Sequencing the USDA core soybean collection reveals gene loss during domestication and breeding

The gene content of plants varies between individuals of the same species due to gene presence/absence variation, and selection can alter the frequency of specific genes in a population. Selection during domestication and breeding will modify the genomic landscape, though the nature of these modifications is only understood for specific genes or on a more general level (e.g., by a loss of genetic diversity). Here we have assembled and analyzed a soybean (Glycine spp.) pangenome representing more than 1,000 soybean accessions derived from the USDA Soybean Germplasm Collection, including both wild and cultivated lineages, to assess genomewide changes in gene and allele frequency during domestication and breeding. We identified 3,765 genes that are absent from the Lee reference genome assembly and assessed the presence/absence of all genes across this population. In addition to a loss of genetic diversity, we found a significant reduction in the average number of protein-coding genes per individual during domestication and subsequent breeding, though with some genes and allelic variants increasing in frequency associated with selection for agronomic traits. This analysis provides a genomic perspective of domestication and breeding in this important oilseed crop

The SPTPoL extended cluster survey

We describe the observations and resultant galaxy cluster catalog from the 2770 deg2 SPTpol Extended Cluster Survey (SPT-ECS). Clusters are identified via the Sunyaev-Zel'dovich (SZ) effect and confirmed with a combination of archival and targeted follow-up data, making particular use of data from the Dark Energy Survey (DES). With incomplete follow-up we have confirmed as clusters 244 of 266 candidates at a detection significance ξ ≥ 5 and an additional 204 systems at 4 4 threshold, and 10% of their measured SZ flux. We associate SZ-selected clusters, from both SPT-ECS and the SPT-SZ survey, with clusters from the DES redMaPPer sample, and we find an offset distribution between the SZ center and central galaxy in general agreement with previous work, though with a larger fraction of clusters with significant offsets. Adopting a fixed Planck-like cosmology, we measure the optical richness-SZ mass (l - M) relation and find it to be 28% shallower than that from a weak-lensing analysis of the DES data-a difference significant at the 4σ level-with the relations intersecting at λ = 60. The SPT-ECS cluster sample will be particularly useful for studying the evolution of massive clusters and, in combination with DES lensing observations and the SPT-SZ cluster sample, will be an important component of future cosmological analyses

Detection of CMB-cluster lensing using polarization data from SPTpol

We report the first detection of gravitational lensing due to galaxy clusters using only the polarization of the cosmic microwave background (CMB). The lensing signal is obtained using a new estimator that extracts the lensing dipole signature from stacked images formed by rotating the cluster-centered Stokes Q U map cutouts along the direction of the locally measured background CMB polarization gradient. Using data from the SPTpol 500     deg 2 survey at the locations of roughly 18 000 clusters with richness λ ≥ 10 from the Dark Energy Survey (DES) Year-3 full galaxy cluster catalog, we detect lensing at 4.8 σ . The mean stacked mass of the selected sample is found to be ( 1.43 ± 0.40 ) × 10 14 M ⊙ which is in good agreement with optical weak lensing based estimates using DES data and CMB-lensing based estimates using SPTpol temperature data. This measurement is a key first step for cluster cosmology with future low-noise CMB surveys, like CMB-S4, for which CMB polarization will be the primary channel for cluster lensing measurements

Dissecting genomic hotspots underlying seed protein, oil, and sucrose content in an interspecific mapping population of soybean using high-density linkage mapping

