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1.  Genome-wide Association Study of Obsessive-Compulsive Disorder 
Stewart, S Evelyn | Yu, Dongmei | Scharf, Jeremiah M | Neale, Benjamin M | Fagerness, Jesen A | Mathews, Carol A | Arnold, Paul D | Evans, Patrick D | Gamazon, Eric R | Osiecki, Lisa | McGrath, Lauren | Haddad, Stephen | Crane, Jacquelyn | Hezel, Dianne | Illman, Cornelia | Mayerfeld, Catherine | Konkashbaev, Anuar | Liu, Chunyu | Pluzhnikov, Anna | Tikhomirov, Anna | Edlund, Christopher K | Rauch, Scott L | Moessner, Rainald | Falkai, Peter | Maier, Wolfgang | Ruhrmann, Stephan | Grabe, Hans-Jörgen | Lennertz, Leonard | Wagner, Michael | Bellodi, Laura | Cavallini, Maria Cristina | Richter, Margaret A | Cook, Edwin H | Kennedy, James L | Rosenberg, David | Stein, Dan J | Hemmings, Sian MJ | Lochner, Christine | Azzam, Amin | Chavira, Denise A | Fournier, Eduardo | Garrido, Helena | Sheppard, Brooke | Umaña, Paul | Murphy, Dennis L | Wendland, Jens R | Veenstra-VanderWeele, Jeremy | Denys, Damiaan | Blom, Rianne | Deforce, Dieter | Van Nieuwerburgh, Filip | Westenberg, Herman GM | Walitza, Susanne | Egberts, Karin | Renner, Tobias | Miguel, Euripedes Constantino | Cappi, Carolina | Hounie, Ana G | Conceição do Rosário, Maria | Sampaio, Aline S | Vallada, Homero | Nicolini, Humberto | Lanzagorta, Nuria | Camarena, Beatriz | Delorme, Richard | Leboyer, Marion | Pato, Carlos N | Pato, Michele T | Voyiaziakis, Emanuel | Heutink, Peter | Cath, Danielle C | Posthuma, Danielle | Smit, Jan H | Samuels, Jack | Bienvenu, O Joseph | Cullen, Bernadette | Fyer, Abby J | Grados, Marco A | Greenberg, Benjamin D | McCracken, James T | Riddle, Mark A | Wang, Ying | Coric, Vladimir | Leckman, James F | Bloch, Michael | Pittenger, Christopher | Eapen, Valsamma | Black, Donald W | Ophoff, Roel A | Strengman, Eric | Cusi, Daniele | Turiel, Maurizio | Frau, Francesca | Macciardi, Fabio | Gibbs, J Raphael | Cookson, Mark R | Singleton, Andrew | Hardy, John | Crenshaw, Andrew T | Parkin, Melissa A | Mirel, Daniel B | Conti, David V | Purcell, Shaun | Nestadt, Gerald | Hanna, Gregory L | Jenike, Michael A | Knowles, James A | Cox, Nancy | Pauls, David L
Molecular psychiatry  2012;18(7):788-798.
Obsessive-compulsive disorder (OCD) is a common, debilitating neuropsychiatric illness with complex genetic etiology. The International OCD Foundation Genetics Collaborative (IOCDF-GC) is a multi-national collaboration established to discover the genetic variation predisposing to OCD. A set of individuals affected with DSM-IV OCD, a subset of their parents, and unselected controls, were genotyped with several different Illumina SNP microarrays. After extensive data cleaning, 1,465 cases, 5,557 ancestry-matched controls and 400 complete trios remained, with a common set of 469,410 autosomal and 9,657 X-chromosome SNPs. Ancestry-stratified case-control association analyses were conducted for three genetically-defined subpopulations and combined in two meta-analyses, with and without the trio-based analysis. In the case-control analysis, the lowest two p-values were located within DLGAP1 (p=2.49×10-6 and p=3.44×10-6), a member of the neuronal postsynaptic density complex. In the trio analysis, rs6131295, near BTBD3, exceeded the genome-wide significance threshold with a p-value=3.84 × 10-8. However, when trios were meta-analyzed with the combined case-control samples, the p-value for this variant was 3.62×10-5, losing genome-wide significance. Although no SNPs were identified to be associated with OCD at a genome-wide significant level in the combined trio-case-control sample, a significant enrichment of methylation-QTLs (p<0.001) and frontal lobe eQTLs (p=0.001) was observed within the top-ranked SNPs (p<0.01) from the trio-case-control analysis, suggesting these top signals may have a broad role in gene expression in the brain, and possibly in the etiology of OCD.
doi:10.1038/mp.2012.85
PMCID: PMC4218751  PMID: 22889921
Obsessive-compulsive disorder; GWAS; Genetic; Genomic; Neurodevelopmental disorder; DLGAP
2.  Analysis of miR-137 Expression and rs1625579 in Dorsolateral Prefrontal Cortex 
Journal of psychiatric research  2013;47(9):1215-1221.
MicroRNAs (miRNAs) are small non-coding RNAs that act as potent regulators of gene expression. A recent GWAS reported the rs1625579 SNP, located downstream of miR-137, as the strongest new association with schizophrenia (Ripke et al., 2011). Prior to this GWAS finding, a schizophrenia imaging-genetic study found miR-137 target genes significantly enriched for association with activation in the dorsolateral prefrontal cortex (DLPFC) (Potkin et al., 2010).
We investigated the expression levels of miR-137 and three candidate target genes (ZNF804A, CACNA1C, TCF4) in the DLPFC of postmortem brain tissue from 2 independent cohorts: 1) 26 subjects (10 control (CTR), 7 schizophrenia (SZ), 9 bipolar disorder (BD)) collected at the UCI brain bank; and 2) 99 subjects (33 CTR, 35 SZ, 31 BD) obtained from the Stanley Medical Research Institute (SMRI). MiR-137 expression in the DLPFC did not differ between diagnoses. We also explored the relationship between rs1625579 genotypes and miR-137 expression. Significantly lower miR-137 expression levels were observed in the homozygous TT subjects compared to TG and GG subjects in the control group (30% decrease, p-value=0.03). Moreover, reduced miR-137 levels in TT subjects corresponded to increased levels of the miR-137 target gene TCF4. The miR-137 expression pattern in 9 brain regions was significant for regional effect (ANOVA p-value=1.83E-12), with amygdala and hippocampus having the highest miR-137 expression level. In conclusion, decreased miR-137 expression is associated with the SZ risk allele of rs1625579, and potential regulation of TCF4, another SZ candidate gene. This study offers additional support for involvement of miR-137 and downstream targets as mechanisms of risk for psychiatric disorders.
doi:10.1016/j.jpsychires.2013.05.021
PMCID: PMC3753093  PMID: 23786914
schizophrenia; bipolar disorder; rs1625579; miR-137; TCF4; gene expression
3.  Increased CNV-Region deletions in mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects in the ADNI sample 
Genomics  2013;102(2):112-122.
We investigated the genome-wide distribution of CNVs in the Alzheimer's disease (AD) Neuroimaging Initiative (ADNI) sample (146 with AD, 313 with Mild Cognitive Impairment (MCI), and 181 controls). Comparison of single CNVs between cases (MCI and AD) and controls shows overrepresentation of large heterozygous deletions in cases (p-value < 0.0001). The analysis of CNV-Regions identifies 44 copy number variable loci of heterozygous deletions, with more CNV-Regions among affected than controls (p = 0.005). Seven of the 44 CNV-Regions are nominally significant for association with cognitive impairment. We validated and confirmed our main findings with genome re-sequencing of selected patients and controls. The functional pathway analysis of the genes putatively affected by deletions of CNV-Regions reveals enrichment of genes implicated in axonal guidance, cell–cell adhesion, neuronal morphogenesis and differentiation. Our findings support the role of CNVs in AD, and suggest an association between large deletions and the development of cognitive impairment
doi:10.1016/j.ygeno.2013.04.004
PMCID: PMC4012421  PMID: 23583670
Alzheimer's disease; Copy Number Variable Regions (CNV-Regions); Copy Number Variations (CNVs); Genome-wide scan; Next Generation Sequencing (NGS)
5.  Genetic Analysis of Quantitative Phenotypes in AD and MCI: Imaging, Cognition and Biomarkers 
Brain imaging and behavior  2014;8(2):183-207.
