Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles.

Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.

Genetic dissection of ten photosynthesis-related traits based on InDel- and SNP-GWAS in soybean.

KEY MESSAGE: A total of 416 InDels and 112 SNPs were significantly associated with soybean photosynthesis-related traits. GmIWS1 and GmCDC48 might be related to chlorophyll fluorescence and gas-exchange parameters, respectively. Photosynthesis is one of the main factors determining crop yield. A better understanding of the genetic architecture for photosynthesis is of great significance for soybean yield improvement. Our previous studies identified 5,410,112 single nucleotide polymorphisms (SNPs) from the resequencing data of 219 natural soybean accessions. Here, we identified 634,106 insertions and deletions (InDels) from these 219 accessions and used these InDel variations to perform principal component and linkage disequilibrium analysis of this population. The genome-wide association study (GWAS) were conducted on six chlorophyll fluorescence parameters (chlorophyll content, light energy absorbed per reaction center, quantum yield for electron transport, probability that a trapped exciton moves an electron into the electron transport chain beyond primary quinone acceptor, maximum quantum yield of photosystem II primary photochemistry in the dark-adapted state, performance index on absorption basis) and four gas-exchange parameters (intercellular carbon dioxide concentration, stomatal conductance, net photosynthesis rate, transpiration rate) and revealed 416 significant InDels and 112 significant SNPs. Based on GWAS results, GmIWS1 (encoding a transcription elongation factor) and GmCDC48 (encoding a cell division cycle protein) with the highest expression in the mapping region were determined as the candidate genes responsible for chlorophyll fluorescence and gas-exchange parameters, respectively. Further identification of favorable haplotypes with higher photosynthesis, seed weight and seed yield were carried out for GmIWS1 and GmCDC48. Overall, this study revealed the natural variations and candidate genes underlying the photosynthesis-related traits based on abundant phenotypic and genetic data, providing valuable insights into the genetic mechanisms controlling photosynthesis and yield in soybean.

Characterizing the polygenic overlap and shared loci between rheumatoid arthritis and cardiovascular diseases.

BACKGROUND: Despite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD. METHODS: Based on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets. RESULTS: Compared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15-1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure. CONCLUSIONS: This study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.

The broad impact of cell death genes on the human disease phenome.

Cell death mediated by genetically defined signaling pathways influences the health and dynamics of all tissues, however the tissue specificity of cell death pathways and the relationships between these pathways and human disease are not well understood. We analyzed the expression profiles of an array of 44 cell death genes involved in apoptosis, necroptosis, and pyroptosis cell death pathways across 49 human tissues from GTEx, to elucidate the landscape of cell death gene expression across human tissues, and the relationship between tissue-specific genetically determined expression and the human phenome. We uncovered unique cell death gene expression profiles across tissue types, suggesting there are physiologically distinct cell death programs in different tissues. Using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank (n ~ 500,000), we evaluated 513 traits encompassing ICD-10 defined diagnoses and laboratory-derived traits. Our analysis revealed hundreds of significant (FDR < 0.05) associations between genetically regulated cell death gene expression and an array of human phenotypes encompassing both clinical diagnoses and hematologic parameters, which were independently validated in another large-scale DNA biobank (BioVU) at Vanderbilt University Medical Center (n = 94,474) with matching phenotypes. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits. Our findings are also concordant with independently published studies (e.g. associations between BCL2L11/BIM expression and platelet & lymphocyte counts). Overall, these results suggest that cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.

Multi-trait GWAS for diverse ancestries: mapping the knowledge gap.

