Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
BMC Medicine
Published in: BMC Medicine 1/2021

Open Access 01-12-2021 | Tourette Syndrome | Research article

A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

Authors: Haojie Lu, Jiahao Qiao, Zhonghe Shao, Ting Wang, Shuiping Huang, Ping Zeng

Published in: BMC Medicine | Issue 1/2021

Login to get access

Abstract

Background

Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.

Methods

We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.

Results

We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.

Conclusions

Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.
Appendix
Available only for authorised users
Literature
1.
Lee PH, Anttila V, Won H, Feng Y-CA, Rosenthal J, Zhu Z, et al. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell. 2019;179(7):1469–1482.e1411.CrossRef
2.
Sullivan PF, Agrawal A, Bulik CM, Andreassen OA, Børglum AD, Breen G, et al. Psychiatric genomics: an update and an agenda. Am J Psychiatry. 2018;175(1):15–27.PubMedCrossRef
3.
van Os J, Linszen D, Reif A. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381(9875):1371–9.CrossRef
4.
5.
Sullivan PF, Daly MJ, O'Donovan M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet. 2012;13(8):537–51.PubMedPubMedCentralCrossRef
6.
Polderman TJ, Benyamin B, De Leeuw CA, Sullivan PF, Van Bochoven A, Visscher PM, et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47(7):702–9.PubMedCrossRef
7.
Smoller JW, Andreassen OA, Edenberg HJ, Faraone SV, Glatt SJ, Kendler KS. Psychiatric genetics and the structure of psychopathology. Mol Psychiatry. 2019;24(3):409–20.PubMedCrossRef
8.
9.
Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748–52.PubMedCrossRef
10.
Lee SH, DeCandia TR, Ripke S, Yang J, Sullivan PF, Goddard ME. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat Genet. 2012;44(3):247–50.PubMedPubMedCentralCrossRef
11.
O'Donovan MC, Owen MJ. The implications of the shared genetics of psychiatric disorders. Nat Med. 2016;22(11):1214–9.PubMedCrossRef
12.
Maier RM, Visscher PM, Robinson MR, Wray NR. Embracing polygenicity: a review of methods and tools for psychiatric genetics research. Psychol Med. 2018;48(7):1055–67.PubMedCrossRef
13.
Martin J, Taylor MJ, Lichtenstein P. Assessing the evidence for shared genetic risks across psychiatric disorders and traits. Psychol Med. 2018;48(11):1759–74.PubMedCrossRef
14.
Sivakumaran S, Agakov F, Theodoratou E, Prendergast JG, Zgaga L, Manolio T, et al. Abundant pleiotropy in human complex diseases and traits. Am J Hum Genet. 2011;89(5):607–18.PubMedPubMedCentralCrossRef
15.
16.
Watanabe K, Stringer S, Frei O, Umićević Mirkov M, de Leeuw C, Polderman TJC, et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019;51(9):1339–48.PubMedCrossRef
17.
18.
Andreassen OA, Thompson WK, Schork AJ, Ripke S, Mattingsdal M, Kelsoe JR, et al. Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate. PLoS Genet. 2013;9(4):e1003455.PubMedPubMedCentralCrossRef
19.
Consortium C-DGotPG: Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381(9875):1371–79.
20.
Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet. 2014;10(11):e1004787.PubMedPubMedCentralCrossRef
21.
Lim CH, Zain SM, Reynolds GP, Zain MA, Roffeei SN, Zainal NZ, et al. Genetic association of LMAN2L gene in schizophrenia and bipolar disorder and its interaction with ANK3 gene polymorphism. Prog Neuro-Psychopharmacol Biol Psychiatry. 2014;54:157–62.CrossRef
22.
Finseth PI, Sønderby IE, Djurovic S, Agartz I, Malt UF, Melle I, et al. Association analysis between suicidal behaviour and candidate genes of bipolar disorder and schizophrenia. J Affect Disord. 2014;163:110–14.PubMedCrossRef
