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Objectives: This study aims to use Mendelian randomisation to identify the causal relationship between a spectrum of 41 inflammatory cytokines and the development of oropharyngeal cancer. Methods: This study investigated genetic variants that have been associated with oral and oropharyngeal cancer using data from a large GWAS. Inflammatory cytokine data were obtained from 8293 asymptomatic individuals. The study primarily used inverse variance weighted and MR-Egger methods to determine the causal relationship between inflammatory cytokines and cancer incidence, complemented by a series of sensitivity analyses including MR-Egger, simple mode, weighted mode, weighted median and leave-one-out approaches. Results: Our study demonstrates that higher levels of interleukin-7 (IL-7) and interleukin-5 (IL-5) slightly increase the odds of oropharyngeal cancer by 0.07% [OR: 1.0007, p = 0.005] and 0.04% [OR: 1.0004, p = 0.015], corresponding to a modest increase. Similarly, increased PDGF-bb and CTACK levels are modestly associated with increased odds of oral and oropharyngeal cancer by 0.22% [OR: 1.0022, p = 0.031] and 0.17% [OR: 1.0017, p = 0.043], respectively. Conclusion: This investigation posits that IL-5 and IL-7 may be pertinent factors in the etiology of Oropharyngeal cancer, while PDGF bb and CTACK are likely implicated in the pathogenesis of both oral and oropharyngeal cancers. [ABSTRACT FROM AUTHOR]

Summary: Harnessing the plant microbiome through plant genetics is of increasing interest to those seeking to improve plant nutrition and health. While genome‐wide association studies (GWAS) have been conducted to identify plant genes driving the plant microbiome, more multidisciplinary studies are required to assess the relationships among plant genetics, plant microbiome and plant fitness.Using a metabarcoding approach, we characterized the rhizosphere bacterial communities of a core collection of 155 Medicago truncatula genotypes along with the plant phenotype and investigated the plant genetic effects through GWAS.The different genotypes within the M. truncatula core collection showed contrasting growth and nutritional strategies but few loci were associated with these ecophysiological traits. To go further, we described its associated rhizosphere bacterial communities, dominated by Proteobacteria, Actinobacteria and Bacteroidetes, and defined a core rhizosphere bacterial community. Next, the occurrences of bacterial candidates predicting plant ecophysiological traits of interest were identified using random forest analyses. Some of them were heritable and plant loci were identified, pinpointing genes related to response to hormone stimulus, systemic acquired resistance, response to stress, nutrient starvation or transport, and root development.Together, these results suggest that plant genetics can affect plant growth and nutritional strategies by harnessing keystone bacteria in a well‐connected interaction network. [ABSTRACT FROM AUTHOR]

Genomic studies of diseases can be divided into two types: i) analyses that reveal causal genes by focusing on linkage disequilibrium between observed and causal variants and ii) those that simultaneously assess numerous genetic markers to estimate the polygenic effects of a particular genomic region or entire genome. The field of human genetics has emphasized the discovery of causal genes, but these represent only a fraction of the total genetic variance. Therefore, alternative approaches, such as the polygenic risk score, which estimates the genetic risk for a given trait or disease based on all genetic markers (rather than on known causal variants only), have begun to garner attention. In many respects, these human genetic methods are similar to those originally developed for the estimation of breeding values (i.e., total additive genetic effects) in livestock. However, despite these similarities in methods, the fields of human and animal genetics still differ markedly in terms of research objectives, target populations, and other characteristics. For example, livestock populations have continually been selected and inbred throughout their history; consequently, their effective population size has shrunk and preferred genes (such as those influencing disease resistance and production traits) have accumulated in the modern breeding populations. By examining the characteristics of these two fields, particularly from the perspectives of disease and disease resistance, this review aims to improve understanding of the intrinsic differences between genomic studies using human compared with livestock populations.

Peanut, also known as groundnut (Arachis hypogaea L.), is an important oilseed and food crop globally, contributing significantly to the economy and food security. However, its productivity is often hampered by pests and diseases. Traditional breeding methods have been used to develop resistant cultivars, but these are often time-consuming and labor-intensive. Recent technological advancements have revolutionized the identification of novel resistance sources and the development of resistant peanut cultivars. This review explores the latest techniques and approaches used in peanut breeding for pest and disease resistance, focusing on the identification of resistance loci and their incorporation into peanut using marker-assisted selection (MAS) and genomic tools. Next-generation sequencing (NGS) technologies, bioinformatics pipelines, comparative genomics, and transcriptomics have helped identify a plethora of candidate genes involved in pest resistance. However, peanut lags behind other cereal crops in terms of phenomics and precision genetic techniques for their functional validation. In conclusion, recent technological advancements have significantly improved the efficiency and precision of peanut breeding for pest and disease resistance and hold great promise for developing durable and sustainable resistance in peanut cultivars, ultimately benefiting peanut farmers and consumers globally. [ABSTRACT FROM AUTHOR]

