Genome-wide Association Studies Panels Research Articles

Novel SNPs Linked to Blast Resistance Genes Identified in Pearl Millet Through Genome-Wide Association Models

Foliar blast, caused by Pyricularia grisea, poses a major challenge to pearl millet (Pennisetum glaucum (L.) R. Br) production, leading to severe yield losses, particularly in rainfed ecologies. This study aimed to elucidate the genetic basis of blast resistance through a genome-wide association study (GWAS) involving 281 diverse pearl millet inbreds. GWAS panel was phenotyped for blast resistance against three distinct isolates of P. grisea collected from Delhi, Gujarat, and Rajasthan locations, revealing a significant variability with 16.7% of the inbreds showing high resistance. Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK) and Multi-Locus Mixed Model (MLMM) models using transformed means identified 68 significant SNPs linked to resistance, with hotspots for resistance-related genes on chromosomes 1, 2, and 6. These regions harbor genes involved in defense mechanisms, including immune response, stress tolerance, signal transduction, transcription regulation, and pathogen defense. Genes, namely 14-3-3-like proteins RGA2, RGA4, hypersensitive-induced response proteins, NHL3, NBS-LRR, LRR-RLK, LRRNT_2, and various transcription factors such as AP2/ERF and WRKY, played a crucial role in the stress-responsive pathways. Analyses of transporter proteins, redox processes, and structural proteins revealed additional mechanisms contributing to blast resistance. This study offers valuable insights into the complex genetic architecture of blast resistance in pearl millet, offering a solid foundation for marker-assisted breeding programs and gene-editing experiments.

First report of ergot caused by Claviceps clavispora in switchgrass (Panicum virgatum L.) in Georgia, USA.

Switchgrass (Panicum virgatum L.) is a North American grass species with biofuel potential. Claviceps spp. is known to infect the florets of various grass species, initially characterized by a sticky honeydew exudate and later as sclerotium replacing the seed in the infected ovary (Tanaka et al 2023). Since 2019, from July to October, switchgrass panicles in Georgia have been observed with honeydew and black sclerotia. The disease was first noted on some of the 285 accessions of the genome-wide association study (GWAS) panels at the University of Georgia's Iron Horse Farm in Watkinsville and Gibbs Farm in Tifton, GA. In Watkinsville, GA, ergot incidence was 5%, 6%, 65% and 54% in 2019, 2020, 2021 and 2022, respectively. Symptomatic panicles with honeydew and sclerotia were collected in 2021 (sample Scl) and 2023 (samples Cla_M and ATH20cl) from Watkinsville, GA. Under microcopy, panicles with honeydew symptoms had mycelium and conidia (9 μm long [range of 5-13 μm] and 4 μm wide [3-5 μm]) consistent with Claviceps spp. (Tooley et al. 2001). Sclerotia were 1.5 mm long (range 1-3 mm). Sclerotia were surface sterilized for 3 minutes in 5% NaOCl, followed by 70% ethanol then rinsed three times in distilled water. Sterilized sclerotia were plated on potato dextrose agar and placed on bench top with a 12-hour day/night cycle at room temperature (22°C) (Singh 1976). After 2 months, the sclerotia produced sterile apothecia. For molecular identification, genomic DNA was extracted from three honeydew samples following the protocol of Doyle and Doyle (1987). The internal transcribed spacer (ITS) region and RNA polymerase second largest subunit (RPB2) gene were amplified using ITS4/ITS5 (White et al. 1990) and 5F2/7CR (Liu et al. 1999) primer sets. The ITS region of ATH20cl, Cla_M, and Scl (GenBank nos. PP546317- PP546319) showed 92.83-97.48% identity to C. clavispora (NR_163506.1). The RPB2 region of ATH20cl, Cla_M, and Scl (GenBank nos. PP573916- PP573918) showed 97.46-97.72% identity to C. clavispora (LT216566.1). The maximum likelihood tree constructed in MEGA-X (Kumar et al. 2018) using concatenated ITS (539 bp) and RPB2 (792 bp) gene sequences from this study and eleven reference sequences from Tanaka et al. (2023), revealed close relatedness of ATH20cl, Cla_M, and Scl to C. clavispora under section Pusillae. The pathogenicity test for samples ATH20cl and Cla_M was conducted in the greenhouse on switchgrass cultivar 'Alamo' grown in injection molded pots containing Sungro professional growing mix. Three replicates plants at reproductive (R3) growth stage were inoculated by immersing panicles in 105 spores/ml suspension for 5 minutes and bagged for 3 days (Tooley et al. 2001). Control plants were immersed in distilled water. Honeydew symptoms and sclerotia appeared within 7- and 90-days post-inoculation, respectively, whereas control plants remained symptom-free. The honeydew collected from the infected Alamo panicles were reconfirmed to produce similar spores to those collected from the field. This is the first report of ergot caused by C. clavispora in switchgrass. Ergot was also observed since 2019 on some accessions from the same GWAS panel planted at the University of Tennessee Plant Sciences Farm in Knoxville, TN. This information will be beneficial to determine the effect of ergot on biomass production and seed quality of switchgrass in the southeastern U.S.

