Genome-wide Quantitative Trait Loci Research Articles

Genome-wide QTL and eQTL mapping reveal genes associated with growth rate trait of the Pacific white shrimp (Litopenaeus vannamei)

BackgroundGrowth rate is a crucial economic trait for farmed animals, but the genetic regulation of this trait is largely unknown in non-model organisms such as shrimp.ResultsIn this study, we performed genome-wide phenotypic quantitative trait loci (QTL) and expression quantitative trait loci (eQTL) mapping analyses to identify genes affecting the growth rate of Pacific white shrimp (Litopenaeus vannamei), which is the most commercially-farmed crustacean worldwide. We used RNA-sequencing of 268 individuals in a mapping population, and subsequently validated our findings through gene silencing and shrimp growth experiments. We constructed a high-density genetic linkage map comprising 5533 markers spanning 44 linkage groups, with a total distance of 6205.75 cM and an average marker interval of 1.12 cM. Our analyses identified 11 QTLs significantly correlated with growth rate, and 117,525 eQTLs. By integrating QTL and eQTL data, we identified a gene (metalloreductase STEAP4) highly associated with shrimp growth rate. RNA interference (RNAi) analysis and growth experiments confirmed that STEAP4 was significantly correlated with growth rate in L. vannamei.ConclusionsOur results indicate that the comprehensive analysis of QTL and eQTL can effectively identify genes involved in complex animal traits. This is important for marker-assisted selection (MAS) of animals. Our work contributes to the development of shrimp breeding and available genetic resources.

Genome-wide QTL mapping for agronomic traits in the winter wheat cultivar Pindong 34 based on 90K SNP array.

Agronomic traits are key components of wheat yield. Exploitation of the major underlying quantitative trait loci (QTLs) can improve the yield potential in wheat breeding. In this study, we constructed a recombinant inbred line (RIL) population from Mingxian 169 (MX169) and Pindong 34 (PD34) to determine the QTLs for grain length (GL), grain width (GW), grain length-to-width ratio (LWR), plant height (PH), spike length (SL), grain number per spike (GNS), and the thousand grain weight (TGW) across four environments using wheat 90K SNP array. A QTL associated with TGW, i.e., QTGWpd.swust-6BS, was identified on chromosome 6B, which explained approximately 14.1%-16.2% of the phenotypic variation. In addition, eight QTLs associated with GL were detected across six chromosomes in four different test environments. These were QGLpd.swust-1BL, QGLpd.swust-2BL, QGLpd.swust-3BL.1, QGLpd.swust-3BL.2, QGLpd.swust-5DL, QGLpd.swust-6AL, QGLpd.swust-6DL.1, and QGLpd.swust-6DL.2. They accounted for 9.0%-21.3% of the phenotypic variation. Two QTLs, namely, QGWpd.swust-3BS and QGWpd.swust-6DL, were detected for GW on chromosomes 3B and 6D, respectively. These QTLs explained 12.8%-14.6% and 10.8%-15.2% of the phenotypic variation, respectively. In addition, two QTLs, i.e., QLWRpd.swust-7AS.1 and QLWRpd.swust-7AS.2, were detected on chromosome 7A for the grain LWR, which explained 10.9%-11.6% and 11.6%-11.2% of the phenotypic variation, respectively. Another QTL, named QGNSpd-swust-6DS, was discovered on chromosome 6D, which determines the GNS and which accounted for 11.4%-13.8% of the phenotypic variation. Furthermore, five QTLs associated with PH were mapped on chromosomes 2D, 3A, 5A, 6B, and 7B. These QTLs were QPHpd.swust-2DL, QPHpd.swust-3AL, QPHpd.swust-5AL, QPHpd.swust-6BL, and QPHpd.swust-7BS, which accounted for 11.3%-19.3% of the phenotypic variation. Lastly, a QTL named QSLpd.swust-3AL, conferring SL, was detected on chromosome 3A and explained 16.1%-17.6% of the phenotypic variation. All of these QTLs were defined within the physical interval of the Chinese spring reference genome. The findings of this study have significant implications for the development of fine genetic maps, for genomic breeding, and for marker-assisted selection to enhance wheat grain yield.

Genome-Wide Association Study Reveals a QTL Region for Ashy Stem Blight Resistance in PRA154 Andean Common Bean.

Ashy stem blight (ASB) caused by Macrophomina phaseolina (Tassi) Goidanich affects the common bean (Phaseolus vulgaris L.) at all growing stages. Higher levels of resistance were observed in Andean common beans, but specific resistant quantitative trait loci (QTLs) conferring resistance to this pathogen have not been reported in this gene pool. The objectives of this research were to: (i) conduct a genome-wide association study (GWAS) and QTL mapping for resistance in the Andean breeding line PRA154; and (ii) identify single nucleotide polymorphism (SNP) markers and candidate genes for ASB resistance. Phenotyping was conducted under greenhouse conditions by inoculating the 107 F6:7 recombinant inbred lines (RILs) derived from the cross between the susceptible cultivar 'Verano' and the partial-resistant breeding line PRA154 twice with the M. phaseolina isolate PRI21. Genotyping was performed with 109,040 SNPs distributed across all 11 P. vulgaris chromosomes. A novel major QTL was located between 28,761,668 and 31,263,845 bp, extending 2.5 Mbp on chromosome Pv07, and the highest significant SNP markers were Chr07_28761668_A_G, Chr07_29131720_G_A, and Chr07_31263845_C_T with the highest LOD (more than 10 in most of the cases) and R-squared values, explaining 40% of the phenotypic variance of the PRI21 isolate. The gene Phvul.007G173900 (methylcrotonyl-CoA carboxylase alpha chain and mitochondrial 3-methylcrotonyl-CoA carboxylase 1 [MCCA]) with a size of 10,891 bp, located between 29,131,591 and 29,142,481 bp on Pv07, was identified as one candidate for ASB resistance in PRA154, and it contained Chr07_29131720_G_A. The QTL and genetic marker information could be used to assist common bean breeders to develop germplasm and cultivars with ASB resistance through molecular breeding.

Development of Gossypium hirsutum-Gossypium raimondii introgression lines and its usages in QTL mapping of agricultural traits

Gossypium raimondii (2n=2x=26, D5), an untapped wild species, is the putative progenitor of the D-subgenome of G. hirsutum (2n=4x=52, AD1), an extensively cultivated species. Here, we developed a G. hirsutum (recipient)-G. raimondii (donor) introgression population to exploit favorable QTLs/genes and mapped potential quantitative trait loci (QTLs) from wild cotton species. The introgression population consists of 256 lines with an introgression rate of 52.33% of the genome G. raimondii. The range of introgression segment length was 0.03-19.12 Mb, with an average of 1.22 Mb. The coverage of total introgression fragments was 386.98 Mb from G. raimondii. Further genome-wide association analysis (Q+K+MLM) and QTL mapping (RSTEP-LRT) identified 59 common QTLs, including 14 stable QTLs and 6 common QTL (co-QTL) clusters, and one hotspot of MIC. The common QTLs for seed index showed all positive additive effects, while the common QTLs for boll weight were all negative additive effects, indicating that the linkage between seed index and boll weight could be broken. QTLs for lint percentage showed positive effect and could be beneficial for improving cotton yield. Most QTLs for fiber quality had negative additive effects, implying these QTLs were domesticated/improved in G. hirsutum. A few fiber quality QTLs showed positive additive effects, which could be used to improve cotton fiber quality. These introgression lines developed would be useful for molecular marker-assisted breeding and facilitate us to map QTLs precisely for mining desirable genes from the wild species G. raimondii to improve cultivated cotton in future via a design-breeding approach.

Genome-wide QTL mapping for stripe rust resistance in spring wheat line PI 660122 using the Wheat 15K SNP array.

