Bayesian association scan reveals loci associated with human lifespan and linked biomarkers

Nature Communications

Volumn 8, Issue , 2017, Pages

  McDaid, Aaron F ^a,b   Joshi, Peter K ^c   Porcu, Eleonora ^b,d   Komljenovic, Andrea ^b,d   Li, Hao ^e   Sorrentino, Vincenzo ^e   Litovchenko, Maria ^b,e   Bevers, Roel P J ^a,b   Ruëger, Sina ^d   Reymond, Alexandre ^a   Bochud, Murielle ^b,e   Deplancke, Bart ^f   Williams, Robert W ^b,d   Robinson Rechavi, Marc ^a   Paccaud, Fred ^a   Rousson, Valentin ^a   Auwerx, Johan ^e   Wilson, James F ^c,g   Kutalik, Zoltán ^a,b  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

^b SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

^c UNIVERSITY OF EDINBURGH   (United Kingdom)

^d UNIVERSITY OF LAUSANNE   (Switzerland)

^e EPFL   (Switzerland)

^f UNIVERSITY OF TENNESSEE HEALTH SCIENCE CENTER   (United States)

^g UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; ARYL SULFOTRANSFERASE; CHRNA5 PROTEIN, HUMAN; NERVE PROTEIN; NICOTINIC RECEPTOR; RBM6 PROTEIN, HUMAN; RNA BINDING PROTEIN; SULT1A1 PROTEIN, HUMAN;

ALLELE; BAYESIAN ANALYSIS; BIOMARKER; BRAIN; DATABASE; DISEASE INCIDENCE; EXPERIMENTAL DESIGN; GENE; GENE EXPRESSION; GENETIC MARKER; LONGEVITY;

ALLELE; ARTICLE; BAYESIAN LEARNING; CHRNA5 GENE; FALSE DISCOVERY RATE; GENE; GENE EXPRESSION; GENOME-WIDE ASSOCIATION STUDY; HUMAN; LIFESPAN; PARAMETERS; RBM6 GENE; SINGLE NUCLEOTIDE POLYMORPHISM; SULT1A1 GENE; AGED; BAYES THEOREM; CAUCASIAN; DISEASES; FEMALE; GENETICS; LONGEVITY; MALE; UNITED KINGDOM; VERY ELDERLY;

UNITED KINGDOM;

AGED; AGED, 80 AND OVER; ARYLSULFOTRANSFERASE; BAYES THEOREM; BIOMARKERS; DISEASE; EUROPEAN CONTINENTAL ANCESTRY GROUP; FEMALE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LONGEVITY; MALE; NERVE TISSUE PROTEINS; POLYMORPHISM, SINGLE NUCLEOTIDE; RECEPTORS, NICOTINIC; RNA-BINDING PROTEINS; UNITED KINGDOM;

EID: 85026374800     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms15842     Document Type: Article

References (63)