TIPS: a novel pathway-guided joint model for transcriptome-wide association studies.

In the past two decades, genome-wide association studies (GWAS) have pinpointed numerous SNPs linked to human diseases and traits, yet many of these SNPs are in non-coding regions and hard to interpret. Transcriptome-wide association studies (TWAS) integrate GWAS and expression reference panels to identify the associations at gene level with tissue specificity, potentially improving the interpretability. However, the list of individual genes identified from univariate TWAS contains little unifying biological theme, leaving the underlying mechanisms largely elusive. In this paper, we propose a novel multivariate TWAS method that Incorporates Pathway or gene Set information, namely TIPS, to identify genes and pathways most associated with complex polygenic traits. We jointly modeled the imputation and association steps in TWAS, incorporated a sparse group lasso penalty in the model to induce selection at both gene and pathway levels and developed an expectation-maximization algorithm to estimate the parameters for the penalized likelihood. We applied our method to three different complex traits: systolic and diastolic blood pressure, as well as a brain aging biomarker white matter brain age gap in UK Biobank and identified critical biologically relevant pathways and genes associated with these traits. These pathways cannot be detected by traditional univariate TWAS + pathway enrichment analysis approach, showing the power of our model. We also conducted comprehensive simulations with varying heritability levels and genetic architectures and showed our method outperformed other established TWAS methods in feature selection, statistical power, and prediction. The R package that implements TIPS is available at https://github.com/nwang123/TIPS.

Genome-wide association analysis identifies genetic variants associated with muscle fatty acids and amino acids in grass carp (Ctenopharyngodon idella)

The composition and content of fatty acids (FAs) and amino acids (AAs) are important nutritional and sensory traits in aquaculture fish. To meet increasing demand while retaining quality, genetic selection in breeding programs is urgently needed to improve these traits. In this study, we performed the genome-wide association study (GWAS) to identify genetic variants and candidate genes potentially linked to FA and AA compositions in grass carp muscle. We focused on key dietary components, including unsaturated FAs and essential AAs. Through profiling of total FA and AA contents, we identified strong positive correlations among the compositions of most FAs and AAs. From our GWAS panel, 4,913,199 high-quality single nucleotide polymorphisms (SNPs) were identified across the genome, of which 32 SNPs linked to 18 candidate genes were significantly associated with FA levels, and 202 SNPs linked to 115 candidate genes associated with AA contents. In addition, we categorized our sequencing population by region and detected allele frequencies of SNPs across four geographic locations. Notably, four FA-associated SNPs and 28 AA-associated SNPs exhibited geographic specificity. These results offered valuable insights into the complex genetic basis of FA and AA metabolism and deposition. Furthermore, this work provides potential genetic markers for selective breeding aimed at enhancing flesh quality in the economically important fish species, grass carp.

A combination of joint linkage and genome-wide association study reveals putative candidate genes associated with resistance to northern corn leaf blight in tropical maize.

