NCCC_OLD204: The Interface of Molecular and Quantitative Genetics in Plant and Animal Breeding

(Multistate Research Coordinating Committee and Information Exchange Group)

Status: Inactive/Terminating

Project Menu

SAES-422 Reports

Annual/Termination Reports:

[05/05/2003] [08/05/2004] [07/07/2005] [07/03/2006] [01/08/2008]

Date of Annual Report: 05/05/2003

Report Information

Annual Meeting Dates: 02/08/2003 - 02/09/2003
Period the Report Covers: 06/01/2002 - 02/01/2003

Participants

Arias, Juan (arias@calshp.cals.wisc.edu) -Univ. of Wisconsin;
Bailey, Ted (tbbailey@iastate.edu) -Iowa State;
Bastiaansen, John (JB@fyf.pig.co.uk) -Sygen;
Dekkers, Jack (jdekkers@iastate.edu) -Iowa State;
Della-Chiesa, Emmanuelle (manu@moulon.inra.fr) -INRA;
Dentine, Margaret (mrdentine@cals.wisc.edu) -Univ. of Wisconsin;
Dillman, Christine (dillmann@moulon.inra.fr) -INRA ;
Du, Fengxing (fengxing.du@monsanto.com) -Industry;
Dudley, John (jdudley@uiuc.edu) -Univ. of Illinois;
Gao, Guimin (guiming@vt.edu) -Virginia Tech;
Gianola, Daniel (gianola@calshp.cals.wisc.edu) -Univ. of Wisconsin;
Grapes, Laura (lgrapes@iastate.edu) -Iowa State;
Hospital, Frederic (fred@moulon.inra.fr) -INRA;
Mao, Yongcai (ycmao@mail.ucr.edu) -Univ. of California Riverside;
McElroy, Joseph (jmclry@iastate.edu) -Iowa State;
Misztal, Ignacy (ignacy@uga.edu) -Univ. of Georgia;
Muir, Bill (bmuir@purdue.edu) -Purdue Univ.;
Romero-Severson, Jeanne (romeros@fnr.purdue.edu) -Purdue Univ. ;
Rosa, Guilherme (rosag@msu.edu) -Michigan State;
Saxton, Arnold (asaxton@utk.edu) -Tennessee;
Servin, Bertrand (servin@moulon.inra.fr) -INRA;
Walsh, Bruce (jbwalsh@u.arizona.edu) -Univ. of Arizona;
Wang, De Chun (wangdech@msu.edu) -Michigan State;
Wang, Tianlin (twang@monsanto.com) -Industry;
Xu, Shizhong (xu@genetics.ucr.edu) -Univ. of California Riverside;

Members absent, no representative sent, not excused: Mike Grossman;
Members absent, no representative sent but excused: Cynthia Ernst, Dow-Agrosciences;

Brief Summary of Minutes

The NCR204 project convened Saturday February 8, 2003 in Ventura, California just prior to the Gordon Conference. William Muir (Chair) restated the administrative structure and the three project objectives. Margaret Dentine (Administrative Advisor) emphasized that the expected outcomes are important and include 1) workshops 2) publicly available software and 3) joint grant and joint papers. The importance of regular attendance was noted, as well as the requirement to fill out and sign appendix E. Richard Fram (the CREES representative) has retired and no replacement is anticipated anytime soon.

The business meeting consisted of discussions on the next meeting place, terms of office, elections, successions, appointments and policies, structure, rules and governance. Guilherme Rosa, Jack Dekkers, and Shizhong Xu were appointed coordinators for objectives 1, 2, and 3 respectively. The new chair is Jeanne Romero-Severson. The new secretary is Jean Luc Jannick.

Xu, Misztal, Saxton, Rosa, Walsh, Romero-Severson, Muir, Dudley, Dekkers and Bailey gave station reports. All stations that are members reported, if the AA report may be counted as Wisconsins report. These stations are UC Riverside, University of Georgia, University of Tennessee, Michigan State, University of Arizona, Purdue University, University of Illinois, Iowa State and University of Wisconsin.

Accomplishments

Objective 1: Develop and compare statistical methodology to map genes Xu (UC Riverside) showed that a Baysian method under the random regression coefficient model can handle a large number of markers in a single model and deal with markers with close to zero expected effect. The Bayesian method produces the L-shaped distribution predicted by the oligogenic hypothesis, wherein most markers have very small or zero effects, while a small number of markers account for most of the observed phenotypic variance. Rosa (Michigan State) used Baysian methods to construct genetic maps and assigning parentage assuming a genotyping error rate between 1-5%. The Baysian approach delivers a more accurate estimate of gene order, minimizes false exclusion due to genotyping errors and can determine whether or not a DNA sample contains a mixture of genotypes (i.e. more than 1 individual). Dudley (University of Illinois) indicated that random mating an F2 population preserves genetic variance while breaking up parental linkage blocks. The increase in the number of observable recombinations enables more precise estimates of QTL location provided that the population size genotyped is large enough to accommodate the additional markers that must added to prevent linkage map decomposition (the breaking apart of linkage groups due to map expansion). Dekkers (Iowa State) described how DNA pooling of phenotypic extremes within half-sib families reduces the genotyping burden while permitting estimation of QTL position and effect with minimal loss of power. Least squares interval mapping analysis of pooling data (pool-LSIM) gave high power to detect the QTL (>97%) when family size was large and moderate power (>55%) for small families. For small families, pool-LSIM had up to 14% greater power than single marker analysis, except for distal QTL with known VTE (technical error variance). Objective 2: Examine the efficiency of incorporating molecular tools in breeding programs through theoretical modeling, computer simulations, and biological testing in actual breeding populations. 1. Microarrays. Rosa summarized the current state of microarray analysis. Different statistical tools have been proposed for the normalization (both location and scale normalizations) and analysis of microarray data, including clustering methods and significance tests, such as permutation-based t-statistics, linear (fixed or mixed) models, mixture models and non-parametric methods. Saxton (U Tennessee) reported that a combination of microarrays, genotyping and cloning is being used in a project aimed at increased genetic resistance to mastitis in dairy cattle. The design and interpretation of microarray data remains a problem. Romero-Severson (Purdue) showed that Bonferroni correction, as applied to simple t tests of pairwise comparisons from Affymetrix microarray data, is too conservative. An ad hoc method followed by qRT-PCR found 12 genes with expression differences in excess of 2x for both of the planned comparisons and appropriate tissue specificity. The design included true biological replication and two treatments but was not analyzed using standard statistical models because of the problem posed by autocorrelation among the entries. Autocorrelation was the explanation offered for the failure of the Bonferroni correction. 2. Breeding techniques and statistical theory. Misztal (U Georgia) examined poultry and dairy cow performance data for changes in genetic correlations among performance traits as selection proceeds. Random regression models (RRM) with regressions on functions of year of birth or year of first recording detected large changes in genetic correlations in Holstein breeding programs. Inaccurate genetic correlations will weaken the predictive value of gain from selection estimates. Muir (Purdue) used computer simulations and real data to show that direct selection for performance traits tends to select those genotypes that require high input and reduced competition (more space, more fertilizer, more feed). Inclusion of the associative effects of one organism on another in the selection scheme allows selection for tolerance of competition and increased performance. Quail experiments showed that reducing the associative effects results in more gain that selecting on direct effects alone. This concept can be applied to plantation forestry and also permits QTL mapping for associative effects. Dudley reported that genetic variance in the long term selection experiments has persisted longer than many anticipated. Current hypotheses include the new mutation theory, which implies that genetic variance for a quantitative trait will not disappear under selection in the absence of genetic drift and nonrandom mating. Objective 3: Use molecular genetics to test hypotheses generated from the fundamental theories of population, quantitative, molecular and evolutionary genetics. Bruce Walsh described the theory that gene duplication generates genetic variation through neofunctionalization (retention of the old function and addition of a new function) and subfunctionalization (partition of the original function into conditional functions which differ between the two genes). The third possibility, loss of function through gene silencing, will eventually result in a pseudogene as mutations accumulate. The probability of neofunctionalization is a function of the selection coefficient and the effective population size. Tandem duplication followed by chromosome rearrangement then neofunctionalization can break up linkage relationships, while subfunctionalization can reduce potential pleiotropic constraints. In both cases, the change allows testing of new physical and functional combinations

Publications

Georgia Tsuruta. S., I. Misztal, T. J. Lawlor, and B. Klei. 2002. Application of a Random Regression Model at Different Ages for Final Scores in Holsteins. J. Dairy Sci. 85:1324:1330. Stranden, I., S. Tsuruta, and I. Misztal. 2002. Simple preconditioners for the conjugate gradient method: experience with the test day model. J. Anim. Breeding Genet. 119 (3):166_174. Misztal, I., S. Tsuruta,T. Strabel, B. Auvray, T. Druet, and D. H. Lee. 2002. BLUPF90 and related programs (BGF90). Proc. 7th World Congress Gen. Appl. Livest. Prod., Monteplier, France. CD_ROM communication 28:07. Lawlor, T. J., S. Tsuruta, L. Klei and I. Misztal. 2002. Use of a random regression model to investigate changes in genetic parameters over time. Proc. 7th World Congress Gen. Appl. Livest. Prod., Monteplier, France. CD_ROM communication 17:06. Tsuruta S., I. Misztal, T. J. Lawlor, and L. Klei. 2002. Estimation of genetic parameters over time for final score in holsteins using a random regression model. Proc. 7th World Congress Gen. Appl. Livest. Prod., Monteplier, France. CD_ROM communication 17:20. Misztal I., S. Tsuruta, T. J. Lawlor, and L. Klei. 2002. Use of a random regression model to investigate changes in genetic parameters over time. 18th Panamerican Congress of Veterinary Sciences, Havana, Cuba. CDROM communication. Tsuruta, S., I. Misztal, T. J. Lawlor, and L. Klei. 2002. Changes of genetic correlation between milk production and body size over time in Holsteins using random regression models. J. Dairy Sci (Suppl. 1) 85: 197. Michigan State Rosa, G. J. M., Yandell, B. S., Gianola, D. A Bayesian approach for constructing genetic maps when markers are miscoded. Genetic, Selection, Evolution, 34(3): 353-369. 2002. Coussens, P. M., Colvin, C. J., Rosa, G. J. M., Laspiur, J. P., Elftman, M. D. Novel gene expression differences in peripheral blood mononuclear cells from M. paratuberculosis infected and control cattle. Infection and Immunity (submitted) Madsen, S. A., Rosa, G. J. M., Mccandless, E., Coussens, P. M., Burton, J. L. Microarray analysis of mRNA profiles in neutrophils from periparturient cows reveals a novel phenotype. Veterinary Immunology and Immunopathology (submitted) Rosa, G. J. M., Gianola, D., Padovani, C. R. Robust linear mixed models with normal/independent distributions and Bayesian MCMC implementation. Biometrical Journal. (accepted) Tennessee Stalder, K. J., A. M. Saxton, R. K. Miller, and R. N. Goodwin. 2003. A comparison of hydrogen ion concentration and pH genetic predictions and fixed effect estimations when assessing pork quality. J. Anim. Sci. 81(3): 611-616. Smiley, R.D., L.G. Stinnett, A.M. Saxton, and E.E. Howell. 2002. Breaking Symmetry: Mutations Engineered into R67 Dihydrofolate Reductase, A D2 Symmetric Homotetramer Possessing a Single Active Site Pore. Biochemistry 41(52): 15664-15675. Pighetti, G. M., J. L. Edwards, F. N. Schrick, A. M. Saxton, C. J. Davies, and S. P. Oliver. 2003. Cloning adult dairy cows: A viable new tool in the fight against mastitis. National Mastitis Council Annual Meeting. Youngerman, S. M., S. P. Oliver, A. M. Saxton, J. L. Edwards, F. N. Schrick, C. J. Davies, and G. M. Pighetti. 2003. Interleukin-8 receptor: A candidate gene for mastitis resistance. National Mastitis Council Annual Meeting. Purdue Muir, W.M. and R.D. Howard. 2002. Methods to Assess Ecological Risks of Transgenic Fish Releases. In Genetically Engineered Organisms: Assessing Environmental and Human Health Effects Eds. D.K. Letourneau and B. E. Burrows. CRC Press p355-383. Muir, WM and R.D. Howard 2002. Environmental Risk Assessment of Transgenic Fish With Implications for Other Diploid Organisms. Transgenic Research 11:101-114. Muir, W.M. 2002. Potential Environmental Risks And Hazards Of Biotechnology. Potential Environmental Risks And Hazards Of Biotechnology Part I: Risks and Hazards. http://www.isb.vt.edu/news/2001/news01.nov.html#nov0105. Information Systems For Biotechnology (online version) Muir, W.M. 2002. Potential Environmental Risks And Hazards Of Biotechnology.Part II: Methods to Estimate Risks and Hazards http://www.isb.vt.edu/news/2002/news02.feb.html#feb0201. Information Systems For Biotechnology (online version) Muir, W.M. and A. Schinckel. 2002. Incorporation of competitive effects in breeding programs to improve productivity and animal well being. Proc. 7th World Congress of Genetics Applied to Livestock Breeding. 32:35-36 Muir, W.M. 2002. Use of molecular genetics in poultry breeding. Proc. 7th World Congress of Genetics Applied to Livestock Breeding. 30:193-200 NRC (National Research Council). 2002. Animal Biotechnology: Science Based Concerns. Washington, DC: National Academy Press. Muir, W.M.. 2002. Indirect Selection for Improvement of Animal Well-Being. In Poultry Breeding and Biotechnology Eds. WM Muir and S Aggrey. CABI Press (in press). Muir, W.M.. 2002. Incorporating Molecular Information in Breeding Programs, Applications and Limitations. In Poultry Breeding and Biotechnology Eds. WM Muir and S Aggrey. CABI Press (in press). Muir, W.M., D. Miles, and A.E. Bell, 2002. Long Term Selection Studies In Tribolium Castaneum, Alternative Selection Strategies, And Associated Nature Of Quantitative Genetic Variation. Plant Breeding Reviews (In Press) Rider, Sj Jr, TE Henderson, RE Jerome. HJ Edenberg, J Romero-Severson, J Ogas. 2003 Coordinate Repression of Regulators of Embryonic Identity by PICKLE During Germination in Arabidopsis. The Plant Journal. In press. Iowa Chaiwong, N., J.C.M. Dekkers, R.L. Fernando, and M.F. Rothschild. 2002. Introgressing multiple QTL in backcross breeding programs of limited size. 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, 22:08. Chakraborty, R., L. Moreau, and J. C. M. Dekkers. 2002. A method to optimize selection on multiple identified quantitative trait loci. Genet. Sel. Evol. 34: 145-170. Cruickshank, J., M. R. Dentine, P. J. Berger, and B. W. Kirkpatrick. 2002. Mapping quantitative trait loci for twinning in Holstein dairy cattle. Proc. 28th Intl. Conf. Anim. Genet. p. 164. Goettingen, Germany. Dekkers, J. C. M. and P. Settar. 2002. Long-term Selection with Known Quantitative Trait Loci. Plant Breeding Reviews. Wiley. (Accepted). Dekkers, J. C. M., R. Chakraborty, and L. Moreau. 2002. Optimal selection on two quantitative trait loci with linkage. Genet. Sel. Evol. 34 171-192. Dekkers, J.C.M., and R. Chakraborty. 2002. Purebred selection for crossbred performance using QTL. 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, 22:15. Fernandez, S. A., and R. L. Fernando. 2002. Determining Peeling Order Using Sparse Matrix Algorithms. J. Dairy Sci. 85:1623-1629. Fernandez, S. A., R. L. Fernando, B. Gulbrandtsen, C. Stricker, M. Schelling, and A. L. Carriquiry. 2002. Irreducibility and efficiency of ESIP to sample marker genotypes in large pedigrees with loops. Genet. Sel Evol. 34:537-555. Fernando, R. L. and L. R. Totir. 2002. Advances in genetic and statistical models to predict breeding values. 7th World Congress Genet. Appl. Livest.\Prod. 20-01. Fernando, R. L., J. C. M. Dekkers and M. Soller. 2002. Controlling the proportion of false positives (PFP) in a multiple test genome scan for marker-QTL linkage. 7th World Congress Genet. Appl. Livest. Prod. 20-01. Fernando, R.L., J.C.M. Dekkers, and M. Soller. 2002. Controlling the proportion of false positive (PFP) in a multiple test genome scan for marker-QTL linkage. 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, 21:37. Grapes, L., R. L. Fernando and M. F. Rothschild. 2002. Analysis of methods for fine mapping quantitative trait loci using linkage disequilibrium. 7th Wld Congress Genet. Appl. Livest. Prod. 21-19. Heifetz, E., R. L. Fernando and M. Soller. 2002. Targeted recombinant progeny: a design for ultra high-resolution mapping of QTL using a saturated marker map. 7th World Congress Genet. Appl. Livest. Prod. 21-25. Hwang, J. T. G. and Nettleton, D. (2003). Principal components regression with data-chosen components and related methods. Accepted for publication in Technometrics. Jannink, J.-L. 2003. Selection dynamics and limits under additive-by-additive epistatic gene action. Crop Sci. 43: In Press. Jannink, J.-L., and X.-L. Wu. 2003. Estimating allelic number and identity in state of QTL in interconnected families. Genet. Res. 81: In Press. Jansen, R.C., J.-L. Jannink, and W.D. Beavis. 2003. Mapping quantitative trait loci in plant breeding populations: use of parental haplotype sharing. Crop Sci. 43:In Press. Kachman, S. D. and R. L. Fernando. 2002. Analysis of generalized linear models with MATVEC. 7th World Congress Genet. Appl. Livest. Prod. 28-04. Lee, H.K., J.C.M. Dekkers, M. Soller, M. Malek, R.L. Fernando, and M.F. Rothschild, 2002. Application of the false discovery rate to QTL interval mapping with multiple traits. Genetics 161: 905-914. McElroy, J.P., D.E. Harry, J.C.M. Dekkers, and S.J. Lamont. 2002. Molecular markers associated with growth and carcass traits in meat-type chickens. 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, 04:04. Settar, P., J.C.M. Dekkers, and H.A.M. van der Steen. 2002. Control of QTL frequency in breeding populations. 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, 23:04. Stricker, C., M. Schelling, F. Du, I. Hoeschelle, S. A. Fernandez and R. L. Fernando. 2002. A comparison of efficient genotype samplers for complex pedigrees and multiple linked loci. 7th World Congress Genet. Appl. Livest. Prod. 21-12. Thomsen, H., J.C.M. Dekkers, H.K. Lee, and M.F. Rothschild. 2002. Characterisation of quantitative loci for growth and meat quality in a breed cross in swine. 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, 15:05. Totir, L.R., R.L. Fernando, J.C.M. Dekkers, and S.A. Fernandez. 2002. A strategy to improve the computational efficiency of marker assisted genetic evaluation under finite locus models. 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, 16:02. Villanueva, B., J.C.M. Dekkers, J.A. Woolliams, and P. Settar. 2002. Maximising genetic gain with QTL information and control of inbreeding. 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, 22:18. Wang, J., M. Soller, and J.C.M. Dekkers. 2002. Mapping QTL with selective DNA pooling by least squares interval mapping. 2002. 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, 21:08. Minnesota SOFTWARE RELEVANT TO NCR-204 GMO Risk Assessment Using Net Fitness Components Approach Available at http://web.ics.purdue.edu/~bmuir/ Author: William Muir COURSES GIVEN ON STATISTICAL GENETICS ANS 824: Methods of Quantitative and Molecular Genetics for Livestock. Spring 2003. Credits 1-0, Michigan State University. Guillerme Rosa. ANS 890/901: Design and analysis of microarray gene expression experiments. Fall 2002. Credits 1-0, Michigan State University. Guillerme Rosa.

Impact Statements

Date of Annual Report: 08/05/2004

Report Information

Annual Meeting Dates: 02/12/2004 - 02/13/2004
Period the Report Covers: 02/01/2003 - 02/01/2004

Participants

Bastiaansen, John (john.bastiaansen@sygeninternational.com)- Sygen;
Deeb, Nader (nader.deeb@pic.com) - Sygen;
Dentine, Margaret (mrdentine@cals.wisc.edu) - Univ. of Wisconsin;
Fernando, Rohan (rohan@iastate.edu) - Iowa State;
Grignola, Fernando (fernando.e.grignola@monsanto.com)- Monsanto;
Henderson, David (dnadave@u.arizona.edu) - Univ. of Arizona;
Jannink, Jean-Luc (jjannink@iastate.edu) - Iowa State;
Kinghorn, Brian (brian.kinghorn@sygeninternational.com) - Sygen;
Misztal, Ignacy (ignacy@uga.edu) - Univ. of Georgia;
Muir, Bill (bmuir@purdue.edu) - Purdue Univ.;
Rocha, John (john.rocha@sygeninternational.com) - Sygen;
Romero-Severson, Jeanne (romeros@fnr.purdue.edu) - Purdue Univ.;
Rosa, Guilherme (rosag@msu.edu) - Michigan State;
Thro, Anne Marie (athro@csrees.usda.gov) - USDA/CSREES;
van der Steen, Hein (hein.vandersteen@sygeninternational.com) - Sygen;
Xu, Shizhong (xu@genetics.usr.edu) - UC Riverside;
Yu, Nan (nan.yu@sygeninternational.com) - Sygen;

Non-attending stations in 2004:
Arkansas, Kotswold, Dow, Illinois, Minnesota, Nebraska , Tennessee, Utah , Virginia;

Non-attending stations in 2003 and 2004:
Minnesota, Nebraska, Arkansas, Utah

Brief Summary of Minutes

The NCR204 project convened Saturday February 12, 2003 in Berkeley, California at Sygen research offices. Jeanne Romero-Severson introduced the new secretary, Jean-Luc Jannink and Anne-Marie Thro, the CSREES representative. The objectives of the project are: 1. Develop and compare statistical methodology to map genes; 2. Examine the efficiency of incorporation molecular tools in breeding programs through theoretical modeling, computer simulations, and biological testing in actual breeding populations; and 3. Use molecular genetics to test hypotheses generated from the fundamental theories of population, quantitative genetics, and molecular evolutionary genetics.

Bill Muir from Purdue, Shizhong Xu from UC Riverside, Ignacy Misztal from U. Georgia, and Jean-Luc Jannink Iowa State University gave research reports.

The business meeting was held 13 February. We discussed: 1. A more rapid mechanism to get the minutes out; 2. A common data set to analyze; 3. Policy surrounding non-attending stations; 4. A secretary is elected, serves as secretary one year and become chair the next year; 5 Elected Guilherme Rosa, Michigan State University, to be secretary in 2004-2005, David Henderson, U. Arizona to be objective coordinator for Objective 1, and Shizhong Xu, UC Riverside, to be ?Local Host? for NCR-204 at the Gordon Conference; 6 Margaret Dentine (Administrative Advisor) discussed CRIS codes in Appendix E of the NCR-204 document, a topic that was recurrent from the 2003 meeting; 7 Mid term review will proceed with an examination of meeting minutes and attendance. A website on the NIMSS can be constructed. Send website materials to Gretel Dentine; 8 Anne Marie Thro discussed the organization of the USDA and where the NCR committees fit, emphasized the importance to Government of the Current Research Information Service (CRIS) codes, and discussed obtaining special grants monies.

Rohan Fernando from Iowa State University, Guilherme Rosa from Michigan State University, John Bastiaansen from Sygen, Jeanne Romero-Severson from Notre Dame, and David Henderson from U. Arizona presented research reports.

Accomplishments

Objective 1: Develop and compare statistical methodology to map genes Xu (UC Riverside) has developed Bayesian QTL mapping methodology that allows for more independent variables in the model than there are observations. This methodology has a fixed-dimensional space and so does not use reversible jump approaches. Its advantages include that it is computationally simpler, converges more rapidly, has excellent estimation properties for the allelic effects, and has output that is simpler to interpret than reversible jump approaches. The approach worked even when >7200 epistatic effects were included in the model. Misztal (U. Georgia) has developed QXPAK, which aims at simplifying QTL analysis and future genetical genomics data analyses implementing a coherent and unified mixed model approach. The goal is to provide a software that can be used in a wide variety of situations with ample genetic and statistical modeling flexibility. The program is modular and so has advantages for teaching. Jannink (Iowa State U.) has assessed different priors in Bayesian analysis for QTL mapping in multiple families. With multiple families, all parents may carry different alleles or some parents may be identical in state for the QTL alleles (that is, they carry alleles that have equal effects on the phenotype). If you make that latter assumption, you do not have to estimate as many QTL effects, and that could be advantageous because it can reduce the entropy of the QTL effect prior. Using ?low entropy priors? did not increase QTL detection power. Fernando (Iowa State U.) has explored fine mapping methods using LD with a series of tightly linked makers, as proposed by Meuwissen and Goddard (2000). Simple regression on single markers can map more accurately than the IBD given IIS approach, in part because simple regression has lower genotyping costs: you do not need to genotype the parents to get marker phase information. Fernando found that the Meuwissen and Goddard approach works optimally with a small number of markers: 4 worked better than 10. This finding is paradoxical because it means that more accurate results are obtained with less information. Fernando also reported on a mapping approach using two markers in which one of the haplotypes is assumed to have carried the mutation. The approach worked better than all others. Rosa (Michigan State U.) reported on simulations of QTL analyses when genotyping errors are present. A 1% error rate can generate a bimodal posterior distribution for QTL position. Model allowing errors in genotyping can correct this problem. In a Bayesian framework, a prior distribution for the error rate is given. Rosa also discussed issues of the error term to use in microarray experiments: technical versus biological replication, the importance of identifying the treatment groups and having biological replication within them. Romero-Severson (Notre Dame) discussed the experimental design of a microarray experiment to identify expression differences between refractory (cannot transmit) and competent (can transmit) mosquitoes with dengue fever infected blood versus non-infected blood including a time course: 1, 2, 6, 12, 24 hrs after blood meal. David Henderson (U. Arizona) discussed the statistical analysis of microarray experiments. Controlling type I errors using Westfall and Young approach versus a Bonferroni approach: the latter is more stringent. Another option is to use Bayesian shrinkage estimators of the residual variances. He also discussed receiver operator curve (ROC), which has its roots in epidemiology and helps you decide what type 1 error you want to optimize the tradeoff between type 1 and type 2 errors. Finally, Henderson discussed stochastic search variable selection procedures for microarray data analysis. Objective 2: Examine the efficiency of incorporating molecular tools in breeding programs through theoretical modeling, computer simulations, and biological testing in actual breeding populations. Muir (Purdue), performed simulations assessing the efficiency of whole genome marker assisted selection. In this approach, each marker haplotype is considered a random effect that is predicted using a mixed-model BLUP approach. Muir assumed 50 possible haplotypes every 1 cM over a 1000 cM genome for a total of 50,000 random effects. Muir found that: 1. Using a population in linkage equilibrium is OK because the smaller populations used to predict haplotype effects generate LD; 2. Using markers with few alleles works as well are markers with many alleles; 3. The approach appears to overcome the Lande and Thompson heritability paradox in that it works for traits with low heritability; 4. After two generations of joint phenotype / marker haplotype data analysis, phenotyping can be skipped for a generation. Bastiaansen (Sygen) gave an overview of Sygen efforts in applying genomics. He did not give specifics on approaches that are being used. Objective 3: Use molecular genetics to test hypotheses generated from the fundamental theories of population, quantitative, molecular and evolutionary genetics. No station reports included results relevant to this objective.