Stripe rust is a global disease of wheat. Identification of new resistance genes is key to developing and growing resistant varieties for control of the disease. Wheat line PI 660122 has exhibited a high level of stripe rust resistance for over a decade. However, the genetics of stripe rust resistance in this line has not been studied. A set of 239 recombinant inbred lines (RILs) was developed from a cross between PI 660122 and an elite Chinese cultivar Zhengmai 9023. The RIL population was phenotyped for stripe rust response in three field environments and genotyped with the Wheat 15K single-nucleotide polymorphism (SNP) array. A total of nine quantitative trait loci (QTLs) for stripe rust resistance were mapped to chromosomes 1B (one QTL), 2B (one QTL), 4B (two QTLs), 4D (two QTLs), 6A (one QTL), 6D (one QTL), and 7D (one QTL), of which seven QTLs were stable and designated as QYrPI660122.swust-4BS, QYrPI660122.swust-4BL, QYrPI660122.swust-4DS, QYrPI660122.swust-4DL, QYrZM9023.swust-6AS, QYrZM9023.swust-6DS, and QYrPI660122.swust-7DS. QYrPI660122.swust-4DS was a major all-stage resistance QTL explaining the highest percentage (10.67%-20.97%) of the total phenotypic variation and was mapped to a 12.15-cM interval flanked by SNP markers AX-110046962 and AX-111093894 on chromosome 4DS. The QTL and their linked SNP markers in this study can be used in wheat breeding to improve resistance to stripe rust. In addition, 26 lines were selected based on stripe rust resistance and agronomic traits in the field for further selection and release of new cultivars.

Network-based quantitative trait linkage analysis of microbiome composition in inflammatory bowel disease families.

Introduction: Inflammatory bowel disease (IBD) is characterized by a dysbiosis of the gut microbiome that results from the interaction of the constituting taxa with one another, and with the host. At the same time, host genetic variation is associated with both IBD risk and microbiome composition. Methods: In the present study, we defined quantitative traits (QTs) from modules identified in microbial co-occurrence networks to measure the inter-individual consistency of microbial abundance and subjected these QTs to a genome-wide quantitative trait locus (QTL) linkage analysis. Results: Four microbial network modules were consistently identified in two cohorts of healthy individuals, but three of the corresponding QTs differed significantly between IBD patients and unaffected individuals. The QTL linkage analysis was performed in a sub-sample of the Kiel IBD family cohort (IBD-KC), an ongoing study of 256 German families comprising 455 IBD patients and 575 first- and second-degree, non-affected relatives. The analysis revealed five chromosomal regions linked to one of three microbial module QTs, namely on chromosomes 3 (spanning 10.79 cM) and 11 (6.69 cM) for the first module, chr9 (0.13 cM) and chr16 (1.20 cM) for the second module, and chr13 (19.98 cM) for the third module. None of these loci have been implicated in a microbial phenotype before. Discussion: Our study illustrates the benefit of combining network and family-based linkage analysis to identify novel genetic drivers of microbiome composition in a specific disease context.

Identification of a QTL region for ashy stem blight resistance using genome-wide association and linage analysis in common bean recombinant inbred lines derived from BAT 477 and NY6020-4.

Ashy stem blight (ASB), caused by the fungus Macrophomina phaseolina (Tassi) Goidanich is an important disease of the common bean (Phaseolus vulgaris L.). It is important to identify quantitative trait loci (QTL) for ASB resistance and introgress into susceptible cultivars of the common bean. The objective of this research was to identify QTL and single nucleotide polymorphism (SNP) markers associated with ASB resistance in recombinant inbred lines (RIL) derived from a cross between BAT 477 and NY6020-4 common bean. One hundred and twenty-six F6:7 RIL were phenotyped for ASB in the greenhouse. Disease severity was scored on a scale of 1-9. Genotyping was performed using whole genome resequencing with 2x common bean genome size coverage, and over six million SNPs were obtained. After being filtered, 72,017 SNPs distributed on 11 chromosomes were used to conduct the genome-wide association study (GWAS) and QTL mapping. A novel QTL region of ~4.28 Mbp from 35,546,329 bp to 39,826,434 bp on chromosome Pv03 was identified for ASB resistance. The two SNPs, Chr03_39824257 and Chr03_39824268 located at 39,824,257 bp and 39,824,268 bp on Pv03, respectively, were identified as the strongest markers associated with ASB resistance. The gene Phvul.003G175900 (drought sensitive, WD repeat-containing protein 76) located at 39,822,021 - 39,824,655 bp on Pv03 was recognized as one candidate for ASB resistance in the RIL, and the gene contained the two SNP markers. QTL and SNP markers may be used to select plants and lines for ASB resistance through marker-assisted selection (MAS) in common bean breeding.

