Quantitative Trait Loci Research Articles

Quantitative genetics of photosynthetic trait variation in maize.

Natural genetic variation in photosynthesis-related traits can aid both in identifying genes involved in regulating photosynthetic processes and developing crops with improved productivity and photosynthetic efficiency. However, rapidly fluctuating environmental parameters create challenges for measuring photosynthetic parameters in large populations under field conditions. We measured chlorophyll fluorescence and absorbance-based photosynthetic traits in a maize diversity panel in the field using an experimental design that allowed us to estimate and control multiple confounding factors. Controlling the impact of day of measurement and light intensity as well as patterns of two-dimensional spatial variation in the field increased heritability for 11 out of 14 traits measured. We were able to identify high confidence GWAS signals associated with variation in four spatially corrected traits (the quantum yield of photosystem II, non-photochemical quenching, redox state of QA, and relative chlorophyll content). Insertion alleles for Arabidopsis orthologs of three candidate genes exhibited phenotypes consistent with our GWAS results. Collectively these results illustrate the potential of applying best practices from quantitative genetics research to address outstanding questions in plant physiology and understand natural variation in photosynthesis.

Improvement of polygenic modeling of blood pressure traits using lifestyle information in the UK Biobank.

Complex traits are determined by the effects of multiple genetic variants, multiple environmental factors, and potentially their interaction. Predicting complex trait phenotypes from genotypes is a fundamental task in quantitative genetics that was pioneered in agricultural breeding for selection purposes. However, it has recently become important in human genetics. While prediction accuracy for some human complex traits is appreciable, this remains low for most traits. A promising way to improve prediction accuracy is by including not only genetic information but also environmental information in prediction models. However, environmental factors can, in turn, be genetically determined. This phenomenon gives rise to collinearity between the genetic and environmental components of the phenotype, which violates the assumptions of most statistical methods for polygenic modeling (i.e., environmental factors are non-randomized over the genetic factors). This phenomenon is also known as "reverse causation", and could lead to biased predictions due to the difficulty in disentangling the genetic and environmental effects. In this work, we investigated the impact of including 27 lifestyle variables as well as genotype information (and their interaction) for predicting diastolic blood pressure, systolic blood pressure, and pulse pressure in older individuals in UK Biobank. The 27 lifestyle variables were included as either raw variables or adjusted for genetic and other non-genetic factors. The results show that proper adjustment of the lifestyle variables allows for improved model performance and reduces the bias generated by reverse causation. Our work confirms the utility of including environmental information in polygenic models of complex traits and highlights the importance of proper handling of the environmental variables.

Targeted multi-layer analysis of PANoptosis-associated genes in the etiology of chronic kidney disease

BackgroundPrevious studies have examined the cellular and molecular interactions between chronic kidney disease (CKD) and PANoptosis, yet the genetic underpinnings remain unclear.MaterialsData at the summary level regarding the methylation, gene expression, and protein levels associated with PANoptosis were obtained from quantitative trait locus (QTL) studies. Genome-wide association study (GWAS) summary statistics for CKD were derived from a GWAS study, supplemented by a replication dataset from the FinnGen database. Genetic variants proximal to or within genes involved in PANoptosis, which showed robust associations with CKD, were utilized as instrumental variables. These variants were the subjected to SMR analysis to explore their causal relationship. The associations among QTLs were systematically analyzed. Additionally, a colocalization analysis was conducted to ascertain whether the signals identified corresponded to a shared genetic basis.ResultsSMR and colocalization analysis revealed 28 methylation sites and 5 genes associated with CKD.Notably, cg01304814 (PRKAR2A) and cg09177106, cg15114474 (CCND1) were inversely associated with CKD risk. Integrating mQTL and eQTL data, we identified four genes (CCND1, GUCY2D, HGF, MADD) causally associated with CKD, with a positive correlation between HGF gene expression and protein levels.ConclusionOur results provide evidence for the PANoptosis-related genes in the pathogenesis of CKD. Notably, PRKAR2A, HGF, CCND1 and MADD, emerged as potential mediators in the pathogenesis of CKD.Trial registrationNot applicable.

