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Schematic of multi-method candidate gene mapping of indexed variants associated with blood lipid levels. We defined indexed variants within the GLGC GWAS summary statistics and performed two similarity-based methods and four locus-based methods to prioritize genes for each of the indexed variants
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Background
Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.
Results
To expand our understa...
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BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our underst...
Citations
... However, the findings across these studies remain contentious. This ongoing controversy can likely be attributed to variations in genetic backgrounds, gender distributions, age demographics, and the geographical locations of the populations under investigation (25)(26)(27). This study aims to investigate not only the association between TG in the circulatory system and urinary stones but also includes other lipid-related substances and lipid-lowering drugs as exposure factors. ...
Background
Previous studies have yielded conflicting findings regarding the association between circulating lipids and lipid-lowering drugs with urinary stones, and the causal relationship between the two remains inconclusive.
Objective
This study aimed to assess the causal relationship between circulating lipids (Triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], apolipoprotein A [APOA], apolipoprotein B [APOB] and Pure hypercholesterolaemia), lipid-lowering drugs (HMGCR [HMG-CoA reductase] inhibitors and PCSK9[Proprotein Convertase Subtilisin/Kexin Type 9] inhibitors) and the risk of urinary stones, using genetic data.
Methods
Genetic instrumental variables (GIVs) for circulating lipids and lipid-lowering drugs were obtained from the UK Biobank and existing literature. Outcome data were extracted from a genetic association database with 3,625 urinary stone cases and 459,308 controls. Two-sample MR analysis, employing the TwoSampleMR software package in R 4.2.3, was conducted to assess the associations between multiple exposures. The primary outcome was determined using the inverse variance weighted (IVW) method for the causal relationship between exposure and outcome, while additional methods such as MR-Egger, weighted median, simple mode, and weighted mode were utilized as supplementary analyses. Robustness of the Mendelian Randomization (MR) analysis results was assessed through leave-one-out analysis and funnel plots.
Results
The MR analysis revealed a significant association between elevated TG levels per 1 standard deviation and the occurrence of urinary stones (odds ratio [OR]: 1.002, 95% confidence interval [CI]: 1.000-1.003, P = 0.010). However, no significant association was observed between factors other than TG exposure and the risk of urinary stone occurrence across all methods(LDL-C: [OR], 1.001; 95% [CI], 1.000-1.003, P=0.132;HDL-C: [OR], 0.999; 95% [CI], 0.998-1.000, P=0.151;APOA:[OR] being 1.000 (95% [CI], 0.999-1.001, P=0.721;APOB: [OR] of 1.001 (95% [CI], 1.000-1.002, P=0.058;Pure hypercholesterolaemia: [OR] of 1.015 (95% [CI], 0.976-1.055, P=0.455) and lipid-lowering drugs (HMGCR inhibitors: [OR], 0.997; 95% [CI], 0.990-1.003, P=0.301 and PCSK9 inhibitors:[OR], 1.002; 95% [CI], 1.000-1.005, P=0.099).
Conclusion
Our findings provide conclusive evidence supporting a causal relationship between an increased risk of urinary stones and elevated serum TG levels. However, we did not find a significant association between urinary stone occurrence and the levels of LDL-C, HDL-C, APOA, APOB, Pure hypercholesterolaemia and lipid-lowering drugs.
... Blood lipid levels, including low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C), are quantitative clinically important traits with welldescribed monogenic and polygenic bases. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] Abnormal blood lipid levels contribute to risk of coronary heart disease (CHD), and in clinical practice, several treatments, including statins and PCSK9 and ANGPTL3 inhibitors, [20][21][22] are available to reduce the risk of developing CHD. Each of these therapeutics has supporting evidence of their efficacy from human genetic analysis of blood lipid levels. ...
... 18 Those significant index variants were identified iteratively starting with the most significant variant and grouping the surrounding region into a locus based on the larger of either 5500 kb or 50.25 cM, followed by a conditional analysis using rareGWAMA, as previously described. 18,19,55 The GLGC results were in genome build 37, and thus we lifted over the positions of GLGC index variants to genome build 38 to match the TOPMed data. For each lncRNA gene, we adjusted for the GLGC index variants falling in a 5500-kb window beyond that lncRNA gene. ...
