Quantitative Trait Nucleotides Research Articles

Detection of quantitative trait nucleotides (QTNs) and QTN-by-environment and QTN-by-QTN interactions for cotton early-maturity traits using the 3VmrMLM method

Early maturity is among the most important and complex agronomic traits of upland cotton (Gossypium hirsutum). Using the recently established 3VmrMLM method, a genome-wide association study was conducted, focusing on three primary early-maturity traits: growth period (GP), the first fruiting branch node (FFBN), and height of the first fruiting branch node (HFFBN). As a result, 45, 37, and 1 quantitative trait nucleotides (QTNs) for GP, 51, 28, and 0 QTNs for FFBN, and 84, 39, and 3 QTNs for HFFBN were detected by the “Single_env”, “Multi_env”, and “Epistasis” modules of IIIVmrMLM software, respectively. Of these QTNs, two for GP, two for FFBN, and three for HFFBN were simultaneously detected in at least three datasets or by at least three modules. The three genes, GH_D08G2175, GH_D08G2201, and GH_D09G2513, associated with the stable QTNs were further verified by qRT-PCR and haplotype analysis. Fourteen QTN-by-environment interactions (QEIs), comprising two for GP, seven for FFBN, and five for HFFBN, were detected, of which nine QEIs exhibited significant phenotypic differences between different genotypes under various environments. Three of the nine QEIs were respectively located within the genes GH_D12G1405, GH_D05G2088, and GH_D11G1027, which are likely to interact with environments and thus affect early maturity. Altogether 21 QTN-by-QTN interactions (QQIs), comprising 5 for GP and 16 for HFFBN, representing interactions between various effects were detected. Ten protein–protein interactions involving 6 QQIs and 10 gene pairs were predicted. The findings offer fresh perspectives on the genes underlying early maturity, advancing the prospects for molecular design breeding in cotton.

Genetic Diversity and Genome-Wide Association in Cowpeas (Vigna unguiculata L. Walp)

Cowpea is one of the most popular dry-land legumes cultivated for food and forage in arid and semi-arid areas. Genetic diversity for global germplasm can be organized into core collections providing optimum resources to serve breeding requirements. Here, we present diversity analysis and genome-wide association study (GWAS) results for part of the cowpea core collection of the United States Department of Agriculture (USDA) along with breeding line controls. Included in the analysis were a total of 373 accessions analyzed with 6880 Single Nucleotide Polymorphism (SNP) markers from Genotyping by Sequencing (GBS). Population structure differentiated accessions into two groups irrespective of geographical origin and formed three clusters based on taxa upon phylogenetic analysis. A total of 56 SNPs were significantly associated to nine traits including pod length (25 Quantitative Trait Nucleotides, QTNs), seed anti-oxidant content (7 QTNs), dry pod color (7 QTNs), plant maturity (5 QTNs), flower color (5 QTNs), seed weight (4 QTNs), tolerance to low phosphate (1 QTN), growth habit (1 QTN), and response to rock phosphate (1 QTN) using Bayesian-information, Linkage-disequilibrium Iteratively Nested Keyway (BLINK), and Fixed and random model Circulating Probability Unification (FarmCPU) association models. Key genes related to all significant SNPs were identified based on annotations of the cowpea reference genome, including a flavonoid gene controlling flower color (Vigun08g040200.1), a root nodulation regulator for tolerance to low phosphate (Vigun11g168000.1), and numerous genes involved in signaling, biosynthesis, metabolite transport, and abiotic stress. Our results highlight the importance of maintaining public phenotyping databases at USDA and strengthening collaborations for data collection in cowpea to maximize research impacts.