Background: The relationship between different types of autoimmune diseases and myelodysplastic syndrome (MDS) is inconclusive. Therefore, we employed Mendelian randomization (MR) to examine whether genetically predicted susceptibility to ten autoimmune diseases is associated with the risk of MDS. Methods: Single nucleotide polymorphisms (SNPs) significantly associated with 10 autoimmune diseases were extracted from the summary statistics of European genome-wide association studies (GWAS). A two-sample MR analysis was performed using summary-level statistics sourced from GWAS datasets. Inverse-variance weighting (IVW), MR–Egger, and weighted median (WM) were further supported by several sensitivity analyses. Results: Four autoimmune diseases showed genetical predisposition to MDS: rheumatoid arthritis (OR = 1.186,95% CI = 1.028-1.367, P = 0.019), multiple sclerosis (OR = 1.247, 95% CI = 1.013-1.534, P = 0.037), myasthenia gravis (OR = 1.326,95% CI = 1.010-1.742, P = 0.042), and Hashimoto thyroiditis(OR = 1.519,95% CI = 1.008-2.290, P = 0.046). Nevertheless, no similar causal relationship was found between the remaining seven autoimmune diseases and MDS. The accuracy and robustness of these findings were confirmed by sensitivity tests. Conclusions: We are the first to use MR analysis to explore the relationship between autoimmune diseases and MDS. The mechanism needs to be further explored. PLAIN LANGUAGE SUMMARY: We employed Mendelian randomization to explore the associations between autoimmune diseases and myelodysplastic syndrome. We found that rheumatoid arthritis, multiple sclerosis, myasthenia gravis and Hashimoto thyroiditis showed a genetical predisposition to myelodysplastic syndrome. The mechanism needs to be further explored. [ABSTRACT FROM AUTHOR]

The everyday harms of structural racism and discrimination, perpetuated through institutions, laws, policies, and practices, constitute social determinants of health, but measures that account for their debilitating effects are largely missing in genetic studies of complex diseases. Drawing on insights from the social sciences and public health, we propose critical methodologies for incorporating tools that measure structural racism and discrimination within genetic analyses. We illustrate how including these measures may strengthen the accuracy and utility of findings for diverse communities, clarify elusive relationships between genetics and environment in a racialized society, and support greater equity within genomics and precision health research. This approach may also support efforts to build and sustain vital partnerships with communities and with other fields of research inquiry, centering community expertise and lived experiences and drawing on valuable knowledge from practitioners in the social sciences and public health to innovate biomedical and genomic study designs aimed at community health priorities. [ABSTRACT FROM AUTHOR]

Genome-wide association Studies (GWAS), initially developed for human genetics, have been highly effective in plant research, particularly for vegetable crops. GWAS is a robust tool for identifying genes associated with key traits such as yield, nutritional value, disease resistance, adaptability, and bioactive compound biosynthesis. Unlike traditional methods, GWAS does not require prior biological knowledge and can accurately pinpoint loci, minimizing false positives. The process involves developing a diverse panel, rigorous phenotyping and genotyping, and sophisticated statistical analysis using various models and software tools. By scanning the entire genome, GWAS identifies specific loci or single nucleotide polymorphisms (SNPs) linked to target traits. When a causal SNP variant is not directly genotyped, GWAS identifies SNPs in linkage disequilibrium (LD) with the causal variant, mapping the genetic interval. The method begins with careful panel selection, phenotyping, and genotyping, controlling for environmental effects and utilizing Best Linear Unbiased Prediction (BLUP). High-correlation, high-heritability traits are prioritized. Various genotyping methods address confounders like population structure and kinship. Bonferroni correction (BC) prevents false positives, and significant associations are shown in Manhattan plots. Candidate genes are identified through LD analysis and fine mapping, followed by functional validation. GWAS offers critical insights for enhancing vegetable crop breeding efficiency and precision, driving breakthroughs through advanced methods. [ABSTRACT FROM AUTHOR]

The expansive collection of genetic and phenotypic data within biobanks offers an unprecedented opportunity for biomedical research. However, the frequent occurrence of missing phenotypes presents a significant barrier to fully leveraging this potential. In our target application, on one hand, we have only a small and complete dataset with both genotypes and phenotypes to build a genetic prediction model, commonly called a polygenic (risk) score (PGS or PRS); on the other hand, we have a large dataset of genotypes (e.g. from a biobank) without the phenotype of interest. Our goal is to leverage the large dataset of genotypes (but without the phenotype) and a separate genome-wide association studies summary dataset of the phenotype to impute the phenotypes, which are then used as an individual-level dataset, along with the small complete dataset, to build a nonlinear model as PGS. More specifically, we trained some nonlinear models to 7 imputed and observed phenotypes from the UK Biobank data. We then trained an ensemble model to integrate these models for each trait, resulting in higher R 2 values in prediction than using only the small complete (observed) dataset. Additionally, for 2 of the 7 traits, we observed that the nonlinear model trained with the imputed traits had higher R 2 than using the imputed traits directly as the PGS, while for the remaining 5 traits, no improvement was found. These findings demonstrate the potential of leveraging existing genetic data and accounting for nonlinear genetic relationships to improve prediction accuracy for some traits. [ABSTRACT FROM AUTHOR]

