Peanut Genome Project 2015-2016 Research …...This report is the 4 th edition of annual research accomplishments from the 5-year The Peanut Genome Project. Achievements in 2015-2016

Peanut Genome Initiative

Peanut Genome Project

2015-2016 Research Accomplishment Report to the

U.S. Peanut Industry

July 31, 2016 V14

2015-2016 Peanut Genomic Research Accomplishments

July 31, 2016 version-14 Page 2

Peanut Genome Project Research Accomplishment Report to the U.S. Peanut Industry

July, 2016

Table of Contents Executive Summary 3 Introduction 5 Research Accomplishment Summaries Key Accomplishments by Program Area 6 Key Accomplishments by Project Component 8 Detailed Description of Research Accomplishments Component 1: Sequencing and Assembly of Peanut Genomes 10 Component 2: Developing Maps and Markers for finding QTL 15 Component 3: Developing Markers for Specific Genes in QTL 18 Component 4: Evaluating New Sequencing and Assembly Technologies 21 Component 5: Identifying Breeding Lines with QTL for Key Traits 23 Component 6: Creating On-line Tools for Genome Assisted Breeding 26 Appendices:

Exhibit 1: Research publications by PGC members relevant to PGP 31 Exhibit 2: Who has invested in this project? 42 Exhibit 3: Members of the Peanut Genome Consortium 43 Exhibit 4: Glossary of Terms and Definitions 44



Executive Summary THE INDUSTRY CHALLENGE: “One of the biggest challenges for the U.S. peanut industry is the ability to compete with other crops for production. Most growers today are focused naturally on yield and production costs. In this Peanut Genome Project (PGP), peanut varieties with improved yielding ability and enhanced disease resistance are being achieved through marker-assisted breeding without using GMO technology. The industry is committed to peanut consumption growth. As consumption grows, we must grow peanut yield potential to sustain our industry. The Peanut Genome Project is the key to a sustainable future for peanuts”-Dr. Darlene Cowart. This report is the 4th edition of annual research accomplishments from the 5-year The Peanut Genome Project. Achievements in 2015-2016 are documented in technical detail within the main body of the full report. Highlights are summarized here. The PGP is one of the best crop genome projects to date based on: 1) degree of difficulty in comparison to other legumes such as soybean; 2) the extraordinary quality of genome maps and gene markers; and 3) the sophistication of on-line databases that make genomics useful in peanut breeding programs. The range of tangible deliverables from scientific publications to new varieties developed by marker-assisted-selection (MAS) also distinguish PGP from other crop genomic programs. • This year the paper on genome sequences in the ancestral parents of cultivated peanut was chosen

as a classic feature article in Nature Genetics • Since 2012, PGP scientists and their associates have published at least 193 relevant papers • Since 2014 PGP breeders have released at least 8 registered commercial varieties including Ole,

Georgia 14N and TifNV-HiO/L which were developed utilizing genetic markers; • New genes and traits are being moved into cultivated germplasm from the USDA collection of wild

peanuts; and • Over 117 accessions have been added to the USDA Peanut Germplasm Collection for breeders use Breeding programs have always been the heart of the PGP. Genome sequence driven MAS technology enables breeders to ‘maximize yield while minimizing inputs’ in a timely manner. This feature makes the PGP special, and inspires a great sense of pride among the PGP workforce as evidenced in a recent statement by Dr. Steven Cannon, Director, Federated Plant Database Initiative for Legumes: “I have been impressed throughout the PGP at the way that researchers have rallied and worked to deliver as much value as possible. I think a key to their success is that the researchers have respected the great effort and good faith involved in fundraising from the growers and others in the peanut industry. The research has been very collaborative, creative, and results-oriented; and has uncovered both results with immediate practical value for breeding programs, as well as results that lay strong foundations for further peanut breeding and research in the long term.” Revealing the genetic secrets of peanuts depends on a steady progression of more informative clues (markers). Think of the evolution of television technology. First there was CRT (BW then Color), next came LCD/Plasma, followed by HD/UHD; the point is, they are all televisions, and each new model gave a better picture. It is the same in genomics: the progression from crude (general vicinity) markers to high-definition markers that pinpoint the exact location of a gene by its exclusive fingerprint. High-definition markers are being used to make it easier to move genes from wild to cultivated peanuts. • PGP scientists used PeanutBase and high-definition markers to transfer root-knot-nematode (RKN)

and other disease resistance genes from wild crosses to cultivated peanut • New high-definition markers from wild species were validated and used to select the trait in

cultivated germplasm



• A systematic way was created to transfer many other disease resistance genes from at least 7 wild peanut populations that are cross compatible with cultivated peanut

As Dr. Scott Jackson, University of Georgia, and chair of the project technical team remarked, “Study of the peanut genome makes a great detective story, many clues are found and linked together to unlock great mysteries of genetics and gene regulation. This is exciting work. We will find answers that help increase peanut productivity, improve crop protection and enhance product quality in a timely manner.” One of the mysteries that came to light in 2015 has to do with how DNA from the two genomes in cultivated peanut (A & B) mix when breeders make a cross. It’s one of the most incredible things learned from study of the peanut genome, so far. It’s totally unexpected, there’s no idea of why or how it occurs; but it explains why it’s so hard to select certain traits. What happens is this. When a cross is made, the A-genes in parent-1 should mix with A-genes in parent-2. Likewise for B-genes. However, peanut A-genes from parent-1 often replace B-genes in parent-2. This causes holes in A-chromosomes (sometimes displacing the key gene), and that results in some really weird inheritance patterns. • This unexpected event was found to have occurred at 3700 places in the cultivated peanut genome • This phenomenon makes it harder to transfer some traits like leaf spot (LS) resistance from Florida

SPT06-06 to hybrids, and also know that this problem can be overcome with high-definition LS markers to find the few hybrid progeny that have the trait

Drs. Corley Holbrook, Tom Isleib, Mark Burow, Barry Tillman, Peggy Ozias-Akins and many others have created a massive pipeline for development of high-definition markers for disease resistance and other traits in cultivated peanuts. Their work is based on the response of the markers in resistant and susceptible lines from at least 20 highly inbred populations. This year the team added validated high-definition markers for early leaf spot (ELS), late leaf spot (LLS), tomato spotted wilt virus (TSWV), RKN, yield and maturity to PeanuBase. It is just the tip of the iceberg. PeanutBase, the on-line Breeders Toolbox, presents worksheets that help breeders design populations for marker-assisted-selection. PeanutBase is the most useful and user-friendly genomic databases ever created. Last year over 7100 users (40% for the first time) accessed PeanutBase. New features include: • A browser that locates genes for a trait, and shows when the genes are active during plant growth • 25 interactive genetic maps annotated with 250 newly validated genes • Over 2000 images of accessions in the peanut germplasm collection plus links to request seed PeanutBase is still under construction, but already is a National treasure that helps breeders use genomic tools to improve peanut productivity and quality via marker-assisted-selection. The address is: http://www.PeanutBase.org/. Take a look, and see what’s there. One more highlight suggests that the PGP has established a launch pad for many future deliverables. High-definition markers are being developed for specific networks of genes. An example showed when genes governing pod and seed traits were active during seed development in Tifrunner and NC3033. A network of genes was tagged for traits that define the period of seed filling and maturation. Dr. Peggy Ozias-Akins, University of Georgia, says, “There are not many methods that help producers determine when a crop is mature. Monitoring the genes that are active at the start of kernel maturation could lead to development of DNA markers that can help improve the selection of early maturing varieties”. Sequencing the reference cultivated peanut genome is 95% complete, and final assembly of a working cultivated peanut genome will be finished within the next year. The workforce getting the job done is led by Drs. Scott Jackson, Peggy Ozias-Akins and Corley Holbrook. PGP members are world class: at the University of Georgia, Texas A&M, NC State University, University of Florida, Auburn University, University of California-Davis; at 7 USDA-ARS locations in GA, MS, IA, NC and OK; with collaborators in India, West Central Africa, Brazil, China, Argentina, Australia, Israel and Japan.

http://www.peanutbase.org/



Peanut Genome Project Research Technical Accomplishment Report to the U.S. Peanut Industry

July, 2016 Introduction Scott Jackson from the University of Georgia and Chair of the Peanut Genome Consortium (PGC) says, “Study of peanut genome structure and order makes a great detective story, where many clues are found and linked together to unlock great mysteries of genetics and gene regulation. This is exciting work”. The high-quality assembly of peanut chromosome structure in the wild species that gave rise to cultivated peanut is an extraordinary resource. It is a major step toward tackling the challenges presented by the size and complexity of cultivated peanut genome which is very large, twice the size of soybean and equal in size to the human genome. Great size makes the puzzle harder to solve. In addition, the peanut genome contains large sections of DNA in which nucleotide sequences repeat themselves many times. These ‘repeating elements’ are like spacers between gene-rich regions of the genome, but when broken into short fragments during sequencing pose problems in fitting the correct order and length when assembling a chromosome. Putting all the pieces together again in the right order requires an elegant strategy, and a team of world-class experts in genomics and peanut biology. Completion of the two wild species genome assemblies in such a short time is evidence that the best and brightest people are working on this project. Scientists working on the peanut genome project bring a great deal of experience from other crop genome sequencing projects, and are among the best in the world, with research partners from the U.S., China, Japan, Brazil, Argentina, Australia, India, Israel, and several countries in Africa. The U.S. is represented by scientists at University of California-Davis; the University of Georgia at Athens and Tifton, Texas A&M University; USDA ARS at Tifton GA, Griffin GA, Stillwater OK, Ames IA and Stoneville MS; NC State University, Auburn University, University of Florida at Marianna; and NCGR at Santa Fe NM. Each U.S. scientist and their international collaborators have a very specific role within the PGP Action Plan. The research contributions of each PGP member are vital to the overall mission of developing useful genetic tools that will accelerate the breeding programs for traits such as disease resistance and drought tolerance; traits that are difficult to achieve with conventional breeding strategies. PGP members are pioneers, clearing new ground with each deliberate step. This report chronicles individual responsibilities, the current state of the genome, and the strategies to move toward completion of the cultivated peanut genome sequence. Appendices: A list of peer-reviewed research publications by PGC members that are relevant to the PGP is shown in Exhibit 1; a list of sponsors who provide financial support for the PGP is presented in Exhibit 2; Peanut Genome Consortium members are listed in Exhibit 3; a glossary of ‘genomic’ terms & definitions is presented in Exhibit 4.



Research Accomplishment Summaries Key Research Accomplishments by Program Area. The Peanut Genome Project generates research along three program areas or avenues of investigation: 1) Generating Detailed Maps of the peanut genome structure, 2) development of Tools for Marker Assisted Selection, and 3) Application of Markers and Maps in Breeding programs. The following highlights research accomplishments by area; a more detailed account is presented in the full report. Generating Detailed Maps of Genome Structure. PGP research indicates that the genomes of two wild peanut species (A. duranensis and A. ipaensis) combined about 10,000 years ago to generate a new species (A. hypogaea) now known as cultivated peanut. Cultivated peanut contains near complete copies of both wild peanut genomes. Maps of the chromosomal structure of these wild peanut genomes have proven to be invaluable for sorting out the genes in the cultivated peanut genome. The first published account of maps of the wild peanut genomes was the feature article in the prestigious journal, Nature Genetics, and merits distinction as a classic contribution to the annals of genomic research (See Publications). One of the many important findings, based on comparisons of the genomes of each wild peanut chromosome to its counterpart in cultivated peanut, revealed explanations for unsolved mysteries about patterns of inheritance for certain genetic traits that have impeded breeding progress. • For example, the first evidence of ‘tetrasomic recombination’ was found in cultivated peanut. A

comparison of chromosomes between wild and cultivated peanuts showed an unexpected genetic recombination in several places within some chromosomes. There was a tendency for parts of a chromosome in the A-genome to displace its counterpart in the B-genome, leaving a void space in the A-genome. Almost 3700 of these events were detected in the cultivated peanut genome. If a gene of interest is located in such a region, the likelihood of it being inherited normally in hybrid progeny becomes quite low. This finding helps explain strange patterns of gene inheritance that may be observed for traits such as resistance to leaf spots. This places greater importance on more robust DNA-markers that tag exact gene-fingerprints to find the few hybrid progeny that inherit the trait. (See Components 1 and 4).

Tools for Marker Assisted Selection. Wild peanut species are an abundant source of genes that can help improve the economics and quality of cultivated peanut production. Transferring those genes from wild to cultivated peanut often requires the development of wild x wild populations which produce lines that are cross compatible with cultivated peanut. Tools to ensure that the proper genes are transferred were developed by sophisticated strategies for generating robust DNA markers. • For example: Genome specific DNA markers that associate with known QTL in wild species were

used to develop high-definition views of the location of targeted genes (alleles) using on-line resources in PeanutBase. Validated markers could be used as noted below to select the trait in wild or cultivated germplasm (Component 2).

Higher-definition genome specific markers are being used to locate (map) QTL for late leaf spot (LLS), early leaf spot (ELS), white mold (WM), cylindrocladium black rot (CBR), (pre-harvest aflatoxin contamination (PAC), tomato spotted wilt virus (TSWV), Sclerotinia and root knot nematode (RKN) resistance plus drought tolerance and oil quality traits in the genomes of at least 20 specially designed and highly inbred cultivated peanut populations. The massive size of these populations was managed by evaluating bulked subsets of lines that tested positive or negative for the targeted trait. Millions of potential markers, generated by whole genome sequencing in lines from each bulked set, were screened to identify a few markers that gave a significant differential response in the same chromosome.



Additional tests were conducted to authenticate the markers and monitor the traits among breeding lines. • This technique proved to be an efficient method for locating the exact position of QTL within the

DNA sequence of a chromosome, and enabled development of several gene specific markers for ELS (3), LLS (3), TSWV (3), % total kernels (3), pod & seed size (1) and yield (2) (See Components 2 & 5).

High-definition markers also were developed for families of genes that encode enzymes in metabolic pathways that mediate seed development in Tifrunner and NC3033. QTL for pod and seed traits were discovered on five chromosomes of the A-genome and one B-genome chromosome. These QTL are examined to locate genes for traits such as: % kernels, kernel weight and size, pod number and density. • This work used gene expression profiling to define the period of seed filling, and confirmed when

seed started to mature. Markers can now be used to help select early-maturing peanuts in breeding programs, and eventually will enable user-friendly tests for judging the stage of maturity in field production (See Component 3).

Application of Markers & Maps in Breeding. Marker Assisted Selection has been shown to reduce the time needed to add several new traits to an agronomic cultivated variety. High-definition markers are being used in breeding programs to select resistance to LLS, ELS, TSWV, RKN, and for high oleic acid. Improved markers are being developed through genome mapping for CBR, WM, peanut rust and drought tolerance. Breeding stratgies such as MAGIC (Multi-parent Advanced Generation Inter-Crossing) and MARS (Marker Assisted Recurrent Selection) have been initiated to stack all these traits in improved varieties for each market type and geographic production area. Full application of peanut genomic research is mediated by an interactive on-line database at http: PeanutBase.org. PeanutBase provides a home for all data generated by the PGP and a platform for the ‘Breeders Tool Box’. • Continued development and use of PeanutBase will help facilitate timely achievement of breeding

objectives that address critical needs of producers, shellers, manufacturers and consumers. In addition, PeanutBase is a first-line of defense for protecting U.S. peanut production from emerging or devastating exotic diseases that may be introduced from other countries.

