Quantitative Trait Loci Research Articles

Marker assisted selection and genome recovery for heat tolerance in segregants of rice (Oryza sativa L.)

Abstract In the present global warming scenario, it is urgent to impart heat tolerance into the popular high yielding rice (Oryza sativa L.) varieties to reduce yield loss to a great extent. Uma (a prevalent red-grained rice variety in Kerala) was crossed with Nagina 22 (N22), the popular donor for heat tolerance. From the seven promising heat-tolerant F3 plants previously identified through marker assisted selection and field screening, F4 lines (58 nos.) were raised and characterised morphologically. Heat tolerance was scored under natural heat stress during hot summer months in field conditions. Among the evaluated F4 progenies, 26 promising plants registered >75% spikelet fertility. Double panicles were observed in some of the tillers of fourteen F4 plants. During the selection with reported polymorphic markers between parents, all the 26 F4 plants produced the corresponding amplicon for RM5749 (linked marker for heat tolerance in the parental cross Uma x N22) and also for RM9, RM201, RM208, RM225, RM242, RM495, RM3586, RM6100, RM6836, and RM26212, corresponding to the heat tolerant parent N22. Genomic analysis revealed 64% recovery of the Uma genome in seven F4 plants, with maximum genomic regions of Uma on the chromosomes 3 and 5. These promising F4 plants could be forwarded to generate stable and high yielding heat tolerant rice varieties. The identified markers could be used for linkage analysis and QTL mapping in future. Keywords: Heat stress; Rice; spikelet fertility; SSR; QTL

Profiling genetically driven alternative splicing across the Indonesian archipelago

One of the regulatory mechanisms influencing the functional capacity of genes is alternative splicing (AS). Previous studies exploring the splicing landscape of human tissues have shown that AS has contributed to human biology, especially in disease progression and the immune response. Nonetheless, this phenomenon remains poorly characterized across human populations, and it is unclear how genetic and environmental variation contribute to AS. Here, we examine a set of 115 Indonesian samples from three traditional island populations spanning the genetic ancestry cline that characterizes Island Southeast Asia. We conduct a global AS analysis between islands to ascertain the degree of functionally significant AS events and their consequences. Using an event-based statistical model, we detected over 1,500 significant differential AS events across all comparisons. Additionally, we identify over 6,000 genetic variants associated with changes in splicing (splicing quantitative trait loci [sQTLs]), some of which are driven by Papuan-like genetic ancestry, and only show partial overlap with other publicly available sQTL datasets derived from other populations. Computational predictions of RNA binding activity reveal that a fraction of these sQTLs directly modulate the binding propensity of proteins involved in the splicing regulation of immune genes. Overall, these results contribute toward elucidating the role of genetic variation in shaping gene regulation in one of the most diverse regions in the world.

Machine vision-assisted genomic prediction and genome-wide association of spleen-related traits in large yellow croaker infected with visceral white-nodules disease

High-resolution and high-throughput genotype-to-phenotype studies in fish are rapidly advancing, driven by innovative technologies that aim to address the challenges of modern breeding models. In recent years, machine vision and deep learning techniques, particularly convolutional neural networks (CNNs), have achieved significant success in image recognition and segmentation. Moreover, qualitative and quantitative analysis of disease resistance has always been a crucial field of research in genetics. This motivation has led us to investigate the potential of large yellow croaker visceral white-nodules disease (VWND) in encoding information on disease resistance for the task of accession classification. In this study, we proposed an image segmentation framework for the feature extraction of the spleen after VWND infection based on machine vision. We utilized deep CNNs and threshold segmentation for automatic feature learning and object segmentation. This approach eliminates subjectivity and enhances work efficiency compared to using hand-crafted features. Additionally, we employed spleen-related traits to conduct genome-wide association analysis (GWAS), which led to the identification of 24 significant SNPs and 10 major quantitative trait loci. The results of function enrichment analysis on candidate genes also indicated potential relationships with immune regulation mechanisms. Furthermore, we explored the use of genomic selection (GS) technology for phenotype prediction of extreme individuals, which further supports the predictability of spleen-related phenotypes for VWND resistance in large yellow croakers. Our findings demonstrate that artificial intelligence (AI)-based phenotyping approaches can deliver state-of-the-art performance for genetics research. We hope this work will provide a paradigm for applying deep learning and machine vision to phenotyping in aquaculture species.

Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction.

Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1 , which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.

Lipid-lowering drugs and risk of rapid renal function decline: a mendelian randomization study.

Chronic kidney disease (CKD) patients face the risk of rapid kidney function decline leading to adverse outcomes like dialysis and mortality. Lipid metabolism might contribute to acute kidney function decline in CKD patients. Here, we utilized the Mendelian Randomization approach to investigate potential causal relationships between drug target-mediated lipid phenotypes and rapid renal function decline. In this study, we utilized two methodologies: summarized data-based Mendelian randomization (SMR) and inverse variance-weighted Mendelian randomization (IVW-MR), to approximate exposure to lipid-lowering drugs. This entailed leveraging expression quantitative trait loci (eQTL) for drug target genes and genetic variants proximal to drug target gene regions, which encode proteins associated with low-density lipoprotein (LDL) cholesterol, as identified in genome-wide association studies. The objective was to investigate causal associations with the progression of rapid kidney function decline. The SMR analysis revealed a potential association between high expression of PCSK9 and rapid kidney function decline (OR = 1.11, 95% CI= [1.001-1.23]; p = 0.044). Similarly, IVW-MR analysis demonstrated a negative association between LDL cholesterol mediated by HMGCR and kidney function decline (OR = 0.74, 95% CI = 0.60-0.90; p = 0.003). Genetically predicted inhibition of HMGCR is linked with the progression of kidney function decline, while genetically predicted PCSK9 inhibition is negatively associated with kidney function decline. Future research should incorporate clinical trials to validate the relevance of PCSK9 in preventing kidney function decline.

Global Epidemiological Impact of PNPLA3 I148M on Liver Disease.

The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) has increased exponentially over the past three decades, in parallel with the global rise in obesity and type 2 diabetes. It is currently the most common cause of liver-related morbidity and mortality. Although obesity has been identified as a key factor in the increased prevalence of MASLD, individual differences in susceptibility are significantly influenced by genetic factors. PNPLA3 I148M (rs738409 C>G) is the variant with the greatest impact on the risk of developing progressive MASLD and likely other forms of steatotic liver disease. This variant is prevalent across the globe, with the risk allele (G) frequency exhibiting considerable variation. Here, we review the contribution of PNPLA3 I148M to global burden and regional differences in MASLD prevalence, focusing on recent evidence emerging from population-based sequencing studies and prevalence assessments. We calculated the population attributable fraction (PAF) as a means of quantifying the impact of the variant on MASLD. Furthermore, we employ quantitative trait locus (QTL) analysis to ascertain the associations between rs738409 and a range of phenotypic traits. This analysis suggests that these QTLs may underpin pleiotropic effects on extrahepatic traits. Finally, we outline potential avenues for further research and identify key areas for investigation in future studies.

Molecular and genetic basis of plant architecture in soybean.

Plant architecture determines canopy coverage, photosynthetic efficiency, and ultimately productivity in soybean (Glycine max). Optimizing plant architecture is a major goal of breeders to develop high yield soybean varieties. Over the past few decades, the yield per unit area of soybean has not changed significantly; however, rice and wheat breeders have succeeded in achieving high yields by generating semi-dwarf varieties. Semi-dwarf crops have the potential to ensure yield stability in high-density planting environments because they can significantly improve responses to fertilizer input, lodging resistance, and enhance resistance to various abiotic and biotic stresses. Soybean has a unique plant architecture, with leaves, inflorescences, and pods growing at each node; internode number greatly affects the final yield. Therefore, producing high-yielding soybean plants with an ideal architecture requires the coordination of effective node formation, effective internode formation, and branching. Dozens of quantitative trait loci (QTLs) controlling plant architecture have been identified in soybean, but only a few genes that control this trait have been cloned and characterized. Here, we review recent progress in understanding the genetic basis of soybean plant architecture. We provide our views and perspectives on how to breed new high-yielding soybean varieties.