Background: Observational studies have indicated that peripheral blood eosinophil count is elevated in individuals diagnosed with ulcerative colitis (UC) and correlates with the disease activity of UC. However, this conclusion contradicts with findings from other studies. Therefore, we employed Mendelian randomization (MR) method to assess the genetic link between eosinophil count and UC. Method: This MR study utilized summary data from genome‐wide association studies (GWAS) on eosinophil count and UC. The main approach used for conducting MR analysis was the inverse variance weighted (IVW) method. Meta‐analysis of the IVW results was performed alongside multiple sensitivity analyses to confirm the robustness of the MR analysis results. Results: The IVW method unveiled a causal relationship between eosinophil count and UC (OR = 1.18, 95% CI: 1.04–1.33, p =.01) in the discovery cohort. This finding was further corroborated by the replication cohorts (OR = 1.16, 95% CI: 1.04–1.29, p =.01; OR = 1.12, 95% CI: 1.01–1.24, p =.03). The meta‐analysis indicated that the overall odds ratio (OR) for all studies was 1.15 (common effect model, 95% CI: 1.08–1.23, p <.01). Sensitivity analysis suggested the absence of heterogeneity and horizontal pleiotropy in all MR analyses. Conclusion: Based on bidirectional two‐sample MR analysis, there is an indication that elevated eosinophil count may increase the risk of UC. However, potential confounding factors cannot be ruled out, and further research is necessary to explore how eosinophils contribute to the onset and progression of UC. [ABSTRACT FROM AUTHOR]

Abstract This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of $$\:{\text{h}}^{2}$$ and $$\:{\text{h}}_{\text{S}\text{N}\text{P}}^{2}$$ . A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D.

Stem rust, caused by the biotrophic fungal pathogen Puccinia graminis f. sp. tritici (Pgt), is an important disease of wheat. However, the majority of Pgt virulence/avirulence loci and underlying genes remain uncharacterized due to the constraints of developing bi-parental populations with this obligate biotroph. Genome-wide association studies (GWAS) using a sexual Pgt population mainly collected from the Pacific Northwestern United States were used to identify candidate virulence/avirulence effector genes corresponding to the six wheat Sr genes: Sr5, Sr21, Sr8a, Sr17, Sr9a, and Sr9d. The Pgt isolates were genotyped using whole-genome shotgun sequencing that identified approximately 1.2 million single nucleotide polymorphisms (SNPs) and were phenotyped at the seedling stage on six Sr gene differential lines. Association mapping analyses identified 17 Pgt loci associated with virulence or avirulence phenotypes on six Pgt resistance genes. Among these loci, 16 interacted with a specific Sr gene, indicating Sr-gene specific interactions. However, one avirulence locus interacted with two separate Sr genes (Sr9a and Sr17), suggesting two distinct Sr genes identifying a single avirulence effector. A total of 24 unique effector gene candidates were identified, and haplotype analysis suggests that within this population, AvrSr5, AvrSr21, AvrSr8a, AvrSr17, and AvrSr9a are dominant avirulence genes, while avrSr9d is a dominant virulence gene. The putative effector genes will be fundamental for future effector gene cloning efforts, allowing for further understanding of rust effector biology and the mechanisms underlying virulence evolution in Pgt with respect to race-specific R-genes. [Figure: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Abstract To sustainably increase wheat yield to meet the growing world population’s food demand in the face of climate change, Conservation Agriculture (CA) is a promising approach. Still, there is a lack of genomic studies investigating the genetic basis of crop adaptation to CA. To dissect the genetic architecture of 19 morpho-physiological traits that could be involved in the enhanced adaptation and performance of genotypes under CA, we performed GWAS to identify MTAs under four contrasting production regimes viz., conventional tillage timely sown (CTTS), conservation agriculture timely sown (CATS), conventional tillage late sown (CTLS) and conservation agriculture late sown (CALS) using an association panel of 183 advanced wheat breeding lines along with 5 checks. Traits like Phi2 (Quantum yield of photosystem II; CATS:0.37, CALS: 0.31), RC (Relative chlorophyll content; CATS:55.51, CALS: 54.47) and PS1 (Active photosystem I centers; CATS:2.45, CALS: 2.23) have higher mean values in CA compared to CT under both sowing times. GWAS identified 80 MTAs for the studied traits across four production environments. The phenotypic variation explained (PVE) by these QTNs ranged from 2.15 to 40.22%. Gene annotation provided highly informative SNPs associated with Phi2, NPQ (Quantum yield of non-photochemical quenching), PS1, and RC which were linked with genes that play crucial roles in the physiological adaptation under both CA and CT. A highly significant SNP AX94651261 (9.43% PVE) was identified to be associated with Phi2, while two SNP markers AX94730536 (30.90% PVE) and AX94683305 (16.99% PVE) were associated with NPQ. Identified QTNs upon validation can be used in marker-assisted breeding programs to develop CA adaptive genotypes.

Linear mixed models (LMMs) are a commonly used method for genome-wide association studies (GWAS) that aim to detect associations between genetic markers and phenotypic measurements in a population of individuals while accounting for population structure and cryptic relatedness. In a standard GWAS, hundreds of thousands to millions of statistical tests are performed, requiring control for multiple hypothesis testing. Typically, static corrections that penalize the number of tests performed are used to control for the family-wise error rate, which is the probability of making at least one false positive. However, it has been shown that in practice this threshold is too conservative for normally distributed phenotypes and not stringent enough for non-normally distributed phenotypes. Therefore, permutation-based LMM approaches have recently been proposed to provide a more realistic threshold that takes phenotypic distributions into account. In this work, we discuss the advantages of permutation-based GWAS approaches, including new simulations and results from a re-analysis of all publicly available Arabidopsis phenotypes from the AraPheno database. [ABSTRACT FROM AUTHOR]

This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of and . A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D. [ABSTRACT FROM AUTHOR]