Genomic, climatic, and cultural diversity of maize landraces from the Himalayan Kingdom of Bhutan

Societal Impact StatementBhutan is an ancient kingdom in the Himalayan range and one of the most rugged, geodiverse, and mountainous agricultural countries in the world. Historically secluded and geographically isolated, Bhutan is a hotspot for Himalayan agrobiodiversity where small‐scale agriculture supports the livelihoods of a large share of the resident population. Here, Bhutanese maize agrobiodiversity is explored to unlock its adaptation potential using genomics and participatory variety selection in combination with climate research. We show that Bhutanese traditional farmers maintain a wealth of diversity that may support the sustainable intensification of maize cropping in the Himalayas and beyond.Summary Bhutan, an ancient kingdom enshrouded in the Himalayas, hosts largely untapped agrobiodiversity that may harness genetic variation useful for adaptation to local climates and user needs. Here, we genotyped‐by‐sequencing 351 pooled samples of local maize (Zea mays L.) landraces, the entire collection of the Bhutan National Gene Bank, comparing their genomic diversity with maize from other countries in the Himalayan range. We reconstructed the adaptation of Bhutanese maize to historical and projected climates, identifying areas of future maladaptation. We then run a common garden experiment involving local smallholder farmers in a participatory evaluation of landraces' performance, aiming at the identification of quantitative trait nucleotides (QTNs) contributing to adaptation, performance, and farmers' choice. We found that Bhutanese maize agrobiodiversity is unique in the Himalayan range, and a locus on Chromosome 5 supports the differentiation of three distinct genetic clusters. We found that a portion of current genomic diversity can be associated with the Bhutanese landscape and that maize cultivation in the southwest of the country may be negatively impacted by projected climates. We also found that Bhutanese maize agrobiodiversity is large and may contribute to adaptation and improvement. A genome‐wide association study identified 117 QTNs for climatic adaptation, agronomic performance, and farmers' preferences. Our results show that Bhutanese maize landraces are a unique source of genetic agrobiodiversity for local adaptation. We found that the integration of genomics, climate science, and participatory methods can speed up the identification of genetic factors contributing to the sustainable intensification of maize cultivation in the Himalayas and beyond.

Genetic loci associated with tissue-specific resistance to powdery mildew in octoploid strawberry (Fragaria × ananassa).

Powdery mildew is one of the most problematic diseases in strawberry production. To date, few commercial strawberry cultivars are deemed to have complete resistance and as such, an extensive spray programme must be implemented to control the pathogen. Here, a large-scale field experiment was used to determine the powdery mildew resistance status of leaf and fruit tissues across a diverse panel of strawberry genotypes. This phenotypic data was used to identify Quantitative Trait Nucleotides (QTN) associated with tissue-specific powdery mildew resistance. In total, six stable QTN were found to be associated with foliar resistance, with one QTN on chromosome 7D associated with a 61% increase in resistance. In contrast to the foliage results, there were no QTN associated with fruit disease resistance and there was a high level of resistance observed on strawberry fruit, with no genetic correlation observed between fruit and foliar symptoms, indicating a tissue-specific response. Beyond the identification of genetic loci, we also demonstrate that genomic selection can lead to rapid gains in foliar resistance across genotypes, with the potential to capture >50% of the genetic foliage resistance present in the population. To date, breeding of robust powdery mildew resistance in strawberry has been impeded by the quantitative nature of natural resistance and a lack of knowledge relating to the genetic control of the trait. These results address this shortfall, through providing the community with a wealth of information that could be utilized for genomic informed breeding, implementation of which could deliver a natural resistance strategy for combatting powdery mildew.

New Insights into the Genetic Basis of Lysine Accumulation in Rice Revealed by Multi-Model GWAS.

Lysine is an essential amino acid that cannot be synthesized in humans. Rice is a global staple food for humans but has a rather low lysine content. Identification of the quantitative trait nucleotides (QTNs) and genes underlying lysine content is crucial to increase lysine accumulation. In this study, five grain and three leaf lysine content datasets and 4,630,367 single nucleotide polymorphisms (SNPs) of 387 rice accessions were used to perform a genome-wide association study (GWAS) by ten statistical models. A total of 248 and 71 common QTNs associated with grain/leaf lysine content were identified. The accuracy of genomic selection/prediction RR-BLUP models was up to 0.85, and the significant correlation between the number of favorable alleles per accession and lysine content was up to 0.71, which validated the reliability and additive effects of these QTNs. Several key genes were uncovered for fine-tuning lysine accumulation. Additionally, 20 and 30 QTN-by-environment interactions (QEIs) were detected in grains/leaves. The QEI-sf0111954416 candidate gene LOC_Os01g21380 putatively accounted for gene-by-environment interaction was identified in grains. These findings suggested the application of multi-model GWAS facilitates a better understanding of lysine accumulation in rice. The identified QTNs and genes hold the potential for lysine-rich rice with a normal phenotype.