BACKGROUND: Approximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores. METHODS: Here, we extend and assess the potential limitations and gains of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits across five ancestries (European (EUR), admixed American (AMR), African (AFR), East Asian (EAS), and South-East Asian (SAS)). RESULTS: We detected 367 new genome-wide significant associations in non-European populations (15 in Admixed American (AMR), 72 in African (AFR) and 280 in East Asian (EAS)). New associations detected represent 5%, 17% and 13% of associations in the AFR, AMR and EAS populations, respectively. Overall, multi-trait testing increases the replication of European associated loci in non-European ancestry by 15%. Pleiotropic effects were highly similar at significant loci across ancestries (e.g. the mean correlation between multi-trait genetic effects of EUR and EAS ancestries was 0.88). For hematological traits, strong discrepancies in multi-trait genetic effects are tied to known evolutionary divergences: the ARKC1 loci, which is adaptive to overcome p.vivax induced malaria. CONCLUSIONS: Multi-trait GWAS can be a valuable tool to narrow the genetic knowledge gap between European and non-European populations.

Epidemiologic association and shared genetic architecture between cataract and hearing difficulties among middle-aged and older adults.

Age-related cataract and hearing difficulties are major sensory disorders that often co-exist in the global-wide elderly and have a tangible influence on the quality of life. However, the epidemiologic association between cataract and hearing difficulties remains unexplored, while little is known about whether the two share their genetic etiology. We first investigated the clinical association between cataract and hearing difficulties using the UK Biobank covering 502,543 individuals. Both unmatched analysis (adjusted for confounders) and a matched analysis (one control matched for each patient with cataract according to confounding factors) were undertaken and confirmed that cataract was associated with hearing difficulties (OR, 2.12; 95% CI, 1.98-2.27; OR, 2.03; 95% CI, 1.86-2.23, respectively). Furthermore, we explored and quantified the shared genetic architecture of these two complex sensory disorders at the common variant level using the bivariate causal mixture model (MiXeR) and conditional/conjunctional false discovery rate method based on the largest available genome-wide association studies of cataract (N = 585,243) and hearing difficulties (N = 323,978). Despite detecting only a negligible genetic correlation, we observe polygenic overlap between cataract and hearing difficulties and identify 6 shared loci with mixed directions of effects. Follow-up analysis of the shared loci implicates candidate genes QKI, STK17A, TYR, NSF, and TCF4 likely contribute to the pathophysiology of cataracts and hearing difficulties. In conclusion, this study demonstrates the presence of epidemiologic association between cataract and hearing difficulties and provides new insights into the shared genetic architecture of these two disorders at the common variant level.

GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction.

BACKGROUND: The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS). RESULTS: Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability. CONCLUSIONS: Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website ( https://github.com/liu-xinrui/GbyE ).

MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups.

Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.

Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection.

Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

Genetic overlap and causality between COVID-19 and multi-site chronic pain: the importance of immunity.

Background: The existence of chronic pain increases susceptibility to virus and is now widely acknowledged as a prominent feature recognized as a major manifestation of long-term coronavirus disease 2019 (COVID-19) infection. Given the ongoing COVID-19 pandemic, it is imperative to explore the genetic associations between chronic pain and predisposition to COVID-19. Methods: We conducted genetic analysis at the single nucleotide polymorphism (SNP), gene, and molecular levels using summary statistics of genome-wide association study (GWAS) and analyzed the drug targets by summary data-based Mendelian randomization analysis (SMR) to alleviate the multi-site chronic pain in COVID-19. Additionally, we performed a latent causal variable (LCV) method to investigate the causal relationship between chronic pain and susceptibility to COVID-19. Results: The cross-trait meta-analysis identified 19 significant SNPs shared between COVID-19 and chronic pain. Coloc analysis indicated that the posterior probability of association (PPH4) for three loci was above 70% in both critical COVID-19 and COVID-19, with the corresponding top three SNPs being rs13135092, rs7588831, and rs13135092. A total of 482 significant overlapped genes were detected from MAGMA and CPASSOC results. Additionally, the gene ANAPC4 was identified as a potential drug target for treating chronic pain (P=7.66E-05) in COVID-19 (P=8.23E-03). Tissue enrichment analysis highlighted that the amygdala (P=7.81E-04) and prefrontal cortex (P=8.19E-05) as pivotal in regulating chronic pain of critical COVID-19. KEGG pathway enrichment further revealed the enrichment of pleiotropic genes in both COVID-19 (P=3.20E-03,Padjust=4.77E-02,hsa05171) and neurotrophic pathways (P=9.03E-04,Padjust =2.55E-02,hsa04621). Finally, the latent causal variable (LCV) model was applied to find the genetic component of critical COVID-19 was causal for multi-site chronic pain (P=0.015), with a genetic causality proportion (GCP) of was 0.60. Conclusions: In this study, we identified several functional genes and underscored the pivotal role of the inflammatory system in the correlation between the paired traits. Notably, heat shock proteins emerged as potential objective biomarkers for chronic pain symptoms in individuals with COVID-19. Additionally, the ubiquitin system might play a role in mediating the impact of COVID-19 on chronic pain. These findings contribute to a more comprehensive understanding of the pleiotropy between COVID-19 and chronic pain, offering insights for therapeutic trials.

