Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.

doi:10.3168/JDS.2009-2730

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Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.

Journal Article•DOI•

Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.

Ignacio Aguilar¹, Ignacy Misztal¹, D. L. Johnson², Andres Legarra³, Shogo Tsuruta¹, T.J. Lawlor - Show less +2 more•Institutions (3)

University of Georgia¹, Livestock Improvement Corporation², Saga Group³

01 Feb 2010-Journal of Dairy Science (Elsevier)-Vol. 93, Iss: 2, pp 743-752

TL;DR: A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure.

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About: This article is published in Journal of Dairy Science.The article was published on 2010-02-01 and is currently open access. It has received 1095 citations till now. The article focuses on the topics: Population.

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Journal Article•DOI•

Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

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Gustavo de los Campos¹, John M. Hickey², Ricardo Pong-Wong³, Hans D. Daetwyler, Mario P. L. Calus⁴ - Show less +1 more•Institutions (4)

University of Alabama at Birmingham¹, University of New England (Australia)², University of Edinburgh³, Wageningen University and Research Centre⁴

01 Feb 2013-Genetics

TL;DR: An overview of available methods for implementing parametric WGR models is provided, selected topics that emerge in applications are discussed, and a general discussion of lessons learned from simulation and empirical data analysis in the last decade are presented.

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Abstract: Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.

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741 citations

Cites background or methods from "Hot topic: a unified approach to ut..."

...The inverse of G, which can be used to compute GBLUP (see Equation 4), has a relatively simple form (Aguilar et al. 2010; Christensen and Lund 2010); however, computing G21 requires inverting the matrix of genomic relationships of individuals with genotypes that may be singular....
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...To circumvent this problem several procedures have been proposed (see Aguilar et al. 2010)....
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...In the proposed methods (Aguilar et al. 2010; Christensen and Lund 2010) the standard genomic relationship matrix, G, is replaced with a matrix, G, computed using observed genotypes (from the subset of genotyped individuals) and pedigree information (which is assumed to include all individuals with…...
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Journal Article•DOI•

Genome-wide association mapping including phenotypes from relatives without genotypes.

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Huiyu Wang¹, Ignacy Misztal¹, Ignacio Aguilar, Andres Legarra², William M. Muir³ - Show less +1 more•Institutions (3)

University of Georgia¹, Saga Group², Purdue University³

01 Apr 2012-Genetics Research

TL;DR: The single-step GBLUP (ssGBLUP) method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.

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Abstract: A common problem for genome-wide association analysis (GWAS) is lack of power for detection of quantitative trait loci (QTLs) and precision for fine mapping. Here, we present a statistical method, termed single-step GBLUP (ssGBLUP), which increases both power and precision without increasing genotyping costs by taking advantage of phenotypes from other related and unrelated subjects. The procedure achieves these goals by blending traditional pedigree relationships with those derived from genetic markers, and by conversion of estimated breeding values (EBVs) to marker effects and weights. Additionally, the application of mixed model approaches allow for both simple and complex analyses that involve multiple traits and confounding factors, such as environmental, epigenetic or maternal environmental effects. Efficiency of the method was examined using simulations with 15 800 subjects, of which 1500 were genotyped. Thirty QTLs were simulated across genome and assumed heritability was 0·5. Comparisons included ssGBLUP applied directly to phenotypes, BayesB and classical GWAS (CGWAS) with deregressed proofs. An average accuracy of prediction 0·89 was obtained by ssGBLUP after one iteration, which was 0·01 higher than by BayesB. Power and precision for GWAS applications were evaluated by the correlation between true QTL effects and the sum of m adjacent single nucleotide polymorphism (SNP) effects. The highest correlations were 0·82 and 0·74 for ssGBLUP and CGWAS with m=8, and 0·83 for BayesB with m=16. Standard deviations of the correlations across replicates were several times higher in BayesB than in ssGBLUP. The ssGBLUP method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.

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413 citations

Cites background or methods from "Hot topic: a unified approach to ut..."