Identification of QTNs, QTN-by-environment interactions, and their candidate genes for salt tolerance related traits in soybean

BackgroundSalt stress significantly reduces soybean yield. To improve salt tolerance in soybean, it is important to mine the genes associated with salt tolerance traits.ResultsSalt tolerance traits of 286 soybean accessions were measured four times between 2009 and 2015. The results were associated with 740,754 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) using three-variance-component multi-locus random-SNP-effect mixed linear model (3VmrMLM). As a result, eight salt tolerance genes (GmCHX1, GsPRX9, Gm5PTase8, GmWRKY, GmCHX20a, GmNHX1, GmSK1, and GmLEA2-1) near 179 significant and 79 suggested QTNs and two salt tolerance genes (GmWRKY49 and GmSK1) near 45 significant and 14 suggested QEIs were associated with salt tolerance index traits in previous studies. Six candidate genes and three gene-by-environment interactions (GEIs) were predicted to be associated with these index traits. Analysis of four salt tolerance related traits under control and salt treatments revealed six genes associated with salt tolerance (GmHDA13, GmPHO1, GmERF5, GmNAC06, GmbZIP132, and GmHsp90s) around 166 QEIs were verified in previous studies. Five candidate GEIs were confirmed to be associated with salt stress by at least one haplotype analysis. The elite molecular modules of seven candidate genes with selection signs were extracted from wild soybean, and these genes could be applied to soybean molecular breeding. Two of these genes, Glyma06g04840 and Glyma07g18150, were confirmed by qRT-PCR and are expected to be key players in responding to salt stress.ConclusionsAround the QTNs and QEIs identified in this study, 16 known genes, 6 candidate genes, and 8 candidate GEIs were found to be associated with soybean salt tolerance, of which Glyma07g18150 was further confirmed by qRT-PCR.

Integrating Genome-Wide Association Study, Transcriptome and Metabolome Reveal Novel QTL and Candidate Genes That Control Protein Content in Soybean.

Protein content (PC) is crucial to the nutritional quality of soybean [Glycine max (L.) Merrill]. In this study, a total of 266 accessions were used to perform a genome-wide association study (GWAS) in three tested environments. A total of 23,131 high-quality SNP markers (MAF ≥ 0.02, missing data ≤ 10%) were identified. A total of 40 association signals were significantly associated with PC. Among them, five novel quantitative trait nucleotides (QTNs) were discovered, and another 32 QTNs were found to be overlapping with the genomic regions of known quantitative trait loci (QTL) related to soybean PC. Combined with GWAS, metabolome and transcriptome sequencing, 59 differentially expressed genes (DEGs) that might control the change in protein content were identified. Meantime, four commonly upregulated differentially abundant metabolites (DAMs) and 29 commonly downregulated DAMs were found. Remarkably, the soybean gene Glyma.08G136900, which is homologous with Arabidopsis hydroxyproline-rich glycoproteins (HRGPs), may play an important role in improving the PC. Additionally, Glyma.08G136900 was divided into two main haplotype in the tested accessions. The PC of haplotype 1 was significantly lower than that of haplotype 2. The results of this study provided insights into the genetic mechanisms regulating protein content in soybean.

GWAS in a Collection of Bulgarian Old and Modern Bread Wheat Accessions Uncovers Novel Genomic Loci for Grain Protein Content and Thousand Kernel Weight.

