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Biomedical subjects

Ran Li

Publications and source records attributed to Ran Li.

2 recordsLinked to original sources

Causality between noise pollution and Alzheimer disease: A Mendelian randomization analysis.

The role of noise pollution as a risk factor for Alzheimer disease (AD) is unclear, with observational studies yielding conflicting results susceptible to confounding and reverse causality. To clarify this relationship, we performed a 2-sample Mendelian randomization (MR) study using summary statistics from large-scale genome-wide association studies of European populations. Genetically predicted daytime and evening noise exposure was used as an instrumental variable to assess a causal effect on AD risk. The primary analysis was conducted using the inverse-variance weighted method, with weighted median and MR-Egger methods as key sensitivity analyses. We assessed instrument validity and pleiotropy using the Cochran Q test, the MR-Egger intercept, and leave-one-out analysis. Our MR analysis found no evidence of a causal association between genetically predicted daytime noise (odds ratio [95% confidence interval] = 0.999 [0.993-1.006], P = .819) or evening noise (odds ratio [95% confidence interval] = 0.999 [0.993-1.005], P = .643) and the risk of AD. Sensitivity analyses were consistent, with no evidence of heterogeneity or directional pleiotropy. In conclusion, this study does not support a direct causal link between noise and AD. While our findings mitigate common observational biases, they do not preclude indirect mechanisms whereby noise may influence AD pathogenesis via established risk pathways, such as chronic sleep disruption and cardiovascular stress. Studies are needed to focus on disentangling these potential indirect effects.

Alzheimer Disease

clusIBD: Robust Detection of Identity-by-descent Segments Using Unphased Genetic Data from Poor-quality Samples.

The detection of identity-by-descent (IBD) segments is widely used to infer relatedness in many fields, including forensics and ancient DNA analysis. However, existing methods are often ineffective for poor-quality DNA samples. Here, we propose a method, clusIBD, which can robustly detect IBD segments using unphased genetic data with a high rate of genotyping error. We evaluated and compared the performance of clusIBD with that of IBIS, TRUFFLE, and IBDseq using simulated data, artificial poor-quality materials, and ancient DNA samples. The results show that clusIBD outperforms these existing tools and could be used for kinship inference in fields such as ancient DNA analysis and criminal investigation. clusIBD is publicly available at GitHub (https://github.com/Ryan620/clusIBD/) and BioCode (https://ngdc.cncb.ac.cn/biocode/tool/BT007882).

Humans