Cardiac Troponin I-Interacting Kinase Affects Cardiomyocyte S-Phase Activity but Not Cardiomyocyte Proliferation.

Identifying genetic variants that affect the level of cell cycle reentry and establishing the degree of cell cycle progression in those variants could help guide development of therapeutic interventions aimed at effecting cardiac regeneration. We observed that C57Bl6/NCR (B6N) mice have a marked increase in cardiomyocyte S-phase activity after permanent coronary artery ligation compared with infarcted DBA/2J (D2J) mice. Cardiomyocyte cell cycle activity after infarction was monitored in D2J, (D2J×B6N)-F1, and (D2J×B6N)-F1×D2J backcross mice by means of bromodeoxyuridine or 5-ethynyl-2'-deoxyuridine incorporation using a nuclear-localized transgenic reporter to identify cardiomyocyte nuclei. Genome-wide quantitative trait locus analysis, fine scale genetic mapping, whole exome sequencing, and RNA sequencing analyses of the backcross mice were performed to identify the gene responsible for the elevated cardiomyocyte S-phase phenotype. (D2J×B6N)-F1 mice exhibited a 14-fold increase in cardiomyocyte S-phase activity in ventricular regions remote from infarct scar compared with D2J mice (0.798±0.09% versus 0.056±0.004%; P<0.001). Quantitative trait locus analysis of (D2J×B6N)-F1×D2J backcross mice revealed that the gene responsible for differential S-phase activity was located on the distal arm of chromosome 3 (logarithm of the odds score=6.38; P<0.001). Additional genetic and molecular analyses identified 3 potential candidates. Of these, Tnni3k (troponin I-interacting kinase) is expressed in B6N hearts but not in D2J hearts. Transgenic expression of TNNI3K in a D2J genetic background results in elevated cardiomyocyte S-phase activity after injury. Cardiomyocyte S-phase activity in both Tnni3k-expressing and Tnni3k-nonexpressing mice results in the formation of polyploid nuclei. These data indicate that Tnni3k expression increases the level of cardiomyocyte S-phase activity after injury.

Abstract 14201: Decoding the Genetic Regulation of Cardiac Long Noncoding RNAs in Male and Female Mice Using Integrative Genomic Analysis

Introduction: Long non-coding RNAs (lncRNAs) are increasingly recognized as important regulators of mammalian heart development and potential contributors to human cardiovascular diseases by regulating RNA transcription and processing. However, the genetic regulation lncRNAs and their interactions with mRNAs are poorly understood. Hypothesis: Cardiac lncRNAs are genetically regulated and they differentially modulate mRNA expression in female vs male hearts. Methods: Deep RNA-seq was performed using Illumina HiSeqTM 4000 sequencer protocol. SNP Array was used for mouse genotyping. Weighted Gene Co-expression Network Analysis (WGCNA) was conducted using R. Association analyses were performed using FaST-LMM. Results: To elucidate lncRNA-mediated transcriptional regulation in heart, we performed integrated genomic analysis of RNA-seq derived transcriptome and SNP genotype data from the hybrid mouse diversity panel (HMDP) of adult male and female mice hearts across 100 strains. Using a 5% FDR P value threshold, genome-wide quantitative trait locus (eQTL) analysis detected total of 408513 and 404661 locally associated (within 1 Mb of genomic distance of a given gene) SNP-lncRNA pairs in female vs male heart, respectively, which corresponded to 6977 and 6357 unique lncRNAs in female vs male heart, respectively. After excluding those with distal eQTLs ( P &lt;4.1e-6), 2687 and 2684 cardiac lncRNAs with cis eQTLs were retained. WGNCA revealed distinct lncRNA-mRNA modules. From module-trait correlation analysis, 14 female and 5 male modules correlated with heart weight, body fat, or insulin resistance (Bicor &gt;0.3, P &lt;0.05). By applying eQTL analysis to the module eigengenes, 11 modules were identified with eigengenes QTLs (FDR&lt;0.05). Lastly, a novel SNP-lncRNA-mRNA regulatory circuit involving the mitochondria transcription elongation factor, TSFM, was revealed from our analysis. Conclusions: We present our efforts to investigate lncRNA-mediated regulation of gene expression in female vs male mouse heart across 100 different strains. Our results reveal novel associations on lncRNAs with complex clinical traits and provide resources to facilitate mechanistic exploration of lncRNAs contribution to cardiovascular diseases.

