Genetic diversity and population structure of 93 rice cultivars (lines) (Oryza sativa Xian group) in Qinba in China by 3 types of genetic markers.

BACKGROUND: The Qinba region is the transition region between Indica and Japonica varieties in China. It has a long history of Indica rice planting of more than 7000 years and is also a planting area for fine-quality Indica rice. The aims of this study are to explore different genetic markers applied to the analysis population structure, genetic diversity, selection and optimization of molecular markers of Indica rice, thus providing more information for the protection and utilization on germplasm resources of Indica rice. METHODS: Fifteen phenotypic traits, a core set of 48 SSR markers which originated protocol for identification of rice varieties-SSR marker method in agricultural industry standard of the People's Republic of China (Ministry of Agriculture of the PRC, NY/T1433-2014, Protocol for identification of rice varieties-SSR marker method, 2014), and SNPs data obtained by genotyping-by-sequencing (GBS, NlaIII and MseI digestion, referred to as SNPs-NlaIII and SNPs-MseI, respectively) for this panel of 93 samples using the Illumina HiSeq2000 sequencing platform, were employed to explore the genetic diversity and population structure of 93 samples. RESULTS: The average of coefficient of variation (CV) and diversity index (He) were 29.72% and 1.83 ranging from 3.07% to 137.43%, and from 1.45 to 2.03, respectively. The correlation coefficient between 15 phenotypic traits ranged from 0.984 to -0.604. The first four PCs accounted for 70.693% phenotypic variation based on phenotypic analysis. A total of 379 alleles were obtained using SSR markers, encompassing an average of 8.0 alleles per primer. Polymorphic bands (PPB) and polymorphism information content (PIC) was 88.65% and 0.77, respectively. The Mantel test showed that the correlation between the genetic distance matrix based on SNPs-NlaIII and SNPs-MseI was the largest (R2=0.88), and that based on 15 phenotypic traits and SSR was the smallest (R2=0.09). The 93 samples could be clustered into two subgroups by 3 types of genetic markers. Molecular variance analysis revealed that the genetic variation was 2% among populations and 98% within populations (the Nm was 0.16), Tajima's D value was 1.66, the FST between the two populations was 0.61 based on 72,824 SNPs. CONCLUSIONS: The population genetic variation explained by SNPs was larger than that explained by SSRs. The gene flow of 93 samples used in this study was larger than that of naturally self-pollinated crops, which may be caused by long-term breeding selection of Indica rice in the Qinba region. The genetic structure of the 93 samples was simple and lacked rare alleles.

Study on population structure of kiwifruit and GWAS for hairiness character.

BACKGROUND: A total of 74,936 SNPs were employed to carry out population structure and genome-wide association studies and post-GWAS for hairiness character of the fifty-six samples including thirty-six Actinidia chinensis var. deliciosa, eighteen A. chinensis var. chinensis, and two A. polygama in the light of morphological observations. RESULTS: The percentage of heterozygous sites of A. chinensis var. deliciosa is higher than that of A. chinensis var. chinensis, which could be one of the reasons for A. chinensis var. deliciosa high disease resistance. Fifty-six samples were divided into two subgroups, in which the genetic distance, ranged from 0.17 to 0.99, according to their genetic divergence. Analysis of molecular variance shows that the frequency of genetic variations within the population is 83.53% and 16.47% between populations. Fst between the two populations is 0.14, and Nm is 1.60. Set at α ≤ 0.05, a total of 327 SNPs and 260 haplotypes were related to the hairiness character. A total of 246 proteins were annotated using GO and KEGG analyses, which indicated the membrane-related genes and stress-resistant metabolic pathways are related to the hairiness character of leaves, stems, and peels of kiwifruit. Protein interaction analysis showed that DNA-directed RNA polymerase was an important node protein that interacted with many proteins. CONCLUSIONS: The genetic basic in the fifty-six genotypes was rich. The results of clustering and morphological observations are not completely consistent, indicating the hairiness character play an important role in the classification of kiwifruit, in which two A. polygama were clustered together with those of A. chinensis var. chinensis. Phylogeny and haplotype analysis showed that the evolution of A. chinensis var. chinensis is later than that of A. chinensis var. deliciosa in A. chinesis. The loss of hairiness character on leaves, stems and peels of A. chinensis var. chinensis compare with A. chinensis var. deliciosa, which is also the result of its poor resistance.

