title: Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans.
abstract: BACKGROUND
  Success in genome-wide association studies and marker-assisted selection depends on good phenotypic and genotypic data. The more complete this data is, the more powerful will be the results of analysis. Nevertheless, there are next-generation technologies that seek to provide genotypic information in spite of great proportions of missing data. The procedures these technologies use to impute genetic data, therefore, greatly affect downstream analyses. This study aims to (1) compare the genetic variance in a single-nucleotide polymorphism panel of soybean with missing data imputed using various methods, (2) evaluate the imputation accuracy and post-imputation quality associated with these methods, and (3) evaluate the impact of imputation method on heritability and the accuracy of genome-wide prediction of soybean traits. The imputation methods we evaluated were as follows: multivariate mixed model, hidden Markov model, logical algorithm, k-nearest neighbor, single value decomposition, and random forest. We used raw genotypes from the SoyNAM project and the following phenotypes: plant height, days to maturity, grain yield, and seed protein composition.
  
  
  RESULTS
  We propose an imputation method based on multivariate mixed models using pedigree information. Our methods comparison indicate that heritability of traits can be affected by the imputation method. Genotypes with missing values imputed with methods that make use of genealogic information can favor genetic analysis of highly polygenic traits, but not genome-wide prediction accuracy. The genotypic matrix captured the highest amount of genetic variance when missing loci were imputed by the method proposed in this paper.
  
  
  CONCLUSIONS
  We concluded that hidden Markov models and random forest imputation are more suitable to studies that aim analyses of highly heritable traits while pedigree-based methods can be used to best analyze traits with low heritability. Despite the notable contribution to heritability, advantages in genomic prediction were not observed by changing the imputation method. We identified significant differences across imputation methods in a dataset missing 20 % of the genotypic values. It means that genotypic data from genotyping technologies that provide a high proportion of missing values, such as GBS, should be handled carefully because the imputation method will impact downstream analysis.
authors.affiliation: Department of Agronomy, Purdue University, Lilly Hall of Life Sciences, 915 W. State St., West Lafayette, Indiana, 47907, USA. xaviera@purdue.edu.
authors.forename: A
authors.initials: A
authors.lastname: Xavier
authors.affiliation: Department of Animal Science, Purdue University, Lilly Hall of Life Sciences, 915 W. State St., West Lafayette, Indiana, 47907, USA. bmuir@purdue.edu.
authors.forename: William M
authors.initials: WM
authors.lastname: Muir
authors.affiliation: Department of Agronomy, Purdue University, Lilly Hall of Life Sciences, 915 W. State St., West Lafayette, Indiana, 47907, USA. krainey@purdue.edu.
authors.forename: Katy M
authors.initials: KM
authors.lastname: Rainey
mesh_terms: D000465:Algorithms; D014644:Genetic Variation / Q000235:genetics*; D018745:Genome, Plant; D055106:Genome-Wide Association Study; D023281:Genomics; D008957:Models, Genetic; D010375:Pedigree; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D040641:Quantitative Trait Loci; D017422:Sequence Analysis, DNA; D013025:Glycine max / Q000235:genetics*
references.citation: G3 (Bethesda). 2014 Jun;4(6):1027-46
references.pmid: 24727289
references.citation: PLoS One. 2011;6(5):e19379
references.pmid: 21573248
references.citation: Genet Sel Evol. 2013;45:15
references.pmid: 23738947
references.citation: Genet Sel Evol. 2011;43:10
references.pmid: 21366914
references.citation: Genome Res. 2010 Oct;20(10):1344-51
references.pmid: 20810666
references.citation: Nat Biotechnol. 2009 Apr;27(4):349-51
references.pmid: 19352374
references.citation: PLoS Genet. 2009 May;5(5):e1000477
references.pmid: 19492015
references.citation: PLoS One. 2014;9(7):e101544
references.pmid: 25025158
references.citation: J Dairy Sci. 2008 Nov;91(11):4414-23
references.pmid: 18946147
references.citation: J Dairy Sci. 2013 Jul;96(7):4666-77
references.pmid: 23684022
references.citation: G3 (Bethesda). 2013 Mar;3(3):427-39
references.pmid: 23449944
references.citation: Stat Appl Genet Mol Biol. 2013 Jun;12(3):375-91
references.pmid: 23629460
references.citation: Animal. 2007 Feb;1(1):21-8
references.pmid: 22444206
references.citation: PLoS One. 2014;9(6):e101025
references.pmid: 24972110
references.citation: Genetics. 2015 Mar;199(3):675-81
references.pmid: 25567991
references.citation: J Dairy Sci. 2013 Jan;96(1):668-78
references.pmid: 23063157
references.citation: J Dairy Sci. 2011 Jul;94(7):3679-86
references.pmid: 21700057
references.citation: BMC Genomics. 2014;15:740
references.pmid: 25174348
references.citation: J Anim Breed Genet. 2006 Aug;123(4):218-23
references.pmid: 16882088
references.citation: Am J Hum Genet. 2010 Apr 9;86(4):526-39
references.pmid: 20303063
references.citation: Bioinformatics. 2012 Jan 1;28(1):112-8
references.pmid: 22039212
references.citation: Am J Hum Genet. 2006 Apr;78(4):629-44
references.pmid: 16532393
references.citation: Genet Sel Evol. 2011;43:25
references.pmid: 21703021
references.citation: Nat Rev Genet. 2010 Jul;11(7):499-511
references.pmid: 20517342
references.citation: Genetics. 2014 Oct;198(2):483-95
references.pmid: 25009151
references.citation: Front Genet. 2014 Mar 24;5:56
references.pmid: 24715901
references.citation: Genet Sel Evol. 2014;46:41
references.pmid: 25022768
references.citation: BMC Proc. 2009 Dec 15;3 Suppl 7:S41
references.pmid: 20018033
references.citation: Genetics. 2013 Feb;193(2):327-45
references.pmid: 22745228
references.citation: Nat Methods. 2014 Apr;11(4):407-9
references.pmid: 24531419
references.citation: Genet Sel Evol. 2013;45:3
references.pmid: 23406470
references.citation: PLoS One. 2013;8(1):e54603
references.pmid: 23372741
references.citation: Front Genet. 2014 Oct 16;5:363
references.pmid: 25360145
references.citation: Genet Sel Evol. 2013;45:12
references.pmid: 23621897
references.citation: J Dairy Sci. 2008 Jan;91(1):360-6
references.pmid: 18096959
references.citation: Genet Epidemiol. 2010 Dec;34(8):816-34
references.pmid: 21058334
references.citation: G3 (Bethesda). 2013 Mar;3(3):481-91
references.pmid: 23450123
references.citation: BMC Genomics. 2015;16:168
references.pmid: 25886991
references.citation: Genetics. 2010 Mar;184(3):789-98
references.pmid: 20008575
references.citation: Genetics. 2013 Jul;194(3):573-96
references.pmid: 23636739
references.citation: Am J Hum Genet. 2007 Nov;81(5):1084-97
references.pmid: 17924348
references.citation: Genetics. 2003 Feb;163(2):789-801
references.pmid: 12618414
references.citation: Genetics. 2001 Apr;157(4):1819-29
references.pmid: 11290733
references.citation: Nat Genet. 2012 Jun;44(6):631-5
references.pmid: 22610117
references.citation: Bioinformatics. 2001 Jun;17(6):520-5
references.pmid: 11395428
references.citation: PLoS Genet. 2009 Jun;5(6):e1000529
references.pmid: 19543373
references.citation: Am J Hum Genet. 2008 Dec;83(6):675-83
references.pmid: 19026399