Source: MIDWEST AREA, AGRICULTURAL RESEARCH SERVICE submitted to NRP
DEVELOPMENT AND FIELD EVALUATION OF GENOME-WIDE MARKER-ASSISTED SELECTION (GWMAS) OVER MULTIPLE GENERATIONS IN COMMERCIAL POULTRY
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
0219130
Grant No.
2009-65205-05665
Cumulative Award Amt.
$1,625,000.00
Proposal No.
2011-03280
Multistate No.
(N/A)
Project Start Date
Sep 1, 2009
Project End Date
Aug 31, 2012
Grant Year
2011
Program Code
[92120]- Animal Genome, Genetics and Breeding
Recipient Organization
MIDWEST AREA, AGRICULTURAL RESEARCH SERVICE
3606 EAST MT HOPE ROAD
EAST LANSING,MI 48823
Performing Department
(N/A)
Non Technical Summary
Modern animal agriculture has been very successful meeting the growing demands of consumers both within and outside the US for high quality, safe, and affordable animal products. To meet the growing demands of consumers, the poultry industry will need to continue to improve methods of selection in breeding programs for production traits. Molecular genetics is an attractive solution as it allows increased selection intensity, increased accuracy of selection, and maintains or integrates new genetic variation. In this project, we assess genome-wide marker-assisted selection (GWMAS), a method first proposed by one of our team members GWMAS utilizes markers spanning the entire genome to increase accuracy and efficiency of estimating breeding values (EBV). This new method promises significant benefits but there are many unanswered questions calling for proof that GWMAS actually works. Although retrospective analysis has shown GWMAS EBV correlate highly with the most current methods, concerns are these analyses will not reflect reality once implemented because favorable GWMAS effects may not persist after the second round of selection. We propose to evaluate the power of GWMAS in a multi-generational selection experiment. We chose to work with chicken as it is the only commercial agricultural animal with adequate genomic resources, reproductive capacity, and short generation interval needed to achieve the objectives within 4 years. With 2 broiler and 3 layer lines, we examine many factors including different breeding objectives, and low to high heritability traits. If GWMAS is found successful, then it can substantially increase genetic progress that will assist the animal agricultural industry to meet the growing demand of consumers. Through its revolutionary impact on breeding value estimation, GWMAS may also provide options for re-designing the structure of breeding programs such as providing novel options for product diversification through custom made selection programs. In short, if successful, GWMAS will provide science-based knowledge and technologies to generate improved high-quality products.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
3033210108050%
3033220108050%
Knowledge Area
303 - Genetic Improvement of Animals;

Subject Of Investigation
3210 - Egg-type chicken, live animal; 3220 - Meat-type chicken, live animal;

Field Of Science
1080 - Genetics;
Goals / Objectives
Modern animal agriculture has been very successful meeting the growing demands of consumers both within and outside the US for high quality, safe, and affordable animal products. To meet the growing demands of consumers, the poultry industry will need to continue to improve methods of selection in breeding programs for production traits. Molecular genetics is an attractive solution as it allows increased selection intensity, increased accuracy of selection, and maintains or integrates new genetic variation. The primary goal is to compare genome-wide marker-assisted selection (GWMAS) with traditional selection over three generations of directional selection. To achieve this goal, we will (1) refine the theoretical and molecular aspects of GWMAS, and (2) perform an actual field assessment of GWMAS. In addition, through the use of our 60K SNP array, the resulting genotypes will greatly aid in the further improvement of the chicken genetic map and, in turn, the genome assembly. The deliverables of this project are many and include a high-density chicken SNP chip, for research purposes, with a large number of validated genetic markers, more efficient and accurate statistical methods for determine breeding values using molecular markers, and a true scientific assessment of GWMAS and its merit compared to the most current methods used in the poultry breeding industry.
Project Methods
To address theoretical aspects of GWMAS, computer simulations will be conducted to compare the statistical methods RR, BayesB, PCR, PLS, and semi-parametric regression for their computational efficiency and accuracy of predicting breeding values in poultry breeding programs, their bias (over/under prediction) of true breeding values, their sensitivity to the assumptions made about the genetic model, and the number of records and genotypes that are computationally feasible. For the GWMAS assessment, we will compare the genetic progress, change of genetic variance, and accuracy over 3 generations of selection and 4 generations of evaluation, in alternative elite commercial lines representing two different commodities (broilers vs. layers) with corresponding differences in breeding objectives and trait types (high vs low heritability) using traditional, state of the art phenotypic selection compared with GWMAS designed to utilize additive effects.