Publications

Georgia Wiggans, G.R., I. Misztal, and C.P. Van Tassell. Calving ease (co)variance components for a sire-maternal grandsire threshold model. J. Dairy Sci. 86:1845?1848. 2003. Van Tassell, C.P., G.R. Wiggans, and I. Misztal. Implementation of a sire-maternal grandsire model for evaluation of calving ease in the United States. J. Dairy Sci. 86:3366?3373. 2003. Oseni, S., I. Misztal, S. Tsuruta, and R. Rekaya. Seasonality of days open in US Holsteins. J. Dairy Sci. 86: 3718?3725. 2003. Nobre, P.R.C., I. Misztal, S. Tsuruta, J.K. Bertrand, L.O.C. Silva, and P.S. Lopes. Analyses of growth curves of Nellore cattle by multiple-trait and random regression models. J. Anim. Sci. 81:918?926. 2003. Nobre, P.R.C., I. Misztal, S. Tsuruta, J.K. Bertrand, L.O.C. Silva, and P.S. Lopes. Genetic evaluation of growth in Nellore cattle by multiple-trait and random regression models. J. Anim. Sci. 81:927?932. 2003. Pribyl, J., I. Misztal, J. Pribylová, and K. ?eba. Multiple-breed, multiple-traits evaluation of beef cattle in the Czech Republic. Czech J. Anim. Sci. 48:519?532. 2003. Tsuruta, S., I. Misztal, T.J. Lawlor, and L. Klei. Estimation of genetic correlations among production, body size, udder, and productive life traits over time in Holsteins (abstract). J. Dairy Sci. 86(Suppl. 1)/J. Anim. Sci. 81(Suppl. 1):38. 2003. Misztal, I., S. Oseni, and S. Tsuruta. Analyses of heat tolerance for milk in Holsteins using different sources of heat-stress information (abstract). J. Dairy Sci. 86(Suppl. 1)/J. Anim. Sci. 81(Suppl. 1):39. 2003. Sapp, R.L., R. Rekaya, J.K. Bertrand, I. Misztal, and K.A. Donoghue. Genetic parameter estimates of udder scores in Gelbvieh cattle (abstract). J. Dairy Sci. 86(Suppl. 1)/J. Anim. Sci. 81(Suppl. 1):88. 2003. Tsuruta, S., I. Misztal, and T. Druet. Comparison of estimation methods for heterogeneous residual variances with random regression models (abstract). J. Dairy Sci. 86(Suppl. 1)/J. Anim. Sci. 81(Suppl. 1):113. 2003. Legarra, A., I. Misztal, and J. Jamrozik. Plotting covariance functions from random regression models (abstract). J. Dairy Sci. 86(Suppl. 1)/J. Anim. Sci. 81(Suppl. 1):114. 2003. Oseni, S., and I. Misztal. Seasonality of days open in US Holsteins (abstract). J. Dairy Sci. 86(Suppl. 1)/J. Anim. Sci. 81(Suppl. 1):131. 2003. Legarra, A., T. Strabel, J.K. Bertrand, and I. Misztal. Setting up the Gelbvieh multiple breed evaluation. J. Dairy Sci. 86(Suppl. 1)/J. Anim. Sci. 81(Suppl.):198. 2003. Bohmanova, J., I. Misztal, and J. Pribyl. Differences in growth trajectories in seven beef breeds (abstract). J. Dairy Sci. 86(Suppl. 1)/J. Anim. Sci. 81(Suppl. 1):198. 2003. Michigan State Rosa, G. J. M. Accounting for genotyping errors in QTL analyses. J. Anim. Sci. 81 (Suppl. 1): 159-160, 2003. De Leon, N., Coors, J. G., Kaeppler, S. M., Rosa, G. J. M. Genetic control of the number of ears per plant and related morphological traits in the Golden Glow maize population. Maize Genetics Conference Abstracts 45: P181, 2003. Dreher, B. P., Rosa, G. J. M., Scribner, K. T., Winterstein, S. R., Lopez, V. A., Libants, S. V., Etter, D. R. How many bears, plus or minus: effects of errors associated with non-invasive population size estimation. Abstracts of the Wildlife Society National Meeting. Burlington, Vermont. September, 2003. Coussens, P. M., Colvin, C. J., Rosa, G. J. M., Laspiur, J. P., Elftman, M. D. Evidence for a novel gene expression program in peripheral blood mononuclear cells from M. paratuberculosis-infected cattle. Infection and Immunity, 71(11): 6487-6498, 2003. Madsen, S. A., Rosa, G. J. M., Mccandless, E., Coussens, P. M., Burton, J. L. Microarray analysis of gene expression in blood neutrophils of parturient cows. Physiological Genomics, 2003. (on line http://physiolgenomics.physiology.org/papbyrecent.shtml). Rosa, G. J. M., Tempelman, R. J., Suchyta, S., Madsen, S. A., Burton, J. L., Coussens, P. M. Normalization, replication, and significance tests in cDNA microarray experiments. J. Dairy. Sci. 86 (Suppl. 1): 159, 2003. Etchebarne, B. E. Silva, L. F. P., Rosa, G. J. M., Coussens, P. M., Weber Nielsen, M. S., Vandehaar, M. J. IGF-I infusion alters gene expression profile of prepuberal bovine mammary parenchyma. J. Dairy. Sci. 86 (Suppl. 1): 165, 2003. Etchebarne, B. E. Silva, L. F. P., Rosa, G. J. M., Coussens, P. M., Weber Nielsen, M. S., Vandehaar, M. J. Leptin intramammary infusion alters gene expression profile of prepuberal bovine mammary parenchyma. J. Dairy. Sci. 86 (Suppl. 1): 166, 2003. Chan, P. S., Schlueter, A. E., Coussens, P. M., Rosa, G. J. M., Haut, R. C., Orth, M. W. (2003) Gene Expression Profile of Mechanically Impacted Bovine Articular Cartilage Explants. Proc. International Symposium on Animal Functional Genomics, East Lansing, MI. May 9-11, 2003. Madsen, S. A., Chang, L.-C., Hickey, M.-C., Coussens, P. M., Rosa, G. J. M., Burton, J. L. (2003) Gene expression profiling and apoptosis phenotyping indicate that parturient steroids promote survival in bovine blood neutrophils. Proc. International Symposium on Animal Functional Genomics, East Lansing, MI. May 9-11, 2003. Steibel, J. P., Tempelman, R. J., Rosa, G. J. M. Thick-tailed and heteroskedastic linear models for the analysis of cDNA microarray data. Proc. International Symposium on Animal Functional Genomics, East Lansing, MI. May 9-11, 2003. Hill, E. W., O?gorman, G. M., Gormley, E., Fitzpatrick, T., Rosa, G. J. M., Coussens, P. M., Machugh, D. E. Functional Genomics Analysis of the Bovine Immune Response of In Vitro Co-Culture with Trypanosomes (Trypanosoma brucei). Proc. International Symposium on Animal Functional Genomics, East Lansing, MI. May 9-11, 2003. Meade, K., Gormley, E., Fitzpatrick, T., Rosa, G. J. M., Coussens, P. M., Machugh, D. E. Analysis of Host Gene Expression in Tuberculosis-Infected Cattle Using In Vitro Stimulation of Peripheral Blood Mononuclear Cells (PBMC) and cDNA Microarrays. Proc. International Symposium on Animal Functional Genomics, East Lansing, MI. May 9-11, 2003. Etchebarne, B. E., Silva, L. F. P., Rosa, G. J. M., Coussens, P. M., Weber Nielsen, M., Vandehaar, M. J. How do the hormones leptin and IGF-I affect the nutritional regulation of mammary development? Proc. International Symposium on Animal Functional Genomics, East Lansing, MI. May 9-11, 2003. Rosa, G. J. M., Gianola, D., Padovani, C. R. Robust linear mixed models with normal/independent distributions and Bayesian MCMC implementation. Biometrical Journal, 45(5): 573-590, 2003. Rosa, G. J. M., Gianola, D., Padovani, C. R. Bayesian longitudinal data analysis with mixed models and thick-tailed distributions using MCMC. Journal of Applied Statistics. (accepted) Purdue Cheng, H.W., P. Singleton and W.M. Muir. 2003. Social stress differentially regulates neuroendocrine responses in laying hens: I. Genetic basis of dopamine responses under three different social conditions. Psychoneuroendocrinology 28:597-611. Cheng, H.W., P. Singleton and W.M. Muir. 2003. Social stress in laying hens: Differential effect of stress on plasma dopamine concentrations and adrenal function in genetically selected chickens. Poult. Sci. 82:192-198. Muir, W.M.. 2003. Incorporating Molecular Information in Breeding Programs, Applications and Limitations. Chapter 28 p549-562. In Poultry Breeding and Biotechnology Eds. WM Muir and S Aggrey. CABI Press Cambridge MA. Muir, W.M.. 2003. Indirect Selection for Improvement of Animal Well-Being. Chapter 14, p247-256. In Poultry Breeding and Biotechnology Eds. WM Muir and S Aggrey. CABI Press Cambridge MA. Muir, W.M., D. Miles, and A.E. Bell, 2003. Long Term Selection Studies In Tribolium Castaneum, Alternative Selection Strategies, And Associated Nature Of Quantitative Genetic Variation. Plant Breeding Reviews (In Press) Hostetler, HA., SL Peck, and W.M. Muir. 2003 High efficiency production of germ-line transgenic Japanese medaka (Oryzias latipes) by electroporation with direct current-shifted radio frequency pulses. Transgene Research (in press) Notre Dame Rider S.D., J.T. Henderson, R.E. Jerome, H.J. Edenberg, J. Romero-Severson, J.P.Ogas. 2003 Coordinate repression of regulators of embryonic identity by pickle during germination in Arabidopsis. The Plant Journal 35 (1): 33-43 Lobo N.F, L.Q. Ton, C.A. Hill, C.Emore, J. Romero-Severson, G.J. Hunt, F.H. Collins. 2003. Genomic Analysis in the sting-2 Quantitative Trait Locus for Defensive Behavior in the Honey Bee, Apis mellifera. Genome Res. 13:2588-2593 Lu H., J. Romero-Severson , R. Bernardo 2003 Genetic basis of heterosis explored by simple sequence repeat markers in a random-mated maize population. Theor Appl Genet 107 (3): 494-502 Feder J.L. J. Roethele, K. Fichak, J. Niebdalski, J. Romero-Severson. 2003 Evidence for inversion polymorphism related to sympatric host race formation in the apple maggot fly, Rhagoletis pomonella. Genetics 163: 939-953 Aldrich P.R., G. R. Parker, C. H. Michler, J. Romero-Severson. 2003 Whole-tree silvic identifications and the microsatellite genetic structure of a red oak species complex in an Indiana old-growth forest. Can J Forest Res 33:2228-2237 Iowa Chen, P., T.J. Baas, J.C.M. Dekkers, K.J. Koehler and J.W. Mabry J. 2003. Evaluation of strategies for selection for lean growth rate in pigs. J. Anim. Sci. 81:1150-1157 Chen, P., T.J. Baas, J.W. Mabry, K.J. Koehler, and J.C.M. Dekkers. 2003. Genetic parameters and trends for litter traits in U.S. Yorkshire, Duroc, Hampshire, and Landrace pigs. J. Animal Sci. 81:46-53 Ciobanu, D.C., J.W.M. Bastiaansen, S.M. Lonergan, H. Thomsen, J.C.M. Dekkers, G.S. Plastow, and M.F. Rothschild. 2004. New alleles in calpastatin gene are associated with meat quality traits in pigs. J. Animal Sci. (Accepted). Dekkers, J.C.M. 2004. Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. J. Anim. Sci. (Accepted subject to minor revision) Dekkers, J.C.M., and P. Settar. 2003. Long-term Selection with Known Quantitative Trait Loci. Plant Breeding Reviews. Wiley. Plant Breeding Reviews, Volume 24, Part 1, Long Term Selection: Maize. edited by Jules Janick. John Wiley&Sons, Inc. Pp: 311-336 (invited presentation). Dekkers, J.C.M., and R. Chakraborty. 2004. Optimizing purebred selection for crossbred performance using QTL. Genet. Sel. Evol. (Accepted) Dorman, K. S., Sinsheimer, J. S. and K. Lange (2004) In the Garden of Branching Processes. SIAM Review. Accepted for publication. Fernando, R.L. and L.R. Totir. 2003. Incorporating Molecular Informationin Breeding Programs: Methodology. In ?Poultry Breeding andBiotechnology?. CABI Publishing, Cambridge. Fernando, R.L., D. Nettleton, B.R. Southey, J.C.M. Dekkers, M.F. Rothschild, and M. Soller. 2004. Controlling the proportion of false positives (PFP) in multiple dependent tests. Genetics (In press) Grapes, L., J.C.M. Dekkers, M.F. Rothschild, and R.L. Fernando. 2004. Comparing linkage disequilibrium-based methods for fine mapping quantitative trait loci. Genetics (Accepted). Jannink, J.-L., and R.L. Fernando. 2003. On the Metropolis-Hastings acceptance probability to add or drop a QTL in MCMC-based Bayesian analyses. Genetics 165:In Press. Jannink, J.-L., and X.-L. Wu. 2003. Estimating allelic number and identity in state of QTL in interconnected families. Genet Res 81:133-144. Kulak, K., J. Wilton, G. Fox and J. Dekkers. 2003. Comparisons of economic values with and without risk for livestock trait improvement Livestock Production Science, 79: 183-191. Lall, S., Nettleton, D., DeCook, R., Che, P., Howell, S. H. (2004). QTLs affecting adventitious shoot formation in tissue culture and the program of shoot development in Arabidopsis. Acceptance pending revisions from Genetics. Thomsen, H., J.C.M. Dekkers, H. K. Lee, and M. F. Rothschild 2004. Characterization of quantitative trait loci for growth and meat quality in a cross between commercial breeds of swine J. Anim. Sci. (Accepted subject to minor revision) Totir, L. R., R.L. Fernando, J.C.M. Dekkers, S.A. Fernandez, and B.Guldbrandtsen. 2003. A comparison of alternative methods to computeconditional genotype probabilities for genetic evaluation with finite locusmodels. Genet. Sol. Evol 35: 1-20. Totir, L.R., R.L. Fernando, and J.C.M. Dekkers. 2003. Response to selectionby marker assisted BLUP with use of approximate gametic variancecovariance matrices. J. Anim. Sci. 81 (Suppl. 1) Totir, L.R., R.L. Fernando, J.C.M. Dekkers, S.A. Fernandez, and B. Guldbrandtsen. 2004. Effect of using approximate gametic variance covariance matrices on marker assisted selection by BLUP. Genet. Sel. Evol. 36:29-48. Totir, L.R., R.L. Fernando, J.C.M. Dekkers, S.A. Fernandez, and B Guldbrandtsen. 2003. A comparison of alternative methods to compute conditional genotype probabilities for genetic evaluation with finite locus models. Genet. Sel. Evol. 35:1-20 Villanueva, B., J.C.M. Dekkers, J.A. Woolliams and P. Settar. 2004. Maximising genetic gain over multiple generations with QTL information and control of inbreeding. Genet. Sel. Evol. (Accepted). Wagter, L.C., B.A. Mallard, B.N. Wilkie, K.E. Leslie, P.J. Boettcher, and J.C.M. Dekkers. 2003. The Relationship Between Milk Production and Antibody Response to Ovalbumin During the Peripartum Period. J. Dairy Sci. 86: 169-173. Wu, X.-L., and J.-L. Jannink. 2003. Optimal sampling of a population to determine QTL location, variance, and allelic number. Theor Appl Genet:Accepted. Zhao, H., M. F. Rothschild, R.L. Fernando, and J.C.M. Dekkers. 2003. Tests of candidate genes in breed cross population for QTL mappping in livestock. Mammalian Genome 14: 472-482 UC Riverside Yi, N., S. Xu and D. B. Allison. 2003. Bayeisan model choice and search strategies for mapping interacting quantitative trait loci. Genetics 165:867-883. Yi, N., S. Xu, V. George and D. B. Allison. 2004. Mapping multiple quantitative trait loci for ordinal traits. Behavior Genetics 34:3-14. Xu, S., N. Yi, D. Burke, A. Galecki, and R. A. Miller. 2003. An EM algorithm for mapping binary disease loci: application to fibrosarcoma in a four-way cross mouse family. Genetical Research 82:127-138. Xu, S. 2003. Theoretical basis of the Beavis effect. Genetics 165:2259-2268. Qu, Y. and S. Xu. 2004. Supervised cluster analysis for microarray data based on multivariate Gaussian mixture. Bioinformatics (in press) Zhang, Y.?M. and S. Xu. 2004. Mapping quantitative trait loci in F2 incorporating phenotypes of F3 progeny. Genetics (in press) Mao, Y. and S. Xu. 2004. Mapping QTL for traits measured as percentage. Genetical Research (in press) Xu, S., C. Xu and Z. Li. 2004. Joint mapping of quantitative trait loci for multiple binary characters. Genetics (accepted, pending revision) Wang, H., X. Li, G. L. Masinde, S. Mohan, D. J. Baylink, and S. Xu. 2004. Bayesian shrinkage estimation of QTL parameters. Genetics (submitted). Mao, Y. and S. Xu. 2004. A Monte Carlo algorithm for computing the IBD matrices using incomplete marker information. Heredity (submitted) Zhang, Y.-M., Y. Mao, C. Xie, H. Smith, L. Luo, and S. Xu. 2004. Mapping QTL using naturally occurring genetic variance among commercial inbred lines. Genetics (submitted). Illinois Bohn, M., T. Magg, D. Klein, and A.E. Melchinger. 2003. Breeding early maturing European Dent maize (Zea mays L.) for improved agronomic performance and resistance against the European corn borer (Ostrinia nubilalis Hb.). Maydica 48:239-247. Magg, T., M. Bohn, D. Klein, and A.E. Melchinger. 2003. Concentration of moniliformin produced by Fusarium species in grains of transgenic Bt maize hybrids compared to their isogenic counterparts and commercial varieties under European corn borer (Ostrinia nubilalis Hb.) pressure. Plant Breeding 122:322-327. Heckenberger, M., M. Bohn, J.R. van der Voort, J. Peleman, and A.E. Melchinger. 2003. Variation of DNA fingerprints among accessions within maize inbred lines and implications for identification of essentially derived varieties: II. Genetic and technical sources of variation in AFLP data and comparison with SSR data. Molecular Breeding. 12:97-106. Reif, J.C., A.E. Melchinger, X.C. Xia, M. Warburton, D.A. Hoisington, S.K. Vasal, D. Beck, M. Bohn, and M. Frisch. 2003. Use of SSRs for establishing heterotic groups in subtropical maize. Theor. Appl. Genet. 107:947-957. Reif, J. C., A. E. Melchinger, X. C. Xia, M. L. Warburton, D. A. Hoisington, S. K. Vasal, G. Srinivasan, M. Bohn, and M. Frisch. 2003. Genetic distance based on simple sequence repeats and heterosis in tropical maize. Crop Sci. 43: 1275-1282. SOFTWARE RELEVANT TO NCR-204 QXPAK to jointly analyze QTL and polygenic traits in single-and multiple-trait models. http://nce.ads.uga.edu/~ignacy/newprograms.html Developed by Ignacy Misztal at U. Georgia

Impact Statements

Several computer programs were developed to analyze marker data for detecting loci affecting quantitative traits including the effects of epistatic interactions.
Several analytical methods were compared as well as the results of assumptions included in the models. Researchers can utilize these methods more confidently and report results and implications more accurately using these tools.
Methods to design microchips for investigations and analytical strategies for interpreting microarray data were investigated and compared. These tools will enable more efficient and effective use of microarray data in investigations.

Date of Annual Report: 07/07/2005

Report Information

Annual Meeting Dates: 02/19/2005 - 02/20/2005
Period the Report Covers: 02/01/2004 - 02/01/2005

Participants

Participants Attachment:

Attachment

Brief Summary of Minutes

Minutes Attachment:

Attachment

Accomplishments

ACCOMPLISHMENTS AND IMPACTS Objective 1: Develop and compare statistical methodology to map genes Xu (UC Riverside) continued developing new statistical methods for QTL mapping in pedigrees. He finished the Monte Carlo method for calculating IBD matrices of pedigrees with arbitrary complexity (Mao and Xu, 2005). The method was further modified to fit pedigrees containing all inbred lines. He analyzed a pedigree containing 404 inbred lines of maize with known genealogical relationship and identified several major QTL responsible for the genetic variance of flowering time (Zhang et al., 2005). Xu has developed also a Bayesian shrinkage method for estimating QTL parameters. The method can simultaneously estimate the effects and positions of several hundred QTL with a single model (Wang et al., 2005). The model assumes that the maximum number of QTL is p. The positions of these QTL along the genome are disjoint and vary based on Metropolis-Hastings rule. The method can handle extremely high marker density. In addition, Xu has been studying statistical methods for clustering expressed genes based on their association with a quantitative trait. He first examined the linear association (Jia and Xu, 2005) and then higher order association using orthogonal polynomials (Qu and Xu, 2005). Both methods have been applied to data collected from 31 subjects in a microarray experiment for Alzheimer disease. He detected many genes that are associated with the disease phenotype MMSE. Wang (Michigan State U.) reported on results of three studies aiming the genetic mapping of quantitative trait loci underlying agronomic traits in soybean. The first experiment referred to identification of putative QTL for yield in interspecific soybean backcross populations. The second study was related to genetic mapping of QTLs that condition waterlogging tolerance in soybean. The third trial aimed the genetic mapping of genes underlying partial resistance to Sclerotinia Stem Rot in Soybean PI 391589B. Misztal (U. Georgia) reported on results of a study aiming the estimation of competitive effects for average daily gain in swine. There were 4,946 records from 2,409 litters and 362 pen-groups. Pen size ranged from 12 to 16. Models included the effects of contemporary group (farm-barn-batch), birth litter, pen-group and two additive genetic effects: direct and associative. The additive genetic variance was a function of the number of competitors in a group, the additive relationships between the animal performing the record and its pen mates, and the additive relationships between pen mates. To partially account for differences in pen size and in relationships among members of the pen a covariable was added to the associative genetic effect. Estimates of direct and associative heritability were 0.15 and 0.03, respectively. Misztal mentioned that the magnitude of competition effects may be larger in commercial populations, where housing is denser and food is limited. Jannink (Iowa State U.) reported on research on selective phenotyping to accurately map QTL. The marker genotypes of the progeny should allow the number of recombination events they carry to be determined such that the most useful progeny could be phenotyped, in a procedure termed selective phenotyping. Two methods to select genotypes for their usefulness in mapping were evaluated, one that maximizes the overall mapping information content in the selected progeny, and one that seeks to maximize both overall mapping information and the uniformity of its distribution across the genome. Simulations showed that both methods successfully decreased the mean squared error for QTL position. Average mean squared errors were similar for the two methods and variability of mean squared error was slightly lower for the latter relative to the former method. Simulations indicated that a large fraction of the decrease in the mean squared error achievable by selective phenotyping could be obtained by genotyping twice the number of progeny than were ultimately phenotyped, though further decreases in the mean squared error were observed when up to sixteen times more progeny were genotyped than phenotyped. The procedure appears to most improve the accuracy of QTL mapping for QTL of small effect or when available markers do not allow marker spacing below 10 cM. Rosa (Michigan State U.) continued development of alternative techniques for the statistical integration of potentially miscoded genotypes in linkage analysis and QTL mapping studies in line crosses and outbred populations. The same ideas are being implemented in the context of paternity assignment with uncertain pedigrees, and on mark-recapture applications using molecular markers. Rosa reported also on research being conducted on linear mixed models suitable for the analysis of either log ratios or log intensity values of microarray data in the presence of multiple sources of variability. These models have been used also to compare the power and efficiency of different microarray experimental designs within a hierarchical replication context. A third area of research reported by Rosa referred to environmental risk assessment, by extending the net fitness components model of Muir and Howard (1999, 2001, 2002) to estimate environmental risk of genetically modified organisms (GMO) by predicting the fate of transgenes introduced into wild populations by escaped GMO. Dekkers (Iowa State U.) discussed a theoretical analysis of alternative measures of LD based on multi-allelic microsattelite markers. Effectiveness of marker-assisted selection (MAS) using population-wide LD depends on the extent of marker-to-QTL (M-Q) LD. To evaluate alternative measures of observable (marker-to-marker) LD as predictors of M-Q LD, LD among 4-allele markers and a biallelic QTL was simulated by 100 generations of random mating of 100 parents. Using 100 individuals in generation 100, M-Q LD was quantified by the R2 of regression of QTL allele on alleles at a single marker. Observable LD was evaluated using: Lewontins D2; r2=pooled square of correlations between alleles weighted by the product of allele frequencies; c2=Chi-square statistic for association between alleles; and a standardized c22=c2/[N(n-1)], where N=number of haplotypes and n=smallest number of alleles across the 2 markers. Extensive M-Q LD existed at short distances but declined rapidly with distance. Observable LD showed similar declines for r2, c2 and c22, but D2 was strongly inflated. Correlations of mean D2, r2, c2 and c22 between markers with mean M-Q LD at corresponding distances (d20cM) were 0.85, 0.96, 0.96 and 0.96. Correlations of means for different population sizes (10 ~ 200) of D2, r2, c2 and c22 at 2cM with means of M-Q LD at 1cM were 0.80, 0.80, -0.66 and 0.75. Corresponding correlations for means at different generations (0 ~ 200; population size of 100) were 0.56, 0.70, 0.33 and 0.74. Although r2 and c22 both correlated well with M-Q LD, c22 is preferred because it ranges from 0 to 1, while r2<1 for multi-allelic markers in complete LD. To assess the decline in LD with distance, LDd=1/(1+4²d) was fitted to data on 100 individuals in generation 100 where d is distance in Morgans, and ² is related to effective population size. Estimates for ² were 51.4 and 63.8 for c22 and M-Q LD, resulting in very similar lines. In conclusion, c22 is a good predictor of LD between markers and QTL when LD is generated by drift alone. Objective 2: Examine the efficiency of incorporating molecular tools in breeding programs through theoretical modeling, computer simulations, and biological testing in actual breeding populations. Muir (Purdue) examined the efficiency of incorporating molecular tools in breeding programs through theoretical modeling, computer simulations, and biological testing in actual breeding populations. For traits of low heritability, initial theoretical examination showed that MAS could increase response to selection by as much as 500%. However, a decade of experimentation and simulations has since demonstrated a much more moderate response. These shortcomings were found to be due a critical assumption: that the quantitative trait loci (QTL, or closely linked makers) affecting such traits were known. In actuality, these QTL associations are found by statistical estimation and hypothesis testing based upon similar data breeders would use to make selection decisions, i.e. have the same limitations of a high environmental variance. Thus QTLs for traits of low heritability are difficult or impossible to locate. A gene level simulation was used to compare results of genome wide MAS (GMAS) with that using conventional methods of BLUP estimation of genetic based on pedigrees, as well as study the number of generations of training needed to accurately estimate the breeding values just from genotypes, and for how many generations after phenotypes were no longer collected the predictions were still good. Results showed that as expected for GMAS, the more generations of training you do, the better it is at prediction. For a trait of heritability of .5, the accuracy of selection with GMAS reached about 88%, whereas the BLUP line only reaches 82% accuracy. The accuracy of BLUP dropped off rapidly in generations where the genotype is predicted based only on ancestors information whereas GMAS continued at a higher accuracy. Bill stressed that the power of genomics is much higher for traits of low heritability. For example, a trait with a heritability of .1, the accuracy of selection was up to almost 70% with GMAS and 3 generations of training while BLUP was only hitting about 60%. Bills sees this as the future of genomics in animal breeding, the only real issue is if we can get the price down. Objective 3: Use molecular genetics to test hypotheses generated from the fundamental theories of population, quantitative, molecular and evolutionary genetics. No station reports included results relevant to this objective. Submitted April 15, 2005 Guilherme J. M. Rosa (Secretary)