The effects of case/control ratio and sample size on genome-wide association studies: A simulation study.

BACKGROUND: Genome-wide association studies (GWAS) is a useful tool for the detection of disease or quantitative trait-related genetic variations in the veterinary field. For a binary trait, a case/control experiment is designed in GWAS. However, there is limited information on the optimal case/control and sample size in GWAS. OBJECTIVES: In this study, it was aimed to detect the effects of case/control ratio and sample size for GWAS using computer simulation under certain assumptions. METHOD: Using the PLINK software, we simulated three different disease scenarios. In scenario 1, we simulated 10 different case/control ratios with increasing ratio of cases to controls. In scenario 2, we did versa of scenario 1 with the increasing ratio of controls to cases. In scenarios 1 and 2, sample size gradually was increased with the change case/control ratios. In scenario 3, the total sample size was fixed to 2000 to see real effects of case/control ratio on the number of disease-related single nucleotide polymorphisms (SNPs). RESULTS: The results showed that the number of disease-related SNPs were the highest when the case/control ratio is close to 1:1 in scenarios 1 and 2 and did not change with an increase in sample size. Similarly, the number of disease-related SNPs was the highest in case/control ratios 1:1 in scenario 3. However, unbalanced case/control ratio caused the detection of lower number of disease-related SNPs in scenario 3. The estimated average power of SNPs was highest when case/control ratio is 1:1 in all scenarios. CONCLUSIONS: All findings led to the conclusion that an increase in sample size may enhance the statistical power of GWAS when the number of cases is small. In addition, case/control ratio 1:1 may be the optimal ratio for GWAS. These findings may be valuable not only for veterinary field but also for human clinical experiments.

Learning epistatic polygenic phenotypes with Boolean interactions.

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

Multiple phenotype association tests based on sliced inverse regression.

BACKGROUND: Joint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in dimensionality has become a very challenging problem in the widespread use of joint analysis. To handle the excessiveness of variables, we consider the sliced inverse regression (SIR) method. Specifically, we propose a novel SIR-based association test that is robust and powerful in testing the association between multiple predictors and multiple outcomes. RESULTS: We conduct simulation studies in both low- and high-dimensional settings with various numbers of Single-Nucleotide Polymorphisms and consider the correlation structure of traits. Simulation results show that the proposed method outperforms the existing methods. We also successfully apply our method to the genetic association study of ADNI dataset. Both the simulation studies and real data analysis show that the SIR-based association test is valid and achieves a higher efficiency compared with its competitors. CONCLUSION: Several scenarios with low- and high-dimensional responses and genotypes are considered in this paper. Our SIR-based method controls the estimated type I error at the pre-specified level α .

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging.

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.

Genetic regulatory effects in response to a high-cholesterol, high-fat diet in baboons.

Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWASs), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context-responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using non-human primate models.