Detection of candidate gene networks involved in resistance to Sclerotinia sclerotiorum in soybean.

Quantitative trait locus (QTL) mapping often yields associations with dissimilar loci/genes as a consequence of diverse factors. One trait for which very limited agreement between mapping studies has been observed is resistance to white mold in soybean. To explore whether different approaches applied to a single data set could lead to more consistent results, haplotype-trait association and epistasis interaction effects were explored as a complement to a more conventional marker-trait analysis. At least 10 genomic regions were significantly associated with Sclerotinia sclerotiorum resistance in soybean, which have not been previously reported. At a significance level of α = 0.05, haplotype-trait association showed that the most prominent signal originated from a haplotype with 4-SNP (single nucleotide polymorphism) on chromosome 17, and single SNP-trait analysis located a nucleotide polymorphism at position rs34387780 on chromosome 3. All of the peak-SNPs (p-value < 0.05) of each chromosome also appeared in their respective haplotypes. Samples with extreme phenotypes were singled-out for association studies, 25-30% from each end of the phenotypic spectrum appeared in the present investigation to be the most appropriate sample size. Some key genes were identified by epistasis interaction analysis. By combining information on the nearest positional genes indicated that most loci have not been previously reported. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses suggest potential candidate genes underlying callose deposition in the cell wall and mitogen-activated protein kinase (MAPK) signaling pathway-plant, as well as plant-pathogen interaction pathway, were activated. Integration of multi-method genome-wide association study (GWAS) revealed novel genomic regions and promising candidate genes in novel regions, which include Glyma.01g048500, Glyma.03g129100, Glyma.17g072200, and the Dishevelled (Dvl) family of proteins on chromosomes 1, 3, 17, and 20, respectively.

Comparative proteomics analysis reveals differentially accumulated proteins associated with male and female A. chinensis var. chinensis bud development.

BACKGROUND: Kiwifruit (Actinidia chinensis var. Chinensis) is abundant with vitamin C and is a rapidly developing crop in China, New Zealand, and other countries. It has been widely used as a raw material for food and kiwifruit wine. Among these, A. chinensis var. chinensis and A. chinensis var. deliciosa are the most valuable kiwifruit in production. Kiwifruit is a typical dioecious plant and its female and male plants have different economic values. Therefore, sex identification, especially at the seedling stage, has important implications for the scientific planning of its production and economic benefits. However, the kiwifruit sex regulation mechanism is very complex and molecular studies are in the initial stages. Currently, there is not a universal and effective sex identification method for A. chinensis. METHODS: In this study, we used a label-free quantitative proteomics approach to investigate differentially accumulated proteins, including their presence/absence and significantly different levels of abundances during A. chinensis var. chinensis male and female flower bud development. RESULTS: A total of 6485 proteins were identified, among which, 203 were identified in male buds, which were mainly associated with phenylalanine metabolism, tyrosine metabolism, and plant hormone signal transduction. In female buds, 241 were identified, which were mainly associated with the ErbB signaling pathway, growth hormone synthesis, secretion and action, and mRNA surveillance pathway. A total of 373 proteins were significantly differentially accumulated proteins (fold change > 2; P < 0.05), of which, 168 were upregulated and 205 were downregulated. Significant differences between proteins involved 13 signaling pathways, most of which were involved in flavonoid biosynthesis, phenylpropanoid biosynthesis, and starch and sucrose metabolism. Protein interaction analysis showed that enriched protein nodes included cell division cycle 5-like protein, 40S ribosomal protein S8, ribosomal protein, and 40S ribosomal protein like, which interact with 35, 25, 22, and 22 proteins, respectively. CONCLUSIONS: This study provide valuable information for cloning key genes that control sex traits and functionally analyze their roles, which lay a foundation to the development of molecular markers for male and female kiwifruit identification.