Molecular characterization and validation of adult-plant stripe rust resistance genes in Yunnan hulled wheat (Triticum aestivum ssp. yunnanense King).

Three stable QTLs for adult-plant resistance to stripe rust were identified on chromosomes 2DL, 5B, and 7BS in Yunnan hulled wheat, providing durable high-level resistance through additive effects. Yunnan hulled wheat, a unique subspecies native to China, exhibits excellent adult-plant resistance (APR) to stripe rust. However, the genetic basis of stripe rust resistance in Yunnan hulled wheat remains largely unexplored. In this study, we investigated an accession from Lancang County, which has shown stable APR under field conditions for over 10years. To identify the stripe rust gene of Lancang hulled wheat (LC), we developed 108 F5:7 recombinant inbred lines (RILs) from the cross LC × Avocet S, followed by multi-environment phenotypic evaluation of APR to stripe rust. Bulked segregant exome capture sequencing and quantitative trait locus (QTL) analysis identified three stable QTLs on chromosomes 2DL (QYr.LC-2DL), 5B (QYr.LC-5B), and 7BS (QYr.LC-7BS), explaining 11.44-15.74%, 11.27-15.31%, and 12.12-20.00% of the phenotypic variance, respectively. To validate these QTLs, we constructed three independent heterogeneous inbred families (HIF) segregating for QYr.LC-2DL, QYr.LC-5B, and QYr.LC-7BS from the RIL populations using QTL-linked molecular markers. Further molecular mapping of these three HIF populations refined the localization of QYr.LC-2DL, QYr.LC-5B, and QYr.LC-7BS to intervals between markers KP2D-617.34 and KP2D-625.65, KP5B-121.75 and KP5B-262.42, and KP7B-52.54 and KP7B-58.54, respectively. Additionally, additive effects were observed for QYr.LC-2DL, QYr.LC-5B, and QYr.LC-7BS, with lines carrying all three loci showing the highest resistance. Moreover, the linked markers of QYr.LC-2DL and QYr.LC-7BS can be effectively utilized for marker-assisted selection in wheat breeding programs. These findings provide valuable insights into the genetic basis of stripe rust resistance in Yunnan hulled wheat and offer germplasm for breeding wheat varieties with durable resistance.

A Comprehensive Genetic Study of Classical Hodgkin Lymphoma Using Circulating Tumor DNA.

A Comprehensive Genetic Study of Classical Hodgkin Lymphoma Using Circulating Tumor DNA.

QTL mapping and multi-omics identify candidate genes for protein and oil in soybean.

QTL mapping, BSA-seq, and RNA-seq revealed key QTL/genes of protein and oil, some of which were aligned with previous QTLs. EMS-mutations and SoyGVD were applied to validate function of candidate genes. Protein and oil are the two principal economic components of soybean seed. Although several related genes regulating soybean protein and oil accumulation have been reported, the regulatory mechanisms remain largely unknown. In this study, quantitative trait loci (QTL) mapping was employed using four generations derived from a cross between Changjiangchun 2 (high protein and oil content) and Yushuxian 2 (low protein and oil content), resulting in the detection of 37 QTLs, including 16 QTLs for protein content and 21 QTLs for oil content. BSA-seq was performed on two parents and two offspring extreme pools from F2 population individuals. Two QTLs, qOIL1.2 and qPRO14.1, were found to overlap with the BSA-seq mapping interval, in which eight genes exhibited single nucleotide polymorphisms (SNPs) or insertion/deletion (InDel) variations within their coding sequences (CDS). RNA-seq was performed on seeds collected at 20, 30, and 40days after-pollination (DAP) from two extreme pools of F2:3 population individuals. A total of 199 deferentially expressed genes (DEGs) were obtained within the QTLs regions. In conjunction with previous research, 10 protein and four oil QTLs intervals were aligned with previously identified QTLs. Notably, based on EMS-induced mutation lines and SoyGVD database, 12 potential candidate genes were screened out and preliminary validated the function for oil and protein in soybeans. These findings lay the groundwork for further research into candidate genes and marker-assisted selection (MAS) in soybean breeding to enhance seed protein and oil content.

