Fast admixture analysis and population tree estimation for SNP and NGS data

Bioinformatics

Volumn 33, Issue 14, 2017, Pages 2148-2155

  Cheng, Jade Yu ^a,b,c   Mailund, Thomas ^a   Nielsen, Rasmus ^b,c  

^a AARHUS UNIVERSITY   (Denmark)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; CLASSIFICATION; DNA SEQUENCE; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN; PHYLOGENY; POPULATION GENETICS; POPULATION GROUP; PROCEDURES; SOFTWARE;

ALGORITHMS; GENETICS, POPULATION; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; PHYLOGENY; POPULATION GROUPS; SEQUENCE ANALYSIS, DNA; SOFTWARE;

EID: 85024488622     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btx098     Document Type: Conference Paper

References (37)

1. Alexander, D.H., et al. (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res., 19, 1655-1664.
2. Alexander, D.H. and Lange, K. (2011) Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinformatics, 12, 1.
3. Athanasiadis, G. et al. (2016) Nationwide genomic study in Denmark reveals remarkable population homogeneity. Genetics, genetics-116.
4. Browning, S.R. and Browning, B.L. (2007) 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.
5. Cavalli-Sforza, L.L. et al. (1964) Analysis of human evolution under random genetic drift. In: Cavalli-Sforza, L.L. and Anthony, W.F. (eds), Cold Spring Harbor Symposia on Quantitative Biology, vol. 29, pp. 923-933. Cold Spring Harbor Laboratory Press.
6. Cavalli-Sforza, L.L. et al. (1967) Phylonative American population historyge-netic analysis. Models and estimation procedures. Am. J. Hum. Genet., 1, 233.
7. Cholesky, A.-L. (2005) Sur la resolution numerique des systemes d'équations lineaires. Bulletin De La Sabix. Societe Des Amis De La Bibliotheque Et De L'Histoire De L'Ecole Polytechnique, 39, 81-95.
8. Coop, G. et al. (2010) Using environmental correlations to identify loci underlying local adaptation. Genetics, 185, 1411-1423.
9. Espeseth, T. et al. (2012) Imaging and cognitive genetics: The Norwegian Cognitive NeuroGenetics sample. Twin Res. Hum. Genet., 15, 442-452.
10. Excoffier, L. et al. (2013) Robust demographic inference from genomic and SNP data. PLoS Genet., 9, e1003905.
11. Felsenstein J. (1985) Phylogenies and the comparative method. Am. Nat., 1-15.
12. Gao, H. et al. (2007) A Markov chain Monte Carlo approach for joint inference of population structure and inbreeding rates from multilocus genotype data. Genetics, 176, 1635-1651.
13. Gunther, T. and Coop, G. (2013) Robust identification of local adaptation from allele frequencies. Genetics, 195, 205-220.
14. International HapMap Consortium. (2005) A haplotype map of the human genome. Nature, 437, 1299-1320.
15. Karush, W. (1939) Minima of functions of several variables with inequalities as side constraints. PhD diss., Master's thesis, Dept. of Mathematics, Univ. of Chicago.
16. Korneliussen, T.S. et al. (2014) ANGSD: analysis of next generation sequencing data. BMC Bioinformatics, 15, 1.
17. Kuhn, H.W.-T. (1951) AW (1951) Nonlinear programming. In: 2nd Berkeley Symposium. University of California Press, Berkeley.
18. Laaksovirta, H. et al. (2010) Chromosome 9p21 in amyotrophic lateral sclerosis in Finland: A genome-wide association study. Lancet Neurol., 9, 978-985.
19. Marjoram, P. and Tavaré, S. (2006) Modern computational approaches for analysing molecular genetic variation data. Nat. Rev. Genet., 7, 759-770.
20. McVean, G.A.T. and Cardin, N.J. (2005) Approximating the coalescent with recombination. Philos. Trans. R. Soc. Lond. B Biol. Sci., 360, 1387-1393.
21. Murty, K.G. and Feng-Tien, Y. (1988) Linear Complementarity, Linear and Nonlinear Programming. Heldermann, Berlin.
22. Nelder, J.A. and Mead, R. (1965) A simplex method for function minimization. Comput. J., 7, 308-313.
23. Nelson, M.R. et al. (2008) The Population Reference Sample, POPRES: A resource for population, disease, and pharmacological genetics research. Am. J. Hum. Genet., 83, 347-358.
24. Pickrell, J.K. and Pritchard, J.K. (2012) Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet., 8, e1002967.
25. Pritchard, J.K. et al. (2000) Inference of population structure using multilocus genotype data. Genetics, 155, 945-959.
26. Purcell, S. et al. (2007) PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet., 81, 559-575.
27. Reich, D. et al. (2012) Reconstructing native American population history. Nature, 488, 370-374.
28. Ripke, S. et al. (2013) Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat. Genet., 45, 1150-1159.
29. Royal, C.D. et al. (2010) Inferring genetic ancestry: opportunities, challenges, and implications. Am. J. Hum. Genet., 86, 661-673.
30. Saitou, N. and Nei, M. (1987) The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evolut., 4, 406-425.
31. Scheet, P. and Stephens, M. (2006) 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.
32. Skotte, L. et al. (2013) Estimating individual admixture proportions from next generation sequencing data. Genetics, 195, 693-702.
33. Tang, H. et al. (2005) Estimation of individual admixture: analytical and study design considerations. Genet. Epidemiol., 28, 289-301.
34. Varadhan, R. and Roland, C. (2008) Simple and globally convergent methods for accelerating the convergence of any EM algorithm. Scand. J. Stat., 35, 335-353.
35. Weiss, K.M. and Long, J.C. (2009) Non-Darwinian estimation: My ancestors, my genes' ancestors. Genome Res., 19, 703-710.
36. Wold, S. (1978) Cross-validatory estimation of the number of components in factor and principal components models. Technometrics, 20, 397-405.
37. Yang, W.-Y. et al. (2012) A model-based approach for analysis of spatial structure in genetic data. Nat. Genet., 44, 725-731.