Multiple Quantitative Trait Research Articles

Unveiling genetic variants for age-related sarcopenia by conducting a genome-wide association study on Korean cohorts

Sarcopenia is an age-related disorder characterised by a progressive decrease in skeletal muscle mass. As the genetic biomarkers for sarcopenia are not yet well characterised, this study aimed to investigate the genetic variations related to sarcopenia in a relatively aged cohort, using genome-wide association study (GWAS) meta-analyses of lean body mass (LBM) in 6961 subjects. Two Korean cohorts were analysed, and subgroup GWAS was conducted for appendicular skeletal muscle mass (ASM) and skeletal muscle index. The effects of significant single nucleotide polymorphisms (SNPs) on gene expression were also investigated using multiple expression quantitative trait loci datasets, differentially expressed gene analysis, and gene ontology analyses. Novel genetic biomarkers were identified for LBM (rs1187118; rs3768582) and ASM (rs6772958). Their related genes, including RPS10, NUDT3, NCF2, SMG7, and ARPC5, were differently expressed in skeletal muscle tissue, while GPD1L was not. Furthermore, the ‘mRNA destabilisation’ biological process was enriched for sarcopenia. Our study identified RPS10, NUDT3, and GPD1L as significant genetic biomarkers for sarcopenia. These genetic loci were related to lipid and energy metabolism, suggesting that genes involved in metabolic dysregulation may lead to the pathogenesis of age-related sarcopenia.

Powerful Identification of Large Quantitative Trait Loci Using Genome-Wide R/glmnet-Based Regression.

R/glmnet has been successfully applied to jointly mapped multiple quantitative trait loci for linkage analysis, along with statistical inference for quantitative trait loci candidates with nonzero genetic effects using R/lm for normally distributed traits, R/glm for discrete traits, and R/coxph for survival times. In this study, we extended R/glmnet to a genome-wide association study by means of parallel computation. A multi-locus genome-wide association study for high-throughput single-nucleotide polymorphisms was implemented in the "Multi-Runking" software written within the R workspace. This software can better detect common and large quantitative trait nucleotides and more accurately estimate than genome-wide mixed model analysis for one single-nucleotide polymorphism at a time and linear mixed models-least absolute shrinkage and selection operator. Its applicability and utility were demonstrated by multi-locus genome-wide association studies for the simulated and real traits distributed normally, binary traits, and survival times.

English

Rathu Heenati is a donor parent commonly used in the development of resistant rice varieties against brown planthopper (BPH), Nilaparvata lugens, which is a devastating insect pest of rice. The variety is reported to have a broad-spectrum resistance against BPH, through the action of multiple genes and quantitative trait loci (QTL) including Bph3, Qbph3, Qbph4 and Qbph10. The present study was carried out to validate the effectiveness of those genes against Malaysian BPH population. The study was conducted using an F2 segregating population which was obtained from a cross between Rathu Heenati and a Malaysian commercial variety, MR219. Plant damage score was used to estimate the degree of plant tolerance, while the amount of honeydew excretion was used to measure the level of antibiosis. The gene presence in an individual plant was determined based on the segregation pattern of the flanking microsatellite markers of the respective reported genes. Results confirmed the role of Bph3 and the three other QTLs in conferring resistance against the Malaysian biotype of BPH. Marker assisted breeding can facilitate the monitoring of the introgressed genes in the plants of a breeding population. © 2022 Friends Science Publishers

Statistical inference for high-dimensional pathway analysis with multiple responses

Pathway analysis, i.e., grouping analysis, has important applications in genomic studies. Existing pathway analysis approaches are mostly focused on a single response and are not suitable for analyzing complex diseases that are often related with multiple response variables. Although a handful of approaches have been developed for multiple responses, these methods are mainly designed for pathways with a moderate number of features. A multi-response pathway analysis approach that is able to conduct statistical inference when the dimension is potentially higher than sample size is introduced. Asymptotical properties of the test statistic are established and theoretical investigation of the statistical power is conducted. Simulation studies and real data analysis show that the proposed approach performs well in identifying important pathways that influence multiple expression quantitative trait loci (eQTL).

Genome-wide association study of disease resilience traits from a natural polymicrobial disease challenge model in pigs identifies the importance of the major histocompatibility complex region.

Infectious diseases cause tremendous financial losses in the pork industry, emphasizing the importance of disease resilience, which is the ability of an animal to maintain performance under disease. Previously, a natural polymicrobial disease challenge model was established, in which pigs were challenged in the late nursery phase by multiple pathogens to maximize expression of genetic differences in disease resilience. Genetic analysis found that performance traits in this model, including growth rate, feed and water intake, and carcass traits, as well as clinical disease phenotypes, were heritable and could be selected for to increase disease resilience of pigs. The objectives of the current study were to identify genomic regions that are associated with disease resilience in this model, using genome-wide association studies and fine-mapping methods, and to use gene set enrichment analyses to determine whether genomic regions associated with disease resilience are enriched for previously published quantitative trait loci, functional pathways, and differentially expressed genes subject to physiological states. Multiple quantitative trait loci were detected for all recorded performance and clinical disease traits. The major histocompatibility complex region was found to explain substantial genetic variance for multiple traits, including for growth rate in the late nursery (12.8%) and finisher (2.7%), for several clinical disease traits (up to 2.7%), and for several feeding and drinking traits (up to 4%). Further fine mapping identified 4 quantitative trait loci in the major histocompatibility complex region for growth rate in the late nursery that spanned the subregions for class I, II, and III, with 1 single-nucleotide polymorphism in the major histocompatibility complex class I subregion capturing the largest effects, explaining 0.8–27.1% of genetic variance for growth rate and for multiple clinical disease traits. This single-nucleotide polymorphism was located in the enhancer of TRIM39 gene, which is involved in innate immune response. The major histocompatibility complex region was pleiotropic for growth rate in the late nursery and finisher, and for treatment and mortality rates. Growth rate in the late nursery showed strong negative genetic correlations in the major histocompatibility complex region with treatment or mortality rates (−0.62 to −0.85) and a strong positive genetic correlation with growth rate in the finisher (0.79). Gene set enrichment analyses found genomic regions associated with resilience phenotypes to be enriched for previously identified disease susceptibility and immune capacity quantitative trait loci, for genes that were differentially expressed following bacterial or virus infection and immune response, and for gene ontology terms related to immune and inflammatory response. In conclusion, the major histocompatibility complex and other quantitative trait loci that harbor immune-related genes were identified to be associated with disease resilience traits in a large-scale natural polymicrobial disease challenge. The major histocompatibility complex region was pleiotropic for growth rate under challenge and for clinical disease traits. Four quantitative trait loci were identified across the class I, II, and III subregions of the major histocompatibility complex for nursery growth rate under challenge, with 1 single-nucleotide polymorphism in the major histocompatibility complex class I subregion capturing the largest effects. The major histocompatibility complex and other quantitative trait loci identified play an important role in host response to infectious diseases and can be incorporated in selection to improve disease resilience, in particular the identified single-nucleotide polymorphism in the major histocompatibility complex class I subregion.

Identification of a Risk Locus at 7p22.3 for Schizophrenia and Bipolar Disorder in East Asian Populations.

Background: Shared psychopathological features and mechanisms have been observed between schizophrenia (SZ) and bipolar disorder (BD), but their common risk genes and full genetic architectures remain to be fully characterized. The genome-wide association study (GWAS) datasets offer the opportunity to explore this scientific question using combined genetic data from enormous samples, ultimately allowing a better understanding of the onset and development of these illnesses. Methods: We have herein performed a genome-wide meta-analysis in two GWAS datasets of SZ and BD respectively (24,600 cases and 40,012 controls in total, discovery sample), followed by replication analyses in an independent sample of 4,918 SZ cases and 5,506 controls of Han Chinese origin (replication sample). The risk SNPs were then explored for their correlations with mRNA expression of nearby genes in multiple expression quantitative trait loci (eQTL) datasets. Results: The single nucleotide polymorphisms (SNPs) rs1637749 and rs3800908 at 7p22.3 region were significant in both discovery and replication samples, and exhibited genome-wide significant associations when combining all East Asian SZ and BD samples (29,518 cases and 45,518 controls). The risk SNPs were also significant in GWAS of SZ and BD among Europeans. Both risk SNPs significantly predicted lower expression of MRM2 in the whole blood and brain samples in multiple datasets, which was consistent with its reduced mRNA level in the brains of SZ patients compared with normal controls. The risk SNPs were also associated with MAD1L1 expression in the whole blood sample. Discussion: We have identified a novel genome-wide risk locus associated with SZ and BD in East Asians, adding further support for the putative common genetic risk of the two illnesses. Our study also highlights the necessity and importance of mining public datasets to explore risk genes for complex psychiatric diseases.

Quantitative Trait Loci and Transcriptome Analysis Reveal Genetic Basis of Fiber Quality Traits in CCRI70 RIL Population of Gossypium hirsutum.

Upland cotton (Gossypium hirsutum) is widely planted around the world for its natural fiber, and producing high-quality fiber is essential for the textile industry. CCRI70 is a hybrid cotton plant harboring superior yield and fiber quality, whose recombinant inbred line (RIL) population was developed from two upland cotton varieties (sGK156 and 901-001) and were used here to investigate the source of high-quality related alleles. Based on the material of the whole population, a high-density genetic map was constructed using specific locus-amplified fragment sequencing (SLAF-seq). It contained 24,425 single nucleotide polymorphism (SNP) markers, spanning a distance of 4,850.47 centimorgans (cM) over 26 chromosomes with an average marker interval of 0.20 cM. In evaluating three fiber quality traits in nine environments to detect multiple environments stable quantitative trait loci (QTLs), we found 289 QTLs, of which 36 of them were stable QTLs and 18 were novel. Based on the transcriptome analysis for two parents and two RILs, 24,941 unique differentially expressed genes (DEGs) were identified, 473 of which were promising genes. For the fiber strength (FS) QTLs, 320 DEGs were identified, suggesting that pectin synthesis, phenylpropanoid biosynthesis, and plant hormone signaling pathways could influence FS, and several transcription factors may regulate fiber development, such as GAE6, C4H, OMT1, AFR18, EIN3, bZIP44, and GAI. Notably, the marker D13_56413025 in qFS-chr18-4 provides a potential basis for enhancing fiber quality of upland cotton via marker-assisted breeding and gene cloning of important fiber quality traits.

Identification of quantitative trait loci underlying five major agronomic traits of soybean in three biparental populations by specific length amplified fragment sequencing (SLAF-seq).

Flowering time, plant height, branch number, node numbers of main stem and pods per plant are important agronomic traits related to photoperiodic sensitivity, plant type and yield of soybean, which are controlled by multiple genes or quantitative trait loci (QTL). The main purpose of this study is to identify new QTL for five major agronomic traits, especially for flowering time. Three biparental populations were developed by crossing cultivars from northern and central China. Specific loci amplified fragment sequencing (SLAF-seq) was used to construct linkage map and QTL mapping was carried out. A total of 10 QTL for flowering time were identified in three populations, some of which were related to E1 and E2 genes or the other reported QTL listed in Soybase. In the Y159 population (Xudou No.9 × Kenfeng No.16), QTL for flowering time on chromosome 4, qFT4_1 and qFT4_2 were new. Compared with the QTL reported in Soybase, 1 QTL for plant height (PH), 3 QTL for branch number (BR), 5 QTL for node numbers of main stem, and 3 QTL for pods per plant were new QTL. Major E genes were frequently detected in different populations indicating that major the E loci had a great effect on flowering time and adaptation of soybean. Therefore, in order to further clone minor genes or QTL, it may be of great significance to carefully select the genotypes of known loci. These results may lay a foundation for fine mapping and clone of QTL/genes related to plant-type, provided a basis for high yield breeding of soybean.

