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The functional spectrum of low-frequency coding variation

Marth, Gabor T., Yu, Fuli, Indap, Amit R, Garimella, Kiran, Gravel, Simon, Leong, Wen, Tyler-Smith, Chris, Bainbridge, Matthew, Blackwell, Tom, Zheng-Bradley, Xiangqun, Chen, Yuan, Challis, Danny, Clarke, Laura, Ball, Edward V, Cibulskis, Kristian, Cooper, David Neil, Fulton, Bob, Hartl, Chris, Koboldt, Dan, Muzny, Donna, Smith, Richard, Sougnez, Carrie, Stewart, Chip, Ward, Alistair, Yu, Jin, Xue, Yali, Altshuler, David, Bustamante, Carlos D., Clark, Andrew G., Daly, Mark, DePristo, Mark, Flicek, Paul, Gabriel, Stacey, Mardis, Elaine, Palotie, Aarno and Gibbs, Richard 2011. The functional spectrum of low-frequency coding variation. Genome Biology 12 (9) , R84. 10.1186/gb-2011-12-9-r84

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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

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: Q Science > QH Natural history > QH426 Genetics
R Medicine > R Medicine (General)
Publisher: Genome Biology
ISSN: 1465-6906
Last Modified: 04 Jun 2017 03:52
URI: http://orca.cf.ac.uk/id/eprint/28416

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