Human Genetics最新文献

Most existing genotype-by-environment interaction (G×E) methods assume a known causal direction as an assumption that often does not hold and can lead to biased estimates and spurious findings. To address this, we introduce the Genetic Causality Inference Model (GCIM), a novel approach designed to infer causal directions in G×E studies. GCIM integrates polygenic risk scores (PRS) for both the exposure and the outcome to strengthen causal inference and reduce spurious interaction signals. We evaluated GCIM using simulated data across varying genetic and residual correlation settings and compared its performance to existing PRS-by-environment (PRS×E) models under both null and alternative G×E scenarios. GCIM was also applied to real-world UK Biobank data in both causal directions. GCIM consistently outperformed existing methods by accurately identifying the absence of G×E variance and avoiding false positives, even in the presence of strong phenotypic heteroscedasticity due to residual heterogeneity. Other methods often generated spurious associations, especially under reverse causality. Applying GCIM to UK Biobank data, we investigated 11 circulating biomarkers (including liver enzymes, lipids, and inflammatory markers) and three anthropometric traits (BMI, body fat, and waist-to-hip ratio [WHR]). GCIM identified that bilirubin modulates genetic effects on BMI and WHR, while body fat modulates genetic effects on C-reactive protein, with associations remaining significant after multiple testing corrections. Overall, GCIM provides a more reliable framework for GxE analysis, particularly under challenging conditions such as residual heterogeneity and uncertain causal direction. However, further development is needed to improve its statistical power.

Xeroderma pigmentosum (XP) and Cockayne syndrome (CS) are diseases provoked by mutations in multifunctional proteins that are involved in DNA repair. DNA-repair deficiency explains the high cancer incidence of XP, whereas cancer-free CS, characterized by growth retardation, neurological degeneration, and premature aging does not present as a classical DNA-repair deficiency disorder. Here, we compared a severe combined XP/CS case provoked by XPG-mutation with an XP "only" patient cell line caused by mutation in the same XPG gene to carve out the pathogenic cellular disturbances that provoke CS. We identified RNA polymerase I transcription and rRNA maturation defects, a highly phosphorylated eukaryotic initiation factor 2 alpha (eIF2alpha), and a shift from cap- to internal ribosomal entry site (IRES) translation, indicating an activated integrated stress response in CS. Disturbances in ribosomal biogenesis and translational control might thus contribute to the development of CS.

Neonatal diabetes mellitus (NDM) typically presents within the first 6 months of life and generally lacks islet autoantibodies. Genetic elucidation of NDM is a prime example of precision medicine in diabetes. However, no published genetic data exist for NDM in Thailand. We aimed to assess the genetic etiology of Thai NDM using whole exome sequencing (WES). We enrolled 14 Thai patients with NDM and measured GAD65, IA-2, and ZnT8 autoantibodies. We then performed WES and analyzed 43 NDM-related genes to identify causative variants. All subjects tested negative for the three islet autoantibodies. Eight harbored variants in well-established NDM genes (KCNJ11 [n = 5], ABCC8 [n = 1], INS [n = 2 in identical twins]). Two patients with KCNJ11 variants (rs80356616: p.Val59Met and rs8035661: p.Arg50Gln) achieved excellent glycemic control on sulfonylureas, illustrating precision therapy. The remaining six carried pathogenic variants in genes associated with monogenic diabetes, including LRBA, EIF2AK3, DOCK8, WFS1, GATA6, CISD2/SLC9B1, and COQ2. This is the first report on the genetic etiology of NDM in Thailand. WES is an effective approach to identifying variants in this rare diabetes subtype. Larger cohort studies are needed to determine the true prevalence of NDM in Thailand.

Tuberculosis (TB) treatment is highly effective, but response to therapy varies by geography and population subgroups. We assessed differences in TB treatment response in a representative and heterogeneous Brazilian population. We estimated genetic ancestry according to major genetic ancestry groups (African, European, and Amerindian) in the Regional Prospective Observational Research in Tuberculosis (RePORT)-Brazil cohort using ADMIXTURE software. RePORT-Brazil is an observational prospective cohort study of individuals with newly-diagnosed, culture-confirmed, pulmonary TB. Outcomes attributed to TB treatment included Grade 2 or higher adverse drug reaction (ADR), Grade 3 or higher ADR, hepatic ADR, and failure/recurrence. Genetic ancestry proportions were evaluated as predictors in univariate and multivariable logistic regression models for each outcome, and in stratified models for each genetic ancestry group. There were 930 pulmonary TB patients included in this study. In multivariable models we observed a decreased risk of Grade 2 + ADR when African ancestry proportion increased by 10% (Odds Ratio [OR] 0.41, 95% Confidence Interval [CI] 0.20-0.85) and an increased risk for Grade 2 + ADR with increasing European genetic ancestry (OR 2.33, 95% CI 1.14-4.76). In secondary analyses evaluating the interaction of HIV and genetic ancestry, we observed a statistically significant interaction between HIV and African genetic ancestry, but significantly decreased risk for Grade 2 + ADR with increasing African ancestry proportion. There were no associations with Amerindian genetic ancestry or for other treatment outcomes. In this Brazilian TB cohort, increased toxicity risk was associated with decreased African and increased European ancestry proportion.