Here we analyse genetic variation, population structure and diversity among 3,010 diverse Asian cultivated rice (Oryza sativa L.) genomes from the 3,000 Rice Genomes Project. Our results are consistent with the five major groups previously recognized, but also suggest several unreported subpopulations that correlate with geographic location. We identified 29 million single nucleotide polymorphisms, 2.4 million small indels and over 90,000 structural variations that contribute to within-and between-population variation. Using pan-genome analyses, we identified more than 10,000 novel full-length protein-coding genes and a high number of presence-absence variations. The complex patterns of introgression observed in domestication genes are consistent with multiple independent rice domestication events. The public availability of data from the 3,000 Rice Genomes Project provides a resource for rice genomics research and breeding.Asian cultivated rice is grown worldwide and comprises the staple food for half of the global population. It is envisaged that by the year 2035 1 feeding this growing population will necessitate that an additional 112 million metric tons of rice be produced on a smaller area of land, using less water and under more fluctuating climatic conditions, which will require that future rice cultivars be higher yielding and resilient to multiple abiotic and biotic stresses. The foundation of the continued improvement of rice cultivars is the rich genetic diversity within domesticated populations and wild relatives [2][3][4] . For over 2,000 years, two major types of O. sativa-O. sativa Xian group (here referred to as Xian/Indica (XI) and also known as , Hsien or Indica) and O. sativa Geng Group (here referred to as Geng/Japonica (GJ) and also known as , Keng or Japonica)-have historically been recognized [5][6][7] . Varied degrees of post-reproductive barriers exist between XI and GJ rice accessions 8 ; this differentiation between XI and GJ rice types and the presence of different varietal groups are well-documented at isozyme and DNA levels 6,9 . Two other distinct groups have also been recognized using molecular markers 10 ; one of these encompasses the Aus, Boro and Rayada ecotypes from Bangladesh and India (which we term the circum-Aus group (cA)) and the other comprises the famous Basmati and Sadri aromatic varieties (which we term the circum-Basmati group (cB)).Approximately 780,000 rice accessions are available in gene banks worldwide 11 . To enable the more efficient use of these accessions in future rice improvement, the Chinese Academy of Agricultural Sciences, BGI-Shenzhen and International Rice Research Institute sequenced over 3,000 rice genomes (3K-RG) as part of the 3,000 Rice Genomes Project 12. Here we present analyses of genetic variation in the 3K-RG that focus on important aspects of O. sativa diversity, single nucleotide polymorphisms (SNPs) and structural variation (deletions, duplications, inversions and translocations). We also construct a species pangenome consisting of 'core...

OBITUARY Heinrich Rohrer, pioneer of scanning tunnelling microscopy, remembered p.30 GENES US Supreme Court patent rulings set a higher bar for innovation p.29 ART Exhibition revels in the power of unconstrained thought p.28 SPACE An elegy for the disappearing dark, banished by science p.26 Feeding the future We must mine the biodiversity in seed banks to help to overcome food shortages, urge Susan McCouch and colleagues. The International Center for Tropical Agriculture in Colombia holds 65,000 crop samples from 141 countries.

We have identified about 20 million rice SNPs by aligning reads from the 3000 rice genomes project with the Nipponbare genome. The SNPs and allele information are organized into a SNP-Seek system (http://www.oryzasnp.org/iric-portal/), which consists of Oracle database having a total number of rows with SNP genotypes close to 60 billion (20 M SNPs × 3 K rice lines) and web interface for convenient querying. The database allows quick retrieving of SNP alleles for all varieties in a given genome region, finding different alleles from predefined varieties and querying basic passport and morphological phenotypic information about sequenced rice lines. SNPs can be visualized together with the gene structures in JBrowse genome browser. Evolutionary relationships between rice varieties can be explored using phylogenetic trees or multidimensional scaling plots.

We describe updates to the Rice SNP-Seek Database since its first release. We ran a new SNP-calling pipeline followed by filtering that resulted in complete, base, filtered and core SNP datasets. Besides the Nipponbare reference genome, the pipeline was run on genome assemblies of IR 64, 93-11, DJ 123 and Kasalath. New genotype query and display features are added for reference assemblies, SNP datasets and indels. JBrowse now displays BAM, VCF and other annotation tracks, the additional genome assemblies and an embedded VISTA genome comparison viewer. Middleware is redesigned for improved performance by using a hybrid of HDF5 and RDMS for genotype storage. Query modules for genotypes, varieties and genes are improved to handle various constraints. An integrated list manager allows the user to pass query parameters for further analysis. The SNP Annotator adds traits, ontology terms, effects and interactions to markers in a list. Web-service calls were implemented to access most data. These features enable seamless querying of SNP-Seek across various biological entities, a step toward semi-automated gene-trait association discovery. URL: http://snp-seek.irri.org.

