivar with root knot nematode resistance introgressed from TxAG‐6, a trispecies interspecific hybrid. At best, results such as these point to the importance of direct measurement of GSIM. At worst, they indicate potential errors in pedigree data.

The cultivars in C.3 (Figure 3) include those that were developed by recurrent selection from landraces prior to wide‐scale hybridization‐based plant breeding. This is evident in the high level of GSIM seen among SE Runner 56‐15, Georgia 119‐20, VA Runner G26, VA Bunch G2, and VA Bunch 67, which ranged from 97.4 to 98.9%. Only Georgia 119‐20 is the product of a hybridization; the others are recurrent or pure‐line selections from landraces. The remaining ssp. hypogaea cultivars are dramatically different from that early group of genetically isolated cultivars, indicating a substantial level of diversification that has occurred over the course of the Georgia breeding program. Milla‐Lewis et al. (2010b) documented an increase in genetic diversity of runner peanut cultivars released after the 1980s, with lines developed by Georgia and Florida exhibiting the highest level of within‐program genetic diversity. The increased diversity after 1980 was attributed to the use of several PIs and diverse germplasm, including interspecific hybrids, to create elite cultivars. This explanation was further supported by Isleib et al. (2001), and more recently by an extensive international diversity assessment using SSR markers (Wang et al., 2016). This paper does not attempt to explain the source of the increased diversity within the Georgia breeding program because many of the parents that likely contributed to that diversity were not included in the analysis. It is, however, undeniable that diversification has occurred over time especially as compared with the earliest cultivars released by the program, and this diversification likely is due to the diverse parents used in these cultivars’ development (Table 1).

Interestingly, cases were identified that supported the assumptions made when using CoP as a measure of GSIM, and some relationships refuted the assumptions (St. Martin, 1982; Tinker & Mather, 1993). Today, with genotyping technologies that are accurate, rapid, and fairly inexpensive, it is feasible even for smaller breeding programs to use genotypic data for evaluating GSIM rather than pedigrees alone. Public research budgets are not likely to increase substantially in the near future, therefore it is incumbent upon public sector plant breeders to maximize the impact of their limited resources. Breeding programs are expensive, especially for peanut, which is a very labor‐intensive crop to harvest and process. Additionally, field space is often limited and requires a great deal of management to maintain a high level of plot quality for breeding research. Given that plots are expensive to plant, manage, harvest, dry, shell, grade, and store, peanut breeding programs must minimize the number of crosses with low potential. One method for achieving this is to use genomic information in conjunction with phenotype data to make better‐informed crossing parent choices. The current state‐of‐the‐art in this direction is genomic selection, where statistical models predict phenotypes of new individuals based on their genotype using phenotypic data from previously studied plants. These methods require a significant investment in expertise and material infrastructure, however, which is not achievable for some small breeding programs. In this case, accurate assessments of GSIM among available breeding materials can be used to make better‐informed crossing decisions to maximize genetic variance in breeding populations.

Low levels of genetic diversity limit potential genetic gain. In the case of cultivated peanut, the very recent polyploidization and domestication of the crop have caused a severe genetic bottleneck. However, genetic diversity of peanut cultivars has increased over the course of modern plant breeding efforts, likely a result of PIs and incorporation of novel germplasm. This is supported by our results as well as other similar studies (Milla‐Lewis et al., 2010a; Milla‐Lewis et al., 2010b; Wang et al., 2016). We are not suggesting that additional efforts to increase diversity are unnecessary or unwarranted; on the contrary, our data indicate that part of the success of modern peanut cultivars has likely been due to incorporating diverse materials such as PIs and outside germplasm. Bringing in more genetic diversity to peanut cultivar breeding programs will likely continue this pattern of improved disease resistance and continue to improve peanut yields. Using modern tools to understand both new and existing genetic variability will contribute to maintaining consistent genetic gain throughout the 21st Century.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

Supporting information

Supplemental Figure S1. Coefficient of parentage matrix for 32 peanut cultivars released by the University of Georgia based on publicly available pedigree information, calculated using the KIN software package.

TPG2-14-e20141-s001.pdf (261.3KB, pdf)

ACKNOWLEDGMENTS

The authors would like to thank the University of Georgia Research Foundation, Georgia Peanut Commission, and Georgia Seed Development for partial funding of this research project and the Peanut Breeding Program at the University of Georgia.

Brown N, Branch WD, Johnson M, Wallace J. Genetic diversity assessment of Georgia peanut cultivars developed during ninety years of breeding. Plant Genome. 2021;14:e20141. 10.1002/tpg2.20141

Assigned to Associate Editor Xiaoping Chen.

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Associated Data

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

Supplementary Materials

Supplemental Figure S1. Coefficient of parentage matrix for 32 peanut cultivars released by the University of Georgia based on publicly available pedigree information, calculated using the KIN software package.

TPG2-14-e20141-s001.pdf (261.3KB, pdf)

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