Genome-wide Data Research Articles

Causal relationship between primary headache mediated by circulating cytokines and cerebral cortex structure: a mediation Mendelian randomization study.

Inflammation may be related to structural changes in the cerebral cortex. We aimed to explore whether cytokines mediate the link between these changes and primary headache. The summary statistics of genome-wide association study (GWAS) related to migraine and its subtypes, cluster headache were derived from the FinnGen Release 10 database, and tension-type headache data was from the GWAS Catalog. Ninety-one cytokines were obtained from genome-wide pQTL mapping data. GWAS data on cortical surface area (SA) and thickness (TH) came from the ENIGMA Consortium. The methods of Mendelian randomization (MR) analysis included the inverse-variance-weighted (IVW), MR-Egger, and weighted median. Migraine reduces the SA of paracentral[β = -1.3645, OR = 0.2555, 95%CI (0.0660, 0.9898)] by fibroblast growth factor-23(FGF-23), with an intermediate ratio (IR) of 38.13%. Migraine may reduce the TH of superior parietal[β = -0.0029, OR = 0.9971, 95%CI (0.9943, 0.9999)] by interleukin (IL)-15RA, with an absolute IR of 11.11%. Migraine without aura may reduce the TH of rostral anterior cingulate[β = -0.0005, OR = 0.9995, 95%CI (0.9991, 0.9999)] by IL-18R1, with an IR of 11.63%. FGF23 and IL-15RA are associated with reduced SA or TH in migraine, while IL-18R1 is associated with increased TH in migraine without aura.

Effect analysis of trimethylamine N-oxide and its precursors on susceptibility to pancreatic diseases

To investigate the causal relationship between trimethylamine N-oxide (TMAO) and its precursors (betaine, carnitine, and choline) and pancreatic diseases based on the Mendelian randomization (MR) method. Genome-wide association study data of TMAO, betaine, carnitine, choline, acute pancreatitis (AP), chronic pancreatitis (CP), pancreatic cancer (PC), and circulating immune cell characteristics (white blood cell, lymphocyte, monocyte, neutrophil, eosinophil and basophil) were collected. According to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)-MR reporting guidelines, the available genetic variants [single nucleotide polymorphism (SNP)] were strictly screened. The causal relationship between exposure (TMAO and its precursors) and outcomes (pancreatic diseases and circulating immune cell characteristics) was evaluated using inverse variance weighting (IVW), MR-Egger regression and weighted median. The reliability of the results was evaluated by sensitivity analysis based on MR-Egger regression, MR-PRESSO, Cochrane's Q test and leave-one-out method. A total of 36 SNP associated with TMAO and its precursors were included. Five of these were associated with TMAO, 13 with betaine, 12 with carnitine, and 6 with choline. (1) MR analysis showed that TMAO may increase the risk of AP [odds ratio (OR) = 1.100, 95% confidence interval (95%CI) was 1.008-1.200, P = 0.032], and choline may reduce the risk of alcoholic acute pancreatitis (AAP; OR = 0.743, 95%CI was 0.585-0.944, P = 0.015). The analysis results of MR-Egger regression and weighted median were consistent with the IVW results. There is no evidence to support a causal relationship between TMAO and its precursors and the risk of CP and PC. Sensitivity analysis indicated that SNP analyzed by MR showed no heterogeneity and low pleiotropy. The leave-one-out method analysis determined that after excluding any SNP, the effect intervals of the remaining SNP on the results were similar to the overall effect intervals, which suggested the robustness of MR results. (2) There was a positive causal relationship between plasma TMAO level and circulating monocyte count (OR = 1.017, 95%CI was 1.000*-1.034, P = 0.048, * represented that the data was obtained by correcting to 3 decimal places from 1.000 1). The causal effect obtained by MR-Egger regression and weighted median analysis was consistent with the results of IVW. Sensitivity analysis illustrated SNP analyzed by MR showed no heterogeneity and pleiotropy. The leave-one-out method analysis determined that after excluding any SNP, the effect intervals of the remaining SNP on the results were similar to the overall effect intervals, which suggested the robustness of MR results. TMAO and choline may change the risk of AP, and TMAO may contribute to the increase of circulating monocyte count in AP.

