Birth Weight and Stroke in Adult Life: Genetic Correlation and Causal Inference With Genome-Wide Association Data Sets

Ting Wang; Zaixiang Tang; Xinghao Yu; Yixing Gao; Fengjun Guan; Chengzong Li; Shuiping Huang; Junnian Zheng; Ping Zeng

doi:10.3389/fnins.2020.00479

Birth Weight and Stroke in Adult Life: Genetic Correlation and Causal Inference With Genome-Wide Association Data Sets

Front Neurosci. 2020 Jun 11:14:479. doi: 10.3389/fnins.2020.00479. eCollection 2020.

Authors

Ting Wang¹, Zaixiang Tang², Xinghao Yu¹, Yixing Gao¹, Fengjun Guan³, Chengzong Li⁴, Shuiping Huang¹, Junnian Zheng^{5

6

7}, Ping Zeng¹

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.
² Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China.
³ Department of Pediatrics, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
⁴ Center of Stroke and Department of Cardiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
⁵ Cancer Institute, Xuzhou Medical University, Xuzhou, China.
⁶ Center of Clinical Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
⁷ Jiangsu Center for the Collaboration and Innovation of Cancer Biotherapy, Cancer Institute, Xuzhou Medical University, Xuzhou, China.

Abstract

Objective: Prior studies have shown that there is an inverse association between birth weight and stroke in adulthood; however, whether such association is causal remains yet known and those studies cannot distinguish between the direct fetal effect and the indirect maternal effect. The aim of the study is to untangle such relationship using novel statistical genetic approaches.

Methods: We first utilized linkage disequilibrium score regression (LDSC) and Genetic analysis incorporating Pleiotropy and Annotation (GPA) to estimate the overall genetic correlation between birth weight and stroke. Then, with a set of valid birth-weight instruments which had adjusted fetal and maternal effects, we performed a two-sample Mendelian randomization (MR) to evaluate its causal effect on stroke based summary statistics from large scale genome-wide association study (GWAS) (n = 264,498 for birth weight and 446,696 for stroke). We further validated the MR results with extensive sensitivity analyses.

Results: Both LDSC and GPA demonstrated significant evidence of shared maternal genetic foundation between birth weight and stroke, with the genetic correlation estimated to -0.176. However, no fetal genetic correlation between birth weight and stroke was detected. Furthermore, the inverse variance weighted MR demonstrated the maternally causal effect of birth weight on stroke was 1.12 (95% confidence interval [CI] 1.00-1.27). The maternal ORs of birth weight on three subtypes of stroke including cardioembolic stroke (CES), large artery stroke (LAS) and small vessel stroke (SVS) were 1.16 (95% CI 0.93-1.43), 1.50 (95% CI 1.14-1.96) and 1.47 (95% CI 1.15-1.87), respectively. In contrast, no fetal causal associations were found between birth weight and stroke or the subtypes. Those results were robust against extensive sensitivity analyses, with Egger regression ruling out the possibility of pleiotropy and multivariable MR excluding the likelihood of confounding or mediation effects of other risk factors of stroke.

Conclusion: This study provides empirically supportive evidence on the fetal developmental origins of stroke and its subtypes. However, further investigation is warranted to understand the pathophysiological role of low birth weight in developing stroke.

Keywords: Mendelian randomization; birth weight; causal association; fetal origins; genetic correlation; ischemic stroke; maternal effect; stroke and subtypes.