Mapping of the QTLs governing grain nutrients in wheat (Triticum aestivum L.) under nitrogen treatment using high-density SNP markers

IntroductionMicronutrient deficiencies, particularly zinc (Zn) and iron (Fe), are prevalent global health issues, especially among children, that lead to hidden hunger. Wheat is a primary food source for billions of people, but it contains low essential minerals. According to recent studies, the optimum application of nitrogen (N) fertilizers can significantly enhance the micronutrient uptake and accumulation in wheat grains.MethodsThe aims of this study were to identify superior wheat recombinant inbred lines (RILs) of RAJ3765 × HD2329 with high nutrients in grain using the multi-trait genotype–ideotype distance index (MGIDI) and to identify quantitative trait loci (QTLs)/genes associated with grain nutrient content using a single-nucleotide polymorphism (SNP)-based genetic linkage map. The parents and their RIL population were grown under control and nitrogen-deficient (NT) conditions, and nutrient content was determined using inductively coupled plasma optical emission spectroscopy (ICP-OES).Results and discussionAnalysis of variance and descriptive statistics showed a significant difference among all the nutrients. The highest mean values of grain iron concentration (GFeC) and grain zinc concentration (GZnC) were 52.729 and 35.137 mg/kg, respectively, under the control condition, while the lowest mean values were 41.016 and 33.117 mg/kg, respectively, recorded under NT; a similar trend was observed in all the elements. Genotyping was carried out using the 35K Axiom® Wheat Breeder’s Array. A genetic linkage map was constructed using 2,499 polymorphic markers identified for parents across 21 wheat chromosomes. Genetic linkage mapping identified a total of 26 QTLs on 17 different chromosomes. A total of 18 QTLs under the control condition and eight QTLs under the nitrogen stress condition were identified. QTLs for each nutrient were selected based on the high percentage of phenotypic variation explained (PVE%) and logarithm of odds (LOD) score value of more than 3. The LOD scores for studied nutrients varied from 3.04 to 13.42, explaining approximately 1.1% to 27.83% of PVE. One QTL was mapped for grain calcium concentration (GCaC), whereas two QTLs each for grain potassium concentration (GKC), GFeC, grain copper concentration (GCuC), and grain nickel concentration (GNiC) were mapped on different chromosomes. Four QTLs were mapped each for GZnC, grain manganese concentration (GMnC), and grain molybdenum concentration (GMoC), while the highest five were linked to grain barium concentration (GBaC). In silico analysis of these chromosomal regions identified putative candidate genes that code for 30 different types of proteins, which play roles in many important biochemical or physiological processes. Putative candidate gene magnesium transporter MRS2-G linked to GFeC and probable histone-arginine methyltransferase CARM1 and ABC transporter C family were found to be linked to GZnC. These QTLs can be utilized to generate cultivars adapted to climate change by marker-assisted gene/QTL transfer.

Genome-Wide Analysis of Copy Number Variations in Three Populations of Nanyang Cattle Using Whole-Genome Resequencing

Copy number variation (CNV) serves as a crucial contributor to genetic diversity, exerting a profound influence on phenotypic diversity, traits of economic significance, and the evolutionary trajectory of livestock species. This study aimed to dissect the genome-wide CNV landscape of the Nanyang cattle line (Nanyang, Pinnan, and Xianan cattle) to identify functionally relevant CNVs associated with key economic traits and breed differentiation. In this study, 27 resequencing datasets were utilized to analyze the genome-wide distribution of CNVs in three breeds of Nanyang cattle (Nanyang cattle, Pinnan cattle, and Xianan cattle) based on the latest reference genome ARS-UCD2.0. This study identified a total of 97,564 CNVs, and after merging CNVs with overlapping genomic positions, we obtained 10,349 CNV regions (CNVRs), accounting for 1.48% of the reference genome. Functional enrichment analysis showed that CNVR genes were mainly involved in organ development, neural regulation, immune regulation, and metabolism. In addition, 131 CNVRs overlapped with 81 quantitative trait loci (QTLs), such as growth and carcass QTL, multiple birth QTL, tenderness score QTL, and antal follicle number QTL. Additionally, AOX1, KRT72, and ZBTB7C were found to overlap with body weight QTLs. Furthermore, a selective sweep analysis of CNVR revealed that numerous genes (KIF26A, SPINT4, OR5W1, etc.) exhibited divergent copy numbers between breeds. Conclusively, this study facilitates comprehension of the genetic characteristics of the Nanyang cattle line at the CNV level and furnishes valuable information for the advancement of the Nanyang cattle line breeding system.

Major Facilitator Superfamily (MFS) transporters balance sugar metabolism in peach.

Sugar content is a key determinant of peach (Prunus persica) fruit quality, influencing taste, consumer preferences, and market value. However, the roles of Major Facilitator Superfamily (MFS) transporters in sugar metabolism and regulation remain largely unexplored. This study employed a combination of spatial metabolomics, quantitative genetics, transcriptomics, comparative genomics, and functional genomics to investigate the role of 67 MFS members in balancing sugar metabolism during peach fruit development. Spatial metabolomics revealed dynamic sugar distribution patterns, with ERD6-like transporters (PpERDL16-1) and tonoplastic sugar transporters 1 (PpTST1) promoting sucrose accumulation and Polyol/monosaccharide transporters 5 (PpPMT5-1) and sucrose transporters 4 (PpSUT4) reducing sucrose transport during fruit ripening. Functional studies confirmed these roles: PpERDL16-1 overexpression enhanced sucrose transport, and PpPMT5-1 or PpSUT4 silencing reduced sugar levels in peach fruit. Quantitative trait locus (QTL) mapping identified a major locus on chromosome 5, upstream of PpTST1, forming distinct haplotypes (Hap1 and Hap2). Hap1 was associated with lower PpTST1 expression and higher sugar and soluble solids content (SSC), while Hap2 was linked to higher PpTST1 expression and lower sugar content. This inverse relationship suggests that upstream genetic variants fine-tune PpTST1 expression in a context-dependent manner, potentially through interactions with transcription factors or epigenetic modifiers. Notably, PpTST1 overexpression increased sugar content but did not alter SSC, indicating compensatory mechanisms such as changes in organic acid metabolism or water content. These results illuminate the molecular mechanisms regulating sugar homeostasis in peach fruits, providing valuable targets for the genetic improvement of fruit quality through breeding programs.

The Functional Impact of the Noncoding SNP rs3741442 on Orofacial Clefting.

Orofacial cleft (OFC) is a common congenital anomaly in humans with variable birth prevalence in different ethnic groups. Although genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with nonsyndromic OFC (nsOFC), understanding of the underlying biological mechanisms of causative SNPs and genes in nsOFC remains limited. Here, we report that the noncoding SNP, rs3741442, has an expression quantitative trait locus (eQTL) effect on epithelial genes associated with periderm differentiation. Using a combination of epigenetic markers and in silico analysis, we prioritized the intergenic SNP rs3741442 as a potential causal factor in nsOFC. The risk allele of rs3741442 is prevalent in East Asian populations, and its presence in CRISPR-edited cells leads to reduced expression of neighboring KRT18 and EIF4B. The transcription factor SP1 differentially binds the risk versus nonrisk alleles of rs3741442. Alongside this cis-eQTL impact, rs3741442 has a trans-eQTL effect on the epithelial gene TP63 that is associated with syndromic forms of OFC and psoriasis. These findings provide insights into the mechanism by which an intergenic SNP can affect palatogenesis through the modulation of gene expression.

