Open AccessExtremely low-coverage sequencing and imputation increases power for genome-wide association studies
Author(s) -
Bogdan Paşaniuc,
Nadin Rohland,
Paul J. McLaren,
Kiran Garimella,
Noah Zaitlen,
Heng Li,
Namrata Gupta,
Benjamin M. Neale,
Mark J. Daly,
Pamela Sklar,
Patrick F. Sullivan,
Sarah E. Bergen,
Jennifer L. Moran,
Christina M. Hultman,
Paul Lichtenstein,
Patrik K. E. Magnusson,
Shaun Purcell,
David W. Haas,
Liming Liang,
Shamil Sunyaev,
Nick Patterson,
Paul I. W. de Bakker,
David Reich,
Alkes L. Price
Publication year - 2012
Publication title -
carolina digital repository (university of north carolina at chapel hill)
Language(s) - English
DOI - 10.17615/haws-ma91
Subject(s) - imputation (statistics) , genome wide association study , genetic association , computational biology , biology , genetics , computer science , single nucleotide polymorphism , machine learning , gene , genotype , missing data