23.
24.
Ruderfer DM, Fanous AH, Ripke S, McQuillin A, Amdur RL, Gejman PV, et al. Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia. Mol Psychiatry. 2014;19(9):1017–24.PubMedCrossRef
25.
Sumner JA, Duncan L, Ratanatharathorn A, Roberts AL, Koenen KC. PTSD has shared polygenic contributions with bipolar disorder and schizophrenia in women. Psychol Med. 2016;46(3):669–71.PubMedCrossRef
26.
Costas J, Carrera N, Alonso P, Gurriarán X, Segalàs C, Real E, et al. Exon-focused genome-wide association study of obsessive-compulsive disorder and shared polygenic risk with schizophrenia. Transl Psychiatry. 2016;6(3):e768.PubMedPubMedCentralCrossRef
27.
Chen J, Bacanu S-A, Yu H, Zhao Z, Jia P, Kendler KS, et al. Genetic relationship between schizophrenia and nicotine dependence. Sci Rep. 2016;6(1):1–10.
28.
Chen X, Long F, Cai B, Chen X, Qin L, Chen G. A novel relationship for schizophrenia, bipolar, and major depressive disorder. Part 8: a hint from chromosome 8 high density association screen. Mol Neurobiol. 2017;54(8):5868–82.PubMedCrossRef
29.
Schork AJ, Won H, Appadurai V, Nudel R, Gandal M, Delaneau O, et al. A genome-wide association study for shared risk across major psychiatric disorders in a nation-wide birth cohort implicates fetal neurodevelopment as a key mediator. bioRxiv. 2017:240911.
30.
Taylor MJ, Martin J, Lu Y, Brikell I, Lundström S, Larsson H, et al. Genetic evidence for shared risks across psychiatric disorders and related traits in a Swedish population twin sample. BioRxiv. 2017;234963.
31.
Consortium TASDWGoTPG. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24. 32 and a significant overlap with schizophrenia. Mol Autism. 2017;8:1–17.
32.
Nivard MG, Gage SH, Hottenga JJ, Van Beijsterveldt CE, Abdellaoui A, Bartels M, et al. Genetic overlap between schizophrenia and developmental psychopathology: longitudinal and multivariate polygenic risk prediction of common psychiatric traits during development. Schizophr Bull. 2017;43(6):1197–207.PubMedPubMedCentralCrossRef
33.
Chung D, Kim HJ, Zhao H. graph-GPA: a graphical model for prioritizing GWAS results and investigating pleiotropic architecture. PLoS Comput Biol. 2017;13(2):e1005388.PubMedPubMedCentralCrossRef
34.
Stergiakouli E, Smith GD, Martin J, Skuse DH, Viechtbauer W, Ring SM, et al. Shared genetic influences between dimensional ASD and ADHD symptoms during child and adolescent development. Mol Autism. 2017;8(1):1–13.CrossRef
35.
Foo JC, Streit F, Treutlein J, Ripke S, Witt SH, Strohmaier J, et al. Shared genetic etiology between alcohol dependence and major depressive disorder. Psychiatr Genet. 2018;28(4):66.PubMedPubMedCentralCrossRef
36.
Kaufmann T, van der Meer D, Doan NT, Schwarz E, Lund MJ, Agartz I, et al. Genetics of brain age suggest an overlap with common brain disorders. BioRxiv. 2018;303164.
37.
Leonenko G, Di Florio A, Allardyce J, Forty L, Knott S, Jones L, et al. A data-driven investigation of relationships between bipolar psychotic symptoms and schizophrenia genome-wide significant genetic loci. Am J Med Genet B Neuropsychiatr Genet. 2018;177(4):468–75.PubMedPubMedCentralCrossRef
38.
Aas M, Melle I, Bettella F, Djurovic S, Le Hellard S, Bjella T, et al. Psychotic patients who used cannabis frequently before illness onset have higher genetic predisposition to schizophrenia than those who did not. Psychol Med. 2018;48(1):43–9.PubMedCrossRef
39.
Pasman JA, Verweij KJ, Gerring Z, Stringer S, Sanchez-Roige S, Treur JL, et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability. Nat Neurosci. 2018;21(9):1161–70.PubMedPubMedCentralCrossRef
40.
Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, Duncan L, et al. Analysis of shared heritability in common disorders of the brain. Science. 2018;360(6395).
41.
Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science. 2018;359(6376):693–7.PubMedPubMedCentralCrossRef
42.
Pasman JA, Verweij KJ, Gerring Z, Stringer S, Sanchez-Roige S, Treur JL, et al. Genome-wide association analysis of lifetime cannabis use (N = 184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use. bioRxiv. 2018:234294.
43.
St Pourcain B, Robinson EB, Anttila V, Sullivan BB, Maller J, Golding J, et al. ASD and schizophrenia show distinct developmental profiles in common genetic overlap with population-based social communication difficulties. Mol Psychiatry. 2018;23(2):263–70.PubMedCrossRef
44.
Duncan LE, Ratanatharathorn A, Aiello AE, Almli LM, Amstadter AB, Ashley-Koch AE, et al. Largest GWAS of PTSD (N = 20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol Psychiatry. 2018;23(3):666–73.PubMedCrossRef
45.
Schork A, Brown T, Hagler D, Thompson W, Chen CH, Dale A, et al. Polygenic risk for psychiatric disorders correlates with executive function in typical development. Genes Brain Behav. 2019;18(4):e12480.PubMedCrossRef
46.
Yilmaz Z, Halvorsen M, Bryois J, Yu D, Thornton LM, Zerwas S, et al. Examination of the shared genetic basis of anorexia nervosa and obsessive–compulsive disorder. Mol Psychiatry. 2020;25(9):2036–46.PubMedCrossRef
47.
Smeland OB, Frei O, Shadrin A, O’Connell K, Fan C-C, Bahrami S, et al. Discovery of shared genomic loci using the conditional false discovery rate approach. Hum Genet. 2020;139(1):85–94.PubMedCrossRef
48.
Bahrami S, Steen NE, Shadrin A, O’Connell K, Frei O, Bettella F, et al. Shared genetic loci between body mass index and major psychiatric disorders: a genome-wide association study. JAMA Psychiat. 2020;77(5):503–12.CrossRef
49.
Ohi K, Otowa T, Shimada M, Sasaki T, Tanii H. Shared genetic etiology between anxiety disorders and psychiatric and related intermediate phenotypes. Psychol Med. 2020;50(4):692–704.PubMedCrossRef
50.
Peyrot WJ, Price AL. Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS. Nat Genet. 2021;53(4):445–54.PubMedPubMedCentralCrossRef
51.
Muntané G, Farré X, Bosch E, Martorell L, Navarro A, Vilella E. The shared genetic architecture of schizophrenia, bipolar disorder and lifespan. Hum Genet. 2021;140(3):441–55.PubMedCrossRef
52.
Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A. 2009;106(23):9362–7.PubMedPubMedCentralCrossRef
53.
Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101(1):5–22.PubMedPubMedCentralCrossRef
54.
Zeng P, Zhao Y, Qian C, Zhang L, Zhang R, Gou J, et al. Statistical analysis for genome-wide association study. J Biomed Res. 2015;29(4):285.PubMed
55.
Cross-Disorder Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet. 2013;45(9):984–94.PubMedCentralCrossRef
56.
van Rheenen W, Peyrot WJ, Schork AJ, Lee SH, Wray NR. Genetic correlations of polygenic disease traits: from theory to practice. Nat Rev Genet. 2019;20(10):567–81.PubMedCrossRef
57.
Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10(5):e1004383.PubMedPubMedCentralCrossRef
58.
Smeland OB, Frei O, Shadrin A, O'Connell K, Fan C-C, Bahrami S, et al. Discovery of shared genomic loci using the conditional false discovery rate approach. Hum Genet. 2020;139(1):85–94.PubMedCrossRef
59.
Zeng P, Hao X, Zhou X. Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models. Bioinformatics. 2018;34(16):2797–807.PubMedPubMedCentralCrossRef
60.
