Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data

Am J Hum Genet. 2012 Nov 2;91(5):839-48. doi: 10.1016/j.ajhg.2012.09.004. Epub 2012 Oct 25.

Abstract

DNA sample contamination is a serious problem in DNA sequencing studies and may result in systematic genotype misclassification and false positive associations. Although methods exist to detect and filter out cross-species contamination, few methods to detect within-species sample contamination are available. In this paper, we describe methods to identify within-species DNA sample contamination based on (1) a combination of sequencing reads and array-based genotype data, (2) sequence reads alone, and (3) array-based genotype data alone. Analysis of sequencing reads allows contamination detection after sequence data is generated but prior to variant calling; analysis of array-based genotype data allows contamination detection prior to generation of costly sequence data. Through a combination of analysis of in silico and experimentally contaminated samples, we show that our methods can reliably detect and estimate levels of contamination as low as 1%. We evaluate the impact of DNA contamination on genotype accuracy and propose effective strategies to screen for and prevent DNA contamination in sequencing studies.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • DNA Contamination*
  • Diabetes Mellitus, Type 2 / diagnosis
  • Diabetes Mellitus, Type 2 / genetics
  • Genotype*
  • Humans
  • Sequence Analysis, DNA*