Phenotypic Variation Explained Research Articles

High-density genetic map construction and QTL mapping of a zigzag-shaped stem trait in tea plant (Camellia sinensis)

The highly unique zigzag-shaped stem phenotype in tea plants boasts significant ornamental value and is exceptionally rare. To investigate the genetic mechanism behind this trait, we developed BC1 artificial hybrid populations. Our genetic analysis revealed the zigzag-shaped trait as a qualitative trait. Utilizing whole-genome resequencing, we constructed a high-density genetic map from the BC1 population, incorporating 5,250 SNP markers across 15 linkage groups, covering 3,328.51 cM with an average marker interval distance of 0.68 cM. A quantitative trait locus (QTL) for the zigzag-shaped trait was identified on chromosome 4, within a 61.2 to 97.2 Mb range, accounting for a phenotypic variation explained (PVE) value of 13.62%. Within this QTL, six candidate genes were pinpointed. To better understand their roles, we analyzed gene expression in various tissues and individuals with erect and zigzag-shaped stems. The results implicated CsXTH (CSS0035625) and CsCIPK14 (CSS0044366) as potential key contributors to the zigzag-shaped stem formation. These discoveries lay a robust foundation for future functional genetic mapping and tea plant genetic enhancement.

Genome-wide association study and genomic prediction of resistance to summer mortality in Pacific oyster (Crassostrea gigas) using whole genome resequencing

The Pacific oyster Crassostrea gigas is one of the most important farmed marine shellfish worldwide. However, massive mortality during the summer months caused significant economic losses to the C. gigas farming industry. Understanding the genetic architecture of resistance traits has been an ongoing research issue, and the incorporation of genomic information into breeding programs is expected to accelerate the process of genetic improvement. Genomic selection (GS), as a new approach of genetic improvement, has great potential for breeding new lines with adaptive advantages. In this study, we conducted a genome-wide association study (GWAS) for summer mortality resistance and estimated the accuracy of genomic predictions using different methods. The heritability of the C. gigas summer mortality resistance was low, with heritability of 0.108 ± 0.036, 0.161 ± 0.102 and 0.134 ± 0.043 for PBLUP, GBLUP and ssGBLUP, respectively. In addition, we detected 9,783,674 SNPs and used a mixed linear model to identify 18 significant SNPs associated with summer mortality resistance. The phenotypic variance explained (PVE) for these SNPs ranged from 8.25% to 10.14%. Based on these significantly related SNPs, nine significant candidate genes were identified (TLR4, HEX, SLC22A8, PDE2A, HUWE1, SLMAP, RAD52, TBK1 and RAPH1). The prediction accuracy using PBLUP, GBLUP, ssGBLUP, and weight ssGBLUP (WssGBLUP) was 0.549 ± 0.026, 0.381 ± 0.074, 0.544 ± 0.026, and 0.869 ± 0.018, respectively. Therefore, WssGBLUP models are more suitable for genomic prediction of summer mortality resistance in C. gigas. Our results suggest a polygenic genetic architecture that provide new perspectives for studying candidate genes for resistance to summer mortality, which may facilitate the genetic improvement for resistance lines in C. gigas.

Linkage-Mapping and Genome-wide Association Study Identified Two Peanut Late Leaf Spot Resistance Loci, PLLSR-1 and PLLSR-2, Using a Nested Association Mapping.

Identification of candidate genes and molecular markers for late leaf spot (LLS) disease resistance in peanut (Arachis hypogaea) has been a focus of molecular breeding for the U.S. industry funded peanut genome project. Efforts have been hindered by limited mapping resolution due to low levels of genetic recombination and marker density available in traditional biparental mapping populations. To address this, a multi-parental nested association mapping (NAM) population has been genotyped with the peanut 58 K SNP array and phenotyped for LLS severity in the field for three years. Joint linkage-based QTL mapping identified nine QTLs for LLS resistance with significant phenotypic variance explained (PVE) up to 47.7%. A genome-wide association study (GWAS) identified 13 SNPs consistently associated with LLS resistance. Two genomic regions harboring the consistent QTLs and SNPs were identified from 1,336 Kb to 1,520 Kb (184 Kb) on chromosome B02 and from 1,026.9 Kb to 1,793.2 Kb (767 Kb) on chromosome B03, designated as peanut late leaf spot resistance loci, PLLSR-1 and PLLSR-2, respectively. PLLSR-1 contains 10 NBS-LRR disease resistant genes. An NBS-LRR disease resistance gene Arahy.VKVT6A was also identified on homoeologous chromosome A02. PLLSR-2 contains five significant SNPs associated with five different genes encoding callose synthase, pollen defective in guidance protein, pentatricopeptide repeat (PPR), acyl-activating enzyme, and C2 GRAM domains-containing protein. This study highlights the power of multi-parent populations such as NAM for genetic mapping and marker-trait association studies in peanuts. Validation of these two LLS resistance loci will be needed for marker-assisted breeding.