• The training exercises and genomic resources in the ‘Breeders Toolbox’ in the website ’PeanutBase’ were accessed by 7107 users between April 28, 2015 and April 27, 2016 (See Component 6).

Publications. • The paper entitled ‘The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid

ancestors of cultivated peanut’, published in Nature Genetics, established the cornerstone upon which peanut genomic research is based, and has strengthened opportunities for additional future funding from outside sources.

• The book ‘Peanuts: Genetics, Processing & Utilization” published by Elsevier Press in conjunction with APRES and AOCS presents the first comprehensive view of the peanut value-chain in nearly three decades.

• Since 2012, PGP members published 193 peer-reviewed research papers, and since 2014 have released 6 new peanut varieties and 117 germplasm lines.



Key Research Accomplishment Summary by Project Component. The following summarizes most important research accomplishments of each component of the Peanut Genome Project in layman’s terms. The remainder of the report provides more detailed descriptions of research contributions toward strategic goals of the six components in 2015/2016. Component 1: Sequencing and Assembly of Peanut Genomes. • Among many key findings the landmark publication, ‘The genome sequences of Arachis duranensis

and Arachis ipaensis, the diploid ancestors of cultivated peanut’ in Nature Genetics, revealed the reason for weird patterns of inheritance that have impeded breeding progress for LLS resistance and other traits. It was shown that segments of some chromosomes in the A-genome had unexpectedly replaced its counterpart in the B-genome chromosome as a result of hybridization, leaving a void space in the donating A-genome chromosome. Almost 3700 of these events were detected in the cultivated peanut genome. This means that inheritance of a gene affected by such an event would be quite low. This places greater importance on more robust DNA-markers that tag exact gene-fingerprints to find the few hybrid progeny that inherit the trait.

Dr. Scott Jackson, University of Georgia, and chair of the project technical team remarked, “Study of peanut genome structure and order makes a great detective story, where many clues are found and linked together to unlock great mysteries of genetics and gene regulation. This is exciting work. We will find answers that help increase peanut productivity, improve crop protection and enhance product quality in a timely manner.” Component 2: Developing Maps and Markers for finding QTL • Higher-definition genome specific markers for QTL in wild peanut species were or will be used to

locate (map) QTL for late leaf spot (LLS), early leaf spot (ELS), white mold (WM), cylindrocladium black rot (CBR), (pre-harvest aflatoxin contamination (PAC), tomato spotted wilt virus (TSWV), Sclerotinia and root knot nematode (RKN) resistance plus drought tolerance and oil quality traits in the genomes of at least 20 specially designed and highly inbred cultivated peanut populations. Markers that show differential responses in sets of lines from each population that tested positive or negative for the targeted trait were used to locate the location of QTL within a chromosome. This technique proved to be an efficient method for locating genes and the development of several gene specific markers for ELS (3), LLS (3), TSWV (3), % total kernels (3), pod & seed size (1) and yield (2).

Team member Dr. Soraya Leal-Bertioli, Geneticist at EMBRAPA and Visiting Professor at the University of Georgia , says, “Wild peanut species have an abundant source of genes that can help improve the economics and quality of cultivated peanut production. Robust DNA markers are needed to track the transfer of those genes from wild to cultivated peanut. These markers also work in cultivated peanut to improve the efficiency of peanut variety development ”. Component 3: Developing Markers for Specific Genes in QTL.

• High-definition markers were developed for networks of genes that function in metabolic pathways and used to evaluate when genes were active during seed development in Tifrunner and NC3033. QTL for pod and seed traits were discovered on five chromosomes of the A sub-genome and one B sub-genome chromosome. These QTL are being examined to locate genes for traits such as: % kernels, kernel weight and size, pod number & density, and kernels per pod.

Dr. Peggy Ozias-Akins, University of Georgia, who is one of the leaders of this component, says, “There are not many methods that help producers determine when a crop is mature. Monitoring the genes that



are active at the start of kernel maturation may lead to development of DNA markers that can help improve the development of early maturing varieties”. Component 4: Evaluating New Sequencing and Assembly Technologies. • Collaborators at Hudson-Alpha (Huntsville, AL) evaluated three new software programs for ability to

assemble wild and cultivated peanut genomes. ‘Meraculous™’ assembler technology (used by JGI in the U.S. Department of Energy) performed best. ‘Meraculous’ assemblies correctly placed 75% of all wild peanut genes in the correct cultivated peanut genome.

“Each year, our researchers test new technologies for improved ability in peanut genome sequencing and assembly. The Meraculous assembler is a major step forward in putting all the pieces of the cultivated peanut genome together in proper order”, said Dr. Rich Wilson, technical consultant to the Peanut Foundation. Component 5: Identifying Breeding Lines with QTL for Key Traits. • The U.S. mini-core collection (112 true breeding lines that represent genetic diversity in 9917

accessions in the U.S. Peanut Germplasm collection) were phenotyped for variation in yield, seed size, grade, resistance to ELS, LLS, WM, TSWV, and seed composition (fatty acids, flavonoids including resveratrol, and minerals). Four germplasm lines (PI 158854, PI 196622, PI 268868, PI 371521) with a major QTL (Ah3) represent sources of LLS resistance.

Corley Holbrook, USDA-ARS, who is a co-leader of the PGP, recently stated “We are beginning to see how recent advances in plant genomic technology can advance the science of peanut breeding and genetics. Although the technology of gene marker assisted selection in peanut is in its infancy, it already has had a tremendous impact on my breeding program”. Component 6: Creating On-line Tools for Genome Assisted Breeding. • New PeanutBase features added to the ‘Breeders Toolbox’ include: 1) a new sequence-search

interface that looks for similarities between genes in peanut and other legumes, 2) genome browsers that show markers that define the chromosomal location of a gene plus information on how the gene is regulated, 3) expanded gene atlases that catalog when each gene is active in different tissues during plant growth and development, 4) 25 interactive genetic maps, 5) 253 mapped traits (QTL), 6) ability to combine marker assisted selection data about critical traits on one page, 7) templates for researchers to contribute data, 8) access to all information from the USDA Germplasm Resource Information Network (GRIN) plus 2000 images of accessions in the U.S Peanut Germplasm collection, and 9) updated software for gene annotation and gene families.

“Resources generated by the PGI are useful when they are accessible to breeders and researchers. In the PeanutBase website we are working to integrate all of the information in the peanut genomics project to help researchers discover the basis for valuable traits, and to make it easier to use this knowledge to make faster breeding progress,” said Dr. Steven Cannon, Research Geneticist at USDA-ARS in Ames, IA. Overall Benefits to Breeders - Mark Burow, peanut breeder at Texas A&M says, “Web-based genome libraries and databases help breeders find markers that can be used in their breeding populations. The Breeder's Toolbox allows breeders to merge genomics and phenotypic information in ways that lead to efficient development of new varieties through marker-assisted breeding”.



Detailed Description of Research Accomplishments Component 1: Sequencing and Assembly of Peanut Genomes On April 2, 2014, The Peanut Foundation (http://www.peanutfoundation.org/) announced the first peanut (groundnut) genome sequences were published on-line and available for public use at http://peanutbase.org/files/genomes/. Thus far this resource not only has helped define the geographic and evolutionary origin of cultivated peanut (Arachis hypogaea), but has revealed knowledge about the structure of Arachis chromosomes that explain the inheritance of key traits needed to increase peanut productivity, enhance crop protection and improve product quality. Aspects of these revelations now have been documented in the first scientific peer-reviewed publication on the genome sequences of the ancestors of Arachis hypogaea:

The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid ancestors of cultivated peanut, in Nature Genetics doi:10.1038/ng.3517

David Bertioli, Steven Cannon, Lutz Froenicke, Guodong Huang, Andrew Farmer, Ethalinda Cannon, Xin Liu, Dongying Gao, Josh Clevenger, Sudhansu Dash, Longhui Ren, Márcio Moretzsohn, Kenta Shirasawa, Wei Huang, Bruna Vidigal, Brian Abernathy, Ye Chu, Chad Niederhuth, Pooja Umale, Ana Cláudia Araújo, Alexander Kozik, Kyung Do Kim, Mark Burow, Rajeev Varshney, Xingjun Wang, Xinyou Zhang, Noelle Barkley, Patrícia Guimarães, Sachiko Isobe, Baozhu Guo, Boshou Liao, Tom Stalker, Robert Schmitz, Brian Scheffler, Soraya Bertioli, Xu Xun, Scott Jackson, Richard Michelmore & Peggy Ozias-Akins What has been learned from these genome sequences so far? Cultivated peanut (Arachis hypogaea) is an allotetraploid (AABB-type genome with 20 chromosomes) derived from hybridization of two diploid species: Arachis duranensis (AA-type genome) and Arachis ipaensis (BB-type genome) each with 10 chromosomes. Genome size: AABB (2.7 Gb), AA (1.25 Gb), BB (1.56 Gb). Diploid pseudomolecule (chromosome) sequences are deposited in GenBank assembly accessions: GCA_000817695.1 and GCA_000816755.1. A high proportion of each AA (62%) and BB (69%) genome is occupied by non-coding transposable elements (repetitive sequence regions). Several families of transposable elements are present some have the highest abundance reported for plant genomes. Gene space contained 36,734 (AA) and 41,840 (BB) high quality non-transposable element genes. Genome wide methylation was about 75% at CG sites in both diploid genomes. Genic methylation patterns provided independent verification of gene annotations. 345 (AA) and 397 (BB) disease resistance genes were identified. Large clusters of these genes were located on pseudomolecules 2, 4 and 9; and were associated with quantitative trait loci (QTL). These assembles facilitated identification of candidate genes for root knot nematode resistance on AA-chromosomes 2 and 9; and peanut rust resistance on AA-chromosome 3. Allele specific markers have been generated for use in marker-assisted selection of those traits. Evolutionary divergence of the AA and BB genomes is estimated at 2.2 million years ago. Divergence of AA from the A-subgenome is estimated at 247,000 years ago; and BB from the B-subgenome at 9400 years ago. The B-subgenome is nearly identical to the A. ipaensis (BB) genome, which is the only B-genome Arachis species found within native geographic origin of A. duranensis. The center of diversity of the most primitive botanical type of cultivated peanut (A. hypogaea subsp. hypogaea var. hypogaea) also is found in the same region, northern Argentina to southern Bolivia. One-to-one equivalence is found between chromosomes of the AA-diploid genome and AA-subgenome (cultivated peanut); and likewise for BB-diploid and BB-subgenomes based on mapping Moleculo reads



from cultivated peanut to the combined diploid chromosomes (99% identity). However, A-genome chromosomes were distinctly less similar to cultivated peanut sequences than B-genome chromosomes. Gene space in the tetraploid peanut genome is represented by 183,000 transcripts of which 48% reside in the A-subgenome, and 52% are assigned to the B-subgenome. What is the most surprising feature of Arachis chromosome sequences? Although one-to-one equivalence is found between chromosomes of AA and BB genomes with high sequence identity (>99%). Chromosomes 2, 3, 4 and 10 are collinear; 5, 6 and 9 exhibit distal end inversions; 7 and 8 have undergone complex rearrangements, primarily in the AA-genome lineage. Genetic recombination also was observed between A- and B-subgenomes of cultivated peanut. As shown in Figure 1, it is apparent that segments of the B-subgenome have invaded the A-subgenome. These events resulted in nullsomic regions in the B-subgenome and tetrasomic (AAAA instead of AABB) genomic symmetry. This is the first evidence of tetrasomic recombination in cultivated peanut.

Figure 1: a) Tiling Path of Moleculo reads for Chromosome A05 (A. hypogaea vs A. duranensis); b) Tiling Path of Moleculo reads for Chromosome B05 (A. hypogaea vs A. ipaensis)

Figure 1b. Ibid Figure 1a legend, except tetraploid B05-subgenome vs diploid B05 genome. Absence of dots in top row shows a region of the tetraploid B-subgenome that is null for B alleles, but tetrasomic for A alleles (lower arrow), a change from expected AABB to AAAA chromosomal symmetry.

Figure 1a Top row of dots = % identity of Moleculo tetraploid subgenome chromosome A05 reads to diploid A05-genome reads. Apparent lack of sequence similarity shows erosion due to recombination between A and B subgenomes. Lower row of dots = density of Moleculo bases mapping at 0.5 Mb



How does tetrasomic recombination change the genotypic segregation pattern for a key trait? • According to Mendelian genetics, homozygous dominant (PP) and homozygous recessive (pp) alleles

should segregate in a 1:2:1 (PP:Pp:pp) genotypic ratio among F2 progeny of a hybrid cross. • An allele that resides in a tetrasomic region (PPPP or pppp) could exhibit 5 genotypic states:

PPPP:PPPp:PPpp:Pppp:pppp in a hybrid population. • For example, the line SPT06-06 has an allele that confers Leaf Spot resistance on chromosome 5.

When mated with a parent harboring a tetrasomic region of the same chromosome, only five progeny would inherit the resistance allele with 3 others with tetraplex genotypes.

• This type of weird genetics severely compromises the efficiency of traditional breeding strategies based solely on phenotypic selection.

How can Genotype x Sequencing strategies help reveal inheritance patterns for key alleles that may be affected by tetrasomic recombination? • One way is to use diploid Arachis specie genome sequences as proxies for the A- and B-subgenomes

of cultivated peanut. • Amphidiploid hybrids derived from given diploid (AA and BB) species can now be screened with

over 13,000 SNPs on an Affymetrix array and 800 genome specific SNP (Kaspar Fluidigm) to detect structural variations between corresponding A- and B-chromosomes.

• QTL that reside in regions of chromosomal rearrangement may now be located by cross referencing with high-density genetic maps and bioinformatic resources.



• Another strategy for tracking/locating the introgression of DNA segments from wild to cultivated germplasm involves development of synthetic RIL amphidiploid mapping populations where each RIL represents a near isogenic line (NIL). The genotype of each NIL was well characterized with KASP or Fluidigm SNP technology. For example, NIL with RKN, LS and other disease resistances have been mapped in a TxAG-6 x Florunner introgression population. About 150 BC5F2 NIL each with one piece of the TxAG-6 genome have been developed. Validated SNPs can then be used to track individual introgressions in breeding populations segregating for the associated trait.

Which key traits reside in a given wild Arachis species? A-genome species A. cardenasii Aflatoxin, Cylindrocladium parasiticum, Early leaf spot Groundnut rosette virus, Late leaf spot,

Peanut Bud Necrosis Virus, Peanut Mottle Virus (PMV), Peanut rust (Puccinia arachidis), Peanut Stripe Virus (PStV), Tomato-Spotted Wilt Virus (TSVW), Peanut Root-Knot Nematode (Meloidogyne arenaria), Northern Root-Knot Nematode (Meloidogyne hapla)

Armyworm (Spodoptera spp.), Corn Earworm (Helicoverpa armigera), Leafminer (Aproaerema modicella), Leafhoppers (Empoasca fabae ), Southern Corn Rootworm (Diabrotica undecimpunctata howardi)

A. duranensis Aflatoxin, Late leaf spot, Peanut Stunt Virus

Armyworm (Spodoptera spp.), Corn Earworm (Heloithis zea), Corn Earworm (Helicoverpa armigera), Groundnut aphid (Aphis craccivora) , Leafminer (Aproaerema modicella), Leafhoppers (Empoasca fabae ), Nematodes (Meloidogyne arenaria), Thrips (Frankliniella fusca), Thrips (Frankliniella schultzei ), Chilli Thrips (Scirtothrips dorsalis)

A. correntina Cylindrocladium parasiticum, Late leaf spot, Peanut Mottle Virus (PMV), Peanut rust (Puccinia arachidis), Tomato-Spotted Wilt Virus (TSVW)

Armyworm (Spodoptera spp.), Corn Earworm (Heloithis zea), Groundnut aphid (Aphis craccivora), Leafhoppers (Empoasca fabae ), Lesser Cornstalk Borer (Elasmopalpus lignosellus), Spider Mites (Tetranychus urticae), Thrips (Frankliniella fusca)

A. diogoi Early leaf spot Groundnut Rosette Disease, Peanut Bud Necrosis Virus, Peanut Mottle Virus (PMV), Peanut Ringspot Virus, Peanut rust (Puccinia arachidis), Peanut Stripe Virus (PStV), Tomato-Spotted Wilt Virus (TSVW), Peanut Root-Knot Nematode (Meloidogyne arenaria), Northern Root-Knot Nematode (Meloidogyne hapla),



Armyworm (Spodoptera spp.) , Corn Earworm (Heloithis zea), Groundnut aphid (Aphis craccivora), Leafhoppers (Empoasca fabae ), Thrips (Frankliniella fusca), Thrips (Frankliniella schultzei ), Chilli Thrips (Scirtothrips dorsalis)

A. stenosperma Early leaf spot Groundnut Rosette Disease , Late leaf spot, Peanut rust (Puccinia arachidis), Tomato-Spotted Wilt Virus (TSVW), Nematodes (Meloidogyne arenaria), Nematodes (Meloidogyne hapla), Nematodes (Meloidogyne javanica)

Corn Earworm (Heloithis zea), Corn Earworm (Helicoverpa armigera) , Groundnut aphid (Aphis craccivora), Leafminer (Aproaerema modicella), Leafhoppers (Empoasca fabae ), Thrips (Enneothrips flavens) , Thrips (Frankliniella fusca)

B genome species A. magna Early leaf spot, Late leaf spot, Sclerotinia Blight (Sclerotinia minor)

A. ipaensis Late leaf spot, Peanut rust (Puccinia arachidis)

Armyworm (Spodoptera spp.), Corn Earworm (Helicoverpa armigera), Leafhoppers (Empoasca fabae ) K genome species (previously named a B genome) A. batizocoi Early leaf spot, Late leaf spot, Peanut rust (Puccinia arachidis), Tomato-Spotted Wilt Virus

(TSVW)

Corn Earworm (Heloithis zea), Groundnut aphid (Aphis craccivora), Leafhoppers (Empoasca fabae ), Nematodes (Meloidogyne arenaria), Thrips (Frankliniella fusca)

Moving forward on the tetraploid (cultivated) peanut genome assembly • Hudson Alpha was contracted to sequence and assemble 6 PCR-free libraries for Tifrunner, A.

duranensis, and A. ipaensis (2 x 400 bp, 2 x 600 bp, 2 x 800 bp) with Rapid HiSeq technology. • Kmers merged into a single copy peak at k96. • Single shear results showed very low AT bias, which indicated that clear separation of A- and B-

genomes was possible. • Assembly of tetraploid data was initiated at 96X coverage of the subject libraries • ABySS (a de novo, parallel, paired-end sequence assembler designed for short reads) assembled 2.7

Gb (Contig L50, 10.4 kb; Scaffold L50, 14.2kb) at 42X fragment coverage. • These data were validated by SSPACE (a program that determines the order, distance and orientation

of contigs in scaffolds). • Unique non-overlapping 100mers from the tetraploid libraries were aligned to A- and B-genomes. • The proportion of 100mers perfectly matched to each genome was: 15% (A), 26% (B), 0.4% (A and

B). 57% of scaffolds could be called A- or B. Only 1.5% of scaffolds were unassigned. • Excellent separation of tetraploid subgenomes and respective alignment with counterpart diploid

genomes was achieved • Next steps include: completing the diploid sequences, developing nexerra pairs with longer reads to

fill gaps in tetraploid scaffolds, and assembling tetraploid data sets with programs from ALLPATHS-LG (for Kmer assembly) and MERACULOUS (a whole genome assembler developed by JGI that uses Illumina sequence to assemble large-sized genomes on commodity clusters).



Component 2: Developing Maps and Markers for finding QTL The utility of the new breeding strategy called “Genotyping By Sequencing (GBS)” depends on ability to identify the most useful QTL markers from thousands of DNA markers (SNPs or Single Nucleotide Polymorphisms) that may be resident among progeny in a breeding population. Zeroing in on key markers for specific triaits requires a targeted methodology in designed QTL mapping populations. A wide range of targeted mapping populations are now available for QTL discovery and annotation of high-density genetic maps with validated SNPs for QTL in A- and/or B- genomes. How are interspecific mapping populations being used for QTL discovery and mapping? Examples of parents are useful in developing allotetraploid populations for QTL discovery.

• A wide range of parental options are available for generating allotetraploid populations that segregate for specific traits, such as RKN resistances. In the case below, 400 A-genome and 400 B-genome SNPs were used to genotype allotetraploids with Fluidigm technology.

Examples of how Improved markers for RKN resistance are generated from wild species



Population TraitsTifrunner x Bailey High O/L Oleic acid; ELS, LLSTifrunner x C76-16 Drought tolerance, PACTifrunner x NC 3033 CBR, LLS, WM, TSWVTifrunner x SPT 06-06 ELS, LLSFlorida-07 x Bailey High O/L WMFlorida-07 x C76-16 Drought tolerance, PACFlorida-07 x NC 3033 CBRFlorida-07 x SPT 06-06 ElLS, LLS, TSWV

Tifrunner x Olin Oleic acid, maturityTifrunner x NM Valencia A TSWV, maturityTifrunner x Florunner TSWVTifrunner x SSD6 ELS, LLSFlorida-07 x Olin SclerotiniaFlorida-07 x NM Valencia A Oleic acid, TSWV, S. rolfsiiFlorida-07 x Florunner Oleic acid, S. rolfsiiFlorida-07 x SSD6 ELS, LLS

PI 158839 (SS4CC) x Tifguard RKN, Drought toleranceGregory x Tifguard RKN, LLSSunOleic 97R x NC 94022 TSWV, Oleic acidTifrunner x GT-C20 TSWV, LLS, ELS, maturity

Highly Inbred A. hypogaea Mapping Populations

• Fluidigm generated KASP SNPs found in regions A-genome and B-genome were associated with

known QTL for RKN resistance, and used to enhance QTL regions of existing genetic maps. • KASP (Kompetitive Allele Specific PCR) is a fluorescence-based genotyping technology that

identifies specific alleles. This technology enables the location of a specific allele (gene copy) to a particular chromosome, and also to the A- or B-genome in DNA from diploid or tetraploid peanuts.

• The chromosome A02 sequence (avaiable via PeanutBase) from A. duranensis that harbors RKN resistance QTL was annotated with the new KASP markers, which zero in on the location of RKN genes within the QTL.

• The same KASP markers were validated with other diploid, allotetraploid and amphidiploid populations. These markers also can be used to select for RKN resistance in cultivated peanut breeding populations.

Examples of how Improved markers are generated from cultivated peanut resources • At least 20 highly inbred A. hypogaea mapping

populations have been developed that provide remarkable resources for LLS, ELS, WM, CBR, PAC, TSWV, Sclerotinia, and RKN resistance plus drought tolerance and oil quality traits. These resources are managed by UGA, NCSU and UFL

• Phenotypic selection in a RIL population was used

to develop resistant and susceptible bulk populations (for disease resistance traits).

• Bulk populations were resequenced to fine map the location of QTL

• SNP markers were validated using SWEEP software for QTL-Seq studies

• Differential expression of SNPs between R and S bulked lines narrowed the search for chromosomal regions that harbor QTL, when compared to reference genome sequences.

• KASP markers were designed within the identified regions, and used to fine map QTL and generate highly linked SNP markers for key traits.



• This strategy has been used to identify QTL for: ELS (3), LLS (3), TSWV (3), %total kernels (3), pod & seed size (1), and yield (2).

• In addition, these resources also are used in Nested Association Mapping (NAM) studies to help

validate phenotypic analyses for variation in traits such as Leaf Spot resistance.



Component 3: Developing Markers for Specific Genes in QTL Genome sequences help define the structure of chromosomes. GBS or resequencing methods usually reveal millions of structural variations like SNPs that help map those structures in an orderly manner. As markers and maps become more refined, it is possible to look closely within genomic regions (QTL) that harbor genes and pinpoint the location of genes of interest. Yet, distinguishing genes for a targeted trait from hundreds of other genes that also may reside within the QTL is a tedious process. This dilemma may be solved in part by associating the sequence of transcribed or expressed genes with genomic sequeinces of potential candidate genes. Again, this innovation is not trival, but a reverse-genomic approach where sequences of exprressed genes can be matched with genomic sequences within a QTL often facilitates candidate gene discovery and development of allele specific markers for those genes. For example: perfect markers for FAD2 genes that mediate oleic acid concentration. In addition, expressed gene profiling also can provide insights to genetic regulation of metabolic pathways which are mediated by interaction of a multitude of genes. For example, investigaton of gene expression patterns in developing seed is helping improve understanding of the biological mechanisms that govern complex traits such as maturity and yielding ability.

What clues can gene expression analysis reveal on control of traits like maturity and yield? Devloping validated markers from expressed genes that may be associated with a desired trait

• Maturity is a selectable trait by traditional breeding methods. Considerable genotypic variation is present among germplasm resources; and expression of that variation is often influenced by environmental factors that impact yield and quality traits.

• There are no certain measures for determining when peanuts are physiologically mature.

• ‘Early maturity’ often is a breeding goal that is associated with improved flavor of roasted peanuts.

• However, there are few reliable genetic markers that help breedgers select for maturity or predict when a peanut kernel may become mature.

• Transcriptome (RNA) based resequencing (GBS) systems were deployed to select sets of highly-polymorphic contigs that help distinguish introgressed gene segments containing candidate genes among progeny of any hybrid cross.

• Primers for validated SNPs were amplified on Fluidigm Access arrays and then sequenced and mapped to identify candidate gene locations within QTL, maturity in this case.



Discovery of genes that mediate physiological maturity in peanut • Scientific literature suggests networks of co-

expressed genes encode key enzymes for metabolic processes (i.e. hormone metaboism, signaling, phtosynthesis, cell division & growth, carbon & nitrogen metabolism). Genes involved in these pathways may provide markers that help montitor how seeds mature.

• Different patterns of gene expression were found among plant organs (ie leaves, seed, roots) at different stages of plant development.

• RNA-Seq analysis of expressed genes in different plant organs during stages of development (R1 to R7) was conducted to characterize differnences in gene activation/deactivation between the cvs. Tifrunner and NC 3033.

• Thousands of genes were differentially expressed in the pericarp, seed coat and embryo of Tifrunner and NC 3033, most notably during R7



• Although only about half of all transcripts were analyzed, differences transcription of co-expressed confirmed that the R7 growth stage was the end of seed filling and start of seed maturation.

• The metabolic function (or enzymatic product) of

major differentially co-expressed genes was determined through comparisons with known analogs in genomic databases from other plant species.

• The transcripts of selected differentially co-expressed genes can now be targeted for allele specific SNP discovery.

• Thus far this process of allele specific SNP discovery helped define QTL for seed and pod traits on chromosomes: A02, A07, A08, A9, A10, and B9

• It is believed that these QTL contain alleles that govern seed maturity traits of peanut.



Component 4: Evaluating New Sequencing and Assembly Technologies Research findings have shown that more than one DNA sequencing technology is needed to properly assemble the peanut genome. There are many options that not only ensure high quality results but also help reduce project costs. What progress was made to complete a sequence/assembly of the cultivated peanut genome? • Resources at Hudson-Alpha (Huntsville, AL) were

recruited to construct 13 tetraploid libraries (160X coverage of 400bp-800bp fragments, and 28X coverage of 3kb to 8kb pairs); 130X coverage of 12 A. duranensis diploid libraries; and 217X coverage of A. ipaensis diploid libraries.

• Libraries were assembled with AllPaths, Abyss, SSPACE, and Meraculous technology. Abyss generated a 2.7 Gb assembly with 32 kb scaffold N50 in 15 to 20 days runtime. AllPaths generated a 1.6 Gb assembly with 54 kb scaffold N50 in 40 to 50 days runtime. Meraculous generated a 1.9 Gb assembly with 78kb scaffold N50 in 12 days.

• These assemblies were evaluated against diploid gene sets containing about 34,800 genes (A-genome) and 39,500 genes (B-genome). The proportion of A- and B-genome genes aligned by a given assembler were similar; but increased from AllPaths (ca 54%) to Abyss (63%) to Meraculous (75%).

• Analysis of each assembly revealed: 1) AllPaths and Abyss tends to collapse subgenomes; 2) Meraculous appeared to keep subgenomes separated, but there were a significant number of regions where a diploid-A and diploid-B gene alignment overlapped on a tetraploid-B scaffold (unexpected recombination). About 46% of a Meraculous 1011 Mb assembly could be assigned to an A- or B-subgenome; while 8% were found in both subgenomes and the remainder of the assembly contained no genes.

• Meraculous scaffolds were integrated with tetraploid Moleculo scaffolds. However, results showed that longer (10kb) pair tetraploid sequence would be helpful for finished scaffolding and patching, and to help overcome problems posed by is gene alignment due to segregation distortion among subgenomes (tetrasomic recombination).

• PacBio technology was chosen to meet that goal. New PacBio chemistry reportedly enables 2 to 3-fold increases in yield with assemblies averaging 13 kb. Such long reads would significantly improve the tetraploid peanut assembly. 70X coverage was recommended for de novo assembly, development of an ordered map and filling gaps in tetraploid subgenome alignment.





Component 5: Identifying Breeding Lines with QTL for Key Traits Efforts in Component 5 will lead to the development of molecular markers for economically significant traits that should greatly improve the speed and efficiency of all peanut breeding programs. The ongoing genome sequencing effort has resulted in the identification of numerous molecular markers. Phenotyping data from component 5 is being used to associate molecular markers with traits of interest for cultivar development. The primary focus has been on five RIL populations for resistance to biotic and abiotic stresses. These populations are also being phenotyping for yield, and grade. In-depth phenotyping and genotyping of these populations will result in the development of markers that can be deployed by breeding programs for the development of improved cultivars. . What was learned from phenotypic analysis of the U.S. Peanut Germplasm Collection?

The U.S. mini-core collection (112 of 9917 accessions) represent a broad sampling of the genetic diversity in cultivated peanut. Accessions in the U.S. mini-core have been purified to provide a homogenous genetic resource for peanut breeding, genetics, and genome research. The U.S. mini-core is being phenotyped for variation in yield, seed size, grade, resistance to ELS, LLS, WM, TSWV, seed chemistry and nutrient quality, and nitrogen fixation. Examples are shown for the range of genetic variations in seed size/grade, fatty acid composition, flavanoids (including resveratrol), and mineral composition.



