The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019

Metabolic Syndrome and Biotherapeutic Activity of Dairy (Cow and Buffalo) Milk Proteins and Peptides: Fast Food-Induced Obesity Perspective-A Narrative Review

Metabolic syndrome (MS) is defined by the outcome of interconnected metabolic factors that directly increase the prevalence of obesity and other metabolic diseases. Currently, obesity is considered one of the most relevant topics of discussion because an epidemic heave of the incidence of obesity in both developing and underdeveloped countries has been reached. According to the World Obesity Atlas 2023 report, 38% of the world population are presently either obese or overweight. One of the causes of obesity is an imbalance of energy intake and energy expenditure, where nutritional imbalance due to consumption of high-calorie fast foods play a pivotal role. The dynamic interactions among different risk factors of obesity are highly complex; however, the underpinnings of hyperglycemia and dyslipidemia for obesity incidence are recognized. Fast foods, primarily composed of soluble carbohydrates, non-nutritive artificial sweeteners, saturated fats, and complexes of macronutrients (protein-carbohydrate, starch-lipid, starch-lipid-protein) provide high metabolic calories. Several experimental studies have pointed out that dairy proteins and peptides may modulate the activities of risk factors of obesity. To justify the results precisely, peptides from dairy milk proteins were synthesized under in vitro conditions and their contributions to biomarkers of obesity were assessed. Comprehensive information about the impact of proteins and peptides from dairy milks on fast food-induced obesity is presented in this narrative review article.

Genetic variation is a key determinant of chromatin accessibility and drives differences in the regulatory landscape of C57BL/6J and 129S1/SvImJ mice

Most common genetic variants associated with disease are located in non-coding regions of the genome. One mechanism by which they function is through altering transcription factor (TF) binding. In this study, we explore how genetic variation is connected to differences in the regulatory landscape of livers from C57BL/6J and 129S1/SvImJ mice fed either chow or a high-fat diet. To identify sites where regulatory variation affects TF binding and nearby gene expression, we employed an integrative analysis of H3K27ac ChIP-seq (active enhancers), ATAC-seq (chromatin accessibility) and RNA-seq (gene expression). We show that, across all these assays, the genetically driven (i.e. strain-specific) differences in the regulatory landscape are more pronounced than those modified by diet. Most notably, our analysis revealed that differentially accessible regions (DARs, N = 29635, FDR < 0.01 and fold change > 50%) are almost always strain-specific and enriched with genetic variation. Moreover, proximal DARs are highly correlated with differentially expressed genes. We also show that TF binding is affected by genetic variation, which we validate experimentally using ChIP-seq for TCF7L2 and CTCF. This study provides detailed insights into how non-coding genetic variation alters the gene regulatory landscape, and demonstrates how this can be used to study the regulatory variation influencing TF binding.

THUMPD2 catalyzes the N2-methylation of U6 snRNA of the spliceosome catalytic center and regulates pre-mRNA splicing and retinal degeneration

The mechanisms by which the relatively conserved spliceosome manages the enormously large number of splicing events that occur in humans (∼200 000 versus ∼300 in yeast) are poorly understood. Here, we show deposition of one RNA modification-N2-methylguanosine (m2G) on the G72 of U6 snRNA (the catalytic center of the spliceosome) promotes efficient pre-mRNA splicing activity in human cells. This modification was identified to be conserved among vertebrates. Further, THUMPD2 was demonstrated as the methyltransferase responsible for U6 m2G72 by explicitly recognizing the U6-specific sequences and structural elements. The knock-out of THUMPD2 eliminated U6 m2G72 and impaired the pre-mRNA splicing activity, resulting in thousands of changed alternative splicing events of endogenous pre-mRNAs in human cells. Notably, the aberrantly spliced pre-mRNA population elicited the nonsense-mediated mRNA decay pathway. We further show that THUMPD2 was associated with age-related macular degeneration and retinal function. Our study thus demonstrates how an RNA epigenetic modification of the major spliceosome regulates global pre-mRNA splicing and impacts physiology and disease.

Genetic basis of pregnancy-associated decreased platelet counts and gestational thrombocytopenia

ABSTRACT

Platelet count reduction occurs throughout pregnancy, with 5% to 12% of pregnant women being diagnosed with gestational thrombocytopenia (GT), characterized by a more marked decrease in platelet count during pregnancy. However, the underlying biological mechanism behind these phenomena remains unclear. Here, we used sequencing data from noninvasive prenatal testing of 100 186 Chinese pregnant individuals and conducted, to our knowledge, the hitherto largest-scale genome-wide association studies on platelet counts during 5 periods of pregnancy (the first, second, and third trimesters, delivery, and the postpartum period) as well as 2 GT statuses (GT platelet count < 150 × 109/L and severe GT platelet count < 100 × 109/L). Our analysis revealed 138 genome-wide significant loci, explaining 10.4% to 12.1% of the observed variation. Interestingly, we identified previously unknown changes in genetic effects on platelet counts during pregnancy for variants present in PEAR1 and CBL, with PEAR1 variants specifically associated with a faster decline in platelet counts. Furthermore, we found that variants present in PEAR1 and TUBB1 increased susceptibility to GT and severe GT. Our study provides insight into the genetic basis of platelet counts and GT in pregnancy, highlighting the critical role of PEAR1 in decreasing platelet counts during pregnancy and the occurrence of GT. Those with pregnancies carrying specific variants associated with declining platelet counts may experience a more pronounced decrease, thereby elevating the risk of GT. These findings lay the groundwork for further investigation into the biological mechanisms and causal implications of GT.

Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretability

Integrating results from genome-wide association studies (GWASs) and studies of molecular phenotypes such as gene expressions can improve our understanding of the biological functions of trait-associated variants and can help prioritize candidate genes for downstream analysis. Using reference expression quantitative trait locus (eQTL) studies, several methods have been proposed to identify gene-trait associations, primarily based on gene expression imputation. To increase the statistical power by leveraging substantial eQTL sharing across tissues, meta-analysis methods aggregating such gene-based test results across multiple tissues or contexts have been developed as well. However, most existing meta-analysis methods have limited power to identify associations when the gene has weaker associations in only a few tissues and cannot identify the subset of tissues in which the gene is "activated." For this, we developed a cross-tissue subset-based transcriptome-wide association study (CSTWAS) meta-analysis method that improves power under such scenarios and can extract the set of potentially associated tissues. To improve applicability, CSTWAS uses only GWAS summary statistics and pre-computed correlation matrices to identify a subset of tissues that have the maximal evidence of gene-trait association. Through numerical simulations, we found that CSTWAS can maintain a well-calibrated type-I error rate, improves power especially when there is a small number of associated tissues for a gene-trait association, and identifies an accurate associated tissue set. By analyzing GWAS summary statistics of three complex traits and diseases, we demonstrate that CSTWAS could identify biological meaningful signals while providing an interpretation of disease etiology by extracting a set of potentially associated tissues.