Causal relationship between antihypertensive drugs and Hashimoto's thyroiditis: a drug-target Mendelian randomization study.

Recent studies have indicated a potential association of hypertension with Hashimoto's thyroiditis (HT) and other autoimmune diseases, yet the impact of antihypertensive drugs on HT risk is not well understood. We employed a drug-target Mendelian randomization approach to investigate the prolonged impact of 9 classes of antihypertensive medications on HT susceptibility in European and Asian populations. Genetic variants close to or within genes associated with the drug targets and systolic blood pressure (SBP) were utilized to mimic the effects of antihypertensive medications. We focused on drugs linked to a lower risk of coronary artery disease for our main analysis. We gathered genetic data on SBP and HT risk from comprehensive genome-wide association studies available for European and Asian groups. For a supplementary analysis, we used expression quantitative trait loci (eQTLs) related to drug target genes as proxies. Our analysis revealed that the use of calcium channel blockers (CCBs) is linked to a reduced risk of HT in both European (OR [95% CI]: 0.96 [0.95 to 0.98] per 1 mmHg decrease in SBP; p = 3.51×10-5) and Asian populations (OR [95% CI]: 0.28 [0.12, 0.66]; p = 3.54×10-3). Moreover, genetically mimicking the use of loop diuretics (OR [95% CI]: 0.94 [0.91, 0.97]; p = 3.57×10-5) and thiazide diuretics (0.98 [0.96, 0.99]; p = 3.83×10-3) showed a significant association with a decreased risk of HT only in European population. These outcomes were confirmed when eQTLs were employed to represent the effects of antihypertensive medications. The study suggests that CCBs and diuretics could potentially reduce the risk of HT in different populations. Additional research is needed to assess the feasibility of repurposing antihypertensive medications for the prevention of HT.

Genetically Supported Drug Targets and Dental Traits: A Mendelian Randomization Study

Current interventions for oral/dental diseases heavily rely on operative/surgical procedures, while the discovery of novel drug targets may enable access to noninvasive pharmacotherapy. Therefore, this study aims to leverage large-scale data and Mendelian randomization (MR) techniques, utilizing genetic variants as instruments, to identify potential therapeutic targets for oral and dental diseases supported by genetic evidence. By intersecting 4,302 druggable genes with expression quantitative trait loci from 31,684 blood samples, we identified 2,580 druggable targets as exposures. Single nucleotide polymorphisms associated with dental disease/symptom traits were collected from FinnGen R9, the Gene–Lifestyle Interactions in Dental Endpoints consortium, and the UK Biobank to serve as outcomes for both discovery and replication purposes. Through MR analysis, we identified 43 druggable targets for various dental disease/symptom traits. To evaluate the viability of these targets, we replicated the analysis using circulating protein quantitative trait loci as exposures. Additionally, we conducted sensitivity, colocalization, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotation, protein–protein interaction analyses, and validated dental trait–associated druggable gene expression in animal models. Among these targets, IL12RB1 (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.01–1.01) and TNF (OR, 0.98; 95% CI, 0.97–0.99) exhibited therapeutic promise for oral ulcers, whereas CXCL10 (OR, 0.84; 95% CI, 0.76–0.91) was for periodontitis. Through a rigorous quality control and validation pipeline, our study yields compelling evidence for these druggable targets, which may enhance the clinical prognosis by developing novel drugs or repurposing existing ones.

Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.

To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation. Microarray and RNA-sequencing datasets (GSE47184, GSE96804, GSE104948, GSE104954, GSE142025 and GSE175759) were obtained from the Gene Expression Omnibus database. Differential expression analysis identified the differentially expressed genes (DEGs) between patients with DN and controls. Diverse machine learning algorithms, including least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, were used to enhance gene selection accuracy and predictive power. We integrated summary-level data from genome-wide association studies on DN with expression quantitative trait loci data to identify genes with potential causal relationships to DN. The predictive performance of the biomarker gene was validated using receiver operating characteristic (ROC) curves. Gene set enrichment and correlation analyses were conducted to investigate potential mechanisms. Finally, the biomarker gene was validated using quantitative real-time polymerase chain reaction in clinical samples from patients with DN and controls. Based on identified 314 DEGs, seven characteristic genes with high predictive performance were identified using three integrated machine learning algorithms. MR analysis revealed 219 genes with significant causal effects on DN, ultimately identifying one co-expressed gene, carbonic anhydrase II (CA2), as a key biomarker for DN. The ROC curves demonstrated the excellent predictive performance of CA2, with area under the curve values consistently above 0.878 across all datasets. Additionally, our analysis indicated a significant association between CA2 and infiltrating immune cells in DN, providing potential mechanistic insights. This biomarker was validated using clinical samples, confirming the reliability of our findings in clinical practice. By integrating machine learning, MR and experimental validation, we successfully identified and validated CA2 as a promising biomarker for DN with excellent predictive performance. The biomarker may play a role in the pathogenesis and progression of DN via immune-related pathways. These findings provide important insights into the molecular mechanisms underlying DN and may inform the development of personalized treatment strategies for this disease.

Integrative multi-omics analysis reveals genetic and heterotic contributions to male fertility and yield in potato.

The genetic analysis of potato is hampered by the complexity of tetrasomic inheritance. An ongoing effort aims to transform the clonally propagated tetraploid potato into a seed-propagated diploid crop, which would make genetic analyses much easier owing to disomic inheritance. Here, we construct and report the large-scale genetic and heterotic characteristics of a diploid F2 potato population derived from the cross of two highly homozygous inbred lines. We investigate 20,382 traits generated from multi-omics dataset and identify 25,770 quantitative trait loci (QTLs). Coupled with gene expression data, we construct a systems-genetics network for gene discovery in potatoes. Importantly, we explore the genetic basis of heterosis in this population, especially for yield and male fertility heterosis. We find that positive heterotic effects of yield-related QTLs and negative heterotic effects of metabolite QTLs (mQTLs) contribute to yield heterosis. Additionally, we identify a PME gene with a dominance heterotic effect that plays an important role in male fertility heterosis. This study provides genetic resources for the potato community and will facilitate the application of heterosis in diploid potato breeding.

Targeting RAC1 might be a potential therapeutic strategy for diabetic kidney disease: a Mendelian randomization study.

This study aimed to ascertain the causal association between Ras-related C3 botulinum toxin substrate 1 (RAC1) and the incidence and progression of diabetic kidney disease (DKD) through Mendelian randomization analysis. RAC1 expression, evaluated using expression quantitative trait loci data from the eQTLGen Consortium, was served as the exposure variable. Outcomes encompassed the risk of DKD, end-stage renal disease (ESRD), albuminuria assessed by the urinary albumin-to-creatinine ratio (ACR), and estimated glomerular filtration rate (eGFR) among individuals with diabetes. Causal associations were computed using the inverse variance weighted (IVW), weighted median, and MR-PRESSO models. Additionally, we conducted analyses for heterogeneity, horizontal pleiotropy, and sensitivity. This study revealed a causal association between the genetic activation of RAC1 and an elevated risk of DKD among individuals with diabetes [IVW, odds ratio (OR) = 1.28, 95% confidence intervals (CI) 1.08-1.51, P = 0.004]. Furthermore, increased expression of RAC1 was linked to a higher risk of ESRD (IVW, OR = 1.20, 95% CI 1.02-1.43, P = 0.032). Excessive RAC1 expression was causally associated with elevated ACR (IVW, β = 0.052, 95% CI 0.003-0.100, P = 0.036). However, the analysis regarding RAC1 and eGFR showed significant heterogeneity and pleiotropy, with no discernible causal relationship. These findings suggested a positive correlation between the genetic activation of RAC1 and the incidence of DKD, the risk of ESRD, and exacerbated albuminuria among individuals with diabetes. Targeting RAC1 might potentially serve as a therapeutic strategy for DKD.

