This article is part of a special issue on exome sequencing.
Research
The functional spectrum of low-frequency coding variation
- † Equal contributors
1 Department of Biology, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA
2 Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
3 Population Genomics Program, Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA
4 Department of Genetics, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
5 The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
6 School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA
7 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
8 Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK
9 The Genome Institute, Washington University School of Medicine, 4444 Forest Park Avenue, St Louis, MO 63108, USA
10 Department of Molecular Biology and Genetics, Cornell University, 107 Biotechnology Building, Ithaca, NY 14853, USA
Genome Biology 2011, 12:R84 doi:10.1186/gb-2011-12-9-r84
Published: 14 September 2011Abstract
Background
Rare coding variants constitute an important class of human genetic variation, but are underrepresented in current databases that are based on small population samples. Recent studies show that variants altering amino acid sequence and protein function are enriched at low variant allele frequency, 2 to 5%, but because of insufficient sample size it is not clear if the same trend holds for rare variants below 1% allele frequency.
Results
The 1000 Genomes Exon Pilot Project has collected deep-coverage exon-capture data in roughly 1,000 human genes, for nearly 700 samples. Although medical whole-exome projects are currently afoot, this is still the deepest reported sampling of a large number of human genes with next-generation technologies. According to the goals of the 1000 Genomes Project, we created effective informatics pipelines to process and analyze the data, and discovered 12,758 exonic SNPs, 70% of them novel, and 74% below 1% allele frequency in the seven population samples we examined. Our analysis confirms that coding variants below 1% allele frequency show increased population-specificity and are enriched for functional variants.
Conclusions
This study represents a large step toward detecting and interpreting low frequency coding variation, clearly lays out technical steps for effective analysis of DNA capture data, and articulates functional and population properties of this important class of genetic variation.