An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of ℒ 1 (lasso) and ℒ 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats

The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

A genome-wide association study provides insights into the genetic etiology of 57 essential and non-essential trace elements in humans

Trace elements are important for human health but may exert toxic or adverse effects. Mechanisms of uptake, distribution, metabolism, and excretion are partly under genetic control but have not yet been extensively mapped. Here we report a comprehensive multi-element genome-wide association study of 57 essential and non-essential trace elements. We perform genome-wide association meta-analyses of 14 trace elements in up to 6564 Scandinavian whole blood samples, and genome-wide association studies of 43 trace elements in up to 2819 samples measured only in the Trøndelag Health Study (HUNT). We identify 11 novel genetic loci associated with blood concentrations of arsenic, cadmium, manganese, selenium, and zinc in genome-wide association meta-analyses. In HUNT, several genome-wide significant loci are also indicated for other trace elements. Using two-sample Mendelian randomization, we find several indications of weak to moderate effects on health outcomes, the most precise being a weak harmful effect of increased zinc on prostate cancer. However, independent validation is needed. Our current understanding of trace element-associated genetic variants may help establish consequences of trace elements on human health.

Smoking-associated gene expression alterations in nasal epithelium reveal immune impairment linked to lung cancer risk

BACKGROUND

Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to modifiable lifestyle risk in the form of tobacco smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, 40% of which occur more than 15 years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk remain unclear. We thus set out to examine whether risk stratification in the clinic and in the general population can be improved upon by the addition of genetic data and to explore the mechanisms of the persisting risk in former smokers.

METHODS

We analysed transcriptomic data from accessible airway tissues of 487 subjects, including healthy volunteers and clinic patients of different smoking statuses. We developed a computational model to assess smoking-associated gene expression changes and their reversibility after smoking is stopped, comparing healthy subjects to clinic patients with and without lung cancer.

RESULTS

We find persistent smoking-associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune- and interferon-related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier.

CONCLUSIONS

Our results provide initial evidence for germline-mediated personalized smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

Innovative target mining stratagems to navigate drug repurposing endeavours

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

Novel subtype of obesity influencing the outcomes of sleeve gastrectomy: Familial aggregation of obesity

BACKGROUND

Differences in the preoperative characteristics and weight loss outcomes after sleeve gastrectomy (SG) between patients with familial aggregation of obesity (FAO) and patients with sporadic obesity (SO) have not been elucidated.

AIM

To explore the impact of SG on weight loss and the alleviation of obesity-related comorbidities in individuals with FAO.

METHODS

A total of 193 patients with obesity who underwent SG were selected. Patients with FAO/SO were matched 1:1 by propensity score matching and were categorized into 4 groups based on the number of first-degree relatives with obesity (1SO vs 1FAO, 2SO vs 2FAO). The baseline characteristics, weight loss outcomes, prevalence of obesity-related comorbidities and incidence of major surgery-related complications were compared between groups.

RESULTS

We defined FAO as the presence of two or more first-degree relatives with obesity. Patients with FAO did not initially show significant differences in baseline data, short-term postoperative weight loss, or obesity-related comorbidities when compared to patients with SO preoperatively. However, distinctions between the two groups became evident at the two-year mark, with statistically significant differences in both percentage of total weight loss (P = 0.006) and percentage of excess weight loss (P < 0.001). The FAO group exhibited weaker remission of type 2 diabetes mellitus (T2DM) (P = 0.031), hyperlipidemia (P = 0.012), and non-alcoholic fatty liver disease (NAFLD) (P = 0.003) as well as a lower incidence of acid reflux (P = 0.038).

CONCLUSION

FAO patients is associated with decreased mid-to-long-term weight loss outcomes; the alleviation of T2DM, hyperlipidemia and NAFLD; and decreased incidence of acid reflux postoperatively.

Vitamin D constrains inflammation by modulating the expression of key genes on Chr17q12-21.1

Vitamin D possesses immunomodulatory functions and vitamin D deficiency has been associated with the rise in chronic inflammatory diseases, including asthma (Litonjua and Weiss, 2007). Vitamin D supplementation studies do not provide insight into the molecular genetic mechanisms of vitamin D-mediated immunoregulation. Here, we provide evidence for vitamin D regulation of two human chromosomal loci, Chr17q12-21.1 and Chr17q21.2, reliably associated with autoimmune and chronic inflammatory diseases. We demonstrate increased vitamin D receptor (Vdr) expression in mouse lung CD4+ Th2 cells, differential expression of Chr17q12-21.1 and Chr17q21.2 genes in Th2 cells based on vitamin D status and identify the IL-2/Stat5 pathway as a target of vitamin D signaling. Vitamin D deficiency caused severe lung inflammation after allergen challenge in mice that was prevented by long-term prenatal vitamin D supplementation. Mechanistically, vitamin D induced the expression of the Ikzf3-encoded protein Aiolos to suppress IL-2 signaling and ameliorate cytokine production in Th2 cells. These translational findings demonstrate mechanisms for the immune protective effect of vitamin D in allergic lung inflammation with a strong molecular genetic link to the regulation of both Chr17q12-21.1 and Chr17q21.2 genes and suggest further functional studies and interventional strategies for long-term prevention of asthma and other autoimmune disorders.

Identification of diagnostic biomarkers and immune cell infiltration in coronary artery disease by machine learning, nomogram, and molecular docking

Background

Coronary artery disease (CAD) is still a lethal disease worldwide. This study aims to identify clinically relevant diagnostic biomarker in CAD and explore the potential medications on CAD.

Methods

GSE42148, GSE180081, and GSE12288 were downloaded as the training and validation cohorts to identify the candidate genes by constructing the weighted gene co-expression network analysis. Functional enrichment analysis was utilized to determine the functional roles of these genes. Machine learning algorithms determined the candidate biomarkers. Hub genes were then selected and validated by nomogram and the receiver operating curve. Using CIBERSORTx, the hub genes were further discovered in relation to immune cell infiltrability, and molecules associated with immune active families were analyzed by correlation analysis. Drug screening and molecular docking were used to determine medications that target the four genes.