Ray D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between type 2 diabetes and prostate cancer. PLoS Genet. 2020;16(12):e1009218.PubMedPubMedCentralCrossRef
61.
62.
Zeng P, Shao Z, Zhou X. Statistical methods for mediation analysis in the era of high-throughput genomics: current successes and future challenges. Comput Struct Biotechnol J. 2021;19:3209–24.PubMedPubMedCentralCrossRef
63.
Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91(434):444–55.CrossRef
64.
Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29(4):722–29.PubMedCrossRef
65.
Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133–63.PubMedCrossRef
66.
67.
Zeng P, Wang T, Zheng J, Zhou X. Causal association of type 2 diabetes with amyotrophic lateral sclerosis: new evidence from Mendelian randomization using GWAS summary statistics. BMC Med. 2019;17(1):225.PubMedPubMedCentralCrossRef
68.
Zeng P, Zhou X. Causal association between birth weight and adult diseases: evidence from a Mendelian randomization analysis. Front Genet. 2019;10:618.
69.
Duncan L, Yilmaz Z, Gaspar H, Walters R, Goldstein J, Anttila V, et al. Significant locus and metabolic genetic correlations revealed in genome-wide association study of anorexia nervosa. Am J Psychiatry. 2017;174(9):850–8.
70.
Otowa T, Hek K, Lee M, Byrne EM, Mirza SS, Nivard MG, et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol Psychiatry. 2016;21(10):1391–9.PubMedPubMedCentralCrossRef
71.
Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019;51(3):431–44.PubMedPubMedCentralCrossRef
72.
Sanchez-Roige S, Palmer AA, Fontanillas P, Elson SL, Adams MJ, Howard DM, et al. Genome-wide association study meta-analysis of the Alcohol Use Disorders Identification Test (AUDIT) in two population-based cohorts. Am J Psychiatry. 2019;176(2):107–18.PubMedCrossRef
73.
Arnold PD, Askland KD, Barlassina C, Bellodi L, Bienvenu OJ, Black D, et al. Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis. Mol Psychiatry. 2018;23(5):1181–8.CrossRef
74.
Stahl EA, Breen G, Forstner AJ, McQuillin A, Ripke S, Trubetskoy V, et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet. 2019;51(5):793–803.PubMedPubMedCentralCrossRef
75.
Zhang Q, Feitosa M, Borecki IB. Estimating and testing pleiotropy of single genetic variant for two quantitative traits. Genet Epidemiol. 2014;38(6):523–30.PubMedPubMedCentralCrossRef
76.
Nievergelt CM, Maihofer AX, Klengel T, Atkinson EG, Chen CY, Choi KW, et al. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nat Commun. 2019;10(1):4558.PubMedPubMedCentralCrossRef
77.
Yu D, Sul JH, Tsetsos F, Nawaz MS, Huang AY, Zelaya I, et al. Interrogating the genetic determinants of Tourette’s syndrome and other tic disorders through genome-wide association studies. Am J Psychiatry. 2019;176(3):217–27.PubMedPubMedCentralCrossRef
78.
Pasman JA, Verweij KJH, Gerring Z, Stringer S, Sanchez-Roige S, Treur JL, et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat Neurosci. 2018;21(9):1161–70.PubMedPubMedCentralCrossRef
79.
Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50(5):668–81.PubMedPubMedCentralCrossRef
80.
Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51(1):63–75.PubMedCrossRef
81.
Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236–41.PubMedPubMedCentralCrossRef
82.
Liu JZ, McRae AF, Nyholt DR, Medland SE, Wray NR, Brown KM, et al. A versatile gene-based test for genome-wide association studies. Am J Hum Genet. 2010;87(1):139–45.PubMedPubMedCentralCrossRef
83.
Huang Y-T. Genome-wide analyses of sparse mediation effects under composite null hypotheses. Ann Appl Stat. 2019;13(1):60–84.CrossRef
84.
