Literature DB >> 23063157

Genomic imputation and evaluation using high-density Holstein genotypes.

P M VanRaden1, D J Null, M Sargolzaei, G R Wiggans, M E Tooker, J B Cole, T S Sonstegard, E E Connor, M Winters, J B C H M van Kaam, A Valentini, B J Van Doormaal, M A Faust, G A Doak.   

Abstract

Genomic evaluations for 161,341 Holsteins were computed by using 311,725 of 777,962 markers on the Illumina BovineHD Genotyping BeadChip (HD). Initial edits with 1,741 HD genotypes from 5 breeds revealed that 636,967 markers were usable but that half were redundant. Holstein genotypes were from 1,510 animals with HD markers, 82,358 animals with 45,187 (50K) markers, 1,797 animals with 8,031 (8K) markers, 20,177 animals with 6,836 (6K) markers, 52,270 animals with 2,683 (3K) markers, and 3,229 nongenotyped dams (0K) with >90% of haplotypes imputable because they had 4 or more genotyped progeny. The Holstein HD genotypes were from 1,142 US, Canadian, British, and Italian sires, 196 other sires, 138 cows in a US Department of Agriculture research herd (Beltsville, MD), and 34 other females. Percentages of correctly imputed genotypes were tested by applying the programs findhap and FImpute to a simulated chromosome for an earlier population that had only 1,112 animals with HD genotypes and none with 8K genotypes. For each chip, 1% of the genotypes were missing and 0.02% were incorrect initially. After imputation of missing markers with findhap, percentages of genotypes correct were 99.9% from HD, 99.0% from 50K, 94.6% from 6K, 90.5% from 3K, and 93.5% from 0K. With FImpute, 99.96% were correct from HD, 99.3% from 50K, 94.7% from 6K, 91.1% from 3K, and 95.1% from 0K genotypes. Accuracy for the 3K and 6K genotypes further improved by approximately 2 percentage points if imputed first to 50K and then to HD instead of imputing all genotypes directly to HD. Evaluations were tested by using imputed actual genotypes and August 2008 phenotypes to predict deregressed evaluations of US bulls proven after August 2008. For 28 traits tested, the estimated genomic reliability averaged 61.1% when using 311,725 markers vs. 60.7% when using 45,187 markers vs. 29.6% from the traditional parent average. Squared correlations with future data were slightly greater for 16 traits and slightly less for 12 with HD than with 50K evaluations. The observed 0.4 percentage point average increase in reliability was less favorable than the 0.9 expected from simulation but was similar to actual gains from other HD studies. The largest HD and 50K marker effects were often located at very similar positions. The single-breed evaluation tested here and previous single-breed or multibreed evaluations have not produced large gains. Increasing the number of HD genotypes used for imputation above 1,074 did not improve the reliability of Holstein genomic evaluations.
Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 23063157     DOI: 10.3168/jds.2012-5702

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  58 in total

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4.  Genomic prediction of genetic merit using LD-based haplotypes in the Nordic Holstein population.

Authors:  Beatriz C D Cuyabano; Guosheng Su; Mogens S Lund
Journal:  BMC Genomics       Date:  2014-12-23       Impact factor: 3.969

5.  A computationally efficient algorithm for genomic prediction using a Bayesian model.

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6.  Fast imputation using medium or low-coverage sequence data.

Authors:  Paul M VanRaden; Chuanyu Sun; Jeffrey R O'Connell
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7.  A function accounting for training set size and marker density to model the average accuracy of genomic prediction.

Authors:  Malena Erbe; Birgit Gredler; Franz Reinhold Seefried; Beat Bapst; Henner Simianer
Journal:  PLoS One       Date:  2013-12-05       Impact factor: 3.240

8.  Strategies for genotype imputation in composite beef cattle.

Authors:  Tatiane C S Chud; Ricardo V Ventura; Flavio S Schenkel; Roberto Carvalheiro; Marcos E Buzanskas; Jaqueline O Rosa; Maurício de Alvarenga Mudadu; Marcos Vinicius G B da Silva; Fabiana B Mokry; Cintia R Marcondes; Luciana C A Regitano; Danísio P Munari
Journal:  BMC Genet       Date:  2015-08-07       Impact factor: 2.797

9.  Sequence- vs. chip-assisted genomic selection: accurate biological information is advised.

Authors:  Miguel Pérez-Enciso; Juan C Rincón; Andrés Legarra
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10.  Imputation of non-genotyped individuals based on genotyped relatives: assessing the imputation accuracy of a real case scenario in dairy cattle.

Authors:  Aniek C Bouwman; John M Hickey; Mario P L Calus; Roel F Veerkamp
Journal:  Genet Sel Evol       Date:  2014-02-03       Impact factor: 4.297

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