Results

There were 191 and 230 key genes respectively identified by the weighted gene co-expression network analysis in two modules. A total of 421 key genes found enriched pathways by functional enrichment analysis. Candidate immune-related genes were then screened and identified by the random forest model and the eXtreme Gradient Boosting algorithm. Finally, four hub genes, namely, CSF3R, EED, HSPA1B, and IL17RA, were obtained and used to establish the nomogram model. The receiver operating curve, the area under curve, and the calibration curve were all used to validate the accuracy and usefulness of the diagnostic model. Immune cell infiltrating was examined, and CAD patients were then divided into high- and low-expression groups for further gene set enrichment analysis. Through targeting the hub genes, we also found potential drugs for anti-CAD treatment by using the molecular docking method.

Conclusions

CSF3R, EED, HSPA1B, and IL17RA are potential diagnostic biomarkers for CAD. CAD pathogenesis is greatly influenced by patterns of immune cell infiltration. Promising drugs offers new prospects for the development of CAD therapy.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

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.

Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds

Large genotyping datasets, obtained from high-density single nucleotide polymorphism (SNP) arrays, developed for different livestock species, can be used to describe and differentiate breeds or populations. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this study, we applied the Boruta algorithm, a wrapper of the machine learning random forest algorithm, on a database of 23 European pig breeds (20 autochthonous and three cosmopolitan breeds) genotyped with a 70k SNP chip, to pre-select informative SNPs. To identify different sets of SNPs, these pre-selected markers were then ranked with random forest based on their mean decrease accuracy and mean decrease gene indexes. We evaluated the efficiency of these subsets for breed classification and the usefulness of this approach to detect candidate genes affecting breed-specific phenotypes and relevant production traits that might differ among breeds. The lowest overall classification error (2.3%) was reached with a subpanel including only 398 SNPs (ranked based on their mean decrease accuracy), with no classification error in seven breeds using up to 49 SNPs. Several SNPs of these selected subpanels were in genomic regions in which previous studies had identified signatures of selection or genes associated with morphological or production traits that distinguish the analysed breeds. Therefore, even if these approaches have not been originally designed to identify signatures of selection, the obtained results showed that they could potentially be useful for this purpose.

Associations of measured and genetically predicted leukocyte telomere length with vascular phenotypes: a population-based study

Shorter leukocyte telomere length (LTL) is associated with cardiovascular dysfunction. Whether this association differs between measured and genetically predicted LTL is still unclear. Moreover, the molecular processes underlying the association remain largely unknown. We used baseline data of the Rhineland Study, an ongoing population-based cohort study in Bonn, Germany [56.2% women, age: 55.5 ± 14.0 years (range 30 - 95 years)]. We calculated genetically predicted LTL in 4180 participants and measured LTL in a subset of 1828 participants with qPCR. Using multivariable regression, we examined the association of measured and genetically predicted LTL, and the difference between measured and genetically predicted LTL (ΔLTL), with four vascular functional domains and the overall vascular health. Moreover, we performed epigenome-wide association studies of three LTL measures. Longer measured LTL was associated with better microvascular and cardiac function. Longer predicted LTL was associated with better cardiac function. Larger ΔLTL was associated with better microvascular and cardiac function and overall vascular health, independent of genetically predicted LTL. Several CpGs were associated (p < 1e-05) with measured LTL (n = 5), genetically predicted LTL (n = 8), and ΔLTL (n = 27). Genes whose methylation status was associated with ΔLTL were enriched in vascular endothelial signaling pathways and have been linked to environmental exposures, cardiovascular diseases, and mortality. Our findings suggest that non-genetic causes of LTL contribute to microvascular and cardiac function and overall vascular health, through an effect on the vascular endothelial signaling pathway. Interventions that counteract LTL may thus improve vascular function.

Better together against genetic heterogeneity: A sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank

Genetic effects can be sex-specific, particularly for traits such as testosterone, a sex hormone. While sex-stratified analysis provides easily interpretable sex-specific effect size estimates, the presence of sex-differences in SNP effect implies a SNP×sex interaction. This suggests the usage of the often overlooked joint test, testing for an SNP's main and SNP×sex interaction effects simultaneously. Notably, even without individual-level data, the joint test statistic can be derived from sex-stratified summary statistics through an omnibus meta-analysis. Utilizing the available sex-stratified summary statistics of the UK Biobank, we performed such omnibus meta-analyses for 290 quantitative traits. Results revealed that this approach is robust to genetic effect heterogeneity and can outperform the traditional sex-stratified or sex-combined main effect-only tests. Therefore, we advocate using the omnibus meta-analysis that captures both the main and interaction effects. Subsequent sex-stratified analysis should be conducted for sex-specific effect size estimation and interpretation.

Genome-wide association and Mendelian randomisation analysis among 30,699 Chinese pregnant women identifies novel genetic and molecular risk factors for gestational diabetes and glycaemic traits

AIMS/HYPOTHESIS

Gestational diabetes mellitus (GDM) is the most common disorder in pregnancy; however, its underlying causes remain obscure. This study aimed to investigate the genetic and molecular risk factors contributing to GDM and glycaemic traits.

METHODS

We collected non-invasive prenatal test (NIPT) sequencing data along with four glycaemic and 55 biochemical measurements from 30,699 pregnant women during a 2 year period at Shenzhen Baoan Women's and Children's Hospital in China. Genome-wide association studies (GWAS) were conducted between genotypes derived from NIPTs and GDM diagnosis, baseline glycaemic levels and glycaemic levels after glucose challenges. In total, 3317 women were diagnosed with GDM, while 19,565 served as control participants. The results were replicated using two independent cohorts. Additionally, we performed one-sample Mendelian randomisation to explore potential causal associations between the 55 biochemical measurements and risk of GDM and glycaemic levels.

RESULTS

We identified four genetic loci significantly associated with GDM susceptibility. Among these, MTNR1B exhibited the highest significance (rs10830963-G, OR [95% CI] 1.57 [1.45, 1.70], p=4.42×10-29), although its effect on type 2 diabetes was modest. Furthermore, we found 31 genetic loci, including 14 novel loci, that were significantly associated with the four glycaemic traits. The replication rates of these associations with GDM, fasting plasma glucose levels and 0 h, 1 h and 2 h OGTT glucose levels were 4 out of 4, 6 out of 9, 10 out of 11, 5 out of 7 and 4 out of 4, respectively. Mendelian randomisation analysis suggested that a genetically regulated higher lymphocytes percentage and lower white blood cell count, neutrophil percentage and absolute neutrophil count were associated with elevated glucose levels and an increased risk of GDM.

