Sorghum Association Panel Whole-Genome Sequencing Establishes Pivotal Resource for Dissecting Genomic Diversity

Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions with distinct histories of evolutionary divergence and local adaptation. We report the whole-genome sequencing (WGS) of 400 sorghum [Sorghum bicolor (L.) Moench] accessions from the Sorghum Association Panel (SAP) at an average coverage of 38X (25X-72X), enabling the development of a high-density genomic-marker set of 43,983,694 variants including SNPs (~38 million), indels (~5 million), and CNVs (~170,000). We observe slightly more deletions among indels and a much higher prevalence of deletions among copy number variants compared to insertions. This new marker set enabled the identification of several putatively novel genomic associations for plant height and tannin content, which were not identified when using previous lower-density marker sets. WGS identified and scored variants in 5 kb bins where available genotyping-by-sequencing (GBS) data captured no variants, with half of all bins in the genome falling into this category. The predictive ability of genomic best unbiased linear predictor (GBLUP) models was increased by an average of 30% by using WGS markers rather than GBS markers. We identified 18 selection peaks across subpopulations that formed due to evolutionary divergence during domestication, and we found six Fst peaks resulting from comparisons between converted lines and breeding lines within the SAP that were distinct from the peaks associated with historic selection. This population has been and continues to serve as a significant public resource for sorghum research and demonstrates the value of improving upon existing genomic resources.

Correlations Among Grain Mold Severity, Seed Weight, and Germination Rate of Sorghum Association Panel Lines Inoculated With Alternaria alternata, Fusarium thapsinum, and Curvularia lunata

The sorghum association panel was evaluated for grain mold severity, seed weight, and germination rate. The 377 accessions were inoculated with Alternaria alternata alone, a mixture of A. alternata, Fusarium thapsinum, and Curvularia lunata, and untreated water-sprayed control during 2010, 2013-2015 growing seasons at the Texas AgriLife Research Farm, Burleson County, Texas. Each accession was evaluated at least twice. Across accessions, Spearman’s rank correlation was performed for non-parametric correlation analysis for grain mold severity, seed weight, and germination rate. There were significant negative correlations between grain mold severity with seed weight and germination rate for the individual treatment and when combined. A significant positive correlation between seed weight and germination rate was observed. The results indicated that higher grain mold severity reduces both sorghum seed weight and germination rate whether deliberately inoculated with fungal pathogens or naturally infected. It can be argued that correlations from this study were more robust due to a large number of accessions from all major sorghum races used and may represent the true association among the three parameters for this pathosystem. Thus, the use of grain mold-resistant lines, resulting in sound seeds and higher germination rates is recommended.

Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels–a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)–for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations.

Genome-Wide Association Study Reveals Novel Genetic Loci for Quantitative Resistance to Septoria Tritici Blotch in Wheat (Triticum aestivum L.)

Septoria tritici blotch, caused by the fungus Zymoseptoria titici, poses serious and persistent challenges to wheat cultivation in Ethiopia and worldwide. Deploying resistant cultivars is a major component of controlling septoria tritici blotch (STB). Thus, the objective of this study was to elucidate the genomic architecture of STB resistance in an association panel of 178 bread wheat genotypes. The association panel was phenotyped for STB resistance, phenology, yield, and yield-related traits in three locations for 2 years. The panel was also genotyped for single nucleotide polymorphism (SNP) markers using the genotyping-by-sequencing (GBS) method, and a total of 7,776 polymorphic SNPs were used in the subsequent analyses. Marker-trait associations were also computed using a genome association and prediction integrated tool (GAPIT). The study then found that the broad-sense heritability for STB resistance ranged from 0.58 to 0.97 and 0.72 to 0.81 at the individual and across-environment levels, respectively, indicating the presence of STB resistance alleles in the association panel. Population structure and principal component analyses detected two sub-groups with greater degrees of admixture. A linkage disequilibrium (LD) analysis in 338,125 marker pairs also detected the existence of significant (p ≤ 0.01) linkage in 27.6% of the marker pairs. Specifically, in all chromosomes, the LD between SNPs declined within 2.26–105.62 Mbp, with an overall mean of 31.44 Mbp. Furthermore, the association analysis identified 53 loci that were significantly (false discovery rate, FDR, <0.05) associated with STB resistance, further pointing to 33 putative quantitative trait loci (QTLs). Most of these shared similar chromosomes with already published Septoria resistance genes, which were distributed across chromosomes 1B, 1D, 2A, 2B, 2D, 3A,3 B, 3D, 4A, 5A, 5B, 6A, 7A, 7B, and 7D. However, five of the putative QTLs identified on chromosomes 1A, 5D, and 6B appeared to be novel. Dissecting the detected loci on IWGSC RefSeq Annotation v2.1 revealed the existence of disease resistance-associated genes in the identified QTL regions that are involved in plant defense responses. These putative QTLs explained 2.7–13.2% of the total phenotypic variation. Seven of the QTLs (R2 = 2.7–10.8%) for STB resistance also co-localized with marker-trait associations (MTAs) for agronomic traits. Overall, this analysis reported on putative QTLs for adult plant resistance to STB and some important agronomic traits. The reported and novel QTLs have been identified previously, indicating the potential to improve STB resistance by pyramiding QTLs by marker-assisted selection.

