Rice grain shape is a major determinant of rice market value and the end-use. We mapped quantitative trait loci (QTL) for grain shape traits in a bi-parental recombinant inbred line population (Trenasse/Jupiter) and discovered two major grain length QTLs—qGL3.1 and qGL7.1. Previously, a major grain shape gene GS3 was reported in the qGL3.1 region and grain length gene GL7 was reported to be encompassing qGL7.1 locus. The re-sequencing SNP data on the International Rice Research Institute (IRRI) 3K Rice Genome Project (RGP) panel were obtained from the IRRI SNP-Seek database for both genes and haplotype diversity was characterized for each gene in this diverse panel. United States rice germplasm was not well represented in the IRRI 3K RGP database. Therefore, a minimum SNP set was identified for each gene that could differentiate all the characterized haplotypes. These haplotypes in the 3K RGP panel were screened across 323 elite U.S. genotypes using the minimum SNP set. The screening of haplotypes and phenotype association confirmed the role of GS3 under qGL3.1. However, screening of the GL7 haplotypes in the U.S. germplasm panel showed that GL7 did not play a role in qGL7.1, and in addition, GL7.1 did not segregate in the Trenasse/Jupiter RIL population. This concluded that qGL7.1 is a novel QTL discovered on chr7 for grain shape in the Trenasse/Jupiter RIL population. A high-throughput KASP-based SNP marker for each locus (GS3 and qGL7.1) was identified and validated in elite U.S. rice germplasm to be used in an applied rice breeding program.

Global changes in Cannabis legislation after decades of stringent regulation, and heightened demand for its industrial and medicinal applications have spurred recent genetic and genomics research. An international research community emerged and identified the need for a web portal to host Cannabis-specific datasets that seamlessly integrates multiple data sources and serves omics-type analyses, fostering information sharing. The Tripal platform was used to host public genome assemblies, gene annotations, QTL and genetic maps, gene and protein expression, metabolic profile and their sample attributes. SNPs were called using public resequencing datasets on three genomes. Additional applications, such as SNP-Seek and MapManJS, were embedded into Tripal. A multi-omics data integration web-service API, developed on top of existing Tripal modules, returns generic tables of sample, property, and values. Use-cases demonstrate the API's utility for various -omics analyses, enabling researchers to perform multi- omics analyses efficiently.

Purpleputtu (Oryza sativa ssp. indica cv. Purpleputtu) is a unique rice landrace from southern India that exhibits predominantly purple color. This study reports the underlying genetic complexity of the trait, associated domestication and de-domestication processes during its coevolution with present day cultivars. Along-with genome level allelic variations in the entire gene repertoire associated with the purple, red coloration of grain and other plant parts. Comparative genomic analysis using ‘a panel of 108 rice lines’ revealed a total of 3,200,951 variants including 67,774 unique variations in Purpleputtu (PP) genome. Multiple sequence alignment uncovered a 14 bp deletion in Rc (Red colored, a transcription factor of bHLH class) locus of PP, a key regulatory gene of anthocyanin biosynthetic pathway. Interestingly, this deletion in Rc gene is a characteristic feature of the present-day white pericarped rice cultivars. Phylogenetic analysis of Rc locus revealed a distinct clade showing proximity to the progenitor species Oryza rufipogon and O. nivara. In addition, PP genome exhibits a well conserved 4.5 Mbp region on chromosome 5 that harbors several loci associated with domestication of rice. Further, PP showed 1,387 unique when SNPs compared to 3,023 lines of rice (SNP-Seek database). The results indicate that PP genome is rich in allelic diversity and can serve as an excellent resource for rice breeding for a variety of agronomically important traits such as disease resistance, enhanced nutritional values, stress tolerance, and protection from harmful UV-B rays.

Additional file 6. SNP effect results from SNP-Seek.

