Exploring and Mobilizing the Gene Bank Biodiversity for Wheat Improvement

Genetic Diversity, Population Structure, and Linkage Disequilibrium of a Core Collection of Ziziphus jujuba Assessed with Genome-wide SNPs Developed by Genotyping-by-sequencing and SSR Markers

Chinese jujube (Ziziphus jujuba Mill) is an economically important fruit species native to China with high nutritious and medicinal value. Genotyping-by-sequencing was used to detect and genotype single nucleotide polymorphisms (SNPs) in a core collection of 150 Chinese jujube accessions and further to characterize their genetic diversity, population structure, and linkage disequilibrium (LD). A total of 4,680 high-quality SNPs were identified, of which 38 sets of tri-allelic SNPs were detected. The average polymorphism information content (PIC) values based on bi-allelic SNPs and tri-allelic SNPs were 0.27 and 0.38, respectively. STRUCTURE and principal coordinate analyses based on SNPs revealed that the 150 accessions could be clustered into two groups. However, neighbor-joining trees indicated the accessions should be grouped into three major clusters. Our data confirm that the resolving power for genetic diversity was similar for the SSRs and SNPs. In contrast, regarding population structure, the resolving power was higher for SSRs than for SNPs. The LD pattern in Chinese jujube was investigated for the first time. We observed a relatively rapid LD decay with a short range (∼10 kb) for all pseudo-chromosomes and for individual pseudo-chromosomes. Our findings provide important information for future genome-wide association analyses and marker-assisted selective breeding of Chinese jujube.

Candidate loci involved in domestication and improvement detected by a published 90K wheat SNP array

Selection is one of the most important forces in crop evolution. Common wheat is a major world food crop and a typical allopolyploid with a huge and complex genome. We applied four approaches to detect loci selected in wheat during domestication and improvement. A total of 7,984 candidate loci were detected, accounting for 23.3% of all 34,317 SNPs analysed, a much higher proportion than estimated in previous reports. We constructed a first generation wheat selection map which revealed the following new insights on genome-wide selection: (1) diversifying selection acted by increasing, decreasing or not affecting gene frequencies; (2) the number of loci under selection during domestication was much higher than that during improvement; (3) the contribution to wheat improvement by the D sub-genome was relatively small due to the bottleneck of hexaploidisation and diversity can be expanded by using synthetic wheat and introgression lines; and (4) clustered selection regions occur throughout the wheat genome, including the centromere regions. This study will not only help future wheat breeding and evolutionary studies, but will also accelerate study of other crops, especially polyploids.

Genetic diversity and association mapping in the Colombian Central Collection of Solanum tuberosum L. Andigenum group using SNPs markers

The potato (Solanum tuberosum L.) is the fourth most important crop food in the world and Colombia has one of the most important collections of potato germplasm in the world (the Colombian Central Collection-CCC). Little is known about its potential as a source of genetic diversity for molecular breeding programs. In this study, we analyzed 809 Andigenum group accessions from the CCC using 5968 SNPs to determine: 1) the genetic diversity and population structure of the Andigenum germplasm and 2) the usefulness of this collection to map qualitative traits across the potato genome. The genetic structure analysis based on principal components, cluster analyses, and Bayesian inference revealed that the CCC can be subdivided into two main groups associated with their ploidy level: Phureja (diploid) and Andigena (tetraploid). The Andigena population was more genetically diverse but less genetically substructured than the Phureja population (three vs. five subpopulations, respectively). The association mapping analysis of qualitative morphological data using 4666 SNPs showed 23 markers significantly associated with nine morphological traits. The present study showed that the CCC is a highly diverse germplasm collection genetically and phenotypically, useful to implement association mapping in order to identify genes related to traits of interest and to assist future potato genetic breeding programs.

Identification of genomic regions for grain yield and yield stability and their epistatic interactions

The task of identifying genomic regions conferring yield stability is challenging in any crop and requires large experimental data sets in conjunction with complex analytical approaches. We report findings of a first attempt to identify genomic regions with stable expression and their individual epistatic interactions for grain yield and yield stability in a large elite panel of wheat under multiple environments via a genome wide association mapping (GWAM) approach. Seven hundred and twenty lines were genotyped using genotyping-by-sequencing technology and phenotyped for grain yield and phenological traits. High gene diversity (0.250) and a moderate genetic structure (five groups) in the panel provided an excellent base for GWAM. The mixed linear model and multi-locus mixed model analyses identified key genomic regions on chromosomes 2B, 3A, 4A, 5B, 7A and 7B. Further, significant epistatic interactions were observed among loci with and without main effects that contributed to additional variation of up to 10%. Simple stepwise regression provided the most significant main effect and epistatic markers resulting in up to 20% variation for yield stability and up to 17% gain in yield with the best allelic combination.

