Literature DB >> 21605789

The genomic evaluation system in the United States: past, present, future.

G R Wiggans1, P M Vanraden, T A Cooper.   

Abstract

Implementation of genomic evaluation has caused profound changes in dairy cattle breeding. All young bulls bought by major artificial insemination organizations now are selected based on such evaluation. Evaluation reliability can reach approximately 75% for yield traits, which is adequate for marketing semen of 2-yr-old bulls. Shortened generation interval from using genomic evaluations is the most important factor in increasing the rate of genetic improvement. Genomic evaluations are based on 42,503 single nucleotide polymorphisms (SNP) genotyped with technology that became available in 2007. The first unofficial USDA genomic evaluations were released in 2008 and became official for Holsteins, Jerseys, and Brown Swiss in 2009. Evaluation accuracy has increased steadily from including additional bulls with genotypes and traditional evaluations (predictor animals). Some of that increase occurs automatically as young genotyped bulls receive a progeny test evaluation at 5 yr of age. Cow contribution to evaluation accuracy is increased by decreasing mean and variance of their evaluations so that they are similar to bull evaluations. Integration of US and Canadian genotype databases was critical to achieving acceptable initial accuracy and continues to benefit both countries. Genotype exchange with other countries added predictor bulls for Brown Swiss. In 2010, a low-density chip with 2,900 SNP and a high-density chip with 777,962 SNP were released. The low-density chip has increased greatly the number of animals genotyped and is expected to replace microsatellites in parentage verification. The high-density chip can increase evaluation accuracy by better tracking of loci responsible for genetic differences. To integrate information from chips of various densities, a method to impute missing genotypes was developed based on splitting each genotype into its maternal and paternal haplotypes and tracing their inheritance through the pedigree. The same method is used to impute genotypes of nongenotyped dams based on genotyped progeny and mates. Reliability of resulting evaluations is discounted to reflect errors inherent in the process. Further increases in evaluation accuracy are expected because of added predictor animals and more SNP. The large population of existing genotypes can be used to evaluate new traits; however, phenotypic observations must be obtained for enough animals to allow estimation of SNP effects with sufficient accuracy for application to the general population.
Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21605789     DOI: 10.3168/jds.2010-3866

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


  56 in total

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Review 2.  Biological underpinnings of breastfeeding challenges: the role of genetics, diet, and environment on lactation physiology.

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Journal:  Am J Physiol Endocrinol Metab       Date:  2016-06-28       Impact factor: 4.310

3.  Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection.

Authors:  Adriana García-Ruiz; John B Cole; Paul M VanRaden; George R Wiggans; Felipe J Ruiz-López; Curtis P Van Tassell
Journal:  Proc Natl Acad Sci U S A       Date:  2016-06-27       Impact factor: 11.205

4.  Moving Beyond Managing Realized Genomic Relationship in Long-Term Genomic Selection.

Authors:  Herman De Beukelaer; Yvonne Badke; Veerle Fack; Geert De Meyer
Journal:  Genetics       Date:  2017-04-04       Impact factor: 4.562

5.  Holsteins are the genomic selection poster cows.

Authors:  Jeremy F Taylor; Kristen H Taylor; Jared E Decker
Journal:  Proc Natl Acad Sci U S A       Date:  2016-06-29       Impact factor: 11.205

6.  A Bayesian antedependence model for whole genome prediction.

Authors:  Wenzhao Yang; Robert J Tempelman
Journal:  Genetics       Date:  2011-11-30       Impact factor: 4.562

7.  Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes.

Authors:  B J Hayes; J Panozzo; C K Walker; A L Choy; S Kant; D Wong; J Tibbits; H D Daetwyler; S Rochfort; M J Hayden; G C Spangenberg
Journal:  Theor Appl Genet       Date:  2017-08-24       Impact factor: 5.699

8.  Effect of sample stratification on dairy GWAS results.

Authors:  Li Ma; George R Wiggans; Shengwen Wang; Tad S Sonstegard; Jing Yang; Brian A Crooker; John B Cole; Curtis P Van Tassell; Thomas J Lawlor; Yang Da
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9.  Fine mapping for Weaver syndrome in Brown Swiss cattle and the identification of 41 concordant mutations across NRCAM, PNPLA8 and CTTNBP2.

Authors:  Matthew McClure; Euisoo Kim; Derek Bickhart; Daniel Null; Tabatha Cooper; John Cole; George Wiggans; Paolo Ajmone-Marsan; Licia Colli; Enrico Santus; George E Liu; Steve Schroeder; Lakshmi Matukumalli; Curt Van Tassell; Tad Sonstegard
Journal:  PLoS One       Date:  2013-03-20       Impact factor: 3.240

10.  Discovery of single nucleotide polymorphisms in candidate genes associated with fertility and production traits in Holstein cattle.

Authors:  Sarah D Cochran; John B Cole; Daniel J Null; Peter J Hansen
Journal:  BMC Genet       Date:  2013-06-07       Impact factor: 2.797

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