The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly.
Aged
Aged, 80 and over
Cohort Studies
Databases, Genetic
Female
Gene Frequency
Genetic Predisposition to Disease
Genetic Variation
Genome, Human
Healthy Volunteers
Humans
Male
Middle Aged
Mitochondria
/ genetics
Neoplasms
/ genetics
Physical Functional Performance
Polymorphism, Single Nucleotide
Whole Genome Sequencing
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
23 01 2020
23 01 2020
Historique:
received:
21
07
2019
accepted:
13
12
2019
entrez:
25
1
2020
pubmed:
25
1
2020
medline:
25
4
2020
Statut:
epublish
Résumé
Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the first release of the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analyse the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. MGRB individuals have fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK Biobank cohorts, consistent with risk depletion. Age-related somatic changes are correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing.
Identifiants
pubmed: 31974348
doi: 10.1038/s41467-019-14079-0
pii: 10.1038/s41467-019-14079-0
pmc: PMC6978518
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
435Subventions
Organisme : NIA NIH HHS
ID : U19 AG062682
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG029824
Pays : United States
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_12028
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Références
Timmers, P. R. et al. Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances. Elife 8, e39856 (2019).
pubmed: 30642433
pmcid: 6333444
doi: 10.7554/eLife.39856
Pilling, L. C. et al. Human longevity: 25 genetic loci associated in 389,166 UK biobank participants. Aging 9, 2504–2520 (2017).
pubmed: 29227965
pmcid: 5764389
doi: 10.18632/aging.101334
Deelen, J. et al. A meta-analysis of genome-wide association studies identifies multiple longevity genes. Nat. Commun. 10, 3669 (2019).
pubmed: 31413261
pmcid: 6694136
doi: 10.1038/s41467-019-11558-2
Erikson, G. A. et al. Whole-genome sequencing of a healthy aging cohort. Cell 165, 1002–1011 (2016).
pubmed: 27114037
pmcid: 4860090
doi: 10.1016/j.cell.2016.03.022
Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).
pubmed: 25426838
pmcid: 4290021
doi: 10.1056/NEJMoa1409405
Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).
pubmed: 25426837
pmcid: 4306669
doi: 10.1056/NEJMoa1408617
Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
pubmed: 28686856
pmcid: 5501872
doi: 10.1016/j.ajhg.2017.06.005
Zuk, O. et al. Searching for missing heritability: designing rare variant association studies. Proc. Natl. Acad. Sci. USA 111, E455–E464 (2014).
pubmed: 24443550
doi: 10.1073/pnas.1322563111
pmcid: 24443550
Zuk, O., Hechter, E., Sunyaev, S. R. & Lander, E. S. The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl. Acad. Sci. USA 109, 1193–1198 (2012).
pubmed: 22223662
doi: 10.1073/pnas.1119675109
pmcid: 22223662
Li, D., Lewinger, J. P., Gauderman, W. J., Murcray, C. E. & Conti, D. Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies. Genet. Epidemiol. 35, 790–799 (2011).
pubmed: 21922541
pmcid: 4238184
doi: 10.1002/gepi.20628
Lacaze, P. The Medical Genome Reference Bank: a whole-genome data resource of 4,000 healthy elderly individuals. Rationale and cohort design. Eur. J. Hum. Genet. 27, 308–316 (2018).
pubmed: 30353151
pmcid: 6336775
doi: 10.1038/s41431-018-0279-z
McNeil, J. J. et al. Baseline characteristics of participants in the ASPREE (ASPirin in Reducing Events in the Elderly) Study. J. Gerontol. A Biol. Sci. Med. Sci. 72, 1586–1593 (2017).
pubmed: 28329340
pmcid: 5861878
doi: 10.1093/gerona/glw342
45 and Up Study Collaborators et al. Cohort profile: the 45 and up study. Int. J. Epidemiol. 37, 941–947 (2008).
doi: 10.1093/ije/dym184
Zook, J. M. et al. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat. Biotechnol. 32, 246–251 (2014).
pubmed: 24531798
doi: 10.1038/nbt.2835
Telenti, A. et al. Deep sequencing of 10,000 human genomes. Proc. Natl. Acad. Sci. USA 113, 11901–11906 (2016).
pubmed: 27702888
doi: 10.1073/pnas.1613365113
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
pubmed: 27535533
pmcid: 5018207
doi: 10.1038/nature19057
Walsh, R. et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet. Med. 19, 192–203 (2017).
pubmed: 27532257
doi: 10.1038/gim.2016.90
Kalia, S. S. et al. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2. 0): a policy statement of the American College of Medical Genetics and Genomics. Genet. Med. 19, 249–255 (2016).
pubmed: 27854360
doi: 10.1038/gim.2016.190
Chen, R. et al. Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat. Biotechnol. 34, 531–538 (2016).
pubmed: 27065010
doi: 10.1038/nbt.3514
Langsted, A., Nordestgaard, B. G., Benn, M., Tybjærg-Hansen, A. & Kamstrup, P. R. PCSK9 R46L loss-of-function mutation reduces Lipoprotein (a), LDL cholesterol, and risk of aortic valve stenosis. J. Clin. Endocrinol. Metab. 101, 3281–3287 (2016).
pubmed: 27218270
doi: 10.1210/jc.2016-1206
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 (2014).
pubmed: 25282103
pmcid: 4250049
doi: 10.1038/ng.3097
Young, A. L., Challen, G. A., Birmann, B. M. & Druley, T. E. Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults. Nat. Commun. 7, 12484 (2016).
pubmed: 27546487
pmcid: 4996934
doi: 10.1038/ncomms12484
van den Akker, E. B. et al. Uncompromised 10-year survival of oldest old carrying somatic mutations in DNMT3A and TET2. Blood 127, 1512–1515 (2016).
pubmed: 26825711
pmcid: 4797027
doi: 10.1182/blood-2015-12-685925
Russler-Germain, D. A. et al. The R882H DNMT3A mutation associated with AML dominantly inhibits wild-type DNMT3A by blocking its ability to form active tetramers. Cancer Cell 25, 442–454 (2014).
pubmed: 24656771
pmcid: 4018976
doi: 10.1016/j.ccr.2014.02.010
Gelsi-Boyer, V. et al. Mutations in ASXL1 are associated with poor prognosis across the spectrum of malignant myeloid diseases. J. Hematol. Oncol. 5, 12 (2012).
pubmed: 22436456
pmcid: 3355025
doi: 10.1186/1756-8722-5-12
Wachsmuth, M., Hübner, A., Li, M., Madea, B. & Stoneking, M. Age-related and heteroplasmy-related variation in human mtDNA copy number. PLoS Genet. 12, e1005939 (2016).
pubmed: 26978189
pmcid: 4792396
doi: 10.1371/journal.pgen.1005939
Kennedy, S. R., Salk, J. J., Schmitt, M. W. & Loeb, L. A. Ultra-sensitive sequencing reveals an age-related increase in somatic mitochondrial mutations that are inconsistent with oxidative damage. PLoS Genet. 9, e1003794 (2013).
pubmed: 24086148
pmcid: 3784509
doi: 10.1371/journal.pgen.1003794
von Zglinicki, T. & Martin-Ruiz, C. M. Telomeres as biomarkers for ageing and age-related diseases. Curr. Mol. Med. 5, 197–203 (2005).
doi: 10.2174/1566524053586545
Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015).
pubmed: 26551669
pmcid: 4783858
doi: 10.1038/ng.3441
Chainani, V. et al. Objective measures of the frailty syndrome (hand grip strength and gait speed) and cardiovascular mortality: a systematic review. Int. J. Cardiol. 215, 487–493 (2016).
pubmed: 27131770
doi: 10.1016/j.ijcard.2016.04.068
Manrai, A. K., Patel, C. J. & Ioannidis, J. P. A. In the era of precision medicine and big data, who is normal? JAMA 319, 1981–1982 (2018).
pubmed: 29710130
doi: 10.1001/jama.2018.2009
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).
pubmed: 24688116
pmcid: 4103672
doi: 10.1093/hmg/ddu139
Dewey, F. E. et al. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354, aaf6814 (2016).
pubmed: 28008009
doi: 10.1126/science.aaf6814
Amendola, L. M. et al. Actionable exomic incidental findings in 6503 participants: challenges of variant classification. Genome Res. 25, 305–315 (2015).
pubmed: 25637381
pmcid: 4352885
doi: 10.1101/gr.183483.114
Dorschner, M. O. et al. Actionable, pathogenic incidental findings in 1,000 participants’ exomes. Am. J. Hum. Genet. 93, 631–640 (2013).
pubmed: 24055113
pmcid: 3791261
doi: 10.1016/j.ajhg.2013.08.006
Martincorena, I. et al. Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880–886 (2015).
pubmed: 25999502
pmcid: 4471149
doi: 10.1126/science.aaa6806
Artandi, S. E. et al. Telomere dysfunction promotes non-reciprocal translocations and epithelial cancers in mice. Nature 406, 641–645 (2000).
pubmed: 10949306
doi: 10.1038/35020592
Chin, L. et al. p53 deficiency rescues the adverse effects of telomere loss and cooperates with telomere dysfunction to accelerate carcinogenesis. Cell 97, 527–538 (1999).
pubmed: 10338216
doi: 10.1016/S0092-8674(00)80762-X
Sahin, E. et al. Telomere dysfunction induces metabolic and mitochondrial compromise. Nature 470, 359–365 (2011).
pubmed: 21307849
pmcid: 3741661
doi: 10.1038/nature09787
Lee, H. W. et al. Essential role of mouse telomerase in highly proliferative organs. Nature 392, 569–574 (1998).
pubmed: 9560153
doi: 10.1038/33345
Dumble, M. et al. The impact of altered p53 dosage on hematopoietic stem cell dynamics during aging. Blood 109, 1736–1742 (2007).
pubmed: 17032926
pmcid: 1794064
doi: 10.1182/blood-2006-03-010413
Loughland, C. et al. Australian Schizophrenia Research Bank: a database of comprehensive clinical, endophenotypic and genetic data for aetiological studies of schizophrenia. Aust. NZ J. Psychiatry 44, 1029–1035 (2010).
Van der Auwera, G. A. et al. From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).
Tange, O. Gnu parallel-the command-line power tool. USENIX Magazine 36, 42–47 (2011).
Meynert, A. M., Ansari, M., FitzPatrick, D. R. & Taylor, M. S. Variant detection sensitivity and biases in whole genome and exome sequencing. BMC Bioinformatics 15, 247 (2014).
pubmed: 25038816
pmcid: 4122774
doi: 10.1186/1471-2105-15-247
Ganna, A. et al. Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nat. Neurosci. 19, 1563–1565 (2016).
pubmed: 27694993
pmcid: 5127781
doi: 10.1038/nn.4404
Conomos, M. P., Miller, M. B., & Thornton, T. Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness. Genet. Epidemiol.39, 276-293 (2015).
Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).
pubmed: 23060615
pmcid: 3519454
doi: 10.1093/bioinformatics/bts606
Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R. J. 8, 289–317 (2016).
pubmed: 27818791
pmcid: 5096736
doi: 10.32614/RJ-2016-021
Cameron, D. L. et al. GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly. Genome Res. 27, 2050–2060 (2017).
pubmed: 29097403
pmcid: 5741059
doi: 10.1101/gr.222109.117
Thung, D. T. et al. Mobster: accurate detection of mobile element insertions in next generation sequencing data. Genome Biol. 15, 488 (2014).
pubmed: 25348035
pmcid: 4228151
doi: 10.1186/s13059-014-0488-x
Nagpal, S., Gibson, G. & Marigorta, U. M. Pervasive modulation of obesity risk by the environment and genomic background. Genes 9, E411 (2018).
pubmed: 30110940
doi: 10.3390/genes9080411
Machiela, M. J. & Chanock, S. J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557 (2015).
pubmed: 26139635
pmcid: 4626747
doi: 10.1093/bioinformatics/btv402
Schumacher, F. R. et al. Genome-wide association study of colorectal cancer identifies six new susceptibility loci. Nat. Commun. 6, 7138 (2015).
pubmed: 26151821
pmcid: 4967357
doi: 10.1038/ncomms8138
Law, M. H. et al. Genome-wide meta-analysis identifies five new susceptibility loci for cutaneous malignant melanoma. Nat. Genet. 47, 987 (2015).
pubmed: 26237428
pmcid: 4557485
doi: 10.1038/ng.3373
Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).
pubmed: 5798588
pmcid: 5798588
doi: 10.1038/nature24284
Hoffmann, T. J. et al. A large multiethnic genome-wide association study of prostate cancer identifies novel risk variants and substantial ethnic differences. Cancer Discov. 5, 878–891 (2015).
pubmed: 26034056
pmcid: 4527942
doi: 10.1158/2159-8290.CD-15-0315
Warren, H. R. et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat. Genet. 49, 403–415 (2017).
pubmed: 28135244
pmcid: 5972004
doi: 10.1038/ng.3768
Thériault, S. et al. Polygenic contribution in individuals with early-onset coronary artery disease. Circ. Genom. Precis. Med. 11, e001849 (2018).
pubmed: 29874178
doi: 10.1161/CIRCGEN.117.001849
Lubitz, S. A. et al. Genetic risk prediction of atrial fibrillation. Circulation 135, 1311–1320 (2017).
pubmed: 27793994
doi: 10.1161/CIRCULATIONAHA.116.024143
Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).
pubmed: 24162737
pmcid: 3896259
doi: 10.1038/ng.2802
Genin, E. et al. APOE and Alzheimer disease: a major gene with semi-dominant inheritance. Mol. Psychiatry 16, 903–907 (2011).
pubmed: 21556001
pmcid: 3162068
doi: 10.1038/mp.2011.52
McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).
pubmed: 4893825
pmcid: 4893825
doi: 10.1186/s13059-016-0974-4
Ding, Z. et al. Estimating telomere length from whole genome sequence data. Nucleic Acids Res. 42, e75 (2014).
pubmed: 24609383
pmcid: 4027178
doi: 10.1093/nar/gku181
Cawthon, R. M. Telomere measurement by quantitative PCR. Nucleic Acids Res. 30, e47 (2002).
pubmed: 12000852
pmcid: 115301
doi: 10.1093/nar/30.10.e47
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
pubmed: 23945592
pmcid: 3776390
doi: 10.1038/nature12477
Gehring, J. S., Fischer, B., Lawrence, M. & Huber, W. SomaticSignatures: inferring mutational signatures from single-nucleotide variants. Bioinformatics 31, 3673–3675 (2015).
pubmed: 26163694
pmcid: 4817139
Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. Assoc. 99, 673–686 (2004).
doi: 10.1198/016214504000000980
Holm, S. A simple sequentially rejective multiple test procedure. Scand. Stat. Theory Appl. 6, 65–70 (1979).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).