Details

Autor(en) / Beteiligte

• Marth, Gabor T
• Yu, Fuli
• Indap, Amit R
• Garimella, Kiran
• Gravel, Simon
• Leong, Wen Fung
• Tyler-Smith, Chris
• Bainbridge, Matthew
• Blackwell, Tom
• Zheng-Bradley, Xiangqun
• Chen, Yuan
• Challis, Danny
• Clarke, Laura
• Ball, Edward V
• Cibulskis, Kristian
• Cooper, David N
• 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
• Gibbs, Richard

Titel

The functional spectrum of low-frequency coding variation

Ist Teil von

• Genome biology, 2011-09, Vol.12 (9), p.R84-R84

Ort / Verlag

England: BioMed Central

Erscheinungsjahr

2011

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. 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. 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.