6715 Short Adult Height in Childhood Cancer Survivors: Prevalence, Risk Factors, and Genetic Contribution

Abstract Disclosure: T. Yoshida: None. J. Baedke: None. F. Wang: None. W. Moon: None. Y. Sapkota: None. J. Miguel Martínez: None. T.E. Merchant: None. C.L. Wilson: None. K.K. Ness: None. M.M. Hudson: None. Y. Yasui: None. A. Delaney: None. Background: Survivors of childhood cancer are at elevated risk for short adult height (SAH) due to cancer and/or its treatment. In the general population, height is a highly polygenic trait; heritability is estimated to be 70-80%. However, the contribution of genetic factors to SAH among childhood cancer survivors is unknown. In addition, the contribution of chemotherapy agents to risk of SAH among survivors has not been established. We assessed: 1) prevalence of SAH; 2) contribution of genetic factors; and 3) impact of cancer therapy including chemotherapy, on SAH in a large cohort of childhood cancer survivors. Methods: Participants included 4461 childhood cancer survivors aged ≥18 years (female 47.5%, mean age 33.2 years old) with measured height information. SAH was defined as height <3rd percentile for age and sex based on the Centers for Disease Control growth charts. Cancer and treatment history were extracted from medical records. We calculated multi-ancestry height polygenic score (PGS) using the latest methodology developed from 5.4 million individuals of diverse ancestries with more than 1 million variants, where lower score associates with shorter height. With a random sample of 75% of survivors, we fit a multivariable logistic regression model for SAH with the PGS, chemotherapy exposures/doses, corticosteroid exposures/doses, and established risk factors for SAH (e.g., age at cancer diagnosis, radiotherapy exposure) as covariates (main model). The remaining 25% of survivors served for validation of the main model and for the calculation of the population attributable fractions (PAF) of the PGS and cancer treatments. Results: The prevalence of SAH was 8.9% among all survivors (9.3% in males; 8.5% in females) and differed widely by primary cancer diagnosis. SAH was associated with lower PGS [odds ratio (OR) 0.47, 95% confidence interval (CI) 0.40-0.55 for a one standard deviation increase], alkylating agent exposure of >12000 mg/m2 (OR 2.19, 95% CI 1.41-3.38, vs. non-exposure), and spinal radiotherapy (OR 3.65, 95% CI 2.23-6.00, vs. non-exposure). Radiotherapy exposure to the hypothalamic-pituitary region and younger age at cancer diagnosis was also associated with SAH in a dose-response manner. The area under the ROC curve of the main model in the validation dataset was 0.80 (95% CI 0.74-0.87), suggesting good predictive ability for SAH by the model. The PAF of SAH calculated from the multiplicative logistic regression model of SAH was 85.7% for cancer treatments and 30.2% and 60.2% for having PGS below the median and the 90th percentile, respectively. Conclusions: Cancer treatments are the primary contributor to SAH risk among survivors with a PAF of 85.7%. Exposure to high-dose alkylating agents contributes to this along with radiotherapy. Inherited genetic factors also affect SAH among survivors but to a much lesser degree than cancer treatments. Presentation: 6/3/2024

Natural variation at the cotton HIC locus increases trichome density and enhances resistance to aphids.

Plant trichomes are an excellent model for studying cell differentiation and development, providing crucial defenses against biotic and abiotic stresses. There is a well-established inverse relationship between trichome density and aphid prevalence, indicating that higher trichome density leads to reduced aphid infestations. Here we present the cloning and characterization of a dominant quantitative trait locus, HIC (hirsute cotton), which significantly enhances cotton trichome density. This enhancement leads to markedly improved resistance against cotton aphids. The HIC encodes an HD-ZIP IV transcriptional activator, crucial for trichome initiation. Overexpression of HIC leads to a substantial increase in trichome density, while knockdown of HIC results in a marked decrease in density, confirming its role in trichome regulation. We identified a variant in the HIC promoter (-810 bp A to C) that increases transcription of HIC and trichome density in hirsute cotton compared with Gossypium hirsutum cultivars with fewer or no trichomes. Interestingly, although the -810 variant in the HIC promoter is the same in G. barbadense and hirsute cotton, the presence of a copia-like retrotransposon insertion in the coding region of HIC in G. barbadense causes premature transcription termination. Further analysis revealed that HIC positively regulates trichome density by directly targeting the EXPANSIN A2 gene, which is involved in cell wall development. Taken together, our results underscore the pivotal function of HIC as a primary regulator during the initial phases of trichome formation, and its prospective utility in enhancing aphid resistance in superior cotton cultivars via selective breeding.

