Transcriptome-wide association studies: a view from Mendelian randomization

Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J., Boomsma, D. I., Wright, F. A., et al. (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet., 48, 245–252 Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) The genotype-tissue expression (GTEx) project. Nat. Genet., 45, 580–585 Lappalainen, T., Sammeth, M., Friedländer, M. R., ’t Hoen, P. A., Monlong, J., Rivas, M. A., González-Porta, M., Kurbatova, N., Griebel, T., Ferreira, P. G., et al. (2013) Transcriptome and genome sequencing uncovers functional variation in humans. Nature, 501, 506–511 Battle, A., Mostafavi, S., Zhu, X., Potash, J. B., Weissman, M. M., McCormick, C., Haudenschild, C. D., Beckman, K. B., Shi, J., Mei, R., et al. (2014) Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res., 24, 14–24 Ramasamy, A., Trabzuni, D., Guelfi, S., Varghese, V., Smith, C., Walker, R., De, T., Coin, L., de Silva, R., Cookson, M. R., et al. (2014) Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci., 17, 1418–1428 Gibbs, J. R., van der Brug, M. P., Hernandez, D. G., Traynor, B. J., Nalls, M. A., Lai, S.-L., Arepalli, S., Dillman, A., Rafferty, I. P., Troncoso, J., et al. (2010) Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet., 6, e1000952 Tung, J., Zhou, X., Alberts, S. C., Stephens, M. and Gilad, Y. (2015) The genetic architecture of gene expression levels in wild baboons. eLife, 4, e04729 Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J. B., Stephens, M., Gilad, Y. and Pritchard, J. K. (2010) Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature, 464, 768–772 Stancáková, A., Civelek, M., Saleem, N. K., Soininen, P., Kangas, A. J., Cederberg, H., Paananen, J., Pihlajamäki, J., Bonnycastle, L. L., Morken, M. A., et al. (2012) Hyperglycemia and a common variant of GCKR are associated with the levels of eight amino acids in 9,369 Finnish men. Diabetes, 61, 1895–1902 Abeshouse, A., Ahn, J., Akbani, R., Ally, A., Amin, S., Andry, C. D., Annala, M., Aprikian, A., Armenia, J., Arora, A., et al. (2015) The molecular taxonomy of primary prostate cancer. Cell, 163, 1011–1025 Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci., 19, 1442–1453 Wright, F. A., Sullivan, P. F., Brooks, A. I., Zou, F., Sun, W., Xia, K., Madar, V., Jansen, R., Chung, W., Zhou, Y. H., et al. (2014) Heritability and genomics of gene expression in peripheral blood. Nat. Genet., 46, 430–437 Raitakari, O. T., Juonala, M., Rönnemaa, T., Keltikangas-Järvinen, L., Räsänen, L., Pietikäinen, M., Hutri-Kähönen, N., Taittonen, L., Jokinen, E., Marniemi, J., et al. (2008) Cohort profile: the cardiovascular risk in Young Finns Study. Int. J. Epidemiol., 37, 1220–1226 Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., Nicolae, D. L., Cox, N. J., et al. (2015) A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet., 47, 1091–1098 Zou, H. and Hastie, T. (2005) Regularization and variable selection via the elastic net. J. R. Stat. Soc. B, 67, 301–320 Zhou, X., Carbonetto, P. and Stephens, M. (2013) Polygenic modeling with bayesian sparse linear mixed models. PLoS Genet., 9, e1003264 Zeng, P. and Zhou, X. (2017) Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models. Nat. Commun., 8, 456 Nagpal, S., Meng, X., Epstein, M. P., Tsoi, L. C., Patrick, M., Gibson, G., De Jager, P. L., Bennett, D. A., Wingo, A. P., Wingo, T. S., et al. (2019) TIGAR: an improved Bayesian tool for transcriptomic data imputation enhances gene mapping of complex traits. Am. J. Hum. Genet., 105, 258–266 Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M. R., Powell, J. E., Montgomery, G. W., Goddard, M. E., Wray, N. R., Visscher, P. M., et al. (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet., 48, 481–187 Zhu, Z., Zheng, Z., Zhang, F., Wu, Y., Trzaskowski, M., Maier, R., Robinson, M. R., McGrath, J. J., Visscher, P. M., Wray, N. R., et al. (2018) Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun., 9, 224 Yuan, Z., Zhu, H., Zeng, P., Yang, S., Sun, S., Yang, C., Liu, J., Zhou, X. (2019) Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies. bioRxiv, 691014 Sanderson, E., Davey Smith, G., Windmeijer, F. and