Browsing by Subject "GENOME-WIDE ASSOCIATION"

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  • Weiss, Alexander; Baselmans, Bart M. L.; Hofer, Edith; Yang, Jingyun; Okbay, Aysu; Lind, Penelope A.; Miller, Mike B.; Nolte, Ilja M.; Zhao, Wei; Hagenaars, Saskia P.; Hottenga, Jouke-Jan; Matteson, Lindsay K.; Snieder, Harold; Faul, Jessica D.; Hartman, Catharina A.; Boyle, Patricia A.; Tiemeier, Henning; Mosing, Miriam A.; Pattie, Alison; Davies, Gail; Liewald, David C.; Schmidt, Reinhold; De Jager, Philip L.; Heath, Andrew C.; Jokela, Markus; Starr, John M.; Oldehinkel, Albertine J.; Johannesson, Magnus; Cesarini, David; Hofman, Albert; Harris, Sarah E.; Smith, Jennifer A.; Keltikangas-Järvinen, Liisa; Pulkki-Råback, Laura; Schmidt, Helena; Smith, Jacqui; Iacono, William G.; McGue, Matt; Bennett, David A.; Pedersen, Nancy L.; Magnusson, Patrik K. E.; Deary, Ian J.; Martin, Nicholas G.; Boomsma, Dorret I.; Bartels, Meike; Luciano, Michelle (2016)
    Approximately half of the variation in wellbeing measures overlaps with variation in personality traits. Studies of non-human primate pedigrees and human twins suggest that this is due to common genetic influences. We tested whether personality polygenic scores for the NEO Five-Factor Inventory (NEO-FFI) domains and for item response theory (IRT) derived extraversion and neuroticism scores predict variance in wellbeing measures. Polygenic scores were based on published genome-wide association (GWA) results in over 17,000 individuals for the NEO-FFI and in over 63,000 for the IRT extraversion and neuroticism traits. The NEO-FFI polygenic scores were used to predict life satisfaction in 7 cohorts, positive affect in 12 cohorts, and general wellbeing in 1 cohort (maximal N = 46,508). Meta-analysis of these results showed no significant association between NEO-FFI personality polygenic scores and the wellbeing measures. IRT extraversion and neuroticism polygenic scores were used to predict life satisfaction and positive affect in almost 37,000 individuals from UK Biobank. Significant positive associations (effect sizes
  • Prokopenko, Inga; Poon, Wenny; Maegi, Reedik; Prasad, Rashmi B.; Salehi, S. Albert; Almgren, Peter; Osmark, Peter; Bouatia-Naji, Nabila; Wierup, Nils; Fall, Tove; Stancakova, Alena; Barker, Adam; Lagou, Vasiliki; Osmond, Clive; Xie, Weijia; Lahti, Jari; Jackson, Anne U.; Cheng, Yu-Ching; Liu, Jie; O'Connell, Jeffrey R.; Blomstedt, Paul A.; Fadista, Joao; Alkayyali, Sami; Dayeh, Tasnim; Ahlqvist, Emma; Taneera, Jalal; Lecoeur, Cecile; Kumar, Ashish; Hansson, Ola; Hansson, Karin; Voight, Benjamin F.; Kang, Hyun Min; Levy-Marchal, Claire; Vatin, Vincent; Palotie, Aarno; Syvanen, Ann-Christine; Mari, Andrea; Weedon, Michael N.; Loos, Ruth J. F.; Ong, Ken K.; Nilsson, Peter; Isomaa, Bo; Tuomi, Tiinamaija; Wareham, Nicholas J.; Stumvoll, Michael; Widen, Elisabeth; Lakka, Timo A.; Langenberg, Claudia; Tonjes, Anke; Rauramaa, Rainer; Kuusisto, Johanna; Frayling, Timothy M.; Froguel, Philippe; Walker, Mark; Eriksson, Johan G.; Ling, Charlotte; Kovacs, Peter; Ingelsson, Erik; McCarthy, Mark I.; Shuldiner, Alan R.; Silver, Kristi D.; Laakso, Markku; Groop, Leif; Lyssenko, Valeriya (2014)
  • Chew, Tracy; Haase, Bianca; Bathgate, Roslyn; Willet, Cali E.; Kaukonen, Maria K.; Mascord, Lisa J.; Lohi, Hannes T.; Wade, Claire M. (2017)