1. Murabito, J. M., Yuan, R., Lunetta, K. L. The search for longevity and healthy aging genes: Insights from epidemiological studies and samples of long-lived individuals. J. Gerontol. A Biol. Sci. Med. Sci. 67, 470-479 (2012).
2. Kaplan, H., Hill, K., Lancaster, J., Hurtado, A. M. A theory of human life history evolution: Diet, intelligence, and longevity. Evol. Anthropol. 9, 156-185 (2000).
3. Rappuoli, R., Mandl, C. W., Black, S., De Gregorio, E. Vaccines for the twenty-first century society. Nat. Rev. Immunol. 11, 865-872 (2011).
4. Greene, V. W. Personal hygiene and life expectancy improvements since 1850: Historic and epidemiologic associations. Am. J. Infect. Control 29, 203-206 (2001).
5. Herskind, A. M. et al. The heritability of human longevity: A population-based study of 2, 872 Danish twin pairs born 1870-1900. Hum. Genet. 97, 319-323 (1996).
6. Perls, T. et al. Exceptional familial clustering for extreme longevity in humans. J. Am. Geriatr. Soc. 48, 1483-1485 (2000).
7. Terry, D. F. et al. Characteristics of Framingham offspring participants with long-lived parents. Arch. Intern. Med. 167, 438-444 (2007).
8. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001-D1006 (2014).
9. Newman, A. B. et al. A meta-analysis of four genome-wide association studies of survival to age 90 years or older: The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. J. Gerontol. A Biol. Sci. Med. Sci. 65, 478-487 (2010).
10. Broer, L. et al. GWAS of longevity in CHARGE consortium confirms APOE and FOXO3 candidacy. J. Gerontol. A Biol. Sci. Med. Sci. 70, 110-118 (2015).
11. Deelen, J. et al. Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age. Hum. Mol. Genet. 23, 4420-4432 (2014).
12. Walter, S. et al. A genome-wide association study of aging. Neurobiol. Aging 32, 2109 e15-e28 (2011).
13. Pilling, L. C. et al. Human longevity is influenced by many genetic variants: Evidence from 75, 000 UK Biobank participants. Aging (Albany NY) 8, 547-563 (2016).
14. Joshi, P. K. et al. Variants near CHRNA3/5 and APOE have age-A nd sex-related effects on human lifespan. Nat. Commun. 7, 11174 (2016).
15. Sudlow, C. et al. UK biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
16. Thorgeirsson, T. E. et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature 452, 638-642 (2008).
17. Corder, E. H. et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science 261, 921-923 (1993).
18. Fortney, K. et al. Genome-wide scan informed by age-related disease identifies loci for exceptional human longevity. PLoS Genet. 11, e1005728 (2015).
19. Neyman, J. On the problem of the most efficient tests of statistical hypotheses. Philos. Trans. R. Soc. Lond. A-Contain. Pap. Math. Phys. Character 231, 289-337 (1933).
20. Pasaniuc, B. et al. Fast and accurate imputation of summary statistics enhances evidence of functional enrichment. Bioinformatics 30, 2906-2914 (2014).
21. Kabat, G. C. et al. Adult stature and risk of cancer at different anatomic sites in a cohort of postmenopausal women. Cancer Epidemiol. Biomarkers Prev. 22, 1353-1363 (2013).
22. Fisher, R. A. Questions and answers. Am. Stat. 2, 30-31 (1948).
23. Consortium, GTEx. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science 348, 648-660 (2015).
24. Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015).
25. Chen, B. H. et al. DNA methylation-based measures of biological age: Metaanalysis predicting time to death. Aging (Albany NY) 8, 1844-1865 (2016).
26. Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238-1243 (2013).
27. Grundberg, E. et al. Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements. Am. J. Hum. Genet. 93, 876-890 (2013).
28. Bonder, M. J. et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat. Genet. 49, 131-138 (2017).
29. Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).
30. Liao, C. Y., Rikke, B. A., Johnson, T. E., Diaz, V., Nelson, J. F. Genetic variation in the murine lifespan response to dietary restriction: From life extension to life shortening. Aging Cell 9, 92-95 (2010).
31. Kang, H. M. et al. Efficient control of population structure in model organism association mapping. Genetics 178, 1709-1723 (2008).
32. Greer, E. L., Brunet, A. Different dietary restriction regimens extend lifespan by both independent and overlapping genetic pathways in C. elegans. Aging Cell 8, 113-127 (2009).
33. Metaxakis, A., Partridge, L. Dietary restriction extends lifespan in wild-derived populations of Drosophila melanogaster. PLoS ONE 8, e74681 (2013).
34. Weindruch, R., Walford, R. L. Dietary restriction in mice beginning at 1 year of age: Effect on life-span and spontaneous cancer incidence. Science 215, 1415-1418 (1982).
35. Mattison, J. A. et al. Caloric restriction improves health and survival of rhesus monkeys. Nat. Commun. 8, 14063 (2017).
36. Heilbronn, L. K. et al. Effect of 6-month calorie restriction on biomarkers of longevity, metabolic adaptation, and oxidative stress in overweight individuals: A randomized controlled trial. JAMA 295, 1539-1548 (2006).
37. Holloszy, J. O., Fontana, L. Caloric restriction in humans. Exp. Gerontol. 42, 709-712 (2007).
38. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236-1241 (2015).
39. Beekman, M. et al. Genome-wide linkage analysis for human longevity: Genetics of Healthy Aging Study. Aging Cell 12, 184-193 (2013).
40. Lamparter, D., Marbach, D., Rueedi, R., Kutalik, Z., Bergmann, S. Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLoS Comput. Biol. 12, e1004714 (2016).
41. White, I. R., Altmann, D. R., Nanchahal, K. Alcohol consumption and mortality: Modelling risks for men and women at different ages. BMJ 325, 191 (2002).
42. Marioni, R. E. et al. Genetic variants linked to education predict longevity. Proc. Natl Acad. Sci. USA 113, 13366-13371 (2016).
43. van Vliet, P., Oleksik, A. M., van Heemst, D., de Craen, A. J., Westendorp, R. G. Dynamics of traditional metabolic risk factors associate with specific causes of death in old age. J. Gerontol. A Biol. Sci. Med. Sci. 65, 488-494 (2010).
44. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539-542 (2016).
45. Rietveld, C. A. et al. GWAS of 126, 559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467-1471 (2013).
46. Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714-719 (2010).
47. Lin, D. Y., Sullivan, P. F. Meta-analysis of genome-wide association studies with overlapping subjects. Am. J. Hum. Genet. 85, 862-872 (2009).
48. Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291-295 (2015).
49. Greene, W. H. Econometric Analysis 1188 (Prentice Hall, 2012).
50. Burgess, S., Thompson, S. G. Multivariable Mendelian randomization: The use of pleiotropic genetic variants to estimate causal effects. Am. J. Epidemiol. 181, 251-260 (2015).
51. Wen, X. Robust Bayesian FDR control using Bayes factors, with applications to multi-tissue eQTL discovery. Stat. Biosci. 9, 28-49 (2017).
52. Dudoit, S., Shaffer, J. P., Boldrick, J. C. Multiple hypothesis testing in microarray experiments. Stat. Sci. 18, 71-103 (2003).
53. Bertolino, F., Cabras, S., Castellanos, M. E., Racugno, W. Unscaled Bayes factors for multiple hypothesis testing in microarray experiments. Stat. Methods Med. Res. 24, 1030-1043 (2015).
54. Cox, D. R. Regression models and life-tables. J. R. Stat. Soc. B Stat. Methodol. 34, 187-220 (1972).
55. Willer, C. J., Li, Y., Abecasis, G. R. METAL: Fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190-2191 (2010).
56. Tsai, S. P., Hardy, R. J., Wen, C. P. The standardized mortality ratio and life expectancy. Am. J. Epidemiol. 135, 824-831 (1992).
57. Barrett, T. et al. NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 41, D991-D995 (2013).
58. Irizarry, R. A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).
59. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
60. Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333-338 (2011).
61. Tobacco & Genetics, C. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441-447 (2010).
62. Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707-713 (2010).
63. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197-206 (2015).