SNP profile for quantitative trait nucleotide in populations with small effective size and its impact on mapping and genomic predictions.

Increasing SNP density by incorporating sequence information only marginally increases prediction accuracies of breeding values in livestock. To find out why, we used statistical models and simulations to investigate the shape of distribution of estimated SNP effects (a profile) around Quantitative Trait Nucleotides (QTN) in populations with a small effective population size (Ne). A QTN profile created by averaging SNP effects around each QTN was similar to the shape of expected pairwise linkage disequilibrium (PLD) based on Ne and genetic distance between SNP, with a distinct peak for the QTN. Populations with smaller Ne showed lower but wider QTN profiles. However, adding more genotyped individuals with phenotypes dragged the profile closer to the QTN. The QTN profile was higher and narrower for populations with larger compared to smaller Ne. Assuming the PLD curve for the QTN profile, 80% of the additive genetic variance explained by each QTN was contained in ± 1/Ne Morgan interval around the QTN, corresponding to 2 Mb in cattle, and 5 Mb in pigs and chickens. With such large intervals, identifying QTN is difficult even if all of them are in the data and the assumed genetic architecture is simplistic. Additional complexity in QTN detection arises from confounding of QTN profiles with signals due to relationships, overlapping profiles with closely-spaced QTN, and spurious signals. However, small Ne allows for accurate predictions with large data even without QTN identification because QTN are accounted for by QTN profiles if SNP density is sufficient to saturate the segments.

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.

BACKGROUND: Salt stress significantly reduces soybean yield. To improve salt tolerance in soybean, it is important to mine the genes associated with salt tolerance traits. RESULTS: Salt 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. CONCLUSIONS: Around 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.

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.

Introduction: 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. Methods: 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. Results: 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. Discussion: 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.

Unraveling the genetics of heat tolerance in chickpea landraces (Cicer arietinum L.) using genome-wide association studies.

Chickpea, being an important grain legume crop, is often confronted with the adverse effects of high temperatures at the reproductive stage of crop growth, drastically affecting yield and overall productivity. The current study deals with an extensive evaluation of chickpea genotypes, focusing on the traits associated with yield and their response to heat stress. Notably, we observed significant variations for these traits under both normal and high-temperature conditions, forming a robust basis for genetic research and breeding initiatives. Furthermore, the study revealed that yield-related traits exhibited high heritability, suggesting their potential suitability for marker-assisted selection. We carried out single-nucleotide polymorphism (SNP) genotyping using the genotyping-by-sequencing (GBS) method for a genome-wide association study (GWAS). Overall, 27 marker-trait associations (MTAs) linked to yield-related traits, among which we identified five common MTAs displaying pleiotropic effects after applying a stringent Bonferroni-corrected p-value threshold of <0.05 [-log10(p) > 4.95] using the BLINK (Bayesian-information and linkage-disequilibrium iteratively nested keyway) model. Through an in-depth in silico analysis of these markers against the CDC Frontier v1 reference genome, we discovered that the majority of the SNPs were located at or in proximity to gene-coding regions. We further explored candidate genes situated near these MTAs, shedding light on the molecular mechanisms governing heat stress tolerance and yield enhancement in chickpeas such as indole-3-acetic acid-amido synthetase GH3.1 with GH3 auxin-responsive promoter and pentatricopeptide repeat-containing protein, etc. The harvest index (HI) trait was associated with marker Ca3:37444451 encoding aspartic proteinase ortholog sequence of Oryza sativa subsp. japonica and Medicago truncatula, which is known for contributing to heat stress tolerance. These identified MTAs and associated candidate genes may serve as valuable assets for breeding programs dedicated to tailoring chickpea varieties resilient to heat stress and climate change.

Identification of QTNs, QTN-by-environment interactions for plant height and ear height in maize multi-environment GWAS.

