A multiple-phenotype imputation method for genetic studies

Nature Genetics

Volumn 48, Issue 4, 2016, Pages 466-472

  Dahl, Andrew ^a   Iotchkova, Valentina ^b   Baud, Amelie ^b   Johansson, Sa ^c   Gyllensten, Ulf ^c   Soranzo, Nicole ^b   Mott, Richard ^a   Kranis, Andreas ^d,e   Marchini, Jonathan ^a,f  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^c UPPSALA UNIVERSITY   (Sweden)

^d Aviagen   (United States)

^e UNIVERSITY OF EDINBURGH   (United Kingdom)

^f UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BAYES THEOREM; GENETIC ALGORITHM; GENETIC ASSOCIATION; GENETIC CORRELATION; GENETICS; HUMAN; MACHINE LEARNING; NONHUMAN; PHENOTYPE; PRIORITY JOURNAL; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; CHICKEN; FEMALE; GENOME-WIDE ASSOCIATION STUDY; MALE; OUTBRED STRAIN; PHYSIOLOGY; PROCEDURES; RAT; SINGLE NUCLEOTIDE POLYMORPHISM; T LYMPHOCYTE; THROMBOCYTE; WHEAT;

ALGORITHMS; ANIMALS; ANIMALS, OUTBRED STRAINS; BAYES THEOREM; BLOOD PLATELETS; CHICKENS; FEMALE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MALE; MODELS, GENETIC; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; RATS; T-LYMPHOCYTES; TRITICUM;

EID: 84959142538     PISSN: 10614036     EISSN: 15461718     Source Type: Journal    
DOI: 10.1038/ng.3513     Document Type: Article

References (59)