Identification and validation of a major quantitative trait locus for precise control of heading date in wheat (Triticum aestivum L.)

BackgroundHeading date (HD) is a crucial agronomic trait in wheat, significantly influencing both adaptation and yield. Despite having identical genotypes for the major heading genes Vrn-1 and Ppd-1, two Korean wheat cultivars, Jokyoung and Joongmo2008, exhibit substantial differences in heading date. However, the underlying genetic factors responsible for this variation remain unclear. To address this, we aimed to identify major quantitative trait loci (QTLs) associated with narrow-sense earliness under field conditions and develop a practical molecular marker for wheat breeding programs.ResultsA recombinant inbred line (RIL) population was developed from a cross between the late-heading Jokyoung and the early-heading Joongmo2008 using speed breeding systems. The RILs were genotyped using a 35 K SNP chip, and a genetic map was constructed. A stable QTL for HD (qDH-3A) was identified on chromosome 3A, with an average logarithm of the odds (LOD) score of 59.4, explaining 72.6% of the phenotypic variance in HD across three years of field phenotyping. This indicates the robustness of qDH-3 A across multiple environments. Additionally, a kompetitive allele-specific PCR (KASP) marker linked to qDH-3A was developed and validated. The marker showed significant genotypic differences and effectiveness across diverse genetic backgrounds, including 616 worldwide wheat accessions.ConclusionsThe successful application of the KASP marker in both the RIL population and broader genetic resources highlights its potential use for marker-assisted selection (MAS) in wheat breeding programs. This study provides valuable insights into the genetic basis of HD in wheat and offers practical tools for developing cultivars better adapted to specific environmental conditions.

FST-Based Marker Prioritization Within Quantitative Trait Loci Regions and Its Impact on Genomic Selection Accuracy: Insights from a Simulation Study with High-Density Marker Panels for Bovines

Background/Objectives: Genomic selection (GS) has improved accuracy compared to traditional methods. However, accuracy tends to plateau beyond a certain marker density. Prioritizing influential SNPs could further enhance the accuracy of GS. The fixation index (FST) allows for the identification of SNPs under selection pressure. Although the FST method was shown to be able to prioritize SNPs across the whole genome and to increase accuracy, its performance could be further improved by focusing on the prioritization process within QTL regions. Methods: A trait with heritability of 0.1 and 0.4 was generated under different simulation scenarios (number of QTL, size of SNP windows around QTL, and number of selected SNPs within a QTL region). In total, six simulation scenarios were analyzed. Each scenario was replicated five times. The population comprised 30K animals from the last 2 generations (G9–G10) of a 10-generation (G1–G10) selection process. All animals in G9–10 were genotyped with a 600K SNP panel. FST scores were calculated for all 600K SNPs. Two prioritization scenarios were used: (1) selecting the top 1% SNPs with the highest FST scores, and (2) selecting a predetermined number of SNPs within each QTL window. GS accuracy was evaluated using the correlation between true and estimated breeding values for 5000 randomly selected animals from G10. Results: Prioritizing SNPs using FST scores within QTL window regions increased accuracy by 5 to 18%, with the 50-SNP windows showing the best performance. Conclusions: The increase in GS accuracy warrants the testing of the algorithm when the number and position of QTL are unknown.

QTL mapping of flowering time in Brassica napus: a study on the interplay between temperature and day length after vernalization

Flowering is a critical life stage for plants, and the regulation of flowering is heavily influenced by environmental factors and is genetically very complex. In oilseed rape (Brassica napus L.), a major oil crop, yield is heavily dependent on successful flowering. Until now, the influences of day length and temperature on flowering time have mostly been studied in spring-type rape, although they also affect flowering in winter oilseed rape after vernalization, and changing climate conditions alter springtime temperatures. In this study, a doubled haploid population derived from a cross between a winter and a spring-type oilseed rape was examined for the effect of cool and warm temperatures (11°C and 22°C) in combination with long and short days (8/16-h light) on flowering time after vernalization. Quantitative trait locus (QTL) analysis revealed major QTLs for flowering time in two homologous regions on chromosomes C06 and A07, which were found to interact epistatically. It was found that temperature can either delay or promote flowering depending on day length and genotype, highlighting the complex interplay between these factors. Our study provides new insights into the genetic basis of flowering time regulation in B. napus, especially after vernalization, and highlights the importance of considering the interplay between temperature and day length in breeding programs for this crop, particularly in the context of climate change.

