Stearns FW. One hundred years of pleiotropy: a retrospective. Genetics. 2010;186:767–73.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gratten J, Visscher PM. Genetic pleiotropy in complex traits and diseases: implications for genomic medicine. Genome Med. 2016;8:1–3.

Article  Google Scholar 

Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27:R195–R208.

Article  CAS  PubMed  PubMed Central  Google Scholar 

MacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol. 2007;58:593.

Article  PubMed  PubMed Central  Google Scholar 

Zeng P, Shao Z, Zhou X. Statistical methods for mediation analysis in the era of high-throughput genomics: current successes and future challenges. Comput Struct Biotechnol J. 2021;19:3209–24.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Huang YT, Liang L, Moffatt MF, Cookson WO, Lin X. iGWAS: integrative genome‐wide association studies of genetic and genomic data for disease susceptibility using mediation analysis. Genet Epidemiol. 2015;39:347–56.

Article  PubMed  PubMed Central  Google Scholar 

Yan Q, Forno E, C. Celedón J, Chen W. A region-based method for causal mediation analysis of DNA methylation data. Epigenetics. 2022;17:286–96.

Article  PubMed  Google Scholar 

Keele GR, et al. Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation. PLoS Genet. 2020;16:e1008537.

Article  PubMed  PubMed Central  Google Scholar 

Siedlinski M, et al. Dissecting direct and indirect genetic effects on chronic obstructive pulmonary disease (COPD) susceptibility. Hum Genet. 2013;132:431–41.

Article  PubMed  PubMed Central  Google Scholar 

Salinas YD, Wang Z, DeWan AT. Discovery and mediation analysis of cross-phenotype associations between asthma and body mass index in 12q13. 2. Am J Epidemiol. 2021;190:85–94.

Article  PubMed  Google Scholar 

Wang N, et al. Innate IL‐23/Type 17 immune responses mediate the effect of the 17q21 locus on childhood asthma. Clin Exp Allergy. 2021;51:892–901.

Article  CAS  PubMed  Google Scholar 

Lutz SM, Hokanson JE. Mediation analysis in genome-wide association studies: current perspectives. Open Access. Bioinformatics. 2015;7:1–5.

CAS  Google Scholar 

Imai K, Keele L, Yamamoto T. Identification, inference and sensitivity analysis for causal mediation effects. Stat Sci. 2010;25:51–71.

Article  Google Scholar 

Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychol Methods. 2010;15:309.

Article  PubMed  Google Scholar 

Lee SL, et al. The association between loneliness and depressive symptoms among adults aged 50 years and older: a 12-year population-based cohort study. Lancet Psychiatry. 2021;8:48–57.

Article  PubMed  PubMed Central  Google Scholar 

Pingault J-B, et al. Genetic sensitivity analysis: adjusting for genetic confounding in epidemiological associations. PLoS Genet. 2021;17:e1009590.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Choi SW, O’Reilly PF. PRSice-2: Polygenic Risk Score software for biobank-scale data. Gigascience. 2019;8:giz082.

Article  PubMed  PubMed Central  Google Scholar 

Choi SW, Mak TS-H, O’Reilly PF. Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc. 2020;15:2759–72.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bogdan R, Baranger DA, Agrawal A. Polygenic risk scores in clinical psychology: bridging genomic risk to individual differences. Annu Rev Clin Psychol. 2018;14:119.

Article  PubMed  PubMed Central  Google Scholar 

Zhao B, Zhu H. On genetic correlation estimation with summary statistics from genome-wide association studies. J Am Stat Assoc. 2022;117:1–11.

Article  CAS  PubMed  Google Scholar 

Rietveld CA, de Vlaming R, Slob EA. The identification of mediating effects using genome-based restricted maximum likelihood estimation. PLoS Genet. 2023;19:e1010638.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jiang SZ, Lu W, Zong XF, Ruan HY, Liu Y. Obesity and hypertension. Exp Therapeutic Med. 2016;12:2395–9.

Article  Google Scholar 

Egan K, et al. Longitudinal associations between asthma and general and abdominal weight status among N orwegian adolescents and young adults: the HUNT Study. Pediatr Obes. 2015;10:345–52.

Article  CAS  PubMed  Google Scholar 

Loh P-R, et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat Genet. 2015;47:1385–92.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Matilainen K, Mäntysaari EA, Lidauer MH, Strandén I, Thompson R. Employing a Monte Carlo algorithm in Newton-type methods for restricted maximum likelihood estimation of genetic parameters. PloS one. 2013;8:e80821.

Article  PubMed  PubMed Central  Google Scholar 

Bulik-Sullivan BK, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhang Y, et al. Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics. Brief Bioinforma. 2021;22:bbaa442.

Article  Google Scholar 

MacKinnon DP, Krull JL, Lockwood CM. Equivalence of the mediation, confounding and suppression effect. Prev Sci. 2000;1:173–81.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wang L, et al. Sex difference in the association between obesity and asthma in US adults: findings from a national study. Respiratory Med. 2015;109:955–62.

Article  Google Scholar 

Zhang Z, et al. Interaction of sex, age, body mass index and race on hypertension risk in the American population: a cross-sectional study. Eur J Public Health. 2021;31:1042–7.

Article  PubMed  Google Scholar 

Ni G, et al. Estimation of genetic correlation via linkage disequilibrium score regression and genomic restricted maximum likelihood. Am J Hum Genet. 2018;102:1185–94.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Srivastava AK, Williams SM, Zhang G. Heritability estimation approaches utilizing genome‐wide data. Curr Protoc. 2023;3:e734.

Article  CAS  PubMed  Google Scholar 

Evans LM, et al. Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. Nat Genet. 2018;50:737–45.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jacobsson JA, et al. Detailed analysis of variants in FTO in association with body composition in a cohort of 70-year-olds suggests a weakened effect among elderly. PloS one. 2011;6:e20158.

Article  CAS  PubMed  PubMed Central  Google Scholar 

He D, et al. FTO gene variant and risk of hypertension: a meta-analysis of 57,464 hypertensive cases and 41,256 controls. Metabolism. 2014;63:633–9.

Article  CAS  PubMed  Google Scholar 

Pausova Z, et al. A common variant of the FTO gene is associated with not only increased adiposity but also elevated blood pressure in French Canadians. Circulation: Cardiovascular Genet. 2009;2:260–9.

CAS  Google Scholar 

Sakaue S, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53:1415–24.

Article  CAS  PubMed  Google Scholar 

Zeggini E, Ioannidis JP. Meta-analysis in genome-wide association studies. 2009.

Jiang J, Jiang W, Paul D, Zhang Y, Zhao, H. High-dimensional asymptotic behavior of inference based on GWAS summary statistics. Statist Sin. 2023;33:1555–76.

VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91:4414–23.

Article  CAS  PubMed  Google Scholar 

Preacher KJ, Hayes AF. SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav Res Methods Instrum Comput. 2004;36:717–31.

Article  PubMed