CONCLUSIONS/INTERPRETATION

Our findings provide new insights into the genetic basis of GDM and glycaemic traits during pregnancy in an East Asian population and highlight the potential role of inflammatory pathways in the aetiology of GDM and variations in glycaemic levels.

DATA AVAILABILITY

Summary statistics for GDM; fasting plasma glucose; 0 h, 1 h and 2h OGTT; and the 55 biomarkers are available in the GWAS Atlas (study accession no.: GVP000001, https://ngdc.cncb.ac.cn/gwas/browse/GVP000001) .

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.

Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis

Genome-wide association studies (GWAS) have associated 76 loci with the risk of developing melanoma. However, understanding the molecular basis of such associations has remained a challenge because most of these loci are in non-coding regions of the genome. Here, we integrated data on epigenomic markers, three-dimensional (3D) genome organization, and expression quantitative trait loci (eQTL) from melanoma-relevant tissues and cell types to gain novel insights into the mechanisms underlying melanoma risk. This integrative approach revealed a total of 151 target genes, both near and far away from the risk loci in linear sequence, with known and novel roles in the etiology of melanoma. Using protein-protein interaction networks, we identified proteins that interact-directly or indirectly-with the products of the target genes. The interacting proteins were enriched for known melanoma driver genes. Further integration of these target genes into tissue-specific gene regulatory networks revealed patterns of gene regulation that connect melanoma to its comorbidities. Our study provides novel insights into the biological implications of genetic variants associated with melanoma risk.

ChIP-seq analysis found IL21R, a target gene of GTF2I-the susceptibility gene for primary biliary cholangitis in Chinese Han

AIMS

Aimed to identify a new susceptibility gene associated with primary biliary cholangitis (PBC) in Chinese Han and investigate the possible mechanism of that gene in PBC.

METHODS

A total of 466 PBC and 694 healthy controls (HC) were included in our study, and genotyping GTF2I gene variants by Sequenom. CD19 + B cells were isolated for Chromatin immunoprecipitation sequencing (ChIP-seq). Additionally, MEME-ChIP was utilized to perform searches for known motifs and de novo motif discovery. The GTF2I ChIP-seq of hematopoietic cell line (K562) results were obtained from ENCODE (GSE176987, GSE177691). The Genomic HyperBrowser was used to determine overlap and hierarchal clustering between ours and ENCODE datasets.

RESULTS

The frequency of the rs117026326 variant T allele was significantly higher in PBC patients than that in HC (20.26% compared with 13.89%, Pc = 1.09E-04). Furthermore, we observed an elevated proportion of GTF2I binding site located in the upstream and 5' UTR of genes in PBC in comparison with HC. Additionally, an in-depth analysis of IL21R region revealed that GTF2I might bind to the IL21R promoter to regulate the expression of the IL21R, with four peaks of GTF2I binding sites, including three increased binding sites in upstream, one increased binding site in 5' UTR. Motif analysis by MEME-ChIP uncovered five significant motifs. A significant overlap between our ChIP and GSE176987, GSE17769 were found by the Genomic HyperBroswer.

CONCLUSIONS

Our study confirmed that GTF2I was associated with PBC in Chinese Han. Furthermore, our gene function analysis indicated that IL21R may be the target gene regulated by GTF2I.

A genome-wide association study meta-analysis in a European sample of stage III/IV grade C periodontitis patients ≤35 years of age identifies new risk loci

AIM

Few genome-wide association studies (GWAS) have been conducted for severe forms of periodontitis (stage III/IV grade C), and the number of known risk genes is scarce. To identify further genetic risk variants to improve the understanding of the disease aetiology, a GWAS meta-analysis in cases with a diagnosis at ≤35 years of age was performed.

MATERIALS AND METHODS

Genotypes from German, Dutch and Spanish GWAS studies of III/IV-C periodontitis diagnosed at age ≤35 years were imputed using TopMed. After quality control, a meta-analysis was conducted on 8,666,460 variants in 1306 cases and 7817 controls with METAL. Variants were prioritized using FUMA for gene-based tests, functional annotation and a transcriptome-wide association study integrating eQTL data.

RESULTS

The study identified a novel genome-wide significant association in the FCER1G gene (p = 1.0 × 10-9 ), which was previously suggestively associated with III/IV-C periodontitis. Six additional genes showed suggestive association with p < 10-5 , including the known risk gene SIGLEC5. HMCN2 showed the second strongest association in this study (p = 6.1 × 10-8 ).

CONCLUSIONS

This study expands the set of known genetic loci for severe periodontitis with an age of onset ≤35 years. The putative functions ascribed to the associated genes highlight the significance of oral barrier tissue stability, wound healing and tissue regeneration in the aetiology of these periodontitis forms and suggest the importance of tissue regeneration in maintaining oral health.

Multicenter integrated analysis of noncoding CRISPRi screens

The ENCODE Consortium's efforts to annotate noncoding cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes. Pooled, noncoding CRISPR screens offer a systematic approach to investigate cis-regulatory mechanisms. The ENCODE4 Functional Characterization Centers conducted 108 screens in human cell lines, comprising >540,000 perturbations across 24.85 megabases of the genome. Using 332 functionally confirmed CRE-gene links in K562 cells, we established guidelines for screening endogenous noncoding elements with CRISPR interference (CRISPRi), including accurate detection of CREs that exhibit variable, often low, transcriptional effects. Benchmarking five screen analysis tools, we find that CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity single guide RNAs. We uncover a subtle DNA strand bias for CRISPRi in transcribed regions with implications for screen design and analysis. Together, we provide an accessible data resource, predesigned single guide RNAs for targeting 3,275,697 ENCODE SCREEN candidate CREs with CRISPRi and screening guidelines to accelerate functional characterization of the noncoding genome.

Systems genetics of influenza A virus-infected mice identifies TRIM21 as a critical regulator of pulmonary innate immune response

Tripartite motif 21 (TRIM21) is a cytosolic Fc receptor that targets antibody-bound, internalized pathogens for destruction. Apart from this intrinsic defense role, TRIM21 is implicated in autoimmune diseases, inflammation, and autophagy. Whether TRIM21 participates in host interactions with influenza A virus (IAV), however, is unknown. By computational modeling of body weight and lung transcriptome data from the BXD parents (C57BL/6 J (B6) and DBA/2 J (D2)) and 41 BXD mouse strains challenged by IAV, we reveal that a Trim21-associated gene network modulates the early host responses to IAV infection. Trim21 transcripts were significantly upregulated in infected mice of both B6 and D2 backgrounds. Its expression was significantly higher in infected D2 than in infected B6 early after infection and significantly correlated with body weight loss. We identified significant trans-eQTL on chromosome 14 that regulates Trim21 expression. Nr1d2 and Il3ra were among the strongest candidate genes. Pathway analysis found Trim21 to be involved in inflammation and immunity related pathways, such as inflammation signaling pathways (TNF, IL-17, and NF-κB), viral detection signaling pathways (NOD-like and RIG-I-like), influenza, and other respiratory viral infections. Knockdown of TRIM21 in human lung epithelial A549 cells significantly augmented IAV-induced expression of IFNB1, IFNL1, CCL5, CXCL10, and IFN-stimulated genes including DDX58 and IFIH1, among others. Our data suggest that a TRIM21-associated gene network is involved in several aspects of inflammation and viral detection mechanisms during IAV infection. We identify and validate TRIM21 as a critical regulator of innate immune responses to IAV in human lung epithelial cells.