Bowden, J. (2019) An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int. J. Epidemiol., 48, 713–727 Burgess, S. and Thompson, S. G. (2015) Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am. J. Epidemiol., 181, 251–260 Rees, J. M. B., Foley, C. N. and Burgess, S. (2019) Factorial Mendelian randomization: using genetic variants to assess interactions. Int. J. Epidemiol., dyz161 Burgess, S., Daniel, R. M., Butterworth, A. S. and Thompson, S. G., and the EPIC-InterAct Consortium. (2015) Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int. J. Epidemiol., 44, 484–495 Porcu, E., Rüeger, S., Lepik, K., the eQTLGen Consortium, the BIOS Consortium, Santoni, F. A., Reymond, A. and Kutalik, Z. (2019) Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat. Commun., 10, 3300 Zuber, V., Colijn, J. M., Klaver, C. and Burgess, S. (2020) Selecting causal risk factors from high-throughput experiments using multivariable Mendelian randomization. Nat. Commun. 11, 29 Barbeira, A. N., Pividori, M., Zheng, J., Wheeler, H. E., Nicolae, D. L. and Im, H. K. (2019) Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet., 15, e1007889 Hu, Y., Li, M., Lu, Q., Weng, H., Wang, J., Zekavat, S. M., Yu, Z., Li, B., Gu, J., Muchnik, S., et al. (2019) A statistical framework for cross-tissue transcriptome-wide association analysis. Nat. Genet., 51, 568–576 Mancuso, N., Gayther, S., Gusev, A., Zheng, W., Penney, K. L., Kote-Jarai, Z., Eeles, R., Freedman, M., Haiman, C. Pasaniuc, B., et al. (2018) Large-scale transcriptome-wide association study identifies new prostate cancer risk regions. Nat. Commun., 9, 4079 Park, Y., Sarkar, A. K., Bhutani, K. and Kellis, M. (2017) Multi-tissue polygenic models for transcriptome-wide association studies. bioRxiv, 107623 Shi, X., Chai, X., Yang, Y., Cheng, Q., Jiao, Y., Huang, J., Yang, C. and Liu, J. (2019) A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies. bioRxiv, 789396 Mancuso, N., Freund, M. K., Johnson, R., Shi, H., Kichaev, G., Gusev, A. and Pasaniuc, B. (2019) Probabilistic fine-mapping of transcriptome-wide association studies. Nat. Genet., 51, 675–682 Wainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A. N., Knowles, D. A., Golan, D., Ermel, R., Ruusalepp, A., Quertermous, T., Hao, K., et al. (2019) Opportunities and challenges for transcriptome-wide association studies. Nat. Genet., 51, 592–599 Barbeira, A. N., Dickinson, S. P., Bonazzola, R., Zheng, J., Wheeler, H. E., Torres, J. M., Torstenson, E. S., Shah, K. P., Garcia, T., Edwards, T. L., et al. (2018) Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun., 9, 1825 Ference, B. A., Robinson, J. G., Brook, R. D., Catapano, A. L., Chapman, M. J., Neff, D. R., Voros, S., Giugliano, R. P., Davey Smith, G., Fazio, S., et al. (2016) Variation in PCSK9 and HMGCR and risk of cardiovascular disease and diabetes. N. Engl. J. Med., 375, 2144–2153 Helgadottir, A., Gretarsdottir, S., Thorleifsson, G., Hjartarson, E., Sigurdsson, A., Magnusdottir, A., Jonasdottir, A., Kristjansson, H., Sulem, P., Oddsson, A., et al. (2016) Variants with large effects on blood lipids and the role of cholesterol and triglycerides in coronary disease. Nat. Genet., 48, 634–639 Pingault, J.-B., O’Reilly, P. F., Schoeler, T., Ploubidis, G. B., Rijsdijk, F. and Dudbridge, F. (2018) Using genetic data to strengthen causal inference in observational research. Nat. Rev. Genet., 19, 566–580 Zheng, J., Baird, D., Borges, M.-C., Bowden, J., Hemani, G., Haycock, P., Evans, D. M. and Smith, G. D. (2017) Recent developments in Mendelian randomization studies. Curr. Epidemiol. Rep., 4, 330–345 Haycock, P. C., Burgess, S., Wade, K. H., Bowden, J., Relton, C. and Davey Smith, G. (2016) Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am. J. Clin. Nutr., 103, 965–978 Lawlor, D. A. (2016) Commentary: Two-sample Mendelian randomization: opportunities and challenges. Int. J. Epidemiol., 45, 908–915 Bowden, J., Davey Smith, G. and Burgess, S. (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol., 44, 512–525 Bowden, J., Davey Smith, G., Haycock, P. C. and Burgess, S. (2016) Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol., 40, 304–314 Smith, G. D. and Ebrahim, S. (2003) ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol., 