The flashfm approach for fine-mapping multiple quantitative traits

Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Here we show flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.

Genome-Wide Analysis of the Phospholipase D Family in Five Cotton Species, and Potential Role of GhPLD2 in Fiber Development and Anther Dehiscence.

Phospholipase D (PLD) and its hydrolysis product phosphatidic acid play an important role in the regulation of several cellular processes, including root growth, pollen tube elongation, and microtubule reorganization. Here, we systematically identified and analyzed the membership, characterization, and evolutionary relationship of PLDs in five species of cotton. The results of the transcriptomic analysis suggested that the evaluated PLD genes showed high expression levels in anther tissue and during the fiber initiation and elongation periods. Quantitative real-time polymerase chain reaction showed differential expression of GhPLD genes in the anthers of photoperiod sensitive male sterility mutant 5 (psm5). Previous research on multiple stable quantitative trait loci also suggests the role of PLD genes in the fiber development. Further analyses showed that GhPLD2 protein is localized to the plasma membrane. The virus-induced gene silencing of GhPLD2 in cotton seedlings repressed its expression by 40–70%, which led to a reduction in reactive oxygen species (ROS) levels, 22% anther indehiscence, and disrupted fiber initiation and elongation. Thus, we inferred that GhPLD2 may promote ROS production, which, in turn, may regulate anther dehiscence and fiber development.

Mapping causal genes and genetic interactions for agronomic traits using a large F2 population in rice.

Dissecting the genetic mechanisms underlying agronomic traits is of great importance for crop breeding. Agronomic traits are usually controlled by multiple quantitative trait loci (QTLs) and genetic interactions, and mapping the underlying causal genes is still labor-intensive and time-consuming. Here, we present a genetic tool for directly targeting the specific causal genes by using a single-gene resolution linkage map that was constructed from 3756 F2 rice plants via targeted sequencing technology and Tukey-Kramer multiple comparisons test. Three large- and moderate-effect QTLs, qHD6-2, qGL3-1, and qGW5-2, were successfully mapped to their specific causal genes, Hd1, GS3, and GW5, respectively. A complex genetic interaction network containing 30 QTL–QTL interactions was constructed, revealing that the alternative allele of hub QTL, qHD6-2, can hide or release the genetic contributions of the alleles at interacting loci. Moreover, arranging genetic interactions in the models lead to more accurate phenotypic predictions. These results provide a community resource and new feasible strategy for deciphering the genetic mechanisms of complex agronomic traits and accelerating crop breeding.

Identification and validation of a major gene for kernel length at the P1 locus in Triticum polonicum

Kernel size, one of the traits that determine wheat yield, is controlled by multiple quantitative trait loci. Polish wheat (Triticum polonicum) has elongated and plump kernel and is a valuable material for breeding high-yielding wheat cultivars. However, genes or loci determining kernel length (KL) in Polish wheat are unknown. We identified and validated a major KL gene, KL-PW, at the P1 locus in Polish wheat. KL-PW is VRT-A2, which encodes a MIKC-type MADS-box protein (MADS55). An insertion/deletion mutation in intron 1 of VRT-A2PW led to an alternatively spliced transcript, VRT-A2PW2. Quantitative PCR analysis showed that VRT-A2PW was more highly expressed in developing seeds than was VRT-A2Ailanmai. Brassinosteroid (BR) sensitivity experiment and the expression of BR-related genes indicated that VRT-A2PW functions as a positive regulator of BR responses. VRT-A2PW significantly increased KL of wheat. These findings not only reveal the molecular basis of KL-PW in controlling KL, but also provide a valuable genetic resource for increasing kernel size in wheat.

