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Jesse Stewart, K. Rand, M. Muldoon, T. Kamarck (2009)
A prospective evaluation of the directionality of the depression–inflammation relationshipBrain, Behavior, and Immunity, 23
J. Marchini, O. Delaneau, K. Sharp (2015)
UK Biobank Phasing and Imputation Documentation Version 1 . 2 13 November 2015 documentation author
S. Purcell, B. Neale, K. Todd-Brown, L. Thomas, M. Ferreira, David Bender, J. Maller, P. Sklar, P. Bakker, M. Daly, P. Sham (2007)
PLINK: a tool set for whole-genome association and population-based linkage analyses.American journal of human genetics, 81 3
M. Maes, C. Dejonckheere, C. Vandervorst, C. Schotte, P. Cosyns, J. Raus, E. Suy (1991)
Abnormal pituitary function during melancholia: reduced alpha-melanocyte-stimulating hormone secretion and increased intact ACTH non-suppression.Journal of affective disorders, 22 3
D. Levinson, S. Mostafavi, Y. Milaneschi, M. Rivera, S. Ripke, N. Wray, P. Sullivan (2014)
Genetic Studies of Major Depressive Disorder: Why Are There No Genome-wide Association Study Findings and What Can We Do About It?Biological Psychiatry, 76
N. Simon, K. McNamara, Candice Chow, R. Maser, G. Papakostas, M. Pollack, A. Nierenberg, M. Fava, Kwok-Kin Wong (2008)
A detailed examination of cytokine abnormalities in Major Depressive DisorderEuropean Neuropsychopharmacology, 18
P. Needleman, J. Turk, B. Jakschik, A. Morrison, J. Lefkowith (1986)
Arachidonic acid metabolism.Annual review of biochemistry, 55
C. Willer, Yun Li, G. Abecasis (2010)
METAL: fast and efficient meta-analysis of genomewide association scansBioinformatics, 26
Doori Park, J. Jung, Beom-Soon Choi, Murukarthick Jayakodi, Jeongsoo Lee, Jongsung Lim, Yeisoo Yu, Yong‐Soo Choi, Myeong-lyeol Lee, Yoonseong Park, I. Choi, Tae-Jin Yang, O. Edwards, G. Nah, H. Kwon (2015)
Uncovering the novel characteristics of Asian honey bee, Apis cerana, by whole genome sequencingBMC Genomics, 16
83 -2.91) 8.50 x 10 -9 1.15 (0.80 -1.59) 4.39 x 10 -1 1.67 (1.40 -1.98) 7.92 x 10 -8 11 *
Andrew Miller (2010)
Depression and immunity: A role for T cells?Brain, Behavior, and Immunity, 24
Melanie Glocke, F. Lang, E. Schaeffeler, T. Lang, M. Schwab, U. Lang (2013)
Impact of Vitamin D Receptor VDR rs2228570 Polymorphism in Oldest OldKidney and Blood Pressure Research, 37
V. Boraska, O. Davis, L. Cherkas, S. Helder, Juliette Harris, I. Krug, Thomas Liao, J. Treasure, I. Ntalla, L. Karhunen, A. Keski-Rahkonen, Danai Christakopoulou, A. Raevuori, S. Shin, G. Dedoussis, J. Kaprio, N. Soranzo, T. Spector, D. Collier, E. Zeggini (2012)
Genome-Wide Association Analysis of Eating Disorder-Related Symptoms, Behaviors, and Personality TraitsAmerican Journal of Medical Genetics, 159B
Vanja Durić, M. Banasr, P. Licznerski, H. Schmidt, C. Stockmeier, A. Simen, S. Newton, R. Duman (2010)
A negative regulator of MAP kinase causes depressive behavior.Nature medicine, 16 11
Seunggeun Lee, G. Abecasis, M. Boehnke, Xihong Lin (2014)
Rare-variant association analysis: study designs and statistical tests.American journal of human genetics, 95 1
S. Hawley, M. Wills, A. Rabalski, A. Bendall, N. Jones (2011)
Expression patterns of ShcD and Shc family adaptor proteins during mouse embryonic developmentDevelopmental Dynamics, 240
C. Surh, J. Sprent (2008)
Homeostasis of naive and memory T cells.Immunity, 29 6
M. Irwin, Andrew Miller (2007)
Depressive disorders and immunity: 20 years of progress and discoveryBrain, Behavior, and Immunity, 21
D. Kasper, R. Planells-Cases, J. Fuhrmann, O. Scheel, O. Zeitz, K. Ruether, A. Schmitt, Mallorie Poët, R. Steinfeld, M. Schweizer, U. Kornak, T. Jentsch (2005)
Loss of the chloride channel ClC‐7 leads to lysosomal storage disease and neurodegenerationThe EMBO Journal, 24
E. Smyth (2010)
Thromboxane and the thromboxane receptor in cardiovascular diseaseClinical Lipidology, 5
Jeff Huffman, C. Celano, S. Beach, S. Motiwala, J. Januzzi (2013)
Depression and Cardiac Disease: Epidemiology, Mechanisms, and DiagnosisCardiovascular Psychiatry and Neurology, 2013
M. Klok, E. Giltay, A. Does, A. Does, J. Geleijnse, N. Antypa, B. Penninx, E. Geus, G. Willemsen, D. Boomsma, N. Leeuwen, F. Zitman, E. Kloet, R. Derijk (2011)
A common and functional mineralocorticoid receptor haplotype enhances optimism and protects against depression in femalesTranslational Psychiatry, 1
G. Abecasis, S. Cherny, W. Cookson, L. Cardon (2002)
Merlin—rapid analysis of dense genetic maps using sparse gene flow treesNature Genetics, 30
K. Frazer, D. Ballinger, D. Cox, D. Hinds, L. Stuve, R. Gibbs, J. Belmont, A. Boudreau, P. Hardenbol, S. Leal, S. Pasternak, D. Wheeler, T. Willis, F. Yu, Huanming Yang, Changqing Zeng, Yang Gao, Haoran Hu, Weitao Hu, Chaohua Li, Wei Lin, Siqi Liu, Hao Pan, Xiaoli Tang, Jian Wang, Wei Wang, Jun Yu, Bo Zhang, Qingrun Zhang, Hongbin Zhao, Hui-Ping Zhao, Jun Zhou, S. Gabriel, Rachel Barry, B. Blumenstiel, Amy Camargo, M. Defelice, M. Faggart, Marie-Anne Goyette, Supriya Gupta, Jamie Moore, Huy Nguyen, R. Onofrio, Melissa Parkin, J. Roy, E. Stahl, E. Winchester, L. Ziaugra, D. Altshuler, Yan Shen, Zhijian Yao, Wei Huang, X. Chu, Yungang He, Li Jin, Yangfan Liu, Yayun Shen, Weiwei Sun, Haifeng Wang, Yi Wang, Ying Wang, Xiao-yan Xiong, Liang Xu, M. Waye, S. Tsui, H. Xue, J. Wong, L. Galver, Jian-Bing Fan, K. Gunderson, S. Murray, A. Oliphant, M. Chee, A. Montpetit, F. Chagnon, Vincent Ferretti, M. Leboeuf, J. Olivier, M. Phillips, Stéphanie Roumy, C. Sallée, A. Verner, T. Hudson, P. Kwok, Dongmei Cai, D. Koboldt, Raymond Miller, L. Pawlikowska, P. Taillon-Miller, M. Xiao, L. Tsui, W. Mak, You-Qiang Song, P. Tam, Yusuke Nakamura, T. Kawaguchi, T. Kitamoto, Takashi Morizono, A. Nagashima, Y. Ohnishi, A. Sekine, Toshihiro Tanaka, T. Tsunoda, P. Deloukas, C. Bird, Marcos Delgado, E. Dermitzakis, R. Gwilliam, S. Hunt, J. Morrison, Don Powell, B. Stranger, P. Whittaker, D. Bentley, M. Daly, P. Bakker, J. Barrett, Y. Chretien, J. Maller, S. Mccarroll, N. Patterson, I. Pe’er, A. Price, S. Purcell, D. Richter, Pardis Sabeti, R. Saxena, S. Schaffner, P. Sham, P. Varilly, Lincoln Stein, Lalitha Krishnan, A. Smith, M. Tello-Ruiz, Gudmundur Thorisson, A. Chakravarti, Peter Chen, D. Cutler, C. Kashuk, Shin Lin, G. Abecasis, W. Guan, Yun Li, Heather Munro, Zhaohui Qin, D. Thomas, G. McVean, A. Auton, L. Bottolo, Niall Cardin, S. Eyheramendy, C. Freeman, J. Marchini, S. Myers, C. Spencer, M. Stephens, P. Donnelly, L. Cardon, G. Clarke, David Evans, A. Morris, B. Weir, J. Mullikin, S. Sherry, M. Feolo, Andrew Skol, Houcan Zhang, I. Matsuda, Y. Fukushima, D. Macer, Eiko Suda, C. Rotimi, C. Adebamowo, I. Ajayi, Toyin Aniagwu, P. Marshall, C. Nkwodimmah, C. Royal, M. Leppert, M. Dixon, A. Peiffer, Renzong Qiu, A. Kent, Kazuto Kato, N. Niikawa, I. Adewole, B. Knoppers, Morris Foster, E. Clayton, Jessica Watkin, D. Muzny, L. Nazareth, E. Sodergren, G. Weinstock, I. Yakub, B. Birren, R. Wilson, L. Fulton, J. Rogers, J. Burton, N. Carter, C. Clee, M. Griffiths, Matthew Jones, K. McLay, R. Plumb, M. Ross, S. Sims, D. Willey, Zhu Chen, Hua Han, L. Kang, M. Godbout, J. Wallenburg, P. L'Archevêque, G. Bellemare, Koji Saeki, Hongguang Wang, Daochang An, Hongbo Fu, Qing Li, Zhen Wang, Ren-hao Wang, A. Holden, L. Brooks, J. Mcewen, M. Guyer, V. Wang, Jane Peterson, Michael Shi, J. Spiegel, L. Sung, Lynn Zacharia, F. Collins, Karen Kennedy, Ruth Jamieson, J. Stewart (2007)
A second generation human haplotype map of over 3.1 million SNPsNature, 449
AM Fernandez-Pujals (2015)
Epidemiology and heritability of major depressive disorder, stratified by age of onset, sex, and illness course in generation scotland: scottish family health study (GS:SFHS)PLoS ONE, 10
C. Raison, L. Capuron, Andrew Miller (2006)
Cytokines sing the blues: inflammation and the pathogenesis of depression.Trends in immunology, 27 1
Jian Yang, N. Zaitlen, M. Goddard, P. Visscher, A. Price (2014)
Advantages and pitfalls in the application of mixed-model association methodsNature Genetics, 46
Niamh Ryan, S. Morris, D. Porteous, Martin Taylor, K. Evans (2014)
SuRFing the genomics wave: an R package for prioritising SNPs by functionalityGenome Medicine, 6
S. Purcell, S. Cherny, P. Sham (2003)
Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traitsBioinformatics, 19 1
E. Bertone-Johnson (2009)
Vitamin D and the occurrence of depression: causal association or circumstantial evidence?Nutrition reviews, 67 8
Jared O'Connell, D. Gurdasani, O. Delaneau, N. Pirastu, Sheila Ulivi, M. Cocca, Michela Traglia, Jie Huang, J. Huffman, I. Rudan, R. McQuillan, Ross Fraser, H. Campbell, O. Polašek, G. Asiki, K. Ekoru, C. Hayward, A. Wright, V. Vitart, P. Navarro, J. Zagury, James Wilson, D. Toniolo, P. Gasparini, N. Soranzo, M. Sandhu, J. Marchini (2014)
A General Approach for Haplotype Phasing across the Full Spectrum of RelatednessPLoS Genetics, 10
N. Zaitlen, P. Kraft, N. Patterson, B. Pasaniuc, G. Bhatia, Samuela Pollack, A. Price (2013)
Using Extended Genealogy to Estimate Components of Heritability for 23 Quantitative and Dichotomous TraitsPLoS Genetics, 9
V. Ganji, Cristiana Milone, M. Cody, F. Mccarty, Y. Wang (2010)
Serum vitamin D concentrations are related to depression in young adult US population: the Third National Health and Nutrition Examination SurveyInternational Archives of Medicine, 3
Yuan-gang You, Wei-qi Li, Y. Gong, B. Yin, B. Qiang, Jiangang Yuan, Xiaozhong Peng (2010)
ShcD interacts with TrkB via its PTB and SH2 domains and regulates BDNF-induced MAPK activation.BMB reports, 43 7
N. Cai, T. Bigdeli, Warren Kretzschmar, Yihan Li, Jieqin Liang, Li Song, Jingchu Hu, Qibin Li, Wei Jin, Zhenfei Hu, Guangbiao Wang, Linmao Wang, Puyi Qian, Yuan Liu, T. Jiang, Yao Lu, Xiuqing Zhang, Ye Yin, Yingrui Li, Xun Xu, Jingfang Gao, M. Reimers, T. Webb, B. Riley, S. Bacanu, Roseann Peterson, Yiping Chen, H. Zhong, Zhengrong Liu, Gang Wang, Jing Sun, H. Sang, G. Jiang, Xiaoyan Zhou, Yi Li, Wei Zhang, Xueyi Wang, X. Fang, R. Pan, G. Miao, Qiwen Zhang, Jian Hu, F. Yu, B. Du, W. Sang, K. Li, Guibing Chen, M. Cai, Lijun Yang, Donglin Yang, B. Ha, X. Hong, H. Deng, Gongying Li, Kan Li, Yan Song, Shugui Gao, Jinbei Zhang, Z. Gan, H. Meng, J. Pan, C. Gao, Kerang Zhang, N. Sun, Youhui Li, Q. Niu, Y Zhang, Tieqiao Liu, Chunmei Hu, Zhen Zhang, L. Lv, Jicheng Dong, Xiao-ping Wang, M. Tao, Xumei Wang, Jing Xia, H. Rong, Qiang He, Tie-bang Liu, Guoping Huang, Q. Mei, Zhenming Shen, Y. Liu, Jianhua Shen, T. Tian, Xiaojuan Liu, Wenyuan Wu, D. Gu, G. Fu, Jian-guo Shi, Yunchun Chen, Xiangchao Gan, Lan-fen Liu, Lina Wang, Fuzhong Yang, E. Cong, J. Marchini, Huanming Yang, Jian Wang, S. Shi, R. Mott, Qi Xu, Jun Wang, K. Kendler, J. Flint (2015)
Sparse whole genome sequencing identifies two loci for major depressive disorderNature, 523
Danny Smith, B. Nicholl, B. Cullen, Daniel Martin, Z. ul-Haq, Jonathan Evans, J. Gill, Beverly Roberts, J. Gallacher, D. Mackay, M. Hotopf, I. Deary, N. Craddock, J. Pell (2013)
Prevalence and Characteristics of Probable Major Depression and Bipolar Disorder within UK Biobank: Cross-Sectional Study of 172,751 ParticipantsPLoS ONE, 8
C. Fabbri, A. Serretti (2016)
Genetics of long-term treatment outcome in bipolar disorderProgress in Neuro-Psychopharmacology and Biological Psychiatry, 65
S. Ramagopalan, C. Wotton, A. Handel, D. Yeates, M. Goldacre (2011)
Risk of venous thromboembolism in people admitted to hospital with selected immune-mediated diseases: record-linkage studyBMC Medicine, 9
D. Kokare, P. Singru, Manoj Dandekar, C. Chopde, N. Subhedar (2008)
Involvement of alpha-melanocyte stimulating hormone (α-MSH) in differential ethanol exposure and withdrawal related depression in rat: Neuroanatomical–behavioral correlatesBrain Research, 1216
P. Gresele, J. Arnout, H. Deckmyn, E. Huybrechts, G. Pieters, J. Vermylen (1987)
Role of proaggregatory and antiaggregatory prostaglandins in hemostasis. Studies with combined thromboxane synthase inhibition and thromboxane receptor antagonism.The Journal of clinical investigation, 80 5
P. Sullivan, M. Neale, K. Kendler (2000)
Genetic epidemiology of major depression: review and meta-analysis.The American journal of psychiatry, 157 10
G. Lubke, J. Hottenga, R. Walters, Charles Laurin, E. Geus, G. Willemsen, J. Smit, C. Middeldorp, B. Penninx, J. Vink, D. Boomsma (2012)
Estimating the Genetic Variance of Major Depressive Disorder Due to All Single Nucleotide PolymorphismsBiological Psychiatry, 72
J. Higgins, S. Thompson (2002)
Quantifying heterogeneity in a meta‐analysisStatistics in Medicine, 21
A. Manichaikul, J. Mychaleckyj, S. Rich, Kathy Daly, M. Sale, Wei-Min Chen (2010)
Robust relationship inference in genome-wide association studiesBioinformatics, 26 22
K. Hoek, N. Schlegel, O. Eichhoff, D. Widmer, C. Praetorius, Steingrímur Einarsson, S. Valgeirsdóttir, K. Bergsteinsdottir, Alexander Schepsky, R. Dummer, E. Steingrímsson (2008)
Novel MITF targets identified using a two‐step DNA microarray strategyPigment Cell & Melanoma Research, 21
M. First, R. Spitzer, Gibbon Miriam, Janet Williams (1997)
Structured clinical interview for DSM-IV axis I disorders : SCID-I : clinical version : scoresheet
S. Ripke, N. Wray, C. Lewis, S. Hamilton, M. Weissman, G. Breen, Enda Byrne, D. Blackwood, D. Boomsma, S. Cichon, A. Heath, F. Holsboer, S. Lucae, P. Madden, N. Martin, P. McGuffin, P. Muglia, M. Noethen, B. Penninx, M. Pergadia, J. Potash, M. Rietschel, D. Lin, B. Müller-Myhsok, Jianxin Shi, S. Steinberg, H. Grabe, P. Lichtenstein, P. Magnusson, R. Perlis, M. Preisig, J. Smoller, K. Stefánsson, R. Uher, Z. Kutalik, K. Tansey, A. Teumer, A. Viktorin, M. Barnes, T. Bettecken, E. Binder, R. Breuer, V. Castro, S. Churchill, W. Coryell, N. Craddock, I. Craig, D. Czamara, E. Geus, F. Degenhardt, A. Farmer, M. Fava, J. Frank, V. Gainer, Patience Gallagher, S. Gordon, Sergey Goryachev, M. Gross, M. Guipponi, A. Henders, S. Herms, I. Hickie, S. Hoefels, W. Hoogendijk, J. Hottenga, D. Iosifescu, M. Ising, I. Jones, L. Jones, T. Jung-Ying, J. Knowles, I. Kohane, M. Kohli, A. Korszun, M. Landén, W. Lawson, G. Lewis, D. Macintyre, W. Maier, M. Mattheisen, P. McGrath, A. McIntosh, A. McLean, C. Middeldorp, L. Middleton, Grant Montgomery, S. Murphy, M. Nauck, W. Nolen, D. Nyholt, M. O’Donovan, H. Oskarsson, N. Pedersen, W. Scheftner, A. Schulz, T. Schulze, Stanley Shyn, E. Sigurdsson, S. Slager, J. Smit, H. Stefánsson, M. Steffens, T. Thorgeirsson, F. Tozzi, J. Treutlein, M. Uhr, E. Oord, G. Grootheest, H. Völzke, J. Weilburg, G. Willemsen, F. Zitman, B. Neale, M. Daly, D. Levinson, P. Sullivan (2013)
A mega-analysis of genome-wide association studies for major depressive disorderMolecular Psychiatry, 18
E. Bromet, L. Andrade, I. Hwang, N. Sampson, J. Alonso, G. Girolamo, R. Graaf, K. Demyttenaere, Chi‐yi Hu, N. Iwata, A. Karam, J. Kaur, S. Kostyuchenko, J. Lépine, D. Levinson, H. Matschinger, M. Mora, M. Browne, J. Posada-Villa, M. Viana, David Williams, R. Kessler (2011)
Cross-national epidemiology of DSM-IV major depressive episodeBMC Medicine, 9
C. Hyde, M. Nagle, C. Tian, Xing Chen, S. Paciga, J. Wendland, J. Tung, D. Hinds, R. Perlis, A. Winslow (2016)
Identification of 15 genetic loci associated with risk of major depression in individuals of European descentNature genetics, 48
M. First, R. Spitzer, F. Gibon, Jbw Williams (2002)
Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research version (SCID-I RV)
E. Hyppönen, C. Power (2007)
Hypovitaminosis D in British adults at age 45 y: nationwide cohort study of dietary and lifestyle predictors.The American journal of clinical nutrition, 85 3
Base pair (bp) positions are based on build GRCh37. * indicates haplotype boundaries 594 defined by the fine mapping approach
Blair Smith, A. Campbell, P. Linksted, B. Fitzpatrick, Cathy Jackson, S. Kerr, I. Deary, D. Macintyre, H. Campbell, M. McGilchrist, L. Hocking, Lucy Wisely, I. Ford, R. Lindsay, R. Morton, C. Palmer, A. Dominiczak, D. Porteous, A. Morris (2013)
Cohort Profile: Generation Scotland: Scottish Family Health Study (GS:SFHS). The study, its participants and their potential for genetic research on health and illness.International journal of epidemiology, 42 3
O. Delaneau, J. Zagury, Jonathan Marchini (2012)
Supplementary Information for ‘ Improved whole chromosome phasing for disease and population genetic studies ’
O Delaneau, JF Zagury, J Marchini (2013)
Improved whole-chromosome phasing for disease and population genetic studiesNat. Methods, 10