Additional file 1:Table S1. Example of DAS-ELISA results on O. glaberrima accessions. DAS-ELISA were performed on the systemic leaves of individual plants harvested 15 days after inoculation as described in [50]. OD is the optical density at 405 nm; ODcor represents OD minus the mean of negative (buffer) controls. Samples were considered positive when ODcor are superior to 0,1. Tog5681 and Tog7291 were included as resistant controls; CG14 and Og82 as susceptible ones. This table presents data acquired in a single experiment, on a subset of 8 plants per accession, on all the accessions identified as resistant in this study, except Og423. Table S2. ID, phenotype and genotype of accessions characterized for RYMV resistance. Resistance to RYMV was evaluated after mechanical inoculation of the BF1 isolate in this study or in previous studies [12, 13]. Alleles on resistance genes or candidates refer to the results presented in the Additional file 1: Table S3, Table S4, Table S5 or in previous studies [12]. Table S3. Genotype on the RYMV1 resistance gene. Only positions where polymorphisms were detected in the O. glaberrima collection analyzed in Cubry et al. [21] were included. Nucleotide positions refer to the IRGSP1.0 reference sequence of the O. sativa Nipponbare accession [51] that was used as mapping reference. The effect of the mutations are based on the ORGLA04G0147000.1 gene model established on the O. glaberrima CG14 accession [1]. Mutations are described according to the nomenclature proposed by Den Dunnen et al. [55], except that synonymous mutations and mutations occurring in an intron are denoted “syn” and “intron”, respectively. Different variants at the protein level were considered as different alleles. Names for resistance alleles were previously attributed by Albar et al. [14] and Thiemele et al. [12], but an additional protein variant observed in susceptible accessions was given the name “Rymv1–1-Og2”, and for greater clarity the allele named “Rymv1–1-Og” in [12] was referred to as “Rymv1–1-Og1”. Table S4. Genotype on the CPR5–1 gene, candidate for RYMV2. Only positions were polymorphisms were detected in the O. glaberrima collection analyzed in Cubry et al. [21] were included. Nucleotide positions referred to the IRGSP1.0 reference sequence of the O. sativa Nipponbare accession [51] that was used as mapping reference. The effects of the mutations are based on the ORGLA01G0359000.1 gene model established on the O. glaberrima CG14 accession [1]. Mutations are described according to the nomenclature proposed by Den Dunnen et al. [55], except that synonymous mutations and mutations occurring in an intron are noted “syn” and “intron”, respectively. Different variants at the protein level were considered as different alleles. The allele names were chosen to distinguish protein variants associated or not with RYMV resistance. Table S5. Genotype on the NLRRYMV3 gene, candidate for RYMV3. Only positions were polymorphisms were detected in to the O. glaberrima collection analyzed in Cubry et al. [21] were included. Nucleotide positions refer to the IRGSP1.0 reference sequence of the O. sativa Nipponbare accession [51] that was used as mapping reference. The effects of the mutations are based on the ORGLA11G0175800.1 gene model established on the O. glaberrima CG14 accession [1]. Mutations are described according to the nomenclature proposed by Den Dunnen et al. [55], except that synonymous mutations and mutations occurring in an intron are noted “syn” and “intron”, respectively. Different variants at the protein level were considered as different alleles. The allele names were chosen to distinguish protein variants associated or not with RYMV resistance. Table S6. Diversity on RYMV resistance genes or candidates in accessions from the 3000 Rice Genomes Project [26]. Only non-synonymous SNPs from the base SNPs set are reported here. SNP effects were retrieved from the SNP-Seek database [25] and indels effects were evaluated manually. The effects of mutations on CDS and proteins are based on the Os04g42140.1 and Os01g68970.1 gene models established on the Nipponbare IRGSP1.0 sequence [51], for RYMV1 and CPR5–1, respectively. For NLRRYMV3, the CDS is based on the Os11g43700.1 gene mode, except that the ATG codon was shifted from 180 nucleotides downstream of the original starting codon to best fit the corresponding CDS of the ORGLA11G0175800.1 gene model established on CG14 reference sequence. Effects on the CDS and protein were thus adapted. Frequency refers to the percentage of the alternate variant in the complete set of accessions. Mutations located in the PFAM domains MA3, MIF4G and LRR and in the HMM Panther hit LRR are indicated.

• Indicates statistically significant results after correction for multiple testing.

BackgroundVulnerabilities to dependence on addictive substances are substantially heritable complex disorders whose underlying genetic architecture is likely to be polygenic, with modest contributions from variants in many individual genes. “Nontemplate” genome wide association (GWA) approaches can identity groups of chromosomal regions and genes that, taken together, are much more likely to contain allelic variants that alter vulnerability to substance dependence than expected by chance.Methodology/Principal FindingsWe report pooled “nontemplate” genome-wide association studies of two independent samples of substance dependent vs control research volunteers (n = 1620), one European-American and the other African-American using 1 million SNP (single nucleotide polymorphism) Affymetrix genotyping arrays. We assess convergence between results from these two samples using two related methods that seek clustering of nominally-positive results and assess significance levels with Monte Carlo and permutation approaches. Both “converge then cluster” and “cluster then converge” analyses document convergence between the results obtained from these two independent datasets in ways that are virtually never found by chance. The genes identified in this fashion are also identified by individually-genotyped dbGAP data that compare allele frequencies in cocaine dependent vs control individuals.Conclusions/SignificanceThese overlapping results identify small chromosomal regions that are also identified by genome wide data from studies of other relevant samples to extents much greater than chance. These chromosomal regions contain more genes related to “cell adhesion” processes than expected by chance. They also contain a number of genes that encode potential targets for anti-addiction pharmacotherapeutics. “Nontemplate” GWA approaches that seek chromosomal regions in which nominally-positive associations are found in multiple independent samples are likely to complement classical, “template” GWA approaches in which “genome wide” levels of significance are sought for SNP data from single case vs control comparisons.

Characteristics of the selected SNPs.