MetaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

Bioinformatics

Volumn 32, Issue 13, 2016, Pages 1981-1989

  Cichonska, Anna ^a,b   Rousu, Juho ^b   Marttinen, Pekka ^b   Kangas, Antti J ^c   Soininen, Pasi ^c,d   Lehtimäki, Terho ^e   Raitakari, Olli T ^f,g   Järvelin, Marjo Riitta ^c,h,i,j   Salomaa, Veikko ^k   Ala Korpela, Mika ^c,d,l   Ripatti, Samuli ^a,m   Pirinen, Matti ^a  

^a UNIVERSITY OF HELSINKI   (Finland)

^b AALTO UNIVERSITY   (Finland)

^c UNIVERSITY OF OULU   (Finland)

^d UNIVERSITY OF EASTERN FINLAND   (Finland)

^e TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

^f TURKU UNIVERSITY HOSPITAL   (Finland)

^g UNIVERSITY OF TURKU   (Finland)

^h IMPERIAL COLLEGE LONDON   (United Kingdom)

ⁱ UNIVERSITY OF OULU   (Finland)

^j OULU UNIVERSITY HOSPITAL   (Finland)

^k NATIONAL PUBLIC HEALTH INSTITUTE   (Finland)

^l UNIVERSITY OF BRISTOL   (United Kingdom)

^m WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

more..

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BIOLOGY; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMAN; META ANALYSIS; MULTIVARIATE ANALYSIS; PHENOTYPE; PROCEDURES; SINGLE NUCLEOTIDE POLYMORPHISM;

ALGORITHMS; COMPUTATIONAL BIOLOGY; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMANS; MULTIVARIATE ANALYSIS; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 85007276054     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw052     Document Type: Article

References (36)

1. 1000 Genomes Project Consortium (2012) An integrated map of genetic variation from 1,092 human genomes. Nature, 491, 56-65.
2. Ardlie,K.G. et al. (2015) The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Sci., 348, 648-660.
3. Deloukas,P. et al. (2013) Large-scale association analysis identifies new risk loci for coronary artery disease. Nat. Genet., 45, 25-33.
4. ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57-74.
5. Evangelou,E. and Ioannidis,J.P. (2013) Meta-analysis methods for genomewide association studies and beyond. Nat. Rev. Genet., 14, 379-389.
6. Feng,S. et al. (2014) RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics, btu367.
7. Ferreira,M.A. and Purcell,S.M. (2009) A multivariate test of association. Bioinformatics, 25, 132-133.
8. Global Lipids Genetics Consortium (2013) Discovery and refinement of loci associated with lipid levels. Nat. Genet., 45, 1274-1283.
9. Hindorff,L.A. et al. (2011) A Catalog of Published Genome-Wide Association Studies. www.genome.gov/gwastudies. (July 2015, date last accessed).
10. Hotelling,H. (1936) Relations between two sets of variates. Biometrika, 28, 321-377.
11. Howie,B.N. et al. (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLos Genet., 5, e1000529.
12. Inouye,M. et al. (2012) Novel Loci for metabolic networks and multi-tissue expression studies reveal genes for atherosclerosis. PLos Genet., 8, e1002907.
13. Kettunen,J. et al. (2012) Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat. Genet., 44, 269-276.
14. Ledoit,O. and Wolf,M. (2003) Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J. Empir. Finance, 10, 603-621.
15. Lee,S. et al. (2014) Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet., 95, 5-23.
16. Mahajan,A. et al. (2014) Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet., 46, 234-244.
17. Marchini,J. and Howie,B. (2010) Genotype imputation for genome-wide association studies. Nat. Rev. Genet., 11, 499-511.
18. Marttinen,P. et al. (2013) Genome-wide association studies with high-dimensional phenotypes. Stat. Appl. Genet. Mol. Biol., 12, 413-431.
19. Marttinen,P. et al. (2014) Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression. Bioinformatics, 30, 2026-2034.
20. Raitakari,O.T. et al. (2008) Cohort profile: The Cardiovascular Risk in Young Finns Study. Int. J. Epidemiol., 37, 1220-1226.
21. Rantakallio,P. (1969) Groups at risk in low birth weight infants and perinatal mortality. Acta Paediatrica Scand., 193, 193.
22. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421-427.
23. Shin,S.Y. et al. (2014) An atlas of genetic influences on human blood metabolites. Nat. Genet., 46, 543-550.
24. Soininen,P. et al. (2009) High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst, 134, 1781-1785.
25. Soininen,P. et al. (2015) Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. Circul.: Cardiovasc. Genet., 8, 192-206.
26. Stephens,M. (2013) A unified framework for association analysis with multiple related phenotypes. PLoS One, 8, e65245.
27. Surakka,I. et al. (2015) The impact of low-frequency and rare variants on lipid levels. Nat. Genet., 47, 589-597.
28. Tang,C.S. and Ferreira,M.A. (2012) A gene-based test of association using canonical correlation analysis. Bioinformatics, 28, 845-850.
29. Tibshirani,R. et al. (2001) Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc.: Ser. B (Stat. Methodol.), 63, 411-423.
30. van der Sluis,S. et al. (2013) TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLos Genet., 9, e1003235.
31. Vartiainen,E. et al. (2010) Thirty-five-year trends in cardiovascular risk factors in Finland. Int. J. Epidemiol., 39, 504-518.
32. Vuckovic,D. et al. (2015) MultiMeta: an R package for meta-analyzing multiphenotype genome-wide association studies. Bioinformatics, btv222.
33. Wall,J.D. and Pritchard,J.K. (2003) Haplotype blocks and linkage disequilibrium in the human genome. Nat. Rev. Genet., 4, 587-597.
34. Wang,Y. et al. (2013) Random forests on Hadoop for Genome-Wide Association Studies of multivariate neuroimaging phenotypes. BMC Bioinf., 14, 1-15.
35. Yang,J. et al. (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet., 44, 369-375.
36. Zhu,X. et al. (2015) Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am. J. Hum. Genet., 96, 21-36.