Plant height (PH) and ear height (EH) are important traits associated with biomass, lodging resistance, and grain yield in maize. There were strong effects of genotype x environment interaction (GEI) on plant height and ear height of maize. In this study, 203 maize inbred lines were grown at five locations across China's Spring and Summer corn belts, and plant height (PH) and ear height (EH) phenotype data were collected and grouped using GGE biplot. Five locations fell into two distinct groups (or mega environments) that coincide with two corn ecological zones called Summer Corn Belt and Spring Corn Belt. In total, 73,174 SNPs collected using GBS sequencing platform were used as genotype data and a recently released multi-environment GWAS software package IIIVmrMLM was employed to identify QTNs and QTN x environment (corn belt) interaction (QEIs); 12 and 11 statistically significant QEIs for PH and EH were detected respectively and their phenotypic effects were further partitioned into Add*E and Dom*E components. There were 28 and 25 corn-belt-specific QTNs for PH and EH identified, respectively. The result shows that there are a large number of genetic loci underlying the PH and EH GEIs and IIIVmrMLM is a powerful tool in discovering QTNs that have significant QTN-by-Environment interaction. PH and EH candidate genes were annotated based on transcriptomic analysis and haplotype analysis. EH related-QEI S10_135 (Zm00001d025947, saur76, small auxin up RNA76) and PH related-QEI S4_4 (Zm00001d049692, mads32, encoding MADS-transcription factor 32), and corn-belt specific QTNs including S10_4 (Zm00001d023333, sdg127, set domain gene127) and S7_1 (Zm00001d018614, GLR3.4, and glutamate receptor 3.4 or Zm00001d018616, DDRGK domain-containing protein) were reported, and the relationship among GEIs, QEIs and phenotypic plasticity and their biological and breeding implications were discussed.

Breeding effects on durum wheat traits detected using GWAS and haplotype block analysis.

Introduction: The recent boosting of genomic data in durum wheat (Triticum turgidum subsp. durum) offers the opportunity to better understand the effects of breeding on the genetic structures that regulate the expression of traits of agronomic interest. Furthermore, the identification of DNA markers useful for marker-assisted selection could also improve the reliability of technical protocols used for variety protection and registration. Methods: Within this motivation context, 123 durum wheat accessions, classified into three groups: landraces (LR), ancient (OC) and modern cultivars (MC), were evaluated in two locations, for 34 agronomic traits, including UPOV descriptors, to assess the impact of changes that occurred during modern breeding. Results: The association mapping analysis, performed with 4,241 SNP markers and six multi-locus-GWAS models, revealed 28 reliable Quantitative Trait Nucleotides (QTNs) related to plant morphology and kernel-related traits. Some important genes controlling flowering time and plant height were in linkage disequilibrium (LD) decay with QTNs identified in this study. A strong association for yellow berry was found on chromosome 6A (Q.Yb-6A) in a region containing the nadh-ubiquinone oxidoreductase subunit, a gene involved in starch metabolism. The Q.Kcp-2A harbored the PPO locus, with the associated marker (Ku_c13700_1196) in LD decay with Ppo-A1 and Ppo-A2. Interestingly, the Q.FGSGls-2B.1, identified by RAC875_c34512_685 for flag leaf glaucosity, mapped less than 1 Mb from the Epistatic inhibitors of glaucousness (Iw1), thus representing a good candidate for supporting the morphological DUS traits also with molecular markers. LD haplotype block approach revealed a higher diversity, richness and length of haploblocks in MC than OC and LR (580 in LR, 585 in OC and 612 in MC), suggesting a possible effect exerted by breeding programs on genomic regions associated with the agronomic traits. Discussion: Our findings pave new ways to support the phenotypic characterization necessary for variety registration by using a panel of cost-effectiveness SNP markers associated also to the UPOV descriptors. Moreover, the panel of associated SNPs might represent a reservoir of favourable alleles to use in durum wheat breeding and genetics.

Genome-Wide Association Studies Using 3VmrMLM Model Provide New Insights into Branched-Chain Amino Acid Contents in Rice Grains.

Rice (Oryza sativa L.) is a globally important food source providing carbohydrates, amino acids, and dietary fiber for humans and livestock. The branched-chain amino acid (BCAA) level is a complex trait related to the nutrient quality of rice. However, the genetic mechanism underlying the BCAA (valine, leucine, and isoleucine) accumulation in rice grains remains largely unclear. In this study, the grain BCAA contents and 239,055 SNPs of a diverse panel containing 422 rice accessions were adopted to perform a genome-wide association study (GWAS) using a recently proposed 3VmrMLM model. A total of 357 BCAA-content-associated main-effect quantitative trait nucleotides (QTNs) were identified from 15 datasets (12 BCAA content datasets and 3 BLUP datasets of BCAA). Furthermore, the allelic variation of two novel candidate genes, LOC_Os01g52530 and LOC_Os06g15420, responsible for the isoleucine (Ile) content alteration were identified. To reveal the genetic basis of the potential interactions between the gene and environmental factor, 53 QTN-by-environment interactions (QEIs) were detected using the 3VmrMLM model. The LOC_Os03g24460, LOC_Os01g55590, and LOC_Os12g31820 were considered as the candidate genes potentially contributing to the valine (Val), leucine (Leu), and isoleucine (Ile) accumulations, respectively. Additionally, 10 QTN-by-QTN interactions (QQIs) were detected using the 3VmrMLM model, which were putative gene-by-gene interactions related to the Leu and Ile contents. Taken together, these findings suggest that the implementation of the 3VmrMLM model in a GWAS may provide new insights into the deeper understanding of BCAA accumulation in rice grains. The identified QTNs/QEIs/QQIs serve as potential targets for the genetic improvement of rice with high BCAA levels.

The Genetic Structures and Molecular Mechanisms Underlying Ear Traits in Maize (Zea mays L.).

Maize (Zea mays L.) is one of the world's staple food crops. In order to feed the growing world population, improving maize yield is a top priority for breeding programs. Ear traits are important determinants of maize yield, and are mostly quantitatively inherited. To date, many studies relating to the genetic and molecular dissection of ear traits have been performed; therefore, we explored the genetic loci of the ear traits that were previously discovered in the genome-wide association study (GWAS) and quantitative trait locus (QTL) mapping studies, and refined 153 QTL and 85 quantitative trait nucleotide (QTN) clusters. Next, we shortlisted 19 common intervals (CIs) that can be detected simultaneously by both QTL mapping and GWAS, and 40 CIs that have pleiotropic effects on ear traits. Further, we predicted the best possible candidate genes from 71 QTL and 25 QTN clusters that could be valuable for maize yield improvement.

Association mapping reveals novel genes and genomic regions controlling grain size architecture in mini core accessions of Indian National Genebank wheat germplasm collection.

Wheat (Triticum aestivum L.) is a staple food crop for the global human population, and thus wheat breeders are consistently working to enhance its yield worldwide. In this study, we utilized a sub-set of Indian wheat mini core germplasm to underpin the genetic architecture for seed shape-associated traits. The wheat mini core subset (125 accessions) was genotyped using 35K SNP array and evaluated for grain shape traits such as grain length (GL), grain width (GW), grain length, width ratio (GLWR), and thousand grain weight (TGW) across the seven different environments (E1, E2, E3, E4, E5, E5, E6, and E7). Marker-trait associations were determined using a multi-locus random-SNP-effect Mixed Linear Model (mrMLM) program. A total of 160 non-redundant quantitative trait nucleotides (QTNs) were identified for four grain shape traits using two or more GWAS models. Among these 160 QTNs, 27, 36, 38, and 35 QTNs were associated for GL, GW, GLWR, and TGW respectively while 24 QTNs were associated with more than one trait. Of these 160 QTNs, 73 were detected in two or more environments and were considered reliable QTLs for the respective traits. A total of 135 associated QTNs were annotated and located within the genes, including ABC transporter, Cytochrome450, Thioredoxin_M-type, and hypothetical proteins. Furthermore, the expression pattern of annotated QTNs demonstrated that only 122 were differentially expressed, suggesting these could potentially be related to seed development. The genomic regions/candidate genes for grain size traits identified in the present study represent valuable genomic resources that can potentially be utilized in the markers-assisted breeding programs to develop high-yielding varieties.

Multi-locus genome-wide association study of fusarium head blight in relation to days to anthesis and plant height in a spring wheat association panel.

Fusarium head blight (FHB) is a highly destructive fungal disease of wheat to which host resistance is quantitatively inherited and largely influenced by the environment. Resistance to FHB has been associated with taller height and later maturity; however, a further understanding of these relationships is needed. An association mapping panel (AMP) of 192 predominantly Canadian spring wheat was genotyped with the wheat 90K single-nucleotide polymorphism (SNP) array. The AMP was assessed for FHB incidence (INC), severity (SEV) and index (IND), days to anthesis (DTA), and plant height (PLHT) between 2015 and 2017 at three Canadian FHB-inoculated nurseries. Seven multi-environment trial (MET) datasets were deployed in a genome-wide association study (GWAS) using a single-locus mixed linear model (MLM) and a multi-locus random SNP-effect mixed linear model (mrMLM). MLM detected four quantitative trait nucleotides (QTNs) for INC on chromosomes 2D and 3D and for SEV and IND on chromosome 3B. Further, mrMLM identified 291 QTNs: 50 (INC), 72 (SEV), 90 (IND), 41 (DTA), and 38 (PLHT). At two or more environments, 17 QTNs for FHB, DTA, and PLHT were detected. Of these 17, 12 QTNs were pleiotropic for FHB traits, DTA, and PLHT on chromosomes 1A, 1D, 2D, 3B, 5A, 6B, 7A, and 7B; two QTNs for DTA were detected on chromosomes 1B and 7A; and three PLHT QTNs were located on chromosomes 4B and 6B. The 1B DTA QTN and the three pleiotropic QTNs on chromosomes 1A, 3B, and 6B are potentially identical to corresponding quantitative trait loci (QTLs) in durum wheat. Further, the 3B pleiotropic QTN for FHB INC, SEV, and IND co-locates with TraesCS3B02G024900 within the Fhb1 region on chromosome 3B and is ~3 Mb from a cloned Fhb1 candidate gene TaHRC. While the PLHT QTN on chromosome 6B is putatively novel, the 1B DTA QTN co-locates with a disease resistance protein located ~10 Mb from a Flowering Locus T1-like gene TaFT3-B1, and the 7A DTA QTN is ~5 Mb away from a maturity QTL QMat.dms-7A.3 of another study. GWAS and QTN candidate genes enabled the characterization of FHB resistance in relation to DTA and PLHT. This approach should eventually generate additional and reliable trait-specific markers for breeding selection, in addition to providing useful information for FHB trait discovery.

Evaluating the Genetic Background Effect on Dissecting the Genetic Basis of Kernel Traits in Reciprocal Maize Introgression Lines.

Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits.

Identification of QTN-by-environment interactions for yield related traits in maize under multiple abiotic stresses.