Regulation of KIF23 by miR-107 controls replicative tumor cell fitness in mouse and human hepatocellular carcinoma

Regulation of KIF23 by miR-107 controls replicative tumor cell fitness in mouse and human hepatocellular carcinoma

P013 EXPLORING THE BIDIRECTIONAL CAUSAL ASSOCIATION BETWEEN FRAILTY AND HYPERTENSION: FROM ASSOCIATION TO CAUSATION

Background: Studies have shown an association between frailty and hypertension. However, it remains uncertain whether this association reflects causality. We performed a Chinese Longitudinal Health Longevity Survey (CLHLS) analysis and a bidirectional Mendelian randomization (MR) study to assess the relationship between frailty and hypertension. Corroborative evidence for other heart diseases, including atrial fibrillation (AF), coronary artery disease (CAD), and heart failure (HF), was also analyzed. Methods: We performed the cross-sectional analysis based on CLHLS 2018 and used multivariable logistic regression to analyze the association between frailty and HTN. Then, we extracted independent single nucleotide polymorphisms for each phenotype at genome-wide significance levels from the latest genome-wide association summary data of European descent, including FI (N=175,226), HTN (N=462,933), AF (N=138,994), and HF (N= 977,323). The inverse variance weighted method was primarily used, followed by sensitivity and validation analyses. Results: In the cross-sectional study, compared to non-frail individuals, frail patients revealed a significantly higher risk of HTN (OR 5.32, 95% CI 3.46-8.17 P< 0.001) and heart disease (OR 13.50, 95% CI 8.18-22.28 P = 0.017) after full adjustment. In the MR study, higher FI significantly increased the risk of HTN (OR 1.12, 95% CI 1.07-1.16 P<0.001) and CAD (OR 2.57, 95% CI 1.72-3.84 P<0.001). FI suggestively increased the risk of AF (OR 1.83 95%CI 1.11-3.01 P<0.05). No association between FI and HF was observed (OR 1.30, 95% CI 1.00-1.71 P>0.05). In the reverse analysis, genetic predisposition to HTN (β 0.662, 95% CI 1.71, 2.19 P<0.001) and CAD (β 0.067, 95% CI 01.049-1.090 P<0.001) significantly increased FI. A suggestive causality from HF (β 0.088, 95% CI 1.000-1.192 P<0.05) to FI was also observed. No causality between AF to FI was observed (β 0.014, 95% CI 0.998-1.030 P>0.05). Conclusion: Our findings add new evidence to support the bidirectional causality between frailty and HTN. A deeper exploration of such associations is imperative to optimize the management of older patients.

Proteome-wide Mendelian randomization and single-cell sequencing analysis identify the association between plasma proteins and gastric cancer.

Targeted therapy is a crucial treatment modality for advanced gastric cancer, with several targets already identified, and the exploration of new targets is important. In this study, our aim was to identify plasma proteins causally associated with gastric cancer to explore novel genetic targets for the disease. Firstly, we utilized protein quantitative trait loci data for 4,907 plasma proteins and genome-wide association study data for gastric cancer to conduct Mendelian randomization (MR) analyses. This was followed by summary-data-based MR analysis on the identified plasma proteins. We then analyzed single-cell sequencing data from the Gene Expression Omnibus database to describe the distribution of genes corresponding to these proteins across different stages and cell types of gastric cancer. MR analysis identified 12 plasma proteins with potential causal associations with gastric cancer, among which motilin (MLN) and THSD1 passed the summary-data-based MR test. These proteins showed no evidence of pleiotropy nor heterogeneity. In single-cell sequencing analysis, EPHB4, KDR, SEMA6B, CDH1, and C1GALT1C1 were found to be enriched in specific cell types within gastric cancer. KDR and LIFR exhibited significant differential expression between gastric cancer and normal tissues. All the 12 genes displayed differential expression across different stages of gastric cancer. Overall, our study identified several plasma proteins with potential causal relationships to gastric cancer. This provides potential candidate targets for gastric cancer research and advances our understanding of the disease's genetic foundations.

Genome-wide identification and data mining reveals major-latex protein (MLP) from the PR-10 protein family played defense-related roles against phytopathogenic challenges in cassava (Manihot esculenta Crantz).