Mining genomic regions associated with agronomic and biochemical traits in quinoa through GWAS

Quinoa (Chenopodium quinoa Willd.), an Andean crop, is a facultative halophyte food crop recognized globally for its high nutritional value and plasticity to adapt to harsh conditions. We conducted a genome-wide association study on a diverse set of quinoa germplasm accessions. These accessions were evaluated for the following agronomic and biochemical traits: days to 50% flowering (DTF), plant height (PH), panicle length (PL), stem diameter (SD), seed yield (SY), grain diameter (GD), and thousand-grain weight (TGW). These accessions underwent genotyping-by-sequencing using the DNBSeq-G400R platform. Among all evaluated traits, TGW represented maximum broad-sense heritability. Our study revealed average SNP density of ≈ 3.11 SNPs/10 kb for the whole genome, with the lowest and highest on chromosomes Cq1B and Cq9A, respectively. Principal component analysis clustered the quinoa population in three main clusters, one clearly representing lowland Chilean accessions, whereas the other two groups corresponded to germplasm from the highlands of Peru and Bolivia. In our germplasm set, we estimated linkage disequilibrium decay to be ≈ 118.5 kb. Marker-trait analyses revealed major and consistent effect associations for DTF on chromosomes 3A, 4B, 5B, 6A, 7A, 7B and 8B, with phenotypic variance explained (PVE) as high as 19.15%. Nine associations across eight chromosomes were also found for saponin content with 20% PVE by qSPN5A.1. More QTLs were identified for PL and TGW on multiple chromosomal locations. We identified putative candidate genes in the genomic regions associated with DTF and saponin content. The consistent and major-effect genomic associations can be used in fast-tracking quinoa breeding for wider adaptation across marginal environments.

Exploiting genomic tools for genetic dissection and improving the resistance to Fusarium stalk rot in tropical maize.

A stable genomic region conferring FSR resistance at ~250 Mb on chromosome 1 was identified by GWAS. Genomic prediction has the potential to improve FSR resistance. Fusarium stalk rot (FSR) is a global destructive disease in maize; the efficiency of phenotypic selection for improving FSR resistance was low. Novel genomic tools of genome-wide association study (GWAS) and genomic prediction (GP) provide an opportunity for genetic dissection and improving FSR resistance. In this study, GWAS and GP analyses were performed on 562 tropical maize inbred lines consisting of two populations. In total, 15 SNPs significantly associated with FSR resistance were identified across two populations and the combinedPOP consisting of all 562 inbred lines, with the P-values ranging from 1.99 × 10-7 to 8.27 × 10-13, and the phenotypic variance explained (PVE) values ranging from 0.94 to 8.30%. The genetic effects of the 15 favorable alleles ranged from -4.29 to -14.21% of the FSR severity. One stable genomic region at ~ 250Mb on chromosome 1 was detected across all populations, and the PVE values of the SNPs detected in this region ranged from 2.16 to 5.18%. Prediction accuracies of FSR severity estimated with the genome-wide SNPs were moderate and ranged from 0.29 to 0.51. By incorporating genotype-by-environment interaction, prediction accuracies were improved between 0.36 and 0.55 in different breeding scenarios. Considering both the genome coverage and the threshold of the P-value of SNPs to select a subset of molecular markers further improved the prediction accuracies. These findings extend the knowledge of exploiting genomic tools for genetic dissection and improving FSR resistance in tropical maize.

Identification of novel QTLs for resistance to late leaf spot in peanut by SNP array and QTL-seq

Late leaf spot (LLS) is one of important diseases that causes severe yield losses in peanut. Peanut has various sources of resistance to LLS, and the identification of resistant QTLs and the development of related molecular markers are of great importance for breeding of LLS-resistant peanut. In this study, 173 individual lines of a recombinant inbred line (RIL) population and 48K SNP array for genotyping were used to construct a high-density genetic map with 1475 SNP marker and 20 linkage groups. A total of 11 QTLs were obtained through QTL analysis using the constructed genetic map. Among them, a stable major QTL qLLS.LG02 was identified on linkage group 2 in all six environments, with a phenotypic variation explained (PVE) ranging from 15.57 to 31.09%. Additionally, QTL-seq technology was also employed for QTL analysis of LLS resistance. As a result, 14 QTL loci related to LLS resistance were identified using the G prime algorithm. Notably, physical position of qLLS02 and qLLS03 are coincided with that of qLLS.LG02 and qLLS.LG03 respectively. Gene annotation analysis within the 14 QTL intervals by QTL-seq revealed that there were a total of 163 NBS-LRR disease resistance genes, accounting for 22.86% of all R genes in peanut genome and showing a 4.26-fold enrichment with a p-value of 5.19e-57. Within the QTL region qLLS02 of the resistant parent Mi-2, there was a 5 Mb structural variation interval (SV) containing 81 NBS-LRR genes. A PCR diagnostic marker was developed, and validation data suggest that this SV might lead to gene deletion or replacement with other genes. This SV has the potential to enhance peanut resistance to late leaf spot disease. This study holds significant implications for improving peanut breeding for LLS resistance through development of associated molecular markers.