Lipid levels and risk of acute pancreatitis using bidirectional Mendelian randomization.

Previous studies found lipid levels, especially triglycerides (TG), are associated with acute pancreatitis, but their causalities and bi-directions were not fully examined. We determined whether abnormal levels of TG, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) are precursors and/or consequences of acute pancreatitis using bidirectional two-sample Mendelian randomization (MR) with two non-overlapping genome-wide association study (GWAS) summary statistics for lipid levels and acute pancreatitis. We found phenotypic associations that both higher TG levels and lower HDL-C levels contributed to increased risk of acute pancreatitis. Our GWAS meta-analysis of acute pancreatitis identified seven independent signals. Genetically predicted TG was positively associated with acute pancreatitis when using the variants specifically associated with TG using univariable MR [Odds ratio (OR), 95% CI 2.02, 1.22-3.31], but the reversed direction from acute pancreatitis to TG was not observed (mean difference = 0.003, SE = 0.002, P-value = 0.138). However, a bidirectional relationship of HDL-C and acute pancreatitis was observed: A 1-SD increment of genetically predicted HDL-C was associated with lower risk of acute pancreatitis (OR, 95% CI 0.84, 0.76-0.92) and genetically predisposed individuals with acute pancreatitis have, on average, 0.005 SD lower HDL-C (mean difference = - 0.005, SE = 0.002, P-value = 0.004). Our MR analysis confirms the evidence of TG as a risk factor of acute pancreatitis but not a consequence. A potential bidirectional relationship of HDL-C and acute pancreatitis occurs and raises the prospect of HDL-C modulation in the acute pancreatitis prevention and treatment.

A genome-wide association study of neutrophil count in individuals associated to an African continental ancestry group facilitates studies of malaria pathogenesis.

BACKGROUND: 'Benign ethnic neutropenia' (BEN) is a heritable condition characterized by lower neutrophil counts, predominantly observed in individuals of African ancestry, and the genetic basis of BEN remains a subject of extensive research. In this study, we aimed to dissect the genetic architecture underlying neutrophil count variation through a linear-mixed model genome-wide association study (GWAS) in a population of African ancestry (N = 5976). Malaria caused by P. falciparum imposes a tremendous public health burden on people living in sub-Saharan Africa. Individuals living in malaria endemic regions often have a reduced circulating neutrophil count due to BEN, raising the possibility that reduced neutrophil counts modulate severity of malaria in susceptible populations. As a follow-up, we tested this hypothesis by conducting a Mendelian randomization (MR) analysis of neutrophil counts on severe malaria (MalariaGEN, N = 17,056). RESULTS: We carried out a GWAS of neutrophil count in individuals associated to an African continental ancestry group within UK Biobank, identifying 73 loci (r2 = 0.1) and 10 index SNPs (GCTA-COJO loci) associated with neutrophil count, including previously unknown rare loci regulating neutrophil count in a non-European population. BOLT-LMM was reliable when conducted in a non-European population, and additional covariates added to the model did not largely alter the results of the top loci or index SNPs. The two-sample bi-directional MR analysis between neutrophil count and severe malaria showed the greatest evidence for an effect between neutrophil count and severe anaemia, although the confidence intervals crossed the null. CONCLUSION: Our GWAS of neutrophil count revealed unique loci present in individuals of African ancestry. We note that a small sample-size reduced our power to identify variants with low allele frequencies and/or low effect sizes in our GWAS. Our work highlights the need for conducting large-scale biobank studies in Africa and for further exploring the link between neutrophils and severe malaria.

Iron Status and Risk of Heart Disease, Stroke, and Diabetes: A Mendelian Randomization Study in European Adults.