Despite being identified in previous articles, the pathogenesis-related 10 (PR-10) protein remains relatively overlooked and has yet to be fully characterized in numerous plant species. This research employs a comprehensive data mining approach to in silico characterize PR-10 proteins in cassava, a vital crop plant globally. In this study, the focus was on in silico identified 53 cassava PR-10 proteins, which can be categorized into two main subgroups: 34 major latex proteins (MLPs) and 13 major allergen proteins, Pru ar 1, based on their phylogenetic relationship. The genome collinearity analysis with the rubber tree showed a possible evolutionary relationship of the PR-10 gene between these two Euphorbiaceae species, specifically on their chromosome 15. Notably, MLP423 and other MLP proteins were identified in various previously published cassava transcriptome datasets in response to biotic treatments from diverse phytopathogens, including anthracnose fungus, viruses, and bacterial blight. Ligand prediction and molecular docking of three MLP423 proteins have revealed potential interaction with cytokinin and abscisic acid hormones. Their expressions and predicted binding affinities are discussed here, highlighting their role as contributors to cassava's defense network against key diseases.

Causal relationship between Interleukin-27 expression levels and osteoporosis: a bidirectional mendelian randomization study

BackgroundThis study aimed to evaluate the causal relationship between Interleukin-27 (IL-27) and osteoporosis by bidirectional Mendelian randomization (MR) analysis.MethodsFirstly, the genome-wide association study summary data of osteoporosis (finn-b-M13_OSTEOPOROSIS) and IL-27 levels (ebi-a-GCST90012017) were picked out from the Integrative Epidemiology Unit (IEU) OpenGWAS database. After filtrating instrumental variables (IVs), the bidirectional MR analysis between IL-27 levels and osteoporosis was performed by MR-Egger, Weighted median, Simple mode, Weighted mode, and Inverse variance weighted (IVW). Subsequently, the sensitivity analysis was adopted to evaluate the reliability of the MR results via the Heterogeneity, Horizontal pleiotropy test and Leave-One-Out (LOO) analysis. Finally, the enrichment analysis of genes corresponding to SNPs related to IL-27 levels derived from eQTLGen database was executed to explore in depth the biological function and regulatory mechanism of these genes on osteoporosis occurrence.ResultsThe bidirectional MR results based on IVW method revealed that IL-27 level as a risk factor was causally related to osteoporosis (P = 0.004, odds ratio (OR) = 1.123, 95% confidence interval (CI) = 1.037–1.217), whereas osteoporosis was not in significant connection with IL-27 levels (P > 0.05). In regard to the sensitivity analysis for forward MR results, there was no heterogeneity and horizontal pleiotropy, and no SNPs relevant to IL-27 levels existed severe bias, suggesting the reliability of forward MR analysis. Furthermore, a total of 74 genes corresponding to 26 SNPs of IL-27 levels were obtained and were mainly involved in immune and inflammatory pathways including MyD88-dependent toll-like receptor signaling pathway, Toll-like receptor signaling pathway, cytosolic DNA-sensing pathway and so forth.ConclusionsThis study supported that IL-27 level as a risk factor was causally connected with osteoporosis and might regulate the disease occurrence and progression by means of immune and inflammatory mechanisms, which could provide important reference and evidence for further exploring the role of IL-27 in the development of osteoporosis.

Mendelian randomization did not support the causal effect of diabetes on aortic diseases.

Observational studies revealed paradoxically inverse associations between diabetes and aortic diseases (aortic aneurysm or aortic dissection), yet the causality remains to be determined. To investigate the causal associations between diabetes and aortic diseases using Mendelian randomization (MR) analyses. Summary-level data for exposures (type 1 diabetes, type 2 diabetes, fasting glucose, fasting insulin, glycated hemoglobin) and outcomes (aortic dissection and aortic aneurysm) were obtained from public genome-wide association study data. The principal analysis was the inverse-variance weighted (IVW) method. Sensitivity analyses were also carried out, including weighted median, MR-Egger, and multivariable MR methods. According to IVW results, type 1 diabetes (odds ratio [OR]: 0.99; 95% confidence interval [CI] 0.93-1.07; P = 0.87), type 2 diabetes (OR: 0.97; 95% CI 0.77-1.20; P = 0.75), fasting glucose (OR: 1.16; 95% CI 0.48-2.84; P = 0.74), fasting insulin (OR: 2.75; 95% CI 0.53-14.26; P = 0.23), or glycated hemoglobin (OR: 0.33; 95% CI 0.09-1.17; P = 0.09) had no causal effect on aortic dissection. Similarly, type 1 diabetes, type 2 diabetes, fasting glucose, fasting insulin, or glycated hemoglobin had no causal effect on aortic aneurysm. Sensitivity analyses revealed consistent results. MR-Egger method and funnel plot yielded no indication of directional pleiotropy. Diabetes had no causal associations with aortic dissection or aortic aneurysm. The observed inverse associations in previous cohort studies may be explained by confounding factors or reverse causation.