Huang Y-T. Variance component tests of multivariate mediation effects under composite null hypotheses. Biometrics. 2019;75(4):1191–204.PubMedCrossRef
85.
Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat. 2001:1165–88.
86.
87.
Koch M, Ilnytskyy Y, Golubov A, Metz L, Yong V, Kovalchuk O. Global transcriptome profiling of mild relapsing-remitting versus primary progressive multiple sclerosis. Eur J Neurol. 2018;25(4):651–8.PubMedCrossRef
88.
Boldanova T, Suslov A, Heim MH, Necsulea A. Transcriptional response to hepatitis C virus infection and interferon-alpha treatment in the human liver. EMBO Mol Med. 2017;9(6):816–34.PubMedPubMedCentralCrossRef
89.
Baum M, Bielau S, Rittner N, Schmid K, Eggelbusch K, Dahms M, et al. Validation of a novel, fully integrated and flexible microarray benchtop facility for gene expression profiling. Nucleic Acids Res. 2003;31(23):e151.PubMedPubMedCentralCrossRef
90.
Khan MM, Poeckel D, Halavatyi A, Zukowska-Kasprzyk J, Stein F, Vappiani J, et al. An integrated multiomic and quantitative label-free microscopy-based approach to study pro-fibrotic signalling in ex vivo human precision-cut lung slices. Eur Respir J. 2021;58(1).
91.
Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, et al. Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 2021;49(D1):D1144–51.PubMedCrossRef
92.
93.
Noyce AJ, Kia DA, Hemani G, Nicolas A, Price TR, De Pablo-Fernandez E, et al. Estimating the causal influence of body mass index on risk of Parkinson disease: a Mendelian randomisation study. PLoS Med. 2017;14(6):e1002314.PubMedPubMedCentralCrossRef
94.
Zeng P, Zhou X. Causal effects of blood lipids on amyotrophic lateral sclerosis: a Mendelian randomization study. Hum Mol Genet. 2019;28(4):688–97.PubMedCrossRef
95.
Choi KW, Chen C-Y, Stein MB, Klimentidis YC, Wang M-J, Koenen KC, et al. Consortium ftMDDWGotPG: Assessment of bidirectional relationships between physical activity and depression among adults: a 2-sample Mendelian randomization studyassessment of bidirectional relationships between physical activity and depression among adultsassessment of bidirectional relationships between physical activity and depression among adults. JAMA Psychiat. 2019;76(4):399–408.CrossRef
96.
Sanna S, van Zuydam NR, Mahajan A, Kurilshikov A, Vich Vila A, Vosa U, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet. 2019;51(4):600-5.
97.
Yang J, Yan B, Zhao B, Fan Y, He X, Yang L, et al. Assessing the causal effects of human serum metabolites on 5 major psychiatric disorders. Schizophr Bull. 2020;46(4):804-13.
98.
Larsson SC, Burgess S, Michaëlsson K. Association of genetic variants related to serum calcium levels with coronary artery disease and myocardial infarction. JAMA. 2017;318(4):371–80.PubMedPubMedCentralCrossRef
99.
Ahmad OS, Morris JA, Mujammami M, Forgetta V, Leong A, Li R, et al. A Mendelian randomization study of the effect of type-2 diabetes on coronary heart disease. Nat Commun. 2015;6(1):1-11.
100.
Yu X, Wang T, Chen Y, Shen Z, Gao Y, Xiao L, et al. Alcohol drinking and amyotrophic lateral sclerosis: an instrumental variable causal inference. Ann Neurol. 2020;88(1):195–8.PubMedCrossRef
101.
Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48(5):481–7.PubMedCrossRef
102.
Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2017;26(5):2333–55.PubMedCrossRef
103.
Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985–98.PubMedPubMedCentralCrossRef
104.
Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol. 2016;45(6):1961–74.PubMedPubMedCentral
105.
Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734–9.PubMedPubMedCentralCrossRef
106.
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14.PubMedPubMedCentralCrossRef
107.
108.
109.
Ruderfer DM, Ripke S, McQuillin A, Boocock J, Stahl EA, Pavlides JMW, et al. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell. 2018;173(7):1705–1715.e1716.PubMedCentralCrossRef
110.
Baurecht H. Genome-wide comparative analysis of atopic dermatitis and psoriasis gives insight into opposing genetic mechanisms. Am J Hum Genet. 2015;96(1):104–20.PubMedPubMedCentralCrossRef
111.
Schmitt J, Schwarz K, Baurecht H, Hotze M, Fölster-Holst R, Rodríguez E, et al. Atopic dermatitis is associated with an increased risk for rheumatoid arthritis and inflammatory bowel disease, and a decreased risk for type 1 diabetes. J Allergy Clin Immunol. 2016;137(1):130–6.PubMedCrossRef
112.
Mühleisen TW, Leber M, Schulze TG, Strohmaier J, Degenhardt F, Treutlein J, et al. Genome-wide association study reveals two new risk loci for bipolar disorder. Nat Commun. 2014;5(1):3339.PubMedCrossRef
113.
Hou L, Bergen SE, Akula N, Song J, Hultman CM, Landén M, et al. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum Mol Genet. 2016;25(15):3383–94.PubMedPubMedCentralCrossRef
114.
Reinbold CS, Forstner AJ, Hecker J, Fullerton JM, Hoffmann P, Hou L, et al. Analysis of the influence of microRNAs in lithium response in bipolar disorder. Frontiers. Psychiatry. 2018;9:207.
115.
Polanczyk G, Caspi A, Williams B, Price TS, Danese A, Sugden K, et al. Protective effect of CRHR1 gene variants on the development of adult depression following childhood maltreatment: replication and extension. Arch Gen Psychiatry. 2009;66(9):978–85.PubMedPubMedCentralCrossRef
116.
Linda K, Lewerissa E, Verboven AH, Gabriele M, Frega M, Gunnewiek TMK, et al. KANSL1 deficiency causes neuronal dysfunction by oxidative stress-induced autophagy. bioRxiv. 2020.
117.
Cheran G, Silverman H, Manoochehri M, Goldman J, Lee S, Wu L, et al. Psychiatric symptoms in preclinical behavioural-variant frontotemporal dementia in MAPT mutation carriers. J Neurol Neurosurg Psychiatry. 2018;89(5):449–55.PubMedCrossRef
118.
Fan Q, Wang W, Hao J, He A, Wen Y, Guo X, et al. Integrating genome-wide association study and expression quantitative trait loci data identifies multiple genes and gene set associated with neuroticism. Prog Neuro-Psychopharmacol Biol Psychiatry. 2017;78:149–52.CrossRef
119.
Levy D, Ronemus M, Yamrom B, Lee Y-h, Leotta A, Kendall J, et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron. 2011;70(5):886–97.
120.
Schenk P, Van Fessem M, Verploegh-Van Rij S, Mathot R, van Gelder T, Vulto A, et al. Association of graded allele-specific changes in CYP2D6 function with imipramine dose requirement in a large group of depressed patients. Mol Psychiatry. 2008;13(6):597–605.PubMedCrossRef
121.
Vandel P, Haffen E, Nezelof S, Broly F, Kantelip J, Sechter D. Clomipramine, fluoxetine and CYP2D6 metabolic capacity in depressed patients. Hum Psychopharmacol: Cli Exp. 2004;19(5):293–8.CrossRef
122.
Murphy GM, Pollock BG, Kirshner MA, Pascoe N, Cheuk W, Mulsant BH, et al. CYP2D6 genotyping with oligonucleotide microarrays and nortriptyline concentrations in geriatric depression. Neuropsychopharmacology. 2001;25(5):737–43.PubMedCrossRef
123.
Maccioni RB, Cambiazo V. Role of microtubule-associated proteins in the control of microtubule assembly. Physiol Rev. 1995;75(4):835–64.PubMedCrossRef
124.
Rojo LE, Alzate-Morales J, Saavedra IN, Davies P, Maccioni RB. Selective interaction of lansoprazole and astemizole with tau polymers: potential new clinical use in diagnosis of Alzheimer's disease. J Alzheimers Dis. 2010;19(2):573–89.PubMedPubMedCentralCrossRef
125.