In addition, Association Mapping based on linkage disequilibrium (LD) of 1000 validated SNP markers provided an effective way to map loci (QTL) for phenotypic traits. For example, over a three year period, association mapping of the mini-core revealed four germplasm lines (PI 158854, PI 196622, PI 268868, PI 371521) that are sourses of genes for leaf spot resistance. A major QTL (Ah3) for LLS resistance was located with four SSR markers (named pPGPseq2D12B, pPGSseq19B1, TC20B05 and TC04F12). TC20B05 alsone explained 15% of the phenotypic variation for leaf spot resistance

Advanced strategies for pinpointing genes for desired traits in mapping populations Phenotypic data was accumulated over several years for RIL populations segregating for resistance to leaf spot. Bulk Segregate subsamples were developed, for example a resistant bulk and a susceptible bulk for leaf spot resistance. DNA from each bulked set of each population was sequenced (QTL-seq) to identify QTL enriched in a particular parental allele. A pipeline (SWEEP) was used to identify and validate SNPs associated with the parent allele. All loci in each bulked population were screened with validated SNPs.



Differences in the number of alleles at each locus between the bulk populations revealed genomic regions within specific chromosomes that could harbor an allele for the targeted trait. Allele specific markers (KASP) were generated to locate alleles on A- and/or B-subgenomes of cultivated peanut. For example, KASP markers located QTL for LLS and ELS resistance on chromosomes A03, B03 and A05 in SPT06 (R) and FL07 (S).

Candidate genes within QTL for leaf spot resistance were identified relative to phenotypic expression of resistance in regenerated embryonic tissues subjected to genome editing techniques (such as CRISP-Cas9) that delete or knock out sequencial segments of cloned DNA. This process helped to fine map genes to exact chromosomal positions.



Component 6: Creating On-line Tools for Genome Assisted Breeding Full application of peanut genomic research is mediated by an interactive on-line database at http: PeanutBase.org. PeanutBase provides a home for all data generated by the PGP and a platform for the ‘Breeders Tool Box’ What is New in PeanutBase.org? New PeanutBase features include: 1. a new sequence-search interface that looks for

similarities between genes in peanut and other legumes

2. genome browsers that show markers that define the chromosomal location of a gene plus information on how the gene is regulated

3. expanded gene atlases that catalogs when each gene is active in different tissues during plant growth and development

4. 25 interactive genetic maps 5. 253 mapped traits (QTL) 6. combination of marker assisted selection data

about critical traits onto one page 7. templates for researchers to contribute data 8. diversity and germplasm information from

GRIN plus 2000 images of accessions in the U.S Peanut Germplasm collection

9. updated software for gene annotation and gene families.

New sequence search interfaces were implemented in close coordination with collaborators at the Hudson Alpha Institute to facilitate assembly of primary scaffold for diploid and tetraploid genomes. Annotation and gene modeling was conducted using the MAKER-P annotation software on iPlant hardware, and augmented with additional (tetraploid) expression sequence from Ozias-Akins et al.



The Genome browser was integrated with other on-line bioinformatic resources (such as LegumeInfo and Reactome) to enable inferred functional annotation of genes into the underlying database at PeanutBase. These search interfaces now provide access to individual and bulk gene sets from gene expression atlas developed by Ozias-Akins et al.



Efforts also have been focused on collection of trait, genetic maps, and marker data from the literature and directly from researchers, and the GRIN-Global catalog of germplasm. In the coming year, a major focus will be handling the large data sets and many association features from the PGC RIL projects



Marker Assisted Selection (MAS) pages combine information about critical traits onto one page. These pages can be created and maintained directly by experts outside the PeanutBase team.

Data collection templates were developed for traits, maps, and markers, along with data loaders and modules to enable researchers to search and view the data. Traits, maps, and markers can also be explored interactively via CMap.

Accessions of the USDA Peanut Germplasm collection are now documented by more than 2000 images representing over 1100 Arachis accessions. Similarly, software modules have been developed to access germplasm resources from GRIN-Global. Data and analyses of GBS genotyping data and results of GWAS analyses are now available for interactive viewing of trait features in these contexts: database text search; genetic map; and genome browser. For map viewers, CMap software will be used initially, and will be replaced mid-2016 with a new interactive viewer under development in the Cannon lab.



PeanutBase reaches out to other genome databases

Genes for a given peanut trait in peanut also may occur in other legume species such as soybean and common bean. Often times those genes have common structural features. PeanutBase can access the other genome databases to capitalize on those similarities in various ways. PeanutBase use statistics From April 28, 2015 and April 27, 2016, 7107 users conducted 15,563 sessions and viewed 126,300 pages. 44% of the users accessed PeanutBase for the first-time.

New features are being added to PeanutBase • Annotation of the tetraploid assembly: • More maps, markers, QTL & GWAS features • Diversity & germplasm information • Metabolic pathway information • Marker sets useful for phenotyping in breeding programs:

targeted MAS & high-throughput assays • Improvement of codebase for long-term maintenance



Appendices Exhibit 1: Research publications by IPGC members and associates since 2012 that are relevant to the PGP

1. Balota M, Isleib TG, Tallury S. 2012. Variability for drought related traits of virginia-type peanut cultivars and advanced breeding lines. Crop Sci. 52: 2702-2713.

2. Devi, M.J., W. Sadok, T.R. Sinclair. 2012. Transpiration response of de-rooted peanut plants to aquaporing inhibitors. Environmental and Experimental Botany 78:167-172.

3. Gill, R., Culbreath, A., Holbrook, C. and Ozias-Akins, P. (2012). Characterization of a recombinant inbred line (RIL) population for resistance to late leaf spot in cultivated peanut (Arachis hypogaea). Phytopathology 102:S2.4.

4. Grabiele M, Chalup L, Robledo G and Seijo G. 2012. Genetic and geographic origin of domesticated peanut as evidenced by 5S rDNA and chloroplast DNA sequences. Plant Systematic and Evolution, 2012:1151–1165.

5. Koilkonda, P., S. Sato, S. Tabata, K. Shirasawa, H. Hirakawa, H. Sakai, S. Sasamoto, A. Watanabe, T. Wada, Y. Kishida, H. Tsuruoka, T. Fujishiro, M. Yamada, M. Kohara, S. Suzuki, M. Hasegawa, H. Kiyoshima, and S. Isobe. 2012. Large-scale development of expressed sequence tag-derived simple sequence repeat markers and diversity analysis in Arachis spp. Mol. Breed. 30:125-138.

6. Mallikarjuna N, Jadhav D R, Reddy K, Husain F and Das K. 2012. Screening new Arachis amphidiploids, and autotetraploids for resistance to late leaf spot by detached leaf technique. European Journal of Plant Pathology, 132:17–21.

7. McDaniel, K.A., White, B.J., Dean, L.L., Sanders, T.H., and Davis, J.P. 2012. Compositional and mechanical properties of peanuts roasted to equivalent colors using different time/temperature combinations. J. Food Sci. 77:C1292-C1298.

8. Moretzsohn MC, Gouvea EG, Inglis PW, Leal-Bertioli SCM, Valls JFM, Bertioli DJ (2012). A study of the relationships of cultivated peanut (Arachis hypogaea) and its most closely related wild species using intron sequences and microsatellite markers. Annals of Botany doi:10.1093/aob/mcs237

9. Nagy, E.D., Y. Guo, S. Tang, J.E. Bowers., R.A. Okashah, C.A. Taylor, D. Zang, S. Khanal, A.F. Heesacker, N. Khalilian, A.D. Farmer, N. Carrasquilla-Garcia, R. Varma Penmetsa, D. Cook, H.T. Stalker, N. Nielsen, P. Ozias-Akinsand, and S.J. Knapp. 2012. A high-density genetic map of Arachis duranensis, a diploid ancestor of cultivated peanut. BMC Genomics 13:469.

10. Pandey, M. K., B. Gautami, T. Jayakumar, M. Sriswathi, H. D. Upadhyaya, M. V. C. Gowda, T. Radhakrishnan, D. J. Bertioli, S. J. Knapp, D. R. Cook, and R. K. Varshney. 2012. Highly informative genic and genomic SSR markers to facilitate molecular breeding in cultivated groundnut (Arachis hypogaea). Plant Breed. 131:139-147.

11. Qin H., Feng S., Chen C., Guo Y., Knapp S., Culbreath A., He G., Wang M.L., Zhang X., Holbrook C.C., Ozias-Akins P., Guo B.Z. (2012) An Integrated genetic linkage map of cultivated peanut (Arachis hypogaea L.) constructed from two RIL populations. Theor Appl Genet 124:653-664. DOI 10.1007/s00122-011-1737-y.

12. Rowland, D.L., W.H. Faircloth, P. Payton, D.T. Tissue, J.A. Farrell, R.S. Sorensen, and C.L. Butts. 2012. Primed acclimation of cultivated peanut (Arachis hypogaea L.) through the use of deficit irrigation timed to crop developmental periods. Agric. Water Management 113:85-95.

13. Shirasawa, K., P. Koilkonda, K. Aoki, H. Hirakawa, S. Tabata, M. Watanabe, M. Hasegawa, H. Kiyoshima, S. Suzuki, C. Kuwata, Y. Naito, T. Kuboyama, A. Nakaya, S. Sasamoto, A. Watanabe, M. Kato, K. Kawashima, Y. Kishida, M. Kohara, A. Kurabayashi, C. Takahashi, H. Tsuruoka, T. Wada, and S. Isobe. 2012. In silico polymorphism analysis for the development of simple sequence repeat and transposon markers and construction of linkage map in cultivated peanut. BMC Plant Biol. 12:80.

14. Shoba D., N. Manivannan , P. Vindhiyavarman, S. N. Nigam. SSR markers associated for late leaf spot disease resistance by bulked segregant analysis in groundnut (Arachis hypogaea L.). 2012, Euphytica. 188 (2) 265-272.

15. Spokas, K.A., Cantrell, K.B., Novak, J.M., Archer, D.W., Ippolito, J.A., Collins, H.P., Boateng, A.A., Lima, I.M., Lamb, M.C., Mcaloon, A.J., Lentz, R.D., Nichols, K.A. 2012. Biochar: A synthesis of its agronomic impact beyond carbon sequestration. Journal of Environmental Quality. 41(4):973-989.



16. Sujay, V., M.V. Gowda, M.K. Pandey, R.S. Bhat, Y.P. Khedikar, H.L. Nadaf, B. Gautami, C. Sarvamangala, S. Lingaraju, T. Radhakrishan, S.J. Knapp, and R.K. Varshney. 2012. 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. Breed. 30(2):773-788.

17. Wang, Hui, R. V. Penmetsa, M. Yuan, L. Gong, Y. Zhao, B. Guo, A. D. Farmer, B. D. Rosen, J. Gao, S. Isobe, D. J. Bertioli, R. K. Varshney, D. R. Cook, Guohao He. (2012). Development and characterization of BAC-end sequence derived SSRs, and their incorporation into a new higher density genetic map for cultivated peanut (Arachis hypogaea L.). BMC Plant Biology, 12:10.

18. Wang, M.L., Chen, C.Y., Pinnow, D.L., Barkley, N.L., Pittman, R.N., Lamb, M.C., Pederson, G.A. 2012. Seed dormancy variability in the U.S. peanut mini-core collection. Research Journal of Seed Science. 5:84-95.

19. Zhao, Yongli, C. S. Prakash, Guohao He. (2012). Characterization and compilation of polymorphic simple sequence repeat (SSR) markers of peanut from public database. BMC Research Notes, 5: 362.

20. Abouziena, H.F., M.S.A. Abd El Wahed, M.A.T. Eldabaa, and E.R. El-Desoki. 2013. Effect of Sowing Date and Reduced Herbicides Rate with Additives on Peanut (Arachis Hypogaea L.) Productivity and Associated Weeds. Journal of Applied Sciences Research, 9:2176-2187.

21. Bertioli, D. J., B. Vidigal, S. Nielen, M. B. Ratnaparkhe, T. H. Lee, S. C. Leal-Bertioli, C. Kim, P. M. Guimaraes, G. Seijo, T. Schwarzacher, A. H. Paterson, P. Heslop-Harrison, and A. C. Araujo. 2013. The repetitive component of the A genome of peanut (Arachis hypogaea) and its role in remodelling intergenic sequence space since its evolutionary divergence from the B genome. Ann. Bot. 112:545-559.

22. Bishi, S. K., L. Kumar, M. C. Dagla, , M. K. Mahatma, A. L. Rathnakumar, H. B. Lalwani and J. B. Misra. 2013. Characterization of Spanish peanut germplasm (Arachis hypogaea L.) for sugar profiling and oil quality. Ind. Crop. Prod. 51:46-50.

23. Chen, C.Y., Roland, D., Nuti, R.C., Faircloth, W.H., Lamb, M.C., and Harvey, E. 2013. Heritability and genetic relationships for drought related traits in peanut. Crop Sci. 53:1392-1402.

24. Guo, B., M.K. Pandey, G. He, R.K. Varshney, X. Zang, B. Liao, A. Culbreath, R.K. Varshney, V. Nwosu, R.F. Wilson, and H.T. Stalker. 2013. Recent advances in molecular genetic linkage maps of cultivated peanut (Arachis hypogaea L.). Peanut Sci. 40:89-94.

25. Holbrook, C.C., T.G. Isleib, P. Ozias-Akins, Y. Chu, S.J. Knapp, B. Tillman, B. Guo, R. Gill, and M.D. Burow. 2013. Development and phenotyping of recombinant inbred line (RIL) populations for peanut (Arachis hypogae). Peanut Sci. 40:89-94.

26. Hui Wang, Manish K. Pandey, Lixian Qiao, Hongde Qin, Albert K. Culbreath, Guohao He, Rajeev K. Varshney, and Baozhu Guo. (2013). Genetic Mapping and QTL Analysis for Disease Resistance Using F2 and F5 Generation-based Genetic Maps Derived from Tifrunner × GT-C20 in Peanut (Arachis hypogaea L.). Plant Genome, 6. doi: 10.3835/plantgenome2013.05.0018

27. Lewis, W.E., Harris, G.K., Sanders, T.H., White, B.L., and Dean, L.L. 2013. Antioxidant and anti-inflammatory effects of peanut skins. Food Nutr. Res., 4:22-32.

28. Shekoofa, A., J. M. Devi, T. R. Sinclair, C. C. Holbrook, and T. G. Isleib. 2013. Divergence in drought-resistance traits among parents of recombinant peanut inbred lines. Crop Sci. 53:2569-2576.

29. Stalker, H.T., S. Tallury, P. Ozias-Akins, S. Lea-Bertioli, D. Bertioli. 2013. The value of diploid peanut relatives for breeding and genomics. Peanut Sci. 40:70-88.

30. Wang M.L., Chen C.Y., Tonnis B., Barkley N.A., Pinnow D.L., Pittman R.N., Davis J., Holbrook C.C., Stalker H.T., Pederson G.A. (2013) Oil, fatty acid, flavonoid, and reseveratrol content variability and FAD2A functional SNP genotypes in the U.S. peanut mini-core collection. J. Agric. Food Chem. 61:2875-2882.

31. Zhao, Yongli, Roxanne Williams, C. S. Prakash, and Guohao He (2013) Identification and characterization of gene-based SSR markers in date palm (Phoenix dactylifera L.). BMC Plant Biology 12: 237.

32. Baidouri MEL, Carpentier MC, Cooke R, Gao D, Lasserre E, Llauro C, Mirouze M, Picault N, Jackson SA, Panaud O. Incorporation of disease resistances from wild species 2014. The flying genes : widespread and frequent horizontal transfers in plants. Genome Research. 24:831-838.