Genetic enhancement of grain production and quality is a priority in wheat breeding projects. In this study, we assessed two key agronomic traits-grain protein content (GPC) and thousand kernel weight (TKW)-across 179 Bulgarian contemporary and historic varieties and landraces across three growing seasons. Significant phenotypic variation existed for both traits among genotypes and seasons, and no discernible difference was evident between the old and modern accessions. To understand the genetic basis of the traits, we conducted a genome-wide association study with MLM using phenotypic data from the crop seasons, best linear unbiased estimators, and genotypic data from the 25K Infinium iSelect array. As a result, we detected 16 quantitative trait nucleotides (QTNs) associated with GPC and 15 associated with TKW, all of which passed the false discovery rate threshold. Seven loci favorably influenced GPC, resulting in an increase of 1.4% to 8.1%, while four loci had a positive impact on TKW with increases ranging from 1.9% to 8.4%. While some loci confirmed previously published associations, four QTNs linked to GPC on chromosomes 2A, 7A, and 7B, as well as two QTNs related to TKW on chromosomes 1B and 6A, may represent novel associations. Annotations for proteins involved in the senescence-associated nutrient remobilization and in the following buildup of resources required for seed germination have been found for selected putative candidate genes. These include genes coding for storage proteins, cysteine proteases, cellulose-synthase, alpha-amylase, transcriptional regulators, and F-box and RWP-RK family proteins. Our findings highlight promising genomic regions for targeted breeding programs aimed at improving grain yield and protein content.

Dissection of quantitative trait nucleotides and candidate genes associated with agronomic and yield-related traits under drought stress in rapeseed varieties: integration of genome-wide association study and transcriptomic analysis.

An important strategy to combat yield loss challenge is the development of varieties with increased tolerance to drought to maintain production. Improvement of crop yield under drought stress is critical to global food security. In this study, we performed multiomics analysis in a collection of 119 diverse rapeseed (Brassica napus L.) varieties to dissect the genetic control of agronomic traits in two watering regimes [well-watered (WW) and drought stress (DS)] for 3 years. In the DS treatment, irrigation continued till the 50% pod development stage, whereas in the WW condition, it was performed throughout the whole growing season. The results of the genome-wide association study (GWAS) using 52,157 single-nucleotide polymorphisms (SNPs) revealed 1,281 SNPs associated with traits. Six stable SNPs showed sequence variation for flowering time between the two irrigation conditions across years. Three novel SNPs on chromosome C04 for plant weight were located within drought tolerance-related gene ABCG16, and their pleiotropically effects on seed weight per plant and seed yield were characterized. We identified the C02 peak as a novel signal for flowering time, harboring 52.77% of the associated SNPs. The 288-kbps LD decay distance analysis revealed 2,232 candidate genes (CGs) associated with traits. The CGs BIG1-D, CAND1, DRG3, PUP10, and PUP21 were involved in phytohormone signaling and pollen development with significant effects on seed number, seed weight, and grain yield in drought conditions. By integrating GWAS and RNA-seq, 215 promising CGs were associated with developmental process, reproductive processes, cell wall organization, and response to stress. GWAS and differentially expressed genes (DEGs) of leaf and seed in the yield contrasting accessions identified BIG1-D, CAND1, and DRG3 genes for yield variation. The results of our study provide insights into the genetic control of drought tolerance and the improvement of marker-assisted selection (MAS) for breeding high-yield and drought-tolerant varieties.

Multi-omics analysis reveals novel loci and a candidate regulatory gene of unsaturated fatty acids in soybean (Glycine max (L.) Merr)

BackgroundSoybean is a major oil crop; the nutritional components of soybean oil are mainly controlled by unsaturated fatty acids (FA). Unsaturated FAs mainly include oleic acid (OA, 18:1), linoleic acid (LLA, 18:2), and linolenic acid (LNA, 18:3). The genetic architecture of unsaturated FAs in soybean seeds has not been fully elucidated, although many independent studies have been conducted. A 3 V multi-locus random single nucleotide polymorphism (SNP)-effect mixed linear model (3VmrMLM) was established to identify quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits.ResultsIn this study, 194 soybean accessions with 36,981 SNPs were calculated using the 3VmrMLM model. As a result, 94 quantitative trait nucleotides (QTNs) and 19 QEIs were detected using single-environment (QTN) and multi-environment (QEI) methods. Three significant QEIs, namely rs4633292, rs39216169, and rs14264702, overlapped with a significant single-environment QTN.ConclusionsFor QTNs and QEIs, further haplotype analysis of candidate genes revealed that the Glyma.03G040400 and Glyma.17G236700 genes were beneficial haplotypes that may be associated with unsaturated FAs. This result provides ideas for the identification of soybean lipid-related genes and provides insights for breeding high oil soybean varieties in the future.

Multi-locus genome-wide association studies reveal the dynamic genetic architecture of flowering time in chrysanthemum.

