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

Jing Ye,Feng Zhang,Xin Qi,Sen Wang,Lu Zhang,Xiaoqiao Xu,Bolun Cheng,Mei Ma,Xiaomeng Chu,Lin Niu,Om Prakash Kafle,Xi Wang,Ping Li,Li Liu,Yan Wen,Chujun Liang,Cuiyan Wu,Yujie Ning,Shiqiang Cheng

doi:10.18632/aging.102812

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.

Highlights

Risky behavior or risk-taking behavior has been defined as either a socially unacceptable volitional behavior with a potentially negative outcome in which precautions are not taken, or a socially accepted behavior in which the danger is r ecognized [1]
For regulatory single nucleotide polymorphisms (rSNPs), we identified 4378 SNPs shared by the four risky behaviors, corresponding to 792 target regulatory genes, such as PLEKHM1, MAPT, APBB2, TFEC and KANSL1-AS1 (Supplementary Table 1)
For MeQTL, we identified 79 candidate gene ontology (GO) terms, such as Neurogenesis, Subpallium development and Striatum development

Summary

Introduction

Risky behavior or risk-taking behavior has been defined as either a socially unacceptable volitional behavior with a potentially negative outcome in which precautions are not taken (e.g. speeding, drinking and driving), or a socially accepted behavior in which the danger is r ecognized (e.g. competitive sports and skydiving) [1]. 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). 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. 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

Objectives

Methods

Results

Conclusion