Genetic causal relationship between placental weight and autism spectrum disorder: A two-sample Mendelian randomization study

孟德尔随机化 孟德尔遗传 自闭症谱系障碍 遗传学 心理学 样品(材料) 临床心理学 因果推理 发展心理学 自闭症 生物 精神科 医学 遗传变异 基因 基因型 化学 色谱法 病理
作者
Zhao Liu
出处
期刊:Journal of Psychosomatic Research [Elsevier BV]
卷期号:184: 111857-111857
标识
DOI:10.1016/j.jpsychores.2024.111857
摘要

Previous research has suggested an association between placental tissue abnormalities and the diagnosis of autism spectrum disorder. This study aims to explore the causal relationship between placental weight and autism spectrum disorder. This study employed Mendelian randomization analysis to investigate the potential causal relationship between placental weight and autism spectrum disorder. The study design involved two sample populations, with data for the exposed population sourced from previous studies focusing on PW, and data for the outcome population obtained from the Integrative Psychiatric Research and the Psychiatric Genomics Consortium study. To ensure the robustness of the results, three sensitivity analyses were performed, including heterogeneity testing, pleiotropy testing, and a leave-one-out analysis. The inverse variance weighted method served as the gold standard for the Mendelian randomization analysis. The results of the first analysis revealed a significant correlation between an increase in placental weight and an elevated risk of autism spectrum disorder (p = 0.02). Sensitivity analysis detected heterogeneity and outliers. After removing two outlier SNPs in the second round of analysis, the results still supported a genetic causal relationship between placental weight and autism spectrum disorder (p = 0.01). The second-round sensitivity analysis did not reveal any heterogeneity or outliers. Our study provides compelling evidence supporting a causal relationship between elevated placental weight and increased risk of autism spectrum disorder. These findings underscore the significance of placental development in the etiology of autism spectrum disorder and propose a potential early predictive indicator for autism spectrum disorder.
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