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70. Sebuliba, R.G. Nalugo, D.K. Okello, and N. Puppala. 2014. Evaluation of the Reaction of Segregating Generations of Improved Groundnuts (Arachis hypogaea) to rosette disease in Uganda. African Journal of Applied Agricultural Sciences and Technologies. 1(1)52-56.

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77. Upadhyaya, H.D., S.L. Dwivedi, S. Sharma, N. Lalitha, S. Singh, R.K. Varshney, C.L.L. Gowda. 2014. Enhancement of the use and impact of germplasm in crop improvement. Plant Genetic Resources: Characterization and Utilization 12: S155-S159

78. Varshakumari, M. V. C. Gowda, V. Tasiwal, M. K. Pandey, R. S. Bhat, N. Mallikarjuna, H. D. Upadhyaya, and R. K. Varshney. 2014. Diversification of primary gene pool through introgression of resistance for foliar diseases from synthetic amphidiploids to cultivated groundnut (Arachis hypogaea L.). Crop J. 2:110-119.

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81. Venkatesh, R., M. V. Kamble, B. N. Motagi, H. L. Nadaf, and R. S. Bhat. 2014. Analysis of a mutant population for its suitability to study taxonomic traits in groundnut. Electron. J. Plant Breed. 5:828-833.

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83. Wan, L., Y. Wu, J. Huang, X. Dai, Y. Lei, L. Yan, H. Jiang, J. Zhang, R.K. Varshney, B. Liao. 2014. Identification of ERF genes in peanuts and functional analysis of AhERF008 and AhERF019 in abiotic stress response. Functional & Integrative Genomics 14: 467-477

84. Wann, D.Q., and R.S. Tubbs. 2014. Interactive effects of hand weeding, tine and sweep cultivation for weed control in organic peanut production. Peanut Sci. 41:124-130

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86. Yeri, S.B., K. Shirasawa, M.K. Pandey, M.V.C. Gowda, V. Sujay, M. Sriswathi, H.L. Nadaf, B.N. Motagi, S. Lingaraju, A.R.S. Bhat, R.K. Varshney, P.U. Krishnaraj, R.S. Bhat. 2014. Development of NILs from heterogeneous inbred families for validating the rust resistance QTLs in peanut (Arachis hypogaea L.). Plant Breeding 133: 80-85

87. Yuannian Jiao and Andrew H. Paterson. 2014. Polyploidy-associated genome modifications following land plant evolution. Philosophical Transactions of the Royal Society B 369:20130355. doi: 10.1098/rstb.2013.0355

88. Zhang, H., Scharfenstein, L., Zhang, D., Chang, P.-K., Montalbano, B.G., Guo, B.Z., Meng, X. and Yu, J. Peanut resistance gene expression in response to Aspergillus flavus infection during seed germination. Journal of Phytopathology: first published online: 3 OCT 2014 | DOI: 10.1111/jph.12311. 2014.

89. Zhou, X., Y. Xia, X. Ren, Y. Chen, L. Huang, S. Huang, B. Liao, Y. Lei, L. Yan, and H. Jiang. 2014. 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 15:351.

90. Zhu, F., Han, J., Liu, S., Chen, X., R.K. Varshney, X. Liang. 2014. Cloning, expression pattern analysis and subcellular localization of resveratrol synthase gene in peanut (Arachis hypogaea L.). American Journal of Plant Sciences 5: 3619-3631



91. Ballen C, Leal-Bertioli S, Guimaraes P, Silva-Júnior OB, Brasileiro ACM, Jackson SA, Bertioli D (2014) Mapping of differentially expressed genes of Arachis stenosperma under Meloidogyne arenaria infection onto Arachis duranensis pseudomolecules. 7th AAGB, Savannah, Nov 10-14, 2014.

92. Bhat, R. S., R. Venkatesh, M. V. Kamble, A. A. Hake, B. N. Motagi, H. L. Nadaf, S. Lingaraju, S. Mondal, A. M. Badigannavar, and M. V. C. Gowda. 2014. Analysis of mutant populations for association of taxonomic and productivity traits with transposable element (TE) markers in peanut. In AAGB 2014 - 7th International Conference on Advances in Arachis through Genomics & Biotechnology. Georgia, USA, 21-22.

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94. Chopra, R., C. E. Simpson, A. Hillhouse, P. Payton, J. Sharm, and M. D. Burow. 2014. Transcript-based SNP map and QTL analysis on plant architecture traits of F2 lines developed from intra-specific cross of Arachis duranensis x Arachis cardenasii. 7th International Conference on Advances in Arachis through Genomics & Biotechnology, Savannah, Georgia.

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97. Pandey, M. K., H. D. Upadhyaya, A. Rathore, V. Vadez, M. S. Sheshashayee, M. Sriswathi, M. Govil, A. Kumar, M. V. C. Gowda, S. Sharma, F. Hamidou, A. Kumar, P. Khera, R. S. Bhat, A. W. Khan, S. Sube, E. Monyo, H. L. Nadaf, G. Mukri, S. Jackson, B. Guo, X. Liang, and R. K. Varshney. 2014. Comprehensive association analysis for 50 agronomic traits in peanut using the ‘reference set’ comprising 300 genotypes from 48 countries of the semi-arid tropics of the world In AAGB 2014 - 7th International Conference on Advances in Arachis through Genomics & Biotechnology. Georgia, USA, 36.

98. Varshney, R. K., L. Froencike, M. K. Pandey, S. N. Nayak L, H. D. Upadhyaya, A. Rathore, B. Liao, N. Barkley, B. Guo, M. V. C. Gowda, C. C. Holbrook, T. B. Brenneman, R. S. Bhat, C. Chen, J. Damicone, M. D. Burrow, T. Isleib, L. Dean, M. L. Wang, V. Vadez, S. Jackson, and R. Michelmore. 2014. An international initiative to conduct comprehensive genome-wide association studies (GWAS) for an array of agronomic traits in peanut In AAGB 2014 - 7th International Conference on Advances in Arachis through Genomics & Biotechnology. Georgia, USA, 41-42.

99. Wilson, J. N. J. N. Wilson, R. Chopra, M. R. Baring, M. Gomez, C.E. Simpson, J. C. Chagoya, and M.D Burow. (2014) Genetic Mapping and QTL Analysis for Oil Concentration in Peanut. Amer. Peanut Res. Educ Soc. Annual Meeting.

100. Yeri, S. B., R. M. Kolekar, B. N. Motagi, H. L. Nadaf, S. Lingaraju, M. V. C. Gowda, and R. S. Bhat. 2014. Development of late leaf spot and rust tolerant genotypes from TMV 2 and JL 24 by marker assisted backcross breeding in groundnut. In AAGB 2014 - 7th International Conference on Advances in Arachis through Genomics & Biotechnology. Georgia, USA, 45.

101. Burow, M. D., M. R. Baring, J. L. Ayers, A. M. Schubert, Y. López. and C. E. Simpson, 2014. Registration of Tamrun OL12' Peanut. J. Plant Regist 8:117-121.

102. Burow, M. D., M. R. Baring, N. Puppala, C. E. Simpson, J. L. Ayers, J. Cason, A. M. Schubert, A. Muitia, and Y. López. 2014. Registration of 'Schubert' Peanut. J. Plant Regist 8:122-126.

103. Chen, C.Y., Noelle A. Barkley, Ming L. Wang, C. Corley Holbrook, and Phat M.Dang. 2014. Registration of purified accessions for the U.S. peanut mini-core germplasm collection. Journal of Plant Registrations. 8(1):77-85.

104. Pandey, M.K., Upadhyaya, H.D., Rathore, A., Vadez, V., Sheshshaye, M.S., Sriswathi, M., Govil, M., Kumar, A., Gowda, M.V.C., Sharma, S., Hamidou, F., Kumar, V.A., Khera, P., Bhat, R.S., Khan, A.W., Singh, S., Li, H., Monyo, E., Nadaf, H.L., Mukri, G., Jackson, S.A., Guo, B., Liang, X. and Varshney, R.K. Genome-wide association studies for 50 agronomic traits in peanut using the 'reference set' comprising 300 genotypes from 48 countries of the semi-arid tropics of the world. PLoS ONE 9(8): e105228. doi:10.1371/journal.pone.0105228. 2014

105. Puppala, N. and S.P. Tallury. 2014. Registration of High Oleic Valencia Peanut Cultivar ‘NuMex-01’. J. Plant Registrations 8:127-130.



106. Tallury, S.P., T.G. Isleib, S.C. Copeland, P. Rosas-Anderson, M. Balota, D. Singh, and H.T. Stalker. 2014. Registration of two multiple disease-resistant peanut germplasm lines derived from Arachis cardenasii Krapov. & W.C. Gregory, GKP 10017. J. Plant Registrations 8:86-89.

107. Abberton, M., 27 other authors, R.K. Varshney, M. Yano. 2015. Global agricultural intensification during climate change: a role for genomics. Plant Biotechnology Journal doi: 10.1111/pbi.12467

108. Bennett, R.S., Payton, M.E., Chamberlin, K.D. 2015. Response to oxalic acid as a resistance assay for Sclerotinia minor in peanut. Peanut Science. 42(1):56-64.

109. Brasileiro ACM, Carrasquilla-Garcia N, Penmetsa RV, Cook D, Morezsohn MC, Bertioli DJ. 2015. Arachis batizocoi: a study of its relationship to cultivated peanut (A. hypogaea L.) and its potential for introgression of wild genes into the peanut crop using induced allotetraploids Annals of Botany 115 (2):237-249.

110. Brasileiro, A.C.M., C. Morgante, A.C.G. Araujo, S.C.M. Leal-Bertioli, A.K. Silva et al., 2015 Transcriptome profiling of wild Arachis from water-limited environments uncovers drought tolerance candidate genes. Plant Molecular Biology Reporter:1-17.

111. Butts, C.L., Lamb, M.C., Sorensen, R.B., Chen, S. 2015. Oven drying times for moisture content determination of single peanut kernels. Trans. ASABE. 57:579-584.

112. Cannon SB, McKain MR, Harkess A, Nelson MN, Dash S, Deyholos MK, Peng Y, Joyce B, Stewart CN Jr, Rolf M, Kutchan T, Tan X, Chen C, Zhang Y, Carpenter E, Wong GK, Doyle JJ, Leebens-Mack J. Multiple polyploidy events in the early radiation of nodulating and nonnodulating legumes. 2015. Mol Biol Evol. Jan;32(1):193-210. doi: 10.1093/molbev/msu296. Epub 2014 Oct 27.

113. Chandran, M., Y. Chu, S. J. Maleki, P. Ozias-Akins. 2015. Stability of transgene expression in reduced allergen peanut (Arachis hypogaea L.) across multiple generations and at different soil sulfur levels. J. Agric. Food Chem. 63:1788-1797.

114. Chen, H., C. Zhang, Y. Deng, S. Zhou, Y. Zheng, S. Ma, R. Tang, R.K. Varshney, W. Zhuang. 2015. Identification of low Ca2+ stress-induced embryo apoptosis response genes in Arachis hypogaea by SSH-associated library lift (SSHaLL). Plant Biotechnology Journal doi: 10.1111/pbi.12415.

115. Chopra, R., Burow, G.B., Farmer, A., Mudge, J., Simpson, C.E., Wilkins, T.A., Baring, M.R., Puppala, N., Chamberlin, K.D., Burow, M.D. 2015. Next-generation transcriptome sequencing, SNP discovery, and SNP validation in four market classes of peanut, Arachis hypogaea L. Molecular Genetics and Genomics. 290(3):1169-1180.

116. Chopra, R., G. Burow, A. Farmer, J. Mudge, C. E. Simpson, T. A. Wilkins, M. R. Baring, N. Puppala, K.D. Chamberlin, and M. D. Burow. 2015 Next-Generation Transcriptome Sequencing, SNP discovery and Validation in Four Market Classes of Peanut, Arachis hypogaea L. Mol. Gen. Genet. 290(3):1169-80. doi: 10.1007/s00438-014-0976-4.

117. Clevenger, J. and P. Ozias-Akins. 2015. SWEEP: A tool for filtering high-quality SNPs in polyploid crops. G3 5: 1797-1803.

118. Clevenger, J., C. Chavarro, S. A. Pearl, P. Ozias-Akins, and S. A. Jackson. 2015. SNP identification in polyploids: a review, example and recommendations. Mol. Plant 8:831-846.

119. Conner, J. A., M. Muruganantham, H. Huo, K. Chae, and P. Ozias-Akins. 2015. A parthenogenesis gene of apomict origin elicits embryo formation from unfertilized eggs in a sexual plant. Proc. Natl. Acad. Sci. USA 112:11205-11210.

120. Dean, L.L., Constanza, K.E., Tallury, S.P., Whaley, J.D., and T.H. Sanders. 2015. Essential oils from leaves of edible peanut (Arachis hypogaea L.) and perennial peanut (Arachis glabrata Benth. and Arachis pintoi) plants. J. Essen. Oil Bear. Plants. 18(3):605-612.

121. Faye, I., M.K. Pandey, F. Hamidou, A. Rathore, O. Ndoye, V. Vadez, R.K. Varshney. 2015. Identification of quantitative trait loci for yield and yield related traits of groundnut (Arachis hypogaea L.) under different water regimes in Niger and Senegal. Euphytica, DOI 10.1007/s10681-015-1472-6.

122. Fountain, J.C., Khera, P., Yang, L., Nayak, S., Scully, B.T., Lee, R.D., Chen, Z.-Y., Kemerait, R.C., Varshney, R.K. and Guo, B.Z. Resistance to Aspergillus flavus in maize and peanut: Biochemistry, breeding, environmental stress and future perspectives. The Crop Journal 3:229-237 (2015).

123. Fountain, J.C., Scully, B.T., Chen, Z.-Y., Gold, S.E., Glenn, A.E., Abbas, H.K., Lee, R.D., Kemerait, R.C., and Guo, B.Z. Effects of hydrogen peroxide on different toxigenic and atoxigenic isolates of Aspergillus flavus. Toxins. 7:2985-2999. 2015.



124. Fraiman-Meir, D., I. Hedvat, Y. Shem-Tov, and R. Hovav. 2015. Identification and characterization of new genetic source for pod wart resistance in peanut (Arachis hypogaea). Crop Sci. In review.

125. Gao D, Jiang N, Wing R, Jiang J, Jackson SA. 2015.Transposons play an important role in evolution and diversification of centromeres among closely related species. Front Plant Sci. 6:216.

126. Gao D, Li Y, Abernathy B, Jackson SA. 2015. Landscape and evolutionary dynamics of terminal-repeat retrotransposons in miniature (TRIMs) in 48 whole plant genomes. Genome Biology (submitted)

127. Guimarães, P.M., L.G. Arrais, C.V. Morgante, O.B. Silva Jr, A.C.G. Araujo et al., 2015 Transcriptome analysis of the wild peanut Arachis stenosperma reveals candidate genes for Meloidogyne arenaria resistance. PLoS One in press.

128. Guo, Y., B. Abernathy, Y. Zeng, and P. Ozias-Akins. 2015. TILLING by sequencing to identify induced mutations in stress resistance genes of peanut (Arachis hypogaea). BMC Genomics 16:1348.