    Progressive retinal atrophy is a common cause of blindness in the dog and affects >100 breeds. It is characterized by gradual vision loss that occurs due to the degeneration of photoreceptor cells in the retina. Similar to the human counterpart retinitis pigmentosa, the canine disorder is clinically and genetically heterogeneous and the underlying cause remains unknown for many cases. We use a positional candidate gene approach to identify putative variants in the Hungarian Puli breed using genotyping data of 14 family-based samples (CanineHD BeadChip array, Illumina) and whole-genome sequencing data of two proband and two parental samples (Illumina HiSeq 2000). A single nonsense SNP in exon 2 of BBS4 (c.58A > T, p.Lys20*) was identified following filtering of high quality variants. This allele is highly associated (P-CHISQ = 3.425e(-14), n = 103) and segregates perfectly with progressive retinal atrophy in the Hungarian Puli. In humans, BBS4 is known to cause Bardet-Biedl syndrome which includes a retinitis pigmentosa phenotype. From the observed coding change we expect that no functional BBS4 can be produced in the affected dogs. We identified canine phenotypes comparable with Bbs4-null mice including obesity and spermatozoa flagella defects. Knockout mice fail to form spermatozoa flagella. In the affected Hungarian Puli spermatozoa flagella are present, however a large proportion of sperm are morphologically abnormal and
  • Kun-Rodrigues, Celia; Orme, Tatiana; Carmona, Susana; Hernandez, Dena G.; Ross, Owen A.; Eicher, John D.; Shepherd, Claire; Parkkinen, Laura; Darwent, Lee; Heckman, Michael G.; Scholz, Sonja W.; Troncoso, Juan C.; Pletnikova, Olga; Dawson, Ted; Rosenthal, Liana; Ansorge, Olaf; Clarimonm, Jordi; Lleo, Alberto; Morenas-Rodriguez, Estrella; Clark, Lorraine; Honig, Lawrence S.; Marder, Karen; Lemstra, Afina; Rogaeva, Ekaterina; St George-Hyslop, Peter; Londos, Elisabet; Zetterberg, Henrik; Barber, Imelda; Braae, Anne; Brown, Kristelle; Morgan, Kevin; Troakes, Claire; Al-Sarraj, Safa; Lashley, Tammaryn; Holton, Janice; Compta, Yaroslau; Van Deerlin, Vivianna; Serrano, Geidy E.; Beach, Thomas G.; Lesage, Suzanne; Galasko, Douglas; Masliah, Eliezer; Santana, Isabel; Pastor, Pau; Diez-Fairen, Monica; Aguilar, Miquel; Tienari, Pentti J.; Myllykangas, Liisa; Oinas, Minna; Revesz, Tamas; Lees, Andrew; Boeve, Brad F.; Petersen, Ronald C.; Ferman, Tanis J.; Escott-Price, Valentina; Graff-Radford, Neill; Cairns, Nigel J.; Morris, John C.; Pickering-Brown, Stuart; Mann, David; Halliday, Glenda M.; Hardy, John; Trojanowski, John Q.; Dickson, Dennis W.; Singleton, Andrew; Stone, David J.; Guerreiro, Rita; Bras, Jose (2019)
    The role of genetic variability in dementia with Lewy bodies (DLB) is now indisputable; however, data regarding copy number variation (CNV) in this disease has been lacking. Here, we used whole-genome genotyping of 1454 DLB cases and 1525 controls to assess copy number variability. We used 2 algorithms to confidently detect CNVs, performed a case-control association analysis, screened for candidate CNVs previously associated with DLB-related diseases, and performed a candidate gene approach to fully explore the data. We identified 5 CNV regions with a significant genome-wide association to DLB; 2 of these were only present in cases and absent from publicly available databases: one of the regions overlapped LAPTM4B, a known lysosomal protein, whereas the other overlapped the NME1 locus and SPAG9. We also identified DLB cases presenting rare CNVs in genes previously associated with DLB or related neurodegenerative diseases, such as SNCA, APP, and MAPT. To our knowledge, this is the first study reporting genome-wide CNVs in a large DLB cohort. These results provide preliminary evidence for the contribution of CNVs in DLB risk. (C) 2019 Elsevier Inc. All rights reserved.