32, 1–22 Burgess, S., Small, D. S. and Thompson, S. G. (2017) A review of instrumental variable estimators for Mendelian randomization. Stat. Methods Med. Res., 26, 2333–2355 Burgess, S., Butterworth, A. and Thompson, S. G. (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol., 37, 658–665 Burgess, S., Dudbridge, F. and Thompson, S. G. (2016) Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat. Med., 35, 1880–1906 Burgess, S. and Thompson, S. G. (2011) Bias in causal estimates from Mendelian randomization studies with weak instruments. Stat. Med., 30, 1312–1323 Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B, 58, 267–288 Hoerl, A. E. and Kennard, R. W. (2000) Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 42, 80–86 Guan, Y. and Stephens, M. (2011) Bayesian variable selection regression for genome-wide association studies and other large-scale problems. Ann. Appl. Stat., 5, 1780–1815 Boyle, E. A., Li, Y. I. and Pritchard, J. K. (2017) An expanded view of complex traits: from polygenic to omnigenic. Cell, 169, 1177–1186 Yang, C., Wan, X., Lin, X., Chen, M., Zhou, X. and Liu, J. (2019) CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information. Bioinformatics, 35, 1644–1652 Yang, Y., Shi, X., Jiao, Y., Huang, J., Chen, M., Zhou, X., Sun, L., Lin, X., Yang, C., Liu, J. (2020) CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies. Bioinformatics, 36, 2009–2016 Hemani, G., Bowden, J. and Davey Smith, G. (2018) Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum. Mol. Genet., 27, R195–R208 Verbanck, M., Chen, C.-Y., Neale, B. and Do, R. (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet., 50, 693–698 Park, Y., Sarkar, A. K., He, L., Davila-Velderrain, J., De Jager, P. L. and Kellis, M. (2017) A Bayesian approach to mediation analysis predicts 206 causal target genes in Alzheimer’s disease. bioRxiv, 219428 Burgess, S. and Thompson, S. G. (2017) Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol., 32, 377–389 Dai, J. Y., Peters, U., Wang, X., Kocarnik, J., Chang-Claude, J., Slattery, M. L., Chan, A., Lemire, M., Berndt, S. I., Casey, G., et al. (2018) Diagnostics for pleiotropy in Mendelian randomization studies: global and individual tests for direct effects. Am. J. Epidemiol., 187, 2672–2680 Qi, G. and Chatterjee, N. (2019) Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects. Nat. Commun., 10, 1941 Berzuini C, Guo H, Burgess S, Bernardinelli L. (2020) A Bayesian approach to Mendelian randomization with multiple pleiotropic variants. 2018. Biostatistics, 21, 86–101 Li, S. (2017) Mendelian randomization when many instruments are invalid: hierarchical empirical Bayes estimation. ArXiv, 170601389 Barfield, R., Feng, H., Gusev, A., Wu, L., Zheng, W., Pasaniuc, B. and Kraft, P. (2018) Transcriptome-wide association studies accounting for colocalization using Egger regression. Genet. Epidemiol., 42, 418–433 Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M. and Lin, X. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet., 89, 82–93 Li, B. and Leal, S. M. (2008) Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet., 83, 311–321 Madsen, B. E. and Browning, S. R. (2009) A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet., 5, e1000384 Price, A. L., Kryukov, G. V., de Bakker, P. I., Purcell, S. M., Staples, J., Wei, L.-J. and Sunyaev, S. R. (2010) Pooled association tests for rare variants in exon-resequencing studies. Am. J. Hum. Genet., 86, 832–838 Zhou, X. (2017) A unified framework for variance component estimation with summary statistics in genome-wide association studies. Ann. Appl. Stat., 11, 2027–2051 Schork, N. J., Murray, S. S., Frazer, K. A. and Topol, E. J. (2009) Common vs. rare allele hypotheses for complex diseases. Curr. Opin. Genet. Dev., 19, 212–219 Eichler, E. E., Flint, J., Gibson, G., Kong, A., Leal, S. M., Moore, J. H. and Nadeau, J. H. (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet., 11, 446–450 Price, A. L., Helgason, A., Thorleifsson, G., McCarroll, S. A., Kong, A. and Stefansson, K. (2011) Single-tissue and cross-tissue heritability of gene expression via identity-by-descent in related or unrelated individuals. PLoS Genet., 7, e1001317