Progress 09/01/09 to 08/31/13

Outputs
Target Audience: People interested in our research include academic and industry scientists working on genomics and genomic selection. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? A number of graduate and postdoctoral students were trained in molecular and computational genetics. How have the results been disseminated to communities of interest? Besides peer-reviewed publications, the scientific team has made numerous presentations at national and international conferences. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? The advantages of genomic selection (GWMAS) are well understood. In summary, GWMAS is most advantageous for traits that are difficult to or costly to measure (carcass quality, feed efficiency) or are collected late in life (full record egg production). This technology is the final realization of genomics, i.e., direct selection on the genotype and can double response to selection per unit of time for some traits. However, we have shown using simulations that a loss in accuracy will result once selection on those markers has commenced. Unanswered questions is how rapidly the decline in accuracy will occur and secondly, how often the retraining process must occur and how much of the lost accuracy can be regained? To address these questions, we analyzed three traits from two pure lines broiler type chickens across 5 generations. The complete populations consisted of 183,784 and 164,246 broilers for two lines. The genotyped subsets consisted of 3,284 and 3,098 broilers. These DNA were query with a custom 60K SNP chip, which was also used to improve the chicken genome assembly. The training population consisted of the first two generations. Following 3 generations of selection based on ssGBLUP (genomic selection), we compared the accuracy with which EBVs could be predicted in those 3 generations based only on the genotypes (ssGBLUP) or pedigree (BLUP). Accuracy was determined as the correlation between predicted breeding value and the phenotype divided by the square root of heritability for that trait. Results showed that the relative accuracy for ssGBLUP remained about 33% higher than BLUP in all generations across both lines. The results differ from the predictions of the simulations from Muir (2007) who used the same set of assumptions as Meuwissen et al (2001). Our conclusion is that the results are characteristic of the infinitesimal model with thousands of QTL rather than a 100 or fewer as assumed by Meuwissen et al. (2001). We also tested GWMAS in a commercial layer breeding company (Hendrix Genetics) using the same 60K SNP chip across the whole genome. We compared the impact of GWMAS and traditional BLUP methods applied side by side in three different lines of egg-laying chicken. Differences were demonstrated between methods. Our results show that GS applies selection pressure much more locally than BLUP, resulting in larger allele frequency changes. With these results, novel insights into the nature of selection on quantitative traits have been gained and important questions regarding the long-term impact of GS were raised. The results are in agreement with the simulations of Muir (2007). The responses to selection as measured by change in mean of index values showed that for all lines, the responses for GS over BLUP were between 21% and 62% depending on line. There are a number of important conclusions from our results. The infinitesimal model is the most likely the correct approximation to the true biological genetic variation for these traits which implies: 1) ssGBLUP is more accurate than any SNP based method (e.g., BayesA, BayesB, or other such methods, which assume a relatively small number of SNPs whose effects can be estimated and selected for; in contrast, the infinitesimal model implies that genomic selection improves accuracy by increasing the precision with which relationships are estimated), 2) The results also imply that GWAS for these traits will find relatively few large effect QTLs, 3) Missing heritability type issues will result if selection is based on small chip with large effects QTL, i.e. will account for small amount of total genetic variation, 4) The results further imply that for these traits it will be possible to do genotype only selection for several generations before retraining will be necessary, 5) The greatest impact of GS is annual rate of progress due to shorter generation intervals; with traditional BLUP, 2 years are required per generation of selection while with GS, selection is possible at the hatch, with breeding occurring 20 weeks later, or 2 generations per year. Thus on an annualized basis, the rate of progress have been increased a minimum of 400% and with the increase in accuracy factored in, the rate of improvement is between 500% and 600%. The economic impact of which is staggering considering that one breeder is multiplied in 550,000 commercial birds. In general, these results are very favorable for genomic selection suggesting that such approaches will be useful in commercial breeding program. In addition, we developed and distributed widely a 60K SNP chip that also improved the chicken genome assembly.