Introduction: Ulcerative colitis (UC) and diabetic kidney disease (DKD) are chronic disorders with multifaceted pathogenesis, posing significant challenges in clinical management. While substantial efforts have been made to investigate the individual causes of these diseases, the interplay between UC and DKD is not well understood. This study aims to elucidate the genetic association between UC and DKD through Mendelian randomization (MR) analysis, offering new insights into common biological pathways and potential clinical implications. Methods: We conducted a bidirectional two-sample MR study utilizing data from large-scale genome-wide association studies (GWAS) for both UC and DKD. Instrumental variables (IVs) were meticulously selected according to genomewide significance and stringent statistical criteria, ensuring robust causal inference. Various MR methodologies, including inverse variance weighting (IVW), were employed to assess the causal relationships between UC and DKD. Sensitivity analyses were also performed to validate the robustness of our findings. Results: Our analysis revealed a significant causal relationship between genetic predisposition to UC and increased susceptibility to DKD. Specifically, individuals with a genetic susceptibility to UC exhibited a 17.3% higher risk of developing DKD. However, we found no evidence of a causal link between DKD and the risk of developing UC. Additionally, we identified shared genetic risk factors andmolecular pathways linking UC and DKD, thereby highlighting potential therapeutic targets. Discussion: This study underscores the intricate genetic interplay between UC and DKD, suggesting that individuals with UC may be at an elevated risk for developing DKD. Understanding these shared genetic pathways could facilitate the development of early detection strategies and targeted interventions for individuals at risk of DKD. Ultimately, these insights could lead to improved clinical outcomes for patients suffering from both conditions. [ABSTRACT FROM AUTHOR]

(1) Background: Alzheimer's disease (AD) is a worldwide neurodegenerative disorder characterized by the buildup of abnormal proteins in the central nervous system and cognitive decline. Since no radical therapy exists, only symptomatic treatments alleviate symptoms temporarily. In this review, we will explore the latest advancements in precision medicine and biomarkers for AD, including their potential to revolutionize the way we diagnose and treat this devastating condition. (2) Methods: A literature search was performed combining the following Medical Subject Heading (MeSH) terms on PubMed: "Alzheimer's disease", "biomarkers", "APOE", "APP", "GWAS", "cerebrospinal fluid", "polygenic risk score", "Aβ42", "τP-181", " p-tau217", "ptau231", "proteomics", "total tau protein", and "precision medicine" using Boolean operators. (3) Results: Genome-wide association studies (GWAS) have identified numerous genetic variants associated with AD risk, while a transcriptomic analysis has revealed dysregulated gene expression patterns in the brains of individuals with AD. The proteomic and metabolomic profiling of biological fluids, such as blood, urine, and CSF, and neuroimaging biomarkers have also yielded potential biomarkers of AD that could be used for the early diagnosis and monitoring of disease progression. (4) Conclusion: By leveraging a combination of the above biomarkers, novel ultrasensitive immunoassays, mass spectrometry methods, and metabolomics, researchers are making significant strides towards personalized healthcare for individuals with AD. [ABSTRACT FROM AUTHOR]

To sustainably increase wheat yield to meet the growing world population's food demand in the face of climate change, Conservation Agriculture (CA) is a promising approach. Still, there is a lack of genomic studies investigating the genetic basis of crop adaptation to CA. To dissect the genetic architecture of 19 morpho-physiological traits that could be involved in the enhanced adaptation and performance of genotypes under CA, we performed GWAS to identify MTAs under four contrasting production regimes viz., conventional tillage timely sown (CTTS), conservation agriculture timely sown (CATS), conventional tillage late sown (CTLS) and conservation agriculture late sown (CALS) using an association panel of 183 advanced wheat breeding lines along with 5 checks. Traits like Phi2 (Quantum yield of photosystem II; CATS:0.37, CALS: 0.31), RC (Relative chlorophyll content; CATS:55.51, CALS: 54.47) and PS1 (Active photosystem I centers; CATS:2.45, CALS: 2.23) have higher mean values in CA compared to CT under both sowing times. GWAS identified 80 MTAs for the studied traits across four production environments. The phenotypic variation explained (PVE) by these QTNs ranged from 2.15 to 40.22%. Gene annotation provided highly informative SNPs associated with Phi2, NPQ (Quantum yield of non-photochemical quenching), PS1, and RC which were linked with genes that play crucial roles in the physiological adaptation under both CA and CT. A highly significant SNP AX94651261 (9.43% PVE) was identified to be associated with Phi2, while two SNP markers AX94730536 (30.90% PVE) and AX94683305 (16.99% PVE) were associated with NPQ. Identified QTNs upon validation can be used in marker-assisted breeding programs to develop CA adaptive genotypes. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is a common neurodegenerative disorder characterized by a multifaceted genetic foundation. Genome-Wide Association Studies (GWAS) have played a crucial role in pinpointing genetic variants linked to PD susceptibility. Current study aims to delve into the mechanistic aspects through which the PD-associated Single Nucleotide Polymorphism (SNP) rs329648, identified in prior GWAS, influences the pathogenesis of PD. Methods and results: Employing the CRISPR/Cas9-mediated genome editing mechanism, we demonstrated the association of the disease-associated allele of rs329648 with increased expression of miR-4697-3p in differentiated SH-SY5Y cells. We revealed that miR-4697-3p contributes to the formation of high molecular weight complexes of α-Synuclein (α-Syn), indicative of α-Syn aggregate formation, as evidenced by Western blot analysis. Furthermore, our study unveiled that miR-4697-3p elevates SNCA112 mRNA levels. The resultant protein product, α-Syn 112, a variant of α-Syn with 112 amino acids, is recognized for augmenting α-Syn aggregation. Notably, this regulatory effect minimally impacts the levels of full-length SNCA140 mRNA, as evidenced by qRT-PCR. Additionally, we observed a correlation between the disease-associated allele and miR-4697-3p with increased cell death, substantiated by assessments including cell viability assays, alterations in cell morphology, and TUNEL assays. Conclusion: Our research reveals that the disease-associated allele of rs329648 is linked to higher levels of miR-4697-3p. This increase in miR-4697-3p leads to elevated SNCA112 mRNA levels, consequently promoting the formation of α-Syn aggregates. Furthermore, miR-4697-3p appears to play a role in increased cell death, potentially contributing to the pathogenesis of PD. [ABSTRACT FROM AUTHOR]

