Get access

Comparisons of Familial DNA Database Searching Strategies

Authors

  • Jianye Ge Ph.D.,

    1. Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107.
    2. Institute of Investigative Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107.
    Search for more papers by this author
  • Ranajit Chakraborty Ph.D.,

    1. Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107.
    2. Institute of Investigative Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107.
    Search for more papers by this author
  • Arthur Eisenberg Ph.D.,

    1. Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107.
    2. Institute of Investigative Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107.
    Search for more papers by this author
  • Bruce Budowle Ph.D.

    1. Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107.
    2. Institute of Investigative Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107.
    Search for more papers by this author

Errata

This article is corrected by:

  1. Errata: Erratum Volume 57, Issue 1, 283, Article first published online: 4 January 2012

Additional information and reprint requests:
Jianye Ge, Ph.D.
Institute of Investigative Genetics
University of North Texas, Health Science Center
3500 Camp Bowie Blvd.
Fort Worth, TX 76107
E-mail: Jianye.Ge@unthsc.edu

Abstract

Abstract:  The current familial searching strategies are generally based on either Identity-By-State (IBS) (i.e., number of shared alleles) or likelihood ratio (i.e., kinship index [KI]) assessments. In this study, the expected IBS match probabilities given relationships and the logic of the likelihood ratio method were addressed. Further, the false-positive and false-negative rates of the strategies were compared analytically or by simulations using Caucasian population data of the 13 CODIS Short Tandem Repeat (STR). IBS ≥ 15, IBS ≥ 16, KI ≥ 1000, or KI ≥ 10,000 were found to be good thresholds for balancing false-positive and false-negative rates. IBS ≥ 17 and/or KI ≥ 1,000,000 can exclude the majority of candidate profiles in the database, either related or not, and may be an initial screening option if a small candidate list is desired. Polices combining both IBS and KI can provide higher accuracy. Typing additional STRs can provide better searching performance, and lineage markers can be extremely useful for reducing false rates.

Get access to the full text of this article

Ancillary