Identifying Cryptic Relationships

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


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 


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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

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