Discovering patterns of pleiotropy in genome-wide association studies
Authors: Zhana Jianan ,   CHARGE ECG Working Group, Setten Jessica van , Brody Jennifer , Swenson Brenton , Butler Anne M. , Campbell Harry , Greco Fabiola Del , Evans Daniel S. , Gibson Quince , Gudbjartsson Daniel F. , Kerr Kathleen F. , Krijthe Bouwe P. , Lyytikäinen Leo-Pekka , Müller Christian , Müller-Nurasyid Martina , Nolte Ilja M. , Padmanabhan Sandosh , Ritchie Marylyn D. , Robino Antonietta , Smith Albert V. , Steri Maristella , Tanaka Toshiko , Teumer Alexander , Trompet Stella , Ulivi Sheila , Verweij Niek , Yin Xiaoyan , Arnar David O. , Asselbergs Folkert W. , Barnard John , Bis Josh , Blankenberg Stefan , Boerwinkle Eric , Bradford Yuki , Buckley Brendan M. , Chung Mina K. , Crawford Dana , Hoed Marcel den , Denny Josh , Dominiczak Anna F. , Ehret Georg B. , Eijgelsheim Mark , Ellinor Patrick , Felix Stephan B. , Franke Lude , Harris Tamara B. , Heckbert Susan R. , Holm Hilma , Thorsteinsdottir Unnur , Ilaria Gandin , Iorio Annamaria , Kähönen Mika , Kolcic Ivana , Kors Jan A. , Lakatta Edward G. , Launer Lenore J. , Lin Honghuang , Lin Henri J. , Liu Yongmei , Loos Ruth , Lubitz Steve , MacFarlane Peter , Magnani Jared W. , Leach Irene Mateo , Meitinger Thomas , Mitchell Braxton , Munzel Thomas , Papanicolaou George J. , Peters Annette , Pfeufer Arne , Pramstaller Peter M. , Raitakari Olli T. , Rotter Jerome I. , Rudan Igor , Samani Nilesh J. , Schlessinger David , Silva Aldana Claudia T. , Sinner Moritz , Smith Jonathan D. , Snieder Harold , Soliman Elsayed , Spector Timothy D. , Stott David J. , Strauch Konstantin , Tarasov Kirill V. , Uitterlinden Andre G. , van Wagoner David R. , Völker Uwe , Völzke Henry , Waldenberger Melanie , Westra Harm Jan , Wild Philipp S. , Zeller Tanja , Alonso Alvaro , Avery Christy L. , Bandinelli Stefania , Benjamin Emelia J. , Cucca Francesco , Cummings Steven R. , Dörr Marcus , Ferrucci Luigi , Gasparini Paolo , Gudnason Vilmundur , Hayward Carolina , Hicks Andrew A. , Jamshidi Yalda , Jukema J. Wouter , Kääb Stefan , Lehtimäki Terho , Munroe Patricia B. , Parsa Afshin , Polasekd Ozren , Psaty Bruce , Roden Dan , Schnabel Renate B. , Sinagra Gianfranco , Stefansson Kari , Stricker Bruno H. , der Harst Pim van , van Duijn Cornelia M. , Wilson James F. , Gharib Sina , de Bakker Paul I.W. , Isaacs Aaron , Arking Dan E. , Sotoodehnia Nona , Arking Dan E. , Baderab Joel S. 
Published: March 2018

Motivation

Genome-wide association studies have had great success in identifying human genetic variants associated with disease, disease risk factors, and other biomedical phenotypes. Many variants are associated with multiple traits, even after correction for trait-trait correlation. Discovering subsets of variants associated with a shared subset of phenotypes could help reveal disease mechanisms, suggest new therapeutic options, and increase the power to detect additional variants with similar pattern of associations. Here we introduce two methods based on a Bayesian framework, SNP And Pleiotropic PHenotype Organization (SAPPHO), one modeling independent phenotypes (SAPPHO-I) and the other incorporating a full phenotype covariance structure (SAPPHO-C). These two methods learn patterns of pleiotropy from genotype and phenotype data, using identified associations to discover additional associations with shared patterns.

Results

The SAPPHO methods, along with other recent approaches for pleiotropic association tests, were assessed using data from the Atherosclerotic Risk in Communities (ARIC) study of 8,000 individuals, whose gold-standard associations were provided by meta-analysis of 40,000 to 100,000 individuals from the CHARGE consortium. Using power to detect gold-standard associations at genome-wide significance (0.05 family-wise error rate) as a metric, SAPPHO performed best. The SAPPHO methods were also uniquely able to select the most significant variants in a parsimonious model, excluding other less likely variants within a linkage disequilibrium block. For meta-analysis, the SAPPHO methods implement summary modes that use sufficient statistics rather than full phenotype and genotype data. Meta-analysis applied to CHARGE detected 16 additional associations to the gold-standard loci, as well as 124 novel loci, at 0.05 false discovery rate. Reasons for the superior performance were explored by performing simulations over a range of scenarios describing different genetic architectures. With SAPPHO we were able to learn genetic structures that were hidden using the traditional univariate tests.

Availability

https://bitbucket.org/baderlab/fast/wiki/Home. SAPPHO software is available under the GNU General Public License, v2.