Lucia Peixoto, M. Wimmer, Shane Poplawski, Jennifer Tudor, C. Kenworthy, Shichong Liu, K. Mizuno, B. Garcia, Nancy Zhang, K. Giese, T. Abel (2015)
Memory acquisition and retrieval impact different epigenetic processes that regulate gene expressionBMC Genomics, 16
C. Pato, M. Pato, A. Kirby, T. Petryshen, T. Petryshen, H. Medeiros, C. Carvalho, A. Macedo, A. Dourado, I. Coelho, J. Valente, M. Soares, C. Ferreira, M. Lei, A. Verner, T. Hudson, C. Morley, J. Kennedy, M. Azevedo, M. Daly, P. Sklar, P. Sklar (2004)
Genome‐wide scan in Portuguese Island families implicates multiple loci in bipolar disorder: Fine mapping adds support on chromosomes 6 and 11American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 127B
J. Horwood (2014)
UK Biobank Data: Come and Get It
D. Hare, S. Toukhsati, P. Johansson, T. Jaarsma (2014)
Depression and cardiovascular disease: a clinical review.European heart journal, 35 21
S. Goyal, D. Kokare, C. Chopde, N. Subhedar (2006)
Alpha-melanocyte stimulating hormone antagonizes antidepressant-like effect of neuropeptide Y in Porsolt's test in ratsPharmacology Biochemistry and Behavior, 85
Edinburgh Research Explorer Epidemiology and Heritability of Major Depressive Disorder, Stratified by Age of Onset, Sex, and Illness Course in Generation Scotland: Scottish Family Health Study (GS:SFHS)
L. Zgaga, L. Zgaga, E. Theodoratou, E. Theodoratou, S. Farrington, F. Agakov, A. Tenesa, M. Walker, S. Knox, A. Wallace, R. Cetnarskyj, G. Mcneill, J. Kyle, M. Porteous, M. Dunlop, Harry Campbell, Harry Campbell (2011)
Diet, environmental factors, and lifestyle underlie the high prevalence of vitamin D deficiency in healthy adults in Scotland, and supplementation reduces the proportion that are severely deficient.The Journal of nutrition, 141 8
S. Pandruvada, J. Beauregard, S. Benjannet, M. Pata, C. Lazure, N. Seidah, J. Vacher (2015)
Role of Ostm1 Cytosolic Complex with Kinesin 5B in Intracellular Dispersion and TraffickingMolecular and Cellular Biology, 36
N. Park, S. Juo, R. Cheng, Jianjun Liu, J. Loth, B. Lilliston, J. Nee, A. Grunn, K. Kanyas, B. Lerer, J. Endicott, T. Gilliam, M. Baron (2004)
Linkage analysis of psychosis in bipolar pedigrees suggests novel putative loci for bipolar disorder and shared susceptibility with schizophreniaMolecular Psychiatry, 9
S. Lehto, A. Huotari, L. Niskanen, K. Herzig, T. Tolmunen, H. Viinamäki, H. Koivumaa-Honkanen, K. Honkalampi, S. Sinikallio, H. Ruotsalainen, J. Hintikka (2010)
Serum IL-7 and G-CSF in major depressive disorderProgress in Neuro-Psychopharmacology and Biological Psychiatry, 34
Ju-Hyun Park, S. Wacholder, M. Gail, U. Peters, K. Jacobs, S. Chanock, N. Chatterjee (2010)
Estimation of effect size distribution from genome-wide association studies and implications for future discoveriesNature Genetics, 42
D. Dick, T. Foroud, L. Flury, Elizabeth Bowman, Marvin Miller, N. Rau, P. Moe, Nalini Samavedy, R. El-Mallakh, H. Manji, D. Glitz, Eric Meyer, Carrie Smiley, Rhoda Hahn, C. Widmark, R. Mckinney, L. Sutton, C. Ballas, D. Grice, W. Berrettini, W. Byerley, W. Coryell, R. Depaulo, D. MacKinnon, E. Gershon, J. Kelsoe, F. McMahon, M. McInnis, D. Murphy, T. Reich, W. Scheftner, J. Nurnberger (2003)
Genomewide linkage analyses of bipolar disorder: a new sample of 250 pedigrees from the National Institute of Mental Health Genetics Initiative.American journal of human genetics, 73 1
Patrick Sullivan, E. Geus, G. Willemsen, Michael James, Johannes Smit, T. Zandbelt, V. Arolt, B. Baune, D. Blackwood, S. Cichon, W. Coventry, K. Domschke, A. Farmer, Maurizio Fava, Scott Gordon, Qianchuan He, A. Heath, Peter Heutink, Florian Holsboer, W. Hoogendijk, J. Hottenga, Yijuan Hu, Martin Kohli, Danyu Lin, S. Lucae, D. Macintyre, W. Maier, K. McGhee, P. McGuffin, Grant Montgomery, W. Muir, W. Nolen, M. Nöthen, R. Perlis, K. Pirlo, D. Posthuma, M. Rietschel, Patizia Rizzu, A. Schosser, August Smit, J. Smoller, Jung‐Ying Tzeng, R. Dyck, M. Verhage, Frans Zitman, Nicholas Martin, N. Wray, D. Boomsma, B.W.J.H. Penninx (2008)
Genomewide Association for Major Depressive Disorder: A possible role for the presynaptic protein PiccoloMolecular psychiatry, 14
B. Browning, S. Browning (2013)
Improving the Accuracy and Efficiency of Identity-by-Descent Detection in Population DataGenetics, 194
C. Kittipatarin, A. Khaled (2007)
Interlinking interleukin-7.Cytokine, 39 1
Jung-Jin Kim, L. Mandelli, C. Pae, D. Ronchi, T. Jun, Chul Lee, I. Paik, A. Patkar, D. Steffens, A. Serretti, Changsu Han (2008)
Is there protective haplotype of dysbindin gene (DTNBP1) 3 polymorphisms for major depressive disorderProgress in Neuro-Psychopharmacology and Biological Psychiatry, 32
P. McGuffin, F. Rijsdijk, M. Andrew, P. Sham, R. Katz, A. Cardno (2003)
The heritability of bipolar affective disorder and the genetic relationship to unipolar depression.Archives of general psychiatry, 60 5
Zaifu Zhang, J. Ni, Jiangtao Zhang, Wenxin Tang, Xiao Li, Zhi-guo Wu, Chen Zhang (2016)
A haplotype in the 5'-upstream region of the NDUFV2 gene is associated with major depressive disorder in Han Chinese.Journal of affective disorders, 190
B. Bulik-Sullivan, Po-ru Loh, H. Finucane, S. Ripke, Jian Yang, N. Patterson, M. Daly, A. Price, B. Neale (2014)
LD Score regression distinguishes confounding from polygenicity in genome-wide association studiesNature Genetics, 47
P. Sham, S. Purcell (2014)
Statistical power and significance testing in large-scale genetic studiesNature Reviews Genetics, 15
J. Knight, N. Rochberg, Scott Saccone, J. Nurnberger, John Rice (2010)
An investigation of candidate regions for association with bipolar disorderAmerican Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 153B
Hee-Ju Kang, Seon-Young Kim, K. Bae, Sung-Wan Kim, I. Shin, Jin-Sang Yoon, Jae-Min Kim (2015)
Comorbidity of Depression with Physical Disorders: Research and Clinical ImplicationsChonnam Medical Journal, 51
Nagesh Aragam, Kesheng Wang, Yue Pan (2011)
Genome-wide association analysis of gender differences in major depressive disorder in the Netherlands NESDA and NTR population-based samples.Journal of affective disorders, 133 3
C. Amador, J. Huffman, H. Trochet, A. Campbell, D. Porteous, James Wilson, N. Hastie, V. Vitart, C. Hayward, P. Navarro, C. Haley (2015)
Recent genomic heritage in ScotlandBMC Genomics, 16
Jian Yang, Beben Benyamin, Brian McEvoy, S. Gordon, A. Henders, D. Nyholt, P. Madden, A. Heath, N. Martin, G. Montgomery, M. Goddard, P. Visscher (2010)
Common SNPs explain a large proportion of the heritability for human heightNature Genetics, 42
A. Nicholson, H. Kuper, H. Hemingway (2006)
Depression as an aetiologic and prognostic factor in coronary heart disease: a meta-analysis of 6362 events among 146 538 participants in 54 observational studies.European heart journal, 27 23
Genome-wide association studies using genotype data have had limited success in the identification of variants associated with major depressive disorder (MDD). Haplotype data provide an alternative method for detecting associations between variants in weak linkage disequilibrium with genotyped variants and a given trait of interest. A genome-wide haplotype association study for MDD was undertaken utilising a family-based population cohort, Generation Scotland: Scottish Family Health Study (n = 18,773), as a discovery cohort with UK Biobank used as a population-based replication cohort (n = 25,035). Fine mapping of haplotype boundaries was used to account for overlapping haplotypes potentially tagging the same causal variant. Within the discovery cohort, two haplotypes −8 exceeded genome-wide significance (P <5× 10 ) for an association with MDD. One of these haplotypes was nominally significant in the replication cohort (P < 0.05) and was located in 6q21, a region which has been previously associated with bipolar disorder, a psychiatric disorder that is phenotypically and genetically correlated with MDD. −7 Several haplotypes with P <10 in the discovery cohort were located within gene coding regions associated with diseases that are comorbid with MDD. Using such haplotypes to highlight regions for sequencing may lead to the identification of the underlying causal variants. Introduction (SNP)-based analyses are unlikely to fully capture the Major depressive disorder (MDD) is a complex and variation in regions surrounding the genotyped markers, clinically heterogeneous condition with core symptoms of including untyped lower-frequency variants and those low mood and/or anhedonia over a period of at least two that are in weak linkage disequilibrium (LD) with the weeks. MDD is frequently comorbid with other clinical common SNPs on many genotyping arrays. 