Network medicine informed multi-omics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. To date, the challenge to establishing effective treatment for ALS remains formidable, partly due to inadequate translation of existing human genetic findings into actionable ALS-specific pathobiology for subsequent therapeutic development. This study evaluates the feasibility of network medicine methodology via integrating human brain-specific multi-omics data to prioritize drug targets and repurposable treatments for ALS. Using human brain-specific genome-wide quantitative trait loci (x-QTLs) under a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in various known ALS pathobiological pathways, including regulation of T cell activation, monocyte differentiation, and lymphocyte proliferation. Specifically, we leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on brain-specific expression quantitative trait loci (QTL) (eQTL), protein QTLs (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Applying network proximity analysis of predicted ALS-associated gene-coding targets and existing drug-target networks under the human protein-protein interactome (PPI) model, we identified a set of potential repurposable drugs (including Diazoxide, Gefitinib, Paliperidone, and Dimethyltryptamine) for ALS. Subsequent validation established preclinical and clinical evidence for top-prioritized repurposable drugs. In summary, we presented a network-based multi-omics framework to identify potential drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Literature mining discerns latent disease-gene relationships

MOTIVATION

Dysregulation of a gene's function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene-pathology relationships is an ever-daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape project, researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells. However, a similar wealth of knowledge is currently not at our finger-tip when it comes to diseases. This is because the genetic manifestation of a disease is often quite diverse and is confounded by several clinical and demographic covariates.

RESULTS

To circumvent this, we mined ∼18 million PubMed abstracts published till May 2019 and automatically selected ∼4.5 million of them that describe roles of particular genes in disease pathogenesis. Further, we fine-tuned the pretrained bidirectional encoder representations from transformers (BERT) for language modeling from the domain of natural language processing to learn vector representation of entities such as genes, diseases, tissues, cell-types, etc., in a way such that their relationship is preserved in a vector space. The repurposed BERT predicted disease-gene associations that are not cited in the training data, thereby highlighting the feasibility of in silico synthesis of hypotheses linking different biological entities such as genes and conditions.

AVAILABILITY AND IMPLEMENTATION

PathoBERT pretrained model: https://github.com/Priyadarshini-Rai/Pathomap-Model. BioSentVec-based abstract classification model: https://github.com/Priyadarshini-Rai/Pathomap-Model. Pathomap R package: https://github.com/Priyadarshini-Rai/Pathomap.

Mechanisms and functions of lncRNAs linked to autoimmune disease risk alleles

Recent advances in human genomics technologies have helped uncover genetic risk alleles for many complex autoimmune diseases. Intriguingly, over 90% of genome-wide association study (GWAS) risk alleles reside within the non-coding regions of the genome. An emerging new frontier of functional and mechanistic studies have shed light on the functional relevance of risk alleles that lie within long noncoding RNAs (lncRNAs). Here, we review the mechanisms and functional implications of five evolutionarily conserved lncRNAs that display risk allele association with highly prevalent autoimmune diseases.

Meta-Research: Understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of -omics studies. To promote the investigation of understudied genes, we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

Deciphering the causal association and co-disease mechanisms between psoriasis and breast cancer

Background

Prior research has indicated a link between psoriasis and the susceptibility to breast cancer (BC); however, a definitive causal relationship remains elusive. This study sought to elucidate the causal connection and shared underlying mechanisms between psoriasis and BC through bidirectional Mendelian randomization (MR) and bioinformatic approaches.

Methods

We employed a bidirectional MR approach to examine the potential causal connection between psoriasis and BC. Genetic data pertaining to psoriasis and BC were sourced from extensive published genome-wide association studies. The inverse -variance weighted or wald ratio served as the primary method for estimating causal effects. Sensitivity analysis of the MR results was applied with multiple methods. Leveraged datasets from the Gene Expression Omnibus and the Cancer Genome Atlas repositories to identify common differentially expressed genes, shedding light on the shared mechanisms underlying these two conditions.

Results

The MR analysis revealed that when considering psoriasis as an exposure factor, the incidences of BC (OR=1.027) and estrogen receptor negative (ER-) BC (OR=1.054) were higher than in the general population. When using Her2+ BC as an exposure factor, the risk of psoriasis was 0.822 times higher (OR=0.822) than in the general population. Sensitivity analysis indicated that the results were robust. Transcriptome analysis showed that CXCL13 and CCL20 were activated in both BC and psoriasis. Both diseases were also linked to neutrophil chemotaxis, the IL-17 pathway, and the chemokine pathway.

Conclusion

The results suggest that psoriasis may increase the risk of BC, especially ER- BC, while reverse MR suggests a decreased risk of psoriasis in Her2+ BC. Transcriptome analysis revealed a shared mechanism between psoriasis and BC.

BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning

Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabolic regulatory systems based on TFs. Although various deep-learning algorithms have been developed for predicting TFBSs, the prediction performance needs to be improved. This paper proposes a bidirectional encoder representations from transformers (BERT)-based model, called BERT-TFBS, to predict TFBSs solely based on DNA sequences. The model consists of a pre-trained BERT module (DNABERT-2), a convolutional neural network (CNN) module, a convolutional block attention module (CBAM) and an output module. The BERT-TFBS model utilizes the pre-trained DNABERT-2 module to acquire the complex long-term dependencies in DNA sequences through a transfer learning approach, and applies the CNN module and the CBAM to extract high-order local features. The proposed model is trained and tested based on 165 ENCODE ChIP-seq datasets. We conducted experiments with model variants, cross-cell-line validations and comparisons with other models. The experimental results demonstrate the effectiveness and generalization capability of BERT-TFBS in predicting TFBSs, and they show that the proposed model outperforms other deep-learning models. The source code for BERT-TFBS is available at https://github.com/ZX1998-12/BERT-TFBS.