1. Marx, V. Human phenotyping on a population scale. Nat. Methods 12, 711-714 (2015
2. Soranzo N., et al. A genome-wide meta-Analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat. Genet. 41, 1182-1190 (2009
3. Zhou, X., & Stephens, M. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat. Methods 11, 407-409 (2014
4. Huffman, J.E., et al. Polymorphisms in B3GAT1, SLC9A9 and MGAT5 are associated with variation within the human plasma N-glycome of 3533 European adults. Hum. Mol. Genet. 20, 5000-5011 (2011
5. Lauc G., et al. Genomics meets glycomics-The first GWAS study of human N-glycome identifies HNF1a as a master regulator of plasma protein fucosylation. PLoS Genet. 6, e1001256 (2010
6. O?Reilly P.F., et al. MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS One 7, e34861 (2012
7. Lee, S.H., Yang, J., Goddard, M.E., Visscher, P.M., & Wray, N.R. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics 28, 2540-2542 (2012
8. Schadt E.E., et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37, 710-717 (2005
9. Almasy, L., & Blangero, J. Multipoint quantitative-Trait linkage analysis in general pedigrees. Am. J. Hum. Genet. 62, 1198-1211 (1998
10. Abecasis, G.R., Cardon, L.R., Cookson, W.O., Sham, P.C., & Cherny, S.S. Association analysis in a variance components framework. Genet. Epidemiol. 21 (suppl. 1), S341-S346 (2001
11. Meuwissen, T.H., Hayes, B.J., & Goddard, M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829 (2001
12. Hai R., et al. Bivariate genome-wide association study suggests that the DARC gene influences lean body mass and age at menarche. Sci. China Life Sci. 55, 516-520 (2012
13. Piccolo S.R., et al. Evaluation of genetic risk scores for lipid levels using genome-wide markers in the Framingham Heart Study. BMC Proc. 3 (suppl. 7), S46 (2009
14. Choi, Y.-H., Chowdhury, R., & Swaminathan, B. Prediction of hypertension based on the genetic analysis of longitudinal phenotypes: a comparison of different modeling approaches for the binary trait of hypertension. BMC Proc. 8 (suppl. 1) Genetic Analysis Workshop 18Vanessa Olmo, S78 (2014
15. Scutari, M., Howell, P., Balding, D.J., & Mackay, I. Multiple quantitative trait analysis using Bayesian networks. Genetics 198, 129-137 (2014
16. Park, S.H., Lee, J.Y., & Kim, S. A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes. BMC Syst. Biol. 5(suppl. 2), S13 (2011
17. Cui, X., Sha, Q., Zhang, S., & Chen, H.-S. A combinatorial approach for detecting gene-gene interaction using multiple traits of Genetic Analysis Workshop 16 rheumatoid arthritis data. BMC Proc. 3 (suppl. 7), S43 (2009
18. Shin S.-Y., et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543-550 (2014
19. Suhre K., et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54-60 (2011
20. Meuwissen, T.H.E., Odegard, J., Andersen-Ranberg, I., & Grindflek, E. On the distance of genetic relationships and the accuracy of genomic prediction in pig breeding. Genet. Sel. Evol. 46, 49 (2014
21. Schifano, E.D., Li, L., Christiani, D.C., & Lin, X. Genome-wide association analysis for multiple continuous secondary phenotypes. Am. J. Hum. Genet. 92, 744-759 (2013
22. Yang J., et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565-569 (2010
23. Igl, W., Johansson, A., & Gyllensten, U. The Northern Swedish Population Health Study (NSPHS)-A paradigmatic study in a rural population combining community health and basic research. Rural Remote Health 10, 1363 (2010
24. Bloom, J.S., Ehrenreich, I.M., Loo, W.T., Lite, T.-L.V., & Kruglyak, L. Finding the sources of missing heritability in a yeast cross. Nature 494, 234-237 (2013
25. Baud A., et al. Combined sequence-based and genetic mapping analysis of complex traits in outbred rats. Nat. Genet. 45, 767-775 (2013
26. Abdollahi-Arpanahi R., et al. Dissection of additive genetic variability for quantitative traits in chickens using SNP markers. J. Anim. Breed. Genet. 131, 183-193 (2014
27. Mackay, I.J., et al. An eight-parent multiparent advanced generation inter-cross population for winter-sown wheat: creation, properties, and validation. G3 (Bethesda) 4, 1603-1610 (2014
28. Ferreira, M.A.R., & Purcell, S.M. A multivariate test of association. Bioinformatics 25, 132-133 (2009
29. Galesloot, T.E., van Steen, K., Kiemeney, L.A.L.M., Janss, L.L., & Vermeulen, S.H. A comparison of multivariate genome-wide association methods. PLoS One 9, e95923 (2014
30. Casale, F.P., Rakitsch, B., Lippert, C., & Stegle, O. Efficient set tests for the genetic analysis of correlated traits. Nat. Methods 12, 755-758 (2015
31. Dahl, A., Hore, V., Iotchkova, V., & Marchini, J. Network inference in matrix-variate Gaussian models with non-independent noise. arXiv http://arxiv.org/abs/1312.1622v1 (2013
32. Mott, R., Talbot, C.J., Turri, M.G., Collins, A.C., & Flint, J. A method for fine mapping quantitative trait loci in outbred animal stocks. Proc. Natl. Acad. Sci. USA 97, 12649-12654 (2000
33. Marchini, J., & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499-511 (2010
34. Hers, I. Insulin-like growth factor-1 potentiates platelet activation via the IRS/PI3Ka pathway. Blood 110, 4243-4252 (2007
35. Cho, J., & Mosher, D.F. Role of fibronectin assembly in platelet thrombus formation. J. Thromb. Haemost. 4, 1461-1469 (2006
36. Prévost, N., et al. Signaling by ephrinB1 and Eph kinases in platelets promotes Rap1 activation, platelet adhesion, and aggregation via effector pathways that do not require phosphorylation of ephrinB1. Blood 103, 1348-1355 (2004
37. Chen Y.-R., et al. Y-box binding protein-1 down-regulates expression of carbamoyl phosphate synthetase-I by suppressing CCAAT enhancer-binding protein-A function in mice. Gastroenterology 137, 330-340 (2009
38. Shinya, H., Matsuo, N., Takeyama, N., & Tanaka, T. Hyperammonemia inhibits platelet aggregation in rats. Thromb. Res. 81, 195-201 (1996
39. Gilson, C.R., Patel, S.R., & Zimring, J.C. CTLA4-Ig prevents alloantibody production and BMT rejection in response to platelet transfusions in mice. Transfusion 52, 2209-2219 (2012
40. Zufferey A., et al. Unraveling modulators of platelet reactivity in cardiovascular patients using omics strategies: towards a network biology paradigm. Adv. Intern. Med. 1, 25-37 (2013
41. Szabo, S.J., et al. A novel transcription factor, T-bet, directs Th1 lineage commitment. Cell 100, 655-669 (2000
42. Zhou, X., Carbonetto, P., & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013
43. GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348 648-660 (2015
44. Little, R.J.A., & Rubin, D.B Statistical Analysis with Missing Data (John Wiley & Sons 1987
45. Giordano, R., Broderick, T., & Jordan, M. Linear response methods for accurate covariance estimates from mean field variational Bayes. arXiv http://arxiv.org/abs/1506.04088v2 (2015
46. Cotsapas C., et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet. 7, e1002254 (2011
47. Solovieff, N., Cotsapas, C., Lee, P.H., Purcell, S.M., & Smoller, J.W. Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483-495 (2013
48. Listgarten J., et al. A powerful and efficient set test for genetic markers that handles confounders. Bioinformatics 29, 1526-1533 (2013
49. Zhou, X., & Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821-824 (2012
50. Almasy, L., Dyer, T.D., & Blangero, J. Bivariate quantitative trait linkage analysis: pleiotropy versus co-incident linkages. Genet. Epidemiol. 14, 953-958 (1997
51. Mazumder, R., Hastie, T., & Tibshirani, R. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11, 2287-2322 (2010
52. Troyanskaya O., et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520-525 (2001
53. Buuren, S.V., & Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1-67 (2011
54. Allen, G.I., & Tibshirani, R. Transposable regularized covariance models with an application to missing data imputation. Ann. Appl. Stat. 4, 764-790 (2010
55. Dawid, A.P. Some matrix-variate distribution theory: notational considerations and a Bayesian application. Biometrika 68, 265-274 (1981
56. Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., & Saul, L.K. An introduction to variational methods for graphical models. Mach. Learn. 37, 183-233 (1999
57. Liu, D., Zhou, T., Qian, H., Xu, C., & Zhang, Z. in Lecture Notes in Computer Science Vol. 8189 (eds. Hutchison, D., et al) 210-225 (Springer, 2013
58. Wang Z., et al. Rank-one matrix pursuit for matrix completion. Proc. 31st Int. Conf. Machine Learning 91-99 (2014
59. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J., & Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955-959 (2012