Northern corn leaf blight (NCLB), caused by Setosphaeria turcica, is a major fungal disease affecting maize production in sub-Saharan Africa. Utilizing host plant resistance to mitigate yield losses associated with NCLB can serve as a cost-effective strategy. In this study, we conducted a high-resolution genome-wide association study (GWAS) in an association mapping panel and linkage mapping with three doubled haploid (DH) and three F3 populations of tropical maize. These populations were phenotyped for NCLB resistance across six hotspot environments in Kenya. Across environments and genotypes, NCLB scores ranged from 2.12 to 5.17 (on a scale of 1-9). NCLB disease severity scores exhibited significant genotypic variance and moderate-to-high heritability. From the six biparental populations, 23 quantitative trait loci (QTLs) were identified, each explaining between 2.7% and 15.8% of the observed phenotypic variance. Collectively, the detected QTLs explained 34.28%, 51.37%, 41.12%, 12.46%, 12.11%, and 14.66% of the total phenotypic variance in DH populations 1, 2, and 3 and F3 populations 4, 5, and 6, respectively. GWAS, using 337,110 high-quality single nucleotide polymorphisms (SNPs), identified 15 marker-trait associations and several putative candidate genes linked to NCLB resistance in maize. Joint linkage association mapping (JLAM) identified 37 QTLs for NCLB resistance. Using linkage mapping, JLAM, and GWAS, several QTLs were identified within the genomic region spanning 4 to 15 Mbp on chromosome 2. This genomic region represents a promising target for enhancing NCLB resistance via marker-assisted breeding. Genome-wide predictions revealed moderate correlations with mean values of 0.45, 0.44, 0.55, and 0.42 for within GWAS panel, DH pop1, DH pop2, and DH pop3, respectively. Prediction by incorporating marker-by-environment interactions did not show much improvement. Overall, our findings indicate that NCLB resistance is quantitative in nature and is controlled by few major-effect and many minor-effect QTLs. We conclude that genomic regions consistently detected across mapping approaches and populations should be prioritized for improving NCLB resistance, while genome-wide prediction results can help incorporate both major- and minor-effect genes. This study contributes to a deeper understanding of the genetic and molecular mechanisms driving maize resistance to NCLB.

Genome-wide association analysis identifies a consistent QTL for powdery mildew resistance on chromosome 3A in Nordic and Baltic spring wheat

Key messageQPm.NOBAL-3A is an important QTL providing robust adult plant powdery mildew resistance in Nordic and Baltic spring wheat, aiding sustainable crop protection and breeding.Powdery mildew, caused by the biotrophic fungal pathogen Blumeria graminis f. sp. tritici, poses a significant threat to bread wheat (Triticum aestivum L.), one of the world’s most crucial cereal crops. Enhancing cultivar resistance against this devastating disease requires a comprehensive understanding of the genetic basis of powdery mildew resistance. In this study, we performed a genome-wide association study (GWAS) using extensive field trial data from multiple environments across Estonia, Latvia, Lithuania, and Norway. The study involved a diverse panel of recent wheat cultivars and breeding lines sourced from the Baltic region and Norway. We identified a major quantitative trait locus (QTL) on chromosome 3A, designated as QPm.NOBAL-3A, which consistently conferred high resistance to powdery mildew across various environments and countries. Furthermore, the consistency of the QTL haplotype effect was validated using an independent Norwegian spring wheat panel. Subsequent greenhouse seedling inoculations with 15 representative powdery mildew isolates on a subset of the GWAS panel indicated that this QTL provides adult plant resistance and is likely of race non-specific nature. Moreover, we developed and validated KASP markers for QPm.NOBAL-3A tailored for use in breeding. These findings provide a critical foundation for marker-assisted selection in breeding programs aimed at pyramiding resistance QTL/genes to achieve durable and broad-spectrum resistance against powdery mildew.