The dynamic genetic architecture of flowering time in chrysanthemum was elucidated by GWAS. Thirty-six known genes and 14 candidate genes were identified around the stable QTNs and QEIs, among which ERF-1 was highlighted. Flowering time (FT) adaptation is one of the major breeding goals in chrysanthemum, a multipurpose ornamental plant. In order to reveal the dynamic genetic architecture of FT in chrysanthemum, phenotype investigation of ten FT-related traits was conducted on 169 entries in 2 environments. The broad-sense heritability of five non-conditional FT traits, i.e., budding (FBD), visible coloring (VC), early opening (EO), full-bloom (OF) and decay period (DP), ranged from 56.93 to 84.26%, which were higher than that of the five derived conditional FT traits (38.51-75.13%). The phenotypic variation coefficients of OF_EO and DP_OF were relatively large ranging from 30.59 to 36.17%. Based on 375,865 SNPs, the compressed variance component mixed linear model 3VmrMLM was applied for a multi-locus genome-wide association study (GWAS). As a result, 313 quantitative trait nucleotides (QTNs) were identified for the non-conditional FT traits in single-environment analysis, while 119 QTNs and 67 QTN-by-environment interactions (QEIs) were identified in multi-environment analysis. As for the conditional traits, 343 QTNs were detected in single-environment analysis, and 119 QTNs and 83 QEIs were identified in multi- environment analysis. Among the genes around stable QTNs and QEIs, 36 were orthologs of known FT genes in Arabidopsis and other plants; 14 candidates were mined by combining the transcriptomics data and functional annotation, including ERF-1, ACA10, and FOP1. Furthermore, the haplotype analysis of ERF-1 revealed six elite accessions with extreme FBD. Our findings contribute to the understanding of dynamic genetic architecture of FT and provide valuable resources for future chrysanthemum molecular breeding programs.

BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies

Abstract Multilocus genome-wide association study has become the state-of-the-art tool for dissecting the genetic architecture of complex and multiomic traits. However, most existing multilocus methods require relatively long computational time when analyzing large datasets. To address this issue, in this study, we propose a fast mrMLM method, namely, best linear unbiased prediction multilocus random-SNP-effect mixed linear model (BLUPmrMLM). First, genome-wide single-marker scanning in mrMLM is replaced by vectorized Wald tests based on the best linear unbiased prediction (BLUP) values of marker effects and their variances in BLUPmrMLM. Then, adaptive best subset selection is used to identify potentially associated markers on each chromosome to reduce computational time when estimating marker effects via empirical bayes. Finally, shared memory and parallel computing schemes were used to reduce the computation time. In simulation studies, BLUPmrMLM outperformed GEMMA, EMMAX, mrMLM, FarmCPU, and the control method of BLUPmrMLM in terms of computational time, power, accuracy for estimating quantitative trait nucleotide positions and effects, false positive rate, false discovery rate, false negative rate, and F1 score. According to the reanalysis of two large rice datasets, compared with the above methods, BLUPmrMLM significantly reduced the computation time and identified more previously reported genes. This study provides an excellent multilocus model method for the analysis of large-scale and multiomic datasets. The software mrMLM v5.1 is available at BioCode (https://ngdc.cncb.ac.cn/biocode/tools/BT007388) or GitHub (https://github.com/YuanmingZhang65/mrMLM).

Comprehensive Identification of Main, Environment Interaction and Epistasis Quantitative Trait Nucleotides for 100-Seed Weight in Soybean (Glycine max (L.) Merr.)