Publications

Publications Attachment:

Attachment

Impact Statements

Date of Annual Report: 07/03/2006

Report Information

Annual Meeting Dates: 02/16/2006 - 02/17/2006
Period the Report Covers: 03/01/2005 - 02/01/2006

Participants

Bastiaansen, John (john.bastiaansen@sygeninternational.com) - Genus plc;
Beyrouty, Craig (beyrouty@purdue.edu) - Purdue University;
Caraviello, Daniel (dcaraviello@dow.com) - Dow AgroSciences;
Dekkers, Jack (jdekkers@iastate.edu) - Iowa State University;
Ernst, Cynthia (caernst@dow.com) - Dow AgroSciences;
Muir, Bill (bmuir@purdue.edu) - Purdue University;
Robbins, Kelly (krobbin1@uga.edu) - University of Georgia;
Romero-Severson, Jeanne (jromeros@nd.edu) - University of Notre Dame;
Rosa, Guilherme J.M. (rosag@msu.edu) - Michigan State University;

Brief Summary of Minutes

Minutes:
The meeting convened at Dow Agrosciences in Indianapolis at 8.30am on the 16th of February 2006. Cynthia Ernst, our local host for this meeting, welcomed everyone and delivered the necessary safety briefing as well as other useful information about our meeting site.
Craig Beyrouty, our new administrative advisor, briefly introduced himself and reminded the group that during this meeting we needed to address the upcoming renewal of our project for which a justification is needed by September 15 and a final proposal by December 2006.
Time requirements for station reports, business meeting, and tour of Dow Research facility were determined with the order of items to be determined as we go along.

Station reports:
Michigan State University: Guilherme Rosa presented on efficient design (of microarray studies). The choice of treatments to investigate and then the allocation of units to treatments can be performed as 2 independent subsequent steps. The optimal allocation is determined by which contrasts/paramters is desired to be tested, an example is given for allocation of samples to detect either additive of dominance effects or both from a transgene affecting expression. An algorithm was developed for optimizing unit to treatment allocation, several results are presented. The algorithm is available in R code.

University of Georgia: Kelly Robbins presented results from the use of a misclassification algorithm applied to gene-expression data from incipient Alzheimer patients diagnosed as such using a cognitive test. The use of gene expression data resulted in 4 out of 14 affected individuals to be switched to non-affected status. These 4 individuals turned out to be borderline diagnosis on the independent cognitive tests.

Dow Agrosciences: Daniel Caraviello presented results from his research at Wisconsin on the use of machine learning algorithms for the prediction of conception in dairy cows based on body condition scores. A comparison of machine learning algorithms was done using Weka Explorer, a freeware from University of Wakaito, New Zealand. An alternating decision tree was used to find predictors from a large set of measures pre and post service, for conceiving at the current service. An example dataset with 103 herds, 17.500 records and 314 variables was analyzed using a 149 PC cluster (i.e. computer intensive analyses). The chosen method is robust to multiple colinearity, outliers, missing values, inclusion of interactions. In the discussion that followed Guilherme Rosa noted that if markers are used as nodes then an application in Marker Assisted Selection could be very useful. Jack Dekkers remarks that methods used are for qualitative traits, however similar approaches exist for quantitative traits.

Genus: John Bastiaansen presents results from genomewide genotypes and association analysis in pigs. A large number of results means the top hits are primarily false positives. Appropriate adjustment and filtering of results based on power of the underlying dataset, and independent confirmation of results are essential.

Purdue University: Bill Muir has investigated the marker density needed to detect QTL. Can QTLs be found through signatures of selection / selective sweeps. In simulated datasets the pattern of heterozygosity and pairwise Linkage Disequilibrium do show evidence of a signature. Jack Dekkers and Guilherme Rosa are interested in using the same simulated datasets, which Bill will make available.
New versus existing variation makes a big difference. With new variation (mutation, introduction, ... ) the selective sweeps can be seen, but when the starting point is a QTL in Linkage Equilibrium the sweep is not seen.

Iowa State University: Jack Dekkers presents results on the impact of Linkage Disequilibrium (LD) between markers on QLT mapping methods, with focus on LD due to drift. Patterns of LD in livestock populations were found to be different from those in human populations and therefore that mapping methods used in human research may not apply well to animals. In some cases single marker regression has been found to have more power then haplotype analysis. Results from simulation also showed that more markers in a model give less precision in QTL mapping. LD due to new mutations gives slightly better power and precision then LD due to drift. Other areas of research are, accounting for population stratification, incomplete marker information, genetic models, combined Linkage and LD analysis.
Marker assisted composite line development as described by Zhang and Smith, was simulated with the use of LD-MAS.
Selection on multiple QTL wit control of gene diversity and inbreeding for long-term benefit. Truncation selection leads to loss of QTL, this can be reduced by putting a penalty factor on the loss of QTL alleles. with a penalty on loss of alleles the short term cumulative selection response is reduced but long term this is increased compared to truncation selection.

University of Notre Dame: Jeanne Romero-Severson presented results and questions on how to find real differential gene expression in any part of the range of fold differences. Results from a control experiment were presented were the same RNA sample was used in both channels with several levels of replication. Small number of RNA molecules in some spots were found to cause large spurious fold-changes.

Business meeting.

Comments from administrative advisor, Craig Beyrouty: Sees very good dynamics in the group, clearly a set of integrators. In light of the renewal that is coming for our project, Craig gives an explanation of the process in the NCAC1 committee. Our new proposal will need to be submitted between Sept and December of 2006.
Craig also provided an update on a proposal to form a National Institute for Food, Agriculture and Natural Resources, similar to the setup of NIH/NSF as a new model to fund agricultural research.
Attendance. This has been an issue with our group for a long time, there is a smaller group of stations that are the core group, but about half the members have not attended and not send reports.
Actions:
- address non-attendance with exp. station directors. Come to decision to drop from the project, or preferably a commitment to attend the meetings.
- Current members to approach potential new members. Each of 17 potential new members will be contacted by a current member (contact commitments circulated).
- Long term, the attendance of graduate students that come along with their advisors has been a very successful recruiting tool.

Next meeting, will be held with the Gordon Conference on Quantitative Genetic and Genomics (Feb18-23th). Our meeting dates will be Feb 17th and 18th. This makes it easier for members to attend + gives the opportunity to attract interesting guests to present to our group and contribute to our discussions.
Jeanne Romero-Severson will serve as the "local" host and make arrangements for hotel, meeting room etc.

Elections: Cynthia Ernst was elected as secretary for our project in the coming year. John Bastiaansen will become chair for the year 2007. Thank you to our outgoing chair Guilherme Rosa.

Project renewal: A writing committee was formed by electing Bill Muir and Jack Dekkers.
A discussion on the title of the project concluded that we should keep this the same, because it describes the contents and we expect it to attract the right membership.
The availability of datasets was discussed. Commercial datasets may be made available by the industry members for the project to address specific data analysis problems. Several commercial animal datasets with marker genotypes + phenotypes are becoming available through consortium projects. These types of datasets exist for plants in Arabidopsis. Members in plant genetics will be working on issues like how to deal with selfing.

Accomplishments

ACCOMPLISHMENTS AND IMPACTS Objective 1: Develop and compare statistical methodology to map genes Xu developed a penalized maximum likelihood method for mapping QTL with epistatic effects (Zhang and Xu 2005a) and a Bayesian shrinkage method for mapping epistatic effects that allows QTL position to vary (Zhang and Xu 2005b). They also developed a Box-Cox transformation for QTL mapping (Yang et al. 2005, in press). A joint paper was published with Dr. Li at University of Chicago entitled "A critical evaluation of the effect of population size and phenotypic measurement on QTL detection and localization using a large F2 murine mapping population". In this paper, a resampling technique was used to examine the effect of sample size on the power of QTL detection using a real mice data. It was found that a population size of 300 is sufficient to obtain the desirable power. Jannink investigated the use of mating designs to uncover QTL and the genetic architecture of complex traits, providing a theoretical derivation of main and interaction effects on F2 family means relative to variance components in a random mating reference population. They show that with a fixed experiment size, QTL detection and estimation of the genetic architecture are competing goals which should be accounted for in experimental design. Dekkers and Fernando investigated power and precision of regression based linkage disequilibrium (LD) mapping of QTL in livestock and concluded that with adequate sample size and expected levels of LD most livestock populations lend themselves to QTL detection by LD with SNPs at medium density (1-2/cM). Research on the impact of mutation versus drift on LD mapping was presented at the meeting, showing that mutation is not essential for sufficient LD to exist to detect QTL and that QTL can be detected even if substantial heterogeneity exists. Evaluation of genomic selection for composite line development using low density markers showed that MAS strategies outperform standard BLUP selection. When phenotypes are only available in the F2 generation, MAS with markers fitted as random performs similar to BLUP where MAS with markers fitted as fixed led ot considerable lower cumulative discounted response. Nettleton and Dekkers have derived and presented a general formula to calculate power of tests under different treatment effect sizes, number of pools number of individuals per pool and number of repeated measurement per pool for gene expression analysis, which is typically more conservative and closer to the true power than the estimates from Kendziorski (2003). Gomez-Raya proposes QTL Mapping Strategies For Adaptability Of Beef Cattle To Rangelands, using 600 cows and a MOET scheme which would have 55% power to detect a 0.2 sP QTL, or using 600 cows in an existing crossbreeding design where power would be 64% to detect a 0.4 sP QTL. Rules were developed to reconstruct sire genotypes using genotype information from large half-sib families. Rosa developed techniques for the statistical integration of potentially miscoded genotypes in linkage analysis and QTL mappping studies in line crosses and outbred populations. Also DNA based mark-recapture models for estimation of populations size have been developed. These new methodologies will attenuate the biases caused by genotyping errors in these analyses. Research has been devoted to design and statistical analysis of two color microarray platforms using mixed linear models with special attention to the use of the correct error terms and in order to compare power and efficiency of different designs within a hierarchical replication context. Objective 2: Examine the efficiency of incorporating molecular tools in breeding programs through theoretical modeling, computer simulations, and biological testing in actual breeding populations. Xu is interested in clustering expressed genes based on their association with a quantitative trait. We first examined the linear association (Jia and Xu 2005, in press) and then higher order association using orthogonal polynomials (Qu and Xu 2005, submitted). Both methods have been applied to data collected from 31 subjects in a microarray experiment for Alzheimer disease. They detected many genes that are associated with the disease phenotype MMSE. Fernando and Dekkers evaluated methods of Marker Assisted Selection on multiple QTL in a crossbred population, showing that QTL detected by backward elimination regression can be used for subsequent selection within the cross, even when markers are 20 cM apart. Selection criterion bases on BLEU of the markers + BLUP of polygenic effects increased Cumulative response by up to 25% in the F4. Dekkers developed a strategy to maximize selection response while conserving diversity and controlling inbreeding which leads to a lower number of QTL lost, an higher average frequency of favorable alleles, lower response in early generations but higher response in later generations. Muir and collaborators have completed a genome wide assessment of commercial poultry populations to determine what subset of SNPs would allow traceability of poultry meat or live offspring to its pure line parent, and using simulations, examine if the informativeness of these markers are adequate for applications with genome-wide marker-assisted selection (GMAS). Linkage disequilibrium analysis showed that many regions of LD highly correlate among lines suggesting regions of selection. Recently they examined the issue of finding signatures of selection through selective sweeps and concluded that up to 100 SNPs/cM are needed to find selective sweeps. Objective 3: Use molecular genetics to test hypotheses generated from the fundamental theories of population, quantitative, molecular and evolutionary genetics. Rosa has worked on expanding the Muir and Howard (1999, 2001, 2002) model for estimating environmental risk of genetically modified organisms, to include stochasticity and uncertainty. They are planning to use transgenic zebrafish to estimate fitness components and to test model predictions using replicated wild-type and GM zebrafish.

