Unified Sequence-Based Association Tests Allowing for Multiple Functional Annotations and Meta-analysis of Noncoding Variation in Metabochip Data

American Journal of Human Genetics

Volumn 101, Issue 3, 2017, Pages 340-352

  He, Zihuai ^a   Xu, Bin ^a   Lee, Seunggeun ^b   Ionita Laza, Iuliana ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

functional annotations; integrative methods; meta anlysis; Metabochip data; sequence based association tests

Indexed keywords

CHOLESTEROL; LOW DENSITY LIPOPROTEIN; ORGANIC CATION TRANSPORTER 3; LIPID; ORGANIC CATION TRANSPORTER; SOLUTE CARRIER FAMILY 22 (ORGANIC CATION TRANSPORTER), MEMBER 3;

ADAPTATION; ARTICLE; BURDEN TEST; CONTROLLED STUDY; DISEASE MODEL; EIGEN FUNCTIONAL SCORE; GENE SEQUENCE; GENETIC ANALYSIS; GENETIC DATABASE; GENETIC RISK; GENETIC TRAIT; GENETIC VARIATION; METABOCHIP DATA; PREDICTION; PRIORITY JOURNAL; SCORING SYSTEM; SIMULATION; SLC22A3 GENE; STATISTICAL ANALYSIS; CARDIOVASCULAR DISEASE; COMPUTER SIMULATION; GENETIC PREDISPOSITION; GENETICS; GENOME-WIDE ASSOCIATION STUDY; HUMAN; HUMAN GENOME; METABOLISM; METABOLOMICS; MOLECULAR GENETICS; PATHOLOGY; PHENOTYPE; PROCEDURES; SINGLE NUCLEOTIDE POLYMORPHISM;

CARDIOVASCULAR DISEASES; COMPUTER SIMULATION; GENETIC PREDISPOSITION TO DISEASE; GENOME, HUMAN; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LIPIDS; METABOLOMICS; MOLECULAR SEQUENCE ANNOTATION; ORGANIC CATION TRANSPORT PROTEINS; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 85028084766     PISSN: 00029297     EISSN: 15376605     Source Type: Journal    
DOI: 10.1016/j.ajhg.2017.07.011     Document Type: Article

References (36)

