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

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

Enhanced Detection of Novel Low-Frequency Gene Fusions via High-Yield Ligation and Multiplexed Enrichment Sequencing.

Panel-based methods are commonly employed for the analysis of novel gene fusions in precision diagnostics and new drug development in cancer. However, these methods are constrained by limitations in ligation yield and the enrichment of novel gene fusions with low variant allele frequencies. In this study, we conducted a pioneering investigation into the stability of double-stranded adapter DNA, resulting in improved ligation yield and enhanced conversion efficiency. Additionally, we implemented blocker displacement amplification, achieving a remarkable 7-fold enrichment of novel gene fusions. Leveraging the pre-enrichment achieved with this approach, we successfully applied it to Nanopore sequencing, enabling ultra-fast analysis of novel gene fusions within one hour with high sensitivity. This method offers a robust and remarkably sensitive mean of analyzing novel gene fusions, promising the discovery of pivotal biomarkers that can significantly improve cancer diagnostics and the development of new therapeutic strategies.

Mendelian randomization suggests a potential causal effect of eosinophil count on influenza vaccination responsiveness.

Currently, the clinical factors affecting immune responses to influenza vaccines have not been systematically explored. The mechanism of low responsiveness to influenza vaccination (LRIV) is complicated and not thoroughly elucidated. Thus, we integrate our in-house genome-wide association studies (GWAS) analysis result of LRIV (N = 111, Ncase [Low Responders] = 34, Ncontrol [Responders] = 77) with the GWAS summary of 10 blood-based biomarkers (sample size ranging from 62 076-108 794) deposited in BioBank Japan (BBJ) to comprehensively explore the shared genetics between LRIV and blood-based biomarkers to investigate the causal relationships between blood-based biomarkers and LRIV by Mendelian randomization (MR). The applications of four MR approaches (inverse-variance-weighted [IVW], weighted median, weighted mode, and generalized summary-data-based MR [GSMR]) suggested that the genetically instrumented LRIV was associated with decreased eosinophil count (ß = -5.517 to -4.422, p = 0.004-0.039). Finally, we conclude that the low level of eosinophil count is a suggestive risk factor for LRIV.

Programming Super DNA-Enzyme Molecules for On-Demand Enzyme Activity Modulation.

Dynamic interactions of enzymes, including programmable configuration and cycling of enzymes, play important roles in the regulation of cellular metabolism. Here, we constructed a super DNA-enzymes molecule (SDEM) that comprises at least two cascade enzymes and multiple linked DNA strands to control and detect metabolism. We found that the programmable SDEM, which comprises glucose oxidase (GOx) and horseradish peroxidase (HRP), has a 20-fold lower detection limit and a 1.6-fold higher reaction rate than free enzymes. An SDEM can be assembled and disassembled using a hairpin structure and a displacement DNA strand to complete multiple cycles. An entropically driven catalytic assembly (catassembly) enables different SDEMs to switch from an SDEM with GOx and HRP cascades to an SDEM with sarcosine oxidase (SOX) and HRP cascades in over six orders of magnitude less time than without the catassembly to detect different metabolisms (GO and sarcosine) on demand.

Identification of Putative Causal Relationships Between Type 2 Diabetes and Blood-Based Biomarkers in East Asians by Mendelian Randomization.

Observational studies have revealed phenotypic associations between type 2 diabetes (T2D) and many biomarkers. However, causality between these conditions in East Asians is unclear. We leveraged genome-wide association study (GWAS) summary statistics on T2D (n = 77,418 cases; n = 356,122 controls) from the Asian Genetic Epidemiology Network (sample recruited during 2001-2011) and GWAS summary statistics on 42 biomarkers (n = 12,303-143,658) from BioBank Japan (sample recruited during 2003-2008) to investigate causal relationships between T2D and biomarkers. Applications of Mendelian randomization approaches consistently revealed genetically instrumented associations of T2D with increased serum potassium levels (liability-scale ß = 0.04-0.10; P = 6.41 × 10-17-9.85 × 10-5) and decreased serum chloride levels (liability-scale ß = -0.16 to -0.06; P = 5.22 × 10-27-3.14 × 10-5), whereas these 2 biomarkers showed no causal effects on T2D. Heritability Estimation Using Summary Statistics (ρ-HESS) and summary-data-based Mendelian randomization highlighted 27 genomic regions and 3 genes (α-1,3-mannosyl-glycoprotein 2-ß-N-acetylglucosaminyltransferase (MGAT1), transducing-like enhancer (TLE) family member 1, transcriptional corepressor (TLE1), and 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR)) that interactively associated with the shared genetics underlying T2D and the 2 biomarkers. Thus, T2D may causally affect serum potassium and chloride levels among East Asians. In contrast, the relationships of potassium and chloride with T2D are not causal, suggesting the importance of monitoring electrolyte disorders for T2D patients.

Genetic evidence for a causal relationship between type 2 diabetes and peripheral artery disease in both Europeans and East Asians.