Genome-Wide Identification of NAC Gene Family Members of Tree Peony (Paeonia suffruticosa Andrews) and Their Expression under Heat and Waterlogging Stress.

An important family of transcription factors (TFs) in plants known as NAC (NAM, ATAF1/2, and CUC2) is crucial for the responses of plants to environmental stressors. In this study, we mined the NAC TF family members of tree peony (Paeonia suffruticosa Andrews) from genome-wide data and analyzed their response to heat and waterlogging stresses in conjunction with transcriptome data. Based on tree peony's genomic information, a total of 48 PsNAC genes were discovered. Based on how similar their protein sequences were, these PsNAC genes were divided into 14 branches. While the gene structures and conserved protein motifs of the PsNAC genes within each branch were largely the same, the cis-acting elements in the promoter region varied significantly. Transcriptome data revealed the presence of five PsNAC genes (PsNAC06, PsNAC23, PsNAC38, PsNAC41, PsNAC47) and one PsNAC gene (PsNAC37) in response to heat and waterlogging stresses, respectively. qRT-PCR analysis reconfirmed the response of these five PsNAC genes to heat stress and one PsNAC gene to waterlogging stress. This study lays a foundation for the study of the functions and regulatory mechanisms of NAC TFs in tree peony. Meanwhile, the NAC TFs of tree peony in response to heat and waterlogging stress were excavated, which is of great significance for the selection and breeding of new tree peony varieties with strong heat and waterlogging tolerance.

An integrated single-cell reference atlas of the human endometrium

The complex and dynamic cellular composition of the human endometrium remains poorly understood. Previous endometrial single-cell atlases profiled few donors and lacked consensus in defining cell types. We introduce the Human Endometrial Cell Atlas (HECA), a high-resolution single-cell reference atlas (313,527 cells) combining published and new endometrial single-cell transcriptomics datasets of 63 women with and without endometriosis. HECA assigns consensus and identifies previously unreported cell types, mapped in situ using spatial transcriptomics and validated using a new independent single-nuclei dataset (312,246 nuclei, 63 donors). In the functionalis, we identify intricate stromal–epithelial cell coordination via transforming growth factor beta (TGFβ) signaling. In the basalis, we define signaling between fibroblasts and an epithelial population expressing progenitor markers. Integration of HECA with large-scale endometriosis genome-wide association study data pinpoints decidualized stromal cells and macrophages as most likely dysregulated in endometriosis. The HECA is a valuable resource for studying endometrial physiology and disorders, and for guiding microphysiological in vitro systems development.

Development of a biomarker prediction model for post-trauma multiple organ failure/dysfunction syndrome based on the blood transcriptome