Publications

  • Type: Book Chapters Status: Published Year Published: 2011 Citation: Ragavendran, A and W.M. Muir 2011. Genomic Selection in Aquaculture: Methods and Practical Considerations. In Next Generation Sequencing and Whole Genome Selection in Aquaculture ... Ed Z. Liu.
  • Type: Book Chapters Status: Published Year Published: 2012 Citation: Van Eenennaam, A. and W.M. Muir 2012. Animal Biotechnologies And Agricultural Sustainability. In The Role of Biotechnology in a Sustainable Food Supply. Eds Jennie Popp, Marty Matlock, Nathan Kemper, Molly Jahn. Cambridge University Press
  • Type: Book Chapters Status: Published Year Published: 2013 Citation: Muir, WM and HW Cheng. 2013. Genetics and the Behaviour of Chickens: Welfare and Productivity. In Genetics and the Behaviour of Domestic Animals, 2nd Edition. Ed Temple Grandin. Academic Press.
  • Type: Journal Articles Status: Published Year Published: 2009 Citation: Groenen, M.A.M., Wahlberg, P., Foglio, M., Cheng, H.H., Megens, H.J., Crooijmans, R.P.M.A., Besnier, F., Lathrop, M., Muir, W.M., Wong, G.K.S., et al. 2009. A high-density SNP-based linkage map of the chicken genome reveals sequence features correlated with recombination rate. Genome Research 19:510-519.
  • Type: Journal Articles Status: Published Year Published: 2009 Citation: Megens, H-J, RPMA Crooijmans, JWM Bastiaansen, HHD Kerstens, A Coster, R Jalving, A Vereijken, P Silva, MW Muir, HH Cheng, O Hanotte and MAM Groenen. 2009. Comparison of linkage disequilibrium and haplotype diversity on macro- and microchromosomes in chicken. BMC Genetics 10:86.
  • Type: Journal Articles Status: Published Year Published: 2010 Citation: Chen, C., I. Misztal, I. Aguilar, S. Tsuruta, T. Meuwissen et al. 2010 Genetic evaluation including phenotypic, full pedigree, and genomic information: An application in broiler chickens. J. Dairy Sci. 93: 532.
  • Type: Journal Articles Status: Published Year Published: 2011 Citation: Chen, C., I. Misztal, I. Aguilar, A. Legarra, and W. Muir. 2011. Effect of different genomic relationship matrices on accuracy and scale. J. Anim. Sci. 89: 2673-2679.
  • Type: Journal Articles Status: Published Year Published: 2011 Citation: Chen, C.Y., I. Misztal, I. Aguilar, S. Tsuruta, T. H. E. Meuwissen, S. E. Aggrey, T. Wing and W. M. Muir. Genome Wide Marker Assisted selection Combining All Pedigree 2011. Phenotypic Information with genotypic data in one step: an example using broiler chickens. J. Anim Sci. 2011. 89:23-28.
  • Type: Journal Articles Status: Published Year Published: 2011 Citation: Groenen, M. A., H. J. Megens, Y. Zare, W. C. Warren, L. W. Hillier, Muir, W.M. et al. 2011 The development and characterization of a 60K SNP chip for chicken. BMC Genomics 12.274.
  • Type: Journal Articles Status: Published Year Published: 2012 Citation: Wang, H, I. Misztal, I. Aguilar, A. Legarra, and W. M. Muir Genome-wide association mapping including phenotypes from relatives without genotypes. 2012. Genetics Research 94:73-83.
  • Type: Journal Articles Status: Published Year Published: 2013 Citation: Misztal I, SE Aggrey and WM. Muir. 2013. Experiences with a Single-Step Genome Evaluation (ssGBLUP). Poultry Sci. 92:2530-4.
  • Type: Journal Articles Status: Submitted Year Published: 2014 Citation: M Heidaritabar, A Vereijken, WM Muir, THE Meuwissen, H Cheng, HJ Megens, MAM Groenen, and JWM Bastiaansen, 2013, revision submitted to Heredity. Systematic differences in the response of genetic variation to pedigree and genome based selection methods.
  • Type: Journal Articles Status: Submitted Year Published: 2014 Citation: X Zhang, I Misztal, M Heidaritabar, JWM Bastiaansen, R Hawken, R Okimoto, T Wing, WM Muir, and H Cheng, Signature of selection reveals large differences in selection traits.
  • Type: Journal Articles Status: Submitted Year Published: 2014 Citation: M Heidaritabar, MPL Calus, A Vereijken, MAM Groenen, and JWM Bastiaansen, Lack of concordance between patterns of allele frequency changes and QTL mapping associations.