Advanced Breeding Strategies and Future Perspectives of Salinity Tolerance in Rice

Rice, generally classified as a typical glycophyte, often faces abiotic stresses such as excessive drought, high salinity, prolonged submergence, cold, and temperature, which significantly affects growth, development, and ultimately, grain yield. Among these negative impacts of abiotic factors in rice production, salinity stress is a major constraint, followed by drought. There is considerable research on the use of marker-assisted selection (MAS), genome editing techniques, and transgenic studies that have profoundly improved the present-day rice breeders’ toolboxes for developing salt-tolerant varieties. Salinity stresses significantly affect rice plants during seedling and reproductive stages. Hence, greater understanding and manipulation of genetic architecture in developing salt-tolerant rice varieties will significantly impact sustainable rice production. Rice plants’ susceptibility or tolerance to high salinity has been reported to be the result of coordinated actions of multiple stress-responsive quantitative trait loci (QTLs)/genes. This paper reviews recent literature, updating the effects of salinity stress on rice plants and germplasm collections and screening for salinity tolerance by different breeding techniques. Mapping and identification of QTLs salt tolerance genes are illuminated. The present review updates recent breeding for improvement in rice tolerance to salinity stress and how state-of-the-art tools such as MAS or genetic engineering and genome editing techniques, including mutagenesis and conventional breeding techniques, can assist in transferring salt-tolerant QTLs genes into elite rice genotypes, accelerating breeding of salt-resistant rice cultivars.

Evaluation of Septoria Nodorum Blotch (SNB) Resistance in Glumes of Wheat (Triticum aestivum L.) and the Genetic Relationship With Foliar Disease Response.

Septoria nodorum blotch (SNB) is a necrotrophic disease of wheat prominent in some parts of the world, including Western Australia (WA) causing significant losses in grain yield. The genetic mechanisms for resistance are complex involving multiple quantitative trait loci. In order to decipher comparable or independent regulation, this study identified the genetic control for glume compared to foliar resistance across four environments in WA against 37 different isolates. High proportion of the phenotypic variation across environments was contributed by genotype (84.0% for glume response and 82.7% for foliar response) with genotype-by-environment interactions accounting for a proportion of the variation for both glume and foliar response (14.7 and 16.2%, respectively). Despite high phenotypic correlation across environments, most of the eight and 14 QTL detected for glume and foliar resistance using genome wide association analysis (GWAS), respectively, were identified as environment-specific. QTL for glume and foliar resistance neither co-located nor were in LD in any particular environment indicating autonomous genetic mechanisms control SNB response in adult plants, regulated by independent biological mechanisms and influenced by significant genotype-by- environment interactions. Known Snn and Tsn loci and QTL were compared with 22 environment-specific QTL. None of the eight QTL for glume or the 14 for foliar response were co-located or in linkage disequilibrium with Snn and only one foliar QTL was in LD with Tsn loci on the physical map. Therefore, glume and foliar response to SNB in wheat is regulated by multiple environment-specific loci which function independently, with limited influence of known NE-Snn interactions for disease progression in Western Australian environments. Breeding for stable resistance would consequently rely on recurrent phenotypic selection to capture and retain favorable alleles for both glume and foliar resistance relevant to a particular environment.

Robust regressionbased genome-wide multi-trait QTL analysis.

