The role of interleukin-6 signalling in pleural infection: observational and genetic analyses.

Background: Pleural infection is associated with marked local and systemic inflammation leading to significant morbidity. It may be possible to therapeutically augment this response and interleukin-6 is a key signalling cascade in inflammatory pathologies.

Methods: We performed a prospective observational study recruiting patients with pleural effusions secondary to infection and measured interleukin-6 in matched pleural fluid and serum (n = 76).

The impact of ancestry on performance of type 1 diabetes genetic risk scores: high discrimination performance is maintained in African ancestry populations, but population specific thresholds may improve risk prediction.

Objective: Genetic risk scores (GRSs) for type 1 diabetes (T1D) may assist T1D classification and prediction but are often developed from European populations. To improve health outcomes, it is important to understand the performance and utility of GRSs in diverse ancestry populations.

Research Design And Methods: We assessed performance of three previously published T1D GRSs in differentiating people with and without Type 1 diabetes in African (with/without T1D=194/235), European (n=1109/125), and Hispanic (266/170) ancestry populations in the USA, and from Cameroon and Uganda (n=144/5001).

Rare Variants in NEUROD1 and PDX1 Are Low-Penetrance Causes of MODY, Whereas Those in APPL1 and WFS1 Are Not Associated With MODY.

Unlabelled: An accurate genetic diagnosis of maturity-onset diabetes of the young (MODY) is critical for personalized treatment. To avoid misdiagnosis, only genes with strong evidence of causality must be tested. Heterozygous variants in NEUROD1, PDX1, APPL1, and WFS1 have been implicated in MODY, but strong genetic evidence supporting causality is lacking.

Clinical utility of self-reported sleep duration and insomnia symptoms in type 2 diabetes prediction.

Aims/hypothesis: Suboptimal sleep health is linked to higher risks for incident type 2 diabetes. We aimed to assess the clinical utility of adding self-reported sleep traits to a type 2 diabetes prediction model.

Methods: In this cohort study, we used UK Biobank data and Cox proportional hazards models to examine how self-reported sleep duration and insomnia symptoms were associated with incident type 2 diabetes risk.

Calculating genetic risk scores directly from summary statistics with an application to type 1 diabetes.

Motivation: Genetic risk scores (GRS) summarise genetic data into a single number and allow for discrimination between cases and controls. Many applications of GRSs would benefit from comparisons with multiple datasets to assess quality of the GRS across different groups. However, genetic data is often unavailable.

A type 1 diabetes genetic risk score discriminates between type 1 diabetes and type 2 diabetes in a Chinese population.

Aims/hypothesis: We aimed to generate a population-specific type 1 diabetes genetic risk score (GRS) and assess whether it could improve discrimination between type 1 diabetes and type 2 diabetes in a Chinese population.

Methods: We performed a genome-wide association analysis on 1303 individuals with type 1 diabetes and 2236 control individuals. An independent replication cohort of 501 individuals with type 1 diabetes and 853 control individuals was used to validate the top common variant associations.

Utility of genome sequencing and group-enrichment to support splice variant interpretation in Marfan syndrome.

Purpose: To quantify the impact of noncanonical FBN1 splice site variants in undiagnosed Marfan syndrome (MFS), a connective tissue disorder associated with skeletal abnormalities and familial thoracic aortic aneurysm disease (FTAAD).

Methods: A systematic analysis of ultrarare FBN1 variants was performed using genome sequencing data from the 100,000 Genomes Project. Variants were annotated with SpliceAI and the significance of enrichment among individuals with FTAAD was assessed using Fisher's exact test.

Evaluating Genotype-Treatment Interactions for High-Risk Medications in British General Practice: Evidence from UK Biobank.

Background Pharmacogenetics has the potential to optimise drug therapy and reduce adverse drug effects (ADEs) by tailoring treatment to a patient's genotype, particularly for chronic disorders managed in general practice (GP). However, the adoption of pharmacogenetics in GP remains slow. Aim This study aimed to evaluate the reproducibility of previously reported associations between genomic variants and medically important adverse drug effects (MIADEs) associated with high-risk medications in GP.

HLA haplotype diversity, islet autoantibody status and discriminative ability of type 1 diabetes genetic risk score in Indians.

Aims: We have reported that a 9SNPs type 1 diabetes (T1D) Genetic Risk Score (GRS) developed from European data had a lower power in Indians to distinguish T1D from type 2 diabetes (T2D). We explore the performance of an improved (67SNPs) T1DGRS and also the potential reasons for lower discriminative ability to classify types of diabetes in Indians.

Methods: We studied the discriminative ability of a 67SNPs European T1DGRS in 611 clinically diagnosed T1D and 1153 T2D patients, and 321 non-diabetic controls, using receiver operating characteristic (ROC) area under the curve (AUC).