BACKGROUND: The relevance of iron status biomarkers for coronary artery disease (CAD), heart failure (HF), ischemic stroke (IS), and type 2 diabetes (T2D) is uncertain. We compared the observational and Mendelian randomization (MR) analyses of iron status biomarkers and hemoglobin with these diseases. METHODS AND RESULTS: Observational analyses of hemoglobin were compared with genetically predicted hemoglobin with cardiovascular diseases and diabetes in the UK Biobank. Iron biomarkers included transferrin saturation, serum iron, ferritin, and total iron binding capacity. MR analyses assessed associations with CAD (CARDIOGRAMplusC4D [Coronary Artery Disease Genome Wide Replication and Meta-Analysis Plus The Coronary Artery Disease Genetics], n=181 522 cases), HF (HERMES [Heart Failure Molecular Epidemiology for Therapeutic Targets), n=115 150 cases), IS (GIGASTROKE, n=62 100 cases), and T2D (DIAMANTE [Diabetes Meta-Analysis of Trans-Ethnic Association Studies], n=80 154 cases) genome-wide consortia. Observational analyses demonstrated J-shaped associations of hemoglobin with CAD, HF, IS, and T2D. In contrast, MR analyses demonstrated linear positive associations of higher genetically predicted hemoglobin levels with 8% higher risk per 1 SD higher hemoglobin for CAD, 10% to 13% for diabetes, but not with IS or HF in UK Biobank. Bidirectional MR analyses confirmed the causal relevance of iron biomarkers for hemoglobin. Further MR analyses in global consortia demonstrated modest protective effects of iron biomarkers for CAD (7%-14% lower risk for 1 SD higher levels of iron biomarkers), adverse effects for T2D, but no associations with IS or HF. CONCLUSIONS: Higher levels of iron biomarkers were protective for CAD, had adverse effects on T2D, but had no effects on IS or HF. Randomized trials are now required to assess effects of iron supplements on risk of CAD in high-risk older people.

Cell-type-specific and disease-associated expression quantitative trait loci in the human lung.

Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis. Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA sequencing of lung tissue from 66 individuals with pulmonary fibrosis and 48 unaffected donors. Using a pseudobulk approach, we mapped expression quantitative trait loci (eQTLs) across 38 cell types, observing both shared and cell-type-specific regulatory effects. Furthermore, we identified disease interaction eQTLs and demonstrated that this class of associations is more likely to be cell-type-specific and linked to cellular dysregulation in pulmonary fibrosis. Finally, we connected lung disease risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression and implicates context-specific eQTLs as key regulators of lung homeostasis and disease.

Serum electrolyte concentrations and risk of atrial fibrillation: an observational and mendelian randomization study.

BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmic condition resulting in increased stroke risk and is associated with high mortality. Electrolyte imbalance can increase the risk of AF, where the relationship between AF and serum electrolytes remains unclear. METHODS: A total of 15,792 individuals were included in the observational study, with incident AF ascertainment in the Atherosclerosis Risk in Communities (ARIC) study. The Cox regression models were applied to calculate the hazard ratio (HR) and 95% confidence interval (CI) for AF based on different serum electrolyte levels. Mendelian randomization (MR) analyses were performed to examine the causal association. RESULTS: In observational study, after a median 19.7 years of follow-up, a total of 2551 developed AF. After full adjustment, participants with serum potassium below the 5th percentile had a higher risk of AF relative to participants in the middle quintile. Serum magnesium was also inversely associated with the risk of AF. An increased incidence of AF was identified in individuals with higher serum phosphate percentiles. Serum calcium levels were not related to AF risk. Moreover, MR analysis indicated that genetically predicted serum electrolyte levels were not causally associated with AF risk. The odds ratio for AF were 0.999 for potassium, 1.044 for magnesium, 0.728 for phosphate, and 0.979 for calcium, respectively. CONCLUSIONS: Serum electrolyte disorders such as hypokalemia, hypomagnesemia and hyperphosphatemia were associated with an increased risk of AF and may also serve to be prognostic factors. However, the present study did not support serum electrolytes as causal mediators for AF development.