1 2 conditions, such as cardiovascular disease , cancer and Haplotype-based analysis may help improve the detec- inflammatory diseases . This complexity and comorbidity tion of causal genetic variants as, unlike single SNP-based suggests heterogeneity of aetiology and may explain why analysis, it is possible to assign the strand of sequence there has been limited success in identifying causal variants and combine information from multiple SNPs to 4–7 10–12 genetic variants , despite heritability estimates ranging identify rarer causal variants. A number of studies 8,9 from 28 to 37% . Single-nucleotide polymorphism have identified haplotypes associated with MDD, albeit by focussing on particular regions of interest. In the current study, a family and population-based cohort Generation Correspondence: David M Howard (D.Howard@ed.ac.uk) Scotland: Scottish Family Health Study (GS:SFHS) was Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK utilised to ascertain genome-wide haplotypes in closely Medical Research Council Human Genetics Unit, Institute of Genetics and 13 and distantly related individuals . A haplotype-based Molecular Medicine, University of Edinburgh, Edinburgh, UK association analysis was conducted using MDD as a Full list of author information is available at the end of the article © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to theCreativeCommons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Translational Psychiatry 1234567890 Howard et al. Translational Psychiatry (2017) 7:1263 Page 2 of 9 phenotype, followed by additional fine mapping of hap- family information . The default window size of 2 Mb lotype boundaries with a replication and meta-analysis was used for UK Biobank and a 5 Mb window was used performed using the UK Biobank cohort . for GS:SFHS as larger window sizes have been demon- strated to be beneficial when there is increased identity by Materials and methods descent (IBD) in the population . The number of con- Discovery cohort ditioning states per SNP was increased from the default of The discovery phase of the study used the family and 100 states to 200 states to improve phasing accuracy, with population-based Generation Scotland: Scottish Family the default effective population size of 15,000 used. To Health Study (GS:SFHS) cohort , consisting of 23,960 calculate the recombination rates between SNPs during individuals of whom 20,195 were genotyped with the phasing the HapMap phase II b37 was used. This build Illumina OmniExpress BeadChip (706,786 SNPs). Indivi- was also used to partition the phased data into haplotypes. duals with a genotype call rate <98% were removed, as Three window sizes (1cM, 0.5cM and 0.25cM) were well as those SNPs with a call rate <98%, a minor allele used to establish the SNPs that formed each haplotype . frequency (MAF) < 0.01 or those deviating from Each window was then moved along the genome by a −6 Hardy–Weinberg equilibrium (P < 10 ). Individuals who quarter of the respective window size. There were a total were identified as population outliers through principal of 97,333 windows with a mean number of SNPs per component analyses of their genotypic information were window of 157, 79 and 34 for the 1, 0.5 and 0.25cM also removed . windows, respectively. Windows that were less five SNPs Following quality control there were 19,904 GS:SFHS in length were removed. The frequency (p) of each individuals (11,731 females and 8173 males) that had observed haplotype (A) was calculated as: genotypic information for 561,125 autosomal SNPs. 2 X obsðÞ AA þ obsðÞ Aa These individuals ranged from 18–99 years of age with an p ¼ 2 XðÞ obsðÞ AA þ obsðÞ Aa þ obsðÞ aa average age of 47.4 years and a standard deviation of 15.0 years. There were 4933 families that had at least two where a represents all other haplotypes in that window. A related individuals, this included 1799 families with two chi-squared test for Hardy–Weinberg equilibrium (X ) for members, 1216 families with three members and 829 each haplotype was calculated as: families with four members. The largest family group 2 2 obsðÞ AA p n obsðÞ Aa 2 pqn obsðÞ aa q n consisted of 31 related individuals and there were 1789 X ¼ þ þ 2 2 individuals that had no other family members within GS: p n 2 pqn q n SFHS. where n is the number of individuals and q = 1 − p. Replication cohort Haplotypes with 0.995 < p < 0.005 or with X > 24 (P < 16 −6 The population-based UK Biobank (provided as part 10 ) were not tested for association, however, they were of project #4844) was used as a replication cohort to included within the alternative haplotype. Following this −6 assess those haplotypes within GS:SFHS with P < 10 . quality control there were a total of 2,618,094 haplotypes The UK Biobank data consisted of 152,249 individuals remaining for analysis. The reported haplotype positions with genomic data for 72,355,667 imputed variants . The relate to the outermost SNPs within each haplotype are in SNPs genotyped in GS:SFHS were extracted from the UK base pair (bp) position according to GRCh37. Biobank data and those variants with an imputation To approximate the number of independently segre- accuracy <0.8 were removed, leaving 555,782 variants in gating haplotypes the clump command within Plink common between the two cohorts. Those genotyped v1.90 was applied. This provides an estimation of the individuals listed as non-white British and those that had Bonferroni correction required for multiple testing. When also participated in GS:SFHS were removed from within applying an LD r threshold of <0.4 there were 1,070,216 UK Biobank, leaving a total of 119,955 individuals. independently segregating haplotypes within GS:SFHS, −8 equating to a P-value < 5 × 10 for genome-wide sig- Genotype phasing and haplotype formation nificance. This threshold is also frequently applied to The genotype data for GS:SFHS and UK Biobank was SNP-based and sequence-based association studies to phased using SHAPEIT v2.r837 . Genome-wide phasing account for multiple testing . was conducted on the GS:SFHS cohort, while the phasing of UK Biobank was conducted on a 50 Mb window Phenotype ascertainment centred on those haplotypes identified within GS:SFHS Discovery cohort −6 with P < 10 . The relatedness within GS:SFHS made it Within GS:SFHS a diagnosis of MDD was made using suitable for the application of the duoHMM method, initial screening questions and the Structured Clinical which improves phasing accuracy by also incorporating Interview for the Diagnostic and Statistical Manual of Translational Psychiatry Howard et al. Translational Psychiatry (2017) 7:1263 Page 3 of 9 Mental Disorders (SCID) . The SCID is an inter- depression. In total there were 8508 cases and 16,527 nationally validated approach to identifying episodes of controls, equating to a trait prevalence of 34.0% in this depression and was conducted by clinical nurses trained cohort, after the removal of individuals with insufficient in its administration. Further details regarding this diag- information or ambiguous phenotypes. nostic assessment have been described previously .In this study, MDD was defined by at least one instance of a Statistical approach major depressive episode which initially identified 2659 Discovery cohort cases, 17,237 controls and 98 missing (phenotype A mixed linear model was used to conduct an associa- unknown) individuals. tion analysis using GCTA v1.25.0: In addition, the psychiatric history of cases and controls y ¼ Xβ þ Z u þ Z v þ ε 1 2 was examined using the Scottish Morbidity Record . Within the control group, 1072 participants were found to have attended at least one psychiatry outpatient where y was the vector of binary observations for MDD. clinic and were excluded