Identification of Clubroot (Plasmodiophora brassicae) Resistance Loci in Chinese Cabbage (Brassica rapa ssp. pekinensis) with Recessive Character.

The soil-borne pathogen Plasmodiophora brassicae is the causal agent of clubroot, a major disease in Chinese cabbage (Brassica rapa ssp. pekinensis). The host's resistance genes often confer immunity to only specific pathotypes and may be rapidly overcome. Identification of novel clubroot resistance (CR) from germplasm sources is necessary. In this study, Bap246 was tested by being crossed with different highly susceptible B. rapa materials and showed recessive resistance to clubroot. An F2 population derived from Bap246 × Bac1344 was used to locate the resistance Quantitative Trait Loci (QTL) by Bulk Segregant Analysis Sequencing (BSA-Seq) and QTL mapping methods. Two QTL on chromosomes A01 (4.67-6.06 Mb) and A08 (10.42-11.43 Mb) were found and named Cr4Ba1.1 and Cr4Ba8.1, respectively. Fifteen and eleven SNP/InDel markers were used to narrow the target regions in the larger F2 population to 4.67-5.17 Mb (A01) and 10.70-10.84 Mb (A08), with 85 and 19 candidate genes, respectively. The phenotypic variation explained (PVE) of the two QTL were 30.97% and 8.65%, respectively. Combined with gene annotation, mutation site analysis, and real-time quantitative polymerase chain reaction (qRT-PCR) analysis, one candidate gene in A08 was identified, namely Bra020861. And an insertion and deletion (InDel) marker (co-segregated) named Crr1-196 was developed based on the gene sequence. Bra013275, Bra013299, Bra013336, Bra013339, Bra013341, and Bra013357 in A01 were the candidate genes that may confer clubroot resistance in Chinese cabbage. The resistance resource and the developed marker will be helpful in Brassica breeding programs.

Dissection of the E8 locus in two early maturing Canadian soybean populations.

Soybean [Glycine max (L.) Merr.] is a short-day crop for which breeders want to expand the cultivation range to more northern agro-environments by introgressing alleles involved in early reproductive traits. To do so, we investigated quantitative trait loci (QTL) and expression quantitative trait loci (eQTL) regions comprised within the E8 locus, a large undeciphered region (~7.0 Mbp to 44.5 Mbp) associated with early maturity located on chromosome GM04. We used a combination of two mapping algorithms, (i) inclusive composite interval mapping (ICIM) and (ii) genome-wide composite interval mapping (GCIM), to identify major and minor regions in two soybean populations (QS15524F2:F3 and QS15544RIL) having fixed E1, E2, E3, and E4 alleles. Using this approach, we identified three main QTL regions with high logarithm of the odds (LODs), phenotypic variation explained (PVE), and additive effects for maturity and pod-filling within the E8 region: GM04:16,974,874-17,152,230 (E8-r1); GM04:35,168,111-37,664,017 (E8-r2); and GM04:41,808,599-42,376,237 (E8-r3). Using a five-step variant analysis pipeline, we identified Protein far-red elongated hypocotyl 3 (Glyma.04G124300; E8-r1), E1-like-a (Glyma.04G156400; E8-r2), Light-harvesting chlorophyll-protein complex I subunit A4 (Glyma.04G167900; E8-r3), and Cycling dof factor 3 (Glyma.04G168300; E8-r3) as the most promising candidate genes for these regions. A combinatorial eQTL mapping approach identified significant regulatory interactions for 13 expression traits (e-traits), including Glyma.04G050200 (Early flowering 3/E6 locus), with the E8-r3 region. Four other important QTL regions close to or encompassing major flowering genes were also detected on chromosomes GM07, GM08, and GM16. In GM07:5,256,305-5,404,971, a missense polymorphism was detected in the candidate gene Glyma.07G058200 (Protein suppressor of PHYA-105). These findings demonstrate that the locus known as E8 is regulated by at least three distinct genomic regions, all of which comprise major flowering genes.