QTLbase2: an enhanced catalog of human quantitative trait loci on extensive molecular phenotypes.

Deciphering the fine-scale molecular mechanisms that shape the genetic effects at disease-associated loci from genome-wide association studies (GWAS) remains challenging. The key avenue is to identify the essential molecular phenotypes that mediate the causal variant and disease under particular biological conditions. Therefore, integrating GWAS signals with context-specific quantitative trait loci (QTLs) (such as different tissue/cell types, disease states, and perturbations) from extensive molecular phenotypes would present important strategies for full understanding of disease genetics. Via persistent curation and systematic data processing of large-scale human molecular trait QTLs (xQTLs), we updated our previous QTLbase database (now QTLbase2, http://mulinlab.org/qtlbase) to comprehensively analyze and visualize context-specific QTLs across 22 molecular phenotypes and over 95 tissue/cell types. Overall, the resource features the following major updates and novel functions: (i) 960 more genome-wide QTL summary statistics from 146 independent studies; (ii) new data for 10 previously uncompiled QTL types; (iii) variant query scope expanded to fit 195 QTL datasets based on whole-genome sequencing; (iv) supports filtering and comparison of QTLs for different biological conditions, such as stimulation types and disease states; (v) a new linkage disequilibrium viewer to facilitate variant prioritization across tissue/cell types and QTL types.

Genome-wide QTL mapping and RNA-seq reveal the genetic variation influencing growth traits in giant grouper (Epinephelus lanceolatus)

Genetic improvement of growth traits is a major target of grouper aquaculture and breeding. As one of the largest groupers, the giant grouper (Epinephelus lanceolatus) has excellent growth performance, although the genetic basis of its growth traits has been rarely studied. In this study, we successfully constructed a high-density genetic linkage map with 2988 SNPs generated by genotype-by-sequencing of 178 individuals, which belong to an F1 full-sibling family. The length of the map was 3231.5 cM with an average interval of 1.21 cM. Then, we performed genome-wide QTL (quantitative trait locus) mapping for five growth traits including all length, body length, body height, body thickness, and body weight in the giant grouper and identified six growth-associated QTLs, which explained 4.65–12.56% of the phenotypic variance. These QTLs were distributed on five linkage groupers (LGs) of our sex-averaged linkage map with two QTLs on LG11, four QTLs on LG7, LG10, LG15, and LG23. The genes identified within the significant QTLs were validated using qPCR and the results showed six significantly upregulated genes (kalrn, ypel1, supt7l, lacs5, ccnd2, mybpc2) in tissues (muscle, fat, liver, gill, etc.) of the fast vs slow growth groups were highlighted as promising candidates. Further, RNA-seq analysis revealed 484 differentially expressed genes (DEGs) between fast/slow growth groups that play roles in the RNA transport, metabolic pathways, PPAR signaling pathway, and carbon metabolism pathways. Notably, 27 DEGs were detected in the identified growth-associated-QTL regions. Those candidate genes shared between QTL mapping and DEGs may play important roles in regulating cell growth, signal transduction, carbohydrate metabolism and skeletal development. Overall, our findings are useful for improving the genetic process for molecular marker-assisted selection and provide new insights into elucidating the molecular mechanism of growth variation in this species.

Multi-environment genomic prediction for soluble solids content in peach (Prunus persica).

Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual’s narrow-sense and broad-sense heritability for SSC were high (0.57–0.73 and 0.66–0.80, respectively), with 19–32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.

Association between central serous chorioretinopathy susceptibility genes and choroidal parameters.