The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer’s disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g. APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g. FRMD6) that were later replicated on different data sets. Several other genes (e.g. APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development.
doi:10.1007/s11682-013-9262-z
PMCID: PMC3976843  PMID: 24092460
Alzheimer’s disease; genetic association study; quantitative traits; neuroimaging; biomarker; cognition
6.  Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis 
Beecham, Ashley H | Patsopoulos, Nikolaos A | Xifara, Dionysia K | Davis, Mary F | Kemppinen, Anu | Cotsapas, Chris | Shahi, Tejas S | Spencer, Chris | Booth, David | Goris, An | Oturai, Annette | Saarela, Janna | Fontaine, Bertrand | Hemmer, Bernhard | Martin, Claes | Zipp, Frauke | D’alfonso, Sandra | Martinelli-Boneschi, Filippo | Taylor, Bruce | Harbo, Hanne F | Kockum, Ingrid | Hillert, Jan | Olsson, Tomas | Ban, Maria | Oksenberg, Jorge R | Hintzen, Rogier | Barcellos, Lisa F | Agliardi, Cristina | Alfredsson, Lars | Alizadeh, Mehdi | Anderson, Carl | Andrews, Robert | Søndergaard, Helle Bach | Baker, Amie | Band, Gavin | Baranzini, Sergio E | Barizzone, Nadia | Barrett, Jeffrey | Bellenguez, Céline | Bergamaschi, Laura | Bernardinelli, Luisa | Berthele, Achim | Biberacher, Viola | Binder, Thomas M C | Blackburn, Hannah | Bomfim, Izaura L | Brambilla, Paola | Broadley, Simon | Brochet, Bruno | Brundin, Lou | Buck, Dorothea | Butzkueven, Helmut | Caillier, Stacy J | Camu, William | Carpentier, Wassila | Cavalla, Paola | Celius, Elisabeth G | Coman, Irène | Comi, Giancarlo | Corrado, Lucia | Cosemans, Leentje | Cournu-Rebeix, Isabelle | Cree, Bruce A C | Cusi, Daniele | Damotte, Vincent | Defer, Gilles | Delgado, Silvia R | Deloukas, Panos | di Sapio, Alessia | Dilthey, Alexander T | Donnelly, Peter | Dubois, Bénédicte | Duddy, Martin | Edkins, Sarah | Elovaara, Irina | Esposito, Federica | Evangelou, Nikos | Fiddes, Barnaby | Field, Judith | Franke, Andre | Freeman, Colin | Frohlich, Irene Y | Galimberti, Daniela | Gieger, Christian | Gourraud, Pierre-Antoine | Graetz, Christiane | Graham, Andrew | Grummel, Verena | Guaschino, Clara | Hadjixenofontos, Athena | Hakonarson, Hakon | Halfpenny, Christopher | Hall, Gillian | Hall, Per | Hamsten, Anders | Harley, James | Harrower, Timothy | Hawkins, Clive | Hellenthal, Garrett | Hillier, Charles | Hobart, Jeremy | Hoshi, Muni | Hunt, Sarah E | Jagodic, Maja | Jelčić, Ilijas | Jochim, Angela | Kendall, Brian | Kermode, Allan | Kilpatrick, Trevor | Koivisto, Keijo | Konidari, Ioanna | Korn, Thomas | Kronsbein, Helena | Langford, Cordelia | Larsson, Malin | Lathrop, Mark | Lebrun-Frenay, Christine | Lechner-Scott, Jeannette | Lee, Michelle H | Leone, Maurizio A | Leppä, Virpi | Liberatore, Giuseppe | Lie, Benedicte A | Lill, Christina M | Lindén, Magdalena | Link, Jenny | Luessi, Felix | Lycke, Jan | Macciardi, Fabio | Männistö, Satu | Manrique, Clara P | Martin, Roland | Martinelli, Vittorio | Mason, Deborah | Mazibrada, Gordon | McCabe, Cristin | Mero, Inger-Lise | Mescheriakova, Julia | Moutsianas, Loukas | Myhr, Kjell-Morten | Nagels, Guy | Nicholas, Richard | Nilsson, Petra | Piehl, Fredrik | Pirinen, Matti | Price, Siân E | Quach, Hong | Reunanen, Mauri | Robberecht, Wim | Robertson, Neil P | Rodegher, Mariaemma | Rog, David | Salvetti, Marco | Schnetz-Boutaud, Nathalie C | Sellebjerg, Finn | Selter, Rebecca C | Schaefer, Catherine | Shaunak, Sandip | Shen, Ling | Shields, Simon | Siffrin, Volker | Slee, Mark | Sorensen, Per Soelberg | Sorosina, Melissa | Sospedra, Mireia | Spurkland, Anne | Strange, Amy | Sundqvist, Emilie | Thijs, Vincent | Thorpe, John | Ticca, Anna | Tienari, Pentti | van Duijn, Cornelia | Visser, Elizabeth M | Vucic, Steve | Westerlind, Helga | Wiley, James S | Wilkins, Alastair | Wilson, James F | Winkelmann, Juliane | Zajicek, John | Zindler, Eva | Haines, Jonathan L | Pericak-Vance, Margaret A | Ivinson, Adrian J | Stewart, Graeme | Hafler, David | Hauser, Stephen L | Compston, Alastair | McVean, Gil | De Jager, Philip | Sawcer, Stephen | McCauley, Jacob L
Nature genetics  2013;45(11):10.1038/ng.2770.
Using the ImmunoChip custom genotyping array, we analysed 14,498 multiple sclerosis subjects and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (p-value < 1.0 × 10-4). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 multiple sclerosis subjects and 26,703 healthy controls. In these 80,094 individuals of European ancestry we identified 48 new susceptibility variants (p-value < 5.0 × 10-8); three found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants in 103 discrete loci outside of the Major Histocompatibility Complex. With high resolution Bayesian fine-mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalogue of multiple sclerosis risk variants and illustrates the value of fine-mapping in the resolution of GWAS signals.
doi:10.1038/ng.2770
PMCID: PMC3832895  PMID: 24076602
7.  The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data 
Thompson, Paul M. | Stein, Jason L. | Medland, Sarah E. | Hibar, Derrek P. | Vasquez, Alejandro Arias | Renteria, Miguel E. | Toro, Roberto | Jahanshad, Neda | Schumann, Gunter | Franke, Barbara | Wright, Margaret J. | Martin, Nicholas G. | Agartz, Ingrid | Alda, Martin | Alhusaini, Saud | Almasy, Laura | Almeida, Jorge | Alpert, Kathryn | Andreasen, Nancy C. | Andreassen, Ole A. | Apostolova, Liana G. | Appel, Katja | Armstrong, Nicola J. | Aribisala, Benjamin | Bastin, Mark E. | Bauer, Michael | Bearden, Carrie E. | Bergmann, Ørjan | Binder, Elisabeth B. | Blangero, John | Bockholt, Henry J. | Bøen, Erlend | Bois, Catherine | Boomsma, Dorret I. | Booth, Tom | Bowman, Ian J. | Bralten, Janita | Brouwer, Rachel M. | Brunner, Han G. | Brohawn, David G. | Buckner, Randy L. | Buitelaar, Jan | Bulayeva, Kazima | Bustillo, Juan R. | Calhoun, Vince D. | Cannon, Dara M. | Cantor, Rita M. | Carless, Melanie A. | Caseras, Xavier | Cavalleri, Gianpiero L. | Chakravarty, M. Mallar | Chang, Kiki D. | Ching, Christopher R. K. | Christoforou, Andrea | Cichon, Sven | Clark, Vincent P. | Conrod, Patricia | Coppola, Giovanni | Crespo-Facorro, Benedicto | Curran, Joanne E. | Czisch, Michael | Deary, Ian J. | de Geus, Eco J. C. | den Braber, Anouk | Delvecchio, Giuseppe | Depondt, Chantal | de Haan, Lieuwe | de Zubicaray, Greig I. | Dima, Danai | Dimitrova, Rali | Djurovic, Srdjan | Dong, Hongwei | Donohoe, Gary | Duggirala, Ravindranath | Dyer, Thomas D. | Ehrlich, Stefan | Ekman, Carl Johan | Elvsåshagen, Torbjørn | Emsell, Louise | Erk, Susanne | Espeseth, Thomas | Fagerness, Jesen | Fears, Scott | Fedko, Iryna | Fernández, Guillén | Fisher, Simon E. | Foroud, Tatiana | Fox, Peter T. | Francks, Clyde | Frangou, Sophia | Frey, Eva Maria | Frodl, Thomas | Frouin, Vincent | Garavan, Hugh | Giddaluru, Sudheer | Glahn, David C. | Godlewska, Beata | Goldstein, Rita Z. | Gollub, Randy L. | Grabe, Hans J. | Grimm, Oliver | Gruber, Oliver | Guadalupe, Tulio | Gur, Raquel E. | Gur, Ruben C. | Göring, Harald H. H. | Hagenaars, Saskia | Hajek, Tomas | Hall, Geoffrey B. | Hall, Jeremy | Hardy, John | Hartman, Catharina A. | Hass, Johanna | Hatton, Sean N. | Haukvik, Unn K. | Hegenscheid, Katrin | Heinz, Andreas | Hickie, Ian B. | Ho, Beng-Choon | Hoehn, David | Hoekstra, Pieter J. | Hollinshead, Marisa | Holmes, Avram J. | Homuth, Georg | Hoogman, Martine | Hong, L. Elliot | Hosten, Norbert | Hottenga, Jouke-Jan | Hulshoff Pol, Hilleke E. | Hwang, Kristy S. | Jack, Clifford R. | Jenkinson, Mark | Johnston, Caroline | Jönsson, Erik G. | Kahn, René