Identifying Cryptic Relationships

  • Lei Sun
  • Apostolos Dimitromanolakis
  • Wei-Min Chen
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1666)

Abstract

Cryptic relationships such as first-degree relatives often appear in studies that collect population samples, including genome-wide association studies (GWAS) and next-generation sequencing (NGS) analyses. Cryptic relatedness not only increases type 1 error rate of association tests but also affects other analytical aspects of GWAS and NGS such as population stratification via principal component analysis. Here, we discuss three effective methods, as implemented in PREST, PLINK, and KING, to detect and correct for the problem of cryptic relatedness using high-throughput SNP data collected from GWAS and NGS experiments. We provide the analytical and practical details involved using three application examples.

Key words

Cryptic relatedness Pedigree error Relationship estimation IBD IBS IIS Kinship coefficient Likelihood EM algorithm Method-of-moments Software PREST PREST-plus PLINK KING GWAS Sequencing 

References

  1. 1.
    Voight BF, Pritchard JK (2005) Confounding from cryptic relatedness in case-control association studies. PLoS Genet 1(3):e32CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Thornton T, McPeek MS (2010) Roadtrips: case-control association testing with partially or completely unknown population and pedigree structure. Am J Hum Genet 86(2):172–184CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Price AL, Zaitlen NA, Reich D, Patterson N (2010) New approaches to population stratification in genome-wide association studies. Nat Rev Genet 11:459–463CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    McPeek MS, Sun L (2000) Statistical tests for detection of misspecified relationships by use of genome-screen data. Am J Hum Genet 66(3):1076–1094CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Dimitromanolakis A, Paterson AD, Sun L (2009) Accurate IBD inference identifies cryptic relatedness in 9 hapmap populations. Abstract no. 1768 presented at the annual meeting of the American Society of Human Genetics.Google Scholar
  6. 6.
    Sun L, Wilder K, McPeek MS (2002) Enhanced pedigree error detection. Hum Hered 54(2):99–110CrossRefPubMedGoogle Scholar
  7. 7.
    Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC (2007) Plink: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM (2010) Robust relationship inference in genome-wide association studies. Bioinformatics 56(22):2867–2873CrossRefGoogle Scholar
  9. 9.
    Chen WM, Manichaikul A, Rich SS (2016) KING 2.0: relationship inference and integrated analysis in one million samples. Abstract #365T presented at the American Society of Human Genetics annual meetingGoogle Scholar
  10. 10.
    The International HapMap Consortium (2007) A second generation human haplotype map of over 3.1 million snps. Nature 449(7164):851–861CrossRefPubMedCentralGoogle Scholar
  11. 11.
    Begleiter H, Reich T, Nurnberger JJ, Li TK, Conneally PM, Edenberg H, Crowe R, Kuperman S, Schuckit M, Bloom F, Hesselbrock V, Porjesz B, Cloninger CR, Rice J, Goate A (1999) Description of the genetic analysis workshop 11 collaborative study on the genetics of alcoholism. Genet Epidemiol 17(Suppl 1):S25–S30CrossRefPubMedGoogle Scholar
  12. 12.
    Antoni G, Morange P, Luo Y, Saut N, Burgos G, Heath S, Germain M, Biron-Andreani C, Schved J, Pernod G, Galan P, Zelenika D, Alessi M, Drouet L, Visvikis-Siest S, Wells P, Lathrop M, Emmerich J, Tregouet D, Gagnon F (2010) A multi-stage multi-design strategy provides strong evidence that the bai3 locus is associated with early-onset venous thromboembolism. J Thromb Haemost 8(12). doi: 10.1111/j.1538-7836.2010.04092.x
  13. 13.
    Browning SR, Browning BL (2010) High-resolution detection of identity by descent in unrelated individuals. Am J Hum Genet 86(4):526–539CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Lei Sun
    • 1
    • 2
  • Apostolos Dimitromanolakis
    • 3
    • 4
  • Wei-Min Chen
    • 5
    • 6
  1. 1.Department of Statistical Sciences, Faculty of Arts and SciencesUniversity of TorontoTorontoCanada
  2. 2.Division of Biostatistics, Dalla Lana School of Public HealthUniversity of TorontoTorontoCanada
  3. 3.Department of Statistical SciencesFaculty of Arts and ScienceTorontoCanada
  4. 4.Lunenfeld-Tanenbaum Research InstituteMount Sinai HospitalTorontoCanada
  5. 5.Center for Public Health GenomicsUniversity of VirginiaCharlottesvilleUSA
  6. 6.Department of Public Health SciencesUniversity of VirginiaCharlottesvilleUSA

Personalised recommendations