[1] Khoury MJ, Dorman JS. The human genome epidemiology network[J]. Am J Epidemiol, 1998, 148(1): 1–3. doi:  10.1093/aje/148.1.1
[2] Khoury MJ. Human genome epidemiology: translating advances in human genetics into population-based data for medicine and public health[J]. Genet Med, 1999, 1(3): 71–73. doi:  10.1097/00125817-199903000-00002
[3] Shen HB, Jin GF. Human genome epidemiology, progress and future[J]. J Biomed Res, 2013, 27(3): 167–169. doi:  10.7555/JBR.27.20130040
[4] Tam V, Patel N, Turcotte M, et al. Benefits and limitations of genome-wide association studies[J]. Nat Rev Genet, 2019, 20(8): 467–484. doi:  10.1038/s41576-019-0127-1
[5] Visscher PM, Wray NR, Zhang Q, et al. 10 Years of GWAS discovery: biology, function, and translation[J]. Am J Hum Genet, 2017, 101(1): 5–22. doi:  10.1016/j.ajhg.2017.06.005
[6] Buniello A, MacArthur JAL, Cerezo M, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019[J]. Nucleic Acids Res, 2019, 47(D1): D1005–D1012. doi:  10.1093/nar/gky1120
[7] Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores[J]. Nat Rev Genet, 2018, 19(9): 581–590. doi:  10.1038/s41576-018-0018-x
[8] Dai JC, Lv J, Zhu M, et al. Identification of risk loci and a polygenic risk score for lung cancer: a large-scale prospective cohort study in Chinese populations[J]. Lancet Respir Med, 2019, 7(10): 881–891. doi:  10.1016/S2213-2600(19)30144-4
[9] Khera AV, Chaffin M, Aragam KG, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations[J]. Nat Genet, 2018, 50(9): 1219–1224. doi:  10.1038/s41588-018-0183-z
[10] Chowell D, Morris LGT, Grigg CM, et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy[J]. Science, 2018, 359(6375): 582–587. doi:  10.1126/science.aao4572
[11] Young AI. Solving the missing heritability problem[J]. PLoS Genet, 2019, 15(6): e1008222. doi:  10.1371/journal.pgen.1008222
[12] Zuk O, Schaffner SF, Samocha K, et al. Searching for missing heritability: designing rare variant association studies[J]. Proc Natl Acad Sci USA, 2014, 111(4): E455–E464. doi:  10.1073/pnas.1322563111
[13] Reuter JA, Spacek DV, Snyder MP. High-throughput sequencing technologies[J]. Mol Cell, 2015, 58(4): 586–597. doi:  10.1016/j.molcel.2015.05.004
[14] Wang QG, Armenia J, Zhang C, et al. Unifying cancer and normal RNA sequencing data from different sources[J]. Sci Data, 2018, 5(1): 180061. doi:  10.1038/sdata.2018.61
[15] Favé MJ, Lamaze FC, Soave D, et al. Gene-by-environment interactions in urban populations modulate risk phenotypes[J]. Nat Commun, 2018, 9(1): 827. doi:  10.1038/s41467-018-03202-2
[16] Idaghdour Y, Awadalla P. Exploiting gene expression variation to capture gene-environment interactions for disease[J]. Front Genet, 2013, 3: 228.
[17] Aschard H, Lutz S, Maus B, et al. Challenges and opportunities in genome-wide environmental interaction (GWEI) studies[J]. Hum Genet, 2012, 131(10): 1591–1613. doi:  10.1007/s00439-012-1192-0
[18] McAllister K, Mechanic LE, Amos C, et al. Current challenges and new opportunities for gene-environment interaction studies of complex diseases[J]. Am J Epidemiol, 2017, 186(7): 753–761. doi:  10.1093/aje/kwx227
[19] Hutter CM, Mechanic LE, Chatterjee N, et al. Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report[J]. Genet Epidemiol, 2013, 37(7): 643–657. doi:  10.1002/gepi.21756
[20] Dong J, Hu ZB, Wu C, et al. Association analyses identify multiple new lung cancer susceptibility loci and their interactions with smoking in the Chinese population[J]. Nat Genet, 2012, 44(8): 895–899. doi:  10.1038/ng.2351
[21] Hu ZB, Wu C, Shi YY, et al. A genome-wide association study identifies two new lung cancer susceptibility loci at 13q12.12 and 22q12.2 in Han Chinese[J]. Nat Genet, 2011, 43(8): 792–796. doi:  10.1038/ng.875
[22] Shao LP, Zuo XL, Yang Y, et al. The inherited variations of a p53-responsive enhancer in 13q12.12 confer lung cancer risk by attenuating TNFRSF19 expression[J]. Genome Biol, 2019, 20(1): 103. doi:  10.1186/s13059-019-1696-1
[23] Shi M, Umbach DM, Weinberg CR. Family-based gene-by-environment interaction studies: revelations and remedies[J]. Epidemiology, 2011, 22(3): 400–407. doi:  10.1097/EDE.0b013e318212fec6
[24] Lund E, Dumeaux V. Systems epidemiology in cancer[J]. Cancer Epidemiol Biomarkers Prev, 2008, 17(11): 2954–2957. doi:  10.1158/1055-9965.EPI-08-0519
[25] Jacobs L, Thijs L, Jin Y, et al. Heart 'omics' in AGEing (HOMAGE): design, research objectives and characteristics of the common database[J]. J Biomed Res, 2014, 28(5): 349–359.
[26] Haring R, Wallaschofski H. Diving through the "-omics": the case for deep phenotyping and systems epidemiology[J]. OMICS, 2012, 16(5): 231–234. doi:  10.1089/omi.2011.0108
[27] Thorsson V, Gibbs DL, Brown SD, et al. The immune landscape of cancer[J]. Immunity, 2018, 48(4): 812–830. doi:  10.1016/j.immuni.2018.03.023
[28] Berger AC, Korkut A, Kanchi RS, et al. A comprehensive pan-cancer molecular study of gynecologic and breast cancers[J]. Cancer Cell, 2018, 33(4): 690–705. doi:  10.1016/j.ccell.2018.03.014