BackgroundMultiple organ failure/dysfunction syndrome (MOF/MODS) is a major cause of mortality and morbidity among severe trauma patients. Current clinical practices entail monitoring physiological measurements and applying clinical score systems to diagnose its onset. Instead, we aimed to develop an early prediction model for MOF outcome evaluated soon after traumatic injury by performing machine learning analysis of genome-wide transcriptome data from blood samples drawn within 24 h of traumatic injury. We then compared its performance to baseline injury severity scores and detection of infections.MethodsBuffy coat transcriptome and linked clinical datasets from blunt trauma patients from the Inflammation and the Host Response to Injury Study (“Glue Grant”) multi-center cohort were used. According to the inclusion/exclusion criteria, 141 adult (age ≥ 16 years old) blunt trauma patients (excluding penetrating) with early buffy coat (≤ 24 h since trauma injury) samples were analyzed, with 58 MOF-cases and 83 non-cases. We applied the Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms to select features and develop models for MOF early outcome prediction.ResultsThe LASSO model included 18 transcripts (AUROC [95% CI]: 0.938 [0.890–0.987] (training) and 0.833 [0.699–0.967] (test)), and the XGBoost model included 41 transcripts (0.999 [0.997–1.000] (training) and 0.907 [0.816–0.998] (test)). There were 16 overlapping transcripts comparing the two panels (0.935 [0.884–0.985] (training) and 0.836 [0.703–0.968] (test)). The biomarker models notably outperformed models based on injury severity scores and sex, which we found to be significantly associated with MOF (APACHEII + sex—0.649 [0.537–0.762] (training) and 0.493 [0.301–0.685] (test); ISS + sex—0.630 [0.516–0.744] (training) and 0.482 [0.293–0.670] (test); NISS + sex—0.651 [0.540–0.763] (training) and 0.525 [0.335–0.714] (test)).ConclusionsThe accurate assessment of MOF from blood samples immediately after trauma is expected to aid in improving clinical decision-making and may contribute to reduced morbidity, mortality and healthcare costs. Moreover, understanding the molecular mechanisms involving the transcripts identified as important for MOF prediction may eventually aid in developing novel interventions.

Blood-based multivariate methylation risk score for cognitive impairment and dementia.

The established link between DNA methylation and pathophysiology of dementia, along with its potential role as a molecular mediator of lifestyle and environmental influences, positions blood-derived DNA methylation as a promising tool for early dementia risk detection. In conjunction with an extensive array of machine learning techniques, we employed whole blood genome-wide DNA methylation data as a surrogate for 14 modifiable and non-modifiable factors in the assessment of dementia risk in independent dementia cohorts. We established a multivariate methylation risk score (MMRS) for identifying mild cognitive impairment cross-sectionally, independent of age and sex (P=2.0×10-3). This score significantly predicted the prospective development of cognitive impairments in independent studies of Alzheimer's disease (hazard ratio for Rey's Auditory Verbal Learning Test (RAVLT)-Learning=2.47) and Parkinson's disease (hazard ratio for MCI/dementia=2.59). Our work shows the potential of employing blood-derived DNA methylation data in the assessment of dementia risk. We used whole blood DNA methylation as a surrogate for 14 dementia risk factors. Created a multivariate methylation risk score for predicting cognitive impairment. Emphasized the role of machine learning and omics data in predicting dementia. The score predicts cognitive impairment development at the population level.

Multi-omics association study of DNA methylation and gene expression levels and diagnoses of cardiovascular diseases in Danish Twins

BackgroundCardiovascular diseases (CVDs) are major causes of mortality and morbidity worldwide; yet the understanding of their molecular basis is incomplete. Multi-omics studies have significant potential to uncover these mechanisms, but such studies are challenged by genetic and environmental confounding—a problem that can be effectively reduced by investigating intrapair differences in twins. Here, we linked data on all diagnoses of the circulatory system from the nationwide Danish Patient Registry (spanning 1977–2022) to a study population of 835 twins holding genome-wide DNA methylation and gene expression data. CVD diagnoses were divided into prevalent or incident cases (i.e., occurring before or after blood sample collection (2007–2011)). The diagnoses were classified into four groups: cerebrovascular diseases, coronary artery disease (CAD), arterial and other cardiovascular diseases (AOCDs), and diseases of the veins and lymphatic system. Statistical analyses were performed by linear (prevalent cases) or cox (incident cases) regression analyses at both the individual-level and twin pair-level. Significant genes (p < 0.05) in both types of biological data and at both levels were inspected by bioinformatic analyses, including gene set enrichment analysis and interaction network analysis.ResultsIn general, more genes were found for prevalent than for incident cases, and bioinformatic analyses primarily found pathways of the immune system, signal transduction and diseases for prevalent cases, and pathways of cell–cell communication, metabolisms of proteins and RNA, gene expression, and chromatin organization groups for incident cases. This potentially reflects biology related to response to CVD (prevalent cases) and mechanisms related to regulation and development of disease (incident cases). Of specific genes, Myosin 1E was found to be central for CAD, and DEAD-Box Helicase 5 for AOCD. These genes were observed in both the prevalent and the incident analyses, potentially reflecting that their DNA methylation and gene transcription levels change both because of disease (prevalent cases) and prior disease (incident cases).ConclusionWe present novel biomarkers for CVD by performing multi-omics analysis in twins, hereby lowering the confounding due to shared genetics and early life environment—a study design that is surprisingly rare in the field of CVD, and where additional studies are highly needed.

