Background Epilepsy affects millions of people worldwide, and drug-resistant epilepsy (DRE) poses significant challenges. This investigation examined the relationship between ABCB1 gene variations and the efficacy of levetiracetam (LEV) in patients with focal epilepsy. Materials and Methods We classified 360 focal epilepsy patients treated with LEV as responders to LEV (n=142), second ASM (n=92), or non-responders (n=126). Five ABCB1 polymorphisms (rs1128503, rs2032582, rs1045642, rs2235035, and rs1922242) were genotyped using TaqMan assays. ABCB1 gene expression was analyzed in whole blood samples from 73 patients. Statistical analyses compared genotype, allele, and haplotype distributions between the two groups using Fisher's exact test and evaluated mean ABCB1 gene expression levels across genotypes with a t-test or analysis of variance. Results Significant genetic associations were identified between the rs2235035 and rs1922242 polymorphisms and the response to LEV or followed by the second ASM. The minor alleles and mutant genotypes of both SNPs were more prevalent among responders, suggesting protective effects against DRE. The 'AAAAT' and GCGAT haplotypes were associated with improved treatment response. Blood samples investigation revealed increased ABCB1 expression in reference genotypes of rs2235035 and rs1922242 compared to heterozygotes. This study identified novel associations between ABCB1 polymorphisms (rs2235035, rs1922242) and LEV response in patients with focal epilepsy. Conclusions The findings suggest potential genetic markers for predicting treatment outcomes and provide insights into the functional implications of these polymorphisms. Additional studies are warranted to confirm these findings and investigate their potential use in individualized treatment of epilepsy.
INTRODUCTION:Coding and noncoding variants of the LDLR gene have been reported to cause familial hyperlipidaemia (FH) and rare coding variants have been shown to increase risk of hyperlipidaemia in the general population. METHODS:Hyperlipidaemia status had previously been assigned for 470,000 UK Biobank participants in a study of rare coding variants. Effects of variants obtained from whole-genome sequencing in the five-prime untranslated region (5'UTR) and promoter region of LDLR were analysed using this phenotype. Variants previously reported to be implicated in FH and associated with altered LDLR expression were entered into logistic regression analysis. Other variants in these regions with minor allele frequency ≤0.01 were entered into a weighted burden test. For this analysis, insertion-deletion variants were weighted according to rarity while single-nucleotide variants were additionally weighted using 2 pathogenicity predictors, CADD and GPN-MSA. RESULTS:The previously reported common variant, rs17248720C>T, was confirmed to slightly reduce hyperlipidaemia risk. Two previously reported rare variants which were associated with markedly reduced LDLR expression, c.-101T>C and c.-121T>C, had at most only moderate effects on hyperlipidaemia risk. In the weighted burden analysis, promoter variants annotated with CADD showed evidence for association with increased risk (p = 0.00097). However, the effect size was small, with variants having CADD scores exceeding 0.5 having OR = 1.07 (0.99-1.15). CONCLUSION:Although rare noncoding variants do have detectable effects on hyperlipidaemia risk, these effects are much smaller than those detected for coding variants in the same sample. This research has been conducted using the UK Biobank Resource.
INTRODUCTION:High-mobility group box 1 (HMGB1) protein plays a significant role in cancer development and treatment response. The current research on the role of HMGB1 in lung cancer and its treatment outcomes is limited and inconsistent. This exploratory study investigated the association between HMGB1 common genetic variants and lung cancer susceptibility, as well as cisplatin chemotherapy response, in a Chinese cohort. METHODS:The current study included 106 individuals diagnosed with lung cancer and 93 healthy subjects, all of whom were part of a Chinese population cohort. HMGB1 polymorphisms (rs1045411, rs1412125, rs2249825, and rs1360485) were genotyped using the TaqMan single-nucleotide polymorphism typing method. HMGB1 gene expression in the lung tissue of patients was quantified using real-time PCR. All patients were administered cisplatin, and their response to the drug was evaluated. All statistical analyses were performed using GraphPad Prism v10. RESULTS:The control group exhibited a higher frequency of heterozygous variants for HMGB1 polymorphisms rs1045411 (p = 0.01, odds ratio [OR] = 0.45) and rs1412125 (p = 0.03, OR = 0.46) than the patients. Moreover, the combined mutant genotypes for HMGB1 rs1045411 (p = 0.0001, OR = 0.15) and rs2249825 (p = 0.003, OR = 0.19) exhibited favorable responses to cisplatin treatment. Moreover, the wild-type variants of rs1045411 and rs2249825 exhibited higher HMGB1 expression than the mutants; however, this difference was not statistically significant. CONCLUSION:This preliminary investigation indicated potential associations between HMGB1 genetic variants and lung cancer susceptibility and treatment response. These exploratory findings necessitate further validation through larger multicenter studies incorporating functional assays to elucidate the biological significance and clinical utility of HMGB1 polymorphisms in the management of lung cancer.
INTRODUCTION:We show that generalizations of stable population (GSPM) and agent-based models (ABMs) are useful tools to simulate trajectories of human phenotypes. Although mathematically very different, both classes of models can simultaneously account for multiple determinants of the population distribution of a phenotype across time and space. These include genetic transmission, assortative mating, differential fertility, vertical and horizontal cultural heredity, gene-environments interactions (GxE), and environmental feedback. We propose an application to obesity, a condition that has spread rapidly around the globe, increasing the risk of adult chronic illnesses and mortality. METHODS:We choose body mass index as the target phenotype, and formulate GSPM and ABMs that include genetic inheritance, GxE, assortative mating, and fertility differentials. We exclude, in this version, the role of vertical cultural heredity. The GSPM is built on time-varying stochastic matrices that trace the trajectory of the phenotype by groups. The ABM models the behavior of individual agents integrating a stochastic component modifying each agent's behavior. RESULTS:There are four key results. First, differential fertility dominates the phenotype's time trajectory, followed by assortative mating and GxE. Second, contrary to research in other phenotypes, the impact of assortative mating cannot offset the influence of fertility differentials. Third, concerning the formal representation of the transmission process, we show that the use of simple Mendelian models to represent complex phenotypes can produce badly biased inferences. Fourth, despite their mathematical differences, the GSPM and ABM produce virtually identical results. CONCLUSIONS:Modeling phenotypes with complex genetic transmission and heavily dependent on regimes of fertility differentials, assortative mating and GxE ought not to rely on excessive simplifications, as has traditional been done in past research. Both, the GSPM and ABM are useful, accessible, and effective tools to introduce more realism in the modeling of these phenotypes, and can be used as guides for policy interventions.
Colocalization analyses assess whether two traits are affected by the same or distinct causal genetic variants in a single gene region. A class of Bayesian enumeration colocalization tests are now routinely used in practice; for example, for genetic analyses in drug development pipelines. In this work, we consider an alternative frequentist approach to colocalization testing that examines the proportionality of genetic associations with each trait. The proportional colocalization approach uses markedly different assumptions to enumeration colocalization tests, and therefore can provide valuable complementary evidence in cases where enumeration colocalization results are inconclusive or sensitive to priors. We propose a novel conditional test of proportional colocalization, prop-coloc-cond, that aims to account for the uncertainty in variant selection, in order to recover accurate type I error control. The test can be implemented straightforwardly, requiring only summary data on genetic associations. Simulation evidence and an empirical investigation into GLP1R gene expression demonstrates how tests of proportional colocalization can offer important insights in conjunction with enumeration colocalization tests.