129. Gupta, K., I. Hedvat, A. Faigenboim-Doron, P. Ozias-Akins, J. Clevenger, and R. Hovav. 2015. Transcriptome profiling of peanut developing seed with a focus on duplicate oil related pathways. BMC genomics, in submission.

130. Hickey, J.M., G. Gorjanc, R.K. Varshney, C. Nettelblad. 2015. Imputation of single nucleotide polymorphism genotypes in biparental, backcross, and topcross populations with a hidden markov model. Crop Science doi:10.2135/cropsci2014.09.0648

131. Holbrook, A. K. Culbreath, R. K. Varshney, and B. Guo. Genetic mapping of QTLs controlling fatty acids provided insights into the genetic control of fatty acid synthesis pathway in peanut (Arachis hypogaea L.). PlosOne 10(4):e0119454.doi:10.1371/journal.pone.0119454. 2015.

132. Huang, E.B., K.L. Verbyla, A.P. Verbyla, C. Raghavan, V.K. Singh, P.M. Gaur, H. Leung, R.K. Varshney, C.R. Cavanagh. 2015. MAGIC populations in crops: current status and future prospects. Theoretical and Applied Genetics 128:999-1017

133. Isleib, T.G., H.E. Pattee, R.S. Tubbs, T.H. Sanders, L.O. Dean, and K.W. Hendrix. 2015. Intensities of sensory attributes in high- and normal-oleic cultivars in the uniform peanut performance test. Peanut Sci. 42:In press.

134. Janila, P., M.K. Pandey, Y. Shasidhar, T.M. Variath, M. Sriswathi, P. Khera, S.S. Manohar, P. Nagesh, M.K. Vishwakarma, G.P. Mishra, T. Radhakrishnan, N. Manivannan, K.L. Dobariya, R.P. Vasanthi, R.K. Varshney. 2015. Molecular breeding for introgression of fatty acid desaturase mutant alleles (ahFAD2A and ahFAD2B) enhances oil quality in high and low oil containing peanut genotypes. Plant Science, doi.org/10.1016/j.plantsci.2015.08.013.

135. Johanningsmeier, S.D., G.K. Harris, and C.M. Klevorn. 2015. Metabolomic technologies for improving the quality of food: Practice and Promise. Annual Review of Food Science and Technology. In Press.

136. Kanade, S.G, A.A. Shaikh, and J.D. Jadhav. 2015. Sowing environments effect on rust (P. arachidis) disease in groundnut (Arachis hypogea L.). International Journal of Plant Protection. 8:174-180

137. Kanyika, B. T. N., D. Lungu, A. M. Mweetwa, E. Kaimoyo, V. M. Njung’e, E. S. Monyo, M. Siambi, Guohao He, C. S. Prakash, Yongli Zhao, Santie De Villiers. (2015). Identification of groundnut (Arachis hypogaea) SSR markers suitable for multiple resistance traits QTL mapping in African germplasm. Electronic Journal of Biotechnology 18(2): 61-67.

138. Klevorn, C.M., K.W. Hendrix, T.H. Sanders, and L.L. Dean. 2015. Differences in development of oleic and linoleic acid in high- and normal-oleic virginia and runner-type peanuts. Peanut Science. In Press.

139. Kole, C.D., 35 other authors, R.K. Varshney, S.D. Wullschleger, M.Yano, M. Prasad. 2015. Application of genomics-assisted breeding for generation of climate resilient crops: Progress and prospects. Frontiers in Plant Science 6:563 doi: 10.3389/fpls.2015.00563

140. Kolekar, R. M., V. Sujay, K. Shirasawa, M. Sukruth, M. V. C. Gowda, M. K. Pandey, R. K. Varshney, and R. S. Bhat. 2015. 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 (Submitted).

141. Leal-Bertioli, S, K. Shirasawa, B. Abernathy, M. Moretzsohn, C. Chavarro, J. Clevenger, P. Ozias-Akins, S. Jackson, and D. Bertioli. 2015. Tetrasomic recombination is surprisingly frequent in allotetraploid Arachis. Genetics 199:1093-1097.

142. Leal-Bertioli, S.C.M., S.P. Santos, K.M. Dantas, P.W. Inglis, S. Nielen et al., 2015. Arachis batizocoi: a study of its relationship to cultivated peanut (A. hypogaea) and its potential for introgression of wild genes into the peanut crop using induced allotetraploids. Annals of Botany 115 (2):237-249.

143. Leal-Bertioli, S.C.M., U. Cavalcante, E.G. Gouvea, C. Ballén-Taborda, K. Shirasawa, P.M. Guimarães, S.A. Jackson, D.J. Bertioli and M.C. Moretzsohn. 2015. Identification of QTL for rust resistance in the peanut wild



species Arachis magna and the development of KASPar markers for marker assisted selection. G3: Genes, Genetics and Genomes doi: 10.1534/g3.115.018796.

144. Leal-Bertioli, SCM, MC Moretzsohn, PA Roberts, C Ballén-Taborda, TCO Borba, Paula A. Valdisser, RP Vianello, ACG Araújo, PM Guimarães, DJ Bertioli. 2015. Genetic mapping of resistance to Meloidogyne arenaria in Arachis stenosperma: a new source of nematode resistance for peanut – Submitted to Genes/Genomes/Genetics.

145. Leon, R.G. and B.L. Tillman. 2015. Postemergence herbicide tolerance variation in peanut germplasm. Weed Science 63:546-554. doi: 10.1614/WS-D-14-00128.1.

146. Lifeng Liu, Phat Dang, and Charles Chen. 2015. Development and Utilization of InDel Markers to Identify Peanut (Arachis hypogaea) Disease Resistance. Frontiers in Plant Science. (Accepted)

147. Luis, J.M., P. Ozias-Akins, C.C. Holbrook, R.C. Kemerait, Jr., J. L. Snider, and V. Liakos. Phenotyping peanut genotypes for drought tolerance. Peanut Sci. (In Press). 2015.

148. Oellrich A, Walls RL, Cannon EK, Cannon SB, Cooper L, Gardiner J, Gkoutos GV, Harper L, He M, Hoehndorf R, Jaiswal P, Kalberer SR, Lloyd JP, Meinke D, Menda N, Moore L, Nelson RT, Pujar A, Lawrence CJ, Huala E. An ontology approach to comparative phenomics in plants. 2015. Plant Methods. Feb 25;11:10. doi: 10.1186/s13007-015-0053-y. eCollection 2015.

149. Paratwagh, S. A., and R. S. Bhat. 2015. Development of superior introgression lines for resistance to foliar diseases and productivity in groundnut (Arachis hypogaea L.). Electron. J. Plant Breed.(Accepted).

150. Peng, Z., M. Gallo, B. L. Tillman, D. Rowland, and J. Wang. 2015. Molecular marker development from transcript sequences and germplasm evaluation for cultivated peanut (Arachis hypogaea L.). Molecular & General Genetics, DOI:10.1007/s00438-015-1115-6

151. Shekoofa, A., P. Rosas-Anderson, T.R. Sinclair, M. Balota, and T.G. Isleib. 2015. Measurement of limited-transpiration trait under high vapor pressure deficit for peanut in chambers and in field. Agron. J. 107: 1019-1024. [doi: 10.2134/agronj14.0570]

152. Smýkal, P., C.J. Coyne, M.J. Ambrose, N. Maxted, H. Schaefer, M.W. Blair, J. Berger, S. Greene, M.N. Nelson, N. Besharat, T. Vymyslický, C. Toker, R.K. Saxena, M. Roorkiwal, M.K. Pandey, J. Hu, Y.H. Li, L.X. Wang, Y. Guo, L.J. Qiu, R.J. Redden, R.K. Varshney. 2015. Legume crops phylogeny and genetic diversity for science and breeding. Critical Reviews in Plant Sciences 34:43-104.

153. Suassuna, T.M.F., N.D. Suassuna, M.C. Moretzsohn, S.C.M. Leal-Bertioli, D.J. Bertioli et al., 2015 Yield, market quality, and leaf spots partial resistance of interspecific peanut progenies. Crop Breeding and Applied Biotechnology 15:175-180.

154. Sukruth, M., S. A. Paratwagh, V. Sujay, Varshakumari, M. V. C. Gowda, H. L. Nadaf, B. N. Motagi, S. Lingaraju, M. K. Pandey, R. K. Varshney, and R. S. Bhat. 2015. Validation of markers linked to late leaf spot and rust resistance, and selection of superior genotypes among diverse recombinant inbred lines and backcross lines in peanut (Arachis hypogaea L.). Euphytica 204:343-351. DOI: 10.1007/s10681-014-1339-2.

155. Thorntong, S.T., M. Gallo, and B.L. Tillman. 2015. Genotypic variability in calcium concentration of peanut (Arachis hypogaea L.) seeds. Crop Science 55: 211-218. doi:10.2135/cropsci2014.04.0302.

156. Tubbs, R.S., R.C. Kemerait, B. Williams, and J.M. Sarver. 2015. Effect of Bradyrhizobia inoculant formulation with phorate in new peanut fields. Peanut Sci. 42:Accepted with revision.

157. Varshney, R.K. 2015. Exciting journey of 10 years from genomes to fields and markets: Some success stories of genomics-assisted breeding in chickpea, pigeonpea and groundnut. Plant Science http://dx.doi.org/10.1016/j.plantsci.2015.09.009

158. Varshney, R.K., H. Kudapa, L. Pazhamala, A. Chitikineni, M. Thudi, A. Bohra, P.M. Gaur, P. Janila, A. Fikre, P. Kimurto, N. Ellis. 2015. Translational genomics in agriculture: some examples in grain legumes. Critical Reviews in Plant Sciences 34:169-194

159. Wang, H., Khera, P., Li, S., Ren, Y., Yuan, M., Zhuang, W., Varshney, R.K., Guo, B.Z. and Xie, L. Application of highly polymorphic SSR markers in peanut breeding and true hybrid identification. Journal of Fujian Agriculture and Forestry University (Natural Science) (in press)

160. Wang, H., P. Khera, B. Huang, M. Yuan, R. Katam, W. Zhuang, K. Harris-Shultz, K.M. Moore, A.K. Culbreath, X. Zhang, R.K. Varshney, L. Xie, B. Guo. 2015. Analysis of genetic diversity and population structure of peanut cultivars and breeding lines from China, India and the US using SSR markers. Journal of Integrative Plant Biology, DOI: 10.1111/jipb.12380

161. Wang, M. L., P. Khera, M. K. Pandey, H. Wang, L. Qiao, S. Feng, B. Tonnis, N. A. Barkley, D. Pinnow, C. C. Holbrook, A. K. Culbreath, R. K. Varshney, and B. Guo. Genetic mapping of QTLs controlling fatty acids provided



insights into the genetic control of fatty acid synthesis pathway in peanut (Arachis hypogaea L.). PlosOne 10(4):e0119454.doi:10.1371/journal.pone.0119454. 2015.

162. Wang, M.L., Khera, P., Pandey, M.K., Wang, H., Qiao, L., Feng, S., Tonnis, B., Barkley, N.A., Pinnow, D., Holbrook, C.C., Culbreath, A.K., Varshney, R.K. and Guo, B.Z. 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 10(4) e0119454. doi:10.1371/journal.pone.0119454. 2015.

163. Wang, M.L., Michael A. Grusak, Charles Y. Chen, Brandon Tonnis Noelle A. Barkley, Stacie Evans, David Pinnow, Jerry Davis, C. Corley Holbrook, and Gary A. Pederson. 2015. Seed Protein Percentage and Mineral Concentration Variability and their Correlation with Other Seed Quality Traits in the U.S. Peanut Mini-Core Collection. Journal of Agricultural and Food Chemistry. (Accepted).

164. Wang, ML, B Tonnis, Y-QC An, D Pinnow, V Tishchenko, GA Pederson (2015) Newly identified natural high-oleate mutant from Aradchis hypogaea L. subsp. hypogaea. Molecular Breeding 35:186.

165. Wang, ML, P Khera, MK Pandey, H Wang, L Qiao, S Feng, B Tonnis, NA Barkley, D Pinnow, CC Holbrook, AK Culbreath, RK Varshney, B Guo (2015) 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 DOI:10.1371/journal.pone.0119454.

166. Wang, Y., Zhang, X.G., Zhao, Y.L., Prakash, C.S., He, G.H., and Yin, D.M. (2015) Insights into the novel members of the FAD2 enzyme involved in high-oleate fluxes. Genome, doi: 10.1139/gen-2015-0008.

167. Wu, C., R. Gill, Y. Chu, C. C. Holbrook, and P. Ozias-Akins. 2015. Fine phenotyping of pod and seed traits in Arachis germplasm accessions using digital image analysis. Peanut Sci. 42 (in press).

168. Yeri, S. B., and R. S. Bhat. 2015. Development of late leaf spot and rust resistant backcross lines in JL 24 variety of groundnut (Arachis hypogaea L.). Electron. J. Plant Breed. (Accepted).

169. Ze, Pengg, M. Gallo, B.L. Tillman, D. Rowland, and J. Wang. 2015. Molecular marker development from transcript sequences and germplasm evaluation for cultivated peanut (Arachis hypogaea L.). MGAG- Accepted Sept. 3, 2015.

170. Bertioli D, Gao D, Vidigal BS, Froenicke L, Cannon S, Abernathy B, Ren L, Guerra AraujoAC, Guimarães PM, Bertioli S, Michelmore R, Jackson S.2015.Transposable elements in theA and B genomes of peanut. XXIV Plant and Animal Genome Conference, San Diego, CA. W866

171. Gao D, Bertioli D, Iwata A, Chu Y, Clevenger JP, Ozias-Akins P, Froenicke L, Liu X, Cannon S. 2015. Annotation of Transposable Elements for Peanut Improvement and Genomics. 47th American Peanut Research and Education Society (APRES) Annual Meeting, Charleston, SC. P28

172. Hake, A. A., M. V. C. Gowda, and R. S. Bhat. 2015. Analysis of TMV 2 × NLM derived RIL population towards mapping genomic regions governing taxonomic and morphological traits in peanut. In 5th International Conference on Next Generation Genomics and Integrated Breeding for Crop Improvement. ICRISAT, Hyderabad, India.

173. Kolekar, R. M., V. Sujay, K. Shirasawa, S. B. Yeri, D. B. Chougale, B. Asha, M. V. C. Gowda, R. K. Varshney, and R. S. Bhat. 2015. Mapping late leaf spot and rust resistance using an improved map from the RILs of TAG 24 × GPBD 4 in peanut (Arachis hypogaea). In 5th International Conference on Next Generation Genomics and Integrated Breeding for Crop Improvement. ICRISAT, Hyderabad, India.

174. Paratwagh, S. A., D. V. Madhumitha, M. Sukruth, D. B. Chougale, Varshakumari, B. N. Motagi, M. V. C. Gowda, and R. S. Bhat. 2015. Introgression lines with improved resistance to late leaf spot and rust in peanut. In 5th International Conference on Next Generation Genomics and Integrated Breeding for Crop Improvement. ICRISAT, Hyderabad, India.

175. Swamy, K. M., M. S. Patil, R. S. Bhat, and A. S. Byadgi. 2015. Development of coat protein mediated resistance in peanut plants against Peanut bud necrosis virus. In 36th Annual Conference & National Symposium on Challenges and Management Approaches for Crop Diseases of National Importance-Status and Prospects. Agricultural College and Research Institute, Madurai, India.

