Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies

被引:0
|
作者
Xiang-He Meng
Hui Shen
Xiang-Ding Chen
Hong-Mei Xiao
Hong-Wen Deng
机构
[1] Hunan Normal University,Laboratory of Molecular and Statistical Genetics, College of Life Sciences
[2] Tulane University,Department of Global Biostatistics and Data Science, Center of Bioinformatics and Genomics, School of Public Health and Tropical Medicine
[3] Central South University,Institute of Reproduction and Stem Cell Engineering, School of Basic Medical Science
来源
Human Genetics | 2018年 / 137卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with diverse complex phenotypes and diseases, and provided tremendous opportunities for further analyses using summary association statistics. Recently, Pickrell et al. developed a robust method for causal inference using independent putative causal SNPs. However, this method may fail to infer the causal relationship between two phenotypes when only a limited number of independent putative causal SNPs identified. Here, we extended Pickrell’s method to make it more applicable for the general situations. We extended the causal inference method by replacing the putative causal SNPs with the lead SNPs (the set of the most significant SNPs in each independent locus) and tested the performance of our extended method using both simulation and empirical data. Simulations suggested that when the same number of genetic variants is used, our extended method had similar distribution of test statistic under the null model as well as comparable power under the causal model compared with the original method by Pickrell et al. But in practice, our extended method would generally be more powerful because the number of independent lead SNPs was often larger than the number of independent putative causal SNPs. And including more SNPs, on the other hand, would not cause more false positives. By applying our extended method to summary statistics from GWAS for blood metabolites and femoral neck bone mineral density (FN-BMD), we successfully identified ten blood metabolites that may causally influence FN-BMD. We extended a causal inference method for inferring putative causal relationship between two phenotypes using summary statistics from GWAS, and identified a number of potential causal metabolites for FN-BMD, which may provide novel insights into the pathophysiological mechanisms underlying osteoporosis.
引用
收藏
页码:247 / 255
页数:8
相关论文
共 50 条
  • [1] Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies
    Meng, Xiang-He
    Shen, Hui
    Chen, Xiang-Ding
    Xiao, Hong-Mei
    Deng, Hong-Wen
    [J]. HUMAN GENETICS, 2018, 137 (03) : 247 - 255
  • [2] Inferring Causal Relationships Between Metabolites and Polycystic Ovary Syndrome Using Summary Statistics from Genome-Wide Association Studies
    Meng, Xiang-He
    Chen, Bin-Bin
    Liu, Xiao-Wen
    Zhang, Jing-Xi
    Xie, Shun
    Liu, Lv-Jun
    Wen, Li-Feng
    Deng, Ai-Min
    Mao, Zeng-Hui
    [J]. REPRODUCTIVE SCIENCES, 2024, 31 (03) : 832 - 839
  • [3] Inferring Causal Relationships Between Metabolites and Polycystic Ovary Syndrome Using Summary Statistics from Genome‑Wide Association Studies
    Xiang-He Meng
    Bin-Bin Chen
    Xiao-Wen Liu
    Jing-Xi Zhang
    Shun Xie
    Lv-Jun Liu
    Li-Feng Wen
    Ai-Min Deng
    Zeng-Hui Mao
    [J]. Reproductive Sciences, 2024, 31 : 832 - 839
  • [4] Adjustment for covariates using summary statistics of genome-wide association studies
    Wang, Tao
    Xue, Xiaonan
    Xie, Xianhong
    Ye, Kenny
    Zhu, Xiaofeng
    Elston, Robert C.
    [J]. GENETIC EPIDEMIOLOGY, 2018, 42 (08) : 812 - 825
  • [5] Inferring Causal Relationships Between Risk Factors and Outcomes from Genome-Wide Association Study Data
    Burgess, Stephen
    Foley, Christopher N.
    Zuber, Verena
    [J]. ANNUAL REVIEW OF GENOMICS AND HUMAN GENETICS, VOL 19, 2018, 19 : 303 - 327
  • [6] Prospects of fine-mapping causal genetic variants using summary statistics from genome-wide association studies
    Benner, C.
    Havulinna, A.
    Jarvelin, M.
    Salomaa, V.
    Ripatti, S.
    Pirinen, M.
    [J]. EUROPEAN JOURNAL OF HUMAN GENETICS, 2018, 26 : 64 - 65
  • [7] CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies
    Wang, Jianhua
    Huang, Dandan
    Zhou, Yao
    Yao, Hongcheng
    Liu, Huanhuan
    Zhai, Sinan
    Wu, Chengwei
    Zheng, Zhanye
    Zhao, Ke
    Wang, Zhao
    Yi, Xianfu
    Zhang, Shijie
    Liu, Xiaorong
    Liu, Zipeng
    Chen, Kexin
    Yu, Ying
    Sham, Pak Chung
    Li, Mulin Jun
    [J]. NUCLEIC ACIDS RESEARCH, 2020, 48 (D1) : D807 - D816
  • [8] Multiple phenotype association tests using summary statistics in genome-wide association studies
    Liu, Zhonghua
    Lin, Xihong
    [J]. BIOMETRICS, 2018, 74 (01) : 165 - 175
  • [9] On Genetic Correlation Estimation With Summary Statistics From Genome-Wide Association Studies
    Zhao, Bingxin
    Zhu, Hongtu
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2022, 117 (537) : 1 - 11
  • [10] Finding hidden treasures in summary statistics from genome-wide association studies
    Prive, Florian
    Zhu, Zhihong
    Vilhjalmsson, Bjarni J.
    [J]. NATURE GENETICS, 2021, 53 (04) : 431 - 432