S. | Kasperaviciute, Dalia | Kelly, Sinead | Kim, Sungeun | Kochunov, Peter | Koenders, Laura | Krämer, Bernd | Kwok, John B. J. | Lagopoulos, Jim | Laje, Gonzalo | Landen, Mikael | Landman, Bennett A. | Lauriello, John | Lawrie, Stephen M. | Lee, Phil H. | Le Hellard, Stephanie | Lemaître, Herve | Leonardo, Cassandra D. | Li, Chiang-shan | Liberg, Benny | Liewald, David C. | Liu, Xinmin | Lopez, Lorna M. | Loth, Eva | Lourdusamy, Anbarasu | Luciano, Michelle | Macciardi, Fabio | Machielsen, Marise W. J. | MacQueen, Glenda M. | Malt, Ulrik F. | Mandl, René | Manoach, Dara S. | Martinot, Jean-Luc | Matarin, Mar | Mather, Karen A. | Mattheisen, Manuel | Mattingsdal, Morten | Meyer-Lindenberg, Andreas | McDonald, Colm | McIntosh, Andrew M. | McMahon, Francis J. | McMahon, Katie L. | Meisenzahl, Eva | Melle, Ingrid | Milaneschi, Yuri | Mohnke, Sebastian | Montgomery, Grant W. | Morris, Derek W. | Moses, Eric K. | Mueller, Bryon A. | Muñoz Maniega, Susana | Mühleisen, Thomas W. | Müller-Myhsok, Bertram | Mwangi, Benson | Nauck, Matthias | Nho, Kwangsik | Nichols, Thomas E. | Nilsson, Lars-Göran | Nugent, Allison C. | Nyberg, Lars | Olvera, Rene L. | Oosterlaan, Jaap | Ophoff, Roel A. | Pandolfo, Massimo | Papalampropoulou-Tsiridou, Melina | Papmeyer, Martina | Paus, Tomas | Pausova, Zdenka | Pearlson, Godfrey D. | Penninx, Brenda W. | Peterson, Charles P. | Pfennig, Andrea | Phillips, Mary | Pike, G. Bruce | Poline, Jean-Baptiste | Potkin, Steven G. | Pütz, Benno | Ramasamy, Adaikalavan | Rasmussen, Jerod | Rietschel, Marcella | Rijpkema, Mark | Risacher, Shannon L. | Roffman, Joshua L. | Roiz-Santiañez, Roberto | Romanczuk-Seiferth, Nina | Rose, Emma J. | Royle, Natalie A. | Rujescu, Dan | Ryten, Mina | Sachdev, Perminder S. | Salami, Alireza | Satterthwaite, Theodore D. | Savitz, Jonathan | Saykin, Andrew J. | Scanlon, Cathy | Schmaal, Lianne | Schnack, Hugo G. | Schork, Andrew J. | Schulz, S. Charles | Schür, Remmelt | Seidman, Larry | Shen, Li | Shoemaker, Jody M. | Simmons, Andrew | Sisodiya, Sanjay M. | Smith, Colin | Smoller, Jordan W. | Soares, Jair C. | Sponheim, Scott R. | Sprooten, Emma | Starr, John M. | Steen, Vidar M. | Strakowski, Stephen | Strike, Lachlan | Sussmann, Jessika | Sämann, Philipp G. | Teumer, Alexander | Toga, Arthur W. | Tordesillas-Gutierrez, Diana | Trabzuni, Daniah | Trost, Sarah | Turner, Jessica | Van den Heuvel, Martijn | van der Wee, Nic J. | van Eijk, Kristel | van Erp, Theo G. M. | van Haren, Neeltje E. M. | van ‘t Ent, Dennis | van Tol, Marie-Jose | Valdés Hernández, Maria C. | Veltman, Dick J. | Versace, Amelia | Völzke, Henry | Walker, Robert | Walter, Henrik | Wang, Lei | Wardlaw, Joanna M. | Weale, Michael E. | Weiner, Michael W. | Wen, Wei | Westlye, Lars T. | Whalley, Heather C. | Whelan, Christopher D. | White, Tonya | Winkler, Anderson M. | Wittfeld, Katharina | Woldehawariat, Girma | Wolf, Christiane | Zilles, David | Zwiers, Marcel P. | Thalamuthu, Anbupalam | Schofield, Peter R. | Freimer, Nelson B. | Lawrence, Natalia S. | Drevets, Wayne
Brain Imaging and Behavior  2014;8(2):153-182.
The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA’s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
doi:10.1007/s11682-013-9269-5
PMCID: PMC4008818  PMID: 24399358
Genetics; MRI; GWAS; Consortium; Meta-analysis; Multi-site
8.  Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture 
Davis, Lea K. | Yu, Dongmei | Keenan, Clare L. | Gamazon, Eric R. | Konkashbaev, Anuar I. | Derks, Eske M. | Neale, Benjamin M. | Yang, Jian | Lee, S. Hong | Evans, Patrick | Barr, Cathy L. | Bellodi, Laura | Benarroch, Fortu | Berrio, Gabriel Bedoya | Bienvenu, Oscar J. | Bloch, Michael H. | Blom, Rianne M. | Bruun, Ruth D. | Budman, Cathy L. | Camarena, Beatriz | Campbell, Desmond | Cappi, Carolina | Cardona Silgado, Julio C. | Cath, Danielle C. | Cavallini, Maria C. | Chavira, Denise A. | Chouinard, Sylvain | Conti, David V. | Cook, Edwin H. | Coric, Vladimir | Cullen, Bernadette A. | Deforce, Dieter | Delorme, Richard | Dion, Yves | Edlund, Christopher K. | Egberts, Karin | Falkai, Peter | Fernandez, Thomas V. | Gallagher, Patience J. | Garrido, Helena | Geller, Daniel | Girard, Simon L. | Grabe, Hans J. | Grados, Marco A. | Greenberg, Benjamin D. | Gross-Tsur, Varda | Haddad, Stephen | Heiman, Gary A. | Hemmings, Sian M. J. | Hounie, Ana G. | Illmann, Cornelia | Jankovic, Joseph | Jenike, Michael A. | Kennedy, James L. | King, Robert A. | Kremeyer, Barbara | Kurlan, Roger | Lanzagorta, Nuria | Leboyer, Marion | Leckman, James F. | Lennertz, Leonhard | Liu, Chunyu | Lochner, Christine | Lowe, Thomas L. | Macciardi, Fabio | McCracken, James T. | McGrath, Lauren M. | Mesa Restrepo, Sandra C. | Moessner, Rainald | Morgan, Jubel | Muller, Heike | Murphy, Dennis L. | Naarden, Allan L. | Ochoa, William Cornejo | Ophoff, Roel A. | Osiecki, Lisa | Pakstis, Andrew J. | Pato, Michele T. | Pato, Carlos N. | Piacentini, John | Pittenger, Christopher | Pollak, Yehuda | Rauch, Scott L. | Renner, Tobias J. | Reus, Victor I. | Richter, Margaret A. | Riddle, Mark A. | Robertson, Mary M. | Romero, Roxana | Rosàrio, Maria C. | Rosenberg, David | Rouleau, Guy A. | Ruhrmann, Stephan | Ruiz-Linares, Andres | Sampaio, Aline S. | Samuels, Jack | Sandor, Paul | Sheppard, Brooke | Singer, Harvey S. | Smit, Jan H. | Stein, Dan J. | Strengman, E. | Tischfield, Jay A. | Valencia Duarte, Ana V. | Vallada, Homero | Van Nieuwerburgh, Filip | Veenstra-VanderWeele, Jeremy | Walitza, Susanne | Wang, Ying | Wendland, Jens R. | Westenberg, Herman G. M. | Shugart, Yin Yao | Miguel, Euripedes C. | McMahon, William | Wagner, Michael | Nicolini, Humberto | Posthuma, Danielle | Hanna, Gregory L. | Heutink, Peter | Denys, Damiaan | Arnold, Paul D. | Oostra, Ben A. | Nestadt, Gerald | Freimer, Nelson B. | Pauls, David L. | Wray, Naomi R. | Stewart, S. Evelyn | Mathews, Carol A. | Knowles, James A. | Cox, Nancy J. | Scharf, Jeremiah M.
PLoS Genetics  2013;9(10):e1003864.
The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained by all SNPs for two phenotypically-related neurobehavioral disorders, obsessive-compulsive disorder (OCD) and Tourette Syndrome (TS), using GCTA. Our analysis yielded a heritability point estimate of 0.58 (se = 0.09, p = 5.64e-12) for TS, and 0.37 (se = 0.07, p = 1.5e-07) for OCD. In addition, we conducted multiple genomic partitioning analyses to identify genomic elements that concentrate this heritability. We examined genomic architectures of TS and OCD by chromosome, MAF bin, and functional annotations. In addition, we assessed heritability for early onset and adult onset OCD. Among other notable results, we found that SNPs with a minor allele frequency of less than 5% accounted for 21% of the TS heritability and 0% of the OCD heritability. Additionally, we identified a significant contribution to TS and OCD heritability by variants significantly associated with gene expression in two regions of the brain (parietal cortex and cerebellum) for which we had available expression quantitative trait loci (eQTLs). Finally we analyzed the genetic correlation between TS and OCD, revealing a genetic correlation of 0.41 (se = 0.15, p = 0.002). These results are very close to previous heritability estimates for TS and OCD based on twin and family studies, suggesting that very little, if any, heritability is truly missing (i.e., unassayed) from TS and OCD GWAS studies of common variation. The results also indicate that there is some genetic overlap between these two phenotypically-related neuropsychiatric disorders, but suggest that the two disorders have distinct genetic architectures.