176. Varshakumari, M. V. C. Gowda, V. Tasiwal, R. S. Bhat, M. K. Pandey, and R. K. Varshney. 2015. Introgression of foliar disease resistance using synthetic amphidiploids and identification of associated QTLs in groundnut (Arachis hypogaea L.). In 5th International Conference on Next Generation Genomics and Integrated Breeding for Crop Improvement. ICRISAT, Hyderabad, India.



177. Yeri, S. B., R. M. Kolekar, M. Sukruth, H. M. Mehghashree, H. L. Nadaf, and R. S. Bhat. 2015. Marker assisted backcrossing to improve foliar disease resistance in JL 24 and TMV 2 varieties of peanut. In 5th International Conference on Next Generation Genomics and Integrated Breeding for Crop Improvement. ICRISAT, Hyderabad, India.

178. Chamberlin, K. D., J. P. Damicone, M. R. Baring, M. D. Burow, C. B. Godseye, R. S. Bennett, H. A. Melouk, and C. E. Simpson. 2015. Registration of High-Oleic Peanut Germplasm Line ARSOK-S1 (TX996784) with Enhanced Resistance to Sclerotinia Blight and Pod Rot. J. Plant Regist. 9(1):103-107. DOI: 10.3198/jpr2013.08.0044crg

179. Chamberlin, K.D., Bennett, R., Damicone, J.P., Godsey, C.B., Melouk, H.A., Keim, K. 2015. Registration of 'OLé' peanut. Journal of Plant Registrations. 9(2):154-158.

180. Isleib, T.G., S.R. Milla-Lewis, H.E. Pattee, S.C. Copeland, M.C. Zuleta, B.B. Shew, J.E. Hollowell, T.H. Sanders, L.O. Dean, K.W. Hendrix, M. Balota, J.W. Chapin, and W.S. Monfort. 2015. Registration of ‘Sugg’ peanut. J. Plant Regist. 9: 44-52. [doi: 10.3198/jpr2013.09.0059crc]

181. Tillman, B.L. and D.W. Gorbet. 2015. Registration of FloRunTM ‘107’. Journal of Plant Registrations 9(2):162-167. doi:10.3198/jpr2014.12.0086crc.

182. Barkley, N. A., H. D. Upadhyaya, B. Liao, and C. C. Holbrook. Global resources of genetic diversity in peanut.In: T. Stalker & R. Wilson Eds. Pp (In Press) Peanuts: Genetics, Processing & Utilization. Elsevier Press. 2016.

183. Bertioli, D. S.C. M. Leal-Bertioli, and H T. Stalker. 2016. The Peanut Genome: The History of the Consortium and the Structure of the Genome of Cultivated Peanut and its Diploid Ancestors. In H.T. Stalker and R.F. Wilson (eds.) Peanuts: Genetics, Processing and Utilization. Elsevier Press., In press.

184. Bertioli, DJ, SB Cannon, L Froenicke, G Huang, AD Farmer, EKS Cannon, X Liu, D Gao, J Clevenger, S Dash, L Ren, MC Moretzsohn, K Shirasawa, W Huang, B Vidigal, B Abernathy, Y Chu, CE Niederhuth, P Umale, ACG Araujo, A Kozik, KD Kim, MD Burow, RK Varshney, X Wang, X Zhang, N Barkley, PM Guimarães, S Isobe, B Guo, B Liao, HT Stalker, RJ Schmitz, BE Scheffler, SCM Leal-Bertioli, X Xun, SA Jackson, R Michelmore, P Ozias-Akins. The genome sequences of cultivated peanut’s diploid ancestors detect homeologous recombination in the allotetraploid state and uncover a living relic from agricultural prehistory – Submitted to Nature Biotechnology.

185. Chu, Y., J. Clevenger, R. Hovav, J. Wang, B. Scheffler, S.A. Jackson, P. Ozias-Akins. 2016. Application of Genomic, Transcriptomic, and Metabolomic Technologies in Arachis Species. In: Peanuts. R. Wilson and H.T. Stalker, Eds. Elsevier Press.

186. Dash, S., E.K.S. Cannon, S.R. Kalberer, A.D. Farmer, S.B. Cannon. 2016. PeanutBase and other bioinformatic resources for peanut. In: Wilson, R., and H.T. Stalker (eds.) Peanuts: Genetics, Processing, and Utilization. Elsevier Press, in press.

187. Guo, B.Z., Khera, P., Wang, H., Peng, Z., Sudini, H., Wang, X., Osiru, M., Chen, J., Vadez, V., Yuan, M., Wang, C.T., Zhang, X., Waliyar, F., Wang, J. and Varshney, R.K. Annotation of trait loci on integrated genetic maps of Arachis species. In: Tom Stalker and Rich Wilson, editors. Peanuts: Genetics, Processing, and Utilization. Elsevier Press (in press), 2016

188. Hammons, R.O., D. Herman, and H.T. Stalker. 2016. Origin and early history of the peanut In H.T. Stalker and R.F. Wilson (eds.) Peanuts: Genetics, Processing and Utilization. Elsevier Press., In press.

189. Holbrook, C.C., M.D. Burow, C. Chen, M.K. Pandey, L Liu, J.C. Chagoya, Y. Chu, P. Ozias-Akins. 2016. Recent advances in peanut breeding and genetics. In: RF Wilson and HT Stalker (eds.) Peanuts: Genetics, Production, Processing and Utilization, Elsevier Press, doi.org/10.1016/B978-1-63067-038-2.00004-6.

190. Khera, P., Pandey, K.P., Mallikarjuna, N., Sriswathi, M., Roorkiwal, M., Janila, P., Shilpa, K., Sudini, H., Guo, B.Z., Varshney, R.K. Development of introgression lines and advanced backcross QTL analysis for disease resistance, oil quality and yield component traits in peanut (Arachis hypogaea L.). Molecular Breeding (in preparation).

191. Khera, P., Wang, H., Culbreath, A.K., Pandey, M.K., Varshney, R.K., Wang, X., Liao, B., Zhang, X., Wang, J., Holbrook, C.C., Guo, B.Z. QTL mapping and quantitative disease resistance to TSWV and leaf spots in a recombinant inbred line population SunOleic 97R and NC94022 of peanut (Arachis hypogaea L.). Phytopathology (in preparation).

192. Stalker, H.T. and R.F. Wilson (eds.). 2016. Peanuts: Genetics, Processing and Utilization. Elsevier Press., Champaign, IL. In press.

193. Stalker, H.T., S. Tallury, G. Seijo, and S. Leal-Bertioli. 2016. Biology, speciation and utilization of peanut species. In H.T. Stalker and R.F. Wilson (eds.) Peanuts: Genetics, Processing and Utilization. Elsevier Press., Champaign, IL. In press.



Exhibit 2: Sponsors who provide financial support for the Peanut Genome Project U.S. Peanut Sheller Companies: • American Peanut Sheller’s Assoc. Birdsong Peanuts – Damascus Peanut Company – Golden Peanut Company – McCleskey Mills – Snyder’s/Lance – Tifton Peanut Company – Williston Peanuts • Southwestern Peanut Sheller’s – Birdsong Peanuts – Clint Williams Company – Golden Peanut Company – Wilco Peanut Company • Virginia Carolina Sheller’s Assoc. Birdsong Peanuts – Golden Peanut Company – Peanut Processors – Severn Peanut Company • American Peanut Growers Group • Brooks Peanut Company • Sessions Company • Tifton Quality Growers Food Manufacturing Companies: • Algood Food Company • American Blanching • Arway Confections, Inc. • Diamond Foods, Inc. • E.J. Cox • Hampton Farms • The Hershey Company • J.B. Sanfilippo • Jimbo’s Jumbo’s • J.M. Smucker • Kraft – Planters • Mars Chocolate • Old Home Foods • Pardoe’s Perky Peanuts • Peanut Butter & Company • The Peanut Shop of Williamsburg • Producers Peanut Company

US Peanut Producer Organizations: • National Peanut Board • Florida Peanut Producers Association • Texas Peanut Producers Association • Georgia Peanut Commission Allied Sector Companies: • B.A.G. • Bayer CropScience • Chips Group • Concordia, LLC • Dothan Warehouse • Early Trucking • Georgia Federal-State Inspection Service • Hofler Brokerage • International Service Group • JLA USA • Jack Wynn & Company • J.R. James Brokerage • Lewis M. Carter • Kelly Manufacturing Company • Lovatt & Rushing • Mazur & Hockman • M.C. McNeill & Co. LLC • National Peanut Brokers Assn. • National Peanut Buying Points Assn. • Nolin Steel • O’Connor & Company • Olam International Limited • RCB Nuts • Reed Marketing, LLC • Satake USA, Inc. • SGL International, LLC • Southern Ag Carriers International Collaborators BGI-Americas Henan Academy of Agricultural Sciences Chinese Academy of Agricultural Sciences Shandong Academy of Agricultural Sciences

Scientific and Technical Contributions to the Peanut Genome Project are provided by: Auburn University BGI-Americas Catholic University-Brasilia Chinese Academy of Agricultural Sciences EMBRAPA Generation Challenge-Gates Foundation Henan Academy of Agricultural Sciences ICRISAT (India, West & Central Africa) Indian Council of Agricultural Research (ICAR) Kazusa DNA Research Institute (Japan)

National Center Genome Resources Peanut Company of Australia Shandong Academy of Agricultural Sciences North Carolina State University Texas A & M University University of California-Davis University of Florida University of Georgia USDA-Agricultural Research Service Volcani Center (Israel)

Exhibit 3: Members of the Peanut Genome Consortium

IPGI, Executive Committee (January 2016) Members Scott Jackson, UGA (Chair) Peggy Ozias-Akins, UGA (Co-chair) Richard Michelmore, UC-Davis Rajeev Varshney, ICRISAT (India) Howard Valentine, TPF Raymond Schnell, MARS, Inc. Victor Nwosu, MARS, Inc. Corley Holbrook, USDA-ARS (Co-chair) Baozhu Guo, USDA-ARS Brian Scheffler, USDA-ARS Steven Cannon, USDA-ARS Andrew Farmer, NCGR Tom Stalker, NCSU Xin Liu, BGI Lutz Froenicke, UC-Davis Kelly Chamberlin, USDA-ARS Barry Tillman, University Florida Haile Desmae, ICRISAT-WCA Boshou Liao, CAAS, (China) David Bertioli, U Brasila (Brazil)

Soraya Bertioli, EMBRAPA Xingyou Zhang, HAAS (China) Xingjun Wang, SAAS (China) Mark Burow, TAMU Graeme Wright (PCA (Australia) Sachiko Isobe, KDRI (Japan) Ran Hovav, ARS TVC (Israel) Guillermo Seijo, IBN (Argentina) Richard Wilson, OBC Ex Officio Roy Scott, USDA-ARS-ONP Maricio Lopez, President, EMBRAPA Jean-Marcel Ribuat, Director, GCP Jeff Ehlers, Program Officer, Gates Foundation David Hoisington, Director Global Programs, UGA Howard Shapiro, Chief Agricultural Officer, MARS, Inc. Xun Xu, Deputy Director, BGI-Shenzhen Steve Brown, Executive Director, TPF T. Radhakrishnan, Director, DGR, ICAR Fuhe Luo, Vice Chairman CPPCC & CAPD CC

Acknowledgements This document was prepared with the support of The Peanut Foundation, Alexandria VA, and Oilseeds & Biosciences Consulting, Raleigh NC

Annual Report Writing Team George Birdsong

Dan Ward Victor Nwosu

Darlene Cowart

Jim Elder Chris Liebold

Howard Valentine Steve Brown

Richard F Wilson, Editor

International Peanut Genomic Research Initiative Strategic Plan, 2017-2021

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Exhibit 4: Terms and Definitions

Abridged from Http://www.panzea.org/infor/faq.html, and http://www.netsci.org/Science/Bioinform/terms.html Allele: Different forms of a gene which occupy the same position on the chromosome. Allotetraploid: A cell containing two pairs of different chromosomes (i.e. Peanut) Autotetraploid: A cell containing two pairs of the same chromosomes (i.e. Soybean) Amplification: The process of repeatedly making copies of the same piece of DNA. Annotation: Text fields of information about a biosequence which are added to a sequence databases. Annotation (the elucidation and description of biologically relevant features in the sequence) consists of the description of the following items: • Function(s) of the protein. • Post-translational modification(s). For example carbohydrates, phosphorylation, acetylation, GPI-

anchor, etc. • Domains and sites. For example calcium binding regions, ATP-binding sites, zinc fingers,

homeobox, kringle, etc. • Secondary structure. • Quaternary structure. For example homodimer, heterotrimer, etc. • Similarities to other proteins. • Disease(s) associated with deficiency(s) in the protein. • Sequence conflicts, variants, etc.

Assembly: The process of placing fragments of DNA that have been sequenced into their correct position within the chromosome. Association Mapping: As in QTL mapping, the goal of association mapping is to find a statistical association between genetic markers and a quantitative trait. However, in association mapping, the genetic markers usually must lie relatively close to a candidate gene. The goal is to identify the actual genes affecting that trait, rather than just (relatively large) chromosomal segments. QTL mapping is performed in a genetically defined population. Association mapping is performed at the population level within a set of unrelated or distantly-related individuals sampled from a population. Association mapping relies on linkage disequilibrium (LD) between the candidate gene markers and the polymorphism in that gene causes the differences in the phenotypic trait. Bacterial artificial chromosome (BAC): A long sequencing vector which is created from a bacterial chromosome by splicing a DNA fragment from another species. Once the foreign DNA has been cloned into the host bacteria, many copies of the new chromosome can be made. Base: One of five molecules which are assembled, along with a ribose and a phosphate, to form nucleotides (Figure 1). Adenine (A), guanine (G), cytosine (C), and thymine (T) are found in DNA while RNA is made from adenine (A), guanine (G), cytosine (C), and uracil (U).

Base pair (BP): The complementary bases on opposite strands of DNA which are held together by hydrogen bonding. The atomic structure of these bases preselect the pairing of adenine with thymine and the pairing of guanine with cytosine (or uracil in RNA).

http://www.panzea.org/infor/faq.html

http://www.netsci.org/Science/Bioinform/terms.html


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Bioinformatics: An absolute definition of bioinformatics has not been agreed upon. The first level, however, can be defined as the design and application of methods for the collection, organization, indexing, storage, and analysis of biological sequences (both nucleic acids [DNA and RNA] and proteins). The next stage of bioinformatics is the derivation of knowledge concerning the pathways, functions, and interactions of these genes (functional genomics) and proteins (proteomics). Bioinformatics is also referred to as computational biology. Candidate Genes: The distinction between "random" and "candidate" genes is of great importance. By random genes we refer to genes without any known function of the proteins (or RNAs) that they encode. They may be selected from a random set of expressed DNA sequences (DNA sequences that are copied, or transcribed, into RNA) at a time in cell development. Candidate genes refer to genes of known or suspected function or traits of interest. Cell: The smallest functional structural unit of living matter. Cells are classed as either procaryotic and eucaryotic. CentiMorgan (cM): The unit of measurement for distance and recombinate frequency on a genetic map. Formally, the length (number of bases) that have a 1% probability of participating in mixing of genes. For humans, the average length of a cM is one million base pairs (or 1 megabase, Mb). cDNA (complementary DNA): An artificial piece of DNA that is synthesized from an mRNA (messenger RNA) template and is created using reverse transcriptase. The single stranded form of cDNA is frequently used as a probe in the preparation of a physical map of a genome. cDNA is preferred for sequence analysis because the introns found in DNA are removed in translation from DNA ----> mRNA ----> cDNA. Chromosome: A collection of DNA and protein which organizes the human genome. Each human cell contains 23 sets of chromosomes; 22 pairs of autosomes (non sex determining chromosomes) and one pair of sex determining chromosomes. The human genome within the 23 sets of chromosomes is made of approximately 30,000 genes which are built from over 3 billion base pairs. While eukaryotic chromosomes are complex sets of proteins and DNA, prokaryotic chromosomal DNA is circular with the entire genome on a single chromosome. Cloning: The technique used to produce copies of a piece of DNA. A DNA fragment that contains a gene of interest is inserted into the genome of a virus or plasmid which is then allowed to replicate. Cloning vector: A piece of DNA from any foreign body which is grafted into a host DNA strand that can then self replicate. Vectors are used to introduce foreign DNA into host cells for the purpose of manufacturing large quantities of the new DNA or the protein that the DNA expresses. Coding region: The portion of a genome that is translated to RNA which in turn codes protein (also see exon). Codon: The set of three nucleotides along a strand of mRNA that determine (or code) the amino acid placement during protein synthesis. The number of possible arrangements of these three nucleotides (or triplet codes) available for protein synthesis is (4 bases)3 = 64. Thus, each amino acid can be coded by up to 6 different triplet codes. Three triplet codes (UAA, UAG, UGA) specify the end of the protein. In the example below, three codons are shown.