Subclinical ketosis (SCK) is a prevalent metabolic disorder that occurs during the transition to lactation period. It is defined as a high blood concentration of ketone bodies (beta-hydroxybutyric acid f ≥ 1.2 mmol/L) within the first few weeks of lactation, and often presents without clinical signs. SCK is mainly caused by negative energy balance (NEB). The objective of this study is to identify single nucleotide polymorphisms (SNPs) associated with SCK using genome-wide association studies (GWAS), and to predict the biological functions of proximal genes using gene-set enrichment analysis (GSEA). Blood samples were collected from 112 Holstein cows between 5 and 18 days postpartum to determine the incidence of SCK. Genomic DNA extracted from both SCK and healthy cows was examined using the Illumina Bovine SNP50K BeadChip for genotyping. GWAS revealed 194 putative SNPs and 163 genes associated with those SNPs. Additionally, GSEA showed that the genes retrieved by Database for Annotation, Visualization, and Integrated Discovery (DAVID) belonged to calcium signaling, starch and sucrose, immune network, and metabolic pathways. Furthermore, the proximal genes were found to be related to germ cell and early embryo development. In summary, this study proposes several feasible SNPs and genes associated with SCK through GWAS and GSEA. These candidates can be utilized in selective breeding programs to reduce the genetic risk for SCK and subfertility in high-performance dairy cows.

Phosphorus (P) is essential for living plants, and P deficiency is one of the key factors limiting the yield in rapeseed production worldwide. As the most important organ for plants, root morphology traits (RMTs) play a key role in P absorption. To investigate the genetic variability of RMT under low P availability, we dissected the genetic structure of RMTs by genome-wide association studies (GWAS), linkage mapping and candidate gene association studies (CGAS). A total of 52 suggestive loci were associated with RMTs under P stress conditions in 405 oilseed rape accessions. The purple acid phosphatase gene BnPAP17 was found to control the lateral root number (LRN) and root dry weight (RDW) under low P stress. The expression of BnPAP17 was increased in shoot tissue in P-efficient cultivars compared to root tissue and P-inefficient cultivars in response to low P stress. Moreover, the haplotype of BnPAP17Hap3 was detected for the selective breeding of P efficiency in oilseed rape. Over-expression of the BnPAP17Hap3 could promote the shoot and root growth with enhanced tolerance to low P stress and organic phosphorus (Po) utilization in oilseed rape. Collectively, these findings increase our understanding of the mechanisms underlying BnPAP17-mediated low P stress tolerance in oilseed rape.

Aim: Endothelial dysfunction has been associated with both cerebrovascular pathology and Alzheimer’s disease (AD). However, the connection between circulating endothelial cells and the risk of AD remains uncertain. The objective was to leverage data from the Framingham Heart Study to investigate various circulating endothelial subtypes and their potential correlations with the risk of AD. Methods: The study conducted data analyses using Cox proportional hazard regression and linear regression methods. Additionally, genome-wide association study (GWAS) was carried out to further explore the data. Results: Among the eleven distinct circulating endothelial subtypes, only circulating endothelial progenitor cells (EPCs) expressing CD34+CD133+ were found to be negatively and dose-dependently associated with reduced AD risk. This association persisted even after adjusting for age, sex, years of education, apolipoprotein E (APOE) ε4 status, and various vascular diseases. Particularly noteworthy was the significant association observed in individuals with hypertension and cerebral microbleeds. Consistently, positive associations were identified between CD34+CD133+ EPCs and specific brain regions, such as higher proportions of circulating CD34+CD133+ cells correlating with increased volumes of white matter and the hippocampus. Additionally, a GWAS study unveiled that CD34+CD133+ cells influenced AD risk specifically in individuals with homozygous genotypes for variants in two stem cell-related genes: kirre like nephrin family adhesion molecule 3 (KIRREL3, rs580382 CC and rs4144611 TT) and exocyst complex component 6B (EXOC6B, rs61619102 CC). Conclusions: The findings suggest that circulating CD34+CD133+ EPCs possess a protective effect and may offer a new therapeutic avenue for AD, especially in individuals with vascular pathology and those carrying specific genotypes of KIRREL3 and EXOC6B genes.

