Publications by authors named "Basile Jumentier"
Age at menarche (AAM) and age at natural menopause (ANM) are highly heritable traits and have been linked to various health outcomes. We aimed to identify circulating proteins associated with altered ANM and AAM using an unbiased two-sample Mendelian randomization (MR) and colocalization approach. By testing causal effects of 1,271 proteins on AAM, we identified 22 proteins causally associated with AAM in MR, among which 13 proteins (GCKR, FOXO3, SEMA3G, PATE4, AZGP1, NEGR1, LHB, DLK1, ANXA2, YWHAB, DNAJB12, RMDN1 and HPGDS) colocalized.
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- High-dimensional mediation analysis (HDMA) assesses multiple mediators to understand environmental impacts on health, but no universally accepted methods exist for optimal analysis.
- The researchers developed a new method called HDMAX2 to evaluate how placental DNA methylation mediates the effects of maternal smoking on gestational age and birth weight.
- HDMAX2 showed increased statistical power compared to existing methods, identifying new aggregated mediator regions (AMRs) linked to lower birth weight and providing a deeper understanding of the mediation pathways.
Article Synopsis
- The text discusses the challenges in linking phenotypes or exposures with genomic and epigenomic data due to factors like unobserved confounding, and introduces penalized latent factor regression models to address these issues.
- These models use penalties to manage high-dimensional data and are shown to improve statistical performance, particularly in sparse latent factor regression compared to other methods.
- The authors applied these models to studies on a flowering trait in plants and smoking status in pregnant women, achieving consistent results with previous findings and identifying new relevant genes.
Gene-environment association (GEA) studies are essential to understand the past and ongoing adaptations of organisms to their environment, but those studies are complicated by confounding due to unobserved demographic factors. Although the confounding problem has recently received considerable attention, the proposed approaches do not scale with the high-dimensionality of genomic data. Here, we present a new estimation method for latent factor mixed models (LFMMs) implemented in an upgraded version of the corresponding computer program.
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