Author Summary
Family and twin studies have shown that genetic risk factors are important in the development of Tourette Syndrome (TS) and obsessive compulsive disorder (OCD). However, efforts to identify the individual genetic risk factors involved in these two neuropsychiatric disorders have been largely unsuccessful. One possible explanation for this is that many genetic variations scattered throughout the genome each contribute a small amount to the overall risk. For TS and OCD, the genetic architecture (characterized by the number, frequency, and distribution of genetic risk factors) is presently unknown. This study examined the genetic architecture of TS and OCD in a variety of ways. We found that rare genetic changes account for more genetic risk in TS than in OCD; certain chromosomes contribute to OCD risk more than others; and variants that influence the level of genes expressed in two regions of the brain can account for a significant amount of risk for both TS and OCD. Results from this study might help in determining where, and what kind of variants are individual risk factors for TS and OCD and where they might be located in the human genome.
doi:10.1371/journal.pgen.1003864
PMCID: PMC3812053  PMID: 24204291
9.  Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers 
Brain Imaging and Behavior  2013;8(2):183-207.
The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer’s disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development.
Electronic supplementary material
The online version of this article (doi:10.1007/s11682-013-9262-z) contains supplementary material, which is available to authorized users.
doi:10.1007/s11682-013-9262-z
PMCID: PMC3976843  PMID: 24092460
Alzheimer’s disease; Genetic association study; Quantitative traits; Neuroimaging; Biomarker; Cognition
10.  MYO1E MUTATIONS AND CHILDHOOD FAMILIAL FOCAL SEGMENTAL GLOMERULOSCLEROSIS 
The New England journal of medicine  2011;365(4):295-306.
Background
Focal segmental glomerulosclerosis (FSGS) is a kidney disease that presents with nephrotic syndrome and is often resistant to glucocorticosteroids and progresses to end-stage kidney disease in 50–70% of patients. Genetic studies in familial FSGS indicate that it is a disease of the podocytes, major components of the glomerular filtration barrier. However the molecular cause of over half of primary FSGS is unknown, and effective treatments have been elusive.
Methods
We performed whole-genome linkage analysis followed by high-throughput sequencing of the positive linkage area in a family with autosomal recessive FSGS and sequenced a newly discovered gene in 52 unrelated FSGS patients. Immunohistochemistry was performed in human kidney biopsies and cultured podocytes. Expression studies in vitro were performed to characterize the functional consequences of the mutations identified.
Results
Two mutations (A159P and Y695X) in MYO1E, encoding the non-muscle class I myosin, myosin 1E (Myo1E), which segregated with FSGS in two independent pedigrees were identified. Patients were homozygous for the mutations and were resistant to glucocorticosteroids. Electron microscopy showed thickening and disorganization of the glomerular basement membrane. Normal expression of Myo1E was documented in control human kidney biopsies in vivo and in glomerular podocytes in vitro. Transfection studies revealed abnormal subcellular localization and function of A159P-Myo1E mutant. The Y695X mutation causes loss of calmodulin binding and the tail domains of Myo1E.
Conclusions
MYO1E mutations lead to childhood onset steroid-resistant FSGS. These data support a role of Myo1E in podocyte function and the consequent integrity of the glomerular permselectivity barrier.
doi:10.1056/NEJMoa1101273
PMCID: PMC3701523  PMID: 21756023
11.  Identification of common variants associated with human hippocampal and intracranial volumes 
Stein, Jason L | Medland, Sarah E | Vasquez, Alejandro Arias | Hibar, Derrek P | Senstad, Rudy E | Winkler, Anderson M | Toro, Roberto | Appel, Katja | Bartecek, Richard | Bergmann, Ørjan | Bernard, Manon | Brown, Andrew A | Cannon, Dara M | Chakravarty, M Mallar | Christoforou, Andrea | Domin, Martin | Grimm, Oliver | Hollinshead, Marisa | Holmes, Avram J | Homuth, Georg | Hottenga, Jouke-Jan | Langan, Camilla | Lopez, Lorna M | Hansell, Narelle K | Hwang, Kristy S | Kim, Sungeun | Laje, Gonzalo | Lee, Phil H | Liu, Xinmin | Loth, Eva | Lourdusamy, Anbarasu | Mattingsdal, Morten | Mohnke, Sebastian | Maniega, Susana Muñoz | Nho, Kwangsik | Nugent, Allison C | O’Brien, Carol | Papmeyer, Martina | Pütz, Benno | Ramasamy, Adaikalavan | Rasmussen, Jerod | Rijpkema, Mark | Risacher, Shannon L | Roddey, J Cooper | Rose, Emma J | Ryten, Mina | Shen, Li | Sprooten, Emma | Strengman, Eric | Teumer, Alexander | Trabzuni, Daniah | Turner, Jessica | van Eijk, Kristel | van Erp, Theo G M | van Tol, Marie-Jose | Wittfeld, Katharina | Wolf, Christiane | Woudstra, Saskia | Aleman, Andre | Alhusaini, Saud | Almasy, Laura | Binder, Elisabeth B | Brohawn, David G | Cantor, Rita M | Carless, Melanie A | Corvin, Aiden | Czisch, Michael | Curran, Joanne E | Davies, Gail | de Almeida, Marcio A A | Delanty, Norman | Depondt, Chantal | Duggirala, Ravi | Dyer, Thomas D | Erk, Susanne | Fagerness, Jesen | Fox, Peter T | Freimer, Nelson B | Gill, Michael | Göring, Harald H H | Hagler, Donald J | Hoehn, David | Holsboer, Florian | Hoogman, Martine | Hosten, Norbert | Jahanshad, Neda | Johnson, Matthew P | Kasperaviciute, Dalia | Kent, Jack W | Kochunov, Peter | Lancaster, Jack L | Lawrie, Stephen M | Liewald, David C | Mandl, René | Matarin, Mar | Mattheisen, Manuel | Meisenzahl, Eva | Melle, Ingrid | Moses, Eric K | Mühleisen, Thomas W | Nauck, Matthias | Nöthen, Markus M | Olvera, Rene L | Pandolfo, Massimo | Pike, G Bruce | Puls, Ralf | Reinvang, Ivar | Rentería, Miguel E | Rietschel, Marcella | Roffman, Joshua L | Royle, Natalie A | Rujescu, Dan | Savitz, Jonathan | Schnack, Hugo G | Schnell, Knut | Seiferth, Nina | Smith, Colin | Steen, Vidar M | Valdés Hernández, Maria C | Van den Heuvel, Martijn | van der Wee, Nic J | Van Haren, Neeltje E M | Veltman, Joris A | Völzke, Henry | Walker, Robert | Westlye, Lars T | Whelan, Christopher D | Agartz, Ingrid | Boomsma, Dorret I | Cavalleri, Gianpiero L | Dale, Anders M | Djurovic, Srdjan | Drevets, Wayne C | Hagoort, Peter | Hall, Jeremy | Heinz, Andreas | Jack, Clifford R | Foroud, Tatiana M | Le Hellard, Stephanie | Macciardi, Fabio | Montgomery, Grant W | Poline, Jean Baptiste | Porteous, David J | Sisodiya, Sanjay M | Starr, John M | Sussmann, Jessika | Toga, Arthur W | Veltman, Dick J | Walter, Henrik | Weiner, Michael W | Bis, Joshua C | Ikram, M Arfan | Smith, Albert V | Gudnason, Vilmundur | Tzourio, Christophe | Vernooij, Meike W | Launer, Lenore J | DeCarli, Charles | Seshadri, Sudha | Andreassen, Ole A | Apostolova, Liana G | Bastin, Mark E | Blangero, John | Brunner, Han G | Buckner, Randy L | Cichon, Sven | Coppola, Giovanni | de Zubicaray, Greig I | Deary, Ian J | Donohoe, Gary | de Geus, Eco J C | Espeseth, Thomas | Fernández, Guillén | Glahn, David C | Grabe, Hans J | Hardy, John | Hulshoff Pol, Hilleke E | Jenkinson, Mark | Kahn, René S | McDonald, Colm | McIntosh, Andrew M | McMahon, Francis J | McMahon, Katie L | Meyer-Lindenberg, Andreas | Morris, Derek W | Müller-Myhsok, Bertram | Nichols, Thomas E | Ophoff, Roel A | Paus, Tomas | Pausova, Zdenka | Penninx, Brenda W | Potkin, Steven G | Sämann, Philipp G | Saykin, Andrew J | Schumann, Gunter | Smoller, Jordan W | Wardlaw, Joanna M | Weale, Michael E | Martin, Nicholas G | Franke, Barbara | Wright, Margaret J | Thompson, Paul M
Nature genetics  2012;44(5):552-561.
Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer’s disease1,2 and is reduced in schizophrenia3, major depression4 and mesial temporal lobe epilepsy5. Whereas many brain imaging phenotypes are highly heritable6,7, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10−16) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10−12). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10−7).
doi:10.1038/ng.2250
PMCID: PMC3635491  PMID: 22504417
12.  HERVs Expression in Autism Spectrum Disorders 
PLoS ONE  2012;7(11):e48831.
