Rapid genotype imputation from sequence without reference panels

Nature Genetics

Volumn 48, Issue 8, 2016, Pages 965-969

  Davies, Robert W ^a   Flint, Jonathan ^b   Myers, Simon ^a,c   Mott, Richard ^a,d  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF OXFORD   (United Kingdom)

^d UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; CFW MOUSE; CHROMOSOME 19; CHROMOSOME 20; CONTROLLED STUDY; EXPERIMENTAL MOUSE; GENE FREQUENCY; GENE SEQUENCE; GENETIC MODEL; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; GENOTYPE IMPUTATION; HAPLOTYPE; HIDDEN MARKOV MODEL; HUMAN; NONHUMAN; PRIORITY JOURNAL; QUALITY CONTROL; SINGLE NUCLEOTIDE POLYMORPHISM; STITCH METHOD; ANIMAL; ASIAN CONTINENTAL ANCESTRY GROUP; BIOLOGY; DNA SEQUENCE; GENETICS; HIGH THROUGHPUT SEQUENCING; MOUSE; OUTBRED STRAIN; POPULATION GENETICS; PROCEDURES;

ALGORITHMS; ANIMALS; ANIMALS, OUTBRED STRAINS; ASIAN CONTINENTAL ANCESTRY GROUP; COMPUTATIONAL BIOLOGY; GENETICS, POPULATION; GENOTYPE; HAPLOTYPES; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MICE; POLYMORPHISM, SINGLE NUCLEOTIDE; SEQUENCE ANALYSIS, DNA;

EID: 84976908696     PISSN: 10614036     EISSN: 15461718     Source Type: Journal    
DOI: 10.1038/ng.3594     Document Type: Article

References (27)

1. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001-D1006 (2014)
2. International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851-861 (2007)
3. 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56-65 (2012)
4. Delaneau, O., Zagury, J.-F. &Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5-6 (2013)
5. Howie, B.N., Donnelly, P. &Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009)
6. Li, Y., Willer, C.J., Ding, J., Scheet, P. &Abecasis, G.R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816-834 (2010)
7. Browning, S.R. &Browning, B.L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084-1097 (2007)
8. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. &Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955-959 (2012)
9. Swarts, K. et al. Novel methods to optimize genotypic imputation for low-coverage, next-generation sequence data in crop plants. Plant Genome http://dx.doi.org/10.3835/plantgenome2014.05.0023 (2014)
10. Huang, B.E. &George, A.W. R/mpMap: a computational platform for the genetic analysis of multiparent recombinant inbred lines. Bioinformatics 27, 727-729 (2011)
11. Sargolzaei, M., Chesnais, J.P. &Schenkel, F.S. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15, 478 (2014)
12. VanRaden, P.M., Sun, C. &O'Connell, J.R. Fast imputation using medium or low-coverage sequence data. BMC Genet. 16, 82 (2015)
13. Didion, J.P. et al. Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias. BMC Genomics 13, 34 (2012)
14. Pasaniuc, B. et al. Extremely low-coverage sequencing and imputation increases power for genome-wide association studies. Nat. Genet. 44, 631-635 (2012)
15. CONVERGE Consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588-591 (2015)
16. Scheet, P. &Stephens, M. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78, 629-644 (2006)
17. Nicod, J. et al. Genome-wide association of multiple complex traits in outbred mice by ultra-low-coverage sequencing. Nat. Genet. http://dx.doi.org/10.1038/ng.3595 (2016)
18. Yalcin, B. et al. Commercially available outbred mice for genome-wide association studies. PLoS Genet. 6, e1001085 (2010)
19. Keane, T.M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289-294 (2011)
20. DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491-498 (2011)
21. Freedman, A.H. et al. Genome sequencing highlights the dynamic early history of dogs. PLoS Genet. 10, e1004016 (2014)
22. Bovine HapMap Consortium. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 324, 528-532 (2009)
23. Daetwyler, H.D. et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat. Genet. 46, 858-865 (2014)
24. VanBuren, R. et al. Single-molecule sequencing of the desiccation-tolerant grass Oropetium thomaeum. Nature 527, 508-511 (2015)
25. Li, H. &Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754-1760 (2009)
26. Lunter, G. &Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 21, 936-939 (2011)
27. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297-1303 (2010)