Utilizing Two Populations Derived from Tropical Maize for Genome-Wide Association Analysis of Banded Leaf and Sheath Blight Resistance

Banded leaf and sheath blight (BLSB) in maize is a soil-borne fungal disease caused by Rhizoctonia solani Kühn, resulting in significant yield losses. Investigating the genes responsible for regulating resistance to BLSB is crucial for yield enhancement. In this study, a multiparent maize population was developed, comprising two recombinant inbred line (RIL) populations totaling 442 F8RILs. The populations were generated by crossing two tropical inbred lines, CML444 and NK40-1, known for their BLSB resistance, as female parents, with the high-yielding but BLSB-susceptible inbred line Ye107 serving as the common male parent. Subsequently, we utilized 562,212 high-quality single nucleotide polymorphisms (SNPs) generated through genotyping-by-sequencing (GBS) for a comprehensive genome-wide association study (GWAS) aimed at identifying genes responsible for BLSB resistance. The objectives of this study were to (1) identify SNPs associated with BLSB resistance through genome-wide association analyses, (2) explore candidate genes regulating BLSB resistance in maize, and (3) investigate pathways involved in BLSB resistance and discover key candidate genes through Gene Ontology (GO) analysis. The GWAS analysis revealed nineteen SNPs significantly associated with BLSB that were consistently identified across four environments in the GWAS, with phenotypic variation explained (PVE) ranging from 2.48% to 11.71%. Screening a 40 kb region upstream and downstream of the significant SNPs revealed several potential candidate genes. By integrating information from maize GDB and the NCBI, we identified five novel candidate genes, namely, Zm00001d009723, Zm00001d009975, Zm00001d009566, Zm00001d009567, located on chromosome 8, and Zm00001d026376, on chromosome 10, related to BLSB resistance. These candidate genes exhibit association with various aspects, including maize cell membrane proteins and cell immune proteins, as well as connections to cell metabolism, transport, transcriptional regulation, and structural proteins. These proteins and biochemical processes play crucial roles in maize defense against BLSB. When Rhizoctonia solani invades maize plants, it induces the expression of genes encoding specific proteins and regulates corresponding metabolic pathways to thwart the invasion of this fungus. The present study significantly contributes to our understanding of the genetic basis of BLSB resistance in maize, offering valuable insights into novel candidate genes that could be instrumental in future breeding efforts to develop maize varieties with enhanced BLSB resistance.

Genome-wide discovery of InDels and validation of PCR-Based InDel markers for earliness in a RIL population and genotypes of lentil (Lens culinaris Medik.).

The systematic identification of insertion/deletion (InDel) length polymorphisms from the entire lentil genome can be used to map the quantitative trait loci (QTL) and also for the marker-assisted selection (MAS) for various linked traits. The InDels were identified by comparing the whole-genome resequencing (WGRS) data of two extreme bulks (early- and late-flowering bulk) and a parental genotype (Globe Mutant) of lentil. The bulks were made by pooling 20 extreme recombinant inbred lines (RILs) each, derived by crossing Globe Mutant (late flowering parent) with L4775 (early flowering parent). Finally, 734,716 novel InDels were identified, which is nearly one InDel per 5,096 bp of lentil genome. Furthermore, 74.94% of InDels were within the intergenic region and 99.45% displayed modifier effects. Of these, 15,732 had insertions or deletions of 20 bp or more, making them amenable to the development of PCR-based markers. An InDel marker I-SP-356.6 (chr. 3; position 356,687,623; positioned 174.5 Kb from the LcFRI gene) was identified as having a phenotypic variance explained (PVE) value of 47.7% for earliness when validated in a RIL population. Thus, I-SP-356.6 marker can be deployed in MAS to facilitate the transfer of the earliness trait to other elite late-maturing cultivars. Two InDel markers viz., I-SP-356.6 and I-SP-383.9 (chr. 3; linked to LcELF3a gene) when tested in 9 lentil genotypes differing for maturity duration, clearly distinguished three early (L4775, ILL7663, Precoz) and four late genotypes (Globe Mutant, MFX, L4602, L830). However, these InDels could not be validated in two genotypes (L4717, L4727), suggesting either absence of polymorphism and/or presence of other loci causing earliness. The identified InDel markers can act as valuable tools for MAS for the development of early maturing lentil varieties.

Genome-Wide Association Study to Identify Possible Candidate Genes of Snap Bean Leaf and Pod Color.

Color can be an indicator of plant health, quality, and productivity, and is useful to researchers to understand plant nutritional content in their studies. Color may be related to chlorophyll content and photosynthetic activity and provides information for those studying diseases and mineral nutrition because every nutrient deficiency and many diseases produce symptoms that affect color. In order to identify significant loci related to both leaf and pod color in a snap bean (Phaseolus vulgaris L.) diversity panel, a genome-wide association study (GWAS) was carried out. Leaf color in one and pod traits in multiple environments were characterized using a colorimeter. L*a*b* color data were recorded and used to calculate chroma (C*) and hue angle (H°). Leaves were evaluated at three positions (lower, middle, and upper) in the canopy and both pod exterior and interior colors were obtained. GWAS was conducted using two reference genomes that represent the Andean (G19833) and Middle American (5-593) domestication centers. Narrow sense heritabilities were calculated using the mixed linear model (MLM) method in genome association and prediction integrated tool (GAPIT), and significant single nucleotide polymorphisms (SNPs) for each color parameter were obtained using the Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) GWAS model with two principal components (PCAs). In comparison to pod color traits, narrow sense heritabilities of leaf traits were low and similar for both reference genomes. Generally, narrow sense heritability for all traits was highest in the lower, followed by middle, and then upper leaf positions. Heritability for both pod interior and exterior color traits was higher using the G19833 reference genome compared to 5-593 when evaluated by year and means across years. Forty-five significant SNPs associated with leaf traits and 872 associated with pods, totaling 917 significant SNPs were identified. Only one SNP was found in common for both leaf and pod traits on Pv03 in the 5-593 reference genome. One-hundred thirteen significant SNPs, 30 in leaves and 83 in pods had phenotypic variation explained (PVE) of 10% or greater. Fourteen SNPs (four from G19833 and ten from 5-593) with ≥10 PVE%, large SNP effect, and largest p-value for L* and H° pod exterior was identified on Pv01, Pv02, Pv03, and Pv08. More SNPs were associated with pod traits than with leaf traits. The pod interior did not exhibit colors produced by anthocyanins or flavonols which allowed the differentiation of potential candidate genes associated with chloroplast and photosynthetic activity compared to the pod exterior where candidate genes related to both flavonoids and photosynthesis affected color. Several SNPs were associated with known qualitative genes including the wax pod locus (y), persistent color (pc), purple pods (V), and two genes expressed in seeds but not previously reported to affect other plant tissues (B and J). An evaluation of significant SNPs within annotated genes found a number, within a 200 kb window, involved in both flavonoid and photosynthetic biosynthetic pathways.

Depicting the genetic and metabolic panorama of chemical diversity in the tea plant.

As a frequently consumed beverage worldwide, tea is rich in naturally important bioactive metabolites. Combining genetic, metabolomic and biochemical methodologies, here, we present a comprehensive study to dissect the chemical diversity in tea plant. A total of 2837 metabolites were identified at high-resolution with 1098 of them being structurally annotated and 63 of them were structurally identified. Metabolite-based genome-wide association mapping identified 6199 and 7823 metabolic quantitative trait loci (mQTL) for 971 and 1254 compounds in young leaves (YL) and the third leaves (TL), respectively. The major mQTL (i.e., P < 1.05 × 10-5, and phenotypic variation explained (PVE) > 25%) were further interrogated. Through extensive annotation of the tea metabolome as well as network-based analysis, this study broadens the understanding of tea metabolism and lays a solid foundation for revealing the natural variations in the chemical composition of the tea plant. Interestingly, we found that galloylations, rather than hydroxylations or glycosylations, were the largest class of conversions within the tea metabolome. The prevalence of galloylations in tea is unusual, as hydroxylations and glycosylations are typically the most prominent conversions of plant specialized metabolism. The biosynthetic pathway of flavonoids, which are one of the most featured metabolites in tea plant, was further refined with the identified metabolites. And we demonstrated the further mining and interpretation of our GWAS results by verifying two identified mQTL (including functional candidate genes CsUGTa, CsUGTb, and CsCCoAOMT) and completing the flavonoid biosynthetic pathway of the tea plant.