Soybean hundred seed weight (HSW) is a complex quantitative trait affected by multiple genes and environmental factors. To date, a large number of quantitative trait nucleotides (QTNs) have been reported, but less information on QTN-by-environment interactions (QEIs) and QTN-QTN interaction (QQIs) for soybean HSW is available. Mapping without QEIs and QQIs result in missing some important QTNs that are significantly related to HSW. Therefore, the present study conducted genome-wide association analysis to map main QTNs, QEIs and QQIs for HSW in a panel with 573 diverse soybean lines tested in three independent environments (E1, E2 and E3) with Mean- and best linear unbiased value (BLUP)- phenotype. In all, 147 main effect QTNs, 11 QEIs, and 24 pairs of QQIs were detected in the Mean-phenotype, and 138 main effect QTNs, 13 QEIs, and 27 pairs of QQIs in the BLUP-phenotype. The total phenotypic variation explained by the main effect QTNs, QEIs, and QQIs were 35.31–39.71, 8.52–8.89 and 34.77–35.09%, respectively, indicating an important role of non-additive effects on HSW. Out of these, 33 QTNs were considered as stable with 23 colocalized with previously known loci, while 10 were novel QTNs. In addition, 10 pairs stable QQIs were simultaneously detected in the two phenotypes. Based on homolog search in Arabidopsis thaliana and in silico transcriptome data, seven genes (Glyma13g42310, Glyma13g42320, Glyma08g19580, Glyma13g44020, Glyma13g43800, Glyma17g16620 and Glyma07g08950) from some main-QTNs and two genes (Glyma06g19000 and Glyma17g09110) of QQIs were identified as potential candidate genes, however their functional role warrant further screening and functional validation. Our results shed light on the involvement of QEIs and QQIs in regulating HSW in soybean, and these together with candidate genes identified would be valuable genomic resources in developing soybean cultivars with desirable seed weight.

Joint-GWAS, Linkage Mapping, and Transcriptome Analysis to Reveal the Genetic Basis of Plant Architecture-Related Traits in Maize.

Plant architecture is one of the key factors affecting maize yield formation and can be divided into secondary traits, such as plant height (PH), ear height (EH), and leaf number (LN). It is a viable approach for exploiting genetic resources to improve plant density. In this study, one natural panel of 226 inbred lines and 150 family lines derived from the offspring of T32 crossed with Qi319 were genotyped by using the MaizeSNP50 chip and the genotyping by sequence (GBS) method and phenotyped under three different environments. Based on the results, a genome-wide association study (GWAS) and linkage mapping were analyzed by using the MLM and ICIM models, respectively. The results showed that 120 QTNs (quantitative trait nucleotides) and 32 QTL (quantitative trait loci) related to plant architecture were identified, including four QTL and 40 QTNs of PH, eight QTL and 41 QTNs of EH, and 20 QTL and 39 QTNs of LN. One dominant QTL, qLN7-2, was identified in the Zhangye environment. Six QTNs were commonly identified to be related to PH, EH, and LN in different environments. The candidate gene analysis revealed that Zm00001d021574 was involved in regulating plant architecture traits through the autophagy pathway, and Zm00001d044730 was predicted to interact with the male sterility-related gene ms26. These results provide abundant genetic resources for improving maize plant architecture traits by using approaches to biological breeding.

New insights into QTNs and potential candidate genes governing rice yield via a multi-model genome-wide association study

BackgroundRice (Oryza sativa L.) is one of the globally important staple food crops, and yield-related traits are prerequisites for improved breeding efficiency in rice. Here, we used six different genome-wide association study (GWAS) models for 198 accessions, with 553,229 single nucleotide markers (SNPs) to identify the quantitative trait nucleotides (QTNs) and candidate genes (CGs) governing rice yield.ResultsAmongst the 73 different QTNs in total, 24 were co-localized with already reported QTLs or loci in previous mapping studies. We obtained fifteen significant QTNs, pathway analysis revealed 10 potential candidates within 100kb of these QTNs that are predicted to govern plant height, days to flowering, and plot yield in rice. Based on their superior allelic information in 20 elite and 6 inferior genotypes, we found a higher percentage of superior alleles in the elite genotypes in comparison to inferior genotypes. Further, we implemented expression analysis and enrichment analysis enabling the identification of 73 candidate genes and 25 homologues of Arabidopsis, 19 of which might regulate rice yield traits. Of these candidate genes, 40 CGs were found to be enriched in 60 GO terms of the studied traits for instance, positive regulator metabolic process (GO:0010929), intracellular part (GO:0031090), and nucleic acid binding (GO:0090079). Haplotype and phenotypic variation analysis confirmed that LOC_OS09G15770, LOC_OS02G36710 and LOC_OS02G17520 are key candidates associated with rice yield.ConclusionsOverall, we foresee that the QTNs, putative candidates elucidated in the study could summarize the polygenic regulatory networks controlling rice yield and be useful for breeding high-yielding varieties.

Multi-locus genome-wide association study reveal genomic regions underlying root system architecture traits in Ethiopian sorghum germplasm.

The identification of genomic regions underlying the root system architecture (RSA) is vital for improving crop abiotic stress tolerance. To improve sorghum (Sorghum bicolor L. Moench) for environmental stress tolerance, information on genetic variability and genomic regions linked to RSA traits is paramount. The aim of this study was, therefore, to investigate common quantitative trait nucleotides (QTNs) via multiple methodologies and identify genomic regions linked to RSA traits in a panel of 274 Ethiopian sorghum accessions. Multi-locus genome-wide association study was conducted using 265,944 high-quality single nucleotide polymorphism markers. Considering the QTN detected by at least three different methods, a total of 17 reliable QTNs were found to be significantly associated with root angle, number, length, and dry weight. Four QTNs were detected on chromosome SBI-05, followed by SBI-01 and SBI-02 with three QTNs each. Among the 17 QTNs, 11 are colocated with previously identified root traits quantitative trait loci and the remaining six are genome regions with novel genes. A total of 118 genes are colocated with these up- and down-streams of the QTNs. Moreover, five QTNs were found intragenic. These QTNs are S5_8994835 (number of nodal roots), S10_55702393 (number of nodal roots), S1_56872999 (nodal root angle), S9_1212069 (nodal root angle), and S5_5667192 (root dry weight) intragenic regions of Sobic.005G073101, Sobic.010G198000, Sobic.001G273000, Sobic.009G013600, and Sobic.005G054700, respectively. Particularly, Sobic.005G073101, Sobic.010G198000, and Sobic.009G013600 were found responsible for the plant growth hormone-induced RSA. These genes may regulate root development in the seedling stage. Further analysis on these genes might be important to explore the genetic structure of RSA of sorghum.

Precise colocalization of sorghum’s major chilling tolerance locus with Tannin1 due to tight linkage drag rather than antagonistic pleiotropy

Chilling tolerance in crops can increase resilience through longer growing seasons, drought escape, and nitrogen use efficiency. In sorghum (Sorghum bicolor [L.] Moench), breeding for chilling tolerance has been stymied by coinheritance of the largest-effect chilling tolerance locus, qSbCT04.62, with the major gene underlying undesirable grain proanthocyanidins, WD40 transcriptional regulator Tannin1. To test if this coinheritance is due to antagonistic pleiotropy of Tannin1, we developed and studied near-isogenic lines (NILs) carrying chilling tolerant haplotypes at qCT04.62. Whole-genome sequencing of the NILs revealed introgressions spanning part of the qCT04.62 confidence interval, including the Tannin1 gene and an ortholog of Arabidopsis cold regulator CBF/DREB1G. Segregation pattern of grain tannin in NILs confirmed the presence of wildtype Tannin1 and the reconstitution of a functional MYB-bHLH-WD40 regulatory complex. Low-temperature germination did not differ between NILs, suggesting that Tannin1 does not modulate this component of chilling tolerance. Similarly, NILs did not differ in seedling growth rate under either of two contrasting controlled environment chilling scenarios. Finally, while the chilling tolerant parent line had notably different photosynthetic responses from the susceptible parent line – including greater non-photochemical quenching before, during, and after chilling – the NIL responses match the susceptible parent. Thus, our findings suggest that tight linkage drag, not pleiotropy, underlies the precise colocalization of Tan1 with qCT04.62 and the qCT04.62 quantitative trait nucleotide lies outside the NIL introgressions. Breaking linkage at this locus should advance chilling tolerance breeding in sorghum and the identification of a novel chilling tolerance regulator.

Genome-wide genetic architecture for plant maturity and drought tolerance in diploid potatoes.

