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

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.

SUBMITTER: Ye J

PROVIDER: S-EPMC7066886 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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

Ye Jing J Liu Li L Xu Xiaoqiao X Wen Yan Y Li Ping P Cheng Bolun B Cheng Shiqiang S Zhang Lu L Ma Mei M Qi Xin X Liang Chujun C Kafle Om Prakash OP Wu Cuiyan C Wang Sen S Wang Xi X Ning Yujie Y Chu Xiaomeng X Niu Lin L Zhang Feng F

Aging 20200222 4

<h4>Background</h4>Risky behaviors can lead to huge economic and health losses. However, limited efforts are paid to explore the genetic mechanisms of risky behaviors.<h4>Result</h4>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 <i>P</i> value = 2.84×10<sup>-5</sup>), autism spect ...[more]

PMID: 32090979

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Project description:BackgroundOsteoarthritis (OA) is a common skeletal system disease that has been partially attributed to genetic factors. The hand is frequently affected, which seriously affects the patient's quality of life. However, the pathogenetic mechanism of hand osteoarthritis (hand OA) is still elusive.MethodsA genome-wide association study (GWAS) summary of hand OA was obtained from the UK Biobank dataset, which contains data from a total of 452,264 White British individuals, including 37,782 OA patients. The transcriptome-wide association study (TWAS) of hand OA was performed using FUnctional Summary-based ImputatiON (FUSION) with the skeletal muscle and blood as gene expression references. The significant genes identified by TWAS were further subjected to gene set enrichment analysis (GSEA) with the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Furthermore, we compared the genes and gene sets identified by our TWAS with that of a knee OA mRNA expression profile to detect the genes and gene sets shared by TWAS and mRNA expression profiles in OA. The mRNA expression profiles of 18 normal knee cartilages and 20 OA knee cartilages were acquired from the Gene Expression Omnibus database (accession number: GSE114007).ResultsTWAS identified 177 genes with P < 0.05 for the skeletal muscle, including ANKRD44 (P = 0.0001), RIC3 (P = 0.0003), and AC005154.6 (P = 0.0004). TWAS identified 423 genes with P < 0.05 for the blood, including CRIM1 (P = 0.0002), ZNF880 (P = 0.0002), and NCKIPSD (P = 0.0003). After comparing the results of the TWAS to those of the mRNA expression profiling of OA, we identified 5 common genes, including DHRS3 (log2fold = - 1.85, P = 3.31 × 10- 9) and SKP2 (log2fold = 1.36, P = 1.62 × 10- 8). GSEA of TWAS identified 51 gene ontology (GO) terms for hand OA, for example, protein binding (P = 0.0003) and cytosol (P = 0.0020). We also detected 6 common GO terms shared by TWAS and mRNA expression profiling, including protein binding (PTWAS = 2.54 × 10- 4, PmRNA = 3.42 × 10- 8), extracellular exosome (PTWAS = 0.02, PmRNA = 1.18 × 10- 4), and cytoplasm (PTWAS = 0.0183, PmRNA = 0.0048).ConclusionIn this study, we identified 5 candidate genes and 6 GO terms related to hand OA, which may help to uncover the pathogenesis of hand OA. It should be noted that the possible difference in the gene expression profiles between hand OA and knee OA may affect our study results, which should be interpreted with caution.

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

Publications

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

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