Lichtenstein P, Yip BH, Björk C, Pawitan Y, Cannon TD, Sullivan PF, et al. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet. 2009;373(9659):234–9.PubMedCrossRef
126.
Craddock N, O'Donovan MC, Owen MJ. Genes for schizophrenia and bipolar disorder? Implications for psychiatric nosology Schizophr Bull. 2005;32(1):9–16.PubMedCrossRef
127.
Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, et al. Powerful SNP-set analysis for case-control genome-wide association studies. Am J Hum Genet. 2010;86(6):929–42.PubMedPubMedCentralCrossRef
128.
Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89(1):82–93.PubMedPubMedCentralCrossRef
129.
Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson DA, et al. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet. 2012;91(2):224–37.PubMedPubMedCentralCrossRef
130.
Schifano ED, Epstein MP, Bielak LF, Jhun MA, Kardia SLR, Peyser PA, et al. SNP set association analysis for familial data. Genet Epidemiol. 2012;36(8):797–810.PubMedPubMedCentral
131.
Wang X, Lee S, Zhu X, Redline S, Lin X. GEE-based SNP set association test for continuous and discrete traits in family-based association studies. Genet Epidemiol. 2013;37(8):778–86.PubMedPubMedCentralCrossRef
132.
Wu MC, Maity A, Lee S, Simmons EM, Harmon QE, Lin X, et al. Kernel machine SNP-set testing under multiple candidate kernels. Genet Epidemiol. 2013;37(3):267–75.PubMedPubMedCentralCrossRef
133.
Lee S, Abecasis Gonçalo R, Boehnke M, Lin X. Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet. 2014;95(1):5–23.PubMedPubMedCentralCrossRef
134.
Zeng P, Zhao Y, Liu J, Liu L, Zhang L, Wang T, et al. Likelihood ratio tests in rare variant detection for continuous phenotypes. Ann Hum Genet. 2014;78(5):320–32.PubMedCrossRef
135.
Han B, Duong D, Sul JH, de Bakker PIW, Eskin E, Raychaudhuri S. A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping. Hum Mol Genet. 2016;25(9):1857–66.PubMedPubMedCentralCrossRef
136.
LeBlanc M, Zuber V, Thompson WK, Andreassen OA, Frigessi A, Andreassen BK. Psychiat Genomics C: A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. BMC Genomics. 2018;19(1):1-15.
137.
138.
Rommelse NNJ, Franke B, Geurts HM, Hartman CA, Buitelaar JK. Shared heritability of attention-deficit/hyperactivity disorder and autism spectrum disorder. Eur Child Adolesc Psychiatry. 2010;19(3):281–95.PubMedPubMedCentralCrossRef
139.
Scahill L, Bitsko R, Visser S, Blumberg S. Prevalence of diagnosed Tourette syndrome in persons aged 6-17 years-United States, 2007. Morb Mortal Wkly Rep. 2009;58(21):581–5.
140.
Kompoliti K, Goetz CG, Morrissey M, Leurgans S. Gilles de la Tourette syndrome: patient's knowledge and concern of adverse effects. Mov Disord. 2006;21(2):248–52.PubMedCrossRef
141.
Freeman RD, Fast DK, Burd L, Kerbeshian J, Robertson MM, Sandor P. An international perspective on Tourette syndrome: selected findings from 3500 individuals in 22 countries. Dev Med Child Neurol. 2000;42(7):436–47.PubMedCrossRef
142.
Bolton D, Rijsdijk F, O'CONNOR TG, Perrin S, Eley TC. Obsessive–compulsive disorder, tics and anxiety in 6-year-old twins. Psychol Med. 2007;37(1):39–48.
143.
Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22(3):343–52.PubMedPubMedCentralCrossRef
144.
Schork AJ, Won H, Appadurai V, Nudel R, Gandal M, Delaneau O, et al. A genome-wide association study of shared risk across psychiatric disorders implicates gene regulation during fetal neurodevelopment. Nat Neurosci. 2019;22(3):353–61.PubMedPubMedCentralCrossRef
145.
146.
147.
O’Brien HE, Hannon E, Hill MJ, Toste CC, Robertson MJ, Morgan JE, et al. Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders. Genome Biol. 2018;19(1):194.PubMedPubMedCentralCrossRef
