Analysis of 50,000 exome-sequenced UK Biobank subjects fails to identify genes influencing probability of developing a mood disorder resulting in psychiatric referral

被引:8
作者
Curtis, David [1 ,2 ]
机构
[1] UCL, UCL Genet Inst, Darwin Bldg,Gower St, London WC1E 6BT, England
[2] Queen Mary Univ London, Ctr Psychiat, Charterhouse Sq, London EC1M 6BQ, England
基金
英国医学研究理事会; 英国生物技术与生命科学研究理事会;
关键词
Depression; Exome; UK Biobank; Gene; SET;
D O I
10.1016/j.jad.2020.12.025
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Background: Depression is moderately heritable but there is no common genetic variant which has a major effect on susceptibility. It is possible that some very rare variants could have substantial effect sizes and these could be identified from exome sequence data. Methods: Data from 50,000 exome-sequenced UK Biobank participants was analysed. Subjects were treated as cases if they had reported having seen a psychiatrist for "nerves, anxiety, tension or depression". Gene-wise weighted burden analysis was performed to see if there were any genes or sets of genes for which there was an excess of rare, functional variants in cases. Results: There were 5,872 cases and 43,862 controls. There were 22,028 informative genes but no gene or gene set produced a statistically significant result after correction for multiple testing. None of the genes or gene sets with the lowest p values appeared to be a biologically plausible candidate. Limitations: The phenotype is based on self-report and the cases are likely to be somewhat heterogeneous. Likewise, it is expected that some of the subjects classed as controls will in fact have suffered from depression or some other psychiatric diagnosis. The number of cases is on the low side for a study of exome sequence data. Conclusions: The results conform exactly with the expectation under the null hypothesis. It seems unlikely that depression genetics research will implicate specific genes having a substantial impact on the risk of developing psychiatric illness severe enough to merit referral to a specialist until far larger samples become available.
引用
收藏
页码:216 / 219
页数:4
相关论文
共 15 条
[1]  
Adzhubei Ivan, 2013, Curr Protoc Hum Genet, VChapter 7, DOI 10.1002/0471142905.hg0720s76
[2]   Second-generation PLINK: rising to the challenge of larger and richer datasets [J].
Chang, Christopher C. ;
Chow, Carson C. ;
Tellier, Laurent C. A. M. ;
Vattikuti, Shashaank ;
Purcell, Shaun M. ;
Lee, James J. .
GIGASCIENCE, 2015, 4
[3]  
Curtis D., 2020, ANAL EXOME SEQUENCED
[4]  
Curtis D., 2020, MULTIPLE LINEAR REGR, V11
[5]   Weighted burden analysis of exome-sequenced late-onset Alzheimer's cases and controls provides further evidence for a role for PSEN1 and suggests involvement of the PI3K/Akt/GSK-3β and WNT signalling pathways [J].
Curtis, David ;
Bakaya, Kaushiki ;
Sharma, Leona ;
Bandyopadhyay, Sreejan .
ANNALS OF HUMAN GENETICS, 2020, 84 (03) :291-302
[6]   Weighted Burden Analysis of Exome-Sequenced Case-Control Sample Implicates Synaptic Genes in Schizophrenia Aetiology [J].
Curtis, David ;
Coelewij, Leda ;
Liu, Shou-Hwa ;
Humphrey, Jack ;
Mott, Richard .
BEHAVIOR GENETICS, 2018, 48 (03) :198-208
[7]   Pathway analysis of whole exome sequence data provides further support for the involvement of histone modification in the aetiology of schizophrenia [J].
Curtis, David .
PSYCHIATRIC GENETICS, 2016, 26 (05) :223-227
[8]  
Curtis David, 2012, Adv Appl Bioinform Chem, V5, P1, DOI 10.2147/AABC.S33049
[9]   Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions [J].
Howard, David M. ;
Adams, Mark J. ;
Clarke, Toni-Kim ;
Hafferty, Jonathan D. ;
Gibson, Jude ;
Shirali, Masoud ;
Coleman, Jonathan R. I. ;
Hagenaars, Saskia P. ;
Ward, Joey ;
Wigmore, Eleanor M. ;
Alloza, Clara ;
Shen, Xueyi ;
Barbu, Miruna C. ;
Xu, Eileen Y. ;
Whalley, Heather C. ;
Marioni, Riccardo E. ;
Porteous, David J. ;
Davies, Gail ;
Deary, Ian J. ;
Hemani, Gibran ;
Berger, Klaus ;
Teismann, Henning ;
Rawal, Rajesh ;
Arolt, Volker ;
Baune, Bernhard T. ;
Dannlowski, Udo ;
Domschke, Katharina ;
Tian, Chao ;
Hinds, David A. ;
Agee, M. ;
Alipanahi, B. ;
Auton, A. ;
Bell, R. K. ;
Bryc, K. ;
Elson, S. L. ;
Fontanillas, P. ;
Furlotte, N. A. ;
Hicks, B. ;
Huber, K. E. ;
Jewett, E. M. ;
Jiang, Y. ;
Kleinman, A. ;
Lin, K. Han. ;
Litterman, N. K. ;
McIntyre, M. H. ;
Mountain, J. L. ;
Noblin, E. S. ;
Northover, C. A. M. ;
Pitts, S. J. ;
Poznik, G. D. .
NATURE NEUROSCIENCE, 2019, 22 (03) :343-+
[10]   Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm [J].
Kumar, Prateek ;
Henikoff, Steven ;
Ng, Pauline C. .
NATURE PROTOCOLS, 2009, 4 (07) :1073-1082