...An advantage of the infinitesimal model is that the resulting genomic relationship matrix is identical for all traits within a population (Aguilar et al., 2010)....
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..., 2002) modified for genomic analyses (Aguilar et al., 2010), and used simulated...
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...…on 02 Dec 2019 at 10:34:20, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms (v) Computations Computations with ssGBLUP involved program BLUPF90 (Misztal et al., 2002) modified for genomic analyses (Aguilar et al., 2010), and used simulated parameters....
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...The ssGBLUP method has been shown to provide more consistent solutions and better accuracy than the multiple-step approach (Aguilar et al., 2010; Chen et al., 2011; Forni et al., 2011)....
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...…2 and se 2 are total genetic additive and residual variances, respectively, and H is a matrix that combines pedigree and genomic relationships as in Aguilar et al. (2010), and its inverse is Hx1=Ax1+ 0 0 0 Gx1xAx122 , ð3Þ where A is a numerator (pedigree) relationship matrix for all animals ; A22…...
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Journal Article•DOI•

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

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Adriana García-Ruiz¹, John B. Cole¹, Paul M. VanRaden¹, G.R. Wiggans¹, Felipe J. Ruiz-López, Curtis P. Van Tassell¹ - Show less +2 more•Institutions (1)

United States Department of Agriculture¹

12 Jul 2016-Proceedings of the National Academy of Sciences of the United States of America

TL;DR: The analysis of the US national dairy database found that generation intervals have decreased dramatically over the past 6 y, and selection intensity for lowly heritable traits has increased considerably, resulting in rapid genetic improvement in fertility, lifespan, and health in a breed where these traits eroded over time.

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Abstract: Seven years after the introduction of genomic selection in the United States, it is now possible to evaluate the impact of this technology on the population. Selection differential(s) (SD) and generation interval(s) (GI) were characterized in a four-path selection model that included sire(s) of bulls (SB), sire(s) of cows (SC), dam(s) of bulls (DB), and dam(s) of cows (DC). Changes in SD over time were estimated for milk, fat, and protein yield; somatic cell score (SCS); productive life (PL); and daughter pregnancy rate (DPR) for the Holstein breed. In the period following implementation of genomic selection, dramatic reductions were seen in GI, especially the SB and SC paths. The SB GI reduced from ∼7 y to less than 2.5 y, and the DB GI fell from about 4 y to nearly 2.5 y. SD were relatively stable for yield traits, although modest gains were noted in recent years. The most dramatic response to genomic selection was observed for the lowly heritable traits DPR, PL, and SCS. Genetic trends changed from close to zero to large and favorable, resulting in rapid genetic improvement in fertility, lifespan, and health in a breed where these traits eroded over time. These results clearly demonstrate the positive impact of genomic selection in US dairy cattle, even though this technology has only been in use for a short time. Based on the four-path selection model, rates of genetic gain per year increased from ∼50–100% for yield traits and from threefold to fourfold for lowly heritable traits.

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380 citations

Cites methods from "Hot topic: a unified approach to ut..."

...Newer statistical methods being adopted are believed to accommodate this preselection (29)....
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Journal Article•DOI•

Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information

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Selma Forni¹, Ignacio Aguilar², Ignacy Misztal²•Institutions (2)

Genus plc¹, University of Georgia²

05 Jan 2011-Genetics Selection Evolution

TL;DR: Parameter estimates may be biased if the genomic relationship coefficients are in a different scale than pedigree-based coefficients, and a reasonable scaling may be obtained by using observed allele frequencies and re-scaling the genomes to obtain average diagonal elements of 1.

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Abstract: The incorporation of genomic coefficients into the numerator relationship matrix allows estimation of breeding values using all phenotypic, pedigree and genomic information simultaneously. In such a single-step procedure, genomic and pedigree-based relationships have to be compatible. As there are many options to create genomic relationships, there is a question of which is optimal and what the effects of deviations from optimality are. Data of litter size (total number born per litter) for 338,346 sows were analyzed. Illumina PorcineSNP60 BeadChip genotypes were available for 1,989. Analyses were carried out with the complete data set and with a subset of genotyped animals and three generations pedigree (5,090 animals). A single-trait animal model was used to estimate variance components and breeding values. Genomic relationship matrices were constructed using allele frequencies equal to 0.5 (G05), equal to the average minor allele frequency (GMF), or equal to observed frequencies (GOF). A genomic matrix considering random ascertainment of allele frequencies was also used (GOF*). A normalized matrix (GN) was obtained to have average diagonal coefficients equal to 1. The genomic matrices were combined with the numerator relationship matrix creating H matrices. In G05 and GMF, both diagonal and off-diagonal elements were on average greater than the pedigree-based coefficients. In GOF and GOF*, the average diagonal elements were smaller than pedigree-based coefficients. The mean of off-diagonal coefficients was zero in GOF and GOF*. Choices of G with average diagonal coefficients different from 1 led to greater estimates of additive variance in the smaller data set. The correlation between EBV and genomic EBV (n = 1,989) were: 0.79 using G05, 0.79 using GMF, 0.78 using GOF, 0.79 using GOF*, and 0.78 using GN. Accuracies calculated by inversion increased with all genomic matrices. The accuracies of genomic-assisted EBV were inflated in all cases except when GN was used. Parameter estimates may be biased if the genomic relationship coefficients are in a different scale than pedigree-based coefficients. A reasonable scaling may be obtained by using observed allele frequencies and re-scaling the genomic relationship matrix to obtain average diagonal elements of 1.