To evaluate the association between central serous chorioretinopathy (CSC) susceptibility genes and choroidal parameters in a large Japanese cohort. Retrospective cohort study. Of the 9850 individuals in the Nagahama study whose second visit was between 2013 and 2016, those with optical coherence tomography (OCT) images with enhanced depth imaging (EDI), axial length, and genome-wide single nucleotide polymorphism (SNP) genotyping data were included. We calculated subfoveal choroidal thickness (SFCT), choroidal vascularity index (CVI), normalized choroidal intensity (NCI), and vertical asymmetry of choroidal thickness. Genome-wide quantitative trait locus (QTL) analyses were performed for each parameter. We screened for four CSC susceptibility SNPs: CFH rs800292, TNFRSF10A rs13278062, GATA5 rs6061548, and VIPR2 rs3793217. Whenever an SNP was not included in the genotyping data after quality control, its proxy SNP was selected. In total, 4586 participants were evaluated. CFH rs800292 was significantly associated with SFCT (P < 0.001) and CVI (P < 0.001). VIPR2 rs3793217 was significantly associated with SFCT (P < 0.001) but not with CVI. Whereas, TNFRSF10A rs13254617 and GATA5 rs6061548 were not significantly associated with SFCT or CVI. None of these SNPs was associated with NCIEDI and asymmetry of choroidal thickness. CFH, VIPR2, TNFRSF10A, and GATA5 showed different association patterns with choroidal parameters. Although the mechanism of CSC pathogenesis by choroidal changes is not fully understood, this finding suggests that each gene may be involved in different mechanisms of CSC development. Our genetic study provides a basis for understanding the role of CSC susceptibility genes.

Genomic insights into variation in thermotolerance between hybridizing swordtail fishes.

Understanding how organisms adapt to changing environments is a core focus of research in evolutionary biology. One common mechanism is adaptive introgression, which has received increasing attention as a potential route to rapid adaptation in populations struggling in the face of ecological change, particularly global climate change. However, hybridization can also result in deleterious genetic interactions that may limit the benefits of adaptive introgression. Here, we used a combination of genome-wide quantitative trait locus mapping and differential gene expression analyses between the swordtail fish species Xiphophorus malinche and X. birchmanni to study the consequences of hybridization on thermotolerance. While these two species are adapted to different thermal environments, we document a complicated architecture of thermotolerance in hybrids. We identify a region of the genome that contributes to reduced thermotolerance in individuals heterozygous for X. malinche and X. birchmanni ancestry, as well as widespread misexpression in hybrids of genes that respond to thermal stress in the parental species, particularly in the circadian clock pathway. We also show that a previously mapped hybrid incompatibility between X. malinche and X. birchmanni contributes to reduced thermotolerance in hybrids. Together, our results highlight the challenges of understanding the impact of hybridization on complex ecological traits and its potential impact on adaptive introgression.

Recent status of Genotyping by Sequencing (GBS) Technology in cucumber (Cucumis sativus L.): a review.

Current and advanced breeding tools are being used to improve economically important horticultural crops to meet the consumers' needs and preferences. Genotyping-by-sequencing (GBS) is an extremely useful tool in the investigation and analysis of the genetic diversity of different cultivars. Based on a broad range of genetic backgrounds like single nucleotide polymorphism (SNPs), GBS is known as a novel technique to facilitate the detection of quantitative trait loci (QTL) regions robustly linked with interested traits compared to genome-wide association study (GWAS) and QTL. GBS has gained popularity among breeders in recent years and it is also employed in cucumber breeding programs. Cucumbers (C. sativus L.) are monoecious, gynoecious and some of them are parthenocarpic species. Cucumber is one of the most economical and essential crops in the Cucurbitaceae family. For time immemorial, cucumber has been produced and consumed all over the world like other cucurbits. To a large extent, cultivated cucurbits are beneficial to human health for providing necessary minerals and fibers.Therefore, this review portrays the current status of advances made by using GBS and its combination with other tools in various studies of cucumber such as the use of GBS and single nucleotide polymorphism (SNP) markers, GBS and GWAS, also with QTL and marker-assisted selection (MAS) are applied to display and detect explicit genetic architecture complex traits in crops and chromosome rearrangements.Cucumber breeding programs have undoubtedly benefited from genotyping-by-sequencing. Using the GBS method, research discovered lots of new candidate genes that control various traits including spine color, fruit stalk-end color, and disease resistance in cucumber lines.