Repeated migration, interbreeding and bottlenecking shaped the phylogeography of the selfing grass Brachypodium stacei.

Brachypodium stacei is the most ancestral lineage in the genus Brachypodium, a model system for grass functional genomics. B. stacei shows striking and sometimes contradictory biological and evolutionary features, including a high selfing rate yet extensive admixture, an ancient Miocene origin yet with recent evolutionary radiation, and adaptation to different dry climate conditions in its narrow distribution range. Therefore, it constitutes an ideal system to study these life history traits. We studied the phylogeography of 17 native circum-Mediterranean B. stacei populations (39 individuals) using genome-wide RADseq SNP data and complete plastome sequences. Nuclear SNP data revealed the existence of six distinct genetic clusters, low levels of intra-population genetic diversity and high selfing rates, albeit with signatures of admixture. Coalescence-based dating analysis detected a recent split between crown lineages in the Late Quaternary. Plastome sequences showed incongruent evolutionary relationships with those recovered by the nuclear data, suggesting interbreeding and chloroplast capture events between genetically distant populations. Demographic and population dispersal coalescent models identified an ancestral origin of B. stacei in the western-central Mediterranean islands, followed by an early colonization of the Canary Islands and two independent colonization events of the eastern Mediterranean region through long-distance dispersal and bottleneck events as the most likely evolutionary history. Climate niche data identified three arid niches of B. stacei in the southern Mediterranean region. Our findings indicate that the phylogeography of B. stacei populations was shaped by recent radiations, frequent extinctions, long-distance dispersal events, occasional interbreeding, and adaptation to local climates.

Genetics of glucose homeostasis in pregnancy and postpartum.

Pregnancy is accompanied by maternal metabolic adaptations to ensure fetal growth and development, including insulin resistance, which occurs primarily during the second and third trimesters of pregnancy, and a decrease in fasting blood sugar levels over the course of pregnancy. Glucose-related traits are regulated by genetic and environmental factors and modulated by physiological variations throughout the life course. We addressed the hypothesis that there are both overlaps and differences between genetic variants associated with glycaemia-related traits during and outside of pregnancy. Genome-wide SNP data were used to identify genetic variations associated with glycaemia-related traits measured during an OGTT performed at ~28 weeks' gestation in 8067 participants in the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) Study. Associations outside of pregnancy were determined in 3977 individuals who also participated in the HAPO Follow-Up Study at 11-14 years postpartum. A Bayesian classification algorithm was used to determine whether SNPs associated with fasting and 2 h glucose and fasting C-peptide during pregnancy had a pregnancy-predominant effect vs a similar effect during pregnancy and postpartum. SNPs in six loci (GCKR, G6PC2, GCK, PPP1R3B, PCSK1 and MTNR1B) were significantly associated with fasting glucose during pregnancy, while SNPs in CDKAL1 and MTNR1B were associated with 1 h glucose and SNPs in MTNR1B and HKDC1 were associated with 2 h glucose. Variants in CDKAL1 and MTNR1B were associated with insulin secretion during pregnancy. Variants in multiple loci were associated with fasting C-peptide during pregnancy, including GCKR, IQSEC1, PPP1R3B, IGF1 and BACE2. GCKR and BACE2 were associated with 1 h C-peptide and GCKR, IQSEC1 and BACE2 with insulin sensitivity during pregnancy. The associations of MTNR1B with 2 h glucose, BACE2 with fasting and 1 h C-peptide and insulin sensitivity, and IQSEC1 with fasting C-peptide and insulin sensitivity that we identified during pregnancy have not been previously reported in non-pregnancy cohorts. The Bayesian classification algorithm demonstrated that the magnitude of effect of the lead SNP was greater during pregnancy compared with 11-14 years postpartum in PCSK1 and PPP1R3B with fasting glucose, in three loci, including MTNR1B, with 2 h glucose, and in six loci, including IGF1, with fasting C-peptide. Our findings support the hypothesis that there are both overlaps and differences between the genetic architecture of glycaemia-related traits during and outside of pregnancy. Genetic variants at several loci, including PCSK1, PPP1R3B, MTNR1B and IGF1, appear to influence glycaemic regulation in a unique fashion during pregnancy. Future studies in larger cohorts will be needed to replicate the present findings, fully characterise the genetics of maternal glycaemia during pregnancy and determine similarities to and differences from the non-gravid state.