The cultivated [Glycine max (L) Merr.] and wild [Glycine soja Siebold & Zucc.] soybean species comprise wide variation in seed composition traits. Compared to wild soybean, cultivated soybean contains low protein, high oil, and high sucrose. In this study, an interspecific population was derived from a cross between G. max (Williams 82) and G. soja (PI 483460B). This recombinant inbred line (RIL) population of 188 lines was sequenced at 0.3× depth. Based on 91 342 single nucleotide polymorphisms (SNPs), recombination events in RILs were defined, and a high‐resolution bin map was developed (4070 bins). In addition to bin mapping, quantitative trait loci (QTL) analysis for protein, oil, and sucrose was performed using 3343 polymorphic SNPs (3K‐SNP), derived from Illumina Infinium BeadChip sequencing platform. The QTL regions from both platforms were compared, and a significant concordance was observed between bin and 3K‐SNP markers. Importantly, the bin map derived from next‐generation sequencing technology enhanced mapping resolution (from 1325 to 50 Kb). A total of five, nine, and four QTLs were identified for protein, oil, and sucrose content, respectively, and some of the QTLs coincided with soybean domestication‐related genomic loci. The major QTL for protein and oil were mapped on Chr. 20 (qPro_20) and suggested negative correlation between oil and protein. In terms of sucrose content, a novel and major QTL were identified on Chr. 8 (qSuc_08) and harbours putative genes involved in sugar transport. In addition, genome‐wide association using 91 342 SNPs confirmed the genomic loci derived from QTL mapping. A QTL‐based haplotype using whole‐genome resequencing of 106 diverse soybean lines identified unique allelic variation in wild soybean that could be utilized to widen the genetic base in cultivated soybean

Phenotypic data from inbred parents can improve genomic prediction in Pearl Millet hybrids

Pearl millet is a non-model grain and fodder crop adapted to extremely hot and dry environments globally. In India, a great deal of public and private sectors’ investment has focused on developing pearl millet single cross hybrids based on the cytoplasmic-genetic male sterility (CMS) system, while in Africa most pearl millet production relies on open pollinated varieties. Pearl millet lines were phenotyped for both the inbred parents and hybrids stage. Many breeding efforts focus on phenotypic selection of inbred parents to generate improved parental lines and hybrids. This study evaluated two genotyping techniques and four genomic selection schemes in pearl millet. Despite the fact that 6× more sequencing data were generated per sample for RAD-seq than for tGBS, tGBS yielded more than 2× as many informative SNPs (defined as those having MAF > 0.05) than RAD-seq. A genomic prediction scheme utilizing only data from hybrids generated prediction accuracies (median) ranging from 0.73-0.74 (1000-grain weight), 0.87-0.89 (days to flowering time), 0.48-0.51 (grain yield) and 0.72-0.73 (plant height). For traits with little to no heterosis, hybrid only and hybrid/inbred prediction schemes performed almost equivalently. For traits with significant mid-parent heterosis, the direct inclusion of phenotypic data from inbred lines significantly (P < 0.05) reduced prediction accuracy when all lines were analyzed together. However, when inbreds and hybrid trait values were both scored relative to the mean trait values for the respective populations, the inclusion of inbred phenotypic datasets moderately improved genomic predictions of the hybrid genomic estimated breeding values. Here we show that modern approaches to genotyping by sequencing can enable genomic selection in pearl millet. While historical pearl millet breeding records include a wealth of phenotypic data from inbred lines, we demonstrate that the naive incorporation of this data into a hybrid breeding program can reduce prediction accuracy, while controlling for the effects of heterosis per se allowed inbred genotype and trait data to improve the accuracy of genomic estimated breeding values for pearl millet hybrids

Phenotypic data from inbred parents can improve genomic prediction in Pearl Millet hybrids

Pearl millet is a non-model grain and fodder crop adapted to extremely hot and dry environments globally. In India, a great deal of public and private sectors’ investment has focused on developing pearl millet single cross hybrids based on the cytoplasmic-genetic male sterility (CMS) system, while in Africa most pearl millet production relies on open pollinated varieties. Pearl millet lines were phenotyped for both the inbred parents and hybrids stage. Many breeding efforts focus on phenotypic selection of inbred parents to generate improved parental lines and hybrids. This study evaluated two genotyping techniques and four genomic selection schemes in pearl millet. Despite the fact that 6× more sequencing data were generated per sample for RAD-seq than for tGBS, tGBS yielded more than 2× as many informative SNPs (defined as those having MAF > 0.05) than RAD-seq. A genomic prediction scheme utilizing only data from hybrids generated prediction accuracies (median) ranging from 0.73-0.74 (1000-grain weight), 0.87-0.89 (days to flowering time), 0.48-0.51 (grain yield) and 0.72-0.73 (plant height). For traits with little to no heterosis, hybrid only and hybrid/inbred prediction schemes performed almost equivalently. For traits with significant mid-parent heterosis, the direct inclusion of phenotypic data from inbred lines significantly (P < 0.05) reduced prediction accuracy when all lines were analyzed together. However, when inbreds and hybrid trait values were both scored relative to the mean trait values for the respective populations, the inclusion of inbred phenotypic datasets moderately improved genomic predictions of the hybrid genomic estimated breeding values. Here we show that modern approaches to genotyping by sequencing can enable genomic selection in pearl millet. While historical pearl millet breeding records include a wealth of phenotypic data from inbred lines, we demonstrate that the naive incorporation of this data into a hybrid breeding program can reduce prediction accuracy, while controlling for the effects of heterosis per se allowed inbred genotype and trait data to improve the accuracy of genomic estimated breeding values for pearl millet hybrids