Identification and functional analysis of GmPsaL regulating pod color in vegetable soybean

BackgroundVegetable soybean is rich in nutrients and has a unique flavor. It is highly preferred by people because of its pharmacological activities, including those that regulate the intestines and lower blood pressure. The pod color of vegetable soybeans is an important quality that indicates their freshness and has a significant impact on their commercialization.ResultsIn this study, pod color was evaluated in 301 vegetable soybean accessions collected from various regions. Genome-wide association analysis was carried out using the Mixed linear model (MLM), a total of 18 quantitative trait loci including 117 SNPs were detected. Two significant QTLs located on chromosomes 6 (qGPCL4 /qGPCa1/qGPCb2) and 18 (qGPCL10/qGPCb3) were consistently detected across different variables. Based on gene functional annotation, 30 candidate genes were identified in these two candidate intervals. Combined with transcriptome analysis, Glyma.18g241700 has been identified as a candidate gene for regulating pod color in vegetable soybeans. Glyma.18g241700 encodes a chlorophyll photosystem I subunit XI. which localizes to the chloroplast named GmPsaL, qRT-PCR analysis showed that GmPsaL was specifically highly expressed in developing pods. Furthermore, overexpression of GmPsaL in transgenetic Arabidopsis plants produced dark green pods.ConclusionsThese findings may be useful for clarifying the genetic basis of the pod color of vegetable soybeans. The identified candidate genes may be useful for the genetic improvement of the appearance quality of vegetable soybeans.

RNF144A as a potential risk gene for endometrial carcinoma: Insights from Mendelian randomization, bulk RNA sequencing, single-cell RNA, and experimental analysis.

Endometrial carcinoma (EC) is a prevalent gynecological malignancy that poses a significant threat to women's health worldwide. However, its pathogenesis and underlying mechanisms remains unclear. In this study, expression quantitative trait loci data, Mendelian randomization analysis, and differential expression analysis were performed to identify potential targets. A prognostic risk signature was subsequently constructed for EC patients based on the expression of these genes. Four bioinformatics algorithms, including generalized linear model, extreme gradient boosting, support vector machine, and random forest, were used to identify hub genes in EC. The expression of ring finger protein 144A (RNF144A) was validated using quantitative real-time polymerase chain reaction. Cellular proliferation and migration ability were evaluated using CCK-8 and Transwell assays, respectively. The genes RNF144A, ketohexokinase, and Rab interacting lysosomal protein like 2 were identified as potential targets for EC. Their differential expression was observed in EC patients, and Mendelian randomization analysis revealed a negative correlation between these genes and the development of EC. Mechanistic analyses suggested a strong association between these genes and the tumor immune microenvironment. The constructed risk signature was significantly associated with the prognosis, age, cancer stage, and grade of EC patients. Furthermore, based on interacted model algorithms, RNF144A was identified as a hub gene in EC. It was found to be significantly downregulated in EC samples, and its expression was positively correlated with the stage and grade of EC patients. In vitro experiments showed that overexpression of RNF144A significantly promoted cell growth and migration in EC cells. In conclusion, this study provides insights into the molecular mechanisms underlying EC progression and identifies preliminary candidate biomarkers for the development of EC treatment strategies.

Genome-wide mapping uncovers significant quantitative trait loci associated with yam mosaic virus infection, yield and dry matter content in White Guinea yam (Dioscorea rotundata Poir.)

Genome-wide mapping uncovers significant quantitative trait loci associated with yam mosaic virus infection, yield and dry matter content in White Guinea yam (Dioscorea rotundata Poir.)