from the study. In addition, β was the matrix of fixed effects, including haplotype, sex, 47 of the MDD cases were found to have additional age and age . Each unique haplotype was represented as a diagnoses of either bipolar disorder or schizophrenia distinct allele and was either coded as 0, 1 or 2 depending in psychiatric inpatient records and were also excluded on the number of haplotypes carried by that individual. u from the study. These participants had given prior con- was fitted as a random effect taking into account the sent for anonymised access to routine administrative genomic relationships (MVN (0,Gσ ), where G was a clinical data. SNP-based genomic relationship matrix ). v was a ran- In total there were 2605 MDD cases and 16,168 controls dom effect fitting a second genomic relationship matrix following the removal of individuals based on patient G (MVN (0,G σ ) which modelled only the more closely records and population stratification, equating to a pre- related individuals . G was equal to G except that off- valence of 13.9% for MDD in this cohort. diagonal elements <0.05 were set to 0. X, Z and Z were 1 2 the corresponding incidence matrices. ε was the vector of Replication cohort residual effects and was assumed to be normally dis- Within the UK Biobank cohort, 25,035 participants tributed, MVN (0,Iσ ). (12,528 males and 12,507 females) completed a touchsc- The inclusion of the second genomic relationship reen assessment of depressive symptoms and previous matrix, G , was deemed desirable as the fitting of the treatment. These participants ranged from 40 to 79 years single matrix G alone resulted in significant population of age with a mean age of 57.8 years and a standard stratification (intercept = 1.029 ± 0.003, λGC = 1.026) deviation of 8.0 years. On the basis of their responses to following examination with LD score regression . The items from the Patient Health Questionnaire, diagnostic fitting of both genomic relationship matrices simulta- status was defined as either ‘probable single lifetime epi- neously produced no evidence of bias due to population sode of major depression’ or ‘probable recurrent major stratification (intercept = 1.002 ± 0.003, λGC = 1.005). depression (moderate and severe)’ and with control status defined as ‘no mood disorder’ using the definitions pro- Replication cohort vided by Smith et al. . MDD Cases were defined by A mixed linear model was used to assess the haplotypes reporting that they had ever been depressed/down for a in UK Biobank, which were identified in the discovery −6 27 whole week (UK Biobank field number 4598); plus this cohort with P < 10 using GCTA v1.25.0: was for at least a two week period (UK Biobank field y ¼ Xβ þ Z u þ ε number 4609); plus this was for at least one episode (UK Biobank field number 4620); plus ever seen a GP (UK Biobank field number 2090) or psychiatrist (UK Biobank where y was the vector of binary observations for MDD. field number 2100) for nerves, anxiety, tension or β was the matrix of fixed effects, including haplotype, sex, depression. Alternatively, MDD cases were also defined by age, age , genotyping batch and recruitment centre. u was reporting that they had ever been uninterested in things fitted as a random effect taking into account the SNP- or unable to enjoy the things you used to for at least a based genomic relationships (MVN (0,Gσ ).X and Z whole week (UK Biobank field number 4631); plus this were the corresponding incidence matrices and ε was the was for at least a two week period (UK Biobank field vector of residual effects and was assumed to be normally number 5375); plus this was for at least one episode (UK distributed, MVN (0, Iσ ). Replication success was judged Biobank field number 5386); plus ever seen a GP (UK on the statistical significance of each haplotype using an Biobank field number 2090) or psychiatrist (UK Biobank inverse variance-weighted meta-analysis across both field number 2100) for nerves, anxiety, tension or cohorts conducted using Metal . Translational Psychiatry Howard et al. Translational Psychiatry (2017) 7:1263 Page 4 of 9 Fig. 1 Manhattan plot representing the –log P-values for an association between each assessed haplotype in the Generation Scotland: Scottish Family Health Study cohort and Major Depressive Disorder Fine mapping haplotypes exceeded genome-wide significance (P < 5 × −8 The method described above examines the effect of 10 ) for an association with MDD, one located on each haplotype against all other haplotypes in that win- chromosome 6 and the other located on chromosome 10. −6 dow. Therefore, a haplotype could be assessed against There were 12 haplotypes with P < 10 in the discovery similar haplotypes containing the same causal variant, cohort with replication sought for these haplotypes using limiting any observed phenotypic association. To inves- UK Biobank. Summary statistics from both cohorts and tigate whether there were causal variants located within the meta-analysis for these 12 haplotypes are provided in directly overlapping haplotypes of the same window size, Table 1. The protein coding genes which overlap these 12 fine mapping of haplotype boundaries was used. Where haplotypes along with the observed haplotype frequencies there were directly overlapping haplotypes, each with P < within the two cohorts are provided in Table 2. The SNPs −3 10 and with an effect in the same direction, i.e., both and alleles that constitute these 12 haplotypes are pro- causal or both preventative, then any shared consecutive vided in Supplementary Table S1. regions formed a new haplotype that was assessed using The two haplotypes on chromosome 6 (LD r = 0.74) −6 the mixed-model described previously. This new haplo- with P < 10 in the discovery cohort both achieved type was assessed using all individuals and was required to nominal significance (P < 0.05) in the replication cohort be at least five SNPs in length. A total of 47 new haplo- (although these would not survive multiple testing cor- types were assessed from within 26 pairs of directly rection for the 12 SNPs tested in the replication data set), overlapping haplotypes. with one reaching genome-wide significance (P < 5 × −8 10 ) in the meta-analysis. A regional association plot of Results the region surrounding these haplotypes within GS:SFHS An association analysis for MDD was conducted using is provided in Fig. 2. Fine mapping was used to form the 2,618,094 haplotypes and 47 fine mapped haplotypes most significant haplotype within the discovery cohort. within the discovery cohort, GS:SFHS. A genome-wide Two directly overlapping 0.5 cM haplotypes consisting of Manhattan plot of –log P-values for these haplotypes is 28 SNPs were identified between 108,335,345 and provided in Fig. 1 with a q–q plot provided in Supple- 108,454,437 bp (rs7749081–rs212829). These two haplo- −5 −5 mentary Fig. S1. Within the discovery cohort, two types had P-values of 3.24 × 10 and 5.57 × 10 , Translational Psychiatry Howard et al. Translational Psychiatry (2017) 7:1263 Page 5 of 9 respectively, and differed at a single SNP (rs7749081). Exclusion of this single SNP defined a new 27 SNP hap- lotype that had a genome-wide significant association −9 with MDD (P = 7.06 × 10 ). Calculating the effect size at the population level , the estimates of the contribution of the two haplotypes to the total genetic variance was −4 −4 2.09 × 10 and 2.38 × 10 , respectively, within GS: SFHS. None of the individual SNPs located within either haplotype were associated with MDD in either cohort (P ≥ 0.05). −9 A genome-wide significant haplotype (P = 8.50 × 10 ) was identified on chromosome 10 within GS:SFHS using a 0.5 cM window. A regional association plot of the region surrounding this haplotype is provided in Fig. 3. This haplotype had an odds ratio (OR) of 2.33 (95% confidence interval (CI): 1.83 – 2.91) in the discovery cohort and an OR of 1.15 (95% CI: 0.80–1.59) in the replication cohort. These were the highest ORs observed in the respective cohorts. The estimate of the contribution of this haplo- −4 type to the total genetic variance was 2.29 × 10 in the discovery cohort. Association analysis of the 92 SNPs