Breeding design in wheat by combining the QTL information in a GWAS panel with a general genetic map and computer simulation

A large amount of genome-wide association study (GWAS) panels together with quantitative-trait locus (QTL) information associated with breeding-targeted traits have been described in wheat (Triticum aestivum L.). However, the application of mapping results from a GWAS panel to conventional wheat breeding remains a challenge. In this study, we first report a general genetic map which was constructed from 44 published linkage maps. It permits the estimation of genetic distances between any two genetic loci with physical map positions, thereby unifying the linkage relationships between QTL, genes, and genomic markers from multiple genetic populations. Second, we describe QTL mapping in a wheat GWAS panel of 688 accessions, identifying 77 QTL associated with 12 yield and grain-quality traits. Because these QTL have known physical map positions, they could be mapped onto the general map. Finally, we present a design approach to wheat breeding by using known QTL information and computer simulation. Potential crosses between parents in the GWAS panel may be evaluated by the relative frequency of the target genotype, trait correlations in simulated progeny populations, and genetic gain of selected progenies. It is possible to simultaneously improve yield and grain quality by suitable parental selection, progeny population size, and progeny selection scheme. Applying the design approach will allow identifying the most promising crosses and selection schemes in advance of the field experiment, increasing predictability and efficiency in wheat breeding.

Genome-wide analyses using multi-locus models revealed marker-trait associations for major agronomic traits in Sorghum bicolor.

Globally, sorghum is the fifth most important cereal crop, and it is a major crop in Ethiopia, where it has a high genetic diversity. The country’s sorghum gene pool contributes significantly to sorghum improvement worldwide. This study aimed to identify genomic regions and candidate genes associated with major agronomic traits in sorghum by using its genetic resources in Ethiopia for a genome-wide association study (GWAS). Phenotypic data of days to flowering (DTF), plant height (PH), panicle length (PALH), panicle width (PAWD), panicle weight (PAWT), and grain yield (GY) were collected from a GWAS panel comprising 324 sorghum accessions grown in three environments. SeqSNP, a targeted genotyping method, was used to genotype the panel using 5,000 gene-based single nucleotide polymorphism (SNP) markers. For marker-trait association (MTA) analyses, fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) models were used. In all traits, high phenotypic variation was observed, with broad-sense heritability ranging from 0.32 (for GY) to 0.90 (for PALH). A population structure, principal component analysis, and kinship analysis revealed that the accessions could be divided into two groups. In total, 54 MTAs were identified, 11 of which were detected by both BLINK and farmCPU. MTAs identified for each trait ranged from five (PAWT and GY) to fourteen (PH) representing both novel and previously identified quantitative trait loci (QTLs). Three SNPs were associated with more than one trait, including a SNP within the Sobic.004G189200 gene that was associated with PH and PAWT. Major effect SNP loci, Sbi2393610 (PVE = 23.3%), Sbi10438246 (PVE = 35.2%), Sbi17789352 (PVE = 11.9%) and Sbi30169733 (PVE = 18.9%) on chromosomes 1, 3, 5 and 9 that showed strong association signals for PAWD, DTF, GY and PALH, respectively, were major findings of this study. The SNP markers and candidate genes identified in this study provide insights into the genetic control of grain yield and related agronomic traits, and once validated, the markers could be used in genomics-led breeding.

Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance

Genomic selection (GS) is a powerful tool for improving genetic gain in maize breeding. However, its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms. Although sequencing-based and array-based genotyping platforms have been used for GS, few studies have compared prediction performance among platforms. In this study, we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing (GBS), a 40K SNP array, and target sequence capture (TSC) using eight GS models. The GBS marker dataset yielded the highest predictabilities for all traits, followed by TSC and SNP array datasets. We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs, and BayesB, GBLUP, and RKHS performed well, while XGBoost performed poorly in most cases. We also selected significant SNP subsets using genome-wide association study (GWAS) analyses in three panels to predict hybrid performance. GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost, but depended heavily on the GWAS panel. We conclude that there is still room for optimization of the existing SNP array, and using genotyping by target sequencing (GBTS) techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.