Background
Autistic Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder, resulting from complex interactions among genetic, genomic and environmental factors. Here we have studied the expression of Human Endogenous Retroviruses (HERVs), non-coding DNA elements with potential regulatory functions, and have tested their possible implication in autism.
Methods
The presence of retroviral mRNAs from four HERV families (E, H, K and W), widely implicated in complex diseases, was evaluated in peripheral blood mononuclear cells (PBMCs) from ASD patients and healthy controls (HCs) by qualitative RT-PCR. We also analyzed the expression of the env sequence from HERV-H, HERV-W and HERV-K families in PBMCs at the time of sampling and after stimulation in culture, in both ASD and HC groups, by quantitative Real-time PCR. Differences between groups were evaluated using statistical methods.
Results
The percentage of HERV-H and HERV-W positive samples was higher among ASD patients compared to HCs, while HERV-K was similarly represented and HERV-E virtually absent in both groups. The quantitative evaluation shows that HERV-H and HERV-W are differentially expressed in the two groups, with HERV-H being more abundantly expressed and, conversely, HERV-W, having lower abundance, in PBMCs from ASDs compared to healthy controls. PMBCs from ASDs also showed an increased potential to up-regulate HERV-H expression upon stimulation in culture, unlike HCs. Furthermore we report a negative correlation between expression levels of HERV-H and age among ASD patients and a statistically significant higher expression in ASD patients with Severe score in Communication and Motor Psychoeducational Profile-3.
Conclusions
Specific HERV families have a distinctive expression profile in ASD patients compared to HCs. We propose that HERV-H expression be explored in larger samples of individuals with autism spectrum in order to determine its utility as a novel biological trait of this complex disorder.
doi:10.1371/journal.pone.0048831
PMCID: PMC3498248  PMID: 23155411
13.  Next Generation Sequence Analysis and Computational Genomics Using Graphical Pipeline Workflows 
Genes  2012;3(3):545-575.
Whole-genome and exome sequencing have already proven to be essential and powerful methods to identify genes responsible for simple Mendelian inherited disorders. These methods can be applied to complex disorders as well, and have been adopted as one of the current mainstream approaches in population genetics. These achievements have been made possible by next generation sequencing (NGS) technologies, which require substantial bioinformatics resources to analyze the dense and complex sequence data. The huge analytical burden of data from genome sequencing might be seen as a bottleneck slowing the publication of NGS papers at this time, especially in psychiatric genetics. We review the existing methods for processing NGS data, to place into context the rationale for the design of a computational resource. We describe our method, the Graphical Pipeline for Computational Genomics (GPCG), to perform the computational steps required to analyze NGS data. The GPCG implements flexible workflows for basic sequence alignment, sequence data quality control, single nucleotide polymorphism analysis, copy number variant identification, annotation, and visualization of results. These workflows cover all the analytical steps required for NGS data, from processing the raw reads to variant calling and annotation. The current version of the pipeline is freely available at http://pipeline.loni.ucla.edu. These applications of NGS analysis may gain clinical utility in the near future (e.g., identifying miRNA signatures in diseases) when the bioinformatics approach is made feasible. Taken together, the annotation tools and strategies that have been developed to retrieve information and test hypotheses about the functional role of variants present in the human genome will help to pinpoint the genetic risk factors for psychiatric disorders.
doi:10.3390/genes3030545
PMCID: PMC3490498  PMID: 23139896
Next Generation Sequencing (NGS); LONI pipeline; SNPs; CNVs; workflow; bioinformatics
14.  An integrative functional genomics approach for discovering biomarkers in schizophrenia 
Briefings in Functional Genomics  2011;10(6):387-399.
Schizophrenia (SZ) is a complex disorder resulting from both genetic and environmental causes with a lifetime prevalence world-wide of 1%; however, there are no specific, sensitive and validated biomarkers for SZ. A general unifying hypothesis has been put forward that disease-associated single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) are more likely to be associated with gene expression quantitative trait loci (eQTL). We will describe this hypothesis and review primary methodology with refinements for testing this paradigmatic approach in SZ. We will describe biomarker studies of SZ and testing enrichment of SNPs that are associated both with eQTLs and existing GWAS of SZ. SZ-associated SNPs that overlap with eQTLs can be placed into gene–gene expression, protein–protein and protein–DNA interaction networks. Further, those networks can be tested by reducing/silencing the gene expression levels of critical nodes. We present pilot data to support these methods of investigation such as the use of eQTLs to annotate GWASs of SZ, which could be applied to the field of biomarker discovery. Those networks that have association with SNP markers, especially cis-regulated expression, might lead to a more clear understanding of important candidate genes that predispose to disease and alter expression. This method has general application to many complex disorders.
doi:10.1093/bfgp/elr036
PMCID: PMC3277082  PMID: 22155586
expression quantitative trait loci; cis-regulatory SNPs; GWAS; gene expression; lymphoblastoid cell lines
15.  Next Generation Sequence Analysis and Computational Genomics Using Graphical Pipeline Workflows 
Genes  2012;3(3):545-575.
Whole-genome and exome sequencing have already proven to be essential and powerful methods to identify genes responsible for simple Mendelian inherited disorders. These methods can be applied to complex disorders as well, and have been adopted as one of the current mainstream approaches in population genetics. These achievements have been made possible by next generation sequencing (NGS) technologies, which require substantial bioinformatics resources to analyze the dense and complex sequence data. The huge analytical burden of data from genome sequencing might be seen as a bottleneck slowing the publication of NGS papers at this time, especially in psychiatric genetics. We review the existing methods for processing NGS data, to place into context the rationale for the design of a computational resource. We describe our method, the Graphical Pipeline for Computational Genomics (GPCG), to perform the computational steps required to analyze NGS data. The GPCG implements flexible workflows for basic sequence alignment, sequence data quality control, single nucleotide polymorphism analysis, copy number variant identification, annotation, and visualization of results. These workflows cover all the analytical steps required for NGS data, from processing the raw reads to variant calling and annotation. The current version of the pipeline is freely available at http://pipeline.loni.ucla.edu. These applications of NGS analysis may gain clinical utility in the near future (e.g., identifying miRNA signatures in diseases) when the bioinformatics approach is made feasible. Taken together, the annotation tools and strategies that have been developed to retrieve information and test hypotheses about the functional role of variants present in the human genome will help to pinpoint the genetic risk factors for psychiatric disorders.
doi:10.3390/genes3030545
PMCID: PMC3490498  PMID: 23139896
Next Generation Sequencing (NGS); LONI pipeline; SNPs; CNVs; workflow; bioinformatics
16.  Enabling collaborative research using the Biomedical Informatics Research Network (BIRN) 
Objective
As biomedical technology becomes increasingly sophisticated, researchers can probe ever more subtle effects with the added requirement that the investigation of small effects often requires the acquisition of large amounts of data. In biomedicine, these data are often acquired at, and later shared between, multiple sites. There are both technological and sociological hurdles to be overcome for data to be passed between researchers and later made accessible to the larger scientific community. The goal of the Biomedical Informatics Research Network (BIRN) is to address the challenges inherent in biomedical data sharing.
Materials and methods
BIRN tools are grouped into ‘capabilities’ and are available in the areas of data management, data security, information integration, and knowledge engineering. BIRN has a user-driven focus and employs a layered architectural approach that promotes reuse of infrastructure. BIRN tools are designed to be modular and therefore can work with pre-existing tools. BIRN users can choose the capabilities most useful for their application, while not having to ensure that their project conforms to a monolithic architecture.
Results
BIRN has implemented a new software-based data-sharing infrastructure that has been put to use in many different domains within biomedicine. BIRN is actively involved in outreach to the broader biomedical community to form working partnerships.
Conclusion
BIRN's mission is to provide capabilities and services related to data sharing to the biomedical research community. It does this by forming partnerships and solving specific, user-driven problems whose solutions are then available for use by other groups.
doi:10.1136/amiajnl-2010-000032
PMCID: PMC3128398  PMID: 21515543
Genomics; statistical genetics; bioinformatics; complex traits; data; machine learning; data sharing; information integration; data mediation; data security; data management; knowledge engineering
17.  Mitochondrial Mutations and Polymorphisms in Psychiatric Disorders 
Frontiers in Genetics  2012;3:103.