on this haplotype revealed that one SNP in GS:SFHS (rs17133585) and two SNPs in UK Biobank (rs12413638 and rs10904290) were nominally significant (P < 0.05), although none had P-values < 0.001. All 12 of the haplotypes with a P-value for association −6 <10 in the GS:SFHS discovery cohort were risk factors for MDD (OR > 1). Within the replication cohort, 7 out of these 12 haplotypes had OR > 1, however, only of two of these had the lower bound of the 95% confidence interval > 1. None of the 95% confidence intervals for the repli- cation ORs overlapped the 95% confidence intervals of the discovery GS:SFHS cohort. Discussion Twelve haplotypes were identified in the discovery −6 cohort with P < 10 of which two were significant at −8 the genome-wide level (P < 5 × 10 ) in the discovery cohort and one which was genome-wide significant −8 33 (P < 5 × 10 ) in the meta-analysis. A power analysis was conducted using the genotype relative risks observed in the discovery cohort, the sample sizes and haplo- type frequencies in the replication cohort and the pre- valence of MDD reported for a structured clinical diag- nosis of MDD in other high income counties (14.6%) . There was sufficient power (>0.99) to detect the twelve −6 haplotypes with P < 10 identified in the discovery cohort within the replication cohort at a significance threshold of 0.05. There are several reasons why the effect sizes observed in the replication cohort were lower than those observed in the discovery cohort. The causal loci may have been in lower LD with the assessed haplotypes in the replication cohort than in the discovery cohort lessening the Translational Psychiatry Table 1 The genetic association between major depressive disorder and 12 haplotypes in the generation Scotland: Scottish Family Health Study (GS:SFHS) −6 discovery cohort (where P <10 ), the replication cohort (UK Biobank) and a meta-analysis Haplotype GS:SFHS UK biobank Meta-analysis Chr. Position (bp) Window size (cM) Odds ratio (95% CI) P-value Odds ratio (95% CI) P-value Odds ratio (95% CI) P-value a −9 −2 −7 6 108,338,267 − 108,454,437 0.34 1.83 (1.53–2.16) 7.06 × 10 1.11 (1.01–1.22) 3.62 × 10 1.26 (1.16–1.37) 3.14 × 10 { −8 −3 −8 6 108,407,662–108,454,437 0.25 1.68 (1.42–1.96) 8.17 × 10 1.14 (1.04–1.24) 4.47 × 10 1.25 (1.16–1.35) 4.38 × 10 −7 −1 −3 7 139,682,412–139,708,901 0.25 2.17 (1.67–2.73) 4.37 × 10 0.87 (0.68–1.08) 2.20 × 10 1.28 (1.08–1.49) 4.67 × 10 −7 −1 −5 8 79,700,362–80,387,861 0.5 1.98 (1.56–2.46) 9.02 × 10 1.06 (0.86–1.28) 5.93 × 10 1.36 (1.18–1.56) 6.29 × 10 −8 −1 −5 8 79,759,499–80,156,474 0.25 1.77 (1.47–2.10) 7.90 × 10 1.05 (0.91–1.21) 5.06 × 10 1.28 (1.15–1.42) 1.14 × 10 −9 −1 −8 10 4,588,261–4,822,210 0.5 2.33 (1.83–2.91) 8.50 × 10 1.15 (0.80–1.59) 4.39 × 10 1.67 (1.40–1.98) 7.92 × 10 a −7 −1 −4 11 2,260,854–2,437,425 0.41 1.64 (1.38–1.91) 2.86 × 10 1.00 (0.87–1.34) 9.91 × 10 1.26 (1.10–1.34) 1.32 × 10 −7 −1 −4 12 48,159,721–48,263,828 0.25 2.00 (1.58–2.47) 4.78 × 10 0.97 (0.79–1.17) 7.36 × 10 1.29 (1.12–1.48) 6.51 × 10 −7 −1 −5 12 116,904,503–117,062,860 0.25 2.13 (1.64–2.69) 9.90 × 10 1.04 (0.79–1.34) 7.79 × 10 1.45 (1.22–1.71) 5.37 × 10 −8 −1 −6 15 49,206,902–49,260,601 0.25 2.03 (1.62–2.48) 9.21 × 10 1.09 (0.88–1.32) 4.04 × 10 1.41 (1.22–1.61) 4.39 × 10 −7 −1 −3 15 93,806,447–93,851,224 0.5 1.58 (1.34–1.83) 4.47 × 10 0.93 (0.81–1.05) 2.38 × 10 1.16 (1.05–1.27) 2.50 × 10 −7 −1 −3 15 93,821,340–93,845,622 0.25 1.52 (1.31–1.75) 8.67 × 10 0.91 (0.81–1.03) 1.37 × 10 1.13 (1.03–1.23) 6.97 × 10 −8 Bold values indicate genome-wide statistical significance (P <5 × 10 ) was achieved in the GS:SFHS cohort or the meta-analysis, or that nominal statistical significance (P < 0.05) was achieved in the UK Biobank. Base pair a 2 (bp) positions are based on build GRCh37. indicates haplotype boundaries defined by the fine mapping approach. { indicates linkage disequilibrium (r ) > 0.5 between haplotypes in the GS:SFHS cohort Howard et al. Translational Psychiatry (2017) 7:1263 Page 6 of 9 observed effect. The phenotypes across the two cohorts phenotype. A complementary approach to replication is were potentially heterogeneous (certainly with regards to to identify the gene coding regions within haplotypes that the prevalence in each population) so the assessed hap- potentially provide a biologically informative explanation lotypes may have had differing effects on each cohort’s for an association with MDD. Those haplotypes with −6 Table 2 Protein coding genes located overlapping with the 12 haplotypes with P <10 in the generation Scotland: Scottish family health study (GS:SFHS) discovery cohort and the frequencies of those haplotypes in GS:SFHS and UK Biobank Haplotype frequency Chr. Position (bp) Protein coding genes GS:SFHS UK Biobank 6 108,338,267–108,454,437 OSTM1 0.0152 0.0197 6 108,407,662–108,454,437 OSTM1 0.0193 0.0241 7 139,682,412–139,708,901 TBXAS1 0.0066 0.0069 8 79,700,362–80,387,861 IL7 0.0076 0.0081 8 79,759,499–80,156,474 IL7 0.0147 0.0157 10 4,588,261–4,822,210 0.0064 0.0027 11 2,260,854–2,437,425 ASCL2, CLorf21, TSPAN32, CD81, TSSC4, TRPM5 0.0196 0.0187 12 48,159,721–48,263,828 SLC48A1, RAPGEF3, HDAC7, VDR 0.0078 0.0090 12 116,904,503–117,062,860 MAP1LC3B2 0.0057 0.0045 15 49,206,902–49,260,601 SHC4 0.0082 0.0080 15 93,806,447–93,851,224 0.0224 0.0206 15 93,821,340–93,845,622 0.0265 0.0243 Base pair (bp) positions are based on build GRCh37 with protein coding regions obtained from Ensembl, GRCh37.p13. Haplotype frequencies were calculated using unrelated individuals and excluding UK Biobank participants recruited in Glasgow or Edinburgh. { indicates a linkage disequilibrium (r ) > 0.5 between haplotypes in the GS:SFHS cohort Fig. 2 Regional association plot representing the –log P-values for an association between haplotypes in the Generation Scotland: Scottish Family Health Study cohort and Major Depressive Disorder within the 107.4–107.6 Mb region on chromosome 6. The start and end position (using build GRCh37) of haplotypes represent the outermost SNP positions within the windows examined. The warmth of colour represents the r with the genome-wide significant haplotype located between 108,338,267 and 108,454,437 bp Translational Psychiatry Howard et al. Translational Psychiatry (2017) 7:1263 Page 7 of 9 Fig. 3 Regional association plot representing the –log P-values for an association between haplotypes in the Generation Scotland: Scottish Family Health Study cohort and Major Depressive Disorder within the 3.6–5.8 Mb region on chromosome 10. The start and end position (using build GRCh37) of haplotypes represent the outermost SNP positions within the windows examined. The warmth of colour represents the r with the genome-wide significant haplotype located between 4,588,261 and 4,822,210 bp −7 P < 10 in the discovery cohort and the gene coding (LINC00704) and long intergenic non-protein coding regions that they overlap are discussed below. RNA 705 (LINC00705). The function of these non-protein The two haplotypes on chromosome 6 overlapped with coding genes is unreported. However, a study of cardiac the Osteopetrosis Associated Transmembrane Protein 1 neonatal lupus, which is a rare autoimmune disease (OSTM1) coding gene. OSTM1 is associated with neuro- demonstrated an association for a SNP (rs1391511) which 35,36 37 degeneration and melanocyte function , and alpha- is 15kb from LINC00705. 