BACKGROUND: Observational studies have revealed that type 2 diabetes (T2D) is associated with an increased risk of peripheral artery disease (PAD). However, whether the two diseases share a genetic basis and whether the relationship is causal remain unclear. It is also unclear as to whether these relationships differ between ethnic groups. METHODS: By leveraging large-scale genome-wide association study (GWAS) summary statistics of T2D (European-based: Ncase = 21,926, Ncontrol = 342,747; East Asian-based: Ncase = 36,614, Ncontrol = 155,150) and PAD (European-based: Ncase = 5673, Ncontrol = 359,551; East Asian-based: Ncase = 3593, Ncontrol = 208,860), we explored the genetic correlation and putative causal relationship between T2D and PAD in both Europeans and East Asians using linkage disequilibrium score regression and seven Mendelian randomization (MR) models. We also performed multi-trait analysis of GWAS and two gene-based analyses to reveal candidate variants and risk genes involved in the shared genetic basis between T2D and PAD. RESULTS: We observed a strong genetic correlation (rg) between T2D and PAD in both Europeans (rg = 0.51; p-value = 9.34 × 10-15) and East Asians (rg = 0.46; p-value = 1.67 × 10-12). The MR analyses provided consistent evidence for a causal effect of T2D on PAD in both ethnicities (odds ratio [OR] = 1.05 to 1.28 for Europeans and 1.15 to 1.27 for East Asians) but not PAD on T2D. This putative causal effect was not influenced by total cholesterol, body mass index, systolic blood pressure, or smoking initiation according to multivariable MR analysis, and the genetic overlap between T2D and PAD was further explored employing an independent European sample through polygenic risk score regression. Multi-trait analysis of GWAS revealed two novel European-specific single nucleotide polymorphisms (rs927742 and rs1734409) associated with the shared genetic basis of T2D and PAD. Gene-based analyses consistently identified one gene ANKFY1 and gene-gene interactions (e.g., STARD10 [European-specific] to AP3S2 [East Asian-specific]; KCNJ11 [European-specific] to KCNQ1 [East Asian-specific]) associated with the trans-ethnic genetic overlap between T2D and PAD, reflecting a common genetic basis for the co-occurrence of T2D and PAD in both Europeans and East Asians. CONCLUSIONS: Our study provides the first evidence for a genetically causal effect of T2D on PAD in both Europeans and East Asians. Several candidate variants and risk genes were identified as being associated with this genetic overlap. Our findings emphasize the importance of monitoring PAD status in T2D patients and suggest new genetic biomarkers for screening PAD risk among patients with T2D.

Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework.

BACKGROUND: Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms. METHODS: Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages. FINDINGS: The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer's disease were found to share genetic architecture with schizophrenia, bipolar disorder and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated. INTERPRETATION: iGOAT can successfully leverage epigenomic data, phenotype-genotype associations, and protein-protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches. FUNDING: The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).

Mendelian randomization study reveals a population-specific putative causal effect of type 2 diabetes in risk of cataract.

BACKGROUND: The epidemiological association between type 2 diabetes and cataract has been well established. However, it remains unclear whether the two diseases share a genetic basis, and if so, whether this reflects a putative causal relationship. METHODS: We used East Asian population-based genome-wide association studies (GWAS) summary statistics of type 2 diabetes (Ncase = 36 614, Ncontrol = 155 150) and cataract (Ncase = 24 622, Ncontrol = 187 831) to comprehensively investigate the shared genetics between the two diseases. We performed: (i) linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) to estimate the genetic correlation and local genetic correlation pattern between type 2 diabetes and cataract; (ii) multiple Mendelian randomization (MR) analyses to infer the putative causality between type 2 diabetes and cataract; and (iii) summary-data-based Mendelian randomization (SMR) to identify candidate risk genes underling the putative causality. Moreover, to investigate the extent of the population-specific genetic effect size underlying the shared genetics between type 2 diabetes and cataract, we applied the same analytical pipeline to perform a comparative analysis on European population-based GWAS of type 2 diabetes (Ncase = 62 892, Ncontrol = 596 424) and cataract (Ncase = 5045, Ncontrol = 356 096). RESULTS: Using East Asian population-based GWAS summary data, we observed a strong genetic correlation [rg = 0.58, 95% confidence interval (CI) = 0.33, 0.83), P-value = 5.60 × 10-6] between type 2 diabetes and cataract. Both ρ-HESS and multiple MR methods consistently showed a putative causal effect of type 2 diabetes on cataract, with estimated liability-scale MR odds ratios (ORs) at around 1.10 (95% CI = 1.06, 1.17). In contrast, no evidence supports a causal effect of cataract on type 2 diabetes. SMR analysis identified two novel genes MIR4453HG (ßSMR = -0.34, 95% CI = -0.46, -0.22, P-value = 6.41 × 10-8) and KCNK17 (ßSMR = -0.07, 95% CI = -0.09, -0.05, P-value = 2.49 × 10-10), whose expression levels were likely involved in the putative causality of type 2 diabetes on cataract. On the contrary, our comparative analysis on European population provided universally weak evidence on the genetic correlation and causal relationship between the two diseases. CONCLUSIONS: Our results provided robust evidence supporting a putative causal effect of type 2 diabetes on the risk of cataract in East Asians, and revealed potential genetic heterogeneity in the shared genetics underlying type 2 diabetes and cataract between East Asians and Europeans. These findings posed new paths on guiding the prevention and early-stage diagnosis of cataract in type 2 diabetes patients.