GEO combined with quantitative protein trait loci identify causative proteins in hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a rare genetic heart disease characterized by a limited patient population and scarce research and treatment resources. This study aimed to identify HCM-associated proteins by integrating cardiac tissue data from the Gene Expression Omnibus (GEO) database with the latest protein quantitative trait locus (pQTL) dataset. We analysed data from the GEO database. The GSE36961 dataset included 106 HCM samples and 39 healthy controls. The GSE180313 dataset included 13 HCM samples and 7 healthy controls. pQTL data were obtained from the plasma of 54 000 UK Biobank participants, covering 1463 proteins. HCM genome-wide association study (GWAS) data were sourced from the FinnGen study, which included 1125 HCM cases and 411 056 controls. We analysed the GEO dataset of cardiac tissue from HCM patients to identify differentially expressed genes (DEGs). These DEGs were compared with pQTL data to identify protein phenotypes suitable for Mendelian randomization (MR) analysis. A two-sample MR analysis was performed to assess the causal association between these protein phenotypes and HCM. The robustness of the study results was further assessed through sensitivity analysis of heterogeneity and horizontal pleiotropy tests. Two proteins were identified as causally associated with HCM risk: carbonic anhydrase 3 (CA3) [inverse variance weighted (IVW): odds ratio (OR)=1.292, 95% confidence interval (CI)=1.021-1.636, P=0.033] and serpin family E member 1 (SERPINE1) [IVW: OR=1.313, 95% CI=1.063-1.621, P=0.011]. Both proteins were associated with increased HCM risk, with no significant heterogeneity (P>0.05) or evidence of horizontal pleiotropy (P>0.05). CA3 and SERPINE1 proteins may exert causal effects on HCM and may serve as characteristic markers and therapeutic targets for this condition.

Potential key genes for giant cell arteritis revealed based on single-cell sequencing and Mendelian randomization analysis.

Giant cell arteritis (GCA) is an autoimmune disease affecting medium and large arteries. It varies in presentation and often recurs, potentially leading to blindness and aneurysms. The pathogenesis of GCA is not well understood. This study aims to identify key genes linked to GCA and explore potential pathogenic mechanisms. This study integrated single-cell RNA sequencing, expression quantitative trait loci, and genome-wide association study data, employing a two-sample Mendelian randomization (MR) method to explore the causal effects of marker genes in CD4+ T cells on the development of GCA. Additionally, colocalization analysis was conducted to determine whether there was a shared causal variant. Through single-cell RNA sequencing and MR analysis, we identified three key genes, RCAN3, RPS6, and HLA-DQB1, that had a causal relationship with a reduced risk of GCA. Specifically, RCAN3 (OR = 0.49, 95% CI = 0.26-0.93, p = 0.03), RPS6 (OR = 0.21, 95% CI = 0.06-0.73, p = 0.01), and HLA-DQB1 (OR = 0.76, 95% CI = 0.62-0.93, p = 0.01) were inversely associated with the disease. Multiple sensitivity analysis methods showed no heterogeneity and pleiotropy, and ruled out potential reverse causality, demonstrating the robustness of MR analysis results. Colocalization analysis revealed that HLA-DQB1 and GCA were related to SNPs within the same genomic region but involved different causal variants. This study identified three potential key genes (RCAN3, RPS6, HLA-DQB1) linked to the causality of GCA, providing new perspectives on the pathogenesis of GCA and new avenues for therapeutic strategies.

Plasma proteins and different onset subtype of COPD: Proteome-wide Mendelian randomization study and co-localization analyses.