Alzheimer's Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD's pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted Web-based Cell-type-Specific Enrichment Analysis (WebCSEA) on genes within the top 10% of dmGWAS modules, highlighting monocyte as the most significant cell type (p < 5 × 10−12). Functional enrichment analysis revealed Gene Ontology Biological Process terms relevant to AD pathology (adjusted p < 0.05). Additionally, drug target enrichment identified five FDA-approved targets (p-value = 0.03) for further research. In summary, dmGWAS integration of genetic and epigenetic signals unveiled new gene interactions related to AD, offering promising avenues for future studies. [ABSTRACT FROM AUTHOR]

Barley is one of the most important cereal crops in the world, and its value as a food is constantly being revealed, so the research into and the use of barley germplasm are very important for global food security. Although a large number of barley germplasm samples have been collected globally, their specific genetic compositions are not well understood, and in many cases their origins are even disputed. In this study, 183 barley germplasm samples from the Shanghai Agricultural Gene Bank were genotyped using genotyping-by-sequencing (GBS) technology, SNPs were identified and their genetic parameters were estimated, principal component analysis (PCA) was preformed, and the phylogenetic tree and population structure of the samples were also analyzed. In addition, a genome-wide association study (GWAS) was carried out for the hulled/naked grain trait, and a KASP marker was developed using an associated SNP. The results showed that a total of 181,906 SNPs were identified, and these barley germplasm samples could be roughly divided into three categories according to the phylogenetic analysis, which was generally consistent with the classification of the traits of row type and hulled/naked grain. Population structure analysis showed that the whole barley population could be divided into four sub-populations (SPs), the main difference from previous classifications being that the two-rowed and the hulled genotypes were sub-divided into two SPs. The GWAS analysis of the hulled/naked trait showed that many associated loci were unrelated to the Nud/nud locus, indicating that there might be new loci controlling the trait. A KASP marker was developed for one exon-type SNP on chromosome 7. Genotyping based on the KASP assay was consistent with that based on SNPs, indicating that the gene of this locus might be associated with the hulled/naked trait. The above work not only lays a good foundation for the future utilization of this barley germplasm population but it provides new loci and candidate genes for the hulled/naked trait. [ABSTRACT FROM AUTHOR]

With the outbreak of coronavirus disease 2019 (COVID-19), there has been an increasing focus on exploring the relationship between SARS-CoV-2 infection and tumors. However, there is no consensus on the association between COVID-19 and lymphoma. In this study, genome-wide association study (GWAS) summary data sets for COVID-19 and lymphoma were obtained from the OPEN GWAS website. Single nucleotide polymorphisms (SNPs) were selected as genetic instrument variants for fulling P < 5 × 10−8 and linkage disequilibrium [LD] r2 < 0.001. Both palindromic and outlier SNPs were removed. Cochran's Q test, the MR‒Egger intercept test, and leave-one-out analysis were employed to assess the sensitivity of the effect of COVID-19 on lymphoma. The results showed that COVID-19 patients with very severe respiratory symptoms have an increased risk of developing diffuse large B-cell lymphoma (IVW, OR = 1.765, 95% CI 1.174–2.651, P = 0.006). There was no association between COVID-19 with very severe respiratory symptoms and Hodgkin's lymphoma or other types of non-Hodgkin's lymphoma. No horizontal or directional pleiotropy was observed in the Mendelian randomization analysis. In conclusion, SARS-CoV-2 infection with very severe respiratory symptoms may be a potential risk factor for diffuse large B-cell lymphoma (DLBCL), and follow-up studies with larger samples are needed. [ABSTRACT FROM AUTHOR]

Both alopecia areata (AA) and vitiligo are distinct, heterogenous, and complex disease entities, characterized by nonscarring scalp terminal hair loss and skin pigment loss, respectively. In AA, inflammatory cell infiltrates are in the deep reticular dermis close to the hair bulb (swarm of bees), whereas in vitiligo the inflammatory infiltrates are in the epidermis and papillary dermis. Immune privilege collapse has been extensively investigated in AA pathogenesis, including the suppression of immunomodulatory factors (e.g., transforming growth factor-β (TGF-β), programmed death-ligand 1 (PDL1), interleukin-10 (IL-10), α-melanocyte-stimulating hormone (α-MSH), and macrophage migration inhibitory factor (MIF)) and enhanced expression of the major histocompatibility complex (MHC) throughout hair follicles. However, immune privilege collapse in vitiligo remains less explored. Both AA and vitiligo are autoimmune diseases that share commonalities in pathogenesis, including the involvement of plasmacytoid dendritic cells (and interferon-α (IFN- α) signaling pathways) and cytotoxic CD8+ T lymphocytes (and activated IFN-γ signaling pathways). Blood chemokine C-X-C motif ligand 9 (CXCL9) and CXCL10 are elevated in both diseases. Common factors that contribute to AA and vitiligo include oxidative stress, autophagy, type 2 cytokines, and the Wnt/β-catenin pathway (e.g., dickkopf 1 (DKK1)). Here, we summarize the commonalities and differences between AA and vitiligo, focusing on their pathogenesis. [ABSTRACT FROM AUTHOR]

To examine whether circulating interleukin-6 (IL-6) levels (CirIL6) have a causal effect on blood pressure using Mendelian randomization (MR) methods. We used data from genome-wide association studies (GWAS) of European ancestry to obtain genetic instruments for circulating IL-6 levels and blood pressure measurements. We applied several robust MR methods to estimate the causal effects and to test for heterogeneity and pleiotropy. We found that circulating IL-6 had a significant positive causal effect on systolic blood pressure (SBP) and pulmonary arterial hypertension (PAH), but not on diastolic blood pressure (DBP) or hypertension. We found that as CirIL6 genetically increased, SBP increased using Inverse Variance Weighted (IVW) method (for ukb-b-20175, β = 0.082 with SE = 0.032, P = 0.011; for ukb-a-360, β = 0.075 with SE = 0.031, P = 0.014) and weighted median (WM) method (for ukb-b-20175, β = 0.061 with SE = 0.022, P = 0.006; for ukb-a-360, β = 0.065 with SE = 0.027, P = 0.014). Moreover, CirIL6 may be associated with an increased risk of PAH using WM method (odds ratio (OR) = 15.503, 95% CI, 1.025–234.525, P = 0.048), but not with IVW method. Our study provides novel evidence that circulating IL-6 has a causal role in the development of SBP and PAH, but not DBP or hypertension. These findings suggest that IL-6 may be a potential therapeutic target for preventing or treating cardiovascular diseases and metabolic disorders. However, more studies are needed to confirm the causal effects of IL-6 on blood pressure and to elucidate the underlying mechanisms and pathways. [ABSTRACT FROM AUTHOR]

Cassava brown streak disease (CBSD) poses a substantial threat to food security. To address this challenge, we used PlantCV to extract CBSD root necrosis image traits from 320 clones, with an aim of identifying genomic regions through genome-wide association studies (GWAS) and candidate genes. Results revealed strong correlations among certain root necrosis image traits, such as necrotic area fraction and necrotic width fraction, as well as between the convex hull area of root necrosis and the percentage of necrosis. Low correlations were observed between CBSD scores obtained from the 1-5 scoring method and all root necrosis traits. Broad-sense heritability estimates of root necrosis image traits ranged from low to moderate, with the highest estimate of 0.42 observed for the percentage of necrosis, while narrow-sense heritability consistently remained low, ranging from 0.03 to 0.22. Leveraging data from 30,750 SNPs obtained through DArT genotyping, eight SNPs on chromosomes 1, 7, and 11 were identified and associated with both the ellipse eccentricity of root necrosis and the percentage of necrosis through GWAS. Candidate gene analysis in the 172.2kb region on the chromosome 1 revealed 24 potential genes with diverse functions, including ubiquitin-protein ligase, DNA-binding transcription factors, and RNA metabolism protein, among others. Despite our initial expectation that image analysis objectivity would yield better heritability estimates and stronger genomic associations than the 1-5 scoring method, the results were unexpectedly lower. Further research is needed to comprehensively understand the genetic basis of these traits and their relevance to cassava breeding and disease management. [ABSTRACT FROM AUTHOR]

The pathophysiology of Type 2 Diabetes (T2D) is intricate, involving three main processes that lead to elevated glucose levels. Insulin resistance hinders glucose utilization in muscles, adipose tissue, and the liver. Additionally, pancreatic dysfunction results in excessive glucose release and disrupts insulin and glucagon levels, contributing to hyperglycemia. Tailoring management strategies to individual needs and stages of the disease is crucial. Genetic factors play a significant role in the development of T2D and must be considered in treatment planning. Genome-Wide Association Studies (GWAS) have identified numerous genetic loci and Single Nucleotide Polymorphisms (SNPs) associated with T2D. A personalized approach considers a wide range of factors, such as patient characteristics, medical history, complications, and genetic makeup. By customizing treatment plans to suit each patient's unique needs, it may be possible to improve outcomes and reduce the impact of T2D on overall health. While some may argue that personalized diabetes care has been utilized for a long time, integrating it into the standard treatment of T2D remains a challenging task with numerous obstacles.The current review aims to describe the vision of personalized medicine in diabetes and offers helpful suggestions for a better understanding of this issue, as well as disseminating information about novel treatment approaches like Next Generation Sequencing (NGS) and pharmacotherapy.

In forestry genetics and industry, tree morphological traits such as height, crown size, and shape are critical for understanding growth dynamics and productivity. Traditional methods for measuring these traits are limited in efficiency, scalability, and accuracy, posing challenges for large-scale forest assessments. This study focuses on integrating unmanned aerial vehicle (UAV) technology with GWAS to improve genomic association studies in slash pine (Pinus elliottii). Seven key morphological traits have been identified (canopy area (CA), crown base height (CBH), crown length (CL), canopy volume (CV), crown width (CW), crown width height (CWH), and tree height (H)) through advanced UAV-based phenotyping. These associations account for a remarkable range of heritability in slash pine, with traits such as CBH, CL, CV, and H showing relatively high heritability across both Single nucleotide polymorphisms (SNP) and pedigree methods, indicating strong genetic influence, while traits such as CWH show lower heritability, suggesting greater environmental influence or non-additive genetic variance. The GWAS identified 28 associations, including 22 different SNPs localized to 16 candidate genes, that were significantly associated with the morphological traits of Slash Pine. Notably, two of these candidate genes, annotated as putative DEAD-like helicase and ethylene-responsive element binding factor (ERF), were present at different mutation sites and were significantly associated with CW and CA traits, respectively. These results demonstrate that the UAV imaging enables a comprehensive analysis of the Morphological growth response of slash pine and can facilitate the discovery of informative alleles to elucidate the genetic structure underlying complex phenotypic variation in conifers.