54,55 melanocyte-stimulating hormone has been shown to have Two Dutch studies have identified a variant 38–40 an effect on depression-like symptoms . This haplo- (rs8023445) on chromosome 15 located within the SRC type lies within the 6q21 region that has been associated (Src homology 2 domain containing) family, member 4 41–45 with bipolar disorder , a disease that shares symptoms (SHC4) gene coding region that has a moderate degree of −5 −6 with MDD and has a correlated phenotypic liability of association with MDD (P = 1.64 × 10 and P = 9 × 10 , 0.64 . This may indicate either a pleiotropic effect or respectively). A variant (rs10519201) within the SHC4 clinical heterogeneity, whereby patients may be mis- coding region was also found to have an association (P = −6 diagnosed, i.e., patients may have MDD and transition to 6.16 × 10 ) with Obsessive-Compulsive Personality bipolar disorder in the future or are sub-threshold for Disorder in a UK-based study . SHC4 is expressed in bipolar disorder and instead given a diagnosis of MDD. neurons and regulates BDNF-induced MAPK activa- The haplotype identified on chromosome 8 overlapped tion , which has been shown to be a key factor in MDD with the Interleukin 7 (IL7) protein coding region. IL7 is pathophysiology . The SHC4 region overlaps with the involved in maintaining T-cell homoeostasis and pro- haplotype on chromosome 15 identified in the discovery liferation , which in turn contributes to the immune cohort (located at 49,206,902–49,260,601 bp) and, there- response to pathogens. It has been proposed that fore, further research to examine the association between impaired T-cell function may be a factor in the develop- the SHC4 region and psychiatric disorders could be ment of MDD , with depressed subjects found to have warranted. 50 51 elevated or depressed levels of IL7 serum. There is Haplotype-based analyses are capable of tagging var- conjecture as to whether MDD causes inflammation or iants due to the LD between the untyped variants and the represents a reaction to an increased inflammatory multiple flanking genotyped variants which make up the 52,53 response , but it is most likely to be a bidirectional inherited haplotype. This approach should provide greater relationship . power when there is comparatively higher IBD sharing, The haplotype on chromosome 10 overlapped with two such as in GS:SFHS which was a family-based cohort, RNA genes: long intergenic non-protein coding RNA 704 where there is a greater likelihood that a single haplotype Translational Psychiatry Howard et al. Translational Psychiatry (2017) 7:1263 Page 8 of 9 is tagging the same causal variant across that population. McI. and T.-K.C. acknowledge support from the Dr. Mortimer and Theresa Sackler Foundation. The UK Biobank was selected as replication cohort as it is a large population-based sample that was expected to be Author details genetically similar to the GS:SFHS discovery cohort. This Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK. Medical Research Council Human Genetics Unit, Institute of was confirmed by the similarity of the observed haplotype Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK. frequencies (Table 2) between the two cohorts. The pre- Centre for Genomic and Experimental Medicine, Institute of Genetics and valence of MDD observed in the discovery cohort (13.7%) Molecular Medicine, University of Edinburgh, Edinburgh, UK. Generation Scotland, Institute of Genetics and Molecular Medicine, University of was comparable to that reported (14.6%) within similar 34 Edinburgh, Edinburgh, UK. Division of Population Health Sciences, University populations . However, in the replication cohort, the of Dundee, Dundee, UK. Aberdeen Biomedical Imaging Centre, University of trait prevalence was notably higher (34.0%), most likely Aberdeen, Aberdeen, UK. Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK. Department of due to the differing methods of phenotypic ascertainment. Psychology, The University of Edinburgh, Edinburgh, UK Additional work could seek to replicate the findings in further cohorts, as well as full meta-analysis of all hap- Competing interests lotypes within those cohorts. An additive model was used D.J.P. and I.J.P. are participants in UK Biobank. The authors declare that they have no competing financial interests. to analyse the haplotypes and alternative approaches could implement a dominant model or an analysis of Publisher's note: Springer Nature remains neutral with regard to jurisdictional diplotypes (haplotype pairs) for association with MDD. claims in published maps and institutional affiliations. Supplementary information Conclusions The online version of this article (https://doi.org/10.1038/s41398-017-0010-9) This study identified two haplotypes within the dis- contains supplementary material. covery cohort that exceeded genome-wide significance for association with a clinically diagnosed MDD phenotype. Received: 20 March 2017 Revised: 16 August 2017 Accepted: 20 August One of these haplotypes was nominally significant in the replication cohort and was in LD with a haplotype that was genome-wide significant in the meta-analysis. The genome-wide significant haplotype on chromosome 6 was References located on 6q21, which has been shown previously to be 1. Huffman,J.C., Celano,C.M., Beach, S. R.,Motiwala, S. R. & Januzzi,J. L. related to psychiatric disorders. There were a number of Depression and cardiac disease: epidemiology, mechanisms, and diagnosis. Cardiovasc. Psychiatr. Neurol. 2013, 14 (2013). haplotypes approaching genome-wide significance located 2. Kang, H. -J. et al. Comorbidity of depression with physical disorders: research within genic regions associated with diseases that are and clinical implications. Chonnam Med. J. 51,8–18 (2015). comorbid with MDD and, therefore, these regions war- 3. Raison, C. L., Capuron, L. & Miller, A. H. Cytokines sing the blues: inflammation and the pathogenesis of depression. Trends Immunol. 27,24–31 (2006). rant further investigation. The total genetic variance 4. Major Depressive Disorder Working Group of the Psychiatric Gwas Con- explained by the haplotypes identified was small, however, sortium. A mega-analysis of genome-wide association studies for major these haplotypes potentially represent biologically infor- depressive disorder. Mol. Psychiatr. 18,497–511 (2013). 5. Converge Consortium. Sparse whole-genome sequencing identifies two loci mative aetiological subtypes for MDD and merit further for major depressive disorder. Nature 523,588–591 (2015). analysis. 6. Levinson, D. F. et al. Genetic studies of major depressive disorder: why are there no genome-wide association study findings and what can we do about Acknowledgements it? Biol. Psychiatr. 76,510–512 (2014). Generation Scotland received core funding from the Chief Scientist Office of 7. Hyde, C.L.etal. Identification of 15 genetic loci associated with risk of major the Scottish Government Health Directorate CZD/16/6 and the Scottish depression in individuals of European descent. Nat. Genet. 48,1031–1036 Funding Council HR03006. Genotyping of GS:SFHS was carried out by the (2016). Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, 8. Lubke, G. H. et al. Estimating the genetic variance of major depressive Edinburgh, Scotland and was funded by the UK’s Medical Research Council disorder due to all single nucleotide polymorphisms. Biol. Psychiatr. 72, and the Wellcome Trust (Wellcome Trust Strategic Award “Stratifying Resilience 707–709 (2012). and Depression Longitudinally” (STRADL) (Reference 104036/Z/14/Z). We are 9. Sullivan, P. F., Neale, M. C. & Kendler, K. S. Genetic epidemiology of grateful to all the families who took part, the general practitioners and the major depression: review and meta-analysis. Am.J.Psychiatr. 157,1552–1562 Scottish School of Primary Care for their help in recruiting them, and the whole (2000). 10. Zhang, Z. et al. A haplotype in the 5’-upstream region of the NDUFV2 gene is Generation Scotland team, which includes interviewers, computer and associated with major depressive disorder in Han Chinese. J. Affect. Disord. 190, laboratory technicians, clerical workers, research scientists, volunteers, 329–332 (2016). managers, receptionists, healthcare assistants and nurses. Ethics approval for 11. Kim, J. -J. et al. Is there protective haplotype of dysbindin gene (DTNBP1) 3 the study was given by the NHS Tayside committee on research ethics polymorphisms for major depressive disorder. Prog. Neuro-Psychopharmacol. (reference 05/S1401/8). This research has been conducted using the UK Biol. Psychiatr. 32,375–379 (2008). Biobank resource–application number 4844; we are grateful to UK Biobank 12. Klok, M. D. et al.A common and functional mineralocorticoid receptor hap- participants. The UK Biobank study was conducted under generic approval lotype enhances optimism and protects against depression in females. Transl. from the NHS National Research Ethics Service (approval letter dated 17th June Psychiatr. 1, e62 (2011). 2011, Ref 11/NW/0382). Y.Z. acknowledges support from China Scholarship 13. Smith,B.H. et al. Cohort profile: Generation Scotland: Scottish Family Health Council. I.J.D. is supported by the Centre for Cognitive Ageing and Cognitive Study (GS:SFHS). The study, its participants and their potential for genetic Epidemiology, which is funded by the Medical Research Council and the research on health and illness. Int. J. Epidemiol. 42,689–700 (2013). Biotechnology and Biological Sciences Research Council (MR/K026992/1). A.M. Translational Psychiatry Howard et al. Translational Psychiatry (2017) 7:1263 Page 9 of 9 14. Smith, D. J. et al. Prevalence and characteristics of probable major depression 38. Maes, M. et al. Abnormal pituitary function during melancholia: Reduced α- and bipolar disorder within UK Biobank: cross-sectional study of 172,751 melanocyte-stimulating hormone secretion and increased intact ACTH non- participants. PLoS ONE 8, e75362 (2013). suppression. J. Affect. Disord. 22,149–157 (1991). 15. Amador, C. et al. Recent genomic heritage in Scotland. BMC Genomics 16, 39. Goyal, S. N., Kokare,D. M., Chopde,C. T.&Subhedar,N.K.Alpha-melanocyte 1–17 (2015). stimulating hormone antagonizes antidepressant-like effect of neuropeptide Y 16. Allen, N. E., Sudlow, C., Peakman, T. & Collins, R. UK biobank data: come and in Porsolt’stestinrats. Pharmacol. Biochem. Behav. 85,369–377 (2006). get it. Sci. Transl. Med. 6, 224ed224 (2014). 40. Kokare, D. M., Singru, P. S., Dandekar, M. P., Chopde, C. T. & Subhedar, N. K. 17. Marchini J. UK Biobank phasing and imputation documentation. Version 1.2: Involvement of alpha-melanocyte stimulating hormone (α-MSH) in differential http://biobank.ctsu.ox.ac.uk/crystal/docs/impute_ukb_v1.pdf (2015). ethanol exposure and withdrawal related depression in rat: 18. Delaneau, O., Zagury, J. -F. & Marchini, J. Improved whole-chromosome Neuroanatomical–behavioral correlates. Brain Res. 1216,53–67 (2008). phasing for disease and population genetic studies. Nat. Methods 10,5–6 41. Knight, J.,Rochberg, N. S.,Saccone,S.F., Nurnberger,J.I.&Rice,J.P.An (2013). investigation of candidate regions for association with bipolar disorder. Am. J. 19. O’Connell, J. et al. A general approach for haplotype phasing across the full Med. Genet. Part B: Neuropsychiatr. Genet. 153B,1292–1297 (2010). spectrum of relatedness. PLoS Genet. 10, e1004234 (2014). 42. Dick, D. M. et al. Genomewide linkage analyses of bipolar disorder: a new 20. The International HapMap Consortium. A second generation human haplo- sample of 250 pedigrees from the national institute of mental health genetics type map of over 3.1 million SNPs. Nature 449,851–861 (2007). initiative. Am.J.Hum.Genet. 73,107–114 (2003). 21. Browning, B. L. & Browning, S. R. Improving the accuracy and efficiency of 43. Park, N. et al. Linkage analysis of psychosis in bipolar pedigrees suggests novel identity-by-descent detection in population data. Genetics 194,459–471 putative loci for bipolar disorder and shared susceptibility with schizophrenia. (2013). Mol. Psychiatr. 9, 1091–1099 (2004). 22. Purcell, S. et al. PLINK: a tool set for whole-genome association and 44. Pato, C. N. et al. Genome-wide scan in Portuguese Island families implicates population-based linkage analyses. Am.J. Hum.Genet. 81, 559–575 (2007). multiple loci in bipolar disorder: Fine mapping adds support on chromo- 23. Sham, P. C. & Purcell, S. M. Statistical power and significance testing in large- somes 6 and 11. Am. J. Med. Genet. Part B: Neuropsychiatr. Genet. 127B,30–34 scale genetic studies. Nat. Rev. Genet. 15, 335–346 (2014). (2004). 24. First,M.B., Spitzer, R. L.,Miriam, G.,Williams,J.B.W. Structured Clinical Interview 45. Fabbri, C. & Serretti, A. Genetics of long-term treatment outcome in bipolar for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. (SCID-I/P) disorder. Prog. Neuro-Psychopharmacol. Biol. Psychiatr. 65,17–24 (2016). (2002). 46. McGuffin, P. et al. The heritability of bipolar affective disorderand the genetic 25. Fernandez-Pujals, A. M. et al. Epidemiology and heritability of major depressive relationship to unipolar depression. Arch. Gen. Psychiatr. 60,497–502 (2003). disorder, stratified by age of onset, sex, and illness course in generation 47. Surh, C. D. & Sprent, J. Homeostasis of naive and memory T Cells. Immunity 29, scotland: scottish family health study (GS:SFHS). PLoS ONE 10, e0142197 (2015). 848–862 (2008). 26. Information Services Division. SMR Data Manual: http://www.ndc.scot.nhs.uk/ 48. Kittipatarin, C. & Khaled, A. R. Interlinking interleukin-7. Cytokine 39,75–83 Data-Dictionary/SMR-Datasets (2016). (2007). 27. Yang, J.,Zaitlen,N.A., Goddard, M.E., Visscher, P.M. & Price, A. L. Advantages 49. Miller, A. H. Depression and immunity: A role for T cells? Brain Behav. Immun. and pitfalls in the application of mixed-model association methods. Nat. Genet. 24,1–8 (2010). 46,100–106 (2014). 50. Simon, N. M. et al. A detailed examination of cytokine abnormalities in major 28. Yang, J. et al. Common SNPs explain a large proportion of the heritability for depressive disorder. Eur. Neuropsychopharmacol. 18,230–233 (2008). human height. Nat. Genet. 42,565–569 (2010). 51. Lehto, S. M. et al. Serum IL-7 and G-CSF in major depressive disorder. Prog. 29. Zaitlen, N. et al. Using extended genealogy to estimate components of her- Neuro-Psychopharmacol. Biol. Psychiatr. 34,846–851 (2010). itability for 23 quantitative and dichotomous traits. PLoS Genet. 9, e1003520 52. Stewart,J.C., Rand, K.L., Muldoon, M. F. & Kamarck,T. W. A prospective (2013). evaluation of the directionality of the depression-inflammation relationship. 30. Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from Brain Behav. Immun. 23,936–944 (2009). polygenicity in genome-wide association studies. Nat. Genet. 47,291–295 53. Irwin, M. R. & Miller, A. H. Depressive disorders and immunity: 20 years of (2015). progress and discovery. Brain Behav. Immun. 21,374–383 (2007). 31. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of 54. Aragam, N., Wang, K. -S. & Pan, Y. Genome-wide association analysis of gender genomewide association scans. Bioinformatics 26, 2190–2191 (2010). differences in major depressive disorder in the Netherlands NESDA and NTR 32. Park, J. -H. et al. Estimation of effect size distribution from genome-wide population-based samples. J. Affect. Disord. 133,516–521 (2011). association studies and implications for future discoveries. Nat. Genet. 42, 55. Sullivan, P. F. et al. Genome-wide association for major depressive disorder: a 570–575 (2010). possible role for the presynaptic protein piccolo. Mol. Psychiatr. 14,359–375 33. Purcell, S., Cherny, S. S. & Sham, P. C. Genetic power calculator: design of (2008). linkage and association genetic mapping studies of complex traits. Bioinfor- 56. Boraska, V. et al. Genome-wide association analysis of eating disorder-related matics 19,149–150 (2003). symptoms,behaviors,and personality traits. Am.J.Med.Genet. 159B, 803–811 34. Bromet, E. et al. Cross-national epidemiology of DSM-IV major depressive (2012). episode. BMC Med. 9,1–16 (2011). 57. Hawley, S. P., Wills, M. K. B., Rabalski, A. J., Bendall, A. J. & Jones, N. Expression 35. Kasper, D. et al. Loss of the chloride channel ClC‐7 leads to lysosomal storage patterns of ShcD and Shc family adaptor proteins during mouse embryonic disease and neurodegeneration. EMBO J. 24,1079–1091 (2005). development. Dev. Dynam. 240,221–231 (2011). 36. Pandruvada, S. N. M. et al. Role of ostm1 cytosolic complex with kinesin 5B in 58. You, Y. et al. ShcD interacts with TrkB via its PTB and SH2 domains and intracellular dispersion and trafficking. Mol. Cell. Biol. 36,507–521 (2016). regulates BDNF-induced MAPK activation. BMB Rep. 43, 485–490 (2010). 37. Hoek, K. S. et al. Novel MITF targets identified using a two-step DNA microarray 59. Duric, V. et al. A negative regulator of MAP kinase causes depressive behavior. strategy. Pigment Cell Melanoma Res. 21,665–676 (2008). Nat. Med. 16, 1328–1332 (2010). Translational Psychiatry
Translational Psychiatry – Springer Journals
Published: Nov 30, 2017
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