Gerardo A Fernandes Júnior1, Roberto Carvalheiro2,3, Henrique N de Oliveira2,3, Mehdi Sargolzaei4,5, Roy Costilla6, Ricardo V Ventura7, Larissa F S Fonseca2, Haroldo H R Neves8, Ben J Hayes6, Lucia G de Albuquerque9,10. 1. School of Agricultural and Veterinarian Sciences, UNESP, Jaboticabal, SP, 14884-900, Brazil. gerardojjunior@yahoo.com.br. 2. School of Agricultural and Veterinarian Sciences, UNESP, Jaboticabal, SP, 14884-900, Brazil. 3. National Council for Scientific and Technological Development, CNPq, Brasília, DF, 71605-001, Brazil. 4. Ontario Veterinary College, UG, Guelph, Canada. 5. Select Sires Inc., Plain City, OH, USA. 6. Queensland Alliance for Agriculture and Food Innovation, UQ, Brisbane, QLD, 4072, Australia. 7. School of Veterinary Medicine and Animal Science, USP, Pirassununga, SP, 13635-900, Brazil. 8. GenSys Associated Consultants, Porto Alegre, RS, 90680-000, Brazil. 9. School of Agricultural and Veterinarian Sciences, UNESP, Jaboticabal, SP, 14884-900, Brazil. galvao.albuquerque@unesp.br. 10. National Council for Scientific and Technological Development, CNPq, Brasília, DF, 71605-001, Brazil. galvao.albuquerque@unesp.br.
Abstract
BACKGROUND: A cost-effective strategy to explore the complete DNA sequence in animals for genetic evaluation purposes is to sequence key ancestors of a population, followed by imputation mechanisms to infer marker genotypes that were not originally reported in a target population of animals genotyped with single nucleotide polymorphism (SNP) panels. The feasibility of this process relies on the accuracy of the genotype imputation in that population, particularly for potential causal mutations which may be at low frequency and either within genes or regulatory regions. The objective of the present study was to investigate the imputation accuracy to the sequence level in a Nellore beef cattle population, including that for variants in annotation classes which are more likely to be functional. METHODS: Information of 151 key sequenced Nellore sires were used to assess the imputation accuracy from bovine HD BeadChip SNP (~ 777 k) to whole-genome sequence. The choice of the sires aimed at optimizing the imputation accuracy of a genotypic database, comprised of about 10,000 genotyped Nellore animals. Genotype imputation was performed using two computational approaches: FImpute3 and Minimac4 (after using Eagle for phasing). The accuracy of the imputation was evaluated using a fivefold cross-validation scheme and measured by the squared correlation between observed and imputed genotypes, calculated by individual and by SNP. SNPs were classified into a range of annotations, and the accuracy of imputation within each annotation classification was also evaluated. RESULTS: High average imputation accuracies per animal were achieved using both FImpute3 (0.94) and Minimac4 (0.95). On average, common variants (minor allele frequency (MAF) > 0.03) were more accurately imputed by Minimac4 and low-frequency variants (MAF ≤ 0.03) were more accurately imputed by FImpute3. The inherent Minimac4 Rsq imputation quality statistic appears to be a good indicator of the empirical Minimac4 imputation accuracy. Both software provided high average SNP-wise imputation accuracy for all classes of biological annotations. CONCLUSIONS: Our results indicate that imputation to whole-genome sequence is feasible in Nellore beef cattle since high imputation accuracies per individual are expected. SNP-wise imputation accuracy is software-dependent, especially for rare variants. The accuracy of imputation appears to be relatively independent of annotation classification.
BACKGROUND: A cost-effective strategy to explore the complete DNA sequence in animals for genetic evaluation purposes is to sequence key ancestors of a population, followed by imputation mechanisms to infer marker genotypes that were not originally reported in a target population of animals genotyped with single nucleotide polymorphism (SNP) panels. The feasibility of this process relies on the accuracy of the genotype imputation in that population, particularly for potential causal mutations which may be at low frequency and either within genes or regulatory regions. The objective of the present study was to investigate the imputation accuracy to the sequence level in a Nellore beef cattle population, including that for variants in annotation classes which are more likely to be functional. METHODS: Information of 151 key sequenced Nellore sires were used to assess the imputation accuracy from bovineHD BeadChip SNP (~ 777 k) to whole-genome sequence. The choice of the sires aimed at optimizing the imputation accuracy of a genotypic database, comprised of about 10,000 genotyped Nellore animals. Genotype imputation was performed using two computational approaches: FImpute3 and Minimac4 (after using Eagle for phasing). The accuracy of the imputation was evaluated using a fivefold cross-validation scheme and measured by the squared correlation between observed and imputed genotypes, calculated by individual and by SNP. SNPs were classified into a range of annotations, and the accuracy of imputation within each annotation classification was also evaluated. RESULTS: High average imputation accuracies per animal were achieved using both FImpute3 (0.94) and Minimac4 (0.95). On average, common variants (minor allele frequency (MAF) > 0.03) were more accurately imputed by Minimac4 and low-frequency variants (MAF ≤ 0.03) were more accurately imputed by FImpute3. The inherent Minimac4 Rsq imputation quality statistic appears to be a good indicator of the empirical Minimac4 imputation accuracy. Both software provided high average SNP-wise imputation accuracy for all classes of biological annotations. CONCLUSIONS: Our results indicate that imputation to whole-genome sequence is feasible in Nellore beef cattle since high imputation accuracies per individual are expected. SNP-wise imputation accuracy is software-dependent, especially for rare variants. The accuracy of imputation appears to be relatively independent of annotation classification.
Authors: Christie L Warburton; Bailey N Engle; Elizabeth M Ross; Roy Costilla; Stephen S Moore; Nicholas J Corbet; Jack M Allen; Alan R Laing; Geoffry Fordyce; Russell E Lyons; Michael R McGowan; Brian M Burns; Ben J Hayes Journal: Genet Sel Evol Date: 2020-05-27 Impact factor: 4.297
Authors: Hans D Daetwyler; Aurélien Capitan; Hubert Pausch; Paul Stothard; Rianne van Binsbergen; Rasmus F Brøndum; Xiaoping Liao; Anis Djari; Sabrina C Rodriguez; Cécile Grohs; Diane Esquerré; Olivier Bouchez; Marie-Noëlle Rossignol; Christophe Klopp; Dominique Rocha; Sébastien Fritz; André Eggen; Phil J Bowman; David Coote; Amanda J Chamberlain; Charlotte Anderson; Curt P VanTassell; Ina Hulsegge; Mike E Goddard; Bernt Guldbrandtsen; Mogens S Lund; Roel F Veerkamp; Didier A Boichard; Ruedi Fries; Ben J Hayes Journal: Nat Genet Date: 2014-07-13 Impact factor: 38.330
Authors: I M MacLeod; P J Bowman; C J Vander Jagt; M Haile-Mariam; K E Kemper; A J Chamberlain; C Schrooten; B J Hayes; M E Goddard Journal: BMC Genomics Date: 2016-02-27 Impact factor: 3.969
Authors: Irene van den Berg; Phil J Bowman; Iona M MacLeod; Ben J Hayes; Tingting Wang; Sunduimijid Bolormaa; Mike E Goddard Journal: Genet Sel Evol Date: 2017-09-21 Impact factor: 4.297
Authors: L Koufariotis; B J Hayes; M Kelly; B M Burns; R Lyons; P Stothard; A J Chamberlain; S Moore Journal: Sci Rep Date: 2018-12-10 Impact factor: 4.379