1. Gudbjartsson, D.F., Helgason, H., Gudjonsson, S.A., Zink, F., Oddson, A., Gylfason, A., Besenbacher, S., Magnusson, G., Halldorsson, B.V., Hjartarson, E., et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47 (2015), 435–444.
2. Adzhubei, I.A., Schmidt, S., Peshkin, L., Ramensky, V.E., Gerasimova, A., Bork, P., Kondrashov, A.S., Sunyaev, S.R., A method and server for predicting damaging missense mutations. Nat. Methods 7 (2010), 248–249.
3. Davydov, E.V., Goode, D.L., Sirota, M., Cooper, G.M., Sidow, A., Batzoglou, S., Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput. Biol., 6, 2010, e1001025.
4. Kircher, M., Witten, D.M., Jain, P., O'Roak, B.J., Cooper, G.M., Shendure, J., A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46 (2014), 310–315.
5. Ionita-Laza, I., McCallum, K., Xu, B., Buxbaum, J.D., A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat. Genet. 48 (2016), 214–220.
6. Capanu, M., Orlow, I., Berwick, M., Hummer, A.J., Thomas, D.C., Begg, C.B., The use of hierarchical models for estimating relative risks of individual genetic variants: an application to a study of melanoma. Stat. Med. 27 (2008), 1973–1992.
7. Capanu, M., Begg, C.B., Hierarchical modeling for estimating relative risks of rare genetic variants: properties of the pseudo-likelihood method. Biometrics 67 (2011), 371–380.
8. Kichaev, G., Yang, W.Y., Lindstrom, S., Hormozdiari, F., Eskin, E., Price, A.L., Kraft, P., Pasaniuc, B., Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet., 10, 2014, e1004722.
9. Ionita-Laza, I., Capanu, M., De Rubeis, S., McCallum, K., Buxbaum, J.D., Identification of rare causal variants in sequence-based studies: methods and applications to VPS13B, a gene involved in Cohen syndrome and autism. PLoS Genet., 10, 2014, e1004729.
10. Lee, S., Abecasis, G.R., Boehnke, M., Lin, X., Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet. 95 (2014), 5–23.
11. Li, B., Leal, S.M., Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83 (2008), 311–321.
12. Madsen, B.E., Browning, S.R., A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet., 5, 2009, e1000384.
13. Neale, B.M., Rivas, M.A., Voight, B.F., Altshuler, D., Devlin, B., Orho-Melander, M., Kathiresan, S., Purcell, S.M., Roeder, K., Daly, M.J., Testing for an unusual distribution of rare variants. PLoS Genet., 7, 2011, e1001322.
14. Wu, M.C., Lee, S., Cai, T., Li, Y., Boehnke, M., Lin, X., Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89 (2011), 82–93.
15. Li, M., He, Z., Zhang, M., Zhan, X., Wei, C., Elston, R.C., Lu, Q., A generalized genetic random field method for the genetic association analysis of sequencing data. Genet. Epidemiol. 38 (2014), 242–253.
16. Derkach, A., Lawless, J.F., Sun, L., Robust and powerful tests for rare variants using Fisher's method to combine evidence of association from two or more complementary tests. Genet. Epidemiol. 37 (2013), 110–121.
17. Sun, J., Zheng, Y., Hsu, L., A unified mixed-effects model for rare-variant association in sequencing studies. Genet. Epidemiol. 37 (2013), 334–344.
18. Lee, S., Emond, M.J., Bamshad, M.J., Barnes, K.C., Rieder, M.J., Nickerson, D.A., Christiani, D.C., Wurfel, M.M., Lin, X., NHLBI GO Exome Sequencing Project—ESP Lung Project Team. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am. J. Hum. Genet. 91 (2012), 224–237.
19. Byrnes, A.E., Wu, M.C., Wright, F.A., Li, M., Li, Y., The value of statistical or bioinformatics annotation for rare variant association with quantitative trait. Genet. Epidemiol. 37 (2013), 666–674.
20. Minica, C.C., Genovese, G., Hultman, C.M., Pool, R., Vink, J.M., Dolan, C.V., Neale, B.M., The weighting is the hardest part: on the behavior of the likelihood ratio test and score test under weight misspecification in rare variant association studies. bioRxiv, 2015, 10.1101/020198.
21. Wu, M.C., Maity, A., Lee, S., Simmons, E.M., Harmon, Q.E., Lin, X., Engel, S.M., Molldrem, J.J., Armistead, P.M., Kernel machine SNP-set testing under multiple candidate kernels. Genet. Epidemiol. 37 (2013), 267–275.
22. Liu, D.J., Peloso, G.M., Zhan, X., Holmen, O.L., Zawistowski, M., Feng, S., Nikpay, M., Auer, P.L., Goel, A., Zhang, H., et al. Meta-analysis of gene-level tests for rare variant association. Nat. Genet. 46 (2014), 200–204.
23. Lee, S., Teslovich, T.M., Boehnke, M., Lin, X., General framework for meta-analysis of rare variants in sequencing association studies. Am. J. Hum. Genet. 93 (2013), 42–53.
24. Purcell, S.M., Moran, J.L., Fromer, M., Ruderfer, D., Solovieff, N., Roussos, P., O'Dushlaine, C., Chambert, K., Bergen, S.E., Kähler, A., et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506 (2014), 185–190.
25. Schaffner, S.F., Foo, C., Gabriel, S., Reich, D., Daly, M.J., Altshuler, D., Calibrating a coalescent simulation of human genome sequence variation. Genome Res. 15 (2005), 1576–1583.
26. Derkach, A., Lawless, J.F., Sun, L., Pooled association tests for rare genetic variants: a review and some new results. Stat. Sci. 29 (2014), 302–321.
27. Abecasis, G.R., Altshuler, D., Auton, A., Brooks, L.D., Durbin, R.M., Gibbs, R.A., Hurles, M.E., McVean, G.A., 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467 (2010), 1061–1073.
28. Trégouët, D.A., König, I.R., Erdmann, J., Munteanu, A., Braund, P.S., Hall, A.S., Grosshennig, A., Linsel-Nitschke, P., Perret, C., DeSuremain, M., et al. Wellcome Trust Case Control Consortium, Cardiogenics Consortium. Genome-wide haplotype association study identifies the SLC22A3-LPAL2-LPA gene cluster as a risk locus for coronary artery disease. Nat. Genet. 41 (2009), 283–285.
29. Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., Foster, B., GTEx Consortium. The genotype-tissue expression (GTEx) project. Nat. Genet. 45 (2013), 580–585.
30. O'Leary, N.A., Wright, M.W., Brister, J.R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44:D1 (2016), D733–D745.
31. Chen, L., Hong, C., Chen, E.C., Yee, S.W., Xu, L., Almof, E.U., Wen, C., Fujii, K., Johns, S.J., Stryke, D., et al. Genetic and epigenetic regulation of the organic cation transporter 3, SLC22A3. Pharmacogenomics J. 13 (2013), 110–120.
32. Chen, H., Meigs, J.B., Dupuis, J., Sequence kernel association test for quantitative traits in family samples. Genet. Epidemiol. 37 (2013), 196–204.
33. Wang, X., Lee, S., Zhu, X., Redline, S., Lin, X., GEE-based SNP set association test for continuous and discrete traits in family-based association studies. Genet. Epidemiol. 37 (2013), 778–786.
34. He, Z., Zhang, M., Lee, S., Smith, J.A., Guo, X., Palmas, W., Kardia, S.L., Diez Roux, A.V., Mukherjee, B., Set-based tests for genetic association in longitudinal studies. Biometrics 71 (2015), 606–615.
35. Breiman, L., Bagging predictors. Mach. Learn. 24 (1996), 123–140.
36. Tewhey, R., Kotliar, D., Park, D.S., Liu, B., Winnicki, S., Reilly, S.K., Andersen, K.G., Mikkelsen, T.S., Lander, E.S., Schaffner, S.F., Sabeti, P.C., Direct identification of hundreds of expression-modulating variants using a multiplexed reporter assay. Cell 165 (2016), 1519–1529.