In genome-wide quantitative trait locus (QTL) mapping studies, multiple quantitative traits are often measured along with the marker genotypes. Multi-trait QTL (MtQTL) analysis, which includes multiple quantitative traits together in a single model, is an efficient technique to increase the power of QTL identification. The two most widely used classical approaches for MtQTL mapping are Gaussian Mixture Model-based MtQTL (GMM-MtQTL) and Linear Regression Model-based MtQTL (LRM-MtQTL) analyses. There are two types of LRM-MtQTL approach known as least squares-based LRM-MtQTL (LS-LRM-MtQTL) and maximum likelihood-based LRM-MtQTL (ML-LRM-MtQTL). These three classical approaches are equivalent alternatives for QTL detection, but ML-LRM-MtQTL is computationally faster than GMM-MtQTL and LS-LRM-MtQTL. However, one major limitation common to all the above classical approaches is that they are very sensitive to outliers, which leads to misleading results. Therefore, in this study, we developed an LRM-based robust MtQTL approach, called LRM-RobMtQTL, for the backcross population based on the robust estimation of regression parameters by maximizing the β-likelihood function induced from the β-divergence with multivariate normal distribution. When β = 0, the proposed LRM-RobMtQTL method reduces to the classical ML-LRM-MtQTL approach. Simulation studies showed that both ML-LRM-MtQTL and LRM-RobMtQTL methods identified the same QTL positions in the absence of outliers. However, in the presence of outliers, only the proposed method was able to identify all the true QTL positions. Real data analysis results revealed that in the presence of outliers only our LRM-RobMtQTL approach can identify all the QTL positions as those identified in the absence of outliers by both methods. We conclude that our proposed LRM-RobMtQTL analysis approach outperforms the classical MtQTL analysis methods.

Cryptic genetic variation in a heat shock protein modifies the outcome of a mutation affecting epidermal stem cell development in C. elegans

A fundamental question in medical genetics is how the genetic background modifies the phenotypic outcome of mutations. We address this question by focusing on the seam cells, which display stem cell properties in the epidermis of Caenorhabditis elegans. We demonstrate that a putative null mutation in the GATA transcription factor egl-18, which is involved in seam cell fate maintenance, is more tolerated in the CB4856 isolate from Hawaii than the lab reference strain N2 from Bristol. We identify multiple quantitative trait loci (QTLs) underlying the difference in phenotype expressivity between the two isolates. These QTLs reveal cryptic genetic variation that reinforces seam cell fate through potentiating Wnt signalling. Within one QTL region, a single amino acid deletion in the heat shock protein HSP-110 in CB4856 is sufficient to modify Wnt signalling and seam cell development, highlighting that natural variation in conserved heat shock proteins can shape phenotype expressivity.

Underground heterosis for yield improvement in melon.

Heterosis, the superiority of hybrids over their parents, is a major genetic force associated with plant fitness and crop yield enhancement. We investigated root-mediated yield heterosis in melons (Cucumis melo) by characterizing a common variety grafted onto 190 hybrid rootstocks, resulting from crossing 20 diverse inbreds in a diallel-mating scheme. Hybrid rootstocks improved yield by more than 40% compared with their parents, and the best hybrid yield outperformed the reference commercial variety by 65% under both optimal and minimal irrigation treatments. To characterize the genetics of underground heterosis we conducted whole genome re-sequencing of the 20 founder lines, and showed that parental genetic distance was no predictor for the level of heterosis. Through inference of the 190 hybrid genotypes from their parental genomes, followed by genome-wide association analysis, we mapped multiple quantitative trait loci for root-mediated yield. Yield enhancement of the four best-performing hybrid rootstocks was validated in multiple experiments with four different scion varieties. Our grafting approach is complementary to the common roots genetic approach that focuses mainly on variation in root system architecture, and is a step towards discovery of candidate genes involved in root function and yield enhancement.

Correlation and co-localization of QTL for stomatal density, canopy temperature, and productivity with and without drought stress in Setaria.

Mechanistic modeling indicates that stomatal conductance could be reduced to improve water use efficiency (WUE) in C4 crops. Genetic variation in stomatal density and canopy temperature was evaluated in the model C4 genus, Setaria. Recombinant inbred lines (RILs) derived from a Setaria italica×Setaria viridis cross were grown with ample or limiting water supply under field conditions in Illinois. An optical profilometer was used to rapidly assess stomatal patterning, and canopy temperature was measured using infrared imaging. Stomatal density and canopy temperature were positively correlated but both were negatively correlated with total above-ground biomass. These trait relationships suggest a likely interaction between stomatal density and the other drivers of water use such as stomatal size and aperture. Multiple quantitative trait loci (QTL) were identified for stomatal density and canopy temperature, including co-located QTL on chromosomes 5 and 9. The direction of the additive effect of these QTL on chromosome 5 and 9 was in accordance with the positive phenotypic relationship between these two traits. This, along with prior experiments, suggests a common genetic architecture between stomatal patterning and WUE in controlled environments with canopy transpiration and productivity in the field, while highlighting the potential of Setaria as a model to understand the physiology and genetics of WUE in C4 species.

