The functional spectrum of low-frequency coding variation View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-09

AUTHORS

Gabor T Marth, Fuli Yu, Amit R Indap, Kiran Garimella, Simon Gravel, Wen Fung Leong, Chris Tyler-Smith, Matthew Bainbridge, Tom Blackwell, Xiangqun Zheng-Bradley, Yuan Chen, Danny Challis, Laura Clarke, Edward V Ball, Kristian Cibulskis, David N Cooper, Bob Fulton, Chris Hartl, Dan Koboldt, Donna Muzny, Richard Smith, Carrie Sougnez, Chip Stewart, Alistair Ward, Jin Yu, Yali Xue, David Altshuler, Carlos D Bustamante, Andrew G Clark, Mark Daly, Mark DePristo, Paul Flicek, Stacey Gabriel, Elaine Mardis, Aarno Palotie, Richard Gibbs, the 1000 Genomes Project

ABSTRACT

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. More... »

PAGES

r84

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/gb-2011-12-9-r84

DOI

http://dx.doi.org/10.1186/gb-2011-12-9-r84

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012643182

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/21917140


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