Detecting and Correcting Contamination in Genetic Data by

DNA sample contamination is a serious problem in DNA sequencing studies, and may result in systematic genotype misclassification and false positive associations. While 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 withinspecies DNA sample contamination based on (1) a combination of sequencing reads and arraybased 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 insilico 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. A This work has been published: Jun, G., Flickinger, M., Hetrick, K.N., Romm, J.M., Doheny, K.F., Abecasis, G.R., Boehnke, M., Kang, H.M. (2012). Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am J Hum Genet 91, 839–848.