Soybean transporter database: A comprehensive database for identification and exploration of natural variants in soybean transporter genes

Transporters, a class of membrane proteins that facilitate exchange of solutes including diverse molecules and ions across the cellular membrane, are vital component for the survival of all organisms. Understanding plant transporters is important to get insight of the basic cellular processes, physiology, and molecular mechanisms including nutrient uptake, signaling, response to external stress, and many more. In this regard, extensive analysis of transporters predicted in soybean and other plant species was performed. In addition, an integrated database for soybean transporter protein, SoyTD, was developed that will facilitate the identification, classification, and extensive characterization of transporter proteins by integrating expression, gene ontology, conserved domain and motifs, gene structure organization, and chromosomal distribution features. A comprehensive analysis was performed to identify highly confident transporters by integrating various prediction tools. Initially, 7541 transmembrane (TM) proteins were predicted in the soybean genome; out of these, 3306 non‐redundant transporter genes carrying two or more transmembrane domains were selected for further analysis. The identified transporter genes were classified according to a standard transporter classification (TC) system. Comparative analysis of transporter genes among 47 plant genomes provided insights into expansion and duplication of transporter genes in land plants. The whole genome resequencing (WGRS) and tissue‐specific transcriptome datasets of soybean were integrated to investigate the natural variants and expression profile associated with transporter(s) of interest. Overall, SoyTD provides a comprehensive interface to study genetic and molecular function of soybean transporters. SoyTD is publicly available at http://artemis.cyverse.org/soykb_dev/SoyTD/

Grain iron and zinc densities in released and commercial cultivars of pearl millet (Pennisetum glaucum)

Crop biofortification is a cost-effective and sustainable agricultural trategy to reduce micronutrient malnutrition arising from iron (Fe) and zinc (Zn) deficiencies. A large number of hybrids and open-pollinated varieties (OPVs) of pearl millet [Pennisetum glaucum (L.) R. Br.] have been released and/or commercialized in India. Eighteen OPVs and 15 high-Fe candidate hybrids were evaluated in multi-location trials for Fe and Zn density to identify those with high density of these micronutrients. The Fe density in OPVs varied from 42 mg/kg to 67 mg/kg, and Zn density from 37 mg/kg to 52 mg/kg with ICTP 8203 having the highest Fe density (67 mg/kg) followed by ICMV 221 (61 mg/kg) and AIMP 92901 (56 mg/kg). While ICTP 8203 had also the highest level of Zn density (52 mg/kg), ICMV 221 and AIMP 92901 had 45-46 mg/kg Zn density. The Fe density in hybrids varied from 46 mg/kg to 56 mg/kg and Zn density from 37 mg/kg to 44 mg/kg. Four hybrids, viz. Ajeet 38, Proagro XL 51, AC 903 and 86M86 had the highest Fe density of 55-56 mg/kg and 39-41 mg/kg Zn density. The six commercial cultivars (2 OPVs and 4 hybrids) identified in this study with high Fe and Zn densities can be undertaken for expanded cultivation in their recommended ecologies to specifically address the Fe and Zn deficiencies in India. This study also enabled to re-define base line for Fe density at 42 mg/kg for hybrids, the most ominant cultivar type grown in India