... The adjusted nearby protein-coding genes can be divided into two categories: the closest protein-coding genes to each lncRNA gene and genes associated with Mendelian lipid disorders, including ANGPTL8, APOA1, APOA5, APOB, APOC1, APOC3, APOE, CETP, LDLR, LPA, LPL, PCSK7, PCSK9, PLA2G15, and TM6SF2. 19 Our primary analysis was to adjust for only rare nonsynonymous variants (MAF <1%) within nearby protein-coding genes. We did two sensitivity analyses: one adjusted for rare synonymous variants (MAF <1%) within nearby protein-coding genes and another adjusted for rare predicted loss-of-function (pLoF) variants (MAF <1%) within nearby protein-coding genes. ...
... We utilize the combination of bioinformatic approach and experimental validation to uncover recurrent fusion genes in different subtypes of breast cancer as exemplified by ESR1-CCDC170, the first identified recurrent gene fusion in ER+ breast cancer tumor with more aggressive phenotypes via genetic rearrangement [6,7] and BCL2L14-ETV6, the first reported TNBC-specific recurrent gene fusion associated with cell motility, invasiveness, EMT and chemoresistance [8]. Recently non-genomic chimeric transcripts have been reported in some solid tumors and studied as a pathogenic driver associated with disease progression [2,5,[9][10][11][12]. Through analysis of RNA-seq data from TCGA, we recently identified a neoplastic fusion transcript RAD51AP1-DYRK4 in luminal B breast cancer (~17.5%) ...
Background
Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key player of lipid metabolism with higher plasma levels in women throughout their life. Statin treatment affects PCSK9 levels also showing evidence of sex-differential effects. It remains unclear whether these differences can be explained by genetics.
Methods
We performed genome-wide association meta-analyses (GWAS) of PCSK9 levels stratified for sex and statin treatment in six independent studies of Europeans (8936 women/11,080 men respectively 14,825 statin-free/5191 statin-treated individuals). Loci associated in one of the strata were tested for statin- and sex-interactions considering all independent signals per locus. Independent variants at the PCSK9 gene locus were then used in a stratified Mendelian Randomization analysis (cis-MR) of PCSK9 effects on low-density lipoprotein cholesterol (LDL-C) levels to detect differences of causal effects between the subgroups.
Results
We identified 11 loci associated with PCSK9 in at least one stratified subgroup ( p < 1.0 × 10 –6 ), including the PCSK9 gene locus and five other lipid loci: APOB , TM6SF2 , FADS1 / FADS2 , JMJD1C , and HP / HPR . The interaction analysis revealed eight loci with sex- and/or statin-interactions. At the PCSK9 gene locus, there were four independent signals, one with a significant sex-interaction showing stronger effects in men (rs693668). Regarding statin treatment, there were two significant interactions in PCSK9 missense mutations: rs11591147 had stronger effects in statin-free individuals, and rs11583680 had stronger effects in statin-treated individuals. Besides replicating known loci, we detected two novel genome-wide significant associations: one for statin-treated individuals at 6q11.1 (within KHDRBS2 ) and one for males at 12q24.22 (near KSR2 / NOS1 ), both with significant interactions. In the MR of PCSK9 on LDL-C, we observed significant causal estimates within all subgroups, but significantly stronger causal effects in statin-free subjects compared to statin-treated individuals.
Conclusions
We performed the first double-stratified GWAS of PCSK9 levels and identified multiple biologically plausible loci with genetic interaction effects. Our results indicate that the observed sexual dimorphism of PCSK9 and its statin-related interactions have a genetic basis. Significant differences in the causal relationship between PCSK9 and LDL-C suggest sex-specific dosages of PCSK9 inhibitors.
Background
Lipid metabolism plays an essential role in nervous system development. Cholesterol deficiency leads to a variety of neurodevelopmental disorders, such as autism spectrum disorder and fragile X syndrome. There have been a lot of efforts to search for biological markers associated with and causal to ADHD, among which lipid is one possible etiological factor that is quite widely studied. We aimed to evaluate the causal relationship between lipids traits, lipid-lowering drugs, and attention deficit hyperactivity disorder (ADHD) outcomes using Mendelian randomization (MR) studies.