Several studies have reported a strong association between plasma proteins and chronic obstructive pulmonary disease (COPD). However, the directionality and causality of the association and whether proteins effected COPD remain unclear. Therefore, we used Proteome-wide Mendelian randomization (MR) study and co-localization analyses to estimate the casual relationship between them. Summary-level data of 2923 plasma protein levels were extracted from a large-scale protein quantitative trait loci study including 54,219 individuals by the UK Biobank Pharma Proteomics Project. The outcome data for COPD and its subtypes were sourced from the FinnGen study. MR analysis was conducted to estimate the associations between protein and COPD and its subtypes risk. Additionally, phenome-wide MR analysis, and candidate drug prediction were employed to identify potential causal circulating proteins and novel drug targets. STROBE MR guidelines are followed for the study. We assessed the effect of 1929 plasma proteins on COPD. We found that Seven proteins, 4 proteins, and 3 proteins were associated with overall COPD, early-onset COPD, and later-onset COPD risk, respectively. MHC class I polypeptide-related sequence B_A (MICB_MICA) and tyrosine-protein kinase receptor tie-1 (TIE-1) would increase 8% and 27% COPD risk (MICB_MICA: odds ratios [OR], 1.08; 95% CI, 1.05-1.10; PFDR = 2.53 × 10-5; TIE-1: OR, 1.27; 95% CI, 1.13-1.43; PFDR = .012). There was negative association of Septin-8 and Butyrophilin subfamily 1 member A1 (BTN1A1) with overall COPD risk (Septin-8: OR, 0.68; 95% CI, 0.57-0.79; PFDR = 8.00 × 10-4 BTN1A1: OR, 0.82; 95% CI, 0.75-0.90; PFDR = .010). There was a protective effect of BTN1A1 on early COPD incidence (OR, 0.72; 95% CI, 0.63-0.83; PFDR = .002). However, there was no evidence indicating a shared causal variant between the other proteins and COPD and its subtypes in these regions (all posterior probability.H4 < .8). The study revealed the causal relationship between several plasma proteins and COPD and its subtypes, providing new theoretical support for understanding COPD.

Identification of potential therapeutic targeting in ovarian aging from genetic screening with clinical validation.

To screen drug targets of ovarian aging from agenetic perspective. Systematic analyses were conducted with cis-expression quantitative trait loci data of druggable genes extracted as instrument variables. Summary statistics were from large genome-wide association studies for age at menopause. The following colocalization analysis was utilized to examine whether identified genes and ovarian aging shared causal variants. Furthermore, clinical validation was conducted by comparing expression of identified genes in granulosa cells from women with normal or diminished ovarian reserve (DOR) who went through in vitro fertilization (IVF) and by evaluating correlation of targeted gene expression with ovarian function and IVF outcomes. Moreover, single-nuclear RNA (snRNA) seq and drug database were analyzed to find target cells within the ovary and potential drugs targeting identified genes. Systematic analyses identified five therapeutic targets of ovarian aging, including four protective factors (BRCA1, KLHL18, PNP, SRPK1) and one risk factor (PDIA3). The change in expression level of four protective factors has been verified in clinical validation. Particularly, both BRCA1 and SRPK1 have been downregulated among advanced-aged women with DOR and were positively correlated with anti-Müllerian hormone and antral follicle count. Specific target cells and potential small molecule targeted drugs of these genes were identified through snRNA analysis and searching in the drug database. By systematic genetic analyses combined with clinical validation, we identified five potential druggable genes for ovarian aging, providing theoretical basis and promising direction of therapeutic genetic targets for ovarian aging in the future.

