Brief Summary of Minutes of Annual Meeting

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 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.

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.