Genetic Dissection of Seedling Root System Architectural Traits in a Diverse Panel of Hexaploid Wheat through Multi-Locus Genome-Wide Association Mapping for Improving Drought Tolerance

Cultivars with efficient root systems play a major role in enhancing resource use efficiency, particularly water absorption, and thus in drought tolerance. In this study, a diverse wheat association panel of 136 wheat accessions including mini core subset was genotyped using Axiom 35k Breeders’ Array to identify genomic regions associated with seedling stage root architecture and shoot traits using multi-locus genome-wide association studies (ML-GWAS). The association panel revealed a wide variation of 1.5- to 50- fold and were grouped into six clusters based on 15 traits. Six different ML-GWAS models revealed 456 significant quantitative trait nucleotides (QTNs) for various traits with phenotypic variance in the range of 0.12–38.60%. Of these, 87 QTNs were repeatedly detected by two or more models and were considered reliable genomic regions for the respective traits. Among these QTNs, eleven were associated with average diameter and nine each for second order lateral root number (SOLRN), root volume (RV) and root length density (RLD). A total of eleven genomic regions were pleiotropic and each controlled two or three traits. Some important candidate genes such as Formin homology 1, Ubiquitin-like domain superfamily and ATP-dependent 6-phosphofructokinase were identified from the associated genomic regions. The genomic regions/genes identified in this study could potentially be targeted for improving root traits and drought tolerance in wheat.

Genome-wide association study suggests an independent genetic basis of zinc and cadmium concentrations in fresh sweet corn kernels

Despite being one of the most consumed vegetables in the United States, the elemental profile of sweet corn (Zea mays L.) is limited in its dietary contributions. To address this through genetic improvement, a genome-wide association study was conducted for the concentrations of 15 elements in fresh kernels of a sweet corn association panel. In concordance with mapping results from mature maize kernels, we detected a probable pleiotropic association of zinc and iron concentrations with nicotianamine synthase5 (nas5), which purportedly encodes an enzyme involved in synthesis of the metal chelator nicotianamine. Additionally, a pervasive association signal was identified for cadmium concentration within a recombination suppressed region on chromosome 2. The likely causal gene underlying this signal was heavy metal ATPase3 (hma3), whose counterpart in rice, OsHMA3, mediates vacuolar sequestration of cadmium and zinc in roots, whereby regulating zinc homeostasis and cadmium accumulation in grains. In our association panel, hma3 associated with cadmium but not zinc accumulation in fresh kernels. This finding implies that selection for low cadmium will not affect zinc levels in fresh kernels. Although less resolved association signals were detected for boron, nickel, and calcium, all 15 elements were shown to have moderate predictive abilities via whole-genome prediction. Collectively, these results help enhance our genomics-assisted breeding efforts centered on improving the elemental profile of fresh sweet corn kernels.

Haplotype Associated RNA Expression (HARE) Improves Prediction of Complex Traits in Maize

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for prediction within populations. However, it usually cannot capture the complex effects due to combination of alleles in haplotypes. Therefore, accuracy across populations has usually been low. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE, RNA expression of genes by haplotype), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism, so it would be more transferrable across different tissues and populations. We showed that HARE estimates captured one-third of the variation in gene expression and were more transferable across diverse tissues than the measured transcript expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels – a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel) – for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues whereas accuracy using HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations.Author summaryThe increasing availability of genomic data has been widely used in the prediction of many traits. However, genome wide prediction has been mostly carried out within populations and without explicit modeling of RNA or protein expression. In this study, we explored the prediction of field traits within and across populations using estimated RNA expression attributable to only the DNA sequence around a gene. We showed that the estimated RNA expression was more transferable than overall measured RNA expression. We improved prediction of field traits up to 15% using estimated gene expression as compared to observed expression or gene sequence alone. Overall, these findings indicate that structural and functional information in the gene sequence are highly transferable.