Genome-wide association study of copy number variations in Parkinson's disease.

Our study investigates the impact of copy number variations (CNVs) on Parkinson's disease (PD) pathogenesis using genome-wide data, aiming to uncover novel genetic mechanisms and improve the understanding of the role of CNVs in sporadic PD. We applied a sliding window approach to perform CNV-GWAS and conducted genome-wide burden analyses on CNV data from 11,035 PD patients (including 2,731 early-onset PD (EOPD)) and 8,901 controls from the COURAGE-PD consortium. We identified 14 genome-wide significant CNV loci associated with PD, including one deletion and 13 duplications. Among these, duplications in 7q22.1, 11q12.3 and 7q33 displayed the highest effect. Two significant duplications overlapped with PD-related genes SNCA and VPS13C, but none overlapped with recent significant SNP-based GWAS findings. Five duplications included genes associated with neurological disease, and four overlapping genes were dosage-sensitive and intolerant to loss-of-function variants. Enriched pathways included neurodegeneration, steroid hormone biosynthesis, and lipid metabolism. In early-onset cases, four loci were significantly associated with EOPD, including three known duplications and one novel deletion in PRKN. CNV burden analysis showed a higher prevalence of CNVs in PD-related genes in patients compared to controls (OR=1.56 [1.18-2.09], p=0.0013), with PRKN showing the highest burden (OR=1.47 [1.10-1.98], p=0.026). Patients with CNVs in PRKN had an earlier disease onset. Burden analysis with controls and EOPD patients showed similar results. This is the largest CNV-based GWAS in PD identifying novel CNV regions and confirming the significant CNV burden in EOPD, primarily driven by the PRKN gene, warranting further investigation.

Inference of host-pathogen interaction matrices from genome-wide polymorphism data.

Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype x genotype (GxG) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of host and pathogen genomes (co-GWAs) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an Approximate Bayesian Computation method to pinpoint loci with relevant GxG interactions and to infer their underlying interaction matrix. In a combined SNP data set of 451 European humans and their infecting Hepatitis C Virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (GxG) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of GxG interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key GxG interactions underpinning HCV adaptation to the European human population after a recent expansion.

Genetic association of serum calcium, phosphate, vitamin D, parathyroid hormone, and FGF23 with the risk of aortic stenosis

Disorders of mineral metabolism, including elevated levels of serum calcium, phosphate, 25-hydroxyvitamin D (25OH-VitD), parathyroid hormone (PTH), and fibroblast growth factor 23 (FGF23), have been reported in patients with calcific aortic valve stenosis (CAVS). However, evidence of the causal role of mineral metabolism in CAVS is still lacking. In this study, we employed a systematic pipeline combining Mendelian randomization (MR), Steiger directionality test, colocalization analysis, protein-protein network, and enrichment analysis to investigate the causal effect of mineral metabolism on CAVS. Genome-wide association study (GWAS) and protein quantitative trait loci data for mineral metabolism markers were extracted from large-scale meta-analyses. Summary statistics for CAVS were obtained from two independent GWAS datasets as discovery and replication cohorts (n = 374,277 and 653,867). In MR analysis, genetic mimicry of serum FGF23 elevation was associated with increased CAVS risk [ORdiscovery = 3.081 (1.649–5.760), Pdiscovery = 4.21 × 10−4; ORreplication = 2.280 (1.461 – 3.558), Preplication = 2.82 × 10−4] without evidence of reverse causation (Psteiger= 7.21 × 10−98). Strong colocalisation association with CAVS was observed for FGF23 expression in the blood (PP.H4 = 0.96). Additionally, we identified some protein-protein interactions between FGF23 and known CAVS-associated genes. Serum calcium, phosphate, 25OH-VitD, and PTH failed to show causal effects on CAVS at Bonferroni-corrected significance (all P > 0.05/5 = 0.01). In conclusion, elevated serum FGF23 level may act as a causal risk factor for CAVS, and its mechanism of action in CAVS development may be independent of its function in regulating mineral metabolism. Hence, FGF23 may serve as a circulating marker and a promising preventive target for CAVS, warranting further investigation.

Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes

In silico variant effect predictions are available for nearly all missense variants but played a minimal role in clinical variant classification because they were deemed to provide only supporting evidence. Recently, the ClinGen Sequence Variant Interpretation (SVI) Working Group updated recommendations for variant effect prediction use. By analyzing control pathogenic and benign variants across all genes, they were able to compute evidence strength for predictor score intervals with some intervals generating moderate, strong, or even very strong evidence. However, this genome-wide approach could obscure heterogeneous predictor performance in different genes. We quantified the gene-by-gene performance of two top predictors, REVEL and BayesDel, by analyzing control variants in each predictor score interval in 3,668 disease-relevant genes. Approximately 10% of intervals had sufficient control variants for analysis, and ∼70% of these intervals exceeded the maximum number of incorrect predictions implied by the SVI recommendations. These trending discordant intervals arose owing to the divergence of the gene-specific distribution of predictions from the genome-wide distribution, suggesting that gene-specific calibration is needed in many cases. Approximately 22% of ClinVar missense variants of uncertain significance in genes we analyzed (REVEL= 100,629, BayesDel= 71,928) had predictions in trending discordant intervals. Thus, genome-wide calibrations could result in many variants receiving inappropriate evidence strength. To facilitate a review of the SVI's calibrations, we developed a web application enabling visualization of gene-specific predictions and trending concordant and discordant intervals.

Mendelian Randomization and Bayesian Colocalization Analysis Implicate Glycoprotein VI as a Potential Drug Target for Cardioembolic Stroke in South Asian Populations.

Circulating plasma proteins are clinically useful biomarkers for stroke risk. We examined the causal links between plasma proteins and stroke risk in individuals of South Asian ancestry. We applied proteome-wide Mendelian randomization and colocalization approaches to understand causality of 2922 plasma proteins on stroke risk in individuals of South Asian ancestry. We obtained genetic instruments (proxies) for plasma proteins from the UK Biobank (N=920). Genome-wide association studies summary data for strokes (N≤11 312) were sourced from GIGASTROKE consortium. Our primary approach involved the Wald ratio or inverse-variance-weighted methods, with statistical significance set at false discovery rate <0.1. Additionally, a Bayesian colocalization approach assessed shared causal variants among proteome, transcriptome, and stroke phenotypes to minimize bias from linkage disequilibrium. We found evidence of a potential causal effect of plasma GP6 (glycoprotein VI) levels on cardioembolic stroke (odds ratio [OR]Wald ratio=2.53 [95% CI, 1.59-4.03]; P=9.2×10-5, false discovery rate=0.059). Generalized Mendelian randomization accounting for correlated single nucleotide polymorphisms (SNPs), with the P value threshold at P<5×10-8 and clumped at r2=0.3, showed consistent direction of effect of GP6 on cardioembolic stroke (ORgeneralized inverse-variance-weighted=2.21 [95% CI, 1.46-3.33]; P=1.6×10-4). Colocalization analysis indicated that plasma GP6 levels colocalize with cardioembolic stroke (posterior probability=91.4%). Multitrait colocalization combining transcriptome, proteome, and cardioembolic stroke showed moderate to strong evidence that these 2 traits colocalize with GP6 expression in the coronary artery and brain tissues (multitrait posterior probability>50%). The potential causal effect of GP6 on cardioembolic stroke was not significant in European populations (ORinverse-variance-weighted=1.08 [95% CI, 0.93-1.26]; P=0.29). Our joint Mendelian randomization and colocalization analyses suggest that genetically predicted GP6 is potentially causally associated with cardioembolic stroke risk in individuals of South Asian ancestry. As genetic data on individuals of South Asian ancestry increase, future Mendelian randomization studies with larger sample size for plasma GP6 levels should be implemented to further validate our findings. Additionally, clinical studies will be necessary to verify GP6 as a therapeutic target for cardioembolic stroke in South Asians.