Identification of Alzheimer’s disease susceptibility genes by the integration of genomics and transcriptomics

ABSTRACT Background Alzheimer’s disease (AD) is a progressive neurodegenerative disease. With the deepening of clinical and genomic research, a series of biomarkers and risk factors related to AD have been identified. However, the exact molecular mechanism of AD is not completely understood. Methods By combining expression quantitative trait loci (eQTLs) analysis with the results of genome-wide association studies (GWAS), the candidate genes (CG) related to AD were screened out accurately. We identified that intersection genes of differentially expressed genes (DEGs) and CG are the key genes. Then, GO, KEGG, and GSEA were utilized for functional enrichment analysis. Finally, we predicted AD responses to immunotherapy by the single sample gene set enrichment analysis (ssGSEA). Results A total of 253 DEGs were identified. The three key genes (VASP, SURF2, and TARBP1) were identified by taking the intersection of DEGs and CG. Through Mendelian randomization (MR) analysis, it was found that the risk of AD was significantly increased when VASP expression increased (OR = 0.1.046), while the risk of AD was significantly decreased when SURF2 (OR = 0.897) and TARBP1(OR = 0.920) expression increased. Subsequently, the functional analysis indicated that the core genes were mainly enriched in Leukocyte Transendothelial Migration, cGMP-PKG signaling pathway, and Rap1 signaling pathway. Through ssGSEA analysis showed that all three core genes were significantly related to M2 macrophages. Conclusions Three core genes were screened by integrating eQTLs data, GWAS data and transfer group data, and the potential mechanism of diagnosis and treatment of AD was revealed.

Human genetic variation determines 24-hour rhythmic gene expression and disease risk

24-hour biological rhythms are essential to maintain physiological homeostasis. Disruption of these rhythms increases the risks of multiple diseases. Biological rhythms are known to have a genetic basis formed by core clock genes, but how individual genetic variation shapes the oscillating transcriptome and contributes to human chronophysiology and disease risk is largely unknown. Here, we mapped interactions between temporal gene expression and genotype to identify quantitative trait loci (QTLs) contributing to rhythmic gene expression. These newly identified QTLs were termed as rhythmic QTLs (rhyQTLs), which determine previously unappreciated rhythmic genes in human subpopulations with specific genotypes. Functionally, rhyQTLs and their associated rhythmic genes contribute extensively to essential chronophysiological processes, including bile acid and lipid metabolism. The identification of rhyQTLs sheds light on the genetic mechanisms of gene rhythmicity, offers mechanistic insights into variations in human disease risk, and enables precision chronotherapeutic approaches for patients.

Future flooding tolerant rice germplasm: Resilience afforded beyond Sub1A gene.

Developing high-yielding, flood-tolerant rice (Oryza sativa L.) varieties is essential for enhancing productivity and livelihoods in flood-prone ecologies. We explored genetic avenues beyond the well-known SUB1A gene to improve flood resilience in rice. We screened a collection of 6274 elite genotypes from IRRI's germplasm repository for submergence and stagnant flooding tolerance over multiple seasons and years. This rigorous screening identified 89 outstanding elite genotypes, among which 37 exhibited high submergence tolerance, surpassing the survival rate of SUB1A introgression genotypes by 40%-50%. Thirty-five genotypes showed significant tolerance to stagnant flooding, and 17 demonstrated dual tolerance capabilities, highlighting their adaptability to varying flood conditions. The genotypes identified have a broader genetic diversity and harbor 86 key quantitative trait loci (QTLs) and genes related to traits such as grain quality, grain yield, herbicide resistance, and various biotic and abiotic traits, highlighting the richness of the identified elite collection. Besides germplasm, we introduce an innovative breeding approach called "Transition from Trait to Environment" (TTE). TTE leverages a parental pool of high-performing genotypes with complete submergence tolerance to drive population improvement and enable genomic selection in the flood breeding program. Our approach of TTE achieved a remarkable 65% increase in genetic gain for submergence tolerance, with the resulting fixed breeding genotypes demonstrating exceptional performance in flood-prone environments of India and Bangladesh. The elite genotypes identified herein represent invaluable genetic resources for the global rice research community. By adopting the TTE approach, which is trait agonistic, we establish a robust framework for developing more resilient genotypes using advanced breeding tools.