Person: Tiemeier, Henning
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Publication Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation
(Springer Science and Business Media LLC, 2021-09) Min, Josine L.; Hemani, Gibran; Hannon, Eilis; Dekkers, Koen F.; Castillo-Fernandez, Juan; Luijk, René; Carnero-Montoro, Elena; Lawson, Daniel J.; Burrows, Kimberley; Suderman, Matthew; Bretherick, Andrew D.; Richardson, Tom G.; Klughammer, Johanna; Iotchkova, Valentina; Sharp, Gemma; Al Khleifat, Ahmad; Shatunov, Aleksey; Iacoangeli, Alfredo; McArdle, Wendy L.; Ho, Karen M.; Kumar, Ashish; Söderhäll, Cilla; Soriano-Tárraga, Carolina; Giralt-Steinhauer, Eva; Kazmi, Nabila; Mason, Dan; McRae, Allan F.; Corcoran, David L.; Sugden, Karen; Kasela, Silva; Cardona, Alexia; Day, Felix R.; Cugliari, Giovanni; Viberti, Clara; Guarrera, Simonetta; Lerro, Michael; Gupta, Richa; Bollepalli, Sailalitha; Mandaviya, Pooja; Zeng, Yanni; Clarke, Toni-Kim; Walker, Rosie M.; Schmoll, Vanessa; Czamara, Darina; Ruiz-Arenas, Carlos; Rezwan, Faisal I.; Marioni, Riccardo E.; Lin, Tian; Awaloff, Yvonne; Germain, Marine; Aïssi, Dylan; Zwamborn, Ramona; van Eijk, Kristel; Dekker, Annelot; van Dongen, Jenny; Hottenga, Jouke-Jan; Willemsen, Gonneke; Xu, Cheng-Jian; Barturen, Guillermo; Català-Moll, Francesc; Kerick, Martin; Wang, Carol; Melton, Phillip; Elliott, Hannah R.; Shin, Jean; Bernard, Manon; Yet, Idil; Smart, Melissa; Gorrie-Stone, Tyler; Shaw, Chris; Al Chalabi, Ammar; Ring, Susan M.; Pershagen, Göran; Melén, Erik; Jiménez-Conde, Jordi; Roquer, Jaume; Lawlor, Deborah A.; Wright, John; Martin, Nicholas G.; Montgomery, Grant W.; Moffitt, Terrie E.; Poulton, Richie; Esko, Tõnu; Milani, Lili; Metspalu, Andres; Perry, John R. B.; Ong, Ken K.; Wareham, Nicholas J.; Matullo, Giuseppe; Sacerdote, Carlotta; Panico, Salvatore; Caspi, Avshalom; Arseneault, Louise; Gagnon, France; Ollikainen, Miina; Kaprio, Jaakko; Felix, Janine F.; Rivadeneira, Fernando; Tiemeier, Henning; van IJzendoorn, Marinus H.; Uitterlinden, André G.; Jaddoe, Vincent W. V.; Haley, Chris; McIntosh, Andrew M.; Evans, Kathryn L.; Murray, Alison; Räikkönen, Katri; Lahti, Jari; Nohr, Ellen A.; Sørensen, Thorkild I. A.; Hansen, Torben; Morgen, Camilla S.; Binder, Elisabeth B.; Lucae, Susanne; Gonzalez, Juan Ramon; Bustamante, Mariona; Sunyer, Jordi; Holloway, John W.; Karmaus, Wilfried; Zhang, Hongmei; Deary, Ian J.; Wray, Naomi R.; Starr, John M.; Beekman, Marian; van Heemst, Diana; Slagboom, P. Eline; Morange, Pierre-Emmanuel; Trégouët, David-Alexandre; Veldink, Jan H.; Davies, Gareth E.; de Geus, Eco J. C.; Boomsma, Dorret I.; Vonk, Judith M.; Brunekreef, Bert; Koppelman, Gerard H.; Alarcón-Riquelme, Marta E.; Huang, Rae-Chi; Pennell, Craig E.; van Meurs, Joyce; Ikram, M. Arfan; Hughes, Alun D.; Tillin, Therese; Chaturvedi, Nish; Pausova, Zdenka; Paus, Tomas; Spector, Timothy D.; Kumari, Meena; Schalkwyk, Leonard C.; Visscher, Peter M.; Davey Smith, George; Bock, Christoph; Gaunt, Tom R.; Bell, Jordana T.; Heijmans, Bastiaan T.; Mill, Jonathan; Relton, Caroline L.Characterising genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. Here we describe results of DNA methylation-quantitative trait loci (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTL of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We reveal that the genetic architecture of DNAm levels is highly polygenic and DNAm exhibits signatures of negative and positive natural selection. Using shared genetic control between distal DNAm sites we construct networks, identifying discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic factors are associated with both DNAm levels and complex diseases but only in a minority of cases these associations reflect direct causal relationships from DNAm to trait or vice versa indicating a more complex genotype-phenotype map than previously anticipated.
Publication Minimal Phenotyping Yields Genome-Wide Association Signals of Low Specificity for Major Depression
(Nature Publishing Group, 2020-03-30) Cai, Na; Revez, Joana A.; Adams, Mark J.; Andlauer, Till F. M.; Breen, Gerome; Byrne, Enda M.; Clarke, Toni-Kim; Forstner, Andreas J.; Grabe, Hans J.; Hamilton, Steven P.; Levinson, Douglas F.; Lewis, Cathryn M.; Lewis, Glyn; Martin, Nicholas G.; Milaneschi, Yuri; Mors, Ole; Muller-Myhsok, Bertram; Penninx, Brenda W. J. H.; Perlis, Roy H.; Pistis, Giorgio; Potash, James B.; Preisig, Martin; Shi, Jianxin; Smoller, Jordan W.; Streit, Fabien; Tiemeier, Henning; Uher, Rudolf; Van der Auwera, Sandra; Viktorin, Alexander; Weissman, Myrna M.; Kendler, Kenneth S.; Flint, JonathanMinimal phenotyping refers to the reliance on the use of a small number of self-report items for disease case identification, increasingly used in genome-wide association studies (GWAS). Here we report differences in genetic architecture between depression defined by minimal phenotyping and strictly defined Major Depressive Disorder (MDD): the former has a lower genotype-derived heritability that cannot be explained by inclusion of milder cases, and a higher proportion of the genome contributing to this shared genetic liability with other conditions than strictly-defined MDD. GWAS on minimal phenotyping definitions identify loci that are not specific to MDD, and though it can generate highly predictive polygenic risk scores, the predictive power can be explained entirely by large sample sizes rather than specificity for MDD. Our results reveal reliance on results from minimal phenotyping may bias our views of the genetic architecture of MDD and impede our ability to identify pathways specific to MDD.