Cultivated potato (Solanum tuberosum) is known to be highly susceptible to drought. With climate change and its frequent episodes of drought, potato growers will face increased challenges to achieving their yield goals. Currently, a high proportion of untapped potato germplasm remains within the diploid potato relatives, and the genetic architecture of the drought tolerance and maturity traits of diploid potatoes is still unknown. As such, a panel of 384 ethyl methanesulfonate-mutagenized diploid potato clones were evaluated for drought tolerance and plant maturity under field conditions. Genome-wide association studies (GWAS) were conducted to dissect the genetic architecture of the traits. The results obtained from the genetic structure analysis of the panel showed five main groups and seven subgroups. Using the Genome Association and Prediction Integrated Tool-mixed linear model GWAS statistical model, 34 and 17 significant quantitative trait nucleotides (QTNs) were found associated with maturity and drought traits, respectively. Chromosome 5 carried most of the QTNs, some of which were also detected by using the restricted two-stage multi-locus multi-allele-GWAS haploblock-based model, and two QTNs were found to be pleiotropic for both maturity and drought traits. Using the non-parametric U-test, one and three QTNs, with 5.13%-7.4% phenotypic variations explained, showed favorable allelic effects that increase the maturity and drought trait values. The quantitaive trait loci (QTLs)/QTNs associated with maturity and drought trait were found co-located in narrow (0.5-1kb) genomic regions with 56 candidate genes playing roles in plant development and senescence and in abiotic stress responses. A total of 127 potato clones were found to be late maturing and tolerant to drought, while nine were early to moderate-late maturing and tolerant to drought. Taken together, the data show that the studied germplasm panel and the identified candidate genes are prime genetic resources for breeders and biologists in conventional breeding and targeted gene editing as climate adaptation tools.

Multi-model genome-wide association studies for appearance quality in rice.

Improving the quality of the appearance of rice is critical to meet market acceptance. Mining putative quality-related genes has been geared towards the development of effective breeding approaches for rice. In the present study, two SL-GWAS (CMLM and MLM) and three ML-GWAS (FASTmrEMMA, mrMLM, and FASTmrMLM) genome-wide association studies were conducted in a subset of 3K-RGP consisting of 198 rice accessions with 553,831 SNP markers. A total of 594 SNP markers were identified using the mixed linear model method for grain quality traits. Additionally, 70 quantitative trait nucleotides (QTNs) detected by the ML-GWAS models were strongly associated with grain aroma (AR), head rice recovery (HRR, %), and percentage of grains with chalkiness (PGC, %). Finally, 39 QTNs were identified using single- and multi-locus GWAS methods. Among the 39 reliable QTNs, 20 novel QTNs were identified for the above-mentioned three quality-related traits. Based on annotation and previous studies, four functional candidate genes (LOC_Os01g66110, LOC_Os01g66140, LOC_Os07g44910, and LOC_Os02g14120) were found to influence AR, HRR (%), and PGC (%), which could be utilized in rice breeding to improve grain quality traits.

Compressed variance component mixed model reveals epistasis associated with flowering in Arabidopsis.

Epistasis is currently a topic of great interest in molecular and quantitative genetics. Arabidopsis thaliana, as a model organism, plays a crucial role in studying the fundamental biology of diverse plant species. However, there have been limited reports about identification of epistasis related to flowering in genome-wide association studies (GWAS). Therefore, it is of utmost importance to conduct epistasis in Arabidopsis. In this study, we employed Levene's test and compressed variance component mixed model in GWAS to detect quantitative trait nucleotides (QTNs) and QTN-by-QTN interactions (QQIs) for 11 flowering-related traits of 199 Arabidopsis accessions with 216,130 markers. Our analysis detected 89 QTNs and 130 pairs of QQIs. Around these loci, 34 known genes previously reported in Arabidopsis were confirmed to be associated with flowering-related traits, such as SPA4, which is involved in regulating photoperiodic flowering, and interacts with PAP1 and PAP2, affecting growth of Arabidopsis under light conditions. Then, we observed significant and differential expression of 35 genes in response to variations in temperature, photoperiod, and vernalization treatments out of unreported genes. Functional enrichment analysis revealed that 26 of these genes were associated with various biological processes. Finally, the haplotype and phenotypic difference analysis revealed 20 candidate genes exhibiting significant phenotypic variations across gene haplotypes, of which the candidate genes AT1G12990 and AT1G09950 around QQIs might have interaction effect to flowering time regulation in Arabidopsis. These findings may offer valuable insights for the identification and exploration of genes and gene-by-gene interactions associated with flowering-related traits in Arabidopsis, that may even provide valuable reference and guidance for the research of epistasis in other species.