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Neuropsychopharmacology. 2019 Feb 16. doi: 10.1038/s41386-019-0345-4. [Epub ahead of print]

Integration of GWAS and brain eQTL identifies FLOT1 as a risk gene for major depressive disorder.

Zhong J1, Li S2,3, Zeng W4, Li X2,3, Gu C1, Liu J2, Luo XJ5,6,7,8.

Author information

1
The first people's hospital of Yunnan province, Psychiatry Department, 650032, Kunming, Yunnan, China.
2
Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, 650223, Kunming, Yunnan, China.
3
Kunming College of Life Science, University of Chinese Academy of Sciences, 650204, Kunming, Yunnan, China.
4
Yunnan Academy of Tobacco Science, 650106, Kunming, Yunnan, China.
5
Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, 650223, Kunming, Yunnan, China. luoxiongjian@mail.kiz.ac.cn.
6
Kunming College of Life Science, University of Chinese Academy of Sciences, 650204, Kunming, Yunnan, China. luoxiongjian@mail.kiz.ac.cn.
7
Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, 650223, Kunming, China. luoxiongjian@mail.kiz.ac.cn.
8
KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, 650223, Kunming, Yunnan, China. luoxiongjian@mail.kiz.ac.cn.

Abstract

Major depressive disorder (MDD) is the most prevalent mental disorder that affects more than 200 million people worldwide. Recent large-scale genome-wide association studies (GWAS) have identified multiple risk variants that show robust association with MDD. Nevertheless, how the identified risk variants confer risk of MDD remains largely unknown. To identify risk variants that are associated with gene expression in human brain and to identify genes whose expression change may contribute to the susceptibility of MDD, we systematically integrated the genetic associations from a large-scale MDD GWAS (N = 480,359) and brain expression quantitative trait loci (eQTL) data (N = 494) using a Bayesian statistical framework (Sherlock). Sherlock integrative analysis showed that FLOT1 was significantly associated with MDD (P = 6.02 × 10-6), suggesting that risk variants may contribute to MDD susceptibility through affecting FLOT1 expression. We further examined the expression level of FLOT1 in MDD cases and controls and found that FLOT1 was significantly upregulated in brains and peripheral blood of MDD cases compared with controls (European sample). Interestingly, we found that FLOT1 expression was also significantly upregulated in peripheral blood of first-episode drug-naive MDD cases compared with controls (P = 1.01 × 10-7, Chinese sample). Our study identified FLOT1 as a novel MDD risk gene whose expression level may play a role in MDD. In addition, our findings also suggest that risk variants may confer risk of MDD through affecting expression of FLOT1. Further functional investigation of FLOT1 may provide new insights for MDD pathogenesis.

PMID:
30771789
DOI:
10.1038/s41386-019-0345-4

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