Progress 09/01/11 to 08/31/12

Outputs
OUTPUTS: The advantages of genomic selection (GWMAS) are well understood. In summary, GWMAS is most advantageous for traits that are difficult to or costly to measure (carcass quality, feed efficiency) or are collected late in life (full record egg production). This technology is the final realization of genomics, i.e., direct selection on the genotype and can double response to selection per unit of time for some traits. However, we have shown using simulations that a loss in accuracy will result once selection on those markers has commenced. Unanswered questions is how rapidly the decline in accuracy will occur and secondly, how often the retraining process must occur and how much of the lost accuracy can be regained To address these questions, we analyzed three traits from two pure lines broiler type chickens across 5 generations. The complete populations consisted of 183,784 and 164,246 broilers for two lines. The genotyped subsets consisted of 3,284 and 3,098 broilers with 57,636 SNPs. The training population consisted of the first two generations. Following 3 generations of selection based on ssGBLUP (genomic selection), we compared the accuracy with which EBVs could be predicted in those 3 generations based only on the genotypes (ssGBLUP) or pedigree (BLUP). Accuracy was determined as the correlation between predicted breeding value and the phenotype divided by the square root of heritability for that trait. Results showed that the relative accuracy for ssGBLUP remained about 33% higher than BLUP in all generations across both lines. The results differ from the predictions of the simulations from Muir (2007) who used the same set of assumptions as Meuwissen et al (2001). Our conclusion is that the results are characteristic of the infinitesimal model with thousands of QTL rather than a 100 or fewer as assumed by Meuwissen et al. (2001). PARTICIPANTS: Nothing significant to report during this reporting period. TARGET AUDIENCES: People interested in our research include academic and industry scientists working on genomics and genomic selection. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
There are a number of important conclusions from our results. The infinitesimal model is the most likely the correct approximation to the true biological genetic variation for these traits which implies: 1) ssGBLUP is more accurate than any SNP based method (e.g., BayesA, BayesB, or other such methods, which assume a relatively small number of SNPs whose effects can be estimated and selected for; in contrast, the infinitesimal model implies that genomic selection improves accuracy by increasing the precision with which relationships are estimated), 2) The results also imply that GWAS for these traits will find relatively few large effect QTLs, 3) Missing heritability type issues will result if selection is based on small chip with large effects QTL, i.e. will account for small amount of total genetic variation, 4) The results further imply that for these traits it will be possible to do genotype only selection for several generations before retraining will be necessary. In general these results are very favorable for genomic selection suggesting that such approaches will be useful in commercial breeding program

Publications

  • Wang,H, I. Misztal, I. Aguilar, A. Legarra, and W. M. Muir Genome-wide association mapping including phenotypes from relatives without genotypes. 2012. Genetics Research 94:73-83.
  • Pungpapong , V. W.M. Muir, X Li, D Zhang, and M. Zhang. A Fast and Efficient Approach for Genomic Selection with High-Density Markers. G3 Genes, Genomes, Genetics 2012. 2: 1179-1184.


Progress 09/01/10 to 08/31/11

Outputs
OUTPUTS: Genome-wide marker-assisted selection (GWMAS), first proposed by one of our team members, utilizes markers spanning the entire genome to increase accuracy and efficiency of estimating breeding values (EBV). This new method promises significant benefits but there are many unanswered questions calling for proof that GWMAS actually works. Although retrospective analysis has shown genome-wide marker-based EBV correlate highly with phenotypic Best Linear Unbiased Prediction (BLUP) EBV, concerns are these analyses will not reflect reality once implemented because selection may rapidly change variances, allele frequencies, and generates unfavorable linkage disequilibrium (LD), which only becomes apparent after the second round of selection. As planned, two meat-type and three egg-type chicken pure lines are being selected in parallel using either traditional or GWMAS. This year, after completing three rounds of selection in broiler (meat-type) chickens, we conclude that compared to birds selected in parallel using current state-of-the-art breeding methods, genomic selection is superior for the vast majority of the traits selected including body weight and breast yield. PARTICIPANTS: The molecular genetic analyses were lead by Bill Muir (Purdue), Ignacy Misztal and Ching-Yi Chen (U. of Georgia), Theo Meuwissen (Norwegian U of Life Sciences, Norway), Guilherme Rosa (U. of Wisconsin), Addie Vereijken (Hendrix Genetics), and Terry Wing (Cobb-Vantress). Molecular biology assistance was provided by Hans Cheng (ARS, East Lansing, MI), Martien Groenen (Wageningen U., The Netherlands), Ron Okimoto (Cobb-Vantress), Pieter van As (Hendrix Genetics), and Tun-Ping Yu and Charles Pick (DNA Landmarks).People interested in our research include academic and industry scientists working on genomics and genomic selection. TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
This research strongly suggests that genomic selection is an improved breeding method. If costs for genetic testing continue to go down, then poultry breeders should be able to economically breed chickens faster using genomic selection and adapt more readily to changing consumer demands. The economic impact could be great since with 1 million meat-type birds processed per hour in the US alone, the net effect of even small improvements are large and worth millions of dollars.