--- UCA CGU CAU --- Ser ------ Arg ------- His

Complementarity: The sequence-specific or shape-specific recognition that occurs when two or more molecules bind together. DNA forms double stranded helixes because the complementary orientation of the bases in each strand facilitate the formation of the hydrogen bonds which hold the strands together. Computational biology: See bioinformatics Consensus sequence: The most commonly occurring amino acid or nucleotide at each position of an aligned series of proteins or polynucleotides.


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Consensus map: The location of all consensus sequences in a series of multiply aligned proteins or polynucleotides. Conserved sequence: A sequence within DNA or protein that is consistent across species or has remained unchanged within the species over its evolutionary period. Contig maps: The representation of the structure of contiguous regions of the genome (contigs) by specifying overlap relationships among a set of clones. Contigs: A series of cloning vectors which are ordered in such a way as to have each sequence overlap that of its neighbors. The result is that the assembly of the series provides a contiguous part of a genome. Diploid: A cell containing two sets of chromosomes. DNA (deoxyribonucleic acid): A double stranded molecule made of a linear assembly of nucleotides. DNA holds the genetic code for an organism in the arrangement of the bases. The double strand of DNA results from the hydrogen bonds formed between bases when two polynucleotide chains, identical, but running in opposite directions, associate. DNA polymerase: The enzyme which assembles DNA into a double helix by adding complementary bases to a single strand of DNA. Linkages are formed by adding nucleotides at the 5' hydroxyl group to the phosphate group located on the 3' hydroxyl. EMBL: The European Molecular Biology Laboratory (http://www.embl-heidelberg.de) which is located in Heidelberg Germany. EMBL Nucleotide Sequence Database: Europe's primary nucleotide sequence resource. Main sources for DNA and RNA sequences are direct submissions from individual researchers, genome sequencing projects and patent applications. The database is produced in collaboration with GenBank and the DNA Database of Japan (DDBJ). Each of the three groups collects a portion of the total sequence data reported worldwide, and all new and updated database entries are exchanged between the groups on a daily basis. Endonuclease: An enzyme that cleaves at internal locations within a nucleotide sequence. The enzyme's site of action is generally a sequence of 8 bases. For E. coli, treatment with a restriction endonuclease will lead to around 70 fragments. Cleavage of human DNA leads to around 50,000 fragments. Enzyme: A protein which catalyzes (or speeds the rate of reaction for) biochemical processes, but which does not alter the nature or direction of the reaction. EST (Expressed Sequence Tag): A partial sequence of a cDNA clone that can be used to identify sites in a gene. Eukaryote: An organism whose genomic DNA is organized as multiple chromosomes within a separate organelle -- the cell nucleus. Exon: The region of DNA which encodes proteins. These regions are usually found scattered throughout a given strand of DNA. During transcription of DNA to RNA, the separate exons are joined to form a continuous coding region. Exonuclease: An enzyme which cleaves nucleotides sequentially starting at the free end of the linear chain of DNA. FASTA: An alignment program for protein sequences created by Pearson and Lipman in 1988. The program is one of the many heuristic algorithms proposed to speed up sequence comparison. The basic idea is to add a fast prescreen step to locate the highly matching segments between two sequences, and then extend these matching segments to local alignments using more rigorous algorithms such as Smith-Waterman.

http://www.embl-heidelberg.de/


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Fingerprinting: The process of identifying overlapping regions at the ends of DNA fragments. FISH: Fluorescence in situ hybridization. A method used to pinpoint the location of a DNA sequence on a chromosome. Frameshift: Genetic mutation which shifts the reading frame used to translate mRNA (see reading frame). Functional genomics: The development and application of experimental approaches to assess gene function by making use of the information and reagents provided by structural genomics. Gene: A section of DNA at a specific position on a particular chromosome that specifies the amino acid sequence for a protein. Gene expression profiling: Determining specifically which genes are “switched on,” with precise definition of the phenotypic trait. Gene mapping: Determining the relative physical locations of genes on a chromosome. Useful for plant and animal breeding. GenBank: The NIH genetic sequence database. An annotated collection of all publicly available DNA sequences which is located at http://www.ncbi.nlm.nih.gov. There are approximately 2,162,000,000 bases in 3,044,000 sequence records as of December 1998. GenBank is part of the International Nucleotide Sequence Database Collaboration, which is comprised of the DNA DataBank of Japan (DDBJ), the European Molecular Biology Laboratory (EMBL), and GenBank at NCBI. These three organizations exchange data on a daily basis. Gene expression: The conversion of the information encoded in a gene to messenger RNA which is in turn converted to protein. Genetic map (Linkage Map): The linear order of genes on a chromosome of a species. Genetic maps are created by observing the recombination of tagged genetic segments (STSs) during meiosis. The map shows the position of known genes and markers relative to each other, but does not show the specific physical points on the chromosomes. Genetic mutation: An inheritable alteration in DNA or RNA which results in a change in the structure, sequence, or function of a gene. Genetic polymorphism: The occurrence of one or more different alleles at the same locus in a one percent or greater of a specific population. Genome: The total genetic material of a given organism. Genomics: The mapping, sequencing, and analysis of an organism's genome. Genomic library: A collection of biomolecules made from DNA fragments of a genome that represent the genetic information of an organism that can be propagated and then systematically screened for particular properties. The DNA may be derived from the genomic DNA of an organism or from DNA copies made from messenger RNA molecules. A computer-based collection of genetic information from these biomolecules can be a virtual genomic library. Genotyping: The use of markers to organize the genetic information found in individual DNA samples and to measure the variation between such samples. Haploid: A cell containing only one set of chromosomes. Hexaploid: A cell containing three sets of the same chromosomes (i.e. Wheat) Hybridization: The formation of a double stranded DNA, RNA, or DNA/RNA from two complementary oligonucleotide strands.

http://www.ncbi.nlm.nih.gov/


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Intron: The portion of a DNA sequence which interrupts the protein coding sequences of the gene. Most introns begin with the nucleotides GT and end with the nucleotides AG.

In vitro: Outside a living organism, usually in a test tube. In vivo: Inside a living organism. Kilobase (kb): A length of DNA equal to 1,000 nucleotides. Linkage analysis: The process used to study genotype variations between affected and healthy individuals wherein specific regions of the genome that may be inherited with, or "linked" to, disease are determined. Linkage Disequilibrium (LD): In population genetics, LD is the association of alleles at two or more loci on same or different chromosome that is greater than random association. Populations where combinations of alleles or genotypes can be found in the expected proportions are said to be in linkage equilibrium. Linkage map: A map which displays the relative positions of genetic loci on a chromosome. Loci: The location of a gene or other marker on the surface of a chromosome. The use of locus is sometimes restricted to mean regions of DNA that are expressed. Mapping: The process of determining the positions of genes and the distances between them on a chromosome. This is accomplished by identifying unique genome markers (ESTs, STSs, etc.) and localizing these to specific sites on the chromosome. There are three types of DNA maps: physical maps, genetic maps, and cytogenetic maps. The types of markers identified differentiate the map produced. Marker: A physical location on a chromosome which can be reliably monitored during replication and inheritance. Markers on the Human Transcript Map are all STSs. Microarray: DNA which has been anchored to a chip as an array of microscopic dots, each one of which represents a gene. Messenger RNA which encodes for known proteins is added and will hybridize with its complementary DNA on the chip. The result will be a fluorescent signal indicating that the specific gene has been activated. Microsatellite: a specific sequence of DNA bases or nucleotides which contains mono, di, tri, or tetra tandem repeats. For example

GGGGGGGG is a (G)8 ACACACAC is referred to as a (AC)4 ATCATCACTACTACT would be referred to as (ATC)5 ATCTATCT would be referred to as (ATCT)2

Microsatallites also are called simple sequence repeats (SSR), short tandem repeats (STR), or variable number tandem repeats (VNTR). Motifs: A pattern of DNA sequence that is similar for genes of similar function. Also a pattern for protein primary structure (sequence motifs) and tertiary structure that is the same across proteins of similar families.


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mRNA (messenger RNA): RNA that is used as the template for protein synthesis. The first codon in a messenger RNA sequence is almost always AUG NCBI: The National Center for Biotechnology Information (http://www.ncgi.nlm.nih.gov), a division of the NIH, is the home of the BLAST and Entrez servers. NCGR: The National Center for Genome Resources (http://www.ncgr.org). Nucleotide (nt): A molecule which contains three components: a sugar (deoxyribose in DNA, ribose in RNA), a phosphate group, and a heterocyclic base.

Oligos (Oligonucleotides): A chain of nucleotides. Pairwise alignment: In the first step, two sequences are padded by gaps so that they are the same length and so that they display the maximum similarity on a residue to residue basis. An optimal Pairwise Alignment is an alignment which has the maximum amount of similarity with the minimum number of residue 'substitutions'. PCR (polymerase chain reaction; in vitro DNA amplification): The laboratory technique for duplicating (or replicating) DNA using the bacterium Thermus aquaticus, a heat stable bacterium from the hot springs of Yellowstone. As with the polymerase reaction that occurs in cells, there are three stages of a PCR process: separation of the DNA double helix, addition of the primer to the section of the DNA strand which is to be copies, and synthesis of the new DNA. Since PCR is run in a single reaction vessel, the reactor contains all of the components necessary for replication: the target DNA, nucleotides, the primer, and the bacterial DNA polymerase. PCR is initiated by heating the reaction vessel to 90° which causes the DNA chains to separate. The temperature is lowered to 55° to allow the primers to bind to the section of the DNA that they were designed to recognize. Replication is then initiated by heating the vessel to 75°. The process is repeated until the quantity of new DNA desired in obtained. Thirty cycles of PCR can produce over 1 million copies of a target DNA. Physical map: The physical locations (and order) on chromosomes of identifiable areas of DNA sequences such as restriction sites, genes, coding regions, etc. Physical maps are used when searching for disease genes by positional cloning strategies and for DNA sequencing. Polymerase: The process of copying DNA in each chromosome during cell division. In the first step the two DNA chains of the double helix unwind and separate into separate strands. Each strand then serves as a template for the DNA polymerase to make a copy of each strand starting at the 3' end of the chain. Polymorphic marker: A length of DNA that displays population-based variability so that its inheritance can be followed. Polymorphism: Individual differences in DNA. Single nucleotide polymorphism (the difference of one nucleotide in a DNA strand) is currently of interest to a number of companies. Quantitative trait locus (QTL): A locus, or location, on a chromosome for genes that govern a measurable trait with continuous variation, such as height, weight, or color intensity. The presence of a QTL is inferred from genetic mapping, where the total variation is partitioned into components linked to a number of discrete chromosome regions.

http://www.ncgi.nlm.nih.gov/

http://www.ncgr.org/


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QTL mapping: QTLs are detected through QTL mapping populations produced from crossing two inbred lines. The first offspring generation (the F1) is uniformly heterozygous. However, in the second generation (the F2) the parental alleles segregate and most chromosomes recombine. Genes and genetic markers that are close together on a chromosome will tend to co-segregate in the F2 (the same allele combinations that occurred in one of the parents will tend to occur together in the offspring). The closer together are two markers or genes on a chromosome, the less likely the parental alleles at the two loci will be split up in the F2 as a result of recombination. This will lead to a statistical association between a gene segregating for alleles that have a measurable difference in their effect on a quantitative trait and segregating alleles at closely linked marker loci. QTLs can thus be localized to specific chromosomal segments if the trait is measured in all the F2 offspring and if all of these offspring are genotyped at hundreds of genetic markers covering the whole genome. Reading frame (also open reading frame): The stretch of triplet sequence of DNA that encodes a protein. The reading frame is designated by the initiation or start codon and is terminated by a stop codon. As an example, the sequence CAGAUGAGGUCAGGCAUA potentially can be translated as follows:

Position 1 CAG AUG AGG UCA GGC AUA gln met arg ser Gly ile Position 2 C AGA UGA GGU CAG GCA UA arg trp gly Gln ala Position 3 CA GAU GAG GUC AGG CAU A asp glu val Arg his

DNA (through RNA) uses a triplet code to specify the amino acid for a given protein. As can be seen above, a given strand of DNA has three possible starting points (position [or reading frame] one, two, or three). Since both strands of DNA can be translated into RNA and then into protein, a sequence of double helical DNA can specify six different reading frames. Recombinant Inbred Lines (RIL): RILs are the highly inbred progeny of a segregating population or QTL mapping resource. Two parental inbred lines are crossed, the F1 are intermated (or selfed) to form an F2 generation. Numerous individuals from the segregating F2 generation then serve as the founders of RILs. Each subsequent generation of a given RIL is formed by selfing in the previous generation and with single seed descent. In this manner each RIL, after several generations, will contain two identical copies of each chromosome, with most of them being recombinant. Scaffold: A series of contigs that are in the correct order, but are not connected in one continuous length. Sequencing: Determining the order of nucleotides in a gene or the order of amino acids in a protein. Shotgun method: A method that uses enzymes to cut DNA into hundreds (or thousands) of random bits which are then reassembled by computer so it looks like the original genome. The Human Genome Project shotgun approach is applied to cloned DNA fragments that already have been mapped so that it is known exactly where they are located on the genome, making assembly easier and much less prone to error. Single nucleotide polymorphism (SNP): The most common type of DNA sequence variation. An SNP is a change in a single base pair at a particular position along the DNA strand. When an SNP occurs, the gene's function may change, as seen in the development of bacterial resistance to antibiotics or of cancer in humans. Transcriptome: The complete collection of RNA molecules transcribed (or processed) from the DNA of a cell. Transcription: The process of copying a strand of DNA to yield a complementary strand of RNA Translation: The process of sequentially converting the codons on mRNA into amino acids which are then linked to form a protein.

Documents

Peanut Genome Project 2015-2016 Research …...This report is the 4 th edition of annual research accomplishments from the 5-year The Peanut Genome Project. Achievements in 2015-2016