Published online 7 August 2006
Nucleic Acids Research, 2006, Vol. 34, No. 13 e99
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.
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High-throughput discovery of rare human nucleotide polymorphisms by Ecotilling
1 Basic Sciences Division, Fred Hutchinson Cancer Research Center Seattle, WA 98109, USA 2 Department of Biology, University of Washington Seattle, WA 98195, USA
*To whom correspondence should be addressed. Tel: +1 206 685 1949; Fax: +1 206 616 2011; Email: btill{at}fhcrc.org
Received May 26, 2006. Revised June 21, 2006. Accepted June 21, 2006.
Human individuals differ from one another at only 
0.1% of nucleotide positions, but these single nucleotide differences account for most heritable phenotypic variation. Large-scale efforts to discover and genotype human variation have been limited to common polymorphisms. However, these efforts overlook rare nucleotide changes that may contribute to phenotypic diversity and genetic disorders, including cancer. Thus, there is an increasing need for high-throughput methods to robustly detect rare nucleotide differences. Toward this end, we have adapted the mismatch discovery method known as Ecotilling for the discovery of human single nucleotide polymorphisms. To increase throughput and reduce costs, we developed a universal primer strategy and implemented algorithms for automated band detection. Ecotilling was validated by screening 90 human DNA samples for nucleotide changes in 5 gene targets and by comparing results to public resequencing data. To increase throughput for discovery of rare alleles, we pooled samples 8-fold and found Ecotilling to be efficient relative to resequencing, with a false negative rate of 5% and a false discovery rate of 4%. We identified 28 new rare alleles, including some that are predicted to damage protein function. The detection of rare damaging mutations has implications for models of human disease.
Present address: Elisabeth Bowers, University of Colorado, Denver, CO 80262, USA
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