BACKGROUND: Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimers disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. METHODS: To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells. RESULTS: Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD. CONCLUSIONS: In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

Aggressive periodontitis (AgP), Stage III or IV and Grade C according to the new periodontitis classification, is characterized by the rapid destruction of periodontal tissues in the systemically healthy population and often causes premature tooth loss. The presence of familial aggregation suggests the involvement of genetic factors in the pathogenesis. However, the genes associated with the onset and progression of the disease and details of its pathogenesis have not yet been fully identified. In recent years, the genome-wide approach (GWAS), a comprehensive genome analysis method using bioinformatics, has been used to search for disease-related genes, and the results have been applied in genomic medicine for various diseases, such as cancer. In this review, we discuss GWAS in the context of AgP. First, we introduce the relationship between single-nucleotide polymorphisms (SNPs) and susceptibility to diseases and how GWAS is useful for searching disease-related SNPs. Furthermore, we summarize the recent findings of disease-related genes using GWAS on AgP inside and outside Japan and a possible mechanism of the pathogenesis of AgP based on available literature and our research findings. These findings will lead to advancements in the prevention, prognosis, and treatment of AgP.

Chronic kidney disease (CKD) is a major health problem with an increasing epidemiological burden, and is the 16th leading cause of years of life lost worldwide. It is estimated that more than 10% of the population have a variable stage of CKD, while about 850 million people worldwide are affected. Nevertheless, public awareness remains low, clinical access is inappropriate in many circumstances and medication is still ineffective due to the lack of clear therapeutic targets. One of the main issues that drives these problems is the fact that CKD remains a clinical entity with significant causal ambiguity. Beyond diabetes mellitus and hypertension, which are the two major causes of kidney disease, there are still many gray areas in the diagnostic context of CKD. Genetics nowadays emerges as a promising field in nephrology. The role of genetic factors in CKD's causes and predisposition is well documented and thousands of genetic variants are well established to contribute to the high burden of disease. Next-generation sequencing is increasingly revealing old and new rare variants that cause Mendelian forms of chronic nephropathy while genome-wide association studies (GWAS) uncover common variants associated with CKD-defining traits in the general population. In this article we review how GWAS has revolutionized—and continues to revolutionize—the old concept of CKD. Furthermore, we present how the investigation of common genetic variants with previously unknown kidney significance has begun to expand our knowledge on disease understanding, providing valuable insights into disease mechanisms and perhaps paving the way for novel therapeutic targets. [ABSTRACT FROM AUTHOR]

Polygenic scores (PGS) can be used for risk stratification by quantifying individuals’ genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare. [ABSTRACT FROM AUTHOR]

BackgroundEvidence from observational studies suggests that chronic hepatitis B (CHB) is associated with cardiovascular disease (CVD). However, results have been inconsistent and causality remains to be established. We utilized two-sample Mendelian randomization (MR) to investigate potential causal associations between CHB and CVD, including atherosclerosis, coronary heart disease, hypertension, and ischemic stroke.MethodsThe analysis was conducted through genome-wide association studies (GWAS), considering chronic hepatitis B as the exposure and cardiovascular disease as the endpoint. The primary method for evaluating causality in this analysis was the inverse-variance weighted (IVW) technique. Additionally, we employed the weighted median, MR-Egger regression, weighted mode, and simple mode methods for supplementary analyses. Finally, heterogeneity tests, sensitivity analyses, and multiple effects analyses were conducted.ResultsIn a random-effects IVW analysis, we found that genetic susceptibility to chronic hepatitis B was associated with an increased risk of atherosclerosis [OR = 1.048, 95% CI (1.022–1.075), P = 3.08E-04], as well as an increased risk of coronary heart disease [OR = 1.039, 95% CI (1.006–1.072), P = 0.020]. However, it was found to be inversely correlated with ischemic stroke risk [OR = 0.972, 95% CI (0.957–0.988), P = 4.13E-04]. There was no evidence that chronic hepatitis B was associated with hypertension [OR = 1.021, 95% CI (0.994–1.049), P = 0.121].ConclusionOur research indicates that chronic hepatitis B has a correlation with an elevated risk of developing atherosclerosis and coronary heart disease, while it is associated with a decreased risk of experiencing an ischemic stroke.

Cassava brown streak disease (CBSD) poses a substantial threat to food security. To address this challenge, we used PlantCV to extract CBSD root necrosis image traits from 320 clones, with an aim of identifying genomic regions through genome-wide association studies (GWAS) and candidate genes. Results revealed strong correlations among certain root necrosis image traits, such as necrotic area fraction and necrotic width fraction, as well as between the convex hull area of root necrosis and the percentage of necrosis. Low correlations were observed between CBSD scores obtained from the 1-5 scoring method and all root necrosis traits. Broad-sense heritability estimates of root necrosis image traits ranged from low to moderate, with the highest estimate of 0.42 observed for the percentage of necrosis, while narrow-sense heritability consistently remained low, ranging from 0.03 to 0.22. Leveraging data from 30,750 SNPs obtained through DArT genotyping, eight SNPs on chromosomes 1, 7, and 11 were identified and associated with both the ellipse eccentricity of root necrosis and the percentage of necrosis through GWAS. Candidate gene analysis in the 172.2kb region on the chromosome 1 revealed 24 potential genes with diverse functions, including ubiquitin-protein ligase, DNA-binding transcription factors, and RNA metabolism protein, among others. Despite our initial expectation that image analysis objectivity would yield better heritability estimates and stronger genomic associations than the 1-5 scoring method, the results were unexpectedly lower. Further research is needed to comprehensively understand the genetic basis of these traits and their relevance to cassava breeding and disease management.