  • Porcu, Eleonora; Medici, Marco; Pistis, Giorgio; Volpato, Claudia B.; Wilson, Scott G.; Cappola, Anne R.; Bos, Steffan D.; Deelen, Joris; den Heijer, Martin; Freathy, Rachel M.; Lahti, Jari; Liu, Chunyu; Lopez, Lorna M.; Nolte, Ilja M.; O'Connell, Jeffrey R.; Tanaka, Toshiko; Trompet, Stella; Arnold, Alice; Bandinelli, Stefania; Beekman, Marian; Bohringer, Stefan; Brown, Suzanne J.; Buckley, Brendan M.; Camaschella, Clara; de Craen, Anton J. M.; Davies, Gail; de Visser, Marieke C. H.; Ford, Ian; Forsen, Tom Johan; Frayling, Timothy M.; Fugazzola, Laura; Goegele, Martin; Hattersley, Andrew T.; Hermus, Ad R.; Hofman, Albert; Houwing-Duistermaat, Jeanine J.; Jensen, Richard A.; Kajantie, Eero; Kloppenburg, Margreet; Lim, Ee M.; Masciullo, Corrado; Mariotti, Stefano; Minelli, Cosetta; Mitchell, Braxton D.; Nagaraja, Ramaiah; Netea-Maier, Romana T.; Palotie, Aarno; Persani, Luca; Piras, Maria G.; Psaty, Bruce M.; Räikkönen, Katri; Richards, J. Brent; Rivadeneira, Fernando; Sala, Cinzia; Sabra, Mona M.; Sattar, Naveed; Shields, Beverley M.; Soranzo, Nicole; Starr, John M.; Stott, David J.; Sweep, Fred C. G. J.; Usala, Gianluca; van der Klauw, Melanie M.; van Heemst, Diana; van Mullem, Alies; Vermeulen, Sita H.; Visser, W. Edward; Walsh, John P.; Westendorp, Rudi G. J.; Widen, Elisabeth; Zhai, Guangju; Cucca, Francesco; Deary, Ian J.; Eriksson, Johan G.; Ferrucci, Luigi; Fox, Caroline S.; Jukema, J. Wouter; Kiemeney, Lambertus A.; Pramstaller, Peter P.; Schlessinger, David; Shuldiner, Alan R.; Slagboom, Eline P.; Uitterlinden, Andre G.; Vaidya, Bijay; Visser, Theo J.; Wolffenbuttel, Bruce H. R.; Meulenbelt, Ingrid; Rotter, Jerome I.; Spector, Tim D.; Hicks, Andrew A.; Toniolo, Daniela; Sanna, Serena; Peeters, Robin P.; Naitza, Silvia (2013)
  • Bodea, Corneliu A.; Neale, Benjamin M.; Ripke, Stephan; Daly, Mark J.; Devlin, Bernie; Roeder, Kathryn; Int IBD Genetics Consortium; Palotie, A. (2016)
    One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data.
  • Emdin, Connor A.; Haas, Mary E.; Khera, Amit V.; Aragam, Krishna; Chaffin, Mark; Klarin, Derek; Hindy, George; Jiang, Lan; Wei, Wei-Qi; Feng, Qiping; Karjalainen, Juha; Havulinna, Aki; Kiiskinen, Tuomo; Bick, Alexander; Ardissino, Diego; Wilson, James G.; Schunkert, Heribert; McPherson, Ruth; Watkins, Hugh; Elosua, Roberto; Bown, Matthew J.; Samani, Nilesh J.; Baber, Usman; Erdmann, Jeanette; Gupta, Namrata; Danesh, John; Saleheen, Danish; Chang, Kyong-Mi; Vujkovic, Marijana; Voight, Ben; Damrauer, Scott; Lynch, Julie; Kaplan, David; Serper, Marina; Tsao, Philip; Program, Million Veteran; Mercader, Josep; Hanis, Craig; Daly, Mark; Denny, Joshua; Gabriel, Stacey; Kathiresan, Sekar (2020)
