Rate of de novo mutations and the importance of father’s age to disease risk (original) (raw)

Nature volume 488, pages 471–475 (2012)Cite this article

Subjects

Abstract

Mutations generate sequence diversity and provide a substrate for selection. The rate of de novo mutations is therefore of major importance to evolution. Here we conduct a study of genome-wide mutation rates by sequencing the entire genomes of 78 Icelandic parent–offspring trios at high coverage. We show that in our samples, with an average father’s age of 29.7, the average de novo mutation rate is 1.20 × 10−8 per nucleotide per generation. Most notably, the diversity in mutation rate of single nucleotide polymorphisms is dominated by the age of the father at conception of the child. The effect is an increase of about two mutations per year. An exponential model estimates paternal mutations doubling every 16.5 years. After accounting for random Poisson variation, father’s age is estimated to explain nearly all of the remaining variation in the de novo mutation counts. These observations shed light on the importance of the father’s age on the risk of diseases such as schizophrenia and autism.

This is a preview of subscription content, access via your institution

Access options

Subscribe to this journal

Receive 51 print issues and online access

$199.00 per year

only $3.90 per issue

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Additional access options:

Similar content being viewed by others

References

  1. Keightley, P. D. Rates and fitness consequences of new mutations in humans. Genetics 190, 295–304 (2012)
    Article Google Scholar
  2. Crow, J. F. The origins, patterns and implications of human spontaneous mutation. Nature Rev. Genet. 1, 40–47 (2000)
    Article CAS Google Scholar
  3. Kondrashov, A. S. Direct estimates of human per nucleotide mutation rates at 20 loci causing Mendelian diseases. Hum. Mutat. 21, 12–27 (2003)
    Article CAS Google Scholar
  4. Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012)
    Article ADS CAS Google Scholar
  5. O’Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012)
    Article ADS Google Scholar
  6. Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012)
    Article ADS CAS Google Scholar
  7. Xue, Y. et al. Human Y chromosome base-substitution mutation rate measured by direct sequencing in a deep-rooting pedigree. Curr. Biol. 19, 1453–1457 (2009)
    Article CAS Google Scholar
  8. Conrad, D. F. et al. Variation in genome-wide mutation rates within and between human families. Nature Genet. 43, 712–714 (2011)
    Article CAS Google Scholar
  9. Roach, J. C. et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328, 636–639 (2010)
    Article ADS CAS Google Scholar
  10. Holm, H. et al. A rare variant in MYH6 is associated with high risk of sick sinus syndrome. Nature Genet. 43, 316–320 (2011)
    Article CAS Google Scholar
  11. Rafnar, T. et al. Mutations in BRIP1 confer high risk of ovarian cancer. Nature Genet. 43, 1104–1107 (2011)
    Article CAS Google Scholar
  12. Sulem, P. et al. Identification of low-frequency variants associated with gout and serum uric acid levels. Nature Genet. 43, 1127–1130 (2011)
    Article CAS Google Scholar
  13. Keightley, P. D. et al. Analysis of the genome sequences of three Drosophila melanogaster spontaneous mutation accumulation lines. Genome Res. 19, 1195–1201 (2009)
    Article CAS Google Scholar
  14. Malaspina, D. Paternal factors and schizophrenia risk: de novo mutations and imprinting. Schizophr. Bull. 27, 379–393 (2001)
    Article CAS Google Scholar
  15. Croen, L. A., Najjar, D. V., Fireman, B. & Grether, J. K. Maternal and paternal age and risk of autism spectrum disorders. Arch. Pediatr. Adolesc. Med. 161, 334–340 (2007)
    Article Google Scholar
  16. Duong, L. et al. Mutations in NRXN1 in a family multiply affected with brain disorders: NRXN1 mutations and brain disorders. Am. J. Med. Genet. 159B, 354–358 (2012)
    Article Google Scholar
  17. Gauthier, J. et al. Truncating mutations in NRXN2 and NRXN1 in autism spectrum disorders and schizophrenia. Hum. Genet. 130, 563–573 (2011)
    Article CAS Google Scholar
  18. Kirov, G. et al. Comparative genome hybridization suggests a role for NRXN1 and APBA2 in schizophrenia. Hum. Mol. Genet. 17, 458–465 (2008)
    Article CAS Google Scholar
  19. Levinson, D. F. et al. Copy number variants in schizophrenia: confirmation of five previous findings and new evidence for 3q29 microdeletions and VIPR2 duplications. Am. J. Psychiatry 168, 302–316 (2011)
    Article Google Scholar
  20. Rujescu, D. et al. Disruption of the neurexin 1 gene is associated with schizophrenia. Hum. Mol. Genet. 18, 988–996 (2009)
    Article CAS Google Scholar
  21. Boyden, L. M. et al. Mutations in kelch-like 3 and cullin 3 cause hypertension and electrolyte abnormalities. Nature 482, 98–102 (2012)
    Article ADS CAS Google Scholar
  22. Lynch, M. Rate, molecular spectrum, and consequences of human mutation. Proc. Natl Acad. Sci. USA 107, 961–968 (2010)
    Article ADS CAS Google Scholar
  23. Nachman, M. W. & Crowell, S. L. Estimate of the mutation rate per nucleotide in humans. Genetics 156, 297–304 (2000)
    CAS PubMed PubMed Central Google Scholar
  24. Coulondre, C., Miller, J. H., Farabaugh, P. J. & Gilbert, W. Molecular basis of base substitution hotspots in Escherichia coli. Nature 274, 775–780 (1978)
    Article ADS CAS Google Scholar
  25. Kong, A. et al. Recombination rate and reproductive success in humans. Nature Genet. 36, 1203–1206 (2004)
    Article CAS Google Scholar
  26. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009)
    Article CAS Google Scholar
  27. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)
    Article CAS Google Scholar

Download references

Acknowledgements

This research was partly funded by The National Institutes of Health grant MH071425 (K.S.); the European Community’s Seventh Framework Programme, PsychCNVs project, grant agreement HEALTH-F2-2009-223423, and NextGene project, grant agreement IAPP-MC-251592; The European Community IMI grant EU-AIMS, grant agreement 115300.

Author information

Authors and Affiliations

  1. deCODE Genetics, Sturlugata 8, 101 Reykjavik, Iceland,
    Augustine Kong, Michael L. Frigge, Gisli Masson, Soren Besenbacher, Patrick Sulem, Gisli Magnusson, Sigurjon A. Gudjonsson, Asgeir Sigurdsson, Aslaug Jonasdottir, Adalbjorg Jonasdottir, Gunnar Sigurdsson, G. Bragi Walters, Stacy Steinberg, Hannes Helgason, Gudmar Thorleifsson, Daniel F. Gudbjartsson, Agnar Helgason, Olafur Th. Magnusson, Unnur Thorsteinsdottir & Kari Stefansson
  2. Bioinformatics Research Centre, Aarhus University, 8000 Aarhus, Denmark,
    Soren Besenbacher
  3. Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK,
    Wendy S. W. Wong
  4. University of Iceland, 101 Reykjavik, Iceland,
    Agnar Helgason
  5. Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland,
    Unnur Thorsteinsdottir & Kari Stefansson

Authors

  1. Augustine Kong
    You can also search for this author inPubMed Google Scholar
  2. Michael L. Frigge
    You can also search for this author inPubMed Google Scholar
  3. Gisli Masson
    You can also search for this author inPubMed Google Scholar
  4. Soren Besenbacher
    You can also search for this author inPubMed Google Scholar
  5. Patrick Sulem
    You can also search for this author inPubMed Google Scholar
  6. Gisli Magnusson
    You can also search for this author inPubMed Google Scholar
  7. Sigurjon A. Gudjonsson
    You can also search for this author inPubMed Google Scholar
  8. Asgeir Sigurdsson
    You can also search for this author inPubMed Google Scholar
  9. Aslaug Jonasdottir
    You can also search for this author inPubMed Google Scholar
  10. Adalbjorg Jonasdottir
    You can also search for this author inPubMed Google Scholar
  11. Wendy S. W. Wong
    You can also search for this author inPubMed Google Scholar
  12. Gunnar Sigurdsson
    You can also search for this author inPubMed Google Scholar
  13. G. Bragi Walters
    You can also search for this author inPubMed Google Scholar
  14. Stacy Steinberg
    You can also search for this author inPubMed Google Scholar
  15. Hannes Helgason
    You can also search for this author inPubMed Google Scholar
  16. Gudmar Thorleifsson
    You can also search for this author inPubMed Google Scholar
  17. Daniel F. Gudbjartsson
    You can also search for this author inPubMed Google Scholar
  18. Agnar Helgason
    You can also search for this author inPubMed Google Scholar
  19. Olafur Th. Magnusson
    You can also search for this author inPubMed Google Scholar
  20. Unnur Thorsteinsdottir
    You can also search for this author inPubMed Google Scholar
  21. Kari Stefansson
    You can also search for this author inPubMed Google Scholar

Contributions

A.K. and K.S. planned and directed the research. A.K. wrote the first draft and together with K.S., S.B., P.S., A.H. and U.T. wrote the final version. O.T.M. and U.T. oversaw the sequencing and laboratory work. G. Masson, G. Magnusson and G.S. processed the raw sequencing data. A.K. and M.L.F. analysed the data, with W.S.W.W., H.H., G.B.W., S.S., G.T. and D.F.G. providing assistance. P.S. and S.A.G. performed functional annotations. S.B. analysed the mutations with respect to sequence content. A.S., Aslaug J. and Adalbjorg J. did the Sanger sequencing. A.H. investigated the contribution of demographics.

Corresponding authors

Correspondence toAugustine Kong or Kari Stefansson.

Ethics declarations

Competing interests

The authors from deCODE Genetics are employees of or own stock options in deCODE Genetics. W.S.W.W. is an employee of Illumina Inc., a public company that develops and markets systems for genetic analysis; she receives stocks as part of her compensation.

Supplementary information

Supplementary Information

This file contains Supplementary Text, additional references, Supplementary Table 2 and Supplementary Figure 1. (PDF 937 kb)

Supplementary Data

This file contains Supplementary Table 1 which shows information for each of the 4,933 de novo mutations individually. They correspond to the summary in Supplementary Table 2. The positions are based on Human Assembly Build 36. (XLS 523 kb)

PowerPoint slides

Rights and permissions

About this article

Cite this article

Kong, A., Frigge, M., Masson, G. et al. Rate of de novo mutations and the importance of father’s age to disease risk.Nature 488, 471–475 (2012). https://doi.org/10.1038/nature11396

Download citation

This article is cited by

Editorial Summary

Fathers' ages linked to disease risk

De novo mutations are important both as sources of diversity in evolution and for their immediate impact on diseases. Scientists at deCODE genetics and their colleagues have used whole-genome sequencing data from 78 Icelandic parent–offspring trios to study mutation rates in humans at the genome-wide level. They find that diversity in the mutation rate of single nucleotide polymorphisms is dominated by the age of the father at the time a child is conceived. For each year increase in the father's age at conception, the number of mutations increases by about two, and once the effects of random variation are accounted for the father's age is estimated to explain almost all of the remaining variation in the de novo mutation counts. Furthermore, the results show that demographic transitions that affect the age at which males reproduce can have a considerable effect on the rate of mutations, and consequently on the risk of diseases such as schizophrenia and autism.

Associated content