Introduction: Quantitative trait nucleotide (QTN)-by-environment interactions (QEIs) play an increasingly essential role in the genetic dissection of complex traits in crops as global climate change accelerates. The abiotic stresses, such as drought and heat, are the major constraints on maize yields. Multi-environment joint analysis can improve statistical power in QTN and QEI detection, and further help us to understand the genetic basis and provide implications for maize improvement. Methods: In this study, 3VmrMLM was applied to identify QTNs and QEIs for three yield-related traits (grain yield, anthesis date, and anthesis-silking interval) of 300 tropical and subtropical maize inbred lines with 332,641 SNPs under well-watered and drought and heat stresses. Results: Among the total 321 genes around 76 QTNs and 73 QEIs identified in this study, 34 known genes were reported in previous maize studies to be truly associated with these traits, such as ereb53 (GRMZM2G141638) and thx12 (GRMZM2G016649) associated with drought stress tolerance, and hsftf27 (GRMZM2G025685) and myb60 (GRMZM2G312419) associated with heat stress. In addition, among 127 homologs in Arabidopsis out of 287 unreported genes, 46 and 47 were found to be significantly and differentially expressed under drought vs well-watered treatments, and high vs. normal temperature treatments, respectively. Using functional enrichment analysis, 37 of these differentially expressed genes were involved in various biological processes. Tissue-specific expression and haplotype difference analysis further revealed 24 candidate genes with significantly phenotypic differences across gene haplotypes under different environments, of which the candidate genes GRMZM2G064159, GRMZM2G146192, and GRMZM2G114789 around QEIs may have gene-by-environment interactions for maize yield. Discussion: All these findings may provide new insights for breeding in maize for yield-related traits adapted to abiotic stresses.

Identification of QTNs, QTN-by-environment interactions, and their candidate genes for grain size traits in main crop and ratoon rice.

Although grain size is an important quantitative trait affecting rice yield and quality, there are few studies on gene-by-environment interactions (GEIs) in genome-wide association studies, especially, in main crop (MC) and ratoon rice (RR). To address these issues, the phenotypes for grain width (GW), grain length (GL), and thousand grain weight (TGW) of 159 accessions of MC and RR in two environments were used to associate with 2,017,495 SNPs for detecting quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) using 3VmrMLM. As a result, 64, 71, 67, 72, 63, and 56 QTNs, and 0, 1, 2, 2, 2, and 1 QEIs were found to be significantly associated with GW in MC (GW-MC), GL-MC, TGW-MC, GW-RR, GL-RR, and TGW-RR, respectively. 3, 4, 7, 2, 2, and 4 genes were found to be truly associated with the above traits, respectively, while 2 genes around the above QEIs were found to be truly associated with GL-RR, and one of the two known genes was differentially expressed under two soil moisture conditions. 10, 7, 1, 8, 4, and 3 candidate genes were found by differential expression and GO annotation analysis to be around the QTNs for the above traits, respectively, in which 6, 3, 1, 2, 0, and 2 candidate genes were found to be significant in haplotype analysis. The gene Os03g0737000 around one QEI for GL-MC was annotated as salt stress related gene and found to be differentially expressed in two cultivars with different grain sizes. Among all the candidate genes around the QTNs in this study, four were key, in which two were reported to be truly associated with seed development, and two (Os02g0626100 for GL-MC and Os02g0538000 for GW-MC) were new. Moreover, 1, 2, and 1 known genes, along with 8 additional candidate genes and 2 candidate GEIs, were found to be around QTNs and QEIs for GW, GL, and TGW, respectively in MC and RR joint analysis, in which 3 additional candidate genes were key and new. Our results provided a solid foundation for genetic improvement and molecular breeding in MC and RR.

Genetic structure and molecular mechanism underlying the stalk lodging traits in maize (Zea mays L.).

Stalk lodging seriously affects yield and quality of crops, and it can be caused by several factors, such as environments, developmental stages, and internal chemical components of plant stalks. Breeding of stalk lodging-resistant varieties is thus an important task for maize breeders. To better understand the genetic basis underlying stalk lodging resistance, several methods such as quantitative trait locus (QTL) mapping and genome-wide association study (GWAS) have been used to mine potential gene resources. Based on different types of genetic populations and mapping methods, many significant loci associated with stalk lodging resistance have been identified so far. However, few work has been performed to compare and integrate these reported genetic loci. In this study, we first collected hundreds of QTLs and quantitative trait nucleotides (QTNs) related to stalk lodging traits in maize. Then we mapped and integrated the QTLs and QTNs in maize genome to identify overlapped hotspot regions. Based on the genomic confidence intervals harboring these overlapped hotspot regions, we predicted candidate genes related to stalk lodging traits. Meanwhile, we mapped reported genes to these hotspot regions. Finally, we constructed molecular regulatory networks underlying stalk lodging resistance in maize. Collectively, this study provides not only useful genetic loci for deeply exploring molecular mechanisms of stalk lodging resistance traits, but also potential candidate genes and targeted strategies for improving stalk lodging resistance to increase crop yields in future.