148.
Smit DJA, Wright MJ, Meyers JL, Martin NG, Ho YYW, Malone SM, et al. Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity. Hum Brain Mapp. 2018;39(11):4183–95.PubMedPubMedCentralCrossRef
149.
Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science. 2018;359(6376):693–7.PubMedPubMedCentralCrossRef
150.
151.
Crone CC, Gabriel GM, DiMartini A. An overview of psychiatric issues in liver disease for the consultation-liaison psychiatrist. Psychosomatics. 2006;47(3):188–205.PubMedCrossRef
152.
De Hert M, Detraux J, Vancampfort D. The intriguing relationship between coronary heart disease and mental disorders. Dialogues Clin Neurosci. 2018;20(1):31–40.PubMedPubMedCentralCrossRef
153.
Lin Y-H, Liu A-H, Xu Y, Tie L, Yu H-M, Li X-J. Effect of chronic unpredictable mild stress on brain–pancreas relative protein in rat brain and pancreas. Behav Brain Res. 2005;165(1):63–71.PubMedCrossRef
154.
Fras I, Litin EM, Pearson JS. Comparison of psychiatric symptoms in carcinoma of the pancreas with those in some other intra-abdominal neoplasms. Am J Psychiatry. 1967;123(12):1553–62.PubMedCrossRef
155.
Casey DE. Metabolic issues and cardiovascular disease in patients with psychiatric disorders. Am J Med Suppl. 2005;118:15–22.
156.
Ferns G. Cause, consequence or coincidence: the relationship between psychiatric disease and metabolic syndrome. Transl Metab Syndr Res. 2018;1:23–38.
157.
Ho CS, Zhang MW, Mak A, Ho RC. Metabolic syndrome in psychiatry: advances in understanding and management. Adv Psychiatr Treat. 2014;20(2):101–12.CrossRef
158.
Maier R, Moser G, Chen G-B, Ripke S, Coryell W, Potash JB, et al. Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. Am J Hum Genet. 2015;96(2):283–94.PubMedPubMedCentralCrossRef
159.
160.
McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JPA, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9(5):356–69.PubMedCrossRef
161.
Lu H, Wang T, Zhang J, Zhang S, Huang S, Zeng P. Evaluating marginal genetic correlation of associated loci for complex diseases and traits between European and East Asian populations. Hum Genet. 2021;140(9):1285–97.PubMedCrossRef
162.
Smeland OB, Frei O, Fan C-C, Shadrin A, Dale AM, Andreassen OA. The emerging pattern of shared polygenic architecture of psychiatric disorders, conceptual and methodological challenges. Psychiatr Genet. 2019;29(5):152–9.PubMedCrossRef
Metadata
Title
A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics
Authors
Haojie Lu
Jiahao Qiao
Zhonghe Shao
Ting Wang
Shuiping Huang
Ping Zeng
Publication date
01-12-2021
Publisher
BioMed Central
Published in
BMC Medicine / Issue 1/2021
Electronic ISSN: 1741-7015
DOI
https://doi.org/10.1186/s12916-021-02186-z

Other articles of this Issue 1/2021

BMC Medicine 1/2021 Go to the issue

Research article

Maternal dietary quality, inflammatory potential and childhood adiposity: an individual participant data pooled analysis of seven European cohorts in the ALPHABET consortium

Research article

The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study

Research article

Insulin resistance, diabetic kidney disease, and all-cause mortality in individuals with type 2 diabetes: a prospective cohort study

Correspondence

The risk of COVID-19 in survivors of domestic violence and abuse

Research article

A follow-up study shows that recovered patients with re-positive PCR test in Wuhan may not be infectious

Correction

Correction to: Following the science? Comparison of methodological and reporting quality of covid-19 and other research from the first wave of the pandemic