Identification of QTLs for plant height and branching related traits in cultivated peanut

Plant height (PH), primary lateral branch length (PBL) and branch number (BN) are architectural components impacting peanut pod yield, biomass production and adaptivity to mechanical harvesting. In this study, a recombinant inbred population consisting of 181 individual lines was used to determine genetic controls of PH, PBL and BN across three environments. Phenotypic data collected from the population demonstrated continuous distributions and transgressive segregation patterns. Broad-sense heritability of PH, PBL and BN was found to be 0.87, 0.88 and 0.92, respectively. Unconditional individual environmental analysis revealed 35 additive QTLs with phenotypic variation explained (PVE) ranging from 4.57 to 21.68%. A two-round meta-analysis resulted in 24 consensus and 17 unique QTLs. Five unique QTLs exhibited pleiotropic effects and their genetic bases (pleiotropy or tight linkage) were evaluated. Joint analysis was performed to estimate the QTL by environment interaction (QEI) effects on PH, PBL and BN, which collectively explained phenotypic variations of 10.80, 11.02, and 7.89%, respectively. We identified 3 major and stable QTL regions (uq9-3, uq10-2 and uq16-1) on chromosomes 9, 10 and 16, spanning 1.43-1.53 Mb genomic regions. Candidate genes involved in phytohormones biosynthesis, signaling and cell wall development were proposed to regulate these morphological traits. These results provide valuable information for further genetic studies and development of molecular markers applicable for peanut architecture improvement.

Kernel Bioassay Evaluation of Maize Ear Rot and Genome-Wide Association Analysis for Identifying Genetic Loci Associated with Resistance to Fusarium graminearum Infection.

Gibberella ear rot (GER) caused by Fusarium graminearum (teleomorph Gibberella zeae) is one of the most destructive diseases in maize, which severely reduces yield and contaminates several potential mycotoxins in the grain. However, few efforts had been devoted to dissecting the genetic basis of maize GER resistance. In the present study, a genome-wide association study (GWAS) was conducted in a maize association panel consisting of 303 diverse inbred lines. The phenotypes of GER severity were evaluated using kernel bioassay across multiple time points in the laboratory. Then, three models, including the fixed and random model circulating probability unification model (FarmCPU), general linear model (GLM), and mixed linear model (MLM), were conducted simultaneously in GWAS to identify single-nucleotide polymorphisms (SNPs) significantly associated with GER resistance. A total of four individual significant association SNPs with the phenotypic variation explained (PVE) ranging from 3.51 to 6.42% were obtained. Interestingly, the peak SNP (PUT-163a-71443302-3341) with the greatest PVE value, was co-localized in all models. Subsequently, 12 putative genes were captured from the peak SNP, and several of these genes were directly or indirectly involved in disease resistance. Overall, these findings contribute to understanding the complex plant-pathogen interactions in maize GER resistance. The regions and genes identified herein provide a list of candidate targets for further investigation, in addition to the kernel bioassay that can be used for evaluating and selecting elite germplasm resources with GER resistance in maize.

Prescreening of large-effect markers with multiple strategies improves the accuracy of genomic prediction

Presently, integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy. Here, we set the genomic and transcriptomic data as the training population data, using BSLMM, TWAS, and eQTL mapping to prescreen features according to | ^βb|>0, top 1% of phenotypic variation explained (PVE), expression-associated single nucleotide polymorphisms (eSNPs), and egenes (false discovery rate (FDR)<0.01), where these loci were set as extra fixed effects (named GBLUP-Fix) and random effects (GFBLUP) to improve the prediction accuracy in the validation population, respectively. The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle (LDM), water holding capacity (WHC), shear force (SF), and pH in Huaxi cattle on average from 2.14 to 8.69%, especially the improvement of GFBLUP-TWAS over GBLUP was 13.66% for SF. These methods also captured more genetic variance than GBLUP. Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracy of genomic prediction.

Rice (Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified Using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers

This study investigated novel quantitative traits loci (QTLs) associated with the control of grain shape and size as well as grain weight in rice. We employed a joint-strategy multiple GAPIT (Genome Association and Prediction Integrated Tool) models [(Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK)), Fixed and random model Circulating Probability Uniform (FarmCPU), Settlement of MLM Under Progressive Exclusive Relationship (SUPER), and General Linear Model (GLM)]–High-Density SNP Chip DNA Markers (60,461) to conduct a Genome-Wide Association Study (GWAS). GWAS was performed using genotype and grain-related phenotypes of 143 recombinant inbred lines (RILs). Data show that parental lines (Ilpum and Tung Tin Wan Hein 1, TTWH1, Oryza sativa L., ssp. japonica and indica, respectively) exhibited divergent phenotypes for all analyzed grain traits), which was reflected in their derived population. GWAS results revealed the association between seven SNP Chip makers and QTLs for grain length, co-detected by all GAPIT models on chromosomes (Chr) 1–3, 5, 7, and 11, were qGL1-1BFSG (AX-95918134, Chr1: 3,820,526 bp) explains 65.2–72.5% of the phenotypic variance explained (PVE). In addition, qGW1-1BFSG (AX-273945773, Chr1: 5,623,288 bp) for grain width explains 15.5–18.9% of PVE. Furthermore, BLINK or FarmCPU identified three QTLs for grain thickness independently, and explain 74.9% (qGT1Blink, AX-279261704, Chr1: 18,023,142 bp) and 54.9% (qGT2-1Farm, AX-154787777, Chr2: 2,118,477 bp) of the observed PVE. For the grain length-to-width ratio (LWR), the qLWR2BFSG (AX-274833045, Chr2: 10,000,097 bp) explains nearly 15.2–32% of the observed PVE. Likewise, the major QTL for thousand-grain weight (TGW) was detected on Chr6 (qTGW6BFSG, AX-115737727, 28,484,619 bp) and explains 32.8–54% of PVE. The qTGW6BFSG QTL coincides with qGW6-1Blink for grain width and explained 32.8–54% of PVE. Putative candidate genes pooled from major QTLs for each grain trait have interesting annotated functions that require functional studies to elucidate their function in the control of grain size, shape, or weight in rice. Genome selection analysis proposed makers useful for downstream marker-assisted selection based on genetic merit of RILs.

