Extremely low-coverage sequencing and imputation increases power for genome-wide association studies | Zendy

Bogdan Paşaniuc | Zendy; Nadin Rohland | Zendy; Paul J. McLaren | Zendy; Kiran Garimella | Zendy; Noah Zaitlen | Zendy; Heng Li | Zendy; Namrata Gupta | Zendy; Benjamin M. Neale | Zendy; Mark J. Daly | Zendy; Pamela Sklar | Zendy; Patrick F. Sullivan | Zendy; Sarah E. Bergen | Zendy; Jennifer L. Moran | Zendy; Christina M. Hultman | Zendy; Paul Lichtenstein | Zendy; Patrik K. E. Magnusson | Zendy; Shaun Purcell | Zendy; David W. Haas | Zendy; Liming Liang | Zendy; Shamil Sunyaev | Zendy; HonCheong So | Zendy; Paul I. W. de Bakker | Zendy; David Reich | Zendy; Alkes L. Price | Zendy

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Extremely 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,

HonCheong So,

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

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