Mitochondrial deficiencies with unknown causes have been observed in schizophrenia (SZ) and bipolar disorder (BD) in imaging and postmortem studies. Polymorphisms and somatic mutations in mitochondrial DNA (mtDNA) were investigated as potential causes with next generation sequencing of mtDNA (mtDNA-Seq) and genotyping arrays in subjects with SZ, BD, major depressive disorder (MDD), and controls. The common deletion of 4,977 bp in mtDNA was compared between SZ and controls in 11 different vulnerable brain regions and in blood samples, and in dorsolateral prefrontal cortex (DLPFC) of BD, SZ, and controls. In a separate analysis, association of mitochondria SNPs (mtSNPs) with SZ and BD in European ancestry individuals (n = 6,040) was tested using Genetic Association Information Network (GAIN) and Wellcome Trust Case Control Consortium 2 (WTCCC2) datasets. The common deletion levels were highly variable across brain regions, with a 40-fold increase in some regions (nucleus accumbens, caudate nucleus and amygdala), increased with age, and showed little change in blood samples from the same subjects. The common deletion levels were increased in the DLPFC for BD compared to controls, but not in SZ. Full mtDNA genome resequencing of 23 subjects, showed seven novel homoplasmic mutations, five were novel synonymous coding mutations. By logistic regression analysis there were no significant mtSNPs associated with BD or SZ after genome wide correction. However, nominal association of mtSNPs (p < 0.05) to SZ and BD were found in the hypervariable region of mtDNA to T195C and T16519C. The results confirm prior reports that certain brain regions accumulate somatic mutations at higher levels than blood. The study in mtDNA of common polymorphisms, somatic mutations, and rare mutations in larger populations may lead to a better understanding of the pathophysiology of psychiatric disorders.
doi:10.3389/fgene.2012.00103
PMCID: PMC3379031  PMID: 22723804
mitochondria; homoplasmy; common deletion; novel mutations; schizophrenia; bipolar disorder
18.  Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis 
Sawcer, Stephen | Hellenthal, Garrett | Pirinen, Matti | Spencer, Chris C.A. | Patsopoulos, Nikolaos A. | Moutsianas, Loukas | Dilthey, Alexander | Su, Zhan | Freeman, Colin | Hunt, Sarah E. | Edkins, Sarah | Gray, Emma | Booth, David R. | Potter, Simon C. | Goris, An | Band, Gavin | Oturai, Annette Bang | Strange, Amy | Saarela, Janna | Bellenguez, Céline | Fontaine, Bertrand | Gillman, Matthew | Hemmer, Bernhard | Gwilliam, Rhian | Zipp, Frauke | Jayakumar, Alagurevathi | Martin, Roland | Leslie, Stephen | Hawkins, Stanley | Giannoulatou, Eleni | D’alfonso, Sandra | Blackburn, Hannah | Boneschi, Filippo Martinelli | Liddle, Jennifer | Harbo, Hanne F. | Perez, Marc L. | Spurkland, Anne | Waller, Matthew J | Mycko, Marcin P. | Ricketts, Michelle | Comabella, Manuel | Hammond, Naomi | Kockum, Ingrid | McCann, Owen T. | Ban, Maria | Whittaker, Pamela | Kemppinen, Anu | Weston, Paul | Hawkins, Clive | Widaa, Sara | Zajicek, John | Dronov, Serge | Robertson, Neil | Bumpstead, Suzannah J. | Barcellos, Lisa F. | Ravindrarajah, Rathi | Abraham, Roby | Alfredsson, Lars | Ardlie, Kristin | Aubin, Cristin | Baker, Amie | Baker, Katharine | Baranzini, Sergio E. | Bergamaschi, Laura | Bergamaschi, Roberto | Bernstein, Allan | Berthele, Achim | Boggild, Mike | Bradfield, Jonathan P. | Brassat, David | Broadley, Simon A. | Buck, Dorothea | Butzkueven, Helmut | Capra, Ruggero | Carroll, William M. | Cavalla, Paola | Celius, Elisabeth G. | Cepok, Sabine | Chiavacci, Rosetta | Clerget-Darpoux, Françoise | Clysters, Katleen | Comi, Giancarlo | Cossburn, Mark | Cournu-Rebeix, Isabelle | Cox, Mathew B. | Cozen, Wendy | Cree, Bruce A.C. | Cross, Anne H. | Cusi, Daniele | Daly, Mark J. | Davis, Emma | de Bakker, Paul I.W. | Debouverie, Marc | D’hooghe, Marie Beatrice | Dixon, Katherine | Dobosi, Rita | Dubois, Bénédicte | Ellinghaus, David | Elovaara, Irina | Esposito, Federica | Fontenille, Claire | Foote, Simon | Franke, Andre | Galimberti, Daniela | Ghezzi, Angelo | Glessner, Joseph | Gomez, Refujia | Gout, Olivier | Graham, Colin | Grant, Struan F.A. | Guerini, Franca Rosa | Hakonarson, Hakon | Hall, Per | Hamsten, Anders | Hartung, Hans-Peter | Heard, Rob N. | Heath, Simon | Hobart, Jeremy | Hoshi, Muna | Infante-Duarte, Carmen | Ingram, Gillian | Ingram, Wendy | Islam, Talat | Jagodic, Maja | Kabesch, Michael | Kermode, Allan G. | Kilpatrick, Trevor J. | Kim, Cecilia | Klopp, Norman | Koivisto, Keijo | Larsson, Malin | Lathrop, Mark | Lechner-Scott, Jeannette S. | Leone, Maurizio A. | Leppä, Virpi | Liljedahl, Ulrika | Bomfim, Izaura Lima | Lincoln, Robin R. | Link, Jenny | Liu, Jianjun | Lorentzen, Åslaug R. | Lupoli, Sara | Macciardi, Fabio | Mack, Thomas | Marriott, Mark | Martinelli, Vittorio | Mason, Deborah | McCauley, Jacob L. | Mentch, Frank | Mero, Inger-Lise | Mihalova, Tania | Montalban, Xavier | Mottershead, John | Myhr, Kjell-Morten | Naldi, Paola | Ollier, William | Page, Alison | Palotie, Aarno | Pelletier, Jean | Piccio, Laura | Pickersgill, Trevor | Piehl, Fredrik | Pobywajlo, Susan | Quach, Hong L. | Ramsay, Patricia P. | Reunanen, Mauri | Reynolds, Richard | Rioux, John D. | Rodegher, Mariaemma | Roesner, Sabine | Rubio, Justin P. | Rückert, Ina-Maria | Salvetti, Marco | Salvi, Erika | Santaniello, Adam | Schaefer, Catherine A. | Schreiber, Stefan | Schulze, Christian | Scott, Rodney J. | Sellebjerg, Finn | Selmaj, Krzysztof W. | Sexton, David | Shen, Ling | Simms-Acuna, Brigid | Skidmore, Sheila | Sleiman, Patrick M.A. | Smestad, Cathrine | Sørensen, Per Soelberg | Søndergaard, Helle Bach | Stankovich, Jim | Strange, Richard C. | Sulonen, Anna-Maija | Sundqvist, Emilie | Syvänen, Ann-Christine | Taddeo, Francesca | Taylor, Bruce | Blackwell, Jenefer M. | Tienari, Pentti | Bramon, Elvira | Tourbah, Ayman | Brown, Matthew A. | Tronczynska, Ewa | Casas, Juan P. | Tubridy, Niall | Corvin, Aiden | Vickery, Jane | Jankowski, Janusz | Villoslada, Pablo | Markus, Hugh S. | Wang, Kai | Mathew, Christopher G. | Wason, James | Palmer, Colin N.A. | Wichmann, H-Erich | Plomin, Robert | Willoughby, Ernest | Rautanen, Anna | Winkelmann, Juliane | Wittig, Michael | Trembath, Richard C. | Yaouanq, Jacqueline | Viswanathan, Ananth C. | Zhang, Haitao | Wood, Nicholas W. | Zuvich, Rebecca | Deloukas, Panos | Langford, Cordelia | Duncanson, Audrey | Oksenberg, Jorge R. | Pericak-Vance, Margaret A. | Haines, Jonathan L. | Olsson, Tomas | Hillert, Jan | Ivinson, Adrian J. | De Jager, Philip L. | Peltonen, Leena | Stewart, Graeme J. | Hafler, David A. | Hauser, Stephen L. | McVean, Gil | Donnelly, Peter | Compston, Alastair
Nature  2011;476(7359):214-219.
Multiple sclerosis (OMIM 126200) is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability.1 Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals;2,3 and systematic attempts to identify linkage in multiplex families have confirmed that variation within the Major Histocompatibility Complex (MHC) exerts the greatest individual effect on risk.4 Modestly powered Genome-Wide Association Studies (GWAS)5-10 have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects play a key role in disease susceptibility.11 Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the Class I region. Immunologically relevant genes are significantly over-represented amongst those mapping close to the identified loci and particularly implicate T helper cell differentiation in the pathogenesis of multiple sclerosis.
doi:10.1038/nature10251
PMCID: PMC3182531  PMID: 21833088
multiple sclerosis; GWAS; genetics
19.  Association of the Type 2 Diabetes Mellitus Susceptibility Gene, TCF7L2, with Schizophrenia in an Arab-Israeli Family Sample 
PLoS ONE  2012;7(1):e29228.