Publications

Bijma, P. and W. M. Muir 2006. Genetic Analysis And Improvement Of Traits Affected By Interaction Among Individuals. Proc. 8th World Congress of Genetics Applied to Livestock Breeding (In press). Burton, J. L., Madsen, S. A., Chang, L.-C., Weber, P. S. D., Coussens, P. M., Rosa, G. J. M., Matukumalli, L. K, Sonstegard, T., Smith, T. P. Immunogenomics and the transition dairy cow: physiological insights and future possibilities for improving animal health. ASAS-ADSA-CSAS Joint Annual Meeting, July 24-28, 2005. Burton, J. L., Madsen, S. A., Chang, L.-C., Weber, P. S. D., Rosa, G. J. M., Matukumalli, L. K, Sonstegard, T. Expression profiles and SNP analysis of genes that regulate neutrophil apoptosis, endothelial adhesion, and extracellular matrix remodeling at parturition in dairy cows. Plant and Animal Genome XIII, p.243, 2005. Cardoso, F. F., Rosa, G. J. M., Tempelman, R. J. Modelos estruturais de variancia heteroscedastica para inferencia robusta no merito genetico de bovines cruzados. 50th Meeting of the Brazilian Region (International Biometry Society), Londrina Brazil, July 4-8, 2005. Cardoso, F. F., Rosa, G. J. M., Tempelman, R. J. Multiple breed genetic inference using heavy-tailed structural models for heterogeneous residual variances. Journal of Animal Science, 83: 1766-1779, 2005. Chan, P. S., Caron, J. P., Rosa, G. J. M., Orth, M. W. Glucosamine and chondroitin sulfate regulate gene expression and synthesis of nitric oxide and prostaglandin E2 in articular cartilage explants. Osteoarthritis and Cartilage, 13(5): 387-394, 2005. Chan, P.S., Schlueter, A.E., Coussens, P.M., Rosa, G. J. M., Haut, R.C., Orth, M.W. Gene expression profile of mechanically impacted bovine articular cartilage explants. Journal of Orthopedic Research 23(5): 1146-1151, 2005. Cheng, H., and W. M. Muir, 2005 The effects of genetic selection for survivability and productivity on chicken physiological homeostasis. Worlds Poultry Science Journal 61: 383-397 DeCook, R., Nettleton, D., Foster, C.M., Wurtele, E. (2006). Identifying differentially expressed genes in unreplicated multiple-treatment microarray timecourse experiments. Computational Statistics and Data Analysis. 50 518-532. De Leon, N. and Rosa, G. J. M. Optimization of selective phenotyping for QTL mapping. Plant and Animal Genome XIII, P860, p.283, 2005. De Leon, N., Coors, J. G., Kaeppler, S.M., Rosa, G. J. M. Genetic control of prolificacy and related traits in the Golden Glow Maize Population: I. Phenotypic evaluation. Crop Science, 45: 1361-1369, 2005. Devlin, RH Sundström, LF and WM Muir. 2006. Interface of biotechnology and ecology for environmental risk assessments of transgenic fish. Trends in Biotechnology 24:89-97. Festucci-Buselli, R. A., A. S. Carvalho-Dias, M. de Oliveira-Andrade, C. Caixeta-Nunes, H. M. LI et al., 2005 Expression of Cyp6g1 and Cyp12d1 in DDT resistant and susceptible strains of Drosophila melanogaster. Insect Molecular Biology 14: 69-77 L. Galina-Pantoja, G. Solano-Aguilar, M. A. Mellencamp, J. Bastiaansen, R. Cabrera, J. K. Lunney. 2006. Relationship between Immune Cell Phenotypes and Pig Growth in a Commercial Farm. Animal Biotechnology (In press). Gilmour, S. G., Bueno Filho, J. S. S. and Rosa, G. J. M. Design of genetical genomics studies which use two-color microarrays. Workshop on Statistics in Genomics and Proteomics, Monte Estoril, Portugal, October 5-8, 2005, on line: http://wsgp.deio.fc.ul.pt/Abstracts%20Partic/Steven_Gilmour.html Gomez-Raya L. 2006. Inferring unknown sires genotype at co-dominant DNA markers in half-sib families. (In preparation). Gomez-Raya L. 2004. Strategies for Marker Assisted Selection in Cattle. Satellite symposium at the ISAG meeting in Tokyo (Japan). Gomez-Raya L., W. M. Rauw, C. Beattie, Y. Da, D. Smith, O. Ash, and M.S. Amoss. 2006. A Selection Experiment in Sinclair Swine Supports that a Tumor Initiator Locus is involved in Melanoma Susceptibility (manuscript to be submitted). Heifetz, E. M., J. E. Fulton, N. O'Sullivan, H. Zhao, J. C. M. Dekkers, and M. Soller. 2005. Extent and consistency across generations of linkage disequilibrium in commercial layer chicken breeding populations. Genetics 171: 1173-1181 Heifetz, E. M., J. E. Fulton, N. OSullivan, H. Zhao, J. C. M. Dekkers, and M. Soller. 2005. Marker to marker linkage disequilibrium in commercial chicken breeding populations. Abstract presented at the 2005 Midwest Animal Science meeting, J. Anim. Sci. 83 (Suppl. 1). Heifetz, E. M., J. E. Fulton, N. OSullivan, M. Soller, and J. C. M. Dekkers. 2005. Interval mapping of QTL for Mareks disease resistance with selective DNA pooling in crosses of commercial layer chicken lines. Abstract presented at the 2005 Midwest Animal Science meeting, J. Anim. Sci. 83 (Suppl. 1). HA Hostetler, P. Collodi, RH Devlin, and WM Muir. 2005. Improved Phytate Phosphorus Utilization by Japanese Medaka Transgenic for the Aspergillus niger Phytase Gene. Zebrafish 2:19-31 Jia, Z. and S. Xu. 2005. Clustering expressed genes based on their association with a quantitative phenotype. Genetical Research (in press). J.-J. Kim, J.-J., M.F. Rothschild, J. Beever, S. Rodriguez-Zas, and J.C.M. Dekkers. 2005. Joint analysis of two breed cross populations in pigs to improve detection and characterization of quantitative trait loci. J. Anim Sci. 83: 1229-1240. Kim, J.J., H.H. Zhao, H. Thomsen, M.F. Rothschild, and J.C.M. Dekkers. 2005. Combined line-cross and half-sib QTL analysis of crosses between outbred lines. Genet. Res. Camb. 85: 235-248. Kim, J.-J., K.S. Kim, M. Rothschild, J. Beever, S. Rodriguez-Zas, and J.C.M. Dekkers. 2005. Joint analysis of two breed-cross populations in pigs to detect polar overdominance QTL. Proc. Integration of Structural and Functional Genomics conference, Sept. 22-25, Iowa State University. B.P. Kinghorn, J.W.M. Bastiaansen, H.A.M. van der Steen, N. Deeb, N. Yu, and A.J. Mileham, 2006. Visually-aided interpretation of results from a genome scan. Proc. 8th World Congress of Genetics Applied to Livestock Breeding (In press). Li, X., R. J. Quigg, J. Zhou, S. Xu, G. Masinde, S. Mohan and D. J. Baylink. 2005. A critical evaluation of the effect of population size and phenotypic measurement on QTL detection and localization using a large F2 murine mapping population. Genetics and Molecular Biology (in press). McElroy, J.P., J.C.M. Dekkers, J.E. Fulton, N.P. O'Sullivan, M. Soller, E. Lipkin, W. Zhang, K.J. Koehler, S.J. Lamont, and H.H. Cheng. 2005. Microsatellite markers associated with resistance to Mareks disease in commercial layer chickens. Poultry Sci. (Accepted). Muir, W. M., 2005 Incorporation of competitive effects in forest tree or animal breeding programs. Genetics 170: 1247-1259 Muir, W.M. and P. Bijma. 2006. Incorporation Of Competitive Effects In Breeding Programs For Improved Performance And Animal Well-Being. Proc. 8th World Congress of Genetics Applied to Livestock Breeding (In press) W.M. Muir, J. Romero-Severson, S.D. Rider Jr., A. Simons, and J. Ogas 2006. Application of One Sided t-tests and a Generalized Experiment Wise Error Rate to High-Density Oligonucleotide Microarray Experiments: An Example Using Arabidopsis J. Data Science, (In Press) Pedra, J. H. F., R. A. Festucci-Buselli, W. L. Sun, W. M. Muir, M. E. Scharf et al., 2005 Profiling of abundant proteins associated with dichlorodiphenyltrichloroethane resistance in Drosophila melanogaster. Proteomics 5: 258-269 Piyasatian, N., L. R. Totir, R. L. Fernando, and J. C. M. Dekkers. 2005. Marker-assisted selection on multiple QTL in a crossbred population. Abstract presented at the 2005 Midwest Animal Science meeting, J. Anim. Sci. 83 (Suppl. 1). Ragavendran, A., Muir, W. M., Howard, R. and Rosa, G. J. M. A Monte Carlo approach for risk assessment of transgene invasion. Symposium on Invasive Species: Challenges and Opportunities, MSU Invasive Species Initiative - Michigan State University, May 13, 2005. Ragavendran, A., Muir, W. M., Howard, R. and Rosa, G. J. M. Probabilistic risk assessment of transgene invasion. V Transgenic Animal Research Conference, Davis, CA, August 14-18, 2005 Rosa, A. J. M., McFarland, D., Vanier, C., Henderson, D., Rosa, G. J. M., Sanborn, A., Dreis, S., Pesall, J. Analysis of gene expression of myogenetic cells having different proliferation rates. Plant and Animal Genome XIII, p.246, 2005 Rosa, G. J. M., Invited talk, Reassessing Design and Analysis of Microarray Experiments Using Mixed Effects Models. Plant and Animal Genome XIII, San Diego, CA, January 15-19, 2005. Rosa, G. J. M, Invited talk, Microarray: Design & Processing. Short Course Fifth Annual Short Course on Statistical Genetics for Obesity & Nutrition Researchers, Birmingham AL, May 16-19, 2005. Rosa, G. J. M, Seminar, Reassessing Design and Analysis of Two-Colour Microarray Experiments Using Mixed Effects Models. Department of Biostatistics and Medical Informatics, UW-Madison, March 11, 2005. Rosa, G. J. M, Seminar, Animal Functional Genomics Project at MSU. Bovine Genome Round Table, University of Guelph, Toronto, Canada, April 13, 2005. Rosa, G. J. M, Seminar, Optimal Designs for Genetical Genomics Studies Using Two-Colour Microarrays. Brian W. Kennedy Memorial Colloquium, East Lansing, MI, May 8-10, 2005. Rosa, G. J. M, Seminar, Optimal Designs for Genetical Genomics Studies Using Two-Colour Microarrays. Department of Animal Sciences, UW-Madison, May 23, 2005. Rosa, G. J. M, Seminar, Bayesian Inference and Monte Carlo Methods in Fisheries and Wildlife Research. University of Notre Dame, Notre Dame, IN, Nov. 11, 2005. Rosa, G. J. M, Course, ANS 824: Methods of Quantitative and Molecular Genetics for Livestock, Michigan State University, Spring 2005. Rosa, G. J. M. Reassessing Design and Analysis of Microarray Experiments Using Mixed Effects Models. Plant and Animal Genome XIII, p.77, 2005. Rosa, G. J. M., Steibel, J. P., Tempelman, R. J. Linear mixed effects models for dual color microarray intensity ratios. ENAR Spring Meeting, March 20-23, 2005. Rosa, G. J. M., Steibel, J. P., Tempelman, R. J. Reassessing design and analysis of two-color microarray experiments using mixed effects models. Comparative and Functional Genomics 6: 123-131, 2005. Scribner, K., Jones, M., Rosa, G. J. M., Gilmore, S. Parentage analysis and estimation of environmental and genetic sources of variation in juvenile sea lamprey body size. Proceedings of the American Fisheries Society Meeting, 2005, on line: http://209.66.94.27/2005Abs/afssearch.cfm. Steibel, J. P. and Rosa, G. J. M. On reference designs for microarray experiments. Plant and Animal Genome XIII, p.248, 2005. Steibel, J. P., Poletto, R., Rosa, G. J. M. Statistical analysis of relative quantification of gene expression using real time RT-PCR data. ASAS-ADSA-CSAS Joint Annual Meeting, July 24-28, 2005. Steibel, J. P. and Rosa, G. J. M. On reference designs for microarray experiments. Statistical Applications in Genetics and Molecular Biology Vol. 4, No. 1, Article 36, 2005. (http://www.bepress.com/sagmb/vol4/iss1/art36) Steibel, J. P., Suchyta, S., Rosa, G. J. M. Tackling high variability in gene expression studies. Genomics & Proteomics 5(1): 30-32, 2005. Varona L., L. Gomez-Raya, W.M. Rauw and J.L. Noguera, 2005. A simulation study on the detection of causal mutations from F2 experiments. Journal of Animal Breeding and Genetics 122:30-36. Varona L., L. Gomez-Raya, W.M. Rauw, C. Ovilo, A. Clop and J.L. Noguera, 2005. The value of prior information for detection of QTL affecting longitudinal traits: an example using Von Bertallanffy growth function. Journal of Animal Breeding and Genetics 122:37-48 Verhoeven, K.J.F., J.-L. Jannink, and L.M. McIntyre. 2006. Using mating designs to uncover QTL and the genetic architecture of complex traits. Heredity 96:139-149. Wang, D., Nettleton, D. (2006). Identifying genes associated with a quantitative trait or quantitative trait locus via selective transcriptional profiling. Biometrics. In press. Yang, R., N. Yi and S. Xu. 2006. Box-Cox Transformation for QTL Mapping, Genetica (in press). Zhang, W., A. Carriquiry, D. Nettleton, and J. Dekkers. 2005. The effect of pooling mRNA in microarray experiments on power. Proc. Integration of Structural and Functional Genomics conference, Sept. 22-25, Iowa State University. Zhang, Y. M. and S. Xu. 2005a. A penalized maximum likelihood method for estimating epistatic effects of QTL. Heredity 95:96-104. Zhang, Y. M. and S. Xu. 2005b. Advanced statistical methods for detecting multiple quantitative trait loci. Recent Research Development in Genetics and Breeding 2:1-23. Zhao, H., H. Gilbert, and J. C. M. Dekkers. 2005 Discriminant analysis for multitrait quantitative trait loci detection in a Berkshire x Yorkshire F2 population. Abstract presented at the 2005 Midwest Animal Science meeting, J. Anim. Sci. 83 (Suppl. 1). Zhao, H., Nettleton, D., Soller, M., Dekkers, J.C.M. (2005). Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between markers and QTL. Genetical Research. 86 77-87. Zhao, H.H., R.L. Fernando, and J.C.M. Dekkers. 2005. Power and precision of linkage disequilibrium mapping of quantitative trait loci in outbred populations. Proc. Integration of Structural and Functional Genomics conference, Sept. 22-25, Iowa State University.

Impact Statements

A lower probability of failed experiments to map genes and to find gene expression differences is possible for all who are involved in gene mapping / gene expression research by the use of statistical methodology developed and improved by project members, for efficient, powerful and economic design of experiments.
Methods for marker assisted selection have been developed that will benefit breeders. Up to 25% increase in responses are possible in crossbreeding schemes.
Decreased loss of genetic diversity and reduced inbreeding will result from the use of methods for marker assisted selection developed.
Models to estimate environmental risk of genetically modified organisms have been expanded which are expected to increase accuracy of predicting these risks of genetically modified organisms.
Linkage disequilibrium analysis of commercial poultry populations contributes to the improved design of marker assisted selection programs and traceability programs in poultry.
new methodologies for the statistical integration of potentially miscoded genotypes in linkage analysis and QTL mappping studies will attenuate the biases caused by genotyping errors in these analyses.

Date of Annual Report: 01/08/2008

Report Information

Annual Meeting Dates: 02/17/2007 - 02/18/2007
Period the Report Covers: 01/01/2006 - 12/01/2006

Participants

Jack Dekkers (Iowa State University); Juan Medrano (University of California, Davis) New Member; Ignacy Misztal (University of Georgia); William Muir (Purdue University); Guilherme Rosa (University of Wisconsin); Frank Siewerdt (University of Maryland) New Member; Robert Tempelmann (Michigan State University); Shizhong Xu (University of California, Riverside); John Bastiaansen (Wageningen University The Netherlands)Other; Cynthia Ernst (Dow AgroSciences LLC) Other; Ricardo Verdugo (University of California, Davis) Guest; Jeanne Romero-Severson (Notre Dame University) Other; David Habier - Representing Rowan Fernando (Iowa State University) Guest; Mario Calus (Wageningen University) Other; Craig Beyrouty (Purdue University) Administrative Advisor

Brief Summary of Minutes

Technical program: The meeting began at 8:30 a.m. on Saturday, February 17 with opening remarks by John Bastiaansen, Chair. New participants were welcome and an agenda was approved for the meeting. Presentations by experiment stations were done in alphabetical order of the respective university names. Presentations by new participants were made at the end of the list. Discussions were held immediately after each presentation was made. The meeting adjourned at 5:00 p.m.
On Sunday February 18 the meeting reconvened at 8:00 a.m. Individual presentations by experiment stations continued. Guest presenters and presenters from other organizations were given time for their presentations at the end of the presentations of the official members. A general discussion followed the end of the presentations.
Specific accomplishments and impact statements are included with the individual experiment station report summaries, presented below.

Accomplishments

BUSINESS MEETING The business meeting was held on Sunday February 18, after the general discussion session. John Bastiaansen, the outgoing Chair of NCCC204 brought to the attention of the group that new officers needed to be chosen. Cynthia Ernst (Dow AgroSciences), the current Secretary, agreed to serve as Chair for the next cycle while Frank Siewerdt (University of Maryland) was elected secretary for the next cycle. It was decided that NCCC204 would attempt to meet with industry in 2008. Two possibilities were raised: (a) Dow AgroSciences (Indianapolis, IN) could serve as a host for next years meeting or (b) NCCC204 could meet in conjunction with the National Breeders Roundtable, in St. Louis, MO. A final decision will be made at a later time. The meeting was adjourned at 4:00 p.m.