Phenotypic evaluation of Canadian × Chinese elite germplasm in a diversity panel for seed yield and seed quality traits

Despite mounting concerns regarding the narrowness of the genetic base of soybean (( Glycine max (L.) Merr.) in North America and the challenges that it may pose in the changing global environment and climate, exotic germplasm remains seldom used by breeders owing to various concerns. The objective of this study was to evaluate a Genome-Wide Association Study (GWAS) genomic diversity panel of 200 soybean genotypes for seed yield, seed quality, and agronomic trait performance. The GWAS panel consisted of lines derived from several generations of bi-parental crosses between elite Canadian and elite Chinese cultivars (CD–CH), elite Canadian cultivars (CD), and exotic elite Chinese cultivars (CH) evaluated at Elora and Woodstock, ON, in 2019 and 2020. In the combined analysis of variance, the CD–CH group showed a significant increase in seed yield, although the performance of this group was otherwise comparable or inferior to the adapted elite Canadian cultivars. Canadian cultivars were superior to both CD–CH and elite, exotic Chinese cultivars in seed oil and seed protein concentration. The yield potential of the exotic-derived soybean lines observed in this study provide a great source of novel genetics for soybean breeders interested in introgressing novel alleles from exotic sources to improve yield to help combat climate change.

Identification of quantitative trait loci associated with seed quality traits between Canadian and Ukrainian mega-environments using genome-wide association study.

Identifying QTL associated with soybean seed quality traits from a diverse GWAS panel cultivated in Canadian and Ukrainian mega-environments may facilitate future cultivar development for foreign markets. Understanding the complex genetic basis of seed quality traits for soybean in the mega-environments (MEs) is critical for developing a marker-assisted selection program that will lead to breeding superior cultivars adapted to specific regions. This study aimed to analyze the accumulation of 14 soybean seed quality traits in Canadian ME and two seed quality traits in Ukrainian ME and identify associated ME specific quantitative trait loci (QTLSP) and ME universal QTL (QTLU) for protein and oil using a genome-wide association study (GWAS) panel consisting of 184 soybean genotypes. The panel was planted in three locations in Canada and two locations in Ukraine in 2018 and 2019. Genotype plus genotype-by-environment biplot analysis was conducted to assess the accumulation of individual seed compounds across different locations. The protein accumulation was high in the Canadian ME and low in the Ukrainian ME, whereas the oil concentration showed the opposite trends between the two MEs. No QTLU were identified across the MEs for protein and oil concentrations. In contrast, nine Canadian QTLSP for protein were identified on various chromosomes, which were co-located with QTL controlling other traits identified in the Canadian ME. The lack of common QTLU for protein and oil suggests that it may be necessary to use QTLSP associated with these traits separately for the Canadian and Ukrainian ME. Additional Ukrainian data for seed compounds other than oil and protein are required to identify novel QTLSP and QTLU for such traits for the individual or combined Canadian and Ukrainian MEs.

Genome-wide association study identifies loci and candidate genes for grain micronutrients and quality traits in wheat (Triticum aestivum L.)

Malnutrition due to micronutrients and protein deficiency is recognized among the major global health issues. Genetic biofortification of wheat is a cost-effective and sustainable strategy to mitigate the global micronutrient and protein malnutrition. Genomic regions governing grain zinc concentration (GZnC), grain iron concentration (GFeC), grain protein content (GPC), test weight (TW), and thousand kernel weight (TKW) were investigated in a set of 184 diverse bread wheat genotypes through genome-wide association study (GWAS). The GWAS panel was genotyped using Breeders' 35 K Axiom Array and phenotyped in three different environments during 2019–2020. A total of 55 marker-trait associations (MTAs) were identified representing all three sub-genomes of wheat. The highest number of MTAs were identified for GPC (23), followed by TKW (15), TW (11), GFeC (4), and GZnC (2). Further, a stable SNP was identified for TKW, and also pleiotropic regions were identified for GPC and TKW. In silico analysis revealed important putative candidate genes underlying the identified genomic regions such as F-box-like domain superfamily, Zinc finger CCCH-type proteins, Serine-threonine/tyrosine-protein kinase, Histone deacetylase domain superfamily, and SANT/Myb domain superfamily proteins, etc. The identified novel MTAs will be validated to estimate their effects in different genetic backgrounds for subsequent use in marker-assisted selection.

Genetic analysis of sucrose concentration in soybean seeds using a historical soybean genomic panel.

Significant QTL for sucrose concentration have been identified using a historical soybean genomic panel, which could aid in the development of food-grade soybean cultivars. Soybean (Glycine max (L.) Merr) is a crop of global importance for both human and animal consumption, which was domesticated in China more than 6000years ago. A concern about losing genetic diversity as a result of decades of breeding has been expressed by soybean researchers. In order to develop new cultivars, it is critical for breeders to understand the genetic variability present for traits of interest in their program germplasm. Sucrose concentration is becoming an increasingly important trait for the production of soy-food products. The objective of this study was to use a genome-wide association study (GWAS) to identify putative QTL for sucrose concentration in soybean seed. A GWAS panel consisting of 266 historic and current soybean accessions was genotyped with 76k genotype-by-sequencing (GBS) SNP data and phenotyped in four field locations in Ontario (Canada) from 2015 to 2017. Seven putative QTL were identified on chromosomes 1, 6, 8, 9, 10, 13 and 14. A key gene related to sucrose synthase (Glyma.06g182700) was found to be associated with the QTL located on chromosome 6. This information will facilitate efforts to increase the available genetic variability for sucrose concentration in soybean breeding programs and develop new and improved high-sucrose soybean cultivars suitable for the soy-food industry.

Genotyping Platforms for Genome-Wide Association Studies: Options and Practical Considerations.

Genome-wide association studies (GWAS) in crops requires genotyping platforms that are capable of producing accurate high density genotyping data on hundreds of plants in a cost-effective manner. Currently there are multiple commercial platforms available that are being effectively used across crops. These platforms include genotyping arrays such as the Illumina Infinium arrays and the Applied Biosystems Axiom Arrays along with a variety of resequencing methods. These methods are being used to genotype tens of thousands of markers up to millions of markers on GWAS panels. They are being used on crops with simple genomes to crops with very complex, large, polyploid genomes. Depending on the crop and the goal of the GWAS, there are several options and practical considerations to take into account when selecting a genotyping technology to ensure that the right coverage, accuracy, and cost for the study is achieved.

How robust are cross-population signatures of polygenic adaptation in humans?

Abstract Over the past decade, summary statistics from genome-wide association studies (GWAS) have been used to detect and quantify polygenic adaptation in humans. Several studies have reported signatures of natural selection at sets of SNPs associated with complex traits, like height and body mass index. However, more recent studies suggest that some of these signals may be caused by biases from uncorrected population stratification in the GWAS data with which these tests are performed. Moreover, past studies have predominantly relied on SNP effect size estimates obtained from GWAS panels of European ancestries, which are known to be poor predictors of phenotypes in non-European populations. Here, we collated GWAS data from multiple anthropometric and metabolic traits that have been measured in more than one cohort around the world, including the UK Biobank, FINRISK, Chinese NIPT, Biobank Japan, APCDR and PAGE. We then evaluated how robust signals of polygenic adaptation are to the choice of GWAS cohort used to identify associated variants and their effect size estimates, while using the same panel to obtain population allele frequencies (The 1000 Genomes Project). We observe many discrepancies across tests performed on the same phenotype and find that GWAS meta-analyses produce scores with strong overdispersion across populations. This results in apparent signatures of polygenic adaptation which are not observed when using effect size estimates from biobank-based GWAS of homogeneous ancestries. Indeed, we were able to artificially create score overdispersion when taking a homogeneous cohort like the UK Biobank, and simulating a meta-analysis on multiple subsets of the cohort. This suggests that extreme caution should be taken in the execution and interpretation of future tests of polygenic adaptation based on population differentiation, especially when using summary statistics from GWAS meta-analyses.

Genome-wide association screening and verification of potential genes associated with root architectural traits in maize (Zea mays L.) at multiple seedling stages