    Author summary Cirrhosis is a leading cause of death worldwide. However, the genetic underpinnings of cirrhosis remain poorly understood. In this study, we analyze twelve thousand individuals with cirrhosis and identify a common missense variant in a gene called MARC1 that protects against cirrhosis. Carriers of this missense variant also have lower blood cholesterol levels, lower liver enzyme levels and reduced liver fat. We identify an additional two low-frequency coding variants in MARC1 that are also associated with lower cholesterol levels, lower liver enzyme levels and protection from cirrhosis. Finally, we identify an individual homozygous for a predicted loss-of-function variant in MARC1 who exhibits very low blood LDL cholesterol levels. These genetic findings suggest that MARC1 deficiency may lower blood cholesterol levels and protect against cirrhosis, pointing to MARC1 as a potential therapeutic target for liver disease. Analyzing 12,361 all-cause cirrhosis cases and 790,095 controls from eight cohorts, we identify a common missense variant in the Mitochondrial Amidoxime Reducing Component 1 gene (MARC1 p.A165T) that associates with protection from all-cause cirrhosis (OR 0.91, p = 2.3*10(-11)). This same variant also associates with lower levels of hepatic fat on computed tomographic imaging and lower odds of physician-diagnosed fatty liver as well as lower blood levels of alanine transaminase (-0.025 SD, 3.7*10(-43)), alkaline phosphatase (-0.025 SD, 1.2*10(-37)), total cholesterol (-0.030 SD, p = 1.9*10(-36)) and LDL cholesterol (-0.027 SD, p = 5.1*10(-30)) levels. We identified a series of additional MARC1 alleles (low-frequency missense p.M187K and rare protein-truncating p.R200Ter) that also associated with lower cholesterol levels, liver enzyme levels and reduced risk of cirrhosis (0 cirrhosis cases for 238 R200Ter carriers versus 17,046 cases of cirrhosis among 759,027 non-carriers, p = 0.04) suggesting that deficiency of the MARC1 enzyme may lower blood cholesterol levels and protect against cirrhosis.
  • JSCRG; TSRHCCG (2018)
    Adolescent idiopathic scoliosis (AIS) is the most common type of spinal deformity and has a significant genetic background. Genome-wide association studies (GWASs) identified several susceptibility loci associated with AIS. Among them is a locus on chromosome 6q24.1 that we identified by a GWAS in a Japanese cohort. The locus is represented by rs6570507 located within GPR126. To ensure the association of rs6570507 with AIS, we conducted a meta-analysis using eight cohorts from East Asia, Northern Europe and USA. The analysis included a total of 6,873 cases and 38,916 controls and yielded significant association (combined P = 2.95 x 10(-20); odds ratio = 1.22), providing convincing evidence of the worldwide association between rs6570507 and AIS susceptibility. In silico analyses strongly suggested that GPR126 is a susceptibility gene at this locus.
  • Escala-Garcia, M.; Abraham, J.; Andrulis, I.L.; Anton-Culver, H.; Arndt, V.; Ashworth, A.; Auer, P.L.; Auvinen, P.; Beckmann, M.W.; Beesley, J.; Behrens, S.; Benitez, J.; Bermisheva, M.; Blomqvist, C.; Blot, W.; Bogdanova, N.V.; Bojesen, S.E.; Bolla, M.K.; Børresen-Dale, A.-L.; Brauch, H.; Brenner, H.; Brucker, S.Y.; Burwinkel, B.; Caldas, C.; Canzian, F.; Chang-Claude, J.; Chanock, S.J.; Chin, S.