Publications

List of Refereed Publications for NCCC204 1. Abasht, B., J.C.M. Dekkers, and S.J. Lamont. 2006. Review of quantitative trait loci identified in the chicken. Poultry Sci. 85: 2079-2096 2. Andreescu, C., J.H. Ralph, A. Hassen, S.J. Lamont, and J.C.M. Dekkers. 2007. Correspondence of relationships between breeding lines based on marker allele frequencies and marker-marker linkage disequilibrium. Midwest Animal Science Meeting, Des Moines. 3. Bijma, P. and W. M. Muir 2006. Genetic analysis and improvement of traits affected by interaction among individuals WCGALP 17:974-980 4. Bijma, P., W.M. Muir, and J.A.M. Van Arendonk. 2006. Quantitative genetics of inheritance and response with multilevel selection. Genetics (In press) 5. Bijma, P., W.M. Muir, E. Ellen, J.B. Wolf and J.A.M. Van Arendonk. 2006. Estimating the genetic parameters determining response to multilevel selection of traits affected by interaction among individuals. Genetics (In press) 6. Bijma, P., William M. Muir, Rolf F. Hoekstra and Johan A. M. Van Arendonk. 2007. Quantitative genetics of kin selection. American Naturalist. 7. Bohmanova, J., I. Misztal and J. Cole. 2007. Comparison of seven temperature humidity indices as indicators of milk production losses due to heat stress in semi-arid and humid climates. J. Dairy Sci. 90:1947-1956. 8. Bueno, J. S. S., Gilmour, S. G. and Rosa, G. J. M. (2006) Design of microarray experiments for genetical genomics studies. Genetics 174(2): 945-957. 9. Bueno, J. S., Gilmour, S. G., Rosa, G. J. M. Design of microarray experiments for genetical genomics studies. Royal Statistical Society 2006, Queen's University Belfast, September 10-14, 2006. 10. Bueno, J. S., Gilmour, S. G., Rosa, G. J. M. Efficiency and robustness of some designs for two color microarray experiments. SCRA 2006-FIM XIII-Thirteenth International Conference of the Forum for Interdisciplinary Mathematics on Interdisciplinary Mathematical and Statistical Techniques, Lisbon-Tomar, Portugal, September 1-4, 2006. 11. Burton, J. L. and Rosa, G. J. M. (2006) Physiological genomics special issue on animal genomics. Physiological Genomics 28: 1-4. 12. Cardoso, F. F., Rosa, G. J. M. and Tempelman, R. J. Accounting for outliers and heteroskedasticity on multiple-breed genetic evatualtions. In: 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil, Aug. 13-18, 2006 (CD Rom, paper 03-05; on line: http://www.wcgalp8.org.br/wcgalp8/articles/paper/3_201-271.pdf) 13. Cardoso, F. F., Rosa, G. J. M., Tempelman, R. J. (2007) Accounting for outliers and heteroskedasticity on multibreed genetic evaluations of post-weaning gain of Hereford-Nelore cattle. Journal of Animal Science (in press) 14. Chen, K., T. Baxter, W.M. Muir, M.A. Groenen, .L.B. Schook. 2006. Genome Evolution and Biodiversity in Pig (Sus scrofa). International Journal of Biological Sciences. In Press 15. Cheng, H., Yong Zhang and W.M. Muir. 2006. Evidence for widespread epistatic interactions influencing Mareks disease virus viremia levels in chicken. Cytogenetic and Genome Research (In press) 16. Dekkers, J.C.M., H.H. Zhao, and RL. Fernando. 2006. Linkage disequilibrium mapping in livestock. 8th World Congress on Genetics Applied to Livestock Production. Paper 21-05. Belo Horizonte, Brazil. 17. Devlin, RH Sundström, LF and WM Muir. 2006. Interface of biotechnology and ecology for environmental risk assessments of transgenic fish. Trends in Biotechnology 24:89-97. 18. Ellen, E.D., William M. Muir and Piter Bijma. 2007. Genetic improvement of traits affected by interactions among individuals. Genetical Research. 19. Farber C. & Medrano J.F. (2007) Subcongenic partitioning of multiple growth and obesity quantitative trait loci (QTL) closely linked in repulsion on mouse chromosome 2. Mammalian Genome 18:635-645. 20. Farber, C.R., J. Chitwood, S.N Lee, R. Verdugo, A. Islas-Trejo, G. Rincon, I. Lindberg and J.F. Medrano 2007. Overexpression of secretory granule neuroendocrine protein 1 (Sgne1) increases enzymatic activity of PCSK2 and is negatively correlated with body weight and obesity in congenic mouse models. BMC Genetics (Submitted) 21. Gianola, D., Fernando, R.L. and Stella, A. 2006. Genomic assisted prediction of genetic value with semi-parametric procedures. Genet. 173:1761-1776. 22. Grapes, L., M. Z. Firat, J.C.M. Dekkers, M.F. Rothschild, and R.L. Fernando. 2006. Optimal haplotype structure for linkage disequilibrium-based fine mapping of quantitative trait loci using identity-by-descent. Genetics 172: 1955-1965. 23. Hasenstein, J.R., J.C.M. Dekkers, and S.J. Lamont. 2006. Chromosomal Linkage Disequilibrium in Two Advanced Intercross Lines of Poultry Designed for Disease Resistance Genetic Studies. In: Proc Stadler Genetics Symposium, Columbia, MO. Oct. 2-4, 2006. 24. Hassen, A., s. Avendano, S.J. Lamont, W.G. Hill, and J.C.M. Dekkers. 2007. The effect of heritability estimates on high-density SNP analyses with related animals. Midwest Animal Science Meeting, Des Moines. 25. Jia, Z. and S. Xu. 2007. Mapping quantitative trait loci for expression abundance. Genetics (in press). 26. Johanson, J.M., Berger, P.J., Tsuruta, S. and Misztal, I. 2006. Genetic parameters for birth weight, dystocia, gestation length, and perinatal mortality in Holstein cattle. J. Dairy Sci. 89(Suppl. 1): M31, Abstract. 27. Khatib H, Rosa G J M, Weigel K, Schiavini F, Santus E and Bagnato A (2007) Confirmation of association between OLR1 and milk fat traits in cattle. Animal Genetics (in press). 28. Khatib, H., Schutzkus, V., Chang, Y. M. and Rosa, G. J. M. (2007) Pattern of expression of the uterine milk protein gene and its association with productive life in dairy cattle. Journal of Dairy Science (accepted for publication) 29. Khatib, H., Zaitoun, I., Wiebelhaus-Finger, J., Chang, Y. M. and Rosa, G. J. M. (2007) The bovine PPARGC1A and OPN genes are associated with milk composition in two independent Holstein cattle populations. Journal of Dairy Science (accepted for publication) 30. Kocabas, A. M., Crosby, J., Ross, J. P., Out, H. H., Beyhan, Z., Can, K., Tam, W. L., Rosa, G. J. M., Halgren, R. G., Lim, B., Fernandez, E., and Cibelli, J. B. (2006) The transcriptome of human oocytes. Proc. Natl. Acad. Sci. USA 103(38): 14027-14032. 31. Kocabas, A. M., Crosby, J., Rosa, G. J. M., Fernandez, E., Cibelli, J. B. Identification of pregnancy associated transcripts in human cumulus cells using Affymetrix GeneChip technology. American Society for Reproductive Medicine (ASRM) Annual Meeting, New Orleans, Louisiana, October 21-25, 2006. 32. Legarra, A., J. K. Bertrand, T. Strabel, R. L. Sapp, J. P. Sanchez, and I. Misztal. 2007. Multi-breed genetic evaluation in a Gelbvieh population. J. Anim. Breed. Genet. 124:286-295. 33. Li, Y., H.N. Kadarmideen, and J.C.M Dekkers. 2006. Selection on multiple QTL with controlled gene diversity and inbreeding for long-term benefit. 8th World Congress on Genetics Applied to Livestock Production. Paper 22-17. Belo Horizonte, Brazil. 34. Livant, E.J., S. Avendano, S. McLeod, X. Ye., S.J. Lamont, J.C.M. Dekkers, and S.J. Ewald. 2007. Mx gene exon 13 polymorphisms in broiler breeder chickens and associations with commercial traits. Animal Genetics (accepted) Madsen-Bouterse, S. A., Rosa, G. J. M. and Burton, J. L. (2006) Glucocorticoid modulation of Bcl-2 family members A1 and Bak during delayed spontaneous apoptosis of bovine blood neutrophils. Endocrinology 147(8): 3826-3834. 35. McElroy, J.P., J.-J. Kim, D.E. Harry, S.R. Brown, J.C.M. Dekkers, and S.J. Lamont. 2006 Identification of Trait Loci Affecting White Meat Percentage and Other Growth and Carcass Traits in Commercial Broiler Chickens. Poultry Sci. 85:593-605 36. McElroy, J.P., W. Zhang, K.J. Koehler, S.J. Lamont, and J.C.M. Dekkers. 2006. Comparison of methods for analysis of selective genotyping survival data. Genet. Sel. Evol. 38: 637-655. 37. Meade, K. G., Gormley, E., Fitzsimons, T., Rosa, G. J. M., Costello, E., Keane, J., Coussens, P. M. and MacHugh, D. E. (2006) Gene expression profiling of peripheral blood mononuclear cells (PBMC) from Mycobacterium bovis infected cattle after in vitro antigenic stimulation with purified protein derivative of tuberculin (PPD). Veterinary Immunology and Immunopathology 113: 73-89, 2006. 38. Minin, V.N., Dorman, K.S., Fang, F. and Suchard, M.A. 2006. Phylogenetic mapping of recombination hot-spots in HIV via spatially smoothed change-point processes. Genetics. Epub 39. Muir, W.M. and P. Bijma. 2006. Incorporation of competitive effects in breeding programs for improved performance and animal well-being. WCGALP 17:806-812 40. Muir, W.M., J. Romero-Severson, S.D. Rider Jr., A. Simons, and J. Ogas. 2006. Application of One Sided t-tests and a Generalized Experiment Wise Error Rate to High-Density Oligonucleotide Microarray Experiments: An Example Using Arabidopsis. J. Data Science 4, 323-341. 41. Muir, W.M., G.J.M. Rosa, B.R. Pittendrigh, S. Xu, S.D. Rider, M. Fountain and J. Ogas. 2007. A Quantitative Genetics Approach to Discovery of Biologically Significant Genes in Microarray Analysis. Genetics. (submitted) 42. Nettleton, D. (2006). A Discussion of statistical methods for design and analysis of microarray experiments for plant scientists. The Plant Cell. 18 2112-2121. 43. Nettleton, D., Hwang, J.T.G., Caldo, R.A., Wise, R.P. (2006). Estimating the number of true null hypotheses from a histogram of p-values. Journal of Agricultural, Biological, and Environmental Statistics. 11 337-356. 44. Nettleton, D., Wang, D. (2006). Selective transcriptional profiling for trait-based eQTL mapping. Animal Genetics. 37 13-17 45. Piyasatian, N., L.R. Totir, R.L. Fernando, and J.C.M. Dekkers. 2006. QTL detection and marker-assisted composite line development. Midwest Animal Science Meeting, Des Moines. 46. Piyasatian, N., R.L. Fernando, and J.C.M. Dekkers. 2006. Genomic selection for composite line development using low density marker maps. 8th World Congress on Genetics Applied to Livestock Production. Paper 22-65. Belo Horizonte, Brazil. 47. Pribyl, J., H. Krejcova, J. Pribylova, I. Misztal, J. Bohmanova, and M. Stipkova. 2007. Trajectory of body weight of performance tested dual-purpose bulls. Czech J. Anim.Sci. 52: 315-324. 48. Qu, L., D. Nettleton, and J. C. M. Dekkers. 2006. The effect of single nucleotide polymorphisms on one-step Tukeys biweight estimates from affymetrix microarray data. Midwest Animal Science Meeting, Des Moines. 49. Qu, L., D. Nettleton, and J.C.M. Dekkers. 2006. Evaluation of common Affymetrix GeneChip preprocessing methods based on performance in statistical tests and the validity of benchmarking datasets. Abstract presented at the 2nd International Symposium on Animal Functional Genomics, Michigan State University, May, 2006 50. Qu, L., D. Nettleton, and J.C.M. Dekkers. 2007. An A-optimal statistical design for post-hybridization washing procedures in Affymetrix microarray experiments. Midwest Animal Science Meeting, Des Moines. 51. Qu, Y. and S. Xu. 2006. Quantitative trait associated microarray gene expression data analysis. Molecular Biology and Evolution 23:1558-1573. 52. Rosa, G. J. M. and Steibel, J. P. Recovery of inter slide information in the analysis of microarray reference designs. In: 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil, Aug. 13-18, 2006 (CD Rom, paper 23-23; on line: http://www.wcgalp8.org.br/wcgalp8/articles/paper/23_970-1896.pdf) 53. Rosa, G. J. M. Design and analysis of genetical genomics studies involving complex traits. In: 34th Annual Meeting of the Statistical Society of Canada, London, ON, 2006. 54. Rosa, G. J. M. Optimal designs for genetical genomics studies. In: Plant and Animal Genome XIV, San Diego, CA, Jan. 14 - 18, 2006. 55. Rosa, G. J. M., de Leon N. and Rosa, A. J. M. (2006) A review of microarray experimental design strategies for genetical genomics studies. Physiological Genomics 28: 15-23. 56. Rosa, G. J. M., de Leon, N., Rosa, A. J. M. On design of microarray experiments for genetical genomics studies. In: 2nd International Symposium on Animal Functional Genomics (2nd ISAFG), East Lansing, MI, May 16-19, 2006. 57. Rosa, G. J. M., Tempelman, R. J., Ernst, C. W. and Bates, R. O. Combining molecular marker information and gene expression profiling for studying complex traits. In: 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil, Aug. 13-18, 2006 (CD Rom, paper 23-11; on line: http://www.wcgalp8.org.br/wcgalp8/articles/paper/23_795-1869.pdf) 58. Ruppert, D., Nettleton, D., Hwang, J.T.G. (2006). Exploring the information in p-values for the analysis and planning of multiple-test experiments. Biometrics. In press. 59. Siewerdt, F., 2006. On the estimation of realized genetic correlations from selection experiments. In: Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil. Paper 28-02 (4p). 60. Smith, C., McFarland, D. C., Oliveira, H. N., Rosa, G. J. M., Sanborn, A. M., Lindblom, S. J. and Rosa, A. J. M. Gene expression analysis of differentiating avian myogenic satellite cells. In: 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil, Aug. 13-18, 2006 (CD Rom, paper 13-24; on line: http://www.wcgalp8.org.br/wcgalp8/articles/paper/13_926-2066.pdf) 61. Smith, G. W. and Rosa, G. J. M. (2007) Interpretation of microarray data: Trudging out of the abyss towards elucidation of biological significance. Journal of Animal Science (in press). 62. Smith, G. W., Rosa, G. J. M., Coussens, P. M., Halgren, R., Evans, A. C. O., Mihm, M., Lonergan, P., and Ireland J. J. Interpretation of microarray data: Trudging out the abyss towards elucidation of biological significance. J. Dairy Sci. Vol 89, Suppl. 1, 2006. 63. Steibel, J. P. and Rosa, G. J. M. Linear mixed model analysis of relative quantification of reverse transcription polymerase chain reaction data. In: 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil, Aug. 13-18, 2006 (CD Rom, paper 23-04; on line: http://www.wcgalp8.org.br/wcgalp8/articles/paper/23_528-1915.pdf) 64. Steibel, J. P., Tempelman, R. J., Rosa, G. J. M. Linear models for microarray experiments: a comprehensive review. 2nd International Symposium on Animal Functional Genomics (2nd ISAFG), East Lansing, MI, May 16-19, 2006. 65. Soller, M., S. Weigend, M.N. Romanov, J.C.M. Dekkers, and S.J. Lamont. 2006. Strategies to Assess Structural Variation in the Chicken Genome and its Associations with Biodiversity and Biological Performance. Poultry Sci. 85: 2061-2078. 66. Sun,W., V. M. Margam, L. Sun, G. Buczkowski, G. W. Bennett, B. Schemerhorn, W. M. Muir and B. R. Pittendrigh 2006. Genome-wide analysis of phenobarbital-inducible genes in Drosophila melanogaster Insect Molecular Biology. Insect Molecular Biology 15: 455464 67. Tarver, M., W. Muir, L. Murocm, B. Pittendrigh, 2006. Pyramiding of insecticidal compounds for control of the cowpea bruchid (Callosobruchus maculatus F.) Pest Management Science (in press) 68. Tuggle, C., L. Qu, Y. Wang, O. Couture, D. Nettleton, J. Dekkers, S. Zhao, J. Uthe, and S. Bearson. 2006. Using a First-Generation Porcine Affymetrix Genechip® to Investigate the Functional Genomics of Immune Response and Reproductive Biology. Plant and Animal Genome XIV Abstract W242, page 61. 69. Urioste, J. I., I. Misztal and J.K. Bertrand. 2007.Fertility traits in spring-calving Aberdeen Angus cattle. 1. Model development and genetic parameters. J. Anim. Sci. 85:2854-2860. 70. Urioste, J. I., I. Misztal and J.K. Bertrand. 2007.Fertility traits in spring-calving Aberdeen Angus cattle. 2. Model comparison . J. Anim. Sci. 85: 2861-2865. 71. Van Mellis, M. H., Eler*, J. P., Oliveira, H. N, Silva, J. A. II V., Ferraz*, J. B. S., Rosa, G. J. M. and Pereira, E. (2007) Study of stayability in Nellore cows using a threshold model. Journal of Animal Science (accepted for publication) 72. Van Mellis, M. H., Eler, J. P., Oliveira, H. N., Rosa, G. J. M., Ferraz, J. B. S. and Pereira, E. Additive genetic relationship among stayability at five, six and seven years. In: 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil, Aug. 13-18, 2006 (CD Rom, paper 03-88; on line: http://www.wcgalp8.org.br/wcgalp8/articles/paper/3_531-1750.pdf) 73. Wang X.*, Rosa, A. J. M., Oliverira, H. N., Rosa, G. J. M., Guo, X., Travnicek, M.# and Girshick, T. (2006) Transcriptome of local innate and adaptive immunity during early phase of infectious bronchitis virus infection. Viral Immunology 19(4): 768-774. 74. Weber, P. S., Madsen-Bouterse, S. A., Rosa, G. J. M., Sipkovsky, S., Ren, X., Almeida, P. E., Kruska, R., Halgren, R. G., Barrick, J. L. and Burton, J. L. (2006) Analysis of the bovine neutrophil transcriptome during glucocorticoid treatment. Physiological Genomics 28: 97-112. 75. Weber, P. S., Madsen-Bouterse, S. A., Rosa, G. J. M., Sipkovsky, S., Ren, X., Almeida, P. E., Kruska, R., Halgren, R. G., Barrick, J. L. and Burton, J. L. Analysis of the bovine neutrophil transcriptome during glucocorticoid treatment. In: 2nd International Symposium on Animal Functional Genomics (2nd ISAFG), East Lansing, MI, May 16-19, 2006. 76. Xu, S. and C. Xu. 2006. A multivariate model for ordinal trait analysis. Heredity 97:409-417. 77. Xu, S. 2007. An empirical Bayes method for estimating epistatic effects of quantitative trait loci. Biometrics (in press) 78. Xu, S. and Z. Jia. 2007. Genome-wide analysis of epistatic effects for quantitative traits in barley. Genetics (in press) 79. Yang, R., Q. Tian and S. Xu. 2006. Mapping QTL for longitudinal traits in line crosses. Genetics 173:2339-2356. 80. Yang, R., J. Li and S. Xu. 2007. Mapping quantitative trait loci for traits defined as ratios. Genetica (in press) 81. Yang, R. and S. Xu. 2007. Bayesian shrinkage analysis of quantitative trait loci for dynamic traits. Genetics (in press) 82. Ye, X. S.R. Brown, K. Nones, L.L. Coutinho, J.C.M. Dekkers, and S.J. Lamont. 2007. Associations of myostatin gene polymorphisms with performance and mortality traits in broiler chickens. Genet. Sel. Evol. 39: 73-89. 83. Ye, X., S. Avendano, D. J.C.M. Dekkers, and S. J. Lamont. 2006. Association of twelve immune-related genes with performance of three broiler lines in two different hygiene environments. Poultry Sci. 85: 1555-1569. 84. Ye, X., S. Avendano, J.C.M. Dekkers, and S.J. Lamont. 2006. Variation in immune genes associated with body weight and feed conversion in elite commercial broiler lines. Proc. Plant & Animal Genome XIV, San Diego, CA. 85. Ye, X., S. McLeod, D. Elfick, J.C. Dekkers, and S.J. Lamont. 2006. Rapid Identification of Single Nucleotide Polymorphisms and Estimation of Allele Frequencies Using Sequence Traces from DNA Pools. Poultry Sci. 85: 1165-1168. 86. Zhang, W., A. Carriquiry, D. Nettleton, and J.C.M. Dekkers. 2006. Estimation of correlations between phenotype and gene expression in microarray experiments with non-random pooling. Midwest Animal Science Meeting, Des Moines. 87. Zhao, H., R. Fernando, and J. Dekkers. 2006. Power and precision of linkage disequilibrium mapping of QTL in outbred populations. Midwest Animal Science Meeting, Des Moines. 88. Zhao, H.H., D. Nettleton, and J.C.M. Dekkers. 2007. Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between single nucleotide polymorphisms. Genetical Research (accepted) 89. Zhao, H.H., J.C.M. Dekkers, and R.L. Fernando. 2006. Power and precision of regression-based linkage disequilibrium mapping of QTL. 8th World Congress on Genetics Applied to Livestock Production. Paper 21-20. Belo Horizonte, Brazil. 90. Zhao, H.H., R.L. Fernando, and J.C.M. Dekkers. 2007. Effects of population structure on power and precision of regression-based linkage disequilibrium mapping of QTL. Midwest Animal Science Meeting, Des Moines. 91. Zhao, H.H., R.L. Fernando, and J.C.M. Dekkers. 2007. Power and precision of alternate methods for linkage disequilibrium mapping of QTL. Genetics: ahead of print pub. 10.1534/genetics.106.066480 92. Zumbach, B., I. Misztal, S. Tsuruta, J. Holl, W. Herring and T. Long. 2007. Genetic correlations between two strains of Durocs and crossbreds from differing production environments for slaughter traits. J. Anim. Sci. 85:901-908.

Impact Statements

See attached minutes