Many reports in different populations have demonstrated linkage of the 10q24–q26 region to schizophrenia, thus encouraging further analysis of this locus for detection of specific schizophrenia genes. Our group previously reported linkage of the 10q24–q26 region to schizophrenia in a unique, homogeneous sample of Arab-Israeli families with multiple schizophrenia-affected individuals, under a dominant model of inheritance. To further explore this candidate region and identify specific susceptibility variants within it, we performed re-analysis of the 10q24-26 genotype data, taken from our previous genome-wide association study (GWAS) (Alkelai et al, 2011). We analyzed 2089 SNPs in an extended sample of 57 Arab Israeli families (189 genotyped individuals), under the dominant model of inheritance, which best fits this locus according to previously performed MOD score analysis. We found significant association with schizophrenia of the TCF7L2 gene intronic SNP, rs12573128, (p = 7.01×10−6) and of the nearby intergenic SNP, rs1033772, (p = 6.59×10−6) which is positioned between TCF7L2 and HABP2. TCF7L2 is one of the best confirmed susceptibility genes for type 2 diabetes (T2D) among different ethnic groups, has a role in pancreatic beta cell function and may contribute to the comorbidity of schizophrenia and T2D. These preliminary results independently support previous findings regarding a possible role of TCF7L2 in susceptibility to schizophrenia, and strengthen the importance of integrating linkage analysis models of inheritance while performing association analyses in regions of interest. Further validation studies in additional populations are required.
doi:10.1371/journal.pone.0029228
PMCID: PMC3256145  PMID: 22247771
20.  An ICA with reference approach in identification of genetic variation and associated brain networks 
To address the statistical challenges associated with genome-wide association studies, we present an independent component analysis (ICA) with reference approach to target a specific genetic variation and associated brain networks. First, a small set of single nucleotide polymorphisms (SNPs) are empirically chosen to reflect a feature of interest and these SNPs are used as a reference when applying ICA to a full genomic SNP array. After extracting the genetic component maximally representing the characteristics of the reference, we test its association with brain networks in functional magnetic resonance imaging (fMRI) data. The method was evaluated on both real and simulated datasets. Simulation demonstrates that ICA with reference can extract a specific genetic factor, even when the variance accounted for by such a factor is so small that a regular ICA fails. Our real data application from 48 schizophrenia patients (SZs) and 40 healthy controls (HCs) include 300K SNPs and fMRI images in an auditory oddball task. Using SNPs with allelic frequency difference in two groups as a reference, we extracted a genetic component that maximally differentiates patients from controls (p < 4 × 10−17), and discovered a brain functional network that was significantly associated with this genetic component (p < 1 × 10−4). The regions in the functional network mainly locate in the thalamus, anterior and posterior cingulate gyri. The contributing SNPs in the genetic factor mainly fall into two clusters centered at chromosome 7q21 and chromosome 5q35. The findings from the schizophrenia application are in concordance with previous knowledge about brain regions and gene function. All together, the results suggest that the ICA with reference can be particularly useful to explore the whole genome to find a specific factor of interest and further study its effect on brain.
doi:10.3389/fnhum.2012.00021
PMCID: PMC3284145  PMID: 22371699
independent component analysis with reference; genome-wide association study; brain network; schizophrenia; single nucleotide polymorphisms; functional magnetic resonance imaging
21.  Identifying Gene Regulatory Networks in Schizophrenia 
NeuroImage  2010;53(3):839-847.
The imaging genetics approach to studying the genetic basis of disease leverages the individual strengths of both neuroimaging and genetic studies by visualizing and quantifying the brain activation patterns in the context of genetic background. Brain imaging as an intermediate phenotype can help clarify the functional link among genes, the molecular networks in which they participate, and brain circuitry and function. Integrating genetic data from a genome-wide association study (GWAS) with brain imaging as a quantitative trait (QT) phenotype can increase the statistical power to identify risk genes. A QT analysis using brain imaging (DLPFC activation during a working memory task) as a quantitative trait has identified unanticipated risk genes for schizophrenia. Several of these genes (RSRC1, ARHGAP18, ROBO1-ROBO2, GPC1, CTXN3-SLC12A2) have functions related to progenitor cell proliferation, migration, and differentiation, axonal connectivity, and development of forebrain structures. These genes, however, do not function in isolation but rather through gene regulatory networks. To obtain a deeper understanding how the GWAS-identified genes participate in larger gene regulatory networks, we measured correlations among transcript levels in the mouse and human postmortem tissue, and performed a gene set enrichment analysis (GSEA). The results of such computational approaches can be further validated in animal experiments in which the networks are experimentally studied and perturbed with specific compounds. Glypican 1 and FGF17 mouse models can be used to study such gene regulatory networks. The model demonstrates epistatic interactions between FGF and glypican on brain development and may be a useful model of negative symptom schizophrenia.
doi:10.1016/j.neuroimage.2010.06.036
PMCID: PMC3055795  PMID: 20600988
22.  Genome-wide meta-analyses identify three loci associated with primary biliary cirrhosis 
Nature genetics  2010;42(8):658-660.
A genome-wide association screen for primary biliary cirrhosis risk alleles was performed in an Italian cohort. The results from the Italian cohort replicated IL12A and IL12RB associations, and a combined meta-analysis using a Canadian dataset identified newly associated loci at SPIB (P = 7.9 × 10–11, odds ratio (OR) = 1.46), IRF5-TNPO3 (P = 2.8 × 10–10, OR = 1.63) and 17q12-21 (P = 1.7 × 10–10, OR = 1.38).
doi:10.1038/ng.627
PMCID: PMC3150510  PMID: 20639880
23.  Association between mitochondrial DNA variations and Alzheimer's Disease in the ADNI cohort 
Neurobiology of aging  2010;31(8):1355-1363.
Despite the central role of amyloid deposition in the development of Alzheimer's disease (AD), the pathogenesis of AD still remains elusive at the molecular level. Increasing evidence suggests that compromised mitochondrial function contributes to the aging process and thus may increase the risk of AD. Dysfunctional mitochondria contribute to reactive oxygen species (ROS) which can lead to extensive macromolecule oxidative damage and the progression of amyloid pathology. Oxidative stress and amyloid toxicity leave neurons chemically vulnerable. Because the brain relies on aerobic metabolism, it is apparent that mitochondria are critical for the cerebral function. Mitochondrial DNA sequence-changes could shift cell dynamics and facilitate neuronal vulnerability. Therefore we postulated that mitochondrial DNA sequence polymorphisms may increase the risk of AD. We evaluated the role of mitochondrial haplogroups derived from 138 mitochondrial polymorphisms in 358 Caucasian ADNI subjects. Our results indicate that the mitochondrial haplogroup UK may confer genetic susceptibility to AD independently of the APOE4 allele.
doi:10.1016/j.neurobiolaging.2010.04.031
PMCID: PMC2918801  PMID: 20538375
ADNI; Alzheimer's disease; mitochondrial polymorphism; mitochondrial haplogroups
24.  Applications of the pipeline environment for visual informatics and genomics computations 
BMC Bioinformatics  2011;12:304.
Background
Contemporary informatics and genomics research require efficient, flexible and robust management of large heterogeneous data, advanced computational tools, powerful visualization, reliable hardware infrastructure, interoperability of computational resources, and detailed data and analysis-protocol provenance. The Pipeline is a client-server distributed computational environment that facilitates the visual graphical construction, execution, monitoring, validation and dissemination of advanced data analysis protocols.
Results
This paper reports on the applications of the LONI Pipeline environment to address two informatics challenges - graphical management of diverse genomics tools, and the interoperability of informatics software. Specifically, this manuscript presents the concrete details of deploying general informatics suites and individual software tools to new hardware infrastructures, the design, validation and execution of new visual analysis protocols via the Pipeline graphical interface, and integration of diverse informatics tools via the Pipeline eXtensible Markup Language syntax. We demonstrate each of these processes using several established informatics packages (e.g., miBLAST, EMBOSS, mrFAST, GWASS, MAQ, SAMtools, Bowtie) for basic local sequence alignment and search, molecular biology data analysis, and genome-wide association studies. These examples demonstrate the power of the Pipeline graphical workflow environment to enable integration of bioinformatics resources which provide a well-defined syntax for dynamic specification of the input/output parameters and the run-time execution controls.
Conclusions
The LONI Pipeline environment http://pipeline.loni.ucla.edu provides a flexible graphical infrastructure for efficient biomedical computing and distributed informatics research. The interactive Pipeline resource manager enables the utilization and interoperability of diverse types of informatics resources. The Pipeline client-server model provides computational power to a broad spectrum of informatics investigators - experienced developers and novice users, user with or without access to advanced computational-resources (e.g., Grid, data), as well as basic and translational scientists. The open development, validation and dissemination of computational networks (pipeline workflows) facilitates the sharing of knowledge, tools, protocols and best practices, and enables the unbiased validation and replication of scientific findings by the entire community.