Only a small fraction of the naturally occurring genetic diversity available in the world's germplasm repositories has been explored to date, but this is expected to change with the advent of affordable, high-throughput genotyping and sequencing technology. It is now possible to examine genome-wide patterns of natural variation and link sequence polymorphisms with downstream phenotypic consequences. In this paper, we discuss how dramatic changes in the cost and efficiency of sequencing and genotyping are revolutionizing the way gene bank scientists approach the responsibilities of their job. Sequencing technology provides a set of tools that can be used to enhance the quality, efficiency, and cost-effectiveness of gene bank operations, the depth of scientific knowledge of gene bank holdings, and the level of public interest in natural variation. As a result, gene banks have the chance to take on new life. Previously seen as "warehouses" where seeds were diligently maintained, but evolutionarily frozen in time, gene banks could transform into vibrant research centers that actively investigate the genetic potential of their holdings. In this paper, we will discuss how genotyping and sequencing can be integrated into the activities of a modern gene bank to revolutionize the way scientists document the genetic identity of their accessions; track seed lots, varieties, and alleles; identify duplicates; and rationalize active collections, and how the availability of genomics data are likely to motivate innovative collaborations with the larger research and breeding communities to engage in systematic and rigorous phenotyping and multilocation evaluation of the genetic resources in gene banks around the world. The objective is to understand and eventually predict how variation at the DNA level helps determine the phenotypic potential of an individual or population. Leadership and vision are needed to coordinate the characterization of collections and to integrate genotypic and phenotypic information in ways that will illuminate the value of these resources. Genotyping of collections represents a powerful starting point that will enable gene banks to become more effective as stewards of crop biodiversity.

BackgroundUnderstanding population structure of the wild progenitor of Asian cultivated rice (O. sativa), the Oryza rufipogon species complex (ORSC), is of interest to plant breeders and contributes to our understanding of rice domestication. A collection of 286 diverse ORSC accessions was evaluated for nuclear variation using genotyping-by-sequencing (113,739 SNPs) and for chloroplast variation using Sanger sequencing (25 polymorphic sites).ResultsSix wild subpopulations were identified, with 25 % of accessions classified as admixed. Three of the wild groups were genetically and geographically closely related to the O. sativa subpopulations, indica, aus and japonica, and carried O. sativa introgressions; the other three wild groups were genetically divergent, had unique chloroplast haplotypes, and were located at the geographical extremes of the species range. The genetic subpopulations were significantly correlated (r 2 = 0.562) with traditional species designations, O. rufipogon (perennial) and O. nivara (annual), differentiated based on morphology and life history. A wild diversity panel of 95 purified (inbred) accessions was developed for future genetic studies.ConclusionsOur results suggest that the cultivated aus subpopulation is most closely related to an annual wild relative, japonica to a perennial wild relative, and indica to an admixed population of diverse annual and perennial wild ancestors. Gene flow between ORSC and O. sativa is common in regions where rice is cultivated, threatening the identity and diversity of wild ORSC populations. The three geographically isolated ORSC populations harbor variation rarely seen in cultivated rice and provide a unique window into the genetic composition of ancient rice subpopulations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12284-016-0119-0) contains supplementary material, which is available to authorized users.

Contents Summary1407I.Introduction1408II.Technological advances and their utility for gene banks and breeding, and longer‐term contributions to SDGs1408III.The challenges that must be overcome to realise emerging R&D opportunities1410IV.Renewed governance structures for PGR (and related big data)1413V.Access and benefit sharing and big data1416VI.Conclusion1417Acknowledgements1417ORCID1417References1417 Summary Over the last decade, there has been an ongoing revolution in the exploration, manipulation and synthesis of biological systems, through the development of new technologies that generate, analyse and exploit big data. Users of Plant Genetic Resources (PGR) can potentially leverage these capacities to significantly increase the efficiency and effectiveness of their efforts to conserve, discover and utilise novel qualities in PGR, and help achieve the Sustainable Development Goals (SDGs). This review advances the discussion on these emerging opportunities and discusses how taking advantage of them will require data integration and synthesis across disciplinary, organisational and international boundaries, and the formation of multi‐disciplinary, international partnerships. We explore some of the institutional and policy challenges that these efforts will face, particularly how these new technologies may influence the structure and role of research for sustainable development, ownership of resources, and access and benefit sharing. We discuss potential responses to political and institutional challenges, ranging from options for enhanced structure and governance of research discovery platforms to internationally brokered benefit‐sharing agreements, and identify a set of broad principles that could guide the global community as it seeks or considers solutions.

A novel QTL cluster for chalkiness on Chr04 was identified using single environment analysis and joint mapping across 9 environments in Asia and South American. QTL NILs showed that each had a significant effect on chalk. Chalk in rice grains leads to a significant loss in the proportion of marketable grains in a harvested crop, leading to a significant financial loss to rice farmers and traders. To identify the genetic basis of chalkiness, two sets of recombinant inbred lines (RILs) derived from reciprocal crosses between Lemont and Teqing were used to find stable QTLs for chalkiness. The RILs were grown in seven locations in Asia and Latin American and in two controlled environments in phytotrons. A total of 32 (21) and 46 (22) QTLs for DEC and PGWC, most of them explaining more than 10% of phenotypic variation, were detected based on single environment analysis in T/L (L/T) population, respectively. Seven (2) and 7 (3) QTLs for DEC and PGWC were identified in the T/L (L/T) population using joined analysis across all environments, respectively. Six major QTLs clusters were found on five chromosomes: 1, 2, 4, 5 and 11. The biggest cluster at id4007289-RM252 on Chr04 was a novelty, including 16 and 4 QTLs detected by single environment analysis and joint mapping across all environments, respectively. The detected digenic epistatic QTLs explained up to 13% of phenotypic variation, suggesting that epistasis play an important role in the genetic control of chalkiness in rice. QTL NILs showed that each QTL cluster had a significant effect on chalk. These chromosomal regions could be targets for MAS, fine mapping and map-based cloning for low chalkiness breeding.