Enhancer promoter interactome and Mendelian randomization identify network of druggable vascular genes in coronary artery disease

Coronary artery disease (CAD) is a multifactorial disorder, which is partly heritable. Herein, we implemented a mapping of CAD-associated candidate genes by using genome-wide enhancer-promoter conformation (H3K27ac-HiChIP) and expression quantitative trait loci (eQTL). Enhancer-promoter anchor loops from human coronary artery smooth muscle cells (HCASMC) explained 22% of the heritability for CAD. 3D enhancer-promoter genome mapping of CAD-genes in HCASMC was enriched in vascular eQTL genes. By using colocalization and Mendelian randomization analyses, we identified 58 causal candidate vascular genes including some druggable targets (MAP3K11, CAMK1D, PDGFD, IPO9 and CETP). A network analysis of causal candidate genes was enriched in TGF beta and MAPK pathways. The pharmacologic inhibition of causal candidate gene MAP3K11 in vascular SMC reduced the expression of athero-relevant genes and lowered cell migration, a cardinal process in CAD. Genes connected to enhancers are enriched in vascular eQTL and druggable genes causally associated with CAD.

Linkage analysis identifies novel genetic modifiers of microbiome traits in families with inflammatory bowel disease

ABSTRACT Dysbiosis of the gut microbiome is a hallmark of inflammatory bowel disease (IBD) and both, IBD risk and microbiome composition, have been found to be associated with genetic variation. Using data from families of IBD patients, we examined the association between genetic and microbiome similarity in a specific IBD context, followed by a genome-wide quantitative trait locus (QTL) linkage analysis of various microbiome traits using the same data. SNP genotypes as well as gut microbiome and phenotype data were obtained from the Kiel IBD family cohort (IBD-KC). The IBD-KC is an ongoing prospective study in Germany currently comprising 256 families with 455 IBD patients and 575 first- and second-degree relatives. Initially focusing upon known IBD risk loci, we noted a statistically significant (FDR<0.05) association between genetic similarity at SNP rs11741861 and overall microbiome dissimilarity among pairs of relatives discordant for IBD. In a genome-wide QTL analysis, 12 chromosomal regions were found to be linked to the abundance of one of seven microbial genera, namely Barnesiella (chromosome 4, region spanning 10.34 cM), Clostridium_XIVa (chr4, 3.86 cM; chr14, two regions spanning 7.05 and 13.02 cM respectively), Pseudoflavonifractor (chr7, 12.80 cM) Parasutterella (chr14, 8.26 cM), Ruminococcus (chr16, two overlapping regions spanning 8.01 and 16.87 cM, respectively), Roseburia (chr19, 7.99 cM), and Odoribacter (chr22, three regions spanning 0.89, 5.57 and 1.71 cM, respectively), as well as the Shannon index of α diversity (chr3, 1.47 cM). Our study thus shows that, in families of IBD patients, pairwise genetic similarity for at least one IBD risk locus is associated with overall microbiome dissimilarity among discordant pairs of relatives, and that hitherto unknown genetic modifiers of microbiome traits are located in at least 12 human genomic regions.

Chromosome-Wide Characterization of Intragenic Crossover in Shiitake Mushroom, Lentinula edodes.

Meiotic crossover plays a critical role in generating genetic variations and is a central component of breeding. However, our understanding of crossover in mushroom-forming fungi is limited. Here, in Lentinula edodes, we characterized the chromosome-wide intragenic crossovers, by utilizing the single-nucleotide polymorphisms (SNPs) datasets of an F1 haploid progeny. A total of 884 intragenic crossovers were identified in 110 single-spore isolates, the majority of which were closer to transcript start sites. About 71.5% of the intragenic crossovers were clustered into 65 crossover hotspots. A 10 bp motif (GCTCTCGAAA) was significantly enriched in the hotspot regions. Crossover frequencies around mating-type A (MAT-A) loci were enhanced and formed a hotspot in L. edodes. Genome-wide quantitative trait loci (QTLs) mapping identified sixteen crossover-QTLs, contributing 8.5–29.1% of variations. Most of the detected crossover-QTLs were co-located with crossover hotspots. Both cis- and trans-QTLs contributed to the nonuniformity of crossover along chromosomes. On chr2, we identified a QTL hotspot that regulated local, global crossover variation and crossover hotspot in L. edodes. These findings and observations provide a comprehensive view of the crossover landscape in L. edodes, and advance our understandings of conservation and diversity of meiotic recombination in mushroom-forming fungi.