Unraveling the shared genetics of common epilepsies and general cognitive ability

Objective

Cognitive impairment is prevalent among individuals with epilepsy, and it is possible that genetic factors can underlie this relationship. Here, we investigated the potential shared genetic basis of common epilepsies and general cognitive ability (COG).

Methods

We applied linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR) to analyze different aspects of genetic overlap between COG and epilepsies. We used the largest available genome-wide association study data on COG (n = 269,867) and common epilepsies (n = 27,559 cases, 42,436 controls), including the broad phenotypes 'all epilepsy', focal epilepsies and genetic generalized epilepsies (GGE), and as well as specific subtypes. We functionally annotated the identified loci using a variety of biological resources and validated the results in independent samples.

Results

Using MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than 'all epilepsy', GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k - 2.9k variants). The other epilepsy phenotypes were insufficiently powered for analysis. We show extensive genetic overlap between COG and epilepsies with significant negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and 'all epilepsy', and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), 'all epilepsy' (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 1.0 × 10-14; 'all epilepsy': p = 5.6 × 10-3).

Significance

Our study demonstrates a substantial genetic basis shared between epilepsies and COG and identifies novel overlapping genomic loci. Enhancing our understanding of the relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments.

Disparities in Genetic Testing for Neurologic Disorders

BACKGROUND AND OBJECTIVES

Genetic testing is now the standard of care for many neurologic conditions. Health care disparities are unfortunately widespread in the US health care system, but disparities in the utilization of genetic testing for neurologic conditions have not been studied. We tested the hypothesis that access to and results of genetic testing vary according to race, ethnicity, sex, socioeconomic status, and insurance status for adults with neurologic conditions.

METHODS

We analyzed retrospective data from patients who underwent genetic evaluation and testing through our institution's neurogenetics program. We tested for differences between demographic groups in 3 steps of a genetic evaluation pathway: (1) attending a neurogenetic evaluation, (2) completing genetic testing, and (3) receiving a diagnostic result. We compared patients on this genetic evaluation pathway with the population of all neurology outpatients at our institution, using univariate and multivariable logistic regression analyses.

RESULTS

Between 2015 and 2022, a total of 128,440 patients were seen in our outpatient neurology clinics and 2,540 patients underwent genetic evaluation. Black patients were less than half as likely as White patients to be evaluated (odds ratio [OR] 0.49, p < 0.001), and this disparity was similar after controlling for other demographic factors in multivariable analysis. Patients from the least wealthy quartile of zip codes were also less likely to be evaluated (OR 0.67, p < 0.001). Among patients who underwent evaluation, there were no disparities in the likelihood of completing genetic testing, nor in the likelihood of a diagnostic result after adjusting for age. Analyses restricted to specific indications for genetic testing supported these findings.

DISCUSSION

We observed unequal utilization of our clinical neurogenetics program for patients from marginalized and minoritized demographic groups, especially Black patients. Among patients who do undergo evaluation, all groups benefit similarly from genetic testing when it is indicated. Understanding and removing barriers to accessing genetic testing will be essential to health care equity and optimal care for all patients with neurologic disorders.

Sex and statin-related genetic associations at the PCSK9 gene locus: results of genome-wide association meta-analysis

BACKGROUND

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key player of lipid metabolism with higher plasma levels in women throughout their life. Statin treatment affects PCSK9 levels also showing evidence of sex-differential effects. It remains unclear whether these differences can be explained by genetics.

METHODS

We performed genome-wide association meta-analyses (GWAS) of PCSK9 levels stratified for sex and statin treatment in six independent studies of Europeans (8936 women/11,080 men respectively 14,825 statin-free/5191 statin-treated individuals). Loci associated in one of the strata were tested for statin- and sex-interactions considering all independent signals per locus. Independent variants at the PCSK9 gene locus were then used in a stratified Mendelian Randomization analysis (cis-MR) of PCSK9 effects on low-density lipoprotein cholesterol (LDL-C) levels to detect differences of causal effects between the subgroups.

RESULTS

We identified 11 loci associated with PCSK9 in at least one stratified subgroup (p < 1.0 × 10-6), including the PCSK9 gene locus and five other lipid loci: APOB, TM6SF2, FADS1/FADS2, JMJD1C, and HP/HPR. The interaction analysis revealed eight loci with sex- and/or statin-interactions. At the PCSK9 gene locus, there were four independent signals, one with a significant sex-interaction showing stronger effects in men (rs693668). Regarding statin treatment, there were two significant interactions in PCSK9 missense mutations: rs11591147 had stronger effects in statin-free individuals, and rs11583680 had stronger effects in statin-treated individuals. Besides replicating known loci, we detected two novel genome-wide significant associations: one for statin-treated individuals at 6q11.1 (within KHDRBS2) and one for males at 12q24.22 (near KSR2/NOS1), both with significant interactions. In the MR of PCSK9 on LDL-C, we observed significant causal estimates within all subgroups, but significantly stronger causal effects in statin-free subjects compared to statin-treated individuals.

CONCLUSIONS

We performed the first double-stratified GWAS of PCSK9 levels and identified multiple biologically plausible loci with genetic interaction effects. Our results indicate that the observed sexual dimorphism of PCSK9 and its statin-related interactions have a genetic basis. Significant differences in the causal relationship between PCSK9 and LDL-C suggest sex-specific dosages of PCSK9 inhibitors.

The genetic architecture of multimodal human brain age

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Human pangenome analysis of sequences missing from the reference genome reveals their widespread evolutionary, phenotypic, and functional roles

Nonreference sequences (NRSs) are DNA sequences present in global populations but absent in the current human reference genome. However, the extent and functional significance of NRSs in the human genomes and populations remains unclear. Here, we de novo assembled 539 genomes from five genetically divergent human populations using long-read sequencing technology, resulting in the identification of 5.1 million NRSs. These were merged into 45284 unique NRSs, with 29.7% being novel discoveries. Among these NRSs, 38.7% were common across the five populations, and 35.6% were population specific. The use of a graph-based pangenome approach allowed for the detection of 565 transcript expression quantitative trait loci on NRSs, with 426 of these being novel findings. Moreover, 26 NRS candidates displayed evidence of adaptive selection within human populations. Genes situated in close proximity to or intersecting with these candidates may be associated with metabolism and type 2 diabetes. Genome-wide association studies revealed 14 NRSs to be significantly associated with eight phenotypes. Additionally, 154 NRSs were found to be in strong linkage disequilibrium with 258 phenotype-associated SNPs in the GWAS catalogue. Our work expands the understanding of human NRSs and provides novel insights into their functions, facilitating evolutionary and biomedical researches.

Haplotype-resolved 3D chromatin architecture of the hybrid pig

In diploid mammals, allele-specific three-dimensional (3D) genome architecture may lead to imbalanced gene expression. Through ultradeep in situ Hi-C sequencing of three representative somatic tissues (liver, skeletal muscle, and brain) from hybrid pigs generated by reciprocal crosses of phenotypically and physiologically divergent Berkshire and Tibetan pigs, we uncover extensive chromatin reorganization between homologous chromosomes across multiple scales. Haplotype-based interrogation of multi-omic data revealed the tissue dependence of 3D chromatin conformation, suggesting that parent-of-origin-specific conformation may drive gene imprinting. We quantify the effects of genetic variations and histone modifications on allelic differences of long-range promoter-enhancer contacts, which likely contribute to the phenotypic differences between the parental pig breeds. We also observe the fine structure of somatically paired homologous chromosomes in the pig genome, which has a functional implication genome-wide. This work illustrates how allele-specific chromatin architecture facilitates concomitant shifts in allele-biased gene expression, as well as the possible consequential phenotypic changes in mammals.

SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations

Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.

Genetic evidence for T-wave area from 12-lead electrocardiograms to monitor cardiovascular diseases in patients taking diabetes medications

Aims Many studies indicated use of diabetes medications can influence the electrocardiogram (ECG), which remains the simplest and fastest tool for assessing cardiac functions. However, few studies have explored the role of genetic factors in determining the relationship between the use of diabetes medications and ECG trace characteristics (ETC). Methods Genome-wide association studies (GWAS) were performed for 168 ETCs extracted from the 12-lead ECGs of 42,340 Europeans in the UK Biobank. The genetic correlations, causal relationships, and phenotypic relationships of these ETCs with medication usage, as well as the risk of cardiovascular diseases (CVDs), were estimated by linkage disequilibrium score regression (LDSC), Mendelian randomization (MR), and regression model, respectively. Results The GWAS identified 124 independent single nucleotide polymorphisms (SNPs) that were study-wise and genome-wide significantly associated with at least one ETC. Regression model and LDSC identified significant phenotypic and genetic correlations of T-wave area in lead aVR (aVR_T-area) with usage of diabetes medications (ATC code: A10 drugs, and metformin), and the risks of ischemic heart disease (IHD) and coronary atherosclerosis (CA). MR analyses support a putative causal effect of the use of diabetes medications on decreasing aVR_T-area, and on increasing risk of IHD and CA. ConclusionPatients taking diabetes medications are prone to have decreased aVR_T-area and an increased risk of IHD and CA. The aVR_T-area is therefore a potential ECG marker for pre-clinical prediction of IHD and CA in patients taking diabetes medications.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BACKGROUND

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

METHODS

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

RESULTS

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

CONCLUSIONS

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Rare copy number variation in autoimmune Addison's disease

Autoimmune Addison's disease (AAD) is a rare but life-threatening endocrine disorder caused by an autoimmune destruction of the adrenal cortex. A previous genome-wide association study (GWAS) has shown that common variants near immune-related genes, which mostly encode proteins participating in the immune response, affect the risk of developing this condition. However, little is known about the contribution of copy number variations (CNVs) to AAD susceptibility. We used the genome-wide genotyping data from Norwegian and Swedish individuals (1,182 cases and 3,810 controls) to investigate the putative role of CNVs in the AAD aetiology. Although the frequency of rare CNVs was similar between cases and controls, we observed that larger deletions (>1,000 kb) were more common among patients (OR = 4.23, 95% CI 1.85-9.66, p = 0.0002). Despite this, none of the large case-deletions were conclusively pathogenic, and the clinical presentation and an AAD-polygenic risk score were similar between cases with and without the large CNVs. Among deletions exclusive to individuals with AAD, we highlight two ultra-rare deletions in the genes LRBA and BCL2L11, which we speculate might have contributed to the polygenic risk in these carriers. In conclusion, rare CNVs do not appear to be a major cause of AAD but further studies are needed to ascertain the potential contribution of rare deletions to the polygenic load of AAD susceptibility.

Validation of Enhancer Regions in Primary Human Neural Progenitor Cells using Capture STARR-seq

Genome-wide association studies (GWAS) and expression analyses implicate noncoding regulatory regions as harboring risk factors for psychiatric disease, but functional characterization of these regions remains limited. We performed capture STARR-sequencing of over 78,000 candidate regions to identify active enhancers in primary human neural progenitor cells (phNPCs). We selected candidate regions by integrating data from NPCs, prefrontal cortex, developmental timepoints, and GWAS. Over 8,000 regions demonstrated enhancer activity in the phNPCs, and we linked these regions to over 2,200 predicted target genes. These genes are involved in neuronal and psychiatric disease-associated pathways, including dopaminergic synapse, axon guidance, and schizophrenia. We functionally validated a subset of these enhancers using mutation STARR-sequencing and CRISPR deletions, demonstrating the effects of genetic variation on enhancer activity and enhancer deletion on gene expression. Overall, we identified thousands of highly active enhancers and functionally validated a subset of these enhancers, improving our understanding of regulatory networks underlying brain function and disease.

Integrating multi-omics data to reveal the effect of genetic variant rs6430538 on Alzheimer's disease risk

Introduction

Growing evidence highlights a potential genetic overlap between Alzheimer's disease (AD) and Parkinson's disease (PD); however, the role of the PD risk variant rs6430538 in AD remains unclear.

Methods

In Stage 1, we investigated the risk associated with the rs6430538 C allele in seven large-scale AD genome-wide association study (GWAS) cohorts. In Stage 2, we performed expression quantitative trait loci (eQTL) analysis to calculate the cis-regulated effect of rs6430538 on TMEM163 in both AD and neuropathologically normal samples. Stage 3 involved evaluating the differential expression of TMEM163 in 4 brain tissues from AD cases and controls. Finally, in Stage 4, we conducted a transcriptome-wide association study (TWAS) to identify any association between TMEM163 expression and AD.

Results

The results showed that genetic variant rs6430538 C allele might increase the risk of AD. eQTL analysis revealed that rs6430538 up-regulated TMEM163 expression in AD brain tissue, but down-regulated its expression in normal samples. Interestingly, TMEM163 showed differential expression in entorhinal cortex (EC) and temporal cortex (TCX). Furthermore, the TWAS analysis indicated strong associations between TMEM163 and AD in various tissues.

Discussion

In summary, our findings suggest that rs6430538 may influence AD by regulating TMEM163 expression. These discoveries may open up new opportunities for therapeutic strategies targeting AD.

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.

Polygenic risk score-based phenome-wide association study of head and neck cancer across two large biobanks

BACKGROUND

Numerous observational studies have highlighted associations of genetic predisposition of head and neck squamous cell carcinoma (HNSCC) with diverse risk factors, but these findings are constrained by design limitations of observational studies. In this study, we utilized a phenome-wide association study (PheWAS) approach, incorporating a polygenic risk score (PRS) derived from a wide array of genomic variants, to systematically investigate phenotypes associated with genetic predisposition to HNSCC. Furthermore, we validated our findings across heterogeneous cohorts, enhancing the robustness and generalizability of our results.

METHODS

We derived PRSs for HNSCC and its subgroups, oropharyngeal cancer and oral cancer, using large-scale genome-wide association study summary statistics from the Genetic Associations and Mechanisms in Oncology Network. We conducted a comprehensive investigation, leveraging genotyping data and electronic health records from 308,492 individuals in the UK Biobank and 38,401 individuals in the Penn Medicine Biobank (PMBB), and subsequently performed PheWAS to elucidate the associations between PRS and a wide spectrum of phenotypes.

RESULTS

We revealed the HNSCC PRS showed significant association with phenotypes related to tobacco use disorder (OR, 1.06; 95% CI, 1.05-1.08; P = 3.50 × 10-15), alcoholism (OR, 1.06; 95% CI, 1.04-1.09; P = 6.14 × 10-9), alcohol-related disorders (OR, 1.08; 95% CI, 1.05-1.11; P = 1.09 × 10-8), emphysema (OR, 1.11; 95% CI, 1.06-1.16; P = 5.48 × 10-6), chronic airway obstruction (OR, 1.05; 95% CI, 1.03-1.07; P = 2.64 × 10-5), and cancer of bronchus (OR, 1.08; 95% CI, 1.04-1.13; P = 4.68 × 10-5). These findings were replicated in the PMBB cohort, and sensitivity analyses, including the exclusion of HNSCC cases and the major histocompatibility complex locus, confirmed the robustness of these associations. Additionally, we identified significant associations between HNSCC PRS and lifestyle factors related to smoking and alcohol consumption.

CONCLUSIONS

The study demonstrated the potential of PRS-based PheWAS in revealing associations between genetic risk factors for HNSCC and various phenotypic traits. The findings emphasized the importance of considering genetic susceptibility in understanding HNSCC and highlighted shared genetic bases between HNSCC and other health conditions and lifestyles.

Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci

Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6, DALRD3, CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index.. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder.

Investigation of the acute pathogenesis of spondyloarthritis/HLA-B27-associated anterior uveitis based on genome-wide association analysis and single-cell transcriptomics

BACKGROUND

Patients with spondyloarthritis (SpA)/HLA-B27-associated acute anterior uveitis (AAU) experience recurring acute flares, which pose significant visual and financial challenges. Despite established links between SpA and HLA-B27-associated AAU, the exact mechanism involved remains unclear, and further understanding is needed for effective prevention and treatment.

METHODS

To investigate the acute pathogenesis of SpA/HLA-B27-associated AAU, Mendelian randomization (MR) and single-cell transcriptomic analyses were employed. The MR incorporated publicly available protein quantitative trait locus data from previous studies, along with genome-wide association study data from public databases. Causal relationships between plasma proteins and anterior uveitis were assessed using two-sample MR. Additionally, colocalization analysis was performed using Bayesian colocalization. Single-cell transcriptome analysis utilized the anterior uveitis dataset from the Gene Expression Omnibus (GEO) database. Dimensionality reduction, clustering, transcription factor analysis, pseudotime analysis, and cell communication analysis were subsequently conducted to explore the underlying mechanisms involved.

RESULTS

Mendelian randomization analysis revealed that circulating levels of AIF1 and VARS were significantly associated with a reduced risk of developing SpA/HLA-B27-associated AAU, with AIF1 showing a robust correlation with anterior uveitis onset. Colocalization analysis supported these findings. Single-cell transcriptome analysis showed predominant AIF1 expression in myeloid cells, which was notably lower in the HLA-B27-positive group. Pseudotime analysis revealed dendritic cell terminal positions in differentiation branches, accompanied by gradual decreases in AIF1 expression. Based on cell communication analysis, CD141+CLEC9A+ classic dendritic cells (cDCs) and the APP pathway play crucial roles in cellular communication in the Spa/HLA-B27 group.

CONCLUSIONS

AIF1 is essential for the pathogenesis of SpA/HLA-B27-associated AAU. Myeloid cell differentiation into DCs and decreased AIF1 levels are also pivotal in this process.

Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework

BACKGROUND

The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies.

METHODS

We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies.

RESULTS

We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue.

CONCLUSION

Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

Classification of Schizophrenia, Bipolar Disorder and Major Depressive Disorder with Comorbid Traits and Deep Learning Algorithms

Recent GWASs have demonstrated that comorbid disorders share genetic liabilities. But whether and how these shared liabilities can be used for the classification and differentiation of comorbid disorders remains unclear. In this study, we use polygenic risk scores (PRSs) estimated from 42 comorbid traits and the deep neural networks (DNN) architecture to classify and differentiate schizophrenia (SCZ), bipolar disorder (BIP) and major depressive disorder (MDD). Multiple PRSs were obtained for individuals from the schizophrenia (SCZ) (cases = 6,317, controls = 7,240), bipolar disorder (BIP) (cases = 2,634, controls 4,425) and major depressive disorder (MDD) (cases = 1,704, controls = 3,357) datasets, and classification models were constructed with and without the inclusion of PRSs of the target (SCZ, BIP or MDD). Models with the inclusion of target PRSs performed well as expected. Surprisingly, we found that SCZ could be classified with only the PRSs from 35 comorbid traits (not including the target SCZ and directly related traits) (accuracy 0.760 ± 0.007, AUC 0.843 ± 0.005). Similar results were obtained for BIP (33 traits, accuracy 0.768 ± 0.007, AUC 0.848 ± 0.009), and MDD (36 traits, accuracy 0.794 ± 0.010, AUC 0.869 ± 0.004). Furthermore, these PRSs from comorbid traits alone could effectively differentiate unaffected controls, SCZ, BIP, and MDD patients (average categorical accuracy 0.861 ± 0.003, average AUC 0.961 ± 0.041). These results suggest that the shared liabilities from comorbid traits alone may be sufficient to classify SCZ, BIP and MDD. More importantly, these results imply that a data-driven and objective diagnosis and differentiation of SCZ, BIP and MDD may be feasible.