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373 citations

Cites background or methods from "Hot topic: a unified approach to ut..."

...Errors in the allele frequency estimates may result in biased relationships and consequently biased GEBVs, especially for young animals [5]....
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...has been successfully applied in dairy cattle [5]....
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...[5] have resulted in different scaling and accuracies of EBV....
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...However, some experiences in the dairy industry, however, have indicated that actual improvement may differ from expected because of inflation of genomic breeding values and reliabilities [5,11]....
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...Different G can lead to different accuracies of EBV [5]....
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Journal Article•DOI•

Bias in genomic predictions for populations under selection.

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Zulma G. Vitezica¹, Ignacio Aguilar, Ignacy Misztal², Andres Legarra•Institutions (2)

University of Toulouse¹, University of Georgia²

18 Jul 2011-Genetics Research

TL;DR: The effect of selection on bias and accuracy of genomic predictions was studied in two simulated animal populations under weak or strong selection and with several heritabilities.

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Abstract: Prediction of genetic merit or disease risk using genetic marker information is becoming a common practice for selection of livestock and plant species. For the successful application of genome-wide marker-assisted selection (GWMAS), genomic predictions should be accurate and unbiased. The effect of selection on bias and accuracy of genomic predictions was studied in two simulated animal populations under weak or strong selection and with several heritabilities. Prediction of genetic values was by best-linear unbiased prediction (BLUP) using data either from relatives summarized in pseudodata for genotyped individuals (multiple-step method) or using all available data jointly (single-step method). The single-step method combined genomic- and pedigree-based relationship matrices. Predictions by the multiple-step method were biased. Predictions by a single-step method were less biased and more accurate but under strong selection were less accurate. When genomic relationships were shifted by a constant, the single-step method was unbiased and the most accurate. The value of that constant, which adjusts for non-random selection of genotyped individuals, can be derived analytically.

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314 citations

Cites background or methods from "Hot topic: a unified approach to ut..."

...This method combines pedigree and all available phenotypes and genotypes, needs no creation of pseudodata, and it has been applied to population sizes in the millions (Aguilar et al., 2010)....
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...The single-step genomic prediction approach (Legarra et al., 2009; Aguilar et al., 2010; Christensen & Lund, 2010) is based on the model y=Xb+Zu+e, where y is the phenotype vector, X and Z are incidence matrices, b denotes fixed effects, e is the residual and p(u)yN(0, Hsu) involves the genetic effect for non-genotyped (u1) and genotyped (u2) individuals and the genetic variance su 2....
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...However, how selection is accounted for in GWMAS procedures is unclear, although this is becoming a serious concern (Aguilar et al., 2010; Mäntysaari et al., 2010; Chen et al., 2011)....
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...A single-step method based on a linear mixed model and a pedigree relationship matrix augmented with genomic information has been developed recently (Legarra et al., 2009; Aguilar et al., 2010; Christensen & Lund, 2010)....
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..., 2009; Aguilar et al., 2010; Christensen & Lund, 2010) is based on the model y=Xb+Zu+e, where y is the phenotype vector, X and Z are incidence matrices, b denotes fixed effects, e is the residual and p(u)yN(0, Hsu) involves the genetic effect for non-genotyped (u1) and genotyped (u2) individuals and the genetic variance su 2. Here Hx1 is derived as in Legarra et al., (2009) and Christensen & Lund (2010) :...
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References

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Open Access

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Journal Article•DOI•

Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps

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Theo H E Meuwissen, Ben J. Hayes, Michael E. Goddard¹•Institutions (1)

University of Melbourne¹

01 Apr 2001-Genetics

TL;DR: It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.

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Abstract: Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.

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6,036 citations

"Hot topic: a unified approach to ut..." refers background or methods in this paper

...When variances are not equal; for example, as in Bayes-A or BayesB (Meuwissen et al., 2001), an equivalent G can be constructed by scaling contributions from different markers....
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...For example, estimation of genomic effects has several options (Meuwissen et al., 2001; Gianola et al., 2006; VanRaden, 2008; de los Campos et al., 2009)....
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Journal Article•DOI•

Efficient Methods to Compute Genomic Predictions

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Paul M. VanRaden¹•Institutions (1)

Agricultural Research Service¹

01 Nov 2008-Journal of Dairy Science

TL;DR: Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously, and a blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects.