Mapping QTL for leaf pigment content at dynamic development stage and analyzing Meta-QTL in rice

Leaf pigment content is a complicated quantitative trait which results from the cooperation of multiple related genes or quantitative trait loci (QTL) in rice. So far, more than 600 QTL for leaf pigment content has been reported. However, most QTL positions could not be directly compared with each other. In this study, our goal was to analyze consistent QTL regions affecting leaf pigment content by applying a genome wide QTL meta-analysis approach. 20 QTL related to leaf pigment content were detected at 5 different growth periods in 134 recombinant inbred lines (RILs) from a typical Indica-Japonica cross derived from PA64s and 9311 (two parents of super hybrid rice Liangyoupei 9). There was a candidate gene GLTP in the region of 3 QTL (qFS_Chla4, qFS_Chla+b4, and qFS_Car4), which related to component of chloroplast thylakoid membranes. After sequencing and qRT-PCR analysis, the gene showed significant difference in parents of the RILs. Meta-analysis was performed on 618 QTL collected from 38 previous studies. Consequently, a total of 64 MQTL (Meta-QTL) were obtained, of which 58.95% confidence intervals (CI) were smaller than the average Cl of the original QTL. In addition, 64 MQTL covered 6 of 20 QTL detected. Among them, four main QTL were included in 3 MQTL, and phenotypic variation of 21 MQTL was greater than 20%. The traits with higher number of QTL had higher number of MQTL, and candidate genes in overlapping MQTL include multiple effects. 7 candidate genes related to leaf pigment content were found in 4 MQTL spanning physical intervals < 0.5 Mb. These findings are not only anticipated to help molecular marker-assisted selection and pyramiding, but also contribute to determine candidate genes that regulate pigment content.

Identification of Novel Loci and Candidate Genes for Resistance to Powdery Mildew in a Resequenced Cucumber Germplasm.

Powdery mildew (PM) is one of the most serious diseases in cucumber and causes huge yield loss. Multiple quantitative trait loci (QTLs) for PM resistance have been reported in previous studies using a limited number of cucumber accessions. In this study, a cucumber core germplasm (CG) consisting of 94 resequenced lines was evaluated for PM resistance in four trials across three years (2013, 2014, and 2016). These trials were performed on adult plants in the field with natural infection. Using genome-wide association study (GWAS), 13 loci (pmG1.1, pmG1.2, pmG2.1, pmG2.2, pmG3.1, pmG4.1, pmG4.2, pmG5.1, pmG5.2, pmG5.3, pmG5.4, pmG6.1, and pmG6.2) associated with PM resistance were detected on all chromosomes except for Chr.7. Among these loci, ten were mapped to chromosomal intervals where QTLs had been reported in previous studies, while, three (pmG2.1, pmG3.1, and pmG4.1) were novel. The loci of pmG2.1, pmG5.2, pmG5.3 showed stronger signal in four trials. Based on the annotation of homologous genes in Arabidopsis and pairwise LD correlation analysis, candidate genes located in the QTL intervals were predicted. SNPs in these candidate genes were analyzed between haplotypes of highly resistant (HR) and susceptible (HS) CG lines, which were defined based on combing disease index data of all trials. Furthermore, candidate genes (Csa5G622830 and CsGy5G015660) reported in previous studies for PM resistance and cucumber orthologues of several PM susceptibility (S) genes (PMR5, PMR-6, and MLO) that are colocalized with certain QTLs, were analyzed for their potential contribution to the QTL effect on both PM and DM in the CG population. This study shows that the CG germplasm is a very valuable resource carrying known and novel QTLs for both PM and DM resistance, which can be exploited in cucumber breeding.