Skip to main content
SpringerLink
Log in
Book cover

Aging pp 67–76Cite as

Human Population Genetics in Aging Studies for Molecular Biologists

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2144))

Abstract

Testing hypotheses in human populations, then translating such findings into an experimental paradigm to test for causality can accelerate the rate of therapeutic discovery for many aging-related diseases. Integration of human genomics data has become much more accessible to molecular biologists in recent years due to the explosion of data availability and wealth of bioinformatic resources, tools, and methods that work together to minimize barriers related to its use. There are specific skill sets that can promote integration of human data into the work of molecular biologists, which include the ability to download, organize, store, and analyze human genomics data. In this chapter, key considerations and resources are presented, focusing on approaches that might be unfamiliar to molecular biologists, with regard to human subjects protection guidelines, heterogeneity in human genetics, data security and storage, programming languages, and training for data analysis.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Tomczak K, Czerwińska P, Wiznerowicz M (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol 19(1A):A68

    Google Scholar 

  2. Consortium EP (2004) The ENCODE (ENCyclopedia of DNA elements) project. Science 306(5696):636–640

    Article  Google Scholar 

  3. Lonsdale J et al (2013) The genotype-tissue expression (GTEx) project. Nat Genet 45(6):580

    Article  CAS  Google Scholar 

  4. Sonnega A et al (2014) Cohort profile: the health and retirement study (HRS). Int J Epidemiol 43(2):576–585

    Article  PubMed  PubMed Central  Google Scholar 

  5. Health UDo, Services H (2009) Basic HHS policy for protection of human research subjects. Title 45 Code of Federal Regulations

    Google Scholar 

  6. Braunschweiger P, Hansen K (2010) Collaborative institutional training initiative (CITI). J Clin Res Best Pract 6:1–6

    Google Scholar 

  7. Regulations COF (2009) Protection of human subjects. National Institutes of Health Office for Protection from Research Risks. 45

    Google Scholar 

  8. Congress U, Health Insurance Portability and Accountability Act of 1996, Privacy Rule. 45 CFR 164, Aug 2002

    Google Scholar 

  9. R Core Team (2017) R: a language and environment for statistical computing. R Found. Stat. Comput. Vienna, Austria. URL: http://www.R-project.org/. page R Foundation for Statistical Computing

  10. Purcell S et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Mailman MD et al (2007) The NCBI dbGaP database of genotypes and phenotypes. Nat Genet 39(10):1181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Arbour U (2006) National Archive of Computerized Data on Aging

    Google Scholar 

  13. Wang L-S et al (2014) Nia genetics of Alzheimer’s disease data storage site (NIAGADS): 2014 update. Alzheimer’s Dement 10(4):P634–P635

    Article  Google Scholar 

  14. Visscher PM et al (2017) 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet 101(1):5–22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Coordinators NR (2016) Database resources of the national center for biotechnology information. Nucleic Acids Res 44(Database issue):D7

    Google Scholar 

  16. Yates AD et al (2020) Ensembl 2020. Nucleic Acids Res 48(D1):D682–D688

    PubMed  Google Scholar 

  17. Hellenthal G et al (2014) A genetic atlas of human admixture history. Science 343(6172):747–751

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Martin AR et al (2017) Human demographic history impacts genetic risk prediction across diverse populations. Am J Hum Genet 100(4):635–649

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Miller B et al (2019) Comparing the utility of mitochondrial and nuclear DNA to adjust for genetic ancestry in association studies. Cell 8(4):306

    Article  CAS  Google Scholar 

  20. Reich D, Price AL, Patterson N (2008) Principal component analysis of genetic data. Nat Genet 40(5):491

    Article  CAS  PubMed  Google Scholar 

  21. Novembre J, Stephens M (2008) Interpreting principal component analyses of spatial population genetic variation. Nat Genet 40(5):646

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Medicine NLo (2018) Genetics home reference

    Google Scholar 

Download references

Acknowledgments

Contributions to this work were partially supported by funding from the National Institutes of Health, with support from the National Institute on Aging (NIA) training grant to B. Miller (T32 AG00037; PI: Eileen Crimmins), from an NIA training grant to A. Haghani (T32 AG052374: PI: Kelvin Davies), and from the NIA through a pilot award to T.E. Arpawong (parent award P30 AG017265; PI: Eileen Crimmins).

Author information

Authors and Affiliations

  1. Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA

    Brendan Miller, Amin Haghani, Jennifer Ailshire & T. Em Arpawong

Authors
  1. Brendan Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amin Haghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jennifer Ailshire
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. Em Arpawong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Em Arpawong .

Editor information

Editors and Affiliations

  1. Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA

    Sean P. Curran

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Miller, B., Haghani, A., Ailshire, J., Arpawong, T.E. (2020). Human Population Genetics in Aging Studies for Molecular Biologists. In: Curran, S. (eds) Aging. Methods in Molecular Biology, vol 2144. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0592-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-0592-9_6

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0591-2

  • Online ISBN: 978-1-0716-0592-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation