Source: UNIVERSITY OF GEORGIA submitted to NRP
THE INTERFACE OF MOLECULAR AND QUANTITATIVE GENETICS IN PLANT AND ANIMAL BREEDING.
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
0213567
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Apr 1, 2008
Project End Date
Sep 30, 2012
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
UNIVERSITY OF GEORGIA
200 D.W. BROOKS DR
ATHENS,GA 30602-5016
Performing Department
Animal & Dairy Science
Non Technical Summary
Quantitative genetics focuses on understanding and modeling the inheritance of so-called complex or quantitative traits, which are phenotypes that affected by multiple genes and the environment Quantitative genetics has had remarkable success in both plant and animal breeding by directing strategies for genetic selection of agronomic traits. Those advancements, however, have been limited by the relatively simplistic assumptions of the model for inheritance of quantitative traits and the tools available for estimating genetic worth. With the advent of molecular genetics, those limitations no longer need apply. We now have the means to uncover the true modes of inheritance of quantitative traits by unlocking the mysteries of the genetic code. Although structural genomics has revealed the DNA sequence of the human genome and of several plant and animal species, less than 1% of the human genetic code can be deciphered into functional genes. The DNA sequence is like the Egyptian hieroglyphics on the Rosetta stone: we have the cipher but do not yet know what it all means. Functional genomics is the painstaking process of extracting meaning from the code. Functional genomics will reveal gene function and regulation, knowledge that we can apply in advanced breeding programs. Several obstacles, however, remain before such a goal can be realized. Those are detailed below. Need: More than ever before, the need for proper statistical methodology and bioinformatics is critical to the advancement of molecular genetics and for the application of those advancements to improvement of agriculturally relevant plant and animal species. The latter is been termed "translational genomics" whereby information gleamed from molecular genetics is transformed into applications in the field. Failure to empower translational genomics through bioinformatics techniques would relegate the finding of genomics to academic interest and not realize the promise of biotechnology.
Animal Health Component
(N/A)
Research Effort Categories
Basic
(N/A)
Applied
(N/A)
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
20139101080100%
Knowledge Area
201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
3910 - Cross-commodity research--multiple animal species;

Field Of Science
1080 - Genetics;
Goals / Objectives
1. Develop and compare statistical and computational methodology for analysis of molecular genetic and genomic data associated with quantitative traits. 2. Examine the efficiency of incorporating molecular tools in plant and animal breeding programs through theoretical modeling, computer simulations, and biological testing in actual breeding populations. 3. Use molecular genetics and genomics tools to test hypotheses generated from the fundamental theories of population, quantitative genetics, and molecular evolutionary genetics.
Project Methods
This group of researchers (NCCC204) is largely responsible for leading the direction of development of statistical tools and methodology for incorporating molecular genetics in plant and animal breeding. The group disseminates tools, results and methods of research (mapping) or tools and methods of genetic improvement among themselves and other interested parties. The "others" will be encouraged to join the project and contribute to the discussion on the direction of research at the interface of molecular and quantitative genetics. Tools and methods developed for breeding or from fundamental theories are used to test hypotheses from applied or fundamental genetics. Where possible these hypothesis are tested against information available from plant and animal populations or breeding programs.

Progress 04/01/08 to 09/30/12

Outputs
OUTPUTS: In the past, the genetic evaluation of farm animals was based on a large number of animals with phenotypic records. Now, SNP chips including thousands of genomic markers are available, enabling a genomic selection. Lately, genomic selection has been practiced in many species and in many organizations. In some cases, the results have been spectacular, and in some not. When the results fall short of expectations, questions remain as to whether they were because of inadequate statistics, too small chip size, problems with quality control or basic issues. In the end, one wonders what the limits of genomic selection are, and what will follow it. Based on published and unpublished results on genomic selection, including those at the University of Georgia, one can understand many issues in the genomic selection much better than a few years ago. PARTICIPANTS: Not relevant to this project. TARGET AUDIENCES: Not relevant to this project. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
It was hoped that information from 1000 genotyped and phenotyped animals will provide accurate predictions for many future generations. Now it seems that the minimum number is at least 2000 for well proven bulls and even higher for animals with less information. Also genomic associations decay under selection and constant genotyping of new animals is needed. Genomic predictions can be derived either by 1) predicting marker effects, or 2) using genomic information from genomic relationships. On average, both approaches provide similar results. Genomic predictions developed for one breed or line usually are not very useful for genomic predictions in another line/breed. This is because marker SNP effects point mostly to common haplotypes of recent ancestors, not common genes. No recent ancestors are shared by different lines/breeds. Previous genetics evaluations used complex models with features such as adjustments, maternal effects, unknown parent groups and heterosis. Many genomic models are very simple. However, poor models lead to poor predictions, no matter whether genomic or not. Much hope has been put into having large chips with many genomic markers, say over half a million. However, increases in accuracy of predictions with such chips are minimal. The genomic relationships were pretty accurate with some fifty thousand markers so increasing the density did not help much unless causative SNPs are identified. Experience indicates a large number of causative SNPs for most traits, and their accurate estimation would require large data sets. In commercial implementation of the genomic selection, the most important factor is attention to detail. One should use all useful phenotypes, refine models and check quality of genotypes.

Publications

  • Misztal, I. 2011. FAQ for genomic selection - Editorial. J. Anim. Breed. Genet. 128: 245-246.
  • Simeone, R., I. Misztal, I. Aguilar, and Z. Vitezica. 2012. Evaluation of a multi-line broiler chicken population using a single-step genomic evaluation procedure. J, Anim. Breed. Genet. 129( 1):3-10.
  • Wang, H., I. Misztal, I. Aguilar, A. Legarra, and W. M. Muir. 2012. Genome-wide association mapping including phenotypes from relatives without genotypes. Genet. Res. 94(2):73-83.


Progress 01/01/11 to 12/31/11

Outputs
OUTPUTS: A complete phenotypic data set (FULL) consisted of 183,784 and 164,246 broilers for two lines across three generations. Genotyped subset (SUB) consisted of 3,284 and 3,098 broilers in lines 1 and 2 with 57,636 SNP available. Traits were body weight at 6 weeks (BW), breast meat (BM), and binary leg defect score (LEG). Some records were missing for BM. Heritabilities with FULL were 0.17-0.20 for BW, 0.30-0.35 for US, and 0.09-0.11 for LEG. Genetic evaluation was performed by regular BLUP, by a single-step procedure (SSP) that combined relationships based on pedigree and the SNP data, and by Bayes A procedure. While BLUP and SSP could use the complete data set, Bayes A could use only the genotyped subset. Genotyped animals in the third generation were treated as validation population. PARTICIPANTS: Not relevant to this project. TARGET AUDIENCES: Not relevant to this project. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
The average accuracies of the validation population with BLUP for BW, US, and LEG were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FULL. With SSP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FULL, respectively. Accuracies with BayesA were similar to SSP with SUB. Accuracies in lines 1 and 2 were similar for BM but different for BW and LEG. For traits with high heritability, the accuracy of the evaluation using the genomic information and only records of genotyped animals may be higher than that using the complete data and BLUP. An opposite is likely for traits with lower heritability, many missing records, or undergoing pre-selection. An optimal genomic evaluation would be multi-trait and would involve all traits and records on which the selection is based.

Publications

  • Chen,C. Y., I. Misztal,S. Tsuruta,B. Zumbach, W.O. Herring,T. Long, and M. Culbertson. 2011. Genetic analyses of stillbirth in relation to litter size using random regression models. J. Animal Sci. 88: 3800-3808.
  • Boonkum, W., I. Misztal, M. Duangjinda, V. Pattarajinda, S. Tumwasorn, and J. Sanpote. 2011. Genetic effects of heat stress on milk yield of Thai Holstein crossbreds. J. Dairy Sci. 94:487-492.
  • Chen, C. Y., I. Misztal, I. Aguilar, S. Tsuruta, T. H. E. Meuwissen, S. E. Aggrey, T. Wing, and W. M. Muir. 2011. Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: an example using broiler chickens. J. Animal Sci. 89:23-28.
  • Aguilar, I., I. Misztal , A. Legarra , S.Tsuruta. 2011. Efficient computation of genomic relationship matrix and other matrices used in single-step evaluation. J. Anim. Breed. Genet. 128(6):422-428.
  • Boonkum, W., I. Misztal, M. Duangjinda, V. Pattarajinda, S. Tumwasorn, and S. Buaban. 2011. Short communication: Genetic effects of heat stress on days open for Thai Holstein crossbreds. J. Dairy Sci. 94:1592-1596.
  • Johanson, J. M., P. J. Berger, S. Tsuruta, and I. Misztal. 2011. A Bayesian Threshold-Linear Model Evaluation of Perinatal Mortality, Dystocia, Birth Weight, and Gestation Length in a Holstein Herd. J. Dairy Sci. 94:450-460.
  • Forni, S., I. Aguilar, and I. Misztal. 2011. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genet. Sel. Evol. 43:1.
  • Perez-Enciso, M., and I. Misztal. 2011. Qxpak.5: Old mixed model solutions for new genomics problems. BMC Bioinformatics.12:202.
  • Aguilar, I., I. Misztal, S. Tsuruta, G. R. Wiggans and T. J. Lawlor. 2011. Multiple trait genomic evaluation of conception rate in Holsteins. J. Dairy Sci. 94:2621-2624.
  • Costa, R. B., I. Misztal, M. A. Elzo, J. K. Bertrand, L. O. C. Silva, and M. Lukaszewicz. 2011. Estimation of genetic parameters for mature weight in Angus cattle. J. Animal Sci. 89:2680-2686.
  • Simeone, R., I. Misztal, I. Aguilar, and A. Legarra. 2011. Evaluation of the utility of genomic relationship matrix as a diagnostic tool to detect mislabeled genotyped animals in a broiler chicken population. J, Anim. Breed. Genet. 128(5):386-393.
  • Chen, C. Y., I. Misztal, I. Aguilar, A. Legarra, and B. Muir. 2011. Effect of different genomic relationship matrix on accuracy and scale. J. Anim. Sci. 89:2673-2679.
  • Tsuruta, S., I. Aguilar, I. Misztal, and T. J. Lawlor. 2011. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J. Dairy Sci. 94:4198-4204.
  • Vitezica, Z. G., I. Aguilar, I. Misztal, and A. Legarra. 2011. Bias in Genomic Predictions for Populations Under Selection. Genet. Res. Camb. 93:357-366.


Progress 01/01/10 to 12/31/10

Outputs
OUTPUTS: The purpose of this work was to determine whether the use of the genomically derived relationships results in increased estimated heritability as opposed to using pedigree relationships. Data included litter sizes for 338,346 sows, of which 1,919 had genotypes using the porcine 60k SNP chip. Analyses involved a complete data set or a subset of genotyped animals and their parents (n=5,090). A genomic relationship matrix was constructed using equal (G05) or observed gene frequencies (GOB). Additional relationship matrices were the pedigree-based relationship matrix (A) and a combined pedigree-genomic matrix (H) as in Aguilar et al. (2010). A normalized matrix (GN) was obtained by multiplying GOB by a constant to achieve an average diagonal of 1. PARTICIPANTS: Not relevant to this project. TARGET AUDIENCES: Not relevant to this project. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
Using A and the complete data set, the estimate of the additive variance was 1.26. With H that included G05, GOB or GN the additive variance estimates were 1.28, 1.28 and 1.27, respectively. Using A and the subset of the data, the estimate of the additive variance was 2.28. With H that included G05, GOB or GN the additive variance estimates were 3.43, 2.42 and 2.25, respectively. When the genomic relationship matrix has a different scale than the pedigree-based matrix, the estimates of the additive variance may be biased especially for small data sets.

Publications

  • Zumbach, B., I. Misztal, C.Y. Chen, S. Tsuruta, M. Lukaszewicz, W.O. Herring, and M. Culbertson. 2010. Use of serial pig body weights for genetic evaluation of daily gain. J. Animal Breed. Genet. 126(6):93-99.
  • Chen,C. Y., I. Misztal, S. Tsuruta, W.O. Herring,J. Holl, and M. Culbertson. 2010. Influence of heritable social status on daily gain and feeding pattern in pigs. J. Animal Breed. Genet. 127(2):107-112.
  • Chen,C. Y., I. Misztal,S. Tsuruta,B. Zumbach, W.O. Herring,T. Long, and M. Culbertson. 2010. Estimation of genetic parameters of feed intake and daily gain in Durocs using data from electronic swine feeders. J. Animal Breed. Genet. 27(3):230-234.
  • Aguilar, I. S. Tsuruta, and I. Misztal. 2010. Computing options for multiple-trait test-day random regression models while accounting for heat tolerance. J. Anim. Breed. Genet. 127(3):235-241.
  • Aguilar, I., I. Misztal, D. L. Johnson, A. Legarra, S. Tsuruta, and T. J. Lawlor. 2010. A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci. 93:743:752.
  • Soyeurt, H, I. Misztal and N. Gengler. 2010. Genetic Variability of Milk Components Based on Mid-Infrared (MIR) Spectral Data. J. Dairy Sci. 93:1722-1728.
  • Aguilar, I., I. Misztal, and S. Tsuruta. 2010. Short Communication: Genetic trends of milk yield under heat stress for US Holsteins. J. Dairy Sci. 93:1754-1758.
  • Scholtz. A. J., S.W.P. Cloete, J.B. van Wyk, I. Misztal, E. du Toit, and T.C. de K. van der Linde. 2010. Genetic (co)variances between wrinkle score and absence of breech strike in mulesed and unmulesed Merino sheep using a threshold model. Animal Production Science. 50:210-218.
  • Koduru, V. K. R., S. Tsuruta, M. Lukaszewicz, I. Misztal, T. J. Lawlor. 2010. Studies on changes of estimated breeding values of U.S. Holstein bulls for final score from first to second crop of daughters. J. Appl. Genet. 52:81-88.


Progress 01/01/09 to 12/31/09

Outputs
OUTPUTS: Currently the genomic evaluations use multiple step procedures, which are complicated and prone to errors. For many traits, predictions involving estimation of SNP effects or BLUP using a genomic relationship matrix are equivalent. A single step procedure may be applicable by modifying the numerator relationship matrix A in a regular evaluation to H= A+Δ, where Δ includes deviations from original relationships. However, the traditional mixed model equations require H-1, which is difficult to obtain for large pedigrees. The computations with H are feasible when the mixed model equations are expressed in an alternate form given by Henderson that also applies for singular H, and when those equations are solved by the conjugate gradient techniques. Then the only computations involving H are in the form of Aq or Δq, where q is a vector; the product Ac can be calculated efficiently in linear time using Colleau's indirect algorithm. The alternative equations have a nonsymmetric left-hand side. PARTICIPANTS: Not relevant to this project. TARGET AUDIENCES: Not relevant to this project. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
The proposed methodology may allow upgrading of an existing evaluation to incorporate the genomic information. The upgrade would eliminate many approximations present in the multi step procedures and would be both simpler and faster. Tests with real data will follow.

Publications

  • Sanchez, J. P., R. Rekaya and I. Misztal. 2009. Reaction norm models subject to threshold response. Genet. Sel. Evol.41:10, doi:10.1186/1297-9686-41-10.
  • Cloete,S.W.P., I. Misztal, and J.J. Olivier. 2009. Genetic parameters and trends for lamb survival and birth weight in a Merino flock divergently selected for multiple rearing ability. J. Animal Sci. 87:2196-2208.
  • Sanchez, J. P., I. Misztal , I. Aguilar, B. Zumbach, and R. Rekaya. 2009. Genetic determination of the onset of heat stress on daily milk production in the US Holstein cattle. J. Dairy Sci. 92: 4035-4045.
  • Legarra, A., I. Aguilar, and I. Misztal. 2009. A relationship matrix including full pedigree and genomic information. J. Dairy Sci. 92:4656-4663
  • Misztal, I., A. Legarra, and I. Aguilar. 2009. Computing procedures for genetic evaluation including phenotypic, full pedigree and genomic information. J. Dairy Sci. 92:4648-4655.
  • Pszczola, M., I. Aguilar, and I. Misztal. 2009. Short Communication: Trends for Monthly Changes in Days Open in Holsteins. J. Dairy Sci. 92:4689-4696.
  • Huang, C., S. Tsuruta, J. K. Bertrand, I. Misztal, T. J. Lawlor, and J. S. Clay. 2009. Trends for conception rate of Holsteins over time in Southeastern USA. J. Dairy Sci. 92:4641-4647.
  • Aguilar, I., I. Misztal, and S. Tsuruta. 2009. Genetic components of heat stress for dairy cattle with multiple lactations. J. Dairy Sci. 92: 5702-5711.
  • Tsuruta, S., I. Misztal, C. Huang, and T. J. Lawlor. 2009. Bivariate Analysis of Conception Rates and Test-Day Milk Yields Using A Threshold-Linear Model with Random Regressions. J. Dairy Sci. 92: 2922-2930.


Progress 01/01/08 to 12/31/08

Outputs
OUTPUTS: Data included 90,242,799 test day records from 5,402,484 Holstein cows in 3 parities. The total number of animals in the pedigree file was 9,326,754. Additionally, daily temperature humidity indexes (THI) from 202 weather stations were available. The effects of herd test day, age at calving, milking frequency and days in milk classes (DIM) were made fixed, and the effects of additive, permanent environment and herd-year were made random. Random effects were fit as random regression. Covariates included linear splines with four knots at 5, 50, 200, 305 DIM, and a function of THI of the 3rd day before the test day from a weather station closest to the farm. The first three lactations were used as separate traits, resulting in 15 by 15 (co)variance matrices for each random effect. The mixed model equations were solved using an iteration on data program with a preconditioned conjugate gradient (PCG) algorithm. Several preconditioners were used: diagonal (D), block diagonal due to traits (BT), and block diagonal due to traits and correlated effects (BTCORR). One run included BT with a "diagonalized" model in which the random effects were reparameterized for diagonal (co)variance matrices among traits (BTDIAG). PARTICIPANTS: Not relevant to this project. TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
Memory requirements were 8.7 Gbytes for D, 10.4 Gbytes for BT and BTDIAG, and 24.3 Gbytes for BTCORR. Computing times (rounds) were 14 d (952) for D, 10.7 d (706) for BT, 7.7 d (494) for BTDIAG and 4.6 d (289) for BTCORR. The convergence criterion for BTCORR showed high fluctuation that required either a moving average or a strict stopping criterion. The convergence pattern was strongly influenced by the choice of fixed effects. When sufficient memory is available, the option BTCORR is the fastest and simplest to implement; the next efficient method, BTDIAG, requires additional steps to diagonalization and back-diagonalization.

Publications

  • Huang, C., S. Tsuruta, J. K. Bertrand, I. Misztal, T. J. Lawlor, and J. S. Clay. 2008. Environmental Effects on Conception Rate of Holsteins in New York and Georgia. J. Dairy Sci. 92:818-825.
  • Legarra, A., and I. Misztal. 2008. Computing strategies in genome-wide selection. J. Dairy Sci. 91:360-366.
  • Bohmanova, J., I. Misztal, S. Tsuruta, H.D. Norman, and T.J. Lawlor. 2008. Heat Stress as a Factor in Genotype x Environment Interaction in U.S. Holsteins. J. Dairy Science. 91:840-846.
  • B. Zumbach, S. Tsuruta, I. Misztal and K.J. Peters. 2008. Use of a test day model for dairy goat milk yield across lactations in Germany. J. Anim. Breed. Gen. 125:160-167.
  • Sanchez, J. P., I. Misztal, I. Aguilar, and J. K. Bertrand. 2008. Genetic evaluation of growth in a multibreed beef cattle population using random regression linear spline models. J. Anim. Sci. 86:267-277.
  • Sanchez, J. P., I. Misztal, and J. K. Bertrand. 2008. Evaluation of methods for computing approximate accuracies in maternal random regression models for growth trait in beef. J. Anim. Sci. 86:1057-1066.
  • Aguilar, I., and I. Misztal. 2008. Recursive algorithm for inbreeding coefficients assuming non-zero inbreeding of unknown parents. J. Dairy Sci. 91:1669-1672.
  • Tsuruta, S. and I. Misztal. 2008. Computing options for genetic evaluation with a large number of genetic markers. J. Anim. Sci. 86:1514-1518.
  • Pribyl, J., H. Krejcova, J. Pribylova, I. Misztal, S. Tsuruta, and N. Mielenz. 2008. Models for evaluation of growth of performance tested bulls. Czech J. Anim. Sci. 53:45-54.