-F.; Clarke, C.L.; Couch, F.J.; Cox, A.; Cross, S.S.; Czene, K.; Daly, M.B.; Dennis, J.; Devilee, P.; Dunn, J.A.; Dunning, A.M.; Dwek, M.; Earl, H.M.; Eccles, D.M.; Eliassen, A.H.; Ellberg, C.; Evans, D.G.; Fasching, P.A.; Figueroa, J.; Flyger, H.; Gago-Dominguez, M.; Gapstur, S.M.; García-Closas, M.; García-Sáenz, J.A.; Gaudet, M.M.; George, A.; Giles, G.G.; Goldgar, D.E.; González-Neira, A.; Grip, M.; Guénel, P.; Guo, Q.; Haiman, C.A.; Håkansson, N.; Hamann, U.; Harrington, P.A.; Hiller, L.; Hooning, M.J.; Hopper, J.L.; Howell, A.; Huang, C.-S.; Huang, G.; Hunter, D.J.; Jakubowska, A.; John, E.M.; Kaaks, R.; Kapoor, P.M.; Keeman, R.; Kitahara, C.M.; Koppert, L.B.; Kraft, P.; Kristensen, V.N.; Lambrechts, D.; Le Marchand, L.; Lejbkowicz, F.; Lindblom, A.; Lubiński, J.; Mannermaa, A.; Manoochehri, M.; Manoukian, S.; Margolin, S.; Martinez, M.E.; Maurer, T.; Mavroudis, D.; Meindl, A.; Milne, R.L.; Mulligan, A.M.; Neuhausen, S.L.; Nevanlinna, H.; Newman, W.G.; Olshan, A.F.; Olson, J.E.; Olsson, H.; Orr, N.; Peterlongo, P.; Petridis, C.; Prentice, R.L.; Presneau, N.; Punie, K.; Ramachandran, D.; Rennert, G.; Romero, A.; Sachchithananthan, M.; Saloustros, E.; Sawyer, E.J.; Schmutzler, R.K.; Schwentner, L.; Scott, C.; Simard, J.; Sohn, C.; Southey, M.C.; Swerdlow, A.J.; Tamimi, R.M.; Tapper, W.J.; Teixeira, M.R.; Terry, M.B.; Thorne, H.; Tollenaar, R.A.E.M.; Tomlinson, I.; Troester, M.A.; Truong, T.; Turnbull, C.; Vachon, C.M.; van der Kolk, L.E.; Wang, Q.; Winqvist, R.; Wolk, A.; Yang, X.R.; Ziogas, A.; Pharoah, P.D.P.; Hall, P.; Wessels, L.F.A.; Chenevix-Trench, G.; Bader, G.D.; Dörk, T.; Easton, D.F.; Canisius, S.; Schmidt, M.K. (2020)
    Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies similar to 7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis.
  • van der Valk, Ralf J. P.; Kreiner-Moller, Eskil; Kooijman, Marjolein N.; Guxens, Monica; Stergiakouli, Evangelia; Saaf, Annika; Bradfield, Jonathan P.; Geller, Frank; Hayes, M. Geoffrey; Cousminer, Diana L.; Koerner, Antje; Thiering, Elisabeth; Curtin, John A.; Myhre, Ronny; Huikari, Ville; Joro, Raimo; Kerkhof, Marjan; Warrington, Nicole M.; Pitkanen, Niina; Ntalla, Ioanna; Horikoshi, Momoko; Veijola, Riitta; Freathy, Rachel M.; Teo, Yik-Ying; Barton, Sheila J.; Evans, David M.; Kemp, John P.; St Pourcain, Beate; Ring, Susan M.; Smith, George Davey; Bergstrom, Anna; Kull, Inger; Hakonarson, Hakon; Mentch, Frank D.; Bisgaard, Hans; Chawes, Bo; Stokholm, Jakob; Waage, Johannes; Eriksen, Patrick; Sevelsted, Astrid; Melbye, Mads; van Duijn, Cornelia M.; Medina-Gomez, Carolina; Hofman, Albert; de Jongste, Johan C.; Taal, H. Rob; Eriksson, Johan; Palotie, Aarno; Knip, Mikael; Widen, Elisabeth; Early Genetics Lifecourse; Genetic Invest ANthropometric; Early Growth Genetics EGG (2015)
  • Ahonen, Saija J.; Kaukonen, Maria; Nussdorfer, Forrest D.; Harman, Christine D.; Komaromy, Andras M.; Lohi, Hannes (2014)