Association Mapping and Transcriptome Analysis Reveal the Genetic Architecture of Maize Kernel Size

Kernel length, kernel width, and kernel thickness are important traits affecting grain yield and product quality. Here, the genetic architecture of the three kernel size traits was dissected in an association panel of 309 maize inbred lines using four statistical methods. Forty-two significant single nucleotide polymorphisms (SNPs; p < 1.72E-05) and 70 genes for the three traits were identified under five environments. One and eight SNPs were co-detected in two environments and by at least two methods, respectively, and they explained 5.87–9.59% of the phenotypic variation. Comparing the transcriptomes of two inbred lines with contrasting seed size, three and eight genes identified in the association panel showed significantly differential expression between the two genotypes at 15 and 39 days after pollination, respectively. Ten and 17 genes identified by a genome-wide association study were significantly differentially expressed between the two development stages in the two genotypes. Combining environment−/method-stable SNPs and differential expression analysis, ribosomal protein L7, jasmonate-regulated gene 21, serine/threonine-protein kinase RUNKEL, AP2-EREBP-transcription factor 16, and Zm00001d035222 (cell wall protein IFF6-like) were important candidate genes for maize kernel size and development.

Guidelines for correlation coefficient threshold settings in metabolite correlation networks exemplified on a potato association panel

Background Correlation network analysis has become an integral tool to study metabolite datasets. Networks are constructed by omitting correlations between metabolites based on two thresholds—namely the r and the associated p-values. While p-value threshold settings follow the rules of multiple hypotheses testing correction, guidelines for r-value threshold settings have not been defined. Results Here, we introduce a method that allows determining the r-value threshold based on an iterative approach, where different networks are constructed and their network topology is monitored. Once the network topology changes significantly, the threshold is set to the corresponding correlation coefficient value. The approach was exemplified on: (i) a metabolite and morphological trait dataset from a potato association panel, which was grown under normal irrigation and water recovery conditions; and validated (ii) on a metabolite dataset of hearts of fed and fasted mice. For the potato normal irrigation correlation network a threshold of Pearson’s |r|≥ 0.23 was suggested, while for the water recovery correlation network a threshold of Pearson’s |r|≥ 0.41 was estimated. For both mice networks the threshold was calculated with Pearson’s |r|≥ 0.84. Conclusions Our analysis corrected the previously stated Pearson’s correlation coefficient threshold from 0.4 to 0.41 in the water recovery network and from 0.4 to 0.23 for the normal irrigation network. Furthermore, the proposed method suggested a correlation threshold of 0.84 for both mice networks rather than a threshold of 0.7 as applied earlier. We demonstrate that the proposed approach is a valuable tool for constructing biological meaningful networks.

Genetic Dissection of Resistance to Common Rust (Puccinia Sorghi) in Tropical Maize (Zea mays L.) by Combined Genetic Mapping and Genomic Prediction

Background: Common rust is one of the major foliar diseases of maize, leading to significant grain yield losses and poor grain quality. The most sustainable strategy for controlling common rust is to develop resistant maize varieties, which requires a further understanding of genetic dissection of common rust resistance. Results: In this study, an association panel and two bi-parental doubled haploid (DH) populations were used to perform genome-wide association study (GWAS), linkage mapping, and genomic prediction analyses. All the populations were phenotyped in multi-environment trials for common rust resistance and genotyped with genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). GWAS revealed six SNPs significantly associated with common rust resistance at bins 1.05, 1.10, 3.04, 3.05, 4.08, and 10.04, respectively. The SNP effect of each SNP ranged from 0.13 to 0.17. Linkage mapping identified six quantitative trait loci (QTL) in the first DH population (DH1) and two QTL in the second DH population (DH2), distributed on chromosomes 1, 2, 3, 4, 6, 7, and 9, respectively. The phenotypic variation explained (PVE) of each QTL ranged from 3.55% to 12.45%. A new major QTL was detected in DH1 on chromosome 7 in the region between 144,585,945 and 149,528,489 bp, it had the highest LOD score of 7.82 and the largest PVE value of 12.45%. The genomic regions located at bins 1.05, 1.10, and 4.08 were detected by both GWAS and linkage mapping. GRMZM2G114893 (bin 1.05) and GRMZM2G138949 (bin 4.08) were identified as the putative candidate genes conferring common rust resistance. The genomic prediction accuracies observed in the association panel and two bi-parental DH populations were 0.61, 0.51, and 0.10, respectively. Conclusions: These results provided new insight into the genetic architecture of common rust resistance in maize and a better understanding of the application of genomic prediction for common rust resistance in maize breeding.