A Systemic Investigation of Genetic Architecture and Gene Resources Controlling Kernel Size-Related Traits in Maize.

Grain yield is the most critical and complex quantitative trait in maize. Kernel length (KL), kernel width (KW), kernel thickness (KT) and hundred-kernel weight (HKW) associated with kernel size are essential components of yield-related traits in maize. With the extensive use of quantitative trait locus (QTL) mapping and genome-wide association study (GWAS) analyses, thousands of QTLs and quantitative trait nucleotides (QTNs) have been discovered for controlling these traits. However, only some of them have been cloned and successfully utilized in breeding programs. In this study, we exhaustively collected reported genes, QTLs and QTNs associated with the four traits, performed cluster identification of QTLs and QTNs, then combined QTL and QTN clusters to detect consensus hotspot regions. In total, 31 hotspots were identified for kernel size-related traits. Their candidate genes were predicted to be related to well-known pathways regulating the kernel developmental process. The identified hotspots can be further explored for fine mapping and candidate gene validation. Finally, we provided a strategy for high yield and quality maize. This study will not only facilitate causal genes cloning, but also guide the breeding practice for maize.

Genome-wide detection of genotype environment interactions for flowering time in Brassica napus.

Flowering time is strongly related to the environment, while the genotype-by-environment interaction study for flowering time is lacking in Brassica napus. Here, a total of 11,700,689 single nucleotide polymorphisms in 490 B. napus accessions were used to associate with the flowering time and related climatic index in eight environments using a compressed variance-component mixed model, 3VmrMLM. As a result, 19 stable main-effect quantitative trait nucleotides (QTNs) and 32 QTN-by-environment interactions (QEIs) for flowering time were detected. Four windows of daily average temperature and precipitation were found to be climatic factors highly correlated with flowering time. Ten main-effect QTNs were found to be associated with these flowering-time-related climatic indexes. Using differentially expressed gene (DEG) analysis in semi-winter and spring oilseed rapes, 5,850 and 5,511 DEGs were found to be significantly expressed before and after vernalization. Twelve and 14 DEGs, including 7 and 9 known homologs in Arabidopsis, were found to be candidate genes for stable QTNs and QEIs for flowering time, respectively. Five DEGs were found to be candidate genes for main-effect QTNs for flowering-time-related climatic index. These candidate genes, such as BnaFLCs, BnaFTs, BnaA02.VIN3, and BnaC09.PRR7, were further validated by the haplotype, selective sweep, and co-expression networks analysis. The candidate genes identified in this study will be helpful to breed B. napus varieties adapted to particular environments with optimized flowering time.

Identification of QTN-by-environment interactions and their candidate genes for soybean seed oil-related traits using 3VmrMLM.

Introduction: Although seed oil content and its fatty acid compositions in soybean were affected by environment, QTN-by-environment (QEIs) and gene-by-environment interactions (GEIs) were rarely reported in genome-wide association studies. Methods: The 3VmrMLM method was used to associate the trait phenotypes, measured in five to seven environments, of 286 soybean accessions with 106,013 SNPs for detecting QTNs and QEIs. Results: Seven oil metabolism genes (GmSACPD-A, GmSACPD-B, GmbZIP123, GmSWEET39, GmFATB1A, GmDGAT2D, and GmDGAT1B) around 598 QTNs and one oil metabolism gene GmFATB2B around 54 QEIs were verified in previous studies; 76 candidate genes and 66 candidate GEIs were predicted to be associated with these traits, in which 5 genes around QEIs were verified in other species to participate in oil metabolism, and had differential expression across environments. These genes were found to be related to soybean seed oil content in haplotype analysis. In addition, most candidate GEIs were co-expressed with drought response genes in co-expression network, and three KEGG pathways which respond to drought were enriched under drought stress rather than control condition; six candidate genes were hub genes in the co-expression networks under drought stress. Discussion: The above results indicated that GEIs, together with drought response genes in co-expression network, may respond to drought, and play important roles in regulating seed oil-related traits together with oil metabolism genes. These results provide important information for genetic basis, molecular mechanisms, and soybean breeding for seed oil-related traits.