  • Ried, Janina S.; Jeff, Janina M.; Chu, Audrey Y.; Bragg-Gresham, Jennifer L.; van Dongen, Jenny; Huffman, Jennifer E.; Ahluwalia, Tarunveer S.; Cadby, Gemma; Eklund, Niina; Eriksson, Joel; Esko, Tonu; Feitosa, Mary F.; Goel, Anuj; Gorski, Mathias; Hayward, Caroline; Heard-Costa, Nancy L.; Jackson, Anne U.; Jokinen, Eero; Kanoni, Stavroula; Kristiansson, Kati; Kutalik, Zoltan; Lahti, Jari; Luan, Jian'an; Maegi, Reedik; Mahajan, Anubha; Mangino, Massimo; Medina-Gomez, Carolina; Monda, Keri L.; Nolte, Ilja M.; Perusse, Louis; Prokopenko, Inga; Qi, Lu; Rose, Lynda M.; Salvi, Erika; Smith, Megan T.; Snieder, Harold; Stancakova, Alena; Sung, Yun Ju; Tachmazidou, Ioanna; Teumer, Alexander; Thorleifsson, Gudmar; van der Harst, Pim; Walker, Ryan W.; Wang, Sophie R.; Wild, Sarah H.; Willems, Sara M.; Wong, Andrew; Zhang, Weihua; Albrecht, Eva; Alves, Alexessander Couto; Bakker, Stephan J. L.; Barlassina, Cristina; Bartz, Traci M.; Beilby, John; Bellis, Claire; Bergman, Richard N.; Bergmann, Sven; Blangero, John; Blueher, Matthias; Boerwinkle, Eric; Bonnycastle, Lori L.; Bornstein, Stefan R.; Bruinenberg, Marcel; Campbell, Harry; Chen, Yii-Der Ida; Chiang, Charleston W. K.; Chines, Peter S.; Collins, Francis S.; Cucca, Fracensco; Cupples, L. Adrienne; D'Avila, Francesca; de Geus, Eco J. C.; Dedoussis, George; Dimitriou, Maria; Doering, Angela; Eriksson, Johan G.; Farmaki, Aliki-Eleni; Farrall, Martin; Ferreira, Teresa; Fischer, Krista; Forouhi, Nita G.; Friedrich, Nele; Gjesing, Anette Prior; Glorioso, Nicola; Graff, Mariaelisa; Grallert, Harald; Grarup, Niels; Graessler, Juergen; Grewal, Jagvir; Hamsten, Anders; Harder, Marie Neergaard; Hartman, Catharina A.; Hassinen, Maija; Hastie, Nicholas; Hattersley, Andrew Tym; Havulinna, Aki S.; Heliovaara, Markku; Hillege, Hans; Hofman, Albert; Holmen, Oddgeir; Homuth, Georg; Hottenga, Jouke-Jan; Hui, Jennie; Husemoen, Lise Lotte; Hysi, Pirro G.; Isaacs, Aaron; Ittermann, Till; Jalilzadeh, Shapour; James, Alan L.; Jorgensen, Torben; Jousilahti, Pekka; Jula, Antti; Justesen, Johanne Marie; Justice, Anne E.; Kahonen, Mika; Karaleftheri, Maria; Khaw, Kay Tee; Keinanen-Kiukaanniemi, Sirkka M.; Kinnunen, Leena; Knekt, Paul B.; Koistinen, Heikki A.; Kolcic, Ivana; Kooner, Ishminder K.; Koskinen, Seppo; Kovacs, Peter; Kyriakou, Theodosios; Laitinen, Tomi; Langenberg, Claudia; Lewin, Alexandra M.; Lichtner, Peter; Lindgren, Cecilia M.; Lindstrom, Jaana; Linneberg, Allan; Lorbeer, Roberto; Lorentzon, Mattias; Luben, Robert; Lyssenko, Valeriya; Mannisto, Satu; Manunta, Paolo; Leach, Irene Mateo; McArdle, Wendy L.; Mcknight, Barbara; Mohlke, Karen L.; Mihailov, Evelin; Milani, Lili; Mills, Rebecca; Montasser, May E.; Morris, Andrew P.; Mueller, Gabriele; Musk, Arthur W.; Narisu, Narisu; Ong, Ken K.; Oostra, Ben A.; Osmond, Clive; Palotie, Aarno; Pankow, James S.; Paternoster, Lavinia; Penninx, Brenda W.; Pichler, Irene; Pilia, Maria G.; Polasek, Ozren; Pramstaller, Peter P.; Raitakari, Olli T.; Rankinen, Tuomo; Rao, D. C.; Rayner, Nigel W.; Ribel-Madsen, Rasmus; Rice, Treva K.; Richards, Marcus; Ridker, Paul M.; Rivadeneira, Fernando; Ryan, Kathy A.; Sanna, Serena; Sarzynski, Mark A.; Scholtens, Salome; Scott, Robert A.; Sebert, Sylvain; Southam, Lorraine; Sparso, Thomas Hempel; Steinthorsdottir, Valgerdur; Stirrups, Kathleen; Stolk, Ronald P.; Strauch, Konstantin; Stringham, Heather M.; Swertz, Morris A.; Swift, Amy J.; Toenjes, Anke; Tsafantakis, Emmanouil; van der Most, Peter J.; Van Vliet-Ostaptchouk, Jana V.; Vandenput, Liesbeth; Vartiainen, Erkki; Venturini, Cristina; Verweij, Niek; Viikari, Jorma