Exploring the application of deep learning methods for polygenic risk score estimation.

. Polygenic risk scores (PRS) summarise genetic information into a single number with clinical and research uses. Deep learning (DL) has revolutionised multiple fields, however, the impact of DL on PRSs has been less significant.

Whole-genome sequencing analysis identifies rare, large-effect noncoding variants and regulatory regions associated with circulating protein levels.

The contribution of rare noncoding genetic variation to common phenotypes is largely unknown, as a result of a historical lack of population-scale whole-genome sequencing data and the difficulty of categorizing noncoding variants into functionally similar groups. To begin addressing these challenges, we performed a cis association analysis using whole-genome sequencing data, consisting of 1.1 billion variants, 123 million noncoding aggregate-based tests and 2,907 circulating protein levels in ~50,000 UK Biobank participants.

Streamlining Large-Scale Genomic Data Management: Insights from the UK Biobank Whole-Genome Sequencing Data.

Biobank-scale Whole-Genome Sequencing (WGS) studies are increasingly pivotal in unraveling the genetic bases of diverse health outcomes. However, managing and analyzing these datasets' sheer volume and complexity presents significant challenges. We propose , an all-in-one toolkit that efficiently converts WGS data from Variant Call Format (VCF) format to the annotated Genomic Data Structure (aGDS) format, significantly reducing data size while supporting seamless genomic and functional data integration for comprehensive genetic analyses.

Standardized Measurement of Type 1 Diabetes Polygenic Risk Across Multi-Ancestry Population Cohorts.

Type 1 diabetes (T1D) polygenic risk scores (PRS) are effective tools for discriminating T1D from other diabetes types and predicting T1D risk, with applications in screening and intervention trials. A previously published T1D Genetic Risk Score 2 (GRS2) is widely adopted, but challenges in standardization and accessibility have hindered broader clinical and research utility. To address this, we introduce GRS2x, a standardized and cross-compatible method for accurate T1D PRS calculation, demonstrating genotyping and reference panel independent performance across diverse datasets.

Genome-wide association analysis of composite sleep health scores in 413,904 individuals.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, so together may provide a more complete picture of sleep health, while illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches.

Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores.

Correct classification of type 1 (T1D) and type 2 diabetes (T2D) is challenging due to overlapping clinical features and the increasingly early onset of T2D, particularly in South Asians. Polygenic risk scores (PRSs) for T1D and T2D have been shown to work relatively well in South Asians, despite being derived from largely European-ancestry samples. Here we used PRSs to investigate the rate of potential misclassification of diabetes amongst British Bangladeshis and Pakistanis.

Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches.

A Type 1 Diabetes Genetic Risk Score (T1DGRS) aids diagnosis and prediction of Type 1 Diabetes (T1D). While traditionally derived from imputed array genotypes, Whole Genome Sequencing (WGS) provides a more direct approach and is now increasingly used in clinical and research studies. We investigated the concordance between WGS-based and array-based T1DGRS across genetic ancestries in 149,265 UK Biobank participants using WGS, TOPMed-imputed, and 1000 Genomes-imputed array genotypes.

Type 1 Diabetes Genetic Risk Contributes to Phenotypic Presentation in Monogenic Autoimmune Diabetes.

There is variability in early-onset autoimmune diabetes presentation in individuals with monogenic autoimmunity; the mechanism(s) underlying this is unclear. We examined whether type 1 diabetes (T1D) polygenic risk contributes to clinical phenotype in monogenic autoimmune diabetes. Individuals with monogenic autoimmune diabetes had higher T1D genetic risk scores compared with control cohorts, driven largely by increased presence of T1D-risk DR3-DQ2 haplotype.

Type 2 diabetes pathway-specific polygenic risk scores elucidate heterogeneity in clinical presentation, disease progression and diabetic complications in 18,217 Chinese individuals with type 2 diabetes.

Aims/hypothesis: Type 2 diabetes is a complex and heterogeneous disease and the aetiological components underlying the heterogeneity remain unclear in the Chinese and East Asian population. Therefore, we aimed to investigate whether specific pathophysiological pathways drive the clinical heterogeneity in type 2 diabetes.

Methods: We employed newly developed type 2 diabetes hard-clustering and soft-clustering pathway-specific polygenic risk scores (psPRSs) to characterise individual genetic susceptibility to pathophysiological pathways implicated in type 2 diabetes in 18,217 Chinese patients from Hong Kong.

A homozygous TARS2 variant is a novel cause of syndromic neonatal diabetes.

Aims: Neonatal diabetes is a monogenic condition which can be the presenting feature of complex syndromes. The aim of this study was to identify novel genetic causes of neonatal diabetes with neurological features including developmental delay and epilepsy.

Methods: We performed genome sequencing in 27 individuals with neonatal diabetes plus epilepsy and/or developmental delay of unknown genetic cause.