Genome-wide association study (GWAS) with high-throughput SNP chip DNA markers identified novel genetic factors for mesocotyl elongation and seedling emergence in rice (Oryza sativa L.) using multiple GAPIT models.

This study employed a joint strategy high-density SNP Chip DNA markers and multiple Genome Association and Prediction Integrated Tool (GAPIT) models [(Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and random model Circulating Probability Uniform (FarmCPU), General Linear Model (GLM), and Settlement of Mixed Linear Model (MLM) Under Progressively Exclusive Relationship (SUPER)], to investigate novel genetic factors controlling mesocotyl elongation and seedling emergence for direct-seeded rice. Genotype data (230,526 SNP Chip DNA makers) of 117 doubled haploid lines (derived from a cross between 93-11 (Oryza sativa L. ssp. indica) and Milyang352 (O. sativa L. ssp. japonica) were used to perform a Genome-Wide Association Study (GWAS). Results revealed the association between five (5) topmost significant SNP markers, of which number two [AX-155741269, Chr2: 15422406 bp, and AX-155200917, Chr7: 23814085 bp, explaining 37.5% and 13.8% of the phenotypic variance explained (PVE)] are linked to the mesocotyl elongation loci, while three (AX-282097034 and AX-283652873, Chr9: 9882817 bp and 1023383 bp, PVE 64.5%, and 20.2%, respectively, and AX-154356231, Chr1: 17413989 bp, PVE 21.1%) are tightly linked to the loci controlling seedling emergence. The qMEL2-1 and qSEM9-1 are identified as major QTLs explaining 37.5% and 64.5% of the PVE for mesocotyl elongation and seedling emergence, respectively. The AX-282097034 (Chr9: 9882817 bp) was co-detected by four GAPIT models (BLINK, FarmCPU, SUPER, and GLM), while AX-155741269 was co-detected by BLINK and SUPER. Furthermore, a high estimated heritability (Mesocotyl elongation: h2 = 0.955; seedling emergence: h2 = 0.863; shoot length: h2 = 0.707) was observed. Genes harbored by qMEL2-1 and qSEM9-1 have interesting annotated molecular functions that could be investigated through functional studies to uncover their roles during mesocotyl elongation and seedling emergence events in rice. Furthermore, the presence of genes encoding transcription factors, growth- and stress response, or signaling-related genes would suggest that mesocotyl elongation and seedling emergence from deep direct-seeded rice might involve an active signaling cascade and transport of molecules, which could be elucidated through functional analysis. Likewise, genomic selection analysis suggested markers useful for downstream marker-assisted selection (MAS).

Identification of QTLs for resistance to 10 pathotypes of Plasmodiophora brassicae in Brassica oleracea cultivar ECD11 through genotyping-by-sequencing