S.; Vitart, Veronique; Vohl, Marie-Claude; Vonk, Judith M.; Waeber, Gerard; Widen, Elisabeth; Willemsen, Gonneke; Wilsgaard, Tom; Winkler, Thomas W.; Wright, Alan F.; Yerges-Armstrong, Laura M.; Zhao, Jing Hua; Zillikens, M. Carola; Boomsma, Dorret I.; Bouchard, Claude; Chambers, John C.; Chasman, Daniel I.; Cusi, Daniele; Gansevoort, Ron T.; Gieger, Christian; Hansen, Torben; Hicks, Andrew A.; Hu, Frank; Hveem, Kristian; Jarvelin, Marjo-Riitta; Kajantie, Eero; Kooner, Jaspal S.; Kuh, Diana; Kuusisto, Johanna; Laakso, Markku; Lakka, Timo A.; Lehtimaeki, Terho; Metspalu, Andres; Njolstad, Inger; Ohlsson, Claes; Oldehinkel, Albertine J.; Palmer, Lyle J.; Pedersen, Oluf; Perola, Markus; Peters, Annette; Psaty, Bruce M.; Puolijoki, Hannu; Rauramaa, Rainer; Rudan, Igor; Salomaa, Veikko; Schwarz, Peter E. H.; Shudiner, Alan R.; Smit, Jan H.; Sorensen, Thorkild I. A.; Spector, Timothy D.; Stefansson, Kari; Stumvoll, Michael; Tremblay, Angelo; Tuomilehto, Jaakko; Uitterlinden, Andre G.; Uusitupa, Matti; Voelker, Uwe; Vollenweider, Peter; Wareham, Nicholas J.; Watkins, Hugh; Wilson, James F.; Zeggini, Eleftheria; Abecasis, Goncalo R.; Boehnke, Michael; Borecki, Ingrid B.; Deloukas, Panos; van Duijn, Cornelia M.; Fox, Caroline; Groop, Leif C.; Heid, Iris M.; Hunter, David J.; Kaplan, Robert C.; McCarthy, Mark I.; North, Kari E.; O'Connell, Jeffrey R.; Schlessinger, David; Thorsteinsdottir, Unnur; Strachan, David P.; Frayling, Timothy; Hirschhorn, Joel N.; Mueller-Nurasyid, Martina; Loos, Ruth J. F. (2016)
    Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.
  • Intl Multiple Sclerosis Genetics; Madireddy, Lohith; Patsopoulos, Niklaos A.; Palotie, Aarno (2019)
    Genome-wide association studies (GWAS) have identified more than 50,000 unique associations with common human traits. While this represents a substantial step forward, establishing the biology underlying these associations has proven extremely difficult. Even determining which cell types and which particular gene(s) are relevant continues to be a challenge. Here, we conduct a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS), which had analyzed a total of 47,351 cases and 68,284 healthy controls and found more than 200 non-MHC genome-wide associations. Our analysis identifies pan immune cell as well as cell-specific susceptibility genes in T cells, B cells and monocytes. Finally, genotype-level data from 2,370 patients and 412 controls is used to compute intraindividual and cell-specific susceptibility pathways that offer a biological interpretation of the individual genetic risk to MS. This approach could be adopted in any other complex trait for which genome-wide data is available.
  • Type 1 Diabet TrialNet Study Grp; Redondo, Maria J.; Geyer, Susan; Steck, Andrea K.; Knip, Mikael (2018)
    OBJECTIVEWe tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals.RESEARCH DESIGN AND METHODSWe studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables.RESULTSHigher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; P = 0.0002).CONCLUSIONSThe T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.