BackgroundBreeding for new maize varieties with propitious root systems has tremendous potential in improving water and nutrients use efficiency and plant adaptation under suboptimal conditions. To date, most of the previously detected root-related trait genes in maize were new without functional verification. In this study, seven seedling root architectural traits were examined at three developmental stages in a recombinant inbred line population (RIL) of 179 RILs and a genome-wide association study (GWAS) panel of 80 elite inbred maize lines through quantitative trait loci (QTL) mapping and genome-wide association study.ResultsUsing inclusive composite interval mapping, 8 QTLs accounting for 6.44–8.83 % of the phenotypic variation in root traits, were detected on chromosomes 1 (qRDWv3-1-1 and qRDW/SDWv3-1-1), 2 (qRBNv1-2-1), 4 (qSUAv1-4-1, qSUAv2-4-1, and qROVv2-4-1), and 10 (qTRLv1-10-1, qRBNv1-10-1). GWAS analysis involved three models (EMMAX, FarmCPU, and MLM) for a set of 1,490,007 high-quality single nucleotide polymorphisms (SNPs) obtained via whole genome next-generation sequencing (NGS). Overall, 53 significant SNPs with a phenotypic contribution rate ranging from 5.10 to 30.2 % and spread all over the ten maize chromosomes exhibited associations with the seven root traits. 17 SNPs were repeatedly detected from at least two growth stages, with several SNPs associated with multiple traits stably identified at all evaluated stages. Within the average linkage disequilibrium (LD) distance of 5.2 kb for the significant SNPs, 46 candidate genes harboring substantial SNPs were identified. Five potential genes viz. Zm00001d038676, Zm00001d015379, Zm00001d018496, Zm00001d050783, and Zm00001d017751 were verified for expression levels using maize accessions with extreme root branching differences from the GWAS panel and the RIL population. The results showed significantly (P < 0.001) different expression levels between the outer materials in both panels and at all considered growth stages.ConclusionsThis study provides a key reference for uncovering the complex genetic mechanism of root development and genetic enhancement of maize root system architecture, thus supporting the breeding of high-yielding maize varieties with propitious root systems.

Genetic Dissection of Quantitative Resistance to Common Rust (Puccinia sorghi) in Tropical Maize (Zea mays L.) by Combined Genome-Wide Association Study, Linkage Mapping, and Genomic Prediction.

Common rust is one of the major foliar diseases in maize, leading to significant grain yield losses and poor grain quality. To dissect the genetic architecture of common rust resistance, a genome-wide association study (GWAS) panel and a bi-parental doubled haploid (DH) population, DH1, were used to perform GWAS and linkage mapping analyses. The GWAS results revealed six single-nucleotide polymorphisms (SNPs) significantly associated with quantitative resistance of common rust at a very stringent threshold of P-value 3.70 × 10–6 at bins 1.05, 1.10, 3.04, 3.05, 4.08, and 10.04. Linkage mapping identified five quantitative trait loci (QTL) at bins 1.03, 2.06, 4.08, 7.03, and 9.00. The phenotypic variation explained (PVE) value of each QTL ranged from 5.40 to 12.45%, accounting for the total PVE value of 40.67%. Joint GWAS and linkage mapping analyses identified a stable genomic region located at bin 4.08. Five significant SNPs were only identified by GWAS, and four QTL were only detected by linkage mapping. The significantly associated SNP of S10_95231291 detected in the GWAS analysis was first reported. The linkage mapping analysis detected two new QTL on chromosomes 7 and 10. The major QTL on chromosome 7 in the region between 144,567,253 and 149,717,562 bp had the largest PVE value of 12.45%. Four candidate genes of GRMZM2G328500, GRMZM2G162250, GRMZM2G114893, and GRMZM2G138949 were identified, which played important roles in the response of stress resilience and the regulation of plant growth and development. Genomic prediction (GP) accuracies observed in the GWAS panel and DH1 population were 0.61 and 0.51, respectively. This study provided new insight into the genetic architecture of quantitative resistance of common rust. In tropical maize, common rust could be improved by pyramiding the new sources of quantitative resistance through marker-assisted selection (MAS) or genomic selection (GS), rather than the implementation of MAS for the single dominant race-specific resistance gene.