Genome-wide QTL mapping of yield and agronomic traits in two widely adapted winter wheat cultivars from multiple mega-environments.

Quantitative trait loci (QTL) analysis could help to identify suitable molecular markers for marker-assisted breeding (MAB). A mapping population of 124 F5:7recombinant inbred lines derived from the cross ‘TAM 112’/‘TAM 111’ was grown under 28 diverse environments and evaluated for grain yield, test weight, heading date, and plant height. The objective of this study was to detect QTL conferring grain yield and agronomic traits from multiple mega-environments. Through a linkage map with 5,948 single nucleotide polymorphisms (SNPs), 51 QTL were consistently identified in two or more environments or analyses. Ten QTL linked to two or more traits were also identified on chromosomes 1A, 1D, 4B, 4D, 6A, 7B, and 7D. Those QTL explained up to 13.3% of additive phenotypic variations with the additive logarithm of odds (LOD(A)) scores up to 11.2. The additive effect increased yield up to 8.16 and 6.57 g m−2 and increased test weight by 2.14 and 3.47 kg m−3 with favorable alleles from TAM 111 and TAM 112, respectively. Seven major QTL for yield and six for TW with one in common were of our interest on MAB as they explained 5% or more phenotypic variations through additive effects. This study confirmed previously identified loci and identified new QTL and the favorable alleles for improving grain yield and agronomic traits.

IRF7 and RNH1 are modifying factors of HIV-1 reservoirs: a genome-wide association analysis

BackgroundCombination antiretroviral treatment (cART) cannot eradicate HIV-1 from the body due to the establishment of persisting viral reservoirs which are not affected by therapy and reinitiate new rounds of HIV-1 replication after treatment interruption. These HIV-1 reservoirs mainly comprise long-lived resting memory CD4+ T cells and are established early after infection. There is a high variation in the size of these viral reservoirs among virally suppressed individuals. Identification of host factors that contribute to or can explain this observed variation could open avenues for new HIV-1 treatment strategies.MethodsIn this study, we conducted a genome-wide quantitative trait locus (QTL) analysis to probe functionally relevant genetic variants linked to levels of cell-associated (CA) HIV-1 DNA, CA HIV-1 RNA, and RNA:DNA ratio in CD4+ T cells isolated from blood from a cohort of 207 (Caucasian) people living with HIV-1 (PLHIV) on long-term suppressive antiretroviral treatment (median = 6.6 years). CA HIV-1 DNA and CA HIV-1 RNA levels were measured with corresponding droplet digital PCR (ddPCR) assays, and genotype information of 522,455 single-nucleotide variants was retrieved via the Infinium Global Screening array platform.ResultsThe analysis resulted in one significant association with CA HIV-1 DNA (rs2613996, P < 5 × 10−8) and two suggestive associations with RNA:DNA ratio (rs7113204 and rs7817589, P < 5 × 10−7). Then, we prioritized PTDSS2, IRF7, RNH1, and DEAF1 as potential HIV-1 reservoir modifiers and validated that higher expressions of IRF7 and RNH1 were accompanied by rs7113204-G. Moreover, RNA:DNA ratio, indicating relative HIV-1 transcription activity, was lower in PLHIV carrying this variant.ConclusionsThe presented data suggests that the amount of CA HIV-1 DNA and RNA:DNA ratio can be influenced through PTDSS2, RNH1, and IRF7 that were anchored by our genome-wide association analysis. Further, these observations reveal potential host genetic factors affecting the size and transcriptional activity of HIV-1 reservoirs and could indicate new targets for HIV-1 therapeutic strategies.