Theory and methodology for utilizing genes as biomarkers to determine potential biological mixtures

Sadeep Shrestha, Michael W. Smith, Terri H. Beaty, Steffanie A. Strathdee

Research output: Contribution to journalArticlepeer-review

Abstract

Genetically determined mixture information can be used as a surrogate for physical or behavioral characteristics in epidemiological studies examining research questions related to socially stigmatized behaviors and horizontally transmitted infections. A new measure, the probability of mixture discrimination (P MD), was developed to aid mixture analysis that estimates the ability to differentiate single from multiple genomes in biological mixtures. Four autosomal short tandem repeats (STRs) were identified, genotyped and evaluated in African American, European American, Hispanic, and Chinese individuals to estimate P MD. Theoretical P MD frameworks were also developed for autosomal and sex-linked (X and Y) STR markers in potential male/male, male/female and female/female mixtures. Autosomal STRs genetically determine the presence of multiple genomes in mixture samples of unknown genders with more power than the apparently simpler X and Y chromosome STRs. Evaluation of four autosomal STR loci enables the detection of mixtures of DNA from multiple sources with above 99% probability in all four racial/ethnic populations. The genetic-based approach has applications in epidemiology that provide viable alternatives to survey-based study designs. The analysis of genes as biomarkers can be used as a gold standard for validating measurements from self-reported behaviors that tend to be sensitive or socially stigmatizing, such as those involving sex and drugs.

Original languageEnglish (US)
Pages (from-to)29-38
Number of pages10
JournalAnnals of epidemiology
Volume15
Issue number1
DOIs
StatePublished - Jan 2005
Externally publishedYes

Keywords

  • Biological Mixtures
  • Genetic Biomarkers
  • Probability of Mixture Determination
  • Short Tandem Repeats

ASJC Scopus subject areas

  • Epidemiology

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