doi:10.1186/1471-2105-12-304
PMCID: PMC3199760  PMID: 21791102
25.  Association analyses of 249,796 individuals reveal eighteen new loci associated with body mass index 
Speliotes, Elizabeth K. | Willer, Cristen J. | Berndt, Sonja I. | Monda, Keri L. | Thorleifsson, Gudmar | Jackson, Anne U. | Allen, Hana Lango | Lindgren, Cecilia M. | Luan, Jian’an | Mägi, Reedik | Randall, Joshua C. | Vedantam, Sailaja | Winkler, Thomas W. | Qi, Lu | Workalemahu, Tsegaselassie | Heid, Iris M. | Steinthorsdottir, Valgerdur | Stringham, Heather M. | Weedon, Michael N. | Wheeler, Eleanor | Wood, Andrew R. | Ferreira, Teresa | Weyant, Robert J. | Segré, Ayellet V. | Estrada, Karol | Liang, Liming | Nemesh, James | Park, Ju-Hyun | Gustafsson, Stefan | Kilpeläinen, Tuomas O. | Yang, Jian | Bouatia-Naji, Nabila | Esko, Tõnu | Feitosa, Mary F. | Kutalik, Zoltán | Mangino, Massimo | Raychaudhuri, Soumya | Scherag, Andre | Smith, Albert Vernon | Welch, Ryan | Zhao, Jing Hua | Aben, Katja K. | Absher, Devin M. | Amin, Najaf | Dixon, Anna L. | Fisher, Eva | Glazer, Nicole L. | Goddard, Michael E. | Heard-Costa, Nancy L. | Hoesel, Volker | Hottenga, Jouke-Jan | Johansson, Åsa | Johnson, Toby | Ketkar, Shamika | Lamina, Claudia | Li, Shengxu | Moffatt, Miriam F. | Myers, Richard H. | Narisu, Narisu | Perry, John R.B. | Peters, Marjolein J. | Preuss, Michael | Ripatti, Samuli | Rivadeneira, Fernando | Sandholt, Camilla | Scott, Laura J. | Timpson, Nicholas J. | Tyrer, Jonathan P. | van Wingerden, Sophie | Watanabe, Richard M. | White, Charles C. | Wiklund, Fredrik | Barlassina, Christina | Chasman, Daniel I. | Cooper, Matthew N. | Jansson, John-Olov | Lawrence, Robert W. | Pellikka, Niina | Prokopenko, Inga | Shi, Jianxin | Thiering, Elisabeth | Alavere, Helene | Alibrandi, Maria T. S. | Almgren, Peter | Arnold, Alice M. | Aspelund, Thor | Atwood, Larry D. | Balkau, Beverley | Balmforth, Anthony J. | Bennett, Amanda J. | Ben-Shlomo, Yoav | Bergman, Richard N. | Bergmann, Sven | Biebermann, Heike | Blakemore, Alexandra I.F. | Boes, Tanja | Bonnycastle, Lori L. | Bornstein, Stefan R. | Brown, Morris J. | Buchanan, Thomas A. | Busonero, Fabio | Campbell, Harry | Cappuccio, Francesco P. | Cavalcanti-Proença, Christine | Chen, Yii-Der Ida | Chen, Chih-Mei | Chines, Peter S. | Clarke, Robert | Coin, Lachlan | Connell, John | Day, Ian N.M. | Heijer, Martin den | Duan, Jubao | Ebrahim, Shah | Elliott, Paul | Elosua, Roberto | Eiriksdottir, Gudny | Erdos, Michael R. | Eriksson, Johan G. | Facheris, Maurizio F. | Felix, Stephan B. | Fischer-Posovszky, Pamela | Folsom, Aaron R. | Friedrich, Nele | Freimer, Nelson B. | Fu, Mao | Gaget, Stefan | Gejman, Pablo V. | Geus, Eco J.C. | Gieger, Christian | Gjesing, Anette P. | Goel, Anuj | Goyette, Philippe | Grallert, Harald | Gräßler, Jürgen | Greenawalt, Danielle M. | Groves, Christopher J. | Gudnason, Vilmundur | Guiducci, Candace | Hartikainen, Anna-Liisa | Hassanali, Neelam | Hall, Alistair S. | Havulinna, Aki S. | Hayward, Caroline | Heath, Andrew C. | Hengstenberg, Christian | Hicks, Andrew A. | Hinney, Anke | Hofman, Albert | Homuth, Georg | Hui, Jennie | Igl, Wilmar | Iribarren, Carlos | Isomaa, Bo | Jacobs, Kevin B. | Jarick, Ivonne | Jewell, Elizabeth | John, Ulrich | Jørgensen, Torben | Jousilahti, Pekka | Jula, Antti | Kaakinen, Marika | Kajantie, Eero | Kaplan, Lee M. | Kathiresan, Sekar | Kettunen, Johannes | Kinnunen, Leena | Knowles, Joshua W. | Kolcic, Ivana | König, Inke R. | Koskinen, Seppo | Kovacs, Peter | Kuusisto, Johanna | Kraft, Peter | Kvaløy, Kirsti | Laitinen, Jaana | Lantieri, Olivier | Lanzani, Chiara | Launer, Lenore J. | Lecoeur, Cecile | Lehtimäki, Terho | Lettre, Guillaume | Liu, Jianjun | Lokki, Marja-Liisa | Lorentzon, Mattias | Luben, Robert N. | Ludwig, Barbara | Manunta, Paolo | Marek, Diana | Marre, Michel | Martin, Nicholas G. | McArdle, Wendy L. | McCarthy, Anne | McKnight, Barbara | Meitinger, Thomas | Melander, Olle | Meyre, David | Midthjell, Kristian | Montgomery, Grant W. | Morken, Mario A. | Morris, Andrew P. | Mulic, Rosanda | Ngwa, Julius S. | Nelis, Mari | Neville, Matt J. | Nyholt, Dale R. | O’Donnell, Christopher J. | O’Rahilly, Stephen | Ong, Ken K. | Oostra, Ben | Paré, Guillaume | Parker, Alex N. | Perola, Markus | Pichler, Irene | Pietiläinen, Kirsi H. | Platou, Carl G.P. | Polasek, Ozren | Pouta, Anneli | Rafelt, Suzanne | Raitakari, Olli | Rayner, Nigel W. | Ridderstråle, Martin | Rief, Winfried | Ruokonen, Aimo | Robertson, Neil R. | Rzehak, Peter | Salomaa, Veikko | Sanders, Alan R. | Sandhu, Manjinder S. | Sanna, Serena | Saramies, Jouko | Savolainen, Markku J. | Scherag, Susann | Schipf, Sabine | Schreiber, Stefan | Schunkert, Heribert | Silander, Kaisa | Sinisalo, Juha | Siscovick, David S. | Smit, Jan H. | Soranzo, Nicole | Sovio, Ulla | Stephens, Jonathan | Surakka, Ida | Swift, Amy J. | Tammesoo, Mari-Liis | Tardif, Jean-Claude | Teder-Laving, Maris | Teslovich, Tanya M. | Thompson, John R. | Thomson, Brian | Tönjes, Anke | Tuomi, Tiinamaija | van Meurs, Joyce B.J. | van Ommen, Gert-Jan | Vatin, Vincent | Viikari, Jorma | Visvikis-Siest, Sophie | Vitart, Veronique | Vogel, Carla I. G. | Voight, Benjamin F. | Waite, Lindsay L. | Wallaschofski, Henri | Walters, G. Bragi | Widen, Elisabeth | Wiegand, Susanna | Wild, Sarah H. | Willemsen, Gonneke | Witte, Daniel R. | Witteman, Jacqueline C. | Xu, Jianfeng | Zhang, Qunyuan | Zgaga, Lina | Ziegler, Andreas | Zitting, Paavo | Beilby, John P. | Farooqi, I. Sadaf | Hebebrand, Johannes | Huikuri, Heikki V. | James, Alan L. | Kähönen, Mika | Levinson, Douglas F. | Macciardi, Fabio | Nieminen, Markku S. | Ohlsson, Claes | Palmer, Lyle J. | Ridker, Paul M. | Stumvoll, Michael | Beckmann, Jacques S. | Boeing, Heiner | Boerwinkle, Eric | Boomsma, Dorret I. | Caulfield, Mark J. | Chanock, Stephen J. | Collins, Francis S. | Cupples, L. Adrienne | Smith, George Davey | Erdmann, Jeanette | Froguel, Philippe | Grönberg, Henrik | Gyllensten, Ulf | Hall, Per | Hansen, Torben | Harris, Tamara B. | Hattersley, Andrew T. | Hayes, Richard B. | Heinrich, Joachim | Hu, Frank B. | Hveem, Kristian | Illig, Thomas | Jarvelin, Marjo-Riitta | Kaprio, Jaakko | Karpe, Fredrik | Khaw, Kay-Tee | Kiemeney, Lambertus A. | Krude, Heiko | Laakso, Markku | Lawlor, Debbie A. | Metspalu, Andres | Munroe, Patricia B. | Ouwehand, Willem H. | Pedersen, Oluf | Penninx, Brenda W. | Peters, Annette | Pramstaller, Peter P. | Quertermous, Thomas | Reinehr, Thomas | Rissanen, Aila | Rudan, Igor | Samani, Nilesh J. | Schwarz, Peter E.H. | Shuldiner, Alan R. | Spector, Timothy D. | Tuomilehto, Jaakko | Uda, Manuela | Uitterlinden, André | Valle, Timo T. | Wabitsch, Martin | Waeber, Gérard | Wareham, Nicholas J. | Watkins, Hugh | Wilson, James F. | Wright, Alan F. | Zillikens, M. 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Nature genetics  2010;42(11):937-948.
Obesity is globally prevalent and highly heritable, but the underlying genetic factors remain largely elusive. To identify genetic loci for obesity-susceptibility, we examined associations between body mass index (BMI) and ~2.8 million SNPs in up to 123,865 individuals, with targeted follow-up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity-susceptibility loci and identified 18 new loci associated with BMI (P<5×10−8), one of which includes a copy number variant near GPRC5B. Some loci (MC4R, POMC, SH2B1, BDNF) map near key hypothalamic regulators of energy balance, and one is near GIPR, an incretin receptor. Furthermore, genes in other newly-associated loci may provide novel insights into human body weight regulation.
doi:10.1038/ng.686
PMCID: PMC3014648  PMID: 20935630

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