  • SUMMIT Consortium (2018)
    To explore novel genetic loci for diabetic nephropathy, we performed genome-wide association studies (GWAS) for diabetic nephropathy in Japanese patients with type 2 diabetes. We analyzed the association of 5,768,242 single nucleotide polymorphisms (SNPs) in Japanese patients with type 2 diabetes, 2,380 nephropathy cases and 5,234 controls. We further performed GWAS for diabetic nephropathy using independent Japanese patients with type 2 diabetes, 429 cases and 358 controls and the results of these two GWAS were combined with an inverse variance meta-analysis (stage-1), followed by a de novo genotyping for the candidate SNP loci (p <1.0 x 10(-4)) in an independent case-control study (Stage-2; 1,213 cases and 1,298 controls). After integrating stage-1 and stage-2 data, we identified one SNP locus, significantly associated with diabetic nephropathy; rs56094641 in FTO, P = 7.74 x 10(-10). We further examined the association of rs56094641 with diabetic nephropathy in independent Japanese patients with type 2 diabetes (902 cases and 1,221 controls), and found that the association of this locus with diabetic nephropathy remained significant after integrating all association data (P = 7.62 x 10(-10)). We have identified FTO locus as a novel locus for conferring susceptibility to diabetic nephropathy in Japanese patients with type 2 diabetes.
  • Kemppainen, Petri; Husby, Arild (2018)
    A fundamental assumption in quantitative genetics is that traits are controlled by many loci of small effect. Using genomic data, this assumption can be tested using chromosome partitioning analyses, where the proportion of genetic variance for a trait explained by each chromosome (h(c)(2)), is regressed on its size. However, as h(c)(2)-estimates are necessarily positive (censoring) and the variance increases with chromosome size (heteroscedasticity), two fundamental assumptions of ordinary least squares (OLS) regression are violated. Using simulated and empirical data we demonstrate that these violations lead to incorrect inference of genetic architecture. The degree of bias depends mainly on the number of chromosomes and their size distribution and is therefore specific to the species; using published data across many different species we estimate that not accounting for this effect overall resulted in 28% false positives. We introduce a new and computationally efficient resampling method that corrects for inflation caused by heteroscedasticity and censoring and that works under a large range of dataset sizes and genetic architectures in empirical datasets. Our new method substantially improves the robustness of inferences from chromosome partitioning analyses.
  • Pittman, Alan M.; Naranjo, Silvia; Jalava, Sanni E.; Twiss, Philip; Ma, Yussanne; Olver, Bianca; Lloyd, Amy; Vijayakrishnan, Jayaram; Qureshi, Mobshra; Broderick, Peter; van Wezel, Tom; Morreau, Hans; Tuupanen, Sari; Aaltonen, Lauri A.; Eva Alonso, M.; Manzanares, Miguel; Gavilan, Angela; Visakorpi, Tapio; Luis Gomez-Skarmeta, Jose; Houlston, Richard S. (2010)
  • Inouye, Michael; Silander, Kaisa; Hämäläinen, Eija; Salomaa, Veikko; Harald, Kennet; Jousilahti, Pekka; Mannisto, Satu; Eriksson, Johan G.; Saarela, Janna; Ripatti, Samuli; Perola, Markus; van Ommen, Gert-Jan B.; Taskinen, Marja-Riitta; Palotie, Aarno; Dermitzakis, Emmanouil T.; Peltonen, Leena (2010)
  • Hannon, Eilis; Dempster, Emma; Viana, Joana; Burrage, Joe; Smith, Adam R.; Macdonald, Ruby; St Clair, David; Mustard, Colette; Breen, Gerome; Therman, Sebastian; Kaprio, Jaakko; Toulopoulou, Timothea; Pol, Hilleke E. Hulshoff; Bohlken, Marc M.; Kahn, Rene S.; Nenadic, Igor; Hultman, Christina M.; Murray, Robin M.; Collier, David A.; Bass, Nick; Gurling, Hugh; McQuillin, Andrew; Schalkwyk, Leonard; Mill, Jonathan (2016)
    Background: Schizophrenia is a highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Results: We performed a multi-stage epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in a total of 1714 individuals from three independent sample cohorts. We have identified multiple differentially methylated positions and regions consistently associated with schizophrenia across the three cohorts; these effects are independent of important confounders such as smoking. We also show that epigenetic variation at multiple loci across the genome contributes to the polygenic nature of schizophrenia. Finally, we show how DNA methylation quantitative trait loci in combination with Bayesian co-localization analyses can be used to annotate extended genomic regions nominated by studies of schizophrenia, and to identify potential regulatory variation causally involved in disease. Conclusions: This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological approach that can be used to inform epigenome-wide association study analyses of other complex traits and diseases. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with etiological variation, and of using DNA methylation quantitative trait loci to refine the functional and regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.