Qtl Mapping for Grain Yield Component Traits in Recombinant Inbred Line Population of Bread Wheat Under Heat Stress Using 35 K Snp Array

Qtl Mapping for Grain Yield Component Traits in Recombinant Inbred Line Population of Bread Wheat Under Heat Stress Using 35 K Snp Array

Exploring Novel Genomic Loci and Candidate Genes Associated with Plant Height in Bulgarian Bread Wheat via Multi-Model GWAS.

In the context of crop breeding, plant height (PH) plays a pivotal role in determining straw and grain yield. Although extensive research has explored the genetic control of PH in wheat, there remains an opportunity for further advancements by integrating genomics with growth-related phenomics. Our study utilizes the latest genome-wide association scan (GWAS) techniques to unravel the genetic basis of temporal variation in PH across 179 Bulgarian bread wheat accessions, including landraces, tall historical, and semi-dwarf modern varieties. A GWAS was performed with phenotypic data from three growing seasons, the calculated best linear unbiased estimators, and the leveraging genotypic information from the 25K Infinium iSelect array, using three statistical methods (MLM, FarmCPU, and BLINK). Twenty-five quantitative trait loci (QTL) associated with PH were identified across fourteen chromosomes, encompassing 21 environmentally stable quantitative trait nucleotides (QTNs), and four haplotype blocks. Certain loci (17) on chromosomes 1A, 1B, 1D, 2A, 2D, 3A, 3B, 4A, 5B, 5D, and 6A remain unlinked to any known Rht (Reduced height) genes, QTL, or GWAS loci associated with PH, and represent novel regions of potential breeding significance. Notably, these loci exhibit varying effects on PH, contribute significantly to natural variance, and are expressed during seedling to reproductive stages. The haplotype block on chromosome 6A contains five QTN loci associated with reduced height and two loci promoting height. This configuration suggests a substantial impact on natural variation and holds promise for accurate marker-assisted selection. The potentially novel genomic regions harbor putative candidate gene coding for glutamine synthetase, gibberellin 2-oxidase, auxin response factor, ethylene-responsive transcription factor, and nitric oxide synthase; cell cycle-related genes, encoding cyclin, regulator of chromosome condensation (RCC1) protein, katanin p60 ATPase-containing subunit, and expansins; genes implicated in stem mechanical strength and defense mechanisms, as well as gene regulators such as transcription factors and protein kinases. These findings enrich the pool of semi-dwarfing gene resources, providing the potential to further optimize PH, improve lodging resistance, and achieve higher grain yields in bread wheat.

Mitochondrial dysfunction gene expression, DNA methylation, and inflammatory cytokines interaction activate Alzheimer’s disease: a multi-omics Mendelian randomization study

BackgroundMitochondrial dysfunction (MD) is increasingly recognized as a key pathophysiological contributor in Alzheimer disease (AD). As differential MD genes expression may serve as either a causative factor or a consequence in AD, and expression of these genes could be influenced by epigenetic modifications or interact with inflammatory cytokines, hence, the precise role of MD in AD remains uncertain.MethodsMeta-analysis of brain transcriptome datasets was conducted to pinpoint differentially expressed genes (DEGs) associated with MD in AD. We utilized three-step SMR to analyze the AD genome-wide association study summaries with expression quantitative trait loci (eQTLs) and DNA methylation QTLs from the blood and brain tissues, respectively. Through SMR and colocalization analysis, we further explored the interactions between brain eQTLs and inflammatory cytokines.ResultsFive datasets were meta-analyzed to prioritize 825 DEGs in AD from 1339 MD-related genes. Among these, seven genes from blood samples such as NDUFS8 and SPG7 and thirty-two genes from brain tissue including CLU and MAPT were identified as candidate AD-causal MD genes and regulated by methylation level. Furthermore, we revealed 13 MD gene expression-inflammatory pathway pairs involving LDLR, ACE and PTPMT1 along with interleukin-17C, interleukin-18 and hepatocyte growth factor.ConclusionsThis study highlighted that the AD-causal MD genes could be regulated by epigenetic changes and interact with inflammatory cytokines, providing evidence for AD prevention and intervention.