Aging
Navigate
Submit an Article
Research Paper|Volume 12, Issue 4|pp 3287—3297

A genome-wide multiphenotypic association analysis identified candidate genes and gene ontology shared by four common risky behaviors

Jing Ye1, Li Liu1, Xiaoqiao Xu2, Yan Wen1, Ping Li1, Bolun Cheng1, Shiqiang Cheng1, Lu Zhang1, Mei Ma1, Xin Qi1, Chujun Liang1, Om Prakash Kafle1, Cuiyan Wu1, Sen Wang1, Xi Wang1, Yujie Ning1, Xiaomeng Chu1, Lin Niu2, Feng Zhang1
  • 1Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, China
  • 2Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Diseases, College of Stomatology, Xi’an Jiaotong University, Xi’an, China
* Equal contribution
Received: August 23, 2019Accepted: January 25, 2020Published: February 22, 2020
https://doi.org/10.18632/aging.102812

Copyright © 2020 Ye et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Risky behaviors can lead to huge economic and health losses. However, limited efforts are paid to explore the genetic mechanisms of risky behaviors.

Result: MASH analysis identified a group of target genes for risky behaviors, such as APBB2, MAPT and DCC. For GO enrichment analysis, FUMA detected multiple risky behaviors related GO terms and brain related diseases, such as regulation of neuron differentiation (adjusted P value = 2.84×10-5), autism spectrum disorder (adjusted P value =1.81×10-27) and intelligence (adjusted P value =5.89×10-15).

Conclusion: We reported multiple candidate genes and GO terms shared by the four risky behaviors, providing novel clues for understanding the genetic mechanism of risky behaviors.

Methods: Multivariate Adaptive Shrinkage (MASH) analysis was first applied to the GWAS data of four specific risky behaviors (automobile speeding, drinks per week, ever-smoker, number of sexual partners) to detect the common genetic variants shared by the four risky behaviors. Utilizing genomic functional annotation data of SNPs, the SNPs detected by MASH were then mapped to target genes. Finally, gene set enrichment analysis of the identified candidate genes were conducted by the FUMA platform to obtain risky behaviors related gene ontology (GO) terms as well as diseases and traits, respectively.