Mining Centuries Old In situ Conserved Turkish Wheat Landraces for Grain Yield and Stripe Rust Resistance Genes

Wheat landraces in Turkey are an important genetic resource for wheat improvement. An exhaustive 5-year (2009-2014) effort made by the International Winter Wheat Improvement Programme (IWWIP), a cooperative program between the Ministry of Food, Agriculture and Livestock of Turkey, the International Center for Maize and Wheat Improvement (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA), led to the collection and documentation of around 2000 landrace populations from 55 provinces throughout Turkey. This study reports the genetic characterization of a subset of bread wheat landraces collected in 2010 from 11 diverse provinces using genotyping-by-sequencing (GBS) technology. The potential of this collection to identify loci determining grain yield and stripe rust resistance via genome-wide association (GWA) analysis was explored. A high genetic diversity (diversity index = 0.260) and a moderate population structure based on highly inherited spike traits was revealed in the panel. The linkage disequilibrium decayed at 10 cM across the whole genome and was slower as compared to other landrace collections. In addition to previously reported QTL, GWA analysis also identified new candidate genomic regions for stripe rust resistance, grain yield, and spike productivity components. New candidate genomic regions reflect the potential of this landrace collection to further increase genetic diversity in elite germplasm.

Breeding Value of Primary Synthetic Wheat Genotypes for Grain Yield

To introduce new genetic diversity into the bread wheat gene pool from its progenitor, Aegilops tauschii (Coss.) Schmalh, 33 primary synthetic hexaploid wheat genotypes (SYN) were crossed to 20 spring bread wheat (BW) cultivars at the International Wheat and Maize Improvement Center. Modified single seed descent was used to develop 97 populations with 50 individuals per population using first back-cross, biparental, and three-way crosses. Individuals from each cross were selected for short stature, early heading, flowering and maturity, minimal lodging, and free threshing. Yield trials were conducted under irrigated, drought, and heat-stress conditions from 2011 to 2014 in Ciudad Obregon, Mexico. Genomic estimated breeding values (GEBVs) of parents and synthetic derived lines (SDLs) were estimated using a genomic best linear unbiased prediction (GBLUP) model with markers in each trial. In each environment, there were SDLs that had higher GEBVs than their recurrent BW parent for yield. The GEBVs of BW parents for yield ranged from -0.32 in heat to 1.40 in irrigated trials. The range of the SYN parent GEBVs for yield was from -2.69 in the irrigated to 0.26 in the heat trials and were mostly negative across environments. The contribution of the SYN parents to improved grain yield of the SDLs was highest under heat stress, with an average GEBV for the top 10% of the SDLs of 0.55 while the weighted average GEBV of their corresponding recurrent BW parents was 0.26. Using the pedigree-based model, the accuracy of genomic prediction for yield was 0.42, 0.43, and 0.49 in the drought, heat and irrigated trials, respectively, while for the marker-based model these values were 0.43, 0.44, and 0.55. The SYN parents introduced novel diversity into the wheat gene pool. Higher GEBVs of progenies were due to introgression and retention of some positive alleles from SYN parents.

Genome-Wide SNP Calling from Genotyping by Sequencing (GBS) Data: A Comparison of Seven Pipelines and Two Sequencing Technologies

Next-generation sequencing (NGS) has revolutionized plant and animal research in many ways including new methods of high throughput genotyping. Genotyping-by-sequencing (GBS) has been demonstrated to be a robust and cost-effective genotyping method capable of producing thousands to millions of SNPs across a wide range of species. Undoubtedly, the greatest barrier to its broader use is the challenge of data analysis. Herein we describe a comprehensive comparison of seven GBS bioinformatics pipelines developed to process raw GBS sequence data into SNP genotypes. We compared five pipelines requiring a reference genome (TASSEL-GBS v1& v2, Stacks, IGST, and Fast-GBS) and two de novo pipelines that do not require a reference genome (UNEAK and Stacks). Using Illumina sequence data from a set of 24 re-sequenced soybean lines, we performed SNP calling with these pipelines and compared the GBS SNP calls with the re-sequencing data to assess their accuracy. The number of SNPs called without a reference genome was lower (13k to 24k) than with a reference genome (25k to 54k SNPs) while accuracy was high (92.3 to 98.7%) for all but one pipeline (TASSEL-GBSv1, 76.1%). Among pipelines offering a high accuracy (>95%), Fast-GBS called the greatest number of polymorphisms (close to 35,000 SNPs + Indels) and yielded the highest accuracy (98.7%). Using Ion Torrent sequence data for the same 24 lines, we compared the performance of Fast-GBS with that of TASSEL-GBSv2. It again called more polymorphisms (25.8K vs 22.9K) and these proved more accurate (95.2 vs 91.1%). Typically, SNP catalogues called from the same sequencing data using different pipelines resulted in highly overlapping SNP catalogues (79-92% overlap). In contrast, overlap between SNP catalogues obtained using the same pipeline but different sequencing technologies was less extensive (~50-70%).

Prospects of Genomic Prediction in the USDA Soybean Germplasm Collection: Historical Data Creates Robust Models for Enhancing Selection of Accessions

The identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K single nucleotide polymorphism data collected on 18,480 domesticated soybean (Glycine max) accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained an appreciable amount of the variation in accession performance in independent validation trials, with correlations between predicted and observed reaching up to 0.92 for oil and protein and 0.79 for yield. The optimization of training set design was explored using a series of cross-validation schemes. It was found that the target population and environment need to be well represented in the training set. Second, genomic prediction training sets appear to be robust to the presence of data from diverse geographical locations and genetic clusters. This finding, however, depends on the influence of shattering and lodging, and may be specific to soybean with its presence of maturity groups. The distribution of 7608 nonphenotyped accessions was examined through the application of genomic prediction models. The distribution of predictions of phenotyped accessions was representative of the distribution of predictions for nonphenotyped accessions, with no nonphenotyped accessions being predicted to fall far outside the range of predictions of phenotyped accessions.