All Publications Since 2008

2016

Peer-reviewed paper

  • Lourenco, D. A. L., S. Tsuruta, B. O. Fragomeni, C. Y. Chen, and I. Misztal. 2016. Crossbred evaluations in single-step genomic BLUP using adjusted realized relationship matrices. J. Anim. Sci. 94:909-919. (Journal)
  • Masuda, Y., I. Misztal, S. Tsuruta, A. Legarra, I. Aguilar, D. Lourenco, B. Fragomeni, and T. L. Lawlor. 2016. Implementation of genomic recursions in single-step genomic BLUP for US Holsteins with a large number of genotyped animals. J. Dairy Sci. 99:1968-1974. (Journal)
  • Misztal, I. 2016. Inexpensive computation of the inverse of the genomic relationship matrix in populations with small effective population size. Genetics 202:411-409. (Journal)
  • Pocrnic, I., D. A. L. Lourenco, Y. Masuda, A. Legarra, and I. Misztal. 2016. The dimensionality of genomic information and its effect on genomic prediction. Genetics 203:573-581. (Journal)
  • Vitezica, Z., L. Varona, J. M. Elsen, I. Misztal, W. Herring, and A. Legarra. 2016. Genomic BLUP including additive and dominant variation in purebreds and F1 crossbreds, with an application in pigs. Genet. Sel. Evol. 48:6. (Journal)

2015

Peer-reviewed paper

  • Forneris, N. S., A. Legarra, Z. G. Vitezica, S. Tsuruta, I. Aguilar, I. Misztal, and R. J. C. Cantet. 2015. Quality control of genotypes using heritability estimates of gene content at the marker. Genetics. doi: 10.1534/genetics.114.173559.
  • Fragomeni, B. O., D.A.L. Lourenco, S. Tsuruta, Y. Masuda, I. Aguilar, A. Legarra, T. J. Lawlor, and I. Misztal. 2015. Use of genomic recursions in single-step genomic BLUP with a large number of genotypes. J. Dairy Sci. 98:4090-4094.
  • Fragomeni, B. O., D. A. L. Lourenco, S. Tsuruta, Y. Masuda, I. Aguilar, and I. Misztal. 2015. Use of Genomic Recursions and Algorithm for Proven and Young Animals for Single-Step Genomic BLUP Analyses – A Simulation Study. J. Anim. Breed. Genet. 132:340-345.
  • González-Cerón, F., R. Rekaya, and S. E. Aggrey. 2015. Genetic analysis of leg problems and growth in a random mating broiler population. Poultry Sci. 94:162-168.
  • González-Cerón, F., R. Rekaya, and S. E. Aggrey. 2015. Genetic analysis of bone quality traits and growth in a random mating broiler population. Poultry Sci. 94:883-889.
  • González-Cerón, F. R. Rekaya, and S. E. Aggrey. 2015. Genetic relationship between leg problems and bone quality traits in a random mating broiler population. Poultry Sci. 94:1787-1790.
  • Hay, E. H., and R. Rekaya. 2015. A multi-compartment model for genomic selection in multi-breed populations. Livest. Sci. 177:1-7.
  • Hay, E. H., and R. Rekaya. 2015. A structural model for genetic similarity in genomic selection of admixed populations . Livest. Sci. 181: 72-76.
  • Lee, J., A. B. Karnuah, R. Rekaya, N. B. Anthony, and S. E. Aggrey. 2015. Transcriptomic analysis to elucidate the molecular mechanisms that underlie feed efficiency in meat-type chickens. Molecular Genetics and Genomics, 290, 1673-168.
  • Legarra, A., O. F. Christensen, Z. G. Vitezica, I. Aguilar, and I. Misztal. 2015. Ancestral relationships using metafounders: finite ancestral populations and across population relationships. Genetics. doi: 10.1534/genetics.115.177014.
  • Lourenco, D. A. L., B. O. Fragomeni, S. Tsuruta, I. Aguilar, B. Zumbach, R. J. Hawken, A. Legarra, and I. Misztal. 2015. Accuracy of estimated breeding values for males and females with genomic information on males, females, or both: a broiler chicken example. Genet. Sel. Evol. 47:56.
  • Lourenco, D. A. L., S. Tsuruta, B. O. Fragomeni, Y. Masuda, I. Aguilar, A. Legarra, J. K. Bertrand, T. S. Amen, L. Wang, D. W. Moser, and I. Misztal. 2015. Genetic evaluation using single-step genomic BLUP in American Angus. J. Anim. Sci. 93:2653-2662.
  • Lukaszewicz, M., R. Davis, J. K. Bertrand, I. Misztal, and S. Tsuruta. 2015. Correlations between purebred and crossbred body weight traits in Limousin and Limousin-Angus populations. J. Anim. Sci. 93:1490-1493.
  • Masuda, Y., S. Tsuruta, I. Aguilar, and I. Misztal. 2015. Technical note: Acceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements. J. Anim. Sci. 93:4670-4674.
  • Rekaya, R., and S. E. Aggrey. 2015. Genetic properties of residual feed intakes for maintenance and growth and the implications of error measurement. J. Anim. Sci. 93:944-948.
  • Tsuruta, S., D. A. L. Lourenco, I. Misztal, and T. J. Lawlor. 2015. Genotype by environment interactions on culling rates and 305-d milk yield of Holstein cows in three US regