Exome sequencing of Finnish isolates enhances rare-variant association power

Whole-genome sequencing identifies variants in ANK1, LRRN1, HAS1, and other genes and regulatory regions for stroke in type 1 diabetes

Individuals with type 1 diabetes (T1D) carry a markedly increased risk of stroke, with distinct clinical and neuroimaging characteristics as compared to those without diabetes. Using whole-exome or whole-genome sequencing of 1,051 individuals with T1D, we aimed to find rare and low-frequency genomic variants associated with stroke in T1D. We analysed the genome comprehensively with single-variant analyses, gene aggregate analyses, and aggregate analyses on genomic windows, enhancers and promoters. In addition, we attempted replication in T1D using a genome-wide association study (N = 3,945) and direct genotyping (N = 3,263), and in the general population from the large-scale population-wide FinnGen project and UK Biobank summary statistics. We identified a rare missense variant on SREBF1 exome-wide significantly associated with stroke (rs114001633, p.Pro227Leu, p-value = 7.30 × 10-8), which replicated for hemorrhagic stroke in T1D. Using gene aggregate analysis, we identified exome-wide significant genes: ANK1 and LRRN1 displayed replication evidence in T1D, and LRRN1, HAS1 and UACA in the general population (UK Biobank). Furthermore, we performed sliding-window analyses and identified 14 genome-wide significant windows for stroke on 4q33-34.1, of which two replicated in T1D, and a suggestive genomic window on LINC01500, which replicated in T1D. Finally, we identified a suggestively stroke-associated TRPM2-AS promoter (p-value = 5.78 × 10-6) with borderline significant replication in T1D, which we validated with an in vitro cell-based assay. Due to the rarity of the identified genetic variants, future replication of the genomic regions represented here is required with sequencing of individuals with T1D. Nevertheless, we here report the first genome-wide analysis on stroke in individuals with diabetes.

Relationship between low molecular weight heparin calcium therapy and prognosis in severe acute kidney injury in sepsis: Mendelian randomized analysis and retrospective study

Background

Sepsis-associated acute kidney injury (SA-AKI) poses an independent risk for mortality due to the absence of highly sensitive biomarkers and a specific treatment plan.

Objective

Investigate the association between low molecular weight heparin (LMWH) calcium therapy and prognosis in critically ill SA-AKI patients, and assess the causal relationship through Mendelian randomization (MR) analysis.

Methods

A single-center, retrospective, cross-sectional study included 90 SA-AKI patients and 30 septic patients without acute kidney injury (AKI) from the intensive care unit (ICU) of the First Hospital of Lanzhou University. SA-AKI patients were categorized into control or LMWH groups based on LMWH calcium usage. Primary outcome was renal function recovery, with secondary outcomes including 28-day mortality, ICU stay length, number of renal replacement therapy (RRT) recipients, and 90-day survival. MR and related sensitivity analyses explored causal effects.

Results

The combination of heparin-binding protein (HBP), heparanase (HPA), and neutrophil gelatinase-associated lipocalin (NGAL) demonstrated high diagnostic value for SA-AKI. MR analysis suggested a potential causal link between gene-predicted HBP and AKI (OR: 1.369, 95%CI: 1.040-1.801, p = 0.024). In the retrospective study, LMWH-treated patients exhibited improved renal function, reduced levels of HPA, HBP, Syndecan-1, and inflammation, along with enhanced immune function compared to controls. However, LMWH did not impact 28-day mortality, 90-day survival, or ICU stay length.

Conclusion

LMWH could enhance renal function in SA-AKI patients. MR analysis supports this causal link, underscoring the need for further validation in randomized controlled trials.

Drug-target Mendelian randomization analysis supports lowering plasma ANGPTL3, ANGPTL4, and APOC3 levels as strategies for reducing cardiovascular disease risk

Aims

APOC3, ANGPTL3, and ANGPTL4 are circulating proteins that are actively pursued as pharmacological targets to treat dyslipidaemia and reduce the risk of atherosclerotic cardiovascular disease. Here, we used human genetic data to compare the predicted therapeutic and adverse effects of APOC3, ANGPTL3, and ANGPTL4 inactivation.

Methods and results

We conducted drug-target Mendelian randomization analyses using variants in proximity to the genes associated with circulating protein levels to compare APOC3, ANGPTL3, and ANGPTL4 as drug targets. We obtained exposure and outcome data from large-scale genome-wide association studies and used generalized least squares to correct for linkage disequilibrium-related correlation. We evaluated five primary cardiometabolic endpoints and screened for potential side effects across 694 disease-related endpoints, 43 clinical laboratory tests, and 11 internal organ MRI measurements. Genetically lowering circulating ANGPTL4 levels reduced the odds of coronary artery disease (CAD) [odds ratio, 0.57 per s.d. protein (95% CI 0.47-0.70)] and Type 2 diabetes (T2D) [odds ratio, 0.73 per s.d. protein (95% CI 0.57-0.94)]. Genetically lowering circulating APOC3 levels also reduced the odds of CAD [odds ratio, 0.90 per s.d. protein (95% CI 0.82-0.99)]. Genetically lowered ANGPTL3 levels via common variants were not associated with CAD. However, meta-analysis of protein-truncating variants revealed that ANGPTL3 inactivation protected against CAD (odds ratio, 0.71 per allele [95%CI, 0.58-0.85]). Analysis of lowered ANGPTL3, ANGPTL4, and APOC3 levels did not identify important safety concerns.

Conclusion

Human genetic evidence suggests that therapies aimed at reducing circulating levels of ANGPTL3, ANGPTL4, and APOC3 reduce the risk of CAD. ANGPTL4 lowering may also reduce the risk of T2D.

Genome-wide characterization of circulating metabolic biomarkers

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

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.

Proteome-wide Mendelian randomization identifies causal plasma proteins in lung cancer

Plasma proteins are promising biomarkers and potential drug targets in lung cancer. To evaluate the causal association between plasma proteins and lung cancer, we performed proteome-wide Mendelian randomization meta-analysis (PW-MR-meta) based on lung cancer genome-wide association studies (GWASs), protein quantitative trait loci (pQTLs) of 4,719 plasma proteins in deCODE and 4,775 in Fenland. Further, causal-protein risk score (CPRS) was developed based on causal proteins and validated in the UK Biobank. 270 plasma proteins were identified using PW-MR meta-analysis, including 39 robust causal proteins (both FDR-q < 0.05) and 78 moderate causal proteins (FDR-q < 0.05 in one and p < 0.05 in another). The CPRS had satisfactory performance in risk stratification for lung cancer (top 10% CPRS:Hazard ratio (HR) (95%CI):4.33(2.65-7.06)). The CPRS [AUC (95%CI): 65.93 (62.91-68.78)] outperformed the traditional polygenic risk score (PRS) [AUC (95%CI): 55.71(52.67-58.59)]. Our findings offer further insight into the genetic architecture of plasma proteins for lung cancer susceptibility.

Mineral Metabolism and Polycystic Ovary Syndrome and Metabolic Risk Factors: A Mendelian Randomization Study

Observational investigations recommend that mineral supplements were associated with a higher risk of polycystic ovary syndrome (PCOS) and its risk factors (insulin resistance, hyperandrogenism, and obesity), but the relationship with risk of PCOS, hyperandrogenism, obesity, and insulin resistance was unclear. This study was to investigate the potential causal impact of genetically predicted levels of magnesium (Mg), calcium (Ca), selenium (Se), zinc (Zn), iron (Fe), and omega-3 (ω-3) on polycystic ovary syndrome (PCOS) and its associated risk factors. A two-sample Mendelian randomization (MR) analysis was conducted. The genetic variations obtained from GWAS of individuals with European ancestry were found to be associated with the genetically predicted levels of Ca, Mg, Zn, Se, Fe, or ω-3. The data obtained from the FinnGen Consortium and MAGIC were utilized for the outcome of GWAS. The study found that there was a correlation between genetically predicted higher levels of Se and a reduced risk of insulin resistance, with a decrease of 2.2% according to random-effect IVW (OR 0.978, 95% CI 0.960-0.996, p = 0.015). The association between genetically determined mineral levels and PCOS was found to be limited, with an odds ratio (OR) ranging from 0.875 (95% CI: 0.637-1.202, p value = 0.411) for Ca. Limited scientific proof was found for the efficacy of other genetically determined mineral levels on hyperandrogenism, obesity, and insulin resistance. These findings suggested a causal relationship between genetically predicted higher levels of Se and a reduced risk of insulin resistance. Nonetheless, there is limited evidence supporting a causal association between various genetically determined mineral levels and the risk factors associated with PCOS.

Clinical and biochemical associations of urinary metabolites: quantitative epidemiological approach on renal-cardiometabolic biomarkers

BACKGROUND

Urinary metabolomics has demonstrated considerable potential to assess kidney function and its metabolic corollaries in health and disease. However, applications in epidemiology remain sparse due to technical challenges.

METHODS

We added 17 metabolites to an open-access urinary nuclear magnetic resonance metabolomics platform, extending the panel to 61 metabolites (n = 994). We also introduced automated quantification for 11 metabolites, extending the panel to 12 metabolites (+creatinine). Epidemiological associations between these 12 metabolites and 49 clinical measures were studied in three independent cohorts (up to 5989 participants). Detailed regression analyses with various confounding factors are presented for body mass index (BMI) and smoking.

RESULTS

Sex-specific population reference concentrations and distributions are provided for 61 urinary metabolites (419 men and 575 women), together with methodological intra-assay metabolite variations as well as the biological intra-individual and epidemiological population variations. For the 12 metabolites, 362 associations were found. These are mostly novel and reflect potential molecular proxies to estimate kidney function, as the associations cannot be simply explained by estimated glomerular filtration rate. Unspecific renal excretion results in leakage of amino acids (and glucose) to urine in all individuals. Seven urinary metabolites associated with smoking, providing questionnaire-independent proxy measures of smoking status in epidemiological studies. Common confounders did not affect metabolite associations with smoking, but insulin had a clear effect on most associations with BMI, including strong effects on 2-hydroxyisobutyrate, valine, alanine, trigonelline and hippurate.

CONCLUSIONS

Urinary metabolomics provides new insight on kidney function and related biomarkers on the renal-cardiometabolic system, supporting large-scale applications in epidemiology.

TargetGene: a comprehensive database of cell-type-specific target genes for genetic variants

Annotating genetic variants to their target genes is of great importance in unraveling the causal variants and genetic mechanisms that underlie complex diseases. However, disease-associated genetic variants are often located in non-coding regions and manifest context-specific effects, making it challenging to accurately identify the target genes and regulatory mechanisms. Here, we present TargetGene (https://ngdc.cncb.ac.cn/targetgene/), a comprehensive database reporting target genes for human genetic variants from various aspects. Specifically, we collected a comprehensive catalog of multi-omics data at the single-cell and bulk levels and from various human tissues, cell types and developmental stages. To facilitate the identification of Single Nucleotide Polymorphism (SNP)-to-gene connections, we have implemented multiple analytical tools based on chromatin co-accessibility, 3D interaction, enhancer activities and quantitative trait loci, among others. We applied the pipeline to evaluate variants from nearly 1300 Genome-wide association studies (GWAS) and assembled a comprehensive atlas of multiscale regulation of genetic variants. TargetGene is equipped with user-friendly web interfaces that enable intuitive searching, navigation and browsing through the results. Overall, TargetGene provides a unique resource to empower researchers to study the regulatory mechanisms of genetic variants in complex human traits.

Deciphering the genetic structure of the Quebec founder population using genealogies

Using genealogy to study the demographic history of a population makes it possible to overcome the models and assumptions often used in population genetics. The Quebec founder population is one of the few populations in the world having access to the complete genealogy of the last 400 years. The goal of this study is to follow the evolution of the Quebec population structure over time from the beginning of European colonization until the present day. To do so, we calculated the kinship coefficients of all ancestors' pairs in the ascending genealogy of 665 subjects from eight regional and ethnocultural groups per 25-year period. We show that the Quebec population structure appeared progressively in the St. Lawrence valley as early as 1750 with the distinction of the Saguenay and Gaspesian groups. At that time, the ancestors of two groups, the Sagueneans and the Acadians from the Gaspé Peninsula, experienced a marked increase in kinship and inbreeding levels which have shaped the structure and led to the contemporary population structure. Interestingly, this structure arose before the colonization of the Saguenay region and at the very beginning of the Gaspé Peninsula settlement. The resulting regional founder effects in these groups led to differences in the present-day identity-by-descent sharing, the Gaspé and North Shore groups sharing more large segments and the Sagueneans more short segments. This is also reflected by the distribution of the number of most recent common ancestors at different generations and their genetic contribution to the studied subjects.

Causal Relationships between Air Pollutant Exposure and Bone Mineral Density and the Risk of Bone Fractures: Evidence from a Two-Stage Mendelian Randomization Analysis

A number of studies from the literature have suggested that exposure to air pollutants is associated with a declined bone mineral density (BMD), and increased risks of osteoporosis (OP) and bone fractures. This study was performed to systemically assess the genetically causal associations of air pollutants with site-/age-specific BMD and risk of bone fractures with the implementation of two-sample Mendelian randomization (TSMR) and multivariate Mendelian randomization (MVMR). The TSMR analysis was implemented to infer the causal associations between air pollutants and BMD and the risk of bone fractures, additional MVMR analysis was used to further estimate the direct causal effects between air pollutants and BMD, the occurrence of OP, and bone fractures. The results showed that NOx exposure contributed to lower femoral neck BMD (FN-BMD) (β = -0.71, 95%CI: -1.22, -0.20, p = 0.006) and total body BMD (TB-BMD) (β = -0.55, 95%CI: -0.90, -0.21, p = 0.002). Additionally, exposure to PM10 was found to be associated with a decreased TB-BMD (B β = -0.42, 95%CI: -0.66, -0.18, p = 0.001), further age-specific subgroup analysis demonstrated the causal effect of PM10 exposure on the decreased TB-BMD in a subgroup aged 45 to 60 years (β = -0.70, 95%CI: -1.12, -0.29, p = 0.001). Moreover, the findings of the MVMR analysis implied that there was a direct causal effect between PM10 exposure and the decreased TB-BMD (45 < age < 60), after adjusting for PM2.5 and PM2.5 -10 exposure. Our study provides additional evidence to support the causal associations of higher concentrations of air pollutant exposure with decreased BMD, especially in those populations aged between 45 to 60 years, suggesting that early intervention measures and public policy should be considered to improve public health awareness and promote bone health.

Targeting TRP Channels for Pain, Itch and Neurogenic Inflammation

Transient receptor potential (TRP) channels are multifunctional signaling molecules with important roles in health and disease [...].

A resource of induced pluripotent stem cell (iPSC) lines including clinical, genomic, and cellular data from genetically isolated families with mood and psychotic disorders

Genome-wide (GWAS) and copy number variant (CNV) association studies have reproducibly identified numerous risk alleles associated with bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ), but biological characterization of these alleles lags gene discovery, owing to the inaccessibility of live human brain cells and inadequate animal models for human psychiatric conditions. Human-derived induced pluripotent stem cells (iPSCs) provide a renewable cellular reagent that can be differentiated into living, disease-relevant cells and 3D brain organoids carrying the full complement of genetic variants present in the donor germline. Experimental studies of iPSC-derived cells allow functional characterization of risk alleles, establishment of causal relationships between genes and neurobiology, and screening for novel therapeutics. Here we report the creation and availability of an iPSC resource comprising clinical, genomic, and cellular data obtained from genetically isolated families with BD and related conditions. Results from the first 324 study participants, 61 of whom have validated pluripotent clones, show enrichment of rare single nucleotide variants and CNVs overlapping many known risk genes and pathogenic CNVs. This growing iPSC resource is available to scientists pursuing functional genomic studies of BD and related conditions.

Walnut peptide alleviates obesity, inflammation and dyslipidemia in mice fed a high-fat diet by modulating the intestinal flora and metabolites

Introduction

Obesity is a chronic disease in which the body stores excess energy in the form of fat, and intestinal bacterial metabolism and inflammatory host phenotypes influence the development of obesity. Walnut peptide (WP) is a small molecule biopeptide, and the mechanism of action of WP against metabolic disorders has not been fully elucidated. In this study, we explored the potential intervention mechanism of WP on high-fat diet (HFD)-induced obesity through bioinformatics combined with animal experiments.

Methods

PPI networks of Amino acids and their metabolites in WP (AMWP) and "obesity" and "inflammation" diseases were searched and constructed by using the database, and their core targets were enriched and analyzed. Subsequently, Cytoscape software was used to construct the network diagram of the AMWP-core target-KEGG pathway and analyze the topological parameters. MOE2019.0102 was used to verify the molecular docking of core AMWP and core target. Subsequently, an obese Mice model induced by an HFD was established, and the effects of WP on obesity were verified by observing weight changes, glucose, and lipid metabolism levels, liver pathological changes, the size of adipocytes in groin adipose tissue, inflammatory infiltration of colon tissue, and intestinal microorganisms and their metabolites.

Results

The network pharmacology and molecular docking showed that glutathione oxide may be the main active component of AMWP, and its main targets may be EGFR, NOS3, MMP2, PLG, PTGS2, AR. Animal experiments showed that WP could reduce weight gain and improve glucose-lipid metabolism in HFD-induced obesity model mice, attenuate hepatic lesions reduce the size of adipocytes in inguinal adipose tissue, and reduce the inflammatory infiltration in colonic tissue. In addition, the abundance and diversity of intestinal flora were remodeled, reducing the phylum Firmicutes/Bacteroidetes (F/B) ratio, while the intestinal mucosal barrier was repaired, altering the content of short-chain fatty acids (SCFAs), and alleviating intestinal inflammation in HFD-fed mice. These results suggest that WP intervenes in HFD-induced obesity and dyslipidemia by repairing the intestinal microenvironment, regulating flora metabolism and anti-inflammation.

Discussion

Our findings suggest that WP intervenes in HFD-induced obesity and dyslipidemia by repairing the intestinal microenvironment, regulating flora metabolism, and exerting anti-inflammatory effects. Thus, WP may be a potential therapeutic strategy for preventing and treating metabolic diseases, and for alleviating the intestinal flora disorders induced by these diseases. This provides valuable insights for the development of WP therapies.

A common variant that alters SUN1 degradation associates with hepatic steatosis and metabolic traits in multiple cohorts

BACKGROUND & AIMS

Non-alcoholic fatty liver disease (NAFLD), and its progressive form steatohepatitis (NASH), represent a genetically and phenotypically diverse entity for which there is no approved therapy, making it imperative to define the spectrum of pathways contributing to its pathogenesis. Rare variants in genes encoding nuclear envelope proteins cause lipodystrophy with early-onset NAFLD/NASH; we hypothesized that common variants in nuclear envelope-related genes might also contribute to hepatic steatosis and NAFLD.

METHODS

Using hepatic steatosis as the outcome of interest, we performed an association meta-analysis of nuclear envelope-related coding variants in three large discovery cohorts (N >120,000 participants), followed by phenotype association studies in large validation cohorts (N >600,000) and functional testing of the top steatosis-associated variant in cell culture.

RESULTS

A common protein-coding variant, rs6461378 (SUN1 H118Y), was the top steatosis-associated variant in our association meta-analysis (p <0.001). In ancestrally distinct validation cohorts, rs6461378 associated with histologic NAFLD and with NAFLD-related metabolic traits including increased serum fatty acids, type 2 diabetes, hypertension, cardiovascular disease, and decreased HDL. SUN1 H118Y was subject to increased proteasomal degradation relative to wild-type SUN1 in cells, and SUN1 H118Y-expressing cells exhibited insulin resistance and increased lipid accumulation.

CONCLUSIONS

Collectively, these data support a potential causal role for the common SUN1 variant rs6461378 in NAFLD and metabolic disease.

IMPACT AND IMPLICATIONS

Non-alcoholic fatty liver disease (NAFLD), with an estimated global prevalence of nearly 30%, is a growing cause of morbidity and mortality for which there is no approved pharmacologic therapy. Our data provide a rationale for broadening current concepts of NAFLD genetics and pathophysiology to include the nuclear envelope, and particularly Sad1 and UNC84 domain containing 1 (SUN1), as novel contributors to this common liver disease. Furthermore, if future studies confirm causality of the common SUN1 H118Y variant, it has the potential to become a broadly relevant therapeutic target in NAFLD and metabolic disease.

Investigating Associations of Omega-3 Fatty Acids, Lung Function Decline, and Airway Obstruction

Rationale: Inflammation contributes to lung function decline and the development of chronic obstructive pulmonary disease. Omega-3 fatty acids have antiinflammatory properties and may benefit lung health. Objectives: To investigate associations of omega-3 fatty acids with lung function decline and incident airway obstruction in a diverse sample of adults from general-population cohorts. Methods: Complementary study designs: 1) longitudinal study of plasma phospholipid omega-3 fatty acids and repeated FEV1 and FVC measures in the NHLBI Pooled Cohorts Study and 2) two-sample Mendelian randomization (MR) study of genetically predicted omega-3 fatty acids and lung function parameters. Measurements and Main Results: The longitudinal study found that higher omega-3 fatty acid levels were associated with attenuated lung function decline in 15,063 participants, with the largest effect sizes for the most metabolically downstream omega-3 fatty acid, docosahexaenoic acid (DHA). An increase in DHA of 1% of total fatty acids was associated with attenuations of 1.4 ml/yr for FEV1 (95% confidence interval [CI], 1.1-1.8) and 2.0 ml/yr for FVC (95% CI, 1.6-2.4) and a 7% lower incidence of spirometry-defined airway obstruction (95% CI, 0.89-0.97). DHA associations persisted across sexes and smoking histories and in Black, White, and Hispanic participants, with associations of the largest magnitude in former smokers and Hispanic participants. The MR study showed similar trends toward positive associations of genetically predicted downstream omega-3 fatty acids with FEV1 and FVC. Conclusions: The longitudinal and MR studies provide evidence supporting beneficial effects of higher levels of downstream omega-3 fatty acids, especially DHA, on lung health.

The causal associations of altered inflammatory proteins with sleep duration, insomnia and daytime sleepiness

STUDY OBJECTIVES

Growing evidence linked inflammation with sleep. This study aimed to evaluate the associations and causal effects of sleep traits including insomnia, excessive daytime sleepiness (EDS), and sleep duration (short: <7 h; normal: 7-9 h; long: ≥9 h), with levels of C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), and interleukins.

METHODS

Standard procedures of quantitative analysis were applied to estimate the expression differences for each protein in compared groups. Then, a two-sample Mendelian randomization (MR) analysis was performed to explore their causal relationships with published genome-wide association study summary statistics. The inverse-variance weighted was used as the primary method, followed by several complementary approaches as sensitivity analyses.

RESULTS

A total of 44 publications with 51 879 participants were included in the quantitative analysis. Our results showed that the levels of CRP, interleukin-1β (IL-1β), IL-6, and TNF-α were higher from 0.36 to 0.58 (after standardization) in insomnia compared with controls, while there was no significant difference between participants with EDS and controls. Besides, there was a U/J-shaped expression of CRP and IL-6 with sleep durations. In MR analysis, the primary results demonstrated the causal effects of CRP on sleep duration (estimate: 0.017; 95% confidence intervals [CI], [0.003, 0.031]) and short sleep duration (estimate: -0.006; 95% CI, [-0.011, -0.001]). Also, IL-6 was found to be associated with long sleep duration (estimate: 0.006; 95% CI, [0.000, 0.013]). These results were consistent in sensitivity analyses.

CONCLUSIONS

There are high inflammatory profiles in insomnia and extremes of sleep duration. Meanwhile, elevated CRP and IL-6 have causal effects on longer sleep duration. Further studies can focus on related upstream and downstream mechanisms.

mtPGS: Leverage multiple correlated traits for accurate polygenic score construction

Accurate polygenic scores (PGSs) facilitate the genetic prediction of complex traits and aid in the development of personalized medicine. Here, we develop a statistical method called multi-trait assisted PGS (mtPGS), which can construct accurate PGSs for a target trait of interest by leveraging multiple traits relevant to the target trait. Specifically, mtPGS borrows SNP effect size similarity information between the target trait and its relevant traits to improve the effect size estimation on the target trait, thus achieving accurate PGSs. In the process, mtPGS flexibly models the shared genetic architecture between the target and the relevant traits to achieve robust performance, while explicitly accounting for the environmental covariance among them to accommodate different study designs with various sample overlap patterns. In addition, mtPGS uses only summary statistics as input and relies on a deterministic algorithm with several algebraic techniques for scalable computation. We evaluate the performance of mtPGS through comprehensive simulations and applications to 25 traits in the UK Biobank, where in the real data mtPGS achieves an average of 0.90%-52.91% accuracy gain compared to the state-of-the-art PGS methods. Overall, mtPGS represents an accurate, fast, and robust solution for PGS construction in biobank-scale datasets.

KIF15 missense variant is associated with the early onset of idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) has an unknown aetiology and limited treatment options. A recent meta-analysis identified three novel causal variants in the TERT, SPDL1, and KIF15 genes. This observational study aimed to investigate whether the aforementioned variants cause clinical phenotypes in a well-characterised IPF cohort.

METHODS

The study consisted of 138 patients with IPF who were diagnosed and treated at the Helsinki University Hospital and genotyped in the FinnGen FinnIPF study. Data on > 25 clinical parameters were collected by two pulmonologists who were blinded to the genetic data for patients with TERT loss of function and missense variants, SPDL1 and KIF15 missense variants, and a MUC5B variant commonly present in patients with IPF, or no variants were separately analysed.

RESULTS

The KIF15 missense variant is associated with the early onset of the disease, leading to progression to early-age transplantation or death. In patients with the KIF15 variant, the median age at diagnosis was 54.0 years (36.5-69.5 years) compared with 72.0 years (65.8-75.3 years) in the other patients (P = 0.023). The proportion of KIF15 variant carriers was 9- or 3.6-fold higher in patients aged < 55 or 65 years, respectively. The variants for TERT and MUC5B had similar effects on the patient's clinical course, as previously described. No distinct phenotypes were observed in patients with the SPDL1 variant.

CONCLUSIONS

Our study indicated the potential of KIF15 to be used in the genetic diagnostics of IPF. Further studies are needed to elucidate the biological mechanisms of KIF15 in IPF.

Heterogeneous effects on type 2 diabetes and cardiovascular outcomes of genetic variants and traits associated with fasting insulin

Hyperinsulinemia is a complex and heterogeneous phenotype that characterizes molecular alterations that precede the development of type 2 diabetes (T2D). It results from a complex combination of molecular processes, including insulin secretion and insulin sensitivity, that differ between individuals. To better understand the physiology of hyperinsulinemia and ultimately T2D, we implemented a genetic approach grouping fasting insulin (FI)-associated genetic variants based on their molecular and phenotypic similarities. We identified seven distinctive genetic clusters representing different physiologic mechanisms leading to rising FI levels, ranging from clusters of variants with effects on increased FI, but without increased risk of T2D (non-diabetogenic hyperinsulinemia), to clusters of variants that increase FI and T2D risk with demonstrated strong effects on body fat distribution, liver, lipid, and inflammatory processes (diabetogenic hyperinsulinemia). We generated cluster-specific polygenic scores in 1,104,258 individuals from five multi-ancestry cohorts to show that the clusters differed in associations with cardiometabolic traits. Among clusters characterized by non-diabetogenic hyperinsulinemia, there was both increased and decreased risk of coronary artery disease despite the non-increased risk of T2D. Similarly, the clusters characterized by diabetogenic hyperinsulinemia were associated with an increased risk of T2D, yet had differing risks of cardiovascular conditions, including coronary artery disease, myocardial infarction, and stroke. The strongest cluster-T2D associations were observed with the same direction of effect in non-Hispanic Black, Hispanic, non-Hispanic White, and non-Hispanic East Asian populations. These genetic clusters provide important insights into granular metabolic processes underlying the physiology of hyperinsulinemia, notably highlighting specific processes that decouple increasing FI levels from T2D and cardiovascular risk. Our findings suggest that increasing FI levels are not invariably associated with adverse cardiometabolic outcomes.

Causal associations between type 1 diabetes mellitus and cardiovascular diseases: a Mendelian randomization study

BACKGROUND

The presence of type 1 diabetes mellitus (T1DM) has been demonstrated to pose an increased risk for developing cardiovascular diseases (CVDs). However, the causal relationships between T1DM and CVDs remain unclear due to the uncontrolled confounding factors and reverse causation bias of the observational studies.

METHODS

Summary statistics of T1DM and seven CVDs from the largest available genome-wide association studies (GWAS) of European ancestry and FinnGen biobank were extracted for the primary MR analysis, and the analysis was replicated using UK biobank (UKBB) for validation. Three complementary methods: inverse variance weighted (IVW), weighted median, and MR-Egger were used for the MR estimates. The potential pleiotropic effects were assessed by MR-Egger intercept and MR-PRESSO global test. Additionally, multivariable MR (MVMR) analysis was performed to examine whether T1DM has independent effects on CVDs with adjustment of potential confounding factors. Moreover, a two-step MR approach was used to assess the potential mediating effects of these factors on the causal effects between T1DM and CVDs.

RESULTS

Causal effects of T1DM on peripheral atherosclerosis (odds ratio [OR] = 1.06, 95% confidence interval [CI]: 1.02-1.10; p = 0.002)] and coronary atherosclerosis (OR = 1.03, 95% CI: 1.01-1.05; p = 0.001) were found. The results were less likely to be biased by the horizontal pleiotropic effects (both p values of MR-Egger intercept and MR-PRESSO Global test > 0.05). In the following MVMR analysis, we found the causal effects of T1DM on peripheral atherosclerosis and coronary atherosclerosis remain significant after adjusting for a series of potential confounding factors. Moreover, we found that hypertension partly mediated the causal effects of T1DM on peripheral atherosclerosis (proportion of mediation effect in total effect: 11.47%, 95% CI: 3.23-19.71%) and coronary atherosclerosis (16.84%, 95% CI: 5.35-28.33%). We didn't find significant causal relationships between T1DM and other CVDs, including heart failure (HF), coronary artery disease (CAD), atrial fibrillation (AF), myocardial infarction (MI) and stroke. For the reverse MR from CVD to T1DM, no significant causal relationships were identified.

CONCLUSION

This MR study provided evidence supporting the causal effect of T1DM on peripheral atherosclerosis and coronary atherosclerosis, with hypertension partly mediating this effect.

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment.

Lipid Metabolism Traits Mediate the Effect of Psoriasis on Myocardial Infarction Risk: A Two-Step Mendelian Randomization Study

Mendelian randomization (MR) analysis was performed to explore the effect of psoriasis on lipid metabolism traits and myocardial infarction (MI) risk and to analyze the proportion of the mediatory effect of lipid metabolism traits. Publicly accessible summary-level data for psoriasis, lipid metabolism traits, and MI were provided by the genome-wide association studies (GWASs) of the FinnGen Biobank, UK Biobank, and CARDIoGRAMplusC4D, respectively. A two-sample MR was carried out to evaluate the association of psoriasis with lipid metabolism traits and MI. Furthermore, the current research focused on determining if the impact of psoriasis on MI is mediated by lipid metabolism traits. The outcomes of the random effect inverse-variance-weighted (IVW) technique indicated a substantial link between genetically predicted psoriasis and a higher risk of low-density lipoprotein (LDL) cholesterol (OR: 1.006, 95% CI: 1.005-1.007, p = 0.024), apolipoprotein B (OR: 1.018, 95% CI: 1.010-1.026, p = 0.015), lipoprotein A (OR: 1.006, 95% CI: 1.002-1.010, p = 0.039), and MI (OR: 1.066, 95% CI: 1.014-1.121, p = 0.012). The percentages of the mediatory effect of LDL cholesterol, apolipoprotein B, and lipoprotein A under psoriasis conditions on MI risk was 7.4%, 10.2%, and 4.1%, respectively. Psoriasis was causally linked to an elevated risk of lipid metabolism levels and MI. This study further demonstrated that LDL cholesterol, apolipoprotein B, and lipoprotein A mediated the effect of psoriasis on MI risk. And timely lipid-lowering treatment should be given to MI patients.

Effect of vitamin supplementation on polycystic ovary syndrome and key pathways implicated in its development: A Mendelian randomization study

BACKGROUND

Many epidemiologic investigations have explored the relationship between viatmins and polycystic ovary syndrome (PCOS). However, the effectiveness of vitamin, vitamin-like nutrient, or mineral supplementation in reducing the risk of PCOS remains a subject of debate.

AIM

To investigate the impact of plasma levels of vitamins A, B12, D, E, and K on PCOS and key pathways implicated in its development, namely, insulin resistance, hyperlipidemia, and obesity, through Mendelian randomization (MR) analysis.

METHODS

Single nucleotide polymorphisms associated with vitamin levels were selected from genome-wide association studies. The primary analysis was performed using the random-effects inverse-variance-weighted approach. Complementary analyses were conducted using the weighted median, MR-Egger, MR-robust adjusted profile score, and MR-PRESSO approaches.

RESULTS

The results provided suggestive evidence of a decreased risk of PCOS with genetically predicted higher levels of vitamin E (odds ratio [OR] = 0.118; 95% confidence interval [CI]: 0.071-0.226; P < 0.001) and vitamin B12 (OR = 0.753, 95%CI: 0.568-0.998, P = 0.048). An association was observed between vitamin E levels and insulin resistance (OR = 0.977, 95%CI: 0.976-0.978, P < 0.001). Additionally, genetically predicted higher concentrations of vitamins E, D, and A were suggested to be associated with a decreased risk of hyperlipidemia. Increased vitamins K and B12 levels were linked to a lower obesity risk (OR = 0.917, 95%CI: 0.848-0.992, P = 0.031).

CONCLUSION

The findings of this MR study suggest a causal relationship between increased vitamins A, D, E, K, and B12 levels and a reduced risk of PCOS or primary pathways implicated in its development.

Association between alcohol and urolithiasis: a mendelian randomization study

The causal relationship between alcohol and urolithiasis remains uncertain, despite previous observational studies reporting an association between the two. To determine the causality, we conducted a two-sample Mendelian randomization (MR) analysis. In this study, we aimed to investigate the causal relationship between alcohol and kidney stones using a two-sample MR approach. Two sets of genetic instruments were utilized in the analysis, both of which were derived from publicly available genetic summary data. The first set consisted of 73 single-nucleotide polymorphisms (SNPs) robustly linked to alcohol intake frequency (AIF) and the second set was comprised of 69 SNPs associated with alcohol consumption (AC). Our MR analysis was performed using several methods including the inverse-variance weighted (IVW) method, weighted median method, MR-Egger regression, MR Pleiotropy RESidual Sum and Outlier test. Our results from the MR analysis revealed a borderline significant association between AIF and the risk of urolithiasis. This was established through the use of the IVW method (OR (95% CI) = 1.29 (1.02, 1.65), p = 0.036) and the weighted median approach (OR (95% CI) = 1.44 (1.10, 1.89), p = 0.008). The MR-Egger model also yielded similar risk estimates (OR (95% CI) = 1.39 (0.66, 2.93), p = 0.386), although the relationship was not statistically significant. Sixty-eight SNPs were identified as having a substantial and independent link with AC. However, the IVW approach revealed no significant effect of AC on the risk of urolithiasis (OR (95% CI) = 0.74 (0.48, 1.14), p = 0.173). The MR analysis suggested a potential causal association between alcohol intake frequency and the risk of urolithiasis, but not alcohol consumption.

Challenges and Opportunities for Data Science in Women's Health

The intersection of women's health and data science is a field of research that has historically trailed other fields, but more recently it has gained momentum. This growth is being driven not only by new investigators who are moving into this area but also by the significant opportunities that have emerged in new methodologies, resources, and technologies in data science. Here, we describe some of the resources and methods being used by women's health researchers today to meet challenges in biomedical data science. We also describe the opportunities and limitations of applying these approaches to advance women's health outcomes and the future of the field, with emphasis on repurposing existing methodologies for women's health.

From target discovery to clinical drug development with human genetics

The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.

A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease

Identification of individuals at highest risk of coronary artery disease (CAD)-ideally before onset-remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10-2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70-1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.

Characterizing common and rare variations in non-traditional glycemic biomarkers using multivariate approaches on multi-ancestry ARIC study

Glycated hemoglobin, fasting glucose, glycated albumin, and fructosamine are biomarkers that reflect different aspects of the glycemic process. Genetic studies of these glycemic biomarkers can shed light on unknown aspects of type 2 diabetes genetics and biology. While there exists several GWAS of glycated hemoglobin and fasting glucose, very few GWAS have focused on glycated albumin or fructosamine. We performed a multi-phenotype GWAS of glycated albumin and fructosamine from 7,395 White and 2,016 Black participants in the Atherosclerosis Risk in Communities (ARIC) study on the common variants from genotyped/imputed data. We found 2 genome-wide significant loci, one mapping to known type 2 diabetes gene (ARAP1/STARD10, p = 2.8 × 10-8) and another mapping to a novel gene (UGT1A, p = 1.4 × 10-8) using multi-omics gene mapping strategies in diabetes-relevant tissues. We identified additional loci that were ancestry-specific (e.g., PRKCA from African ancestry individuals, p = 1.7 × 10-8) and sex-specific (TEX29 locus in males only, p = 3.0 × 10-8). Further, we implemented multi-phenotype gene-burden tests on whole-exome sequence data from 6,590 White and 2,309 Black ARIC participants. Eleven genes across different rare variant aggregation strategies were exome-wide significant only in multi-ancestry analysis. Four out of 11 genes had notable enrichment of rare predicted loss of function variants in African ancestry participants despite smaller sample size. Overall, 8 out of 15 loci/genes were implicated to influence these biomarkers via glycemic pathways. This study illustrates improved locus discovery and potential effector gene discovery by leveraging joint patterns of related biomarkers across entire allele frequency spectrum in multi-ancestry analyses. Most of the loci/genes we identified have not been previously implicated in studies of type 2 diabetes, and future investigation of the loci/genes potentially acting through glycemic pathways may help us better understand risk of developing type 2 diabetes.

The Newfoundland and Labrador mosaic founder population descends from an Irish and British diaspora from 300 years ago

The founder population of Newfoundland and Labrador (NL) is a unique genetic resource, in part due to its geographic and cultural isolation, where historical records describe a migration of European settlers, primarily from Ireland and England, to NL in the 18th and 19th centuries. Whilst its historical isolation, and increased prevalence of certain monogenic disorders are well appreciated, details of the fine-scale genetic structure and ancestry of the population are lacking. Understanding the genetic origins and background of functional, disease causing, genetic variants would aid genetic mapping efforts in the Province. Here, we leverage dense genome-wide SNP data on 1,807 NL individuals to reveal fine-scale genetic structure in NL that is clustered around coastal communities and correlated with Christian denomination. We show that the majority of NL European ancestry can be traced back to the south-east and south-west of Ireland and England, respectively. We date a substantial population size bottleneck approximately 10-15 generations ago in NL, associated with increased haplotype sharing and autozygosity. Our results reveal insights into the population history of NL and demonstrate evidence of a population conducive to further genetic studies and biomarker discovery.

Cohort profile: SUPER-Finland - the Finnish study for hereditary mechanisms of psychotic disorders

PURPOSE

SUPER-Finland is a large Finnish collection of psychosis cases. This cohort also represents the Finnish contribution to the Stanley Global Neuropsychiatric Genetics Initiative, which seeks to diversify genetic sample collection to include Asian, Latin American and African populations in addition to known population isolates, such as Finland.

PARTICIPANTS

10 474 individuals aged 18 years or older were recruited throughout the country. The subjects have been genotyped with a genome-wide genotyping chip and exome sequenced. A subset of 897 individuals selected from known population sub-isolates were selected for whole-genome sequencing. Recruitment was done between November 2015 and December 2018.

FINDINGS TO DATE

5757 (55.2%) had a diagnosis of schizophrenia, 944 (9.1%) schizoaffective disorder, 1612 (15.5%) type I or type II bipolar disorder, 532 (5.1 %) psychotic depression, 1047 (10.0%) other psychosis and for 530 (5.1%) self-reported psychosis at recruitment could not be confirmed from register data. Mean duration of schizophrenia was 22.0 years at the time of the recruitment. By the end of the year 2018, 204 of the recruited individuals had died. The most common cause of death was cardiovascular disease (n=61) followed by neoplasms (n=40). Ten subjects had psychiatric morbidity as the primary cause of death.

FUTURE PLANS

Compare the effects of common variants, rare variants and copy number variations (CNVs) on severity of psychotic illness. In addition, we aim to track longitudinal course of illness based on nation-wide register data to estimate how phenotypic and genetic differences alter it.

Birth weight and cardiometabolic risk factors: a discordant twin study in the UK Biobank

One of the longstanding debates in life-course epidemiology is whether an adverse intrauterine environment, often proxied by birth weight, causally increases the future risk of cardiometabolic disease. The use of a discordant twin study design, which controls for the influence of shared genetic and environmental confounding factors, may be useful to investigate whether this relationship is causal. We conducted a discordant twin study of 120 monozygotic (MZ) and 148 dizygotic (DZ) twin pairs from the UK Biobank to explore the potential causal relationships between birth weight and a broad spectrum of later-life cardiometabolic risk factors. We used a linear mixed model to investigate the association between birth weight and later-life cardiometabolic risk factors for twins, allowing for both within-pair differences and between-pair differences in birth weight. Of primary interest is the within-pair association between differences in birth weight and cardiometabolic risk factors, which could reflect an intrauterine effect on later-life risk factors. We found no strong evidence of association in MZ twins between the within-pair differences in birth weight and most cardiometabolic risk factors in later life, except for nominal associations with C-reactive protein and insulin-like growth factor 1. However, these associations were not replicated in DZ twin pairs. Our study provided no strong evidence for intrauterine effects on later-life cardiometabolic risk factors, which is consistent with previous large-scale studies of singletons testing the potential causal relationship. It does not support the hypothesis that adverse intrauterine environments increase the risk of cardiometabolic disease in later life.

The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases

Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.

Causal relationship between type 1 diabetes mellitus and six high-frequency infectious diseases: A two-sample mendelian randomization study

Purpose

Type 1 diabetes mellitus (T1DM) is associated with different types of infections; however, studies on the causal relationship between T1DM and infectious diseases are lacking. Therefore, our study aimed to explore the causalities between T1DM and six high-frequency infections using a Mendelian randomization (MR) approach.

Methods

Two-sample MR studies were conducted to explore the causalities between T1DM and six high-frequency infections: sepsis, acute lower respiratory infections (ALRIs), intestinal infections (IIs), infections of the genitourinary tract (GUTIs) in pregnancy, infections of the skin and subcutaneous tissues (SSTIs), and urinary tract infections (UTIs). Data on summary statistics for T1DM and infections were obtained from the European Bioinformatics Institute database, the United Kingdom Biobank, FinnGen biobank, and Medical Research Council Integrative Epidemiology Unit. All data obtained for summary statistics were from European countries. The inverse-variance weighted (IVW) method was employed as the main analysis. Considering the multiple comparisons, statistical significance was set at p< 0.008. If univariate MR analyses found a significant causal association, multivariable MR (MVMR) analyses were performed to adjust body mass index (BMI) and glycated hemoglobin (HbA1c). MVMR-IVW was performed as the primary analysis, and the least absolute shrinkage and selection operator (LASSO) regression and MVMR-Robust were performed as complementary analyses.

Results

MR analysis showed that susceptibility to IIs increased in patients with T1DM by 6.09% using the IVW-fixed method [odds ratio (OR)=1.0609; 95% confidence interval (CI): 1.0281-1.0947, p=0.0002]. Results were still significant after multiple testing. Sensitivity analyses did not show any significant horizontal pleiotropy or heterogeneity. After adjusting for BMI and HbA1c, MVMR-IVW (OR=1.0942; 95% CI: 1.0666-1.1224, p<0.0001) showed significant outcomes that were consistent with those of LASSO regression and MVMR-Robust. However, no significant causal relationship was found between T1DM and sepsis susceptibility, ALRI susceptibility, GUTI susceptibility in pregnancy, SSTI susceptibility, and UTI susceptibility.

Conclusions

Our MR analysis genetically predicted increased susceptibility to IIs in T1DM. However, no causality between T1DM and sepsis, ALRIs, GUTIs in pregnancy, SSTIs, or UTIs was found. Larger epidemiological and metagenomic studies are required to further investigate the observed associations between the susceptibility of certain infectious diseases with T1DM.

High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease

AIMS/HYPOTHESIS

Type 2 diabetes is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of type 2 diabetes loci can be leveraged to identify such pathways.

METHODS

We developed a high-throughput pipeline to enable clustering of type 2 diabetes loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for type 2 diabetes and related traits to generate a matrix of 323 variants × 64 trait associations and applied Bayesian non-negative matrix factorisation (bNMF) to identify genetic components of type 2 diabetes. Epigenomic enrichment analysis was performed in 28 cell types and single pancreatic cells. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance.

RESULTS

We identified ten clusters of genetic loci, recapturing the five from our prior analysis as well as novel clusters related to beta cell dysfunction, pronounced insulin secretion, and levels of alkaline phosphatase, lipoprotein A and sex hormone-binding globulin. Four clusters related to mechanisms of insulin deficiency, five to insulin resistance and one had an unclear mechanism. The clusters displayed tissue-specific epigenomic enrichment, notably with the two beta cell clusters differentially enriched in functional and stressed pancreatic beta cell states. Additionally, cluster-specific polygenic scores were differentially associated with patient clinical characteristics and outcomes. The pipeline was applied to coronary artery disease and chronic kidney disease, identifying multiple overlapping clusters with type 2 diabetes.

CONCLUSIONS/INTERPRETATION

Our approach stratifies type 2 diabetes loci into physiologically interpretable genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.

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), that improves the accuracy of gene expression prediction by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models using SUMMIT-FA with a comprehensive functional database MACIE and the eQTL summary-level data from the eQTLGen consortium. By applying the resulting models to GWASs for 24 complex traits and exploring it through a simulation study, we show that SUMMIT-FA improves the accuracy of gene expression prediction models in whole blood, identifies significantly more gene-trait associations, and improves predictive power for identifying "silver standard" genes compared to several benchmark methods.

Using human genetics to improve safety assessment of therapeutics

Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.

Investigating associations of omega-3 fatty acids, lung function decline, and airway obstruction

Rationale

Inflammation contributes to lung function decline and the development of chronic obstructive pulmonary disease. Omega-3 fatty acids have anti-inflammatory properties and may benefit lung health.

Objectives

Investigate associations of omega-3 fatty acids with lung function decline and incident airway obstruction in adults of diverse races/ethnicities from general population cohorts.

Methods

Complementary study designs: (1) longitudinal study of plasma phospholipid omega-3 fatty acids and repeated FEV 1 and FVC measures in the National Heart, Lung, and Blood Institute Pooled Cohorts Study, and (2) two-sample Mendelian Randomization (MR) study of genetically predicted omega-3 fatty acids and lung function parameters.

Measurements and Main Results

The longitudinal study found that higher omega-3 fatty acid concentrations were associated with attenuated lung function decline in 15,063 participants, with the largest effect sizes for docosahexaenoic acid (DHA). One standard deviation higher DHA was associated with an attenuation of 1.8 mL/year for FEV 1 (95% confidence interval [CI] 1.3-2.2) and 2.4 mL/year for FVC (95% CI 1.9-3.0). One standard deviation higher DHA was also associated with a 9% lower incidence of spirometry-defined airway obstruction (95% CI 0.86-0.97). DHA associations persisted across sexes, smoking histories, and Black, white and Hispanic participants, with the largest magnitude associations in former smokers and Hispanics. The MR study showed positive associations of genetically predicted omega-3 fatty acids with FEV 1 and FVC, with statistically significant findings across multiple MR methods.

Conclusions

The longitudinal and MR studies provide evidence supporting beneficial effects of higher circulating omega-3 fatty acids, especially DHA, on lung health.

Serum metabolites associated with wholegrain consumption using nontargeted metabolic profiling: a discovery and reproducibility study

PURPOSE

To identify fasting serum metabolites associated with WG intake in a free-living population adjusted for potential confounders.

METHODS

We selected fasting serum samples at baseline from a subset (n = 364) of the prospective population-based Kuopio Ischaemic Heart Disease Risk Factor Study (KIHD) cohort. The samples were analyzed using nontargeted metabolomics with liquid chromatography coupled with mass spectrometry (LC-MS). Association with WG intake was investigated using both random forest followed by linear regression adjusted for age, BMI, smoking, physical activity, energy and alcohol consumption, and partial Spearman correlation adjusted for the same covariates. Features selected by any of these models were shortlisted for annotation. We then checked if we could replicate the findings in an independent subset from the same cohort (n = 200).

RESULTS

Direct associations were observed between WG intake and pipecolic acid betaine, tetradecanedioic acid, four glucuronidated alkylresorcinols (ARs), and an unknown metabolite both in discovery and replication cohorts. The associations remained significant (FDR<0.05) even after adjustment for the confounders in both cohorts. Sinapyl alcohol was positively correlated with WG intake in both cohorts after adjustment for the confounders but not in linear models in the replication cohort. Some microbial metabolites, such as indolepropionic acid, were positively correlated with WG intake in the discovery cohort, but the correlations were not replicated in the replication cohort.

CONCLUSIONS

The identified associations between WG intake and the seven metabolites after adjusting for confounders in both discovery and replication cohorts suggest the potential of these metabolites as robust biomarkers of WG consumption.

LAVAA: a lightweight association viewer across ailments

Motivation

Biobank scale genetic associations results over thousands of traits can be difficult to visualize and navigate.

Results

We have created LAVAA, a visualization web-application to generate genetic volcano plots for simultaneously considering the P-value, effect size, case counts, trait class and fine-mapping posterior probability at a single-nucleotide polymorphism (SNP) across a range of traits from a large set of genome-wide association study. We find that user interaction with association results in LAVAA can enrich and enhance the biological interpretation of individual loci.

Availability and implementation

LAVAA is available as a stand-alone web service (https://geneviz.aalto.fi/LAVAA/) and will be available in future releases of the finngen.fi website starting with release 10 in late 2023.

Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases

Little is known about the genetic determinants of medication use in preventing cardiometabolic diseases. Using the Finnish nationwide drug purchase registry with follow-up since 1995, we performed genome-wide association analyses of longitudinal patterns of medication use in hyperlipidemia, hypertension and type 2 diabetes in up to 193,933 individuals (55% women) in the FinnGen study. In meta-analyses of up to 567,671 individuals combining FinnGen with the Estonian Biobank and the UK Biobank, we discovered 333 independent loci (P < 5 × 10-9) associated with medication use. Fine-mapping revealed 494 95% credible sets associated with the total number of medication purchases, changes in medication combinations or treatment discontinuation, including 46 credible sets in 40 loci not associated with the underlying treatment targets. The polygenic risk scores (PRS) for cardiometabolic risk factors were strongly associated with the medication-use behavior. A medication-use enhanced multitrait PRS for coronary artery disease matched the performance of a risk factor-based multitrait coronary artery disease PRS in an independent sample (UK Biobank, n = 343,676). In summary, we demonstrate medication-based strategies for identifying cardiometabolic risk loci and provide genome-wide tools for preventing cardiovascular diseases.

Mono- and biallelic variant effects on disease at biobank scale

Identifying causal factors for Mendelian and common diseases is an ongoing challenge in medical genetics¹. Population bottleneck events, such as those that occurred in the history of the Finnish population, enrich some homozygous variants to higher frequencies, which facilitates the identification of variants that cause diseases with recessive inheritance^2,3. Here we examine the homozygous and heterozygous effects of 44,370 coding variants on 2,444 disease phenotypes using data from the nationwide electronic health records of 176,899 Finnish individuals. We find associations for homozygous genotypes across a broad spectrum of phenotypes, including known associations with retinal dystrophy and novel associations with adult-onset cataract and female infertility. Of the recessive disease associations that we identify, 13 out of 20 would have been missed by the additive model that is typically used in genome-wide association studies. We use these results to find many known Mendelian variants whose inheritance cannot be adequately described by a conventional definition of dominant or recessive. In particular, we find variants that are known to cause diseases with recessive inheritance with significant heterozygous phenotypic effects. Similarly, we find presumed benign variants with disease effects. Our results show how biobanks, particularly in founder populations, can broaden our understanding of complex dosage effects of Mendelian variants on disease.

Severe COVID-19 May Impact Hepatic Fibrosis /Hepatic Stellate Cells Activation as Indicated by a Pathway and Population Genetic Study

Coronavirus disease 19 (COVID-19) has affected over 112 million people and killed more than 2.5 million worldwide. When the pandemic was declared, Spain and Italy accounted for 29% of the total COVID-19 related deaths in Europe, while most infected patients did not present severe illness. We hypothesised that shared genomic characteristics, distinct from the rest of Europe, could be a contributor factor to a poor prognosis in these two populations. To identify pathways related to COVID-19 severity, we shortlisted 437 candidate genes associated with host viral intake and immune evasion from SARS-like viruses. From these, 21 were associated specifically with clinically aggressive COVID-19. To determine the potential mechanism of viral infections, we performed signalling pathway analysis with either the full list (n = 437) or the subset group (n = 21) of genes. Four pathways were significantly associated with the full gene list (Caveolar-mediated Endocytosis and the MSP-RON Signalling) or with the aggressive gene list (Hepatic Fibrosis/Hepatic Stellate Cell (HSC) Activation and the Communication between Innate and Adaptive Immune Cells). Single nucleotide polymorphisms (SNPs) from the ±1 Mb window of all genes related to these four pathways were retrieved from the dbSNP database. We then performed Principal Component analysis for these SNPs in individuals from the 1000 Genomes of European ancestry. Only the Hepatic Fibrosis/HSC Activation pathway showed population-specific segregation. The Spanish and Italian populations clustered together and away from the rest of the European ancestries, with the first segregating further from the rest. Additional in silico analysis identified potential genetic markers and clinically actionable therapeutic targets in this pathway, that may explain the severe disease.

Biobanking as a Tool for Genomic Research: From Allele Frequencies to Cross-Ancestry Association Studies

In recent years, great advances have been made in the field of collection, storage, and analysis of biological samples. Large collections of samples, biobanks, have been established in many countries. Biobanks typically collect large amounts of biological samples and associated clinical information; the largest collections include over a million samples. In this review, we summarize the main directions in which biobanks aid medical genetics and genomic research, from providing reference allele frequency information to allowing large-scale cross-ancestry meta-analyses. The largest biobanks greatly vary in the size of the collection, and the amount of available phenotype and genotype data. Nevertheless, all of them are extensively used in genomics, providing a rich resource for genome-wide association analysis, genetic epidemiology, and statistical research into the structure, function, and evolution of the human genome. Recently, multiple research efforts were based on trans-biobank data integration, which increases sample size and allows for the identification of robust genetic associations. We provide prominent examples of such data integration and discuss important caveats which have to be taken into account in trans-biobank research.

Statistical learning for sparser fine-mapped polygenic models: The prediction of LDL-cholesterol

Polygenic risk scores quantify the individual genetic predisposition regarding a particular trait. We propose and illustrate the application of existing statistical learning methods to derive sparser models for genome-wide data with a polygenic signal. Our approach is based on three consecutive steps. First, potentially informative loci are identified by a marginal screening approach. Then, fine-mapping is independently applied for blocks of variants in linkage disequilibrium, where informative variants are retrieved by using variable selection methods including boosting with probing and stochastic searches with the Adaptive Subspace method. Finally, joint prediction models with the selected variants are derived using statistical boosting. In contrast to alternative approaches relying on univariate summary statistics from genome-wide association studies, our three-step approach enables to select and fit multivariable regression models on large-scale genotype data. Based on UK Biobank data, we develop prediction models for LDL-cholesterol as a continuous trait. Additionally, we consider a recent scalable algorithm for the Lasso. Results show that statistical learning approaches based on fine-mapping of genetic signals result in a competitive prediction performance compared to classical polygenic risk approaches, while yielding sparser risk models.

Association of demographic and clinical factors with risk of acute pancreatitis: An exposure-wide Mendelian randomization study

BACKGROUND

The incidence of acute pancreatitis (AP) is increasing over years, which brings enormous economy and health burden. However, the aetiologies of AP and underlying mechanisms are still unclear. Here, we performed a two-sample Mendelian randomization (MR) analysis to investigate the associations between all reported possible risk factors and AP using publicly available genome-wide association study summary statistics.

METHODS

A series of quality control steps were taken in our analysis to select eligible instrumental single nucleotide polymorphisms which were strongly associated with exposures. To make the conclusions more robust and reliable, we utilized several analytical methods (inverse-variance weighting, MR-PRESSO method, weighted median, MR-Egger regression) that are based on different assumptions of two-sample MR analysis. The MR-Egger intercept test, radial regression and leave-one-out sensitivity analysis were performed to evaluate the horizontal pleiotropy, heterogeneities, and stability of these genetic variants on each exposure. A two-step MR method was applied to explore mediators in significant associations.

RESULTS

Genetic predisposition to cholelithiasis (effect estimate: 17.30, 95% CI: 12.25-22.36, p = 1.95 E-11), body mass index (0.32, 95% CI: 0.13-0.51, p < 0.001), body fat percentage (0.57, 95% CI: 0.31-0.83, p = 1.31 E-05), trunk fat percentage (0.36, 95% CI: 0.14-0.59, p < 0.005), ever smoked (1.61, 95% CI: 0.45-2.77, p = 0.007), and limbs fat percentage (0.55, 95% CI: 0.41-0.69, p < 0.001) were associated with an increased risk of AP. In addition, whole-body fat-free mass (-0.32, 95% CI: -0.55 to -0.10, p = 0.004) was associated with a decrease risk of AP.

CONCLUSION

Genetic predisposition to cholelithiasis, obesity and smoking could be causally associated with an increased risk of AP, and whole body fat-free mass could be associated with a decreased risk of AP.

SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification

Genes with moderate to low expression heritability may explain a large proportion of complex trait etiology, but such genes cannot be sufficiently captured in conventional transcriptome-wide association studies (TWASs), partly due to the relatively small available reference datasets for developing expression genetic prediction models to capture the moderate to low genetically regulated components of gene expression. Here, we introduce a method, the Summary-level Unified Method for Modeling Integrated Transcriptome (SUMMIT), to improve the expression prediction model accuracy and the power of TWAS by using a large expression quantitative trait loci (eQTL) summary-level dataset. We apply SUMMIT to the eQTL summary-level data provided by the eQTLGen consortium. Through simulation studies and analyses of genome-wide association study summary statistics for 24 complex traits, we show that SUMMIT improves the accuracy of expression prediction in blood, successfully builds expression prediction models for genes with low expression heritability, and achieves higher statistical power than several benchmark methods. Finally, we conduct a case study of COVID-19 severity with SUMMIT and identify 11 likely causal genes associated with COVID-19 severity.

Genetic Determinants of 25-Hydroxyvitamin D Concentrations and Their Relevance to Public Health

Twin studies suggest a considerable genetic contribution to the variability in 25-hydroxyvitamin D (25(OH)D) concentrations, reporting heritability estimates up to 80% in some studies. While genome-wide association studies (GWAS) suggest notably lower rates (13−16%), they have identified many independent variants that associate with serum 25(OH)D concentrations. These discoveries have provided some novel insight into the metabolic pathway, and in this review we outline findings from GWAS studies to date with a particular focus on 35 variants which have provided replicating evidence for an association with 25(OH)D across independent large-scale analyses. Some of the 25(OH)D associating variants are linked directly to the vitamin D metabolic pathway, while others may reflect differences in storage capacity, lipid metabolism, and pathways reflecting skin properties. By constructing a genetic score including these 25(OH)D associated variants we show that genetic differences in 25(OH)D concentrations persist across the seasons, and the odds of having low concentrations (<50 nmol/L) are about halved for individuals in the highest 20% of vitamin D genetic score compared to the lowest quintile, an impact which may have notable influences on retaining adequate levels. We also discuss recent studies on personalized approaches to vitamin D supplementation and show how Mendelian randomization studies can help inform public health strategies to reduce adverse health impacts of vitamin D deficiency.

Rare germline deleterious variants increase susceptibility for lung cancer

Although multiple common susceptibility loci for lung cancer (LC) have been identified by genome-wide association studies, they can explain only a small portion of heritability. The etiological contribution of rare deleterious variants (RDVs) to LC risk is not fully characterized and may account for part of the missing heritability. Here, we sequenced the whole exomes of 2777 participants from the Environment and Genetics in Lung cancer Etiology study, a homogenous population including 1461 LC cases and 1316 controls. In single-variant analyses, we identified a new RDV, rs77187983 [EHBP1, odds ratio (OR) = 3.13, 95% confidence interval (CI) = 1.34-7.30, P = 0.008] and replicated two previously reported RDVs, rs11571833 (BRCA2, OR = 2.18; 95% CI = 1.25-3.81, P = 0.006) and rs752672077 (MPZL2, OR = 3.70, 95% CI = 1.04-13.15, P = 0.044). In gene-based analyses, we confirmed BRCA2 (P = 0.007) and ATM (P = 0.014) associations with LC risk and identified TRIB3 (P = 0.009), involved in maintaining genome stability and DNA repair, as a new candidate susceptibility gene. Furthermore, cases were enriched with RDVs in homologous recombination repair [carrier frequency (CF) = 22.9% versus 19.5%, P = 0.017] and Fanconi anemia (CF = 12.5% versus 10.2%, P = 0.036) pathways. Our results were not significant after multiple testing corrections but were enriched in cases versus controls from large scale public biobank resources, including The Cancer Genome Atlas, FinnGen and UK Biobank. Our study identifies novel candidate genes and highlights the importance of RDVs in DNA repair-related genes for LC susceptibility. These findings improve our understanding of LC heritability and may contribute to the development of risk stratification and prevention strategies.

Multinational Genome-Wide Association Study and Functional Genomics Analysis Implicates Decreased SIRT3 Expression Underlying Intracranial Aneurysm Risk

BACKGROUND

The genetic mechanisms regulating intracranial aneurysm (IA) formation and rupture are largely unknown. To identify germline-genetic risk factors for IA, we perform a multinational genome-wide association study (GWAS) of individuals from the United Kingdom, Finland, and Japan.

OBJECTIVE

To identify a shared, multinational genetic basis of IA.

METHODS

Using GWAS summary statistics from UK Biobank, FinnGen, and Biobank Japan, we perform a meta-analysis of IA, containing ruptured and unruptured IA cases. Logistic regression was used to identify IA-associated single-nucleotide polymorphisms. Effect size was calculated using the coefficient r , estimating the contribution of the single-nucleotide polymorphism to the genetic variance of the trait. Genome-wide significance was set at 5.0 × 10 -8 . Expression quantitative trait loci mapping and functional genomics approaches were used to infer mechanistic consequences of implicated variants.

RESULTS

Our cohort contained 155 154 individuals (3132 IA cases and 152 022 controls). We identified 4 genetic loci reaching genome-wide: rs73392700 ( SIRT3 , effect size = 0.28, P = 4.3 × 10 -12 ), rs58721068 ( EDNRA , effect size = -0.20, P = 4.8 × 10 -12 ), rs4977574 ( AL359922.1 , effect size = 0.18, P = 7.9 × 10 -12 ), and rs11105337 ( ATP2B1 , effect size = -0.15, P = 3.4 × 10 -8 ). Expression quantitative trait loci mapping suggests that rs73392700 has a large effect size on SIRT3 gene expression in arterial and muscle, but not neurological, tissues. Functional genomics analysis suggests that rs73392700 causes decreased SIRT3 gene expression.

CONCLUSION

We perform a multinational GWAS of IA and identify 4 genetic risk loci, including 2 novel IA risk loci ( SIRT3 and AL359922.1 ). Identification of high-risk genetic loci across ancestries will enable population-genetic screening approaches to identify patients with IA.