Key messageTwo major quantitative trait loci (QTLs) and five minor QTLs for 10 pathotypes were identified on chromosomes C01, C03, C04 and C08 through genotyping-by-sequencing from Brassica oleracea.Clubroot caused by Plasmodiophora brassicae is an important disease in brassica crops. Managing clubroot disease of canola on the Canadian prairie is challenging due to the continuous emergence of new pathotypes. Brassica oleracea is considered a major source of quantitative resistance to clubroot. Genotyping-by-sequencing (GBS) was performed in the parental lines; T010000DH3 (susceptible), ECD11 (resistant) and 124 BC1 plants. A total of 4769 high-quality polymorphic SNP loci were obtained and distributed on 9 chromosomes of B. oleracea. Evaluation of 124 BC1S1 lines for resistance to 10 pathotypes: 3A, 2B, 5C, 3D, 5G, 3H, 8J, 5K, 5L and 3O of P. brassicae, was carried out. Seven QTLs, 5 originating from ECD11 and 2 from T010000DH3, were detected. One major QTL designated as Rcr_C03-1 on C03 contributed 16.0–65.6% of phenotypic variation explained (PVE) for 8 pathotypes: 2B, 5C, 5G, 3H, 8J, 5K, 5L and 3O. Another major QTL designated as Rcr_C08-1 on C08 contributed 8.3 and 23.5% PVE for resistance to 8J and 5K, respectively. Five minor QTLs designated as Rcr_C01-1, Rcr_C03-2, Rcr_C03-3, Rcr_C04-1 and Rcr_C08-2 were detected on chromosomes C01, C03, C04 and C08 that contributed 8.3–23.5% PVE for 5 pathotypes each of 3A, 2B, 3D, 8J and 5K. There were 1, 10 and 4 genes encoding TIR-NBS-LRR/CC-NBS-LRR class disease resistance proteins in the Rcr_C01-1, Rcr_C03-1 and Rcr_C08-1 flanking regions. The syntenic regions of the two major QTLs Rcr_C03-1 and Rcr_C08-1 in the B. rapa genome ‘Chiifu’ were searched.

Identification of oil content QTL on Arahy12 and Arahy16 and development of KASP markers in cultivated peanut (Arachis hypogaea L.)

Peanut kernels rich in oil, particularly those with oleic acid as their primary fatty acid, are sought after by consumers, the food industry, and farmers due to their superior nutritional content, extended shelf life, and health benefits. The oil content and fatty acid composition are governed by multiple genetic factors. Identifying the quantitative trait loci (QTL) related to these attributes would facilitate marker-assisted selection or genomic selection, thus enhancing the quality-focused peanut breeding program. For this purpose, we developed a population of 521 recombinant inbred lines (RIL) and tested their kernel quality traits across five different environments. We identified two major and stable QTLs for oil content (qOCAh12.1 and qOCAh16.1). The markers linked to these QTLs were designed by competitive allele-specific PCR (KASP) and were subsequently validated. Moreover, we found that the superior haplotype of oil content in the qOCAh16.1 region was conserved within the PI germplasm cluster, as evidenced by a diverse peanut accession panel. In addition, we determined that qAh09 and qAh19.1, which harbor the key gene encoding fatty acid desaturase 2 (FAD2), influence all seven fatty acids, including palmitic, stearic, oleic, linoleic, arachidic, gadoleic, and behenic acids. As for protein content and the long-chain saturated fatty acid behenic acid, qAh07 emerged as the major and stable QTLs, accounting for over 10% of the phenotypic variation explained (PVE). These findings would enhance marker-assisted selection in peanut breeding, aiming to improve oil content, and deepen our understanding of the genetic mechanisms that shape fatty acid composition.

Genetic analysis of genotype-specific parameters in the DSSTA-CROPGRO-soybean phenology simulation model via a multi-GWAS method

ContextGenotype-specific parameters (GSP) are the fundamental elements for phenotypic prediction in the DSSAT-CROPGRO soybean phenology simulation model (DCS-PSM). Yet, it is unclear about the quantitative relationship between GSP and genomics, which limits the crop models used for breeding research. ObjectiveExploring the genetic composition of GSP related to four soybean phenological traits in DCS-PSM for providing a basis for whether GSP are genetic coefficient. Highlighting the breeding value of DCS-PSM from a genetic perspective and strengthening its genetic understanding, which could support a further crop model-assisted molecular breeding. MethodsWe conducted a multi-genome-wide association study (GWAS) method by intrgrating the single-locus with multi-locus GWAS models for GSP associated quantitative trait nucleotides (QTN) detection. And the heritability, functional annotation of QTN and genotype-environment interaction (GEI) effect were further analyzed. Results(i). All GSP had high broad-sense heritability, ranging form 88.20–98.80%. (ii). Multi-locus GWAS significantly increased the QTN detection efficiency, the GSP associated QTN detected by multi-GWAS method contributed 22.11–50.25% phenotypic variation explained (PVE) of GSP. Besides, the GSP associated QTN accounted for a high PVE of four phenological traits, ranging from 54.25% to 74.75%. (iii). Most QTN overlapped with the function-known genes which regulate soybean phenology through photoperiod-temperature pathways. (iv). GSP associated QTN could reveal the GEI effect and contributed 1.45–6.27% PVE of soybean phenology. ConclusionsThis study indicated that the genetic factor was the primary source of GSP’s variation, and the multi-GWAS method could be an alternative way for the genetic analysis of GSP. Phenology-related GSP had genetic potential and could be regarded as genetic coefficients and breeding targets.