Identification of QTNs, QTN-by-environment interactions and genes for yield-related traits in rice using 3VmrMLM.

Rice, which supports more than half the population worldwide, is one of the most important food crops. Thus, potential yield-related quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) have been used to develop efficient rice breeding strategies. In this study, a compressed variance component mixed model, 3VmrMLM, in genome-wide association studies was used to detect QTNs for eight yield-related traits of 413 rice accessions with 44,000 single nucleotide polymorphisms. These traits include florets per panicle, panicle fertility, panicle length, panicle number per plant, plant height, primary panicle branch number, seed number per panicle, and flowering time. Meanwhile, QTNs and QEIs were identified for flowering times in three different environments and five subpopulations. In the detections, a total of 7~23 QTNs were detected for each trait, including the three single-environment flowering time traits. In the detection of QEIs for flowering time in the three environments, 21 QTNs and 13 QEIs were identified. In the five subpopulation analyses, 3~9 QTNs and 2~4 QEIs were detected for each subpopulation. Based on previous studies, we identified 87 known genes around the significant/suggested QTNs and QEIs, such as LOC_Os06g06750 (OsMADS5) and LOC_Os07g47330 (FZP). Further differential expression analysis and functional enrichment analysis identified 30 candidate genes. Of these candidate genes, 27 genes had high expression in specific tissues, and 19 of these 27 genes were homologous to known genes in Arabidopsis. Haplotype difference analysis revealed that LOC_Os04g53210 and LOC_Os07g42440 are possibly associated with yield, and LOC_Os04g53210 may be useful around a QEI for flowering time. These results provide insights for future breeding for high quality and yield in rice.

Genetic Dissection of Epistatic Interactions Contributing Yield-Related Agronomic Traits in Rice Using the Compressed Mixed Model.

Rice (Oryza sativa) is one of the most important cereal crops in the world, and yield-related agronomic traits, including plant height (PH), panicle length (PL), and protein content (PC), are prerequisites for attaining the desired yield and quality in breeding programs. Meanwhile, the main effects and epistatic effects of quantitative trait nucleotides (QTNs) are all important genetic components for yield-related quantitative traits. In this study, we conducted genome-wide association studies (GWAS) for 413 rice germplasm resources, with 36,901 single nucleotide polymorphisms (SNPs), to identify QTNs, QTN-by-QTN interaction (QQI), and their candidate genes, using a multi-locus compressed variance component mixed model, 3VmrMLM. As a result, two significant QTNs and 56 paired QQIs were detected, amongst 5219 genes of these QTNs, and 26 genes were identified as the yield-related confirmed genes, such as LCRN1, OsSPL3, and OsVOZ1 for PH, and LOG and QsBZR1 for PL. To reveal the substantial contributions related to the variation of yield-related agronomic traits in rice, we further implemented an enrichment analysis and expression analysis. As the results showed, 114 genes, nearly all significant QQIs, were involved in 37 GO terms; for example, the macromolecule metabolic process (GO:0043170), intracellular part (GO:0044424), and binding (GO:0005488). It was revealed that most of the QQIs and the candidate genes were significantly involved in the biological process, molecular function, and cellular component of the target traits. The demonstrated genetic interactions play a critical role in yield-related agronomic traits of rice, and such epistatic interactions contributed to large portions of the missing heritability in GWAS. These results help us to understand the genetic basis underlying the inheritance of the three yield-related agronomic traits and provide implications for rice improvement.