# Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics

**Authors:** Azam Yazdani, Akram Yazdani, Sarah H. Elsea, Daniel J. Schaid, Michael R. Kosorok, Gita Dangol, Ahmad Samiei  
**arXiv:** [1904.12652](https://arxiv.org/abs/1904.12652)  
**Date:** 2019-04-29  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1904.12652v1/

## Key points

- Study focuses on understanding functional causes of genomic associations with traits
- Investigated effects of annotated loss-of-function mutations on serum metabolites in African-Americans and European-Americans
- Built metabolomic causal networks for each population separately
- Used structural equation modeling to assess findings
- Validated findings with independent samples
- Showed that affected metabolites are risk predictors in causal pathway of disease
- Identified gene KIAA1755 as elevating levels of eicosapentaenoate, associated with increased essential hypertension
- Gene KIAA1755 also involved in pathway to triglycerides, which are risk factors for metabolomic disorder and heart attack
- Gene CLDN17 had pleiotropic actions on metabolites from amino acid and lipid pathways
- Study integrates biological processes using systems biology approaches for analyzing metabolomics and genetic data
- Provides insights into how genetic variants connect with complex diseases
- Implications for identifying potential risk predictors and pathways related to essential hypertension and metabolomic disorders

## Summary

This study focuses on understanding the functional causes of genomic associations with traits by utilizing systems approaches and intermediate molecular phenotypes. The researchers investigated the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites in two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study. They built metabolomic causal networks for each population separately and used structural equation modeling to assess their findings. The researchers validated their findings with independent samples. Using methods based on concepts of Mendelian randomization, the study showed that some affected metabolites are risk predictors in the causal pathway of disease. For example, LoF mutations in the gene KIAA1755 were found to elevate the levels of eicosapentaenoate, an essential fatty acid clinically associated with increased essential hypertension. This gene was also identified to be involved in the pathway to triglycerides, where both triglycerides and essential hypertension are risk factors for metabolomic disorder and heart attack. Additionally, the study identified that the gene CLDN17, harboring loss-of-function mutations, had pleiotropic actions on metabolites from amino acid and lipid pathways. By integrating several biological processes using systems biology approaches for analyzing metabolomics and genetic data, this study provides insights into how genetic variants may functionally connect with complex diseases. Overall, this research contributes to a better understanding of the functional causes underlying genomic associations with traits by investigating intermediate molecular phenotypes and building causal networks. These findings have implications for identifying potential risk predictors and pathways related to complex diseases such as essential hypertension and metabolomic disorders.