Abstract 2829: Identifying germline variants for metastatic risk assessment in sporadic colorectal carcinomas

Abstract Colorectal carcinoma (CRC) is the third highest incidence cancer and leading cause of cancer mortality worldwide. Metastasis to distal organ is the major cause of cancer mortality. This trend is expected to be exacerbated by an aging population in the developed world. Genetic predisposition plays a greater role in disease progression than disease etiology in sporadic CRC. However, the underlying genetic factors for metastasis are currently unclear. We identified germline single nucleotide polymorphism (SNP) variants contributing to metastasis risk from a case-case (metastasis-positive vs metastasis-negative) genome-wide association study (GWAS) comprising 3,000 sporadic Chinese CRC cases with definitive metastasis status. Metastasis-positive case is defined as one with distal-organ involvement attributable to primary CRC; metastasis-negative case is defined as metastasis-free with 5 years or more follow-up. By interrogating summarized SNP statistics from the case-case GWAS using the Functional Mapping and Annotation (FUMA) software, we identified 5 genomic risk loci and 74 disease-relevant genes based on gene set enrichment analysis and the Molecular Signatures Database. These genes are in various pathways implicated in different metastasis steps, from epithelial-mesenchymal transition (EMT) to implantation at distal organs. They are different from the risk variants identified by case-control GWAS for disease occurrence. We will expression-profile 250 metastasis-positive vs metastasis negative cases from the GWAS panel to validate the bioinformatics analysis by quantitative real-time PCR. We will focus first on the five genes implicated in glycolysis as this is a biochemical fingerprint of malignant cells that represents one of the ‘hallmarks of cancer'. Expression profile of these genes will be validated in a second independent panel. Kaplan Meier survival analysis as function of the median expression value will be performed to assess prognostic value of these genes. The ultimate goal is to improve metastasis risk assessment for secondary prevention to reduce cancer mortality and morbidity for individual patient as well as reducing public health expenditure. Citation Format: PehYean Cheah, Lai Fun Thean, Michelle Wong, Michelle Lo, Emile Tan, Choong Leong Tang. Identifying germline variants for metastatic risk assessment in sporadic colorectal carcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2829.

Natural variations in the P-type ATPase heavy metal transporter gene ZmHMA3 control cadmium accumulation in maize grains.

Cadmium (Cd) accumulation in maize grains is detrimental to human health. Developing maize varieties with low Cd content is important for safe consumption of maize grains. However, the key genes controlling maize grain Cd accumulation have not been cloned. Here, we identified one major locus for maize grain Cd accumulation (qCd1) using a genome-wide association study (GWAS) and bulked segregant RNA-seq analysis with a biparental segregating population of Jing724 (low-Cd line) and Mo17 (high-Cd line). The candidate gene ZmHMA3 was identified by fine mapping and encodes a tonoplast-localized heavy metal P-type ATPase transporter. An ethyl methane sulfonate mutant analysis and an allelism test confirmed that ZmHMA3 influences maize grain Cd accumulation. A transposon in intron 1 of ZmHMA3 is responsible for the abnormal amino acid sequence in Mo17. Based on the natural sequence variations in the ZmHMA3 gene of diverse maize lines, four PCR-based molecular markers were developed, and these were successfully used to distinguish five haplotypes with different grain Cd contents in the GWAS panel and to predict grain Cd contents of widely used maize inbred lines and hybrids. These molecular markers can be used to breed elite maize varieties with low grain Cd contents.

Genome-Wide Association Study Uncover the Genetic Architecture of Salt Tolerance-Related Traits in Common Wheat (Triticum aestivum L.).

Soil salinity is a serious threat to wheat yield affecting sustainable agriculture. Although salt tolerance is important for plant establishment at seedling stage, its genetic architecture remains unclear. In the present study, we have evaluated eight salt tolerance–related traits at seedling stage and identified the loci for salt tolerance by genome-wide association study (GWAS). This GWAS panel comprised 317 accessions and was genotyped with the wheat 90 K single-nucleotide polymorphism (SNP) chip. In total, 37 SNPs located at 16 unique loci were identified, and each explained 6.3 to 18.6% of the phenotypic variations. Among these, six loci were overlapped with previously reported genes or quantitative trait loci, whereas the other 10 were novel. Besides, nine loci were detected for two or more traits, indicating that the salt-tolerance genetic architecture is complex. Furthermore, five candidate genes were identified for salt tolerance–related traits, including kinase family protein, E3 ubiquitin-protein ligase-like protein, and transmembrane protein. SNPs identified in this study and the accessions with more favorable alleles could further enhance salt tolerance in wheat breeding. Our results are useful for uncovering the genetic mechanism of salt tolerance in wheat at seeding stage.