Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization

Jiang, Lin; Miao, Lin; Yi, Guorong; Li, Xiangyi; Xue, Changxi; Plewczynski, Dariusz; Huang, Hailiang; Li, Miaoxin

doi:10.1016/j.ajhg.2022.04.004

Cited by 9 publications

(9 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the GO and KEGG databases, we conducted gene-set enrichment analyses to identify biological pathways. Using DESE (driver tissue estimation by selective expression) 18 , we explored the tissue/cell types specificity associated with diseases through the phenotype-cell-gene association analysis (PCGA) [18][19][20][21] website.…”

Section: Discussionmentioning

confidence: 99%

Role of Blood Lipids in the Shared Genetic Etiology Between Major Depressive Disorder and Myocardial Infarction: A Large-scale Multi-trait Association Analysis

Zhu,

Wu,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: The genetic role of blood lipids (BL) in the shared genetic etiology between major depressive disorder (MDD) and myocardial infarction (MI) has not been fully characterized. Methods: We first evaluated genetic associations and causal inferences between MDD, MI and the quantitative traits of BL. To further unravel the underlying genetic mechanisms, we performed multi-trait association analysis to identify novel or pleiotropic genomic risk loci, and shared causal variants for diseases involving BL. Using multiple post-GWAS methods, we explored potential genes, pathways, tissues, cells, and therapeutic targets associated with diseases from different perspectives. Findings: We found extensive global and local genetic correlations between MDD, MI and the traits of BL. Mendelian randomization (MR) analyses showed that lipid metabolism mediated 26.5% of the mediating effect of MDD leading to MI. Multi-trait association analysis successfully identified 13 MDD- and 36 MI- novel risk loci which have never been reported before. Notably, many pleiotropic loci and shared causal variants were identified across risk loci for both diseases, such as 11q23.3 (rs117937125) and 12q13.3 (rs188571756), which also colocalized for traits of BL. Pathway enrichment analysis further highlighted shared biological pathways primarily involving synaptic function, arterial development, and lipid metabolism. Lastly, gene-mapping，gene-based, transcriptome-wide and proteome-wide association, and MR-proteomic analyses revealed candidate pathogenic genes and therapeutic targets (such as ANGPTL4 and TMEM106B). Interpretation: These findings not only provide novel insights into the role of BL in the comorbidity between MDD and MI, but also benefit the development of preventive or therapeutic drugs for diseases.

show abstract

Section: Discussionmentioning

confidence: 99%

Role of Blood Lipids in the Shared Genetic Etiology Between Major Depressive Disorder and Myocardial Infarction: A Large-scale Multi-trait Association Analysis

Zhu,

Wu,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, we elucidated the biological pathways by conducting gen-set enrichment analyses using the GO and KEGG databases. In addition, we used the DESE (driver tissue estimation by selective expression) approach implemented in phenotype-cell-gene association analysis (PCGA) (21)(22)(23)(24) website to further explore the tissue/cell types specificity and similar phenotypes for HF MTAG .…”

Section: Discussionmentioning

confidence: 99%

“…FUMA mapped 1466 [including 1251 positional mapped genes, 327 expression quantitative trait loci (eQTL) mapped genes and 540 3D Chromatin Interaction mapped genes], 486 (including 406 positional mapped genes, 108 eQTL mapped genes and 145 3D Chromatin Interaction mapped genes), 500 (including 435 positional mapped genes, 113 eQTL mapped genes and 190 3D Chromatin Interaction mapped genes) genes for HF BL , HF BP , and HF BG , respectively (Supplementary Table [22][23][24].…”

Section: Gene-based and Transcriptome-wide Association Analysismentioning

confidence: 99%

Exploring novel risk loci for heart failure and the shared genetic etiology with blood lipids, blood pressure, and blood glucose: a large-scale multi-trait association analysis

Zhu,

Wang,

Cui

et al. 2023

Preprint

View full text Add to dashboard Cite

BackgroundThe comprehensive exploration of genomic risk loci for heart failure (HF) remains constrained, and the genetic role of blood lipids (BL), blood pressure (BP) and blood glucose (BG) in HF has not been fully characterized.MethodsWe first assessed the global and local genetic correlations between HF and the quantitative traits of BL, BP, and BG. We then employed multi-trait association analysis and multi-trait colocalization analysis to identify novel and pleiotropic genomic risk loci for HF. Furthermore, we explored potential genes, pathways, tissues, and cells associated with HF involving BL, BP, and BG. Lastly, we investigated potential therapeutic targets for HF.FindingsWe found extensive global and local genetic correlations between HF and the traits of BL, BP, and BG. Multi-trait association analysis successfully identified 154 novel genomic risk loci for HF. Multi-trait colocalization analysis further revealed 46, 35, and 14 co-localized loci shared by HF with BL, BP, and BG, respectively. We found that the loci shared by HF with these traits rarely overlapped, indicating distinct shared mechanisms. Gene-mapping, gene-based, and transcriptome-wide association analyses prioritized noteworthy candidate genes (such as LPL, GRK5, and TNNC1) for HF. In enrichment analysis, HF exhibited comparable characteristics with cardiovascular traits and metabolic correlated to BL, BP, and BG. We provided genetic evidence for putative drugs, and highlighted 33 robust potential protein targets.InterpretationThese findings will provide biological insights into the pathogenesis for HF, and benefit the development of preventive or therapeutic drugs for HF.

show abstract

“…The authors declare they have no competing interests. (Burgess et al, 2013), dIVW (Ye et al, 2021), pIVW (Xu et al, 2022), MR-Egger (Bowden et al, 2016), MRAID (Yuan et al, 2022), MRMix (Qi & Chatterjee, 2019), MR-cML (Xue et al, 2021), MVMR-cML (Lin et al, 2023), MR-PRESSO (Verbanck et al, 2018), IMRP (Zhu et al, 2021), MR-Median (Bowden et al, 2016), MR-MaxLike (Burgess, Dudbridge, et al, 2016), MR-Corr2 (Cheng, Qiu, et al, 2022), MR-Robust (Rees et al, 2019), MR-Lasso (Kang et al, 2016), MR-Conmix (Burgess et al, 2020), MR-CUE, EMIC (Jiang, Miao, et al, 2022), MR-Mode (Hartwig et al, 2017), MRBEE (Lorincz-Comi et al, 2023), MR-Lap (Mounier & Kutalik, 2023), the Wald test (Palmer et al, 2008), MR-BMA (Zuber et al, 2020), MR-Robin (Gleason et al, 2020), JAM (Newcombe et al, 2016), MR using factor analysis (Patel et al, 2023), moPMR-Egger (Liu et al, 2021), MR-Clust (Foley et al, 2021), MR using PCA (Burgess et al, 2017), MR-CUE (Cheng, Zhang, et al, 2022), mixIE (Lin et al, 2021), MRMO (Deng et al, 2022), BWMR (Zhao, Ming, et al, 2020), sisVIVE (Kang et al, 2016), MR-LDP (Cheng et al, 2020), MR-CIP (Xu et al, 2021), MR-PATH (Iong et al, 2020), BMRE (Schmidt & Dudbridge, 2018)…”

Section: Conflict Of Interest Statementmentioning

confidence: 99%

simmrd: An open‐source tool to perform simulations in Mendelian randomization

Lorincz‐Comi,

Yang,

Zhu

2024

Genetic Epidemiology

View full text Add to dashboard Cite

Mendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open‐source software for performing simulations to evaluate the performance of MR methods in a range of scenarios encountered in practice. Researchers can directly modify the simmrd source code so that the research community may arrive at a widely accepted framework for researchers to evaluate the performance of different MR methods.

show abstract

Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization

Cited by 9 publications

References 77 publications

Role of Blood Lipids in the Shared Genetic Etiology Between Major Depressive Disorder and Myocardial Infarction: A Large-scale Multi-trait Association Analysis

Role of Blood Lipids in the Shared Genetic Etiology Between Major Depressive Disorder and Myocardial Infarction: A Large-scale Multi-trait Association Analysis

Exploring novel risk loci for heart failure and the shared genetic etiology with blood lipids, blood pressure, and blood glucose: a large-scale multi-trait association analysis

simmrd: An open‐source tool to perform simulations in Mendelian randomization

Contact Info

Product

Resources

About