Publications

  • Aguilar, I., I. Misztal, D. L. Johnson, A. Legarra, S. Tsuruta et al. 2010. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci. 93: 743-752.
  • Calus, M. P. L., T. H. E. Meuwissen, J. J. Windig, E. F. Knol, C. Schrooten et al. 2009. Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values. Gen. Sel. Evol. 41: 11.
  • Chen, C. Y., I. Misztal, I. Aguilar, S. Tsuruta, T. H. E. Meuwissen et al. 2011. Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: An example using broiler chickens. J. Anim. Sci. 89: 23-28.
  • de los Campos, G., D. Gianola, and G. J. M. Rosa. 2009. Reproducing kernel Hilbert spaces regression: A general framework for genetic evaluation. J. Anim. Sci. 87: 1883-1887.
  • de los Campos, G., D. Gianola, G. J. M. Rosa, K. A. Weigel, and J. Crossa, 2010. Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet. Res. 92: 295-308.
  • Forni, S., I. Aguilar, and I. Misztal. 2011. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Gen. Sel. Evol. 43: 1.
  • Goddard, M. E., B. J. Hayes, and T. H. E. Meuwissen. 2010. Genomic selection in livestock populations. Genet. Res. 92: 413-421.
  • Gonzalez-Recio, O., K. A. Weigel, D. Gianola, H. Naya, and G. J. M. Rosa. 2010. L-2-Boosting algorithm applied to high-dimensional problems in genomic selection. Genet. Res. 92: 227-237.
  • Legarra, A., and I. Misztal. 2008. Technical note: Computing strategies in genome-wide selection. J. Dairy Sci. 91: 360-366.
  • Nielsen, H. M., A. K. Sonesson, and T. H. E. Meuwissen. 2011. Optimum contribution selection using traditional best linear unbiased prediction and genomic breeding values in aquaculture breeding schemes. J. Anim. Sci. 89: 630-638.
  • Rosa, G. J. M., B. D. Valente, G. de los Campos, X. L. Wu, D. Gianola et al. 2011. Inferring causal phenotype networks using structural equation models. Gen. Sel. Evol. 43: 6.
  • Shepherd, R. K., T. H. E. Meuwissen, and J. A. Woolliams. 2010. Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers. BMC Bioinformatics 11.


Progress 09/01/09 to 08/31/10

Outputs
OUTPUTS: Genome-wide marker-assisted selection (GWMAS), first proposed by one of our team members, utilizes markers spanning the entire genome to increase accuracy and efficiency of estimating breeding values (EBV). This new method promises significant benefits but there are many unanswered questions calling for proof that GWMAS actually works. Although retrospective analysis has shown genome-wide marker-based EBV correlate highly with phenotypic Best Linear Unbiased Prediction (BLUP) EBV, concerns are these analyses will not reflect reality once implemented because selection may rapidly change variances, allele frequencies, and generates unfavorable linkage disequilibrium (LD), which only becomes apparent after the second round of selection. As planned, two meat-type and three egg-type chicken pure lines are being selected in parallel using either traditional or GWMAS. This year, after completing two rounds of selection, we conclude that compared to birds selected in parallel using current state-of-the-art breeding methods, genomic selection is superior for the vast majority of the traits selected including body weight and breast yield. PARTICIPANTS: The molecular genetic analyses were lead by Bill Muir (Purdue), Ignacy Misztal and Ching-Yi Chen (U. of Georgia), Theo Meuwissen (Norwegian U of Life Sciences, Norway), Guilherme Rosa (U. of Wisconsin), Addie Vereijken (Hendrix Genetics), and Terry Wing (Cobb-Vantress). Molecular biology assistance was provided by Hans Cheng (ARS, East Lansing, MI), Martien Groenen (Wageningen U., The Netherlands), Ron Okimoto (Cobb-Vantress), Pieter van As (Hendrix Genetics), and Tun-Ping Yu and Charles Pick (DNA Landmarks). TARGET AUDIENCES: People interested in our research include academic and industry scientists working on genomics and genomic selection. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
This research strongly suggests that genomic selection is an improved breeding method. If costs for genetic testing continue to go down, then poultry breeders should be able to economically breed chickens faster using genomic selection and adapt more readily to changing consumer demands. The economic impact could be great since with 1 million meat-type birds processed per hour in the US alone, the net effect of even small improvements are large and worth millions of dollars.

Publications

  • Chen, C.Y., Misztal, I., Aguilar, I., Tsuruta, S., Meuwissen, T.H.E., Aggrey, S.E., and Muir, W.M. 2010. Genome wise marker assisted selection in chicken: making the most of all data, pedigree, phenotypic, and genotmic in a simple one step procedure. Proc. WCGALP, 288.