Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases

Nature Genetics

Volumn 50, Issue 3, 2018, Pages 390-400

  Kanai, Masahiro ^a,b,c   Akiyama, Masato ^b   Takahashi, Atsushi ^b,d   Matoba, Nana ^b   Momozawa, Yukihide ^b   Ikeda, Masashi ^e   Iwata, Nakao ^e   Ikegawa, Shiro ^b   Hirata, Makoto ^f   Matsuda, Koichi ^f   Kubo, Michiaki ^b   Okada, Yukinori ^a,b,g   Kamatani, Yoichiro ^b,h  

^a OSAKA UNIVERSITY GRADUATE SCHOOL OF MEDICINE   (Japan)

^b RIKEN Center for Integrative Medical Sciences   (Japan)

^c HARVARD MEDICAL SCHOOL   (United States)

^d NATIONAL CEREBRAL AND CARDIOVASCULAR CENTER   (Japan)

^e FUJITA HEALTH UNIVERSITY SCHOOL OF MEDICINE   (Japan)

^f UNIVERSITY OF TOKYO   (Japan)

^g OSAKA UNIVERSITY   (Japan)

^h KYOTO UNIVERSITY GRADUATE SCHOOL OF MEDICINE   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CELLS; GENE FREQUENCY; GENETIC ANALYSIS; GENETIC CORRELATION; GENOME-WIDE ASSOCIATION STUDY; HUMAN; JAPANESE (PEOPLE); PATHOGENESIS; PATHOLOGY; PHENOTYPE; PLEIOTROPY; PRIORITY JOURNAL; QUANTITATIVE TRAIT; QUANTITATIVE TRAIT LOCUS; ADULT; ASIAN CONTINENTAL ANCESTRY GROUP; CELL LINEAGE; DISEASES; DNA SEQUENCE; EPIDEMIOLOGY; FEMALE; GENETIC ASSOCIATION STUDY; GENETIC PREDISPOSITION; GENETICS; JAPAN; MALE; MULTIFACTORIAL INHERITANCE; POPULATION GENETICS; PROCEDURES; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICS AND NUMERICAL DATA;

ADULT; ASIAN CONTINENTAL ANCESTRY GROUP; CELL LINEAGE; DISEASE; FEMALE; GENETIC ASSOCIATION STUDIES; GENETIC PREDISPOSITION TO DISEASE; GENETICS, POPULATION; GENOME-WIDE ASSOCIATION STUDY; HUMANS; JAPAN; MALE; MULTIFACTORIAL INHERITANCE; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; QUANTITATIVE TRAIT LOCI; SEQUENCE ANALYSIS, DNA;

EID: 85041590577     PISSN: 10614036     EISSN: 15461718     Source Type: Journal    
DOI: 10.1038/s41588-018-0047-6     Document Type: Article

References (57)

1. Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173-1186 (2014).
2. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197-206 (2015).
3. Akiyama, M. et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat. Genet. 49, 1458-1467 (2017).
4. Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274-1283 (2013).
5. Surakka, I. et al. The impact of low-frequency and rare variants on lipid levels. Nat. Genet. 47, 589-597 (2015).
6. Okada, Y. et al. Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations. Nat. Genet. 44, 904-909 (2012).
7. Pattaro, C. et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat. Commun. 7, 10023 (2016).
8. Kamatani, Y. et al. Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat. Genet. 42, 210-215 (2010).
9. Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415-1429 (2016).
10. Surendran, P. et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat. Genet. 48, 1151-1161 (2016).
11. Liu, C. et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat. Genet. 48, 1162-1170 (2016).
12. Ehret, G. B. et al. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat. Genet. 48, 1171-1184 (2016).
13. Sivakumaran, S. et al. Abundant pleiotropy in human complex diseases and traits. Am. J. Hum. Genet. 89, 607-618 (2011).
14. Han, B. et al. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nat. Genet. 48, 803-810 (2016).
15. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236-1241 (2015).
16. 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).
17. Davey Smith, G. &Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89-R98 (2014).
18. Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2-S8 (2017).
19. Hirata, M. et al. Cross-sectional analysis of BioBank Japan clinical data: A large cohort of 200,000 patients with 47 common diseases. J. Epidemiol. 27, S9-S21 (2017).
20. 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68-74 (2015).
21. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291-295 (2015).
22. Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979-986 (2015).
23. Tsai, W.-N. et al. Serum total bilirubin concentrations are inversely associated with total white blood cell counts in an adult population. Ann. Clin. Biochem. 52, 251-258 (2015).
24. Liu, Y. et al. Bilirubin possesses powerful immunomodulatory activity and suppresses experimental autoimmune encephalomyelitis. J. Immunol. 181, 1887-1897 (2008).
25. Hirota, T. et al. Genome-wide association study identifies eight new susceptibility loci for atopic dermatitis in the Japanese population. Nat. Genet. 44, 1222-1226 (2012).
26. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376-381 (2014).
27. Okada, Y. et al. Construction of a population-specific HLA imputation reference panel and its application to Graves' disease risk in Japanese. Nat. Genet. 47, 798-802 (2015).
28. Ogura, Y. et al. A functional SNP in BNC2 is associated with adolescent idiopathic scoliosis. Am. J. Hum. Genet. 97, 337-342 (2015).
29. Ikeda, M. et al. A genome-wide association study identifies two novel susceptibility loci and trans population polygenicity associated with bipolar disorder. Mol. Psychiatry https://doi.org/10.1038/mp.2016.259 (2017).
30. Low, S.-K. et al. Identification of six new genetic loci associated with atrial fibrillation in the Japanese population. Nat. Genet. 49, 953-958 (2017).
31. Liu, J., Au Yeung, S. L., Lin, S. L., Leung, G. M. &Schooling, C. M. Liver enzymes and risk of ischemic heart disease and type 2 diabetes mellitus: A Mendelian randomization study. Sci. Rep. 6, 38813 (2016).
32. Rosenberg, M. A. et al. Genetic variants related to height and risk of atrial fibrillation: the cardiovascular health study. Am. J. Epidemiol. 180, 215-222 (2014).
33. Khankari, N. K. et al. Association between adult height and risk of colorectal, lung, and prostate cancer: results from meta-analyses of prospective studies and Mendelian randomization analyses. PLoS Med. 13, e1002118 (2016).
34. Wu, A. H., Gladden, J. D., Ahmed, M., Ahmed, A. &Filippatos, G. Relation of serum uric acid to cardiovascular disease. Int. J. Cardiol. 213, 4-7 (2016).
35. Azab, B. et al. Value of albumin-globulin ratio as a predictor of all-cause mortality after non-ST elevation myocardial infarction. Angiology 64, 137-145 (2013).
36. Perlstein, T. S., Pande, R. L., Beckman, J. A. &Creager, M. A. Serum total bilirubin level and prevalent lower-extremity peripheral arterial disease: National Health and Nutrition Examination Survey (NHANES) 1999 to 2004. Arterioscler. Thromb. Vasc. Biol. 28, 166-172 (2008).
37. Timio, F., Kerry, S. M., Anson, K. M., Eastwood, J. B. &Cappuccio, F. P. Calcium urolithiasis, blood pressure and salt intake. Blood Press. 12, 122-127 (2003).
38. Bowden, J., Davey Smith, G. &Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512-525 (2015).
39. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228-1235 (2015).
40. Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
41. Trynka, G. et al. Disentangling the effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complextrait loci. Am. J. Hum. Genet. 97, 139-152 (2015).
42. Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317-330 (2015).
43. Sidney, L. E., Branch, M. J., Dunphy, S. E., Dua, H. S. &Hopkinson, A. Concise review: evidence for CD34 as a common marker for diverse progenitors. Stem Cells 32, 1380-1389 (2014).
44. Ziegler-Heitbrock, H. W. L. &Ulevitch, R. J. CD14: cell surface receptor and differentiation marker. Immunol. Today 14, 121-125 (1993).
45. Gadhoum, S. Z. &Sackstein, R. CD15 expression in human myeloid cell differentiation is regulated by sialidase activity. Nat. Chem. Biol. 4, 751-757 (2008).
46. Clark, E. A. &Lane, P. J. L. Regulation of human B-cell activation and adhesion. Annu. Rev. Immunol. 9, 97-127 (1991).
47. Sakaguchi, S., Sakaguchi, N., Asano, M., Itoh, M. &Toda, M. Immunologic self-tolerance maintained by activated T cells expressing IL-2 receptor alpha-chains (CD25). Breakdown of a single mechanism of self-tolerance causes various autoimmune diseases. J. Immunol. 155, 1151-1164 (1995).
48. Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284-290 (2015).
49. International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52-58 (2010).
50. Li, Y., Willer, C. J., Ding, J., Scheet, P. &Abecasis, G. R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816-834 (2010).
51. Kanai, M., Tanaka, T. &Okada, Y. Empirical estimation of genome-wide significance thresholds based on the 1000 Genomes Project data set. J. Hum. Genet. 61, 861-866 (2016).
52. Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336-2337 (2010).
53. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
54. Krzywinski, M. et al. Circos: An information aesthetic for comparative genomics. Genome Res. 19, 1639-1645 (2009).
55. Zheng, J. et al. LD Hub: A centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272-279 (2017).
56. Hirata, J. et al. Variants at HLA-A, HLA-C, and HLA-DQB1 confer risk of psoriasis vulgaris in Japanese. J. Invest. Dermatol. https://doi.org/10.1016/j. jid.2017.10.001 (2017).
57. Yavorska, O. O. &Burgess, S. MendelianRandomization: An R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 46, 1734-1739 (2017).