Methods
We used summary data from genome-wide association studies to explore the causal relationships between circulating lipid-related traits and ADHD. Then, quantitative trait loci for the expression of lipid-lowering drug target genes and genetic variants associated with lipid traits were extracted. Summary-data-based MR and inverse-variance-weighted MR (IVW-MR) were used to investigate the correlation between the expression of these drug-target genes and ADHD.
Results
After rigorous screening, 939 instrumental variables were finally included for univariable mendelian randomization analysis. However, there is no correlation between lipid profile and ADHD risk. Drug target analysis by IVW-MR method observed that APOB-mediated low-density lipoprotein cholesterol was associated with lower ADHD risk (odds ratio [ OR] = 0.90, 95% confidence interval [CI] [0.84, 0.97]; p = .007), whereas LPL-mediated triglycerides levels were associated with a higher risk of ADHD ( OR = 1.13, 95% CI [1.06, 1.21]; p < .001).
Conclusion
Our results suggest that APOB gene and LPL gene may be candidate drug target genes for the treatment of ADHD.
Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.
Background
The relationship between mitochondrial DNA copy number (mtDNA CN) and cardiovascular disease remains elusive.
Methods and Results
We performed cross‐sectional and prospective association analyses of blood‐derived mtDNA CN and cardiovascular disease outcomes in 27 316 participants in 8 cohorts of multiple racial and ethnic groups with whole‐genome sequencing. We also performed Mendelian randomization to explore causal relationships of mtDNA CN with coronary heart disease (CHD) and cardiometabolic risk factors (obesity, diabetes, hypertension, and hyperlipidemia). P <0.01 was used for significance. We validated most of the previously reported associations between mtDNA CN and cardiovascular disease outcomes. For example, 1‐SD unit lower level of mtDNA CN was associated with 1.08 (95% CI, 1.04–1.12; P <0.001) times the hazard for developing incident CHD, adjusting for covariates. Mendelian randomization analyses showed no causal effect from a lower level of mtDNA CN to a higher CHD risk (β=0.091; P =0.11) or in the reverse direction (β=−0.012; P =0.076). Additional bidirectional Mendelian randomization analyses revealed that low‐density lipoprotein cholesterol had a causal effect on mtDNA CN (β=−0.084; P <0.001), but the reverse direction was not significant ( P =0.059). No causal associations were observed between mtDNA CN and obesity, diabetes, and hypertension, in either direction. Multivariable Mendelian randomization analyses showed no causal effect of CHD on mtDNA CN, controlling for low‐density lipoprotein cholesterol level ( P =0.52), whereas there was a strong direct causal effect of higher low‐density lipoprotein cholesterol on lower mtDNA CN, adjusting for CHD status (β=−0.092; P <0.001).
Conclusions
Our findings indicate that high low‐density lipoprotein cholesterol may underlie the complex relationships between mtDNA CN and vascular atherosclerosis.
Aim
Genome‐wide association studies ( GWAS ) have identified multiple susceptibility loci associated with insulin resistance ( IR )‐relevant phenotypes. However, the genes responsible for these associations remain largely unknown. We aim to identify susceptibility genes for IR ‐relevant phenotypes via a transcriptome‐wide association study.
Materials and Methods
We conducted a large‐scale multi‐tissue transcriptome‐wide association study for IR (Insulin Sensitivity Index, homeostasis model assessment‐IR, fasting insulin) and lipid‐relevant traits (high‐density lipoprotein cholesterol, triglycerides, low‐density lipoprotein cholesterol and total cholesterol) using the largest GWAS summary statistics and precomputed gene expression weights of 49 human tissues. Conditional and joint analyses were implemented to identify significantly independent genes. Furthermore, we estimated the causal effects of independent genes by Mendelian randomization causal inference analysis.
Results
We identified 1190 susceptibility genes causally associated with IR‐relevant phenotypes, including 58 genes that were not implicated in the original GWAS. Among them, 11 genes were further supported in differential expression analyses or a gene knockout mice database, such as KRIT1 showed both significantly differential expression and IR‐related phenotypic effects in knockout mice. Meanwhile, seven proteins encoded by susceptibility genes were targeted by clinically approved drugs, and three of these genes ( H6PD , CACNB2 and DRD2 ) have been served as drug targets for IR‐related diseases/traits. Moreover, drug repurposing analysis identified four compounds with profiles opposing the expression of genes associated with IR risk.
Conclusions
Our study provided new insights into IR aetiology and avenues for therapeutic development.