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4,196 citations

"Hot topic: a unified approach to ut..." refers background or methods in this paper

...Genomic evaluations are currently calculated with a multiple-step procedure (VanRaden, 2008; Hayes et al., 2009)....
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...To facilitate inversion, final analyses used a weighted G as proposed by VanRaden (2008): G = 0.95Gb + 0.05A22....
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...(VanRaden, 2008), which assumes a priori independence of SNP effects (Gianola et al....
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...The scaling parameter k was defined as k p pj j= ∑ −2 1( ) (VanRaden, 2008), which assumes a priori independence of SNP effects (Gianola et al., 2009)....
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...For example, estimation of genomic effects has several options (Meuwissen et al., 2001; Gianola et al., 2006; VanRaden, 2008; de los Campos et al., 2009)....
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Journal Article•DOI•

Invited review: Genomic selection in dairy cattle: progress and challenges.

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Ben J. Hayes, Phil J. Bowman, Amanda J. Chamberlain, Michael E. Goddard¹•Institutions (1)

University of Melbourne¹

01 Feb 2009-Journal of Dairy Science

TL;DR: In this article, a new technology called genomic selection is revolutionizing dairy cattle breeding, which refers to selection decisions based on genomic breeding values (GEBV) and is calculated as the sum of the effects of dense genetic markers, or haplotypes of these markers, across the entire genome, thereby capturing all the quantitative trait loci (QTL) that contribute to variation in a trait.

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1,461 citations

Journal Article•

Invited review: Genomic selection in dairy cattle: progress and challenges (vol 92, pg 433, 2009)

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Ben J. Hayes, PJ Bowman, Amanda J. Chamberlain, Michael E. Goddard

01 Jan 2009-Journal of Dairy Science

TL;DR: The reliabilities of GEBV achieved were significantly greater than the reliability of parental average breeding values, the current criteria for selection of bull calves to enter progeny test teams, and the increase in reliability is sufficiently high that at least 2 dairy breeding companies are already marketing bull teams for commercial use based on their GEBv only.

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1,380 citations

"Hot topic: a unified approach to ut..." refers background or methods in this paper

...Genomic evaluations are currently calculated with a multiple-step procedure (VanRaden, 2008; Hayes et al., 2009)....
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...…such as deregressed evaluations or daughter deviations (DD), 3) estimation of genomic effects for genotyped animals usually using simple sire models, and possibly 4) combining the genomic index with traditional parent averages (PA) and EBV (Hayes et al., 2009; VanRaden et al., 2009b)....
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...A typical evaluation requires 1) traditional evaluation with an animal model, 2) extraction of pseudo-observations such as deregressed evaluations or daughter deviations (DD), 3) estimation of genomic effects for genotyped animals usually using simple sire models, and possibly 4) combining the genomic index with traditional parent averages (PA) and EBV (Hayes et al., 2009; VanRaden et al., 2009b)....
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...Experiences with actual data from dairy cattle (Hayes et al., 2009; VanRaden et al., 2009b) indicated that using a large number of markers with equal variance for all markers is appropriate for most traits....
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Journal Article•DOI•

Invited review: reliability of genomic predictions for North American Holstein bulls.

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Paul M. VanRaden¹, C.P. Van Tassell¹, G.R. Wiggans¹, Tad S. Sonstegard¹, Robert D. Schnabel², Jeremy F. Taylor², Flavio S Schenkel³ - Show less +3 more•Institutions (3)

Agricultural Research Service¹, University of Missouri², University of Guelph³

01 Jan 2009-Journal of Dairy Science

TL;DR: Genotypes for 38,416 markers and August 2003 genetic evaluations for 3,576 Holstein bulls born before 1999 were used to predict January 2008 daughter deviations and genomic prediction improves reliability by tracing the inheritance of genes even with small effects.

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1,166 citations

Additional excerpts

...G A G A λ λ λ λ Comparisons were based on the regressions DD = μ + δEBV04 + e and EBV09 = μ + δEBV04 + e, where DD were deregressed evaluations (VanRaden et al., 2009b) from genotyped bulls without daughter records in 2004 but with daughter records in 2009 that were computed with complete final…...
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...Genomic evaluations are more accurate than PA and approach the accuracy of evaluations for progeny-tested bulls, but they also seem inflated (VanRaden et al., 2009a)....
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...Results for EBV09 (Table 1) generally were similar to those for DD but with a slight advantage for the multiple-step approach....
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...…such as deregressed evaluations or daughter deviations (DD), 3) estimation of genomic effects for genotyped animals usually using simple sire models, and possibly 4) combining the genomic index with traditional parent averages (PA) and EBV (Hayes et al., 2009; VanRaden et al., 2009b)....
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...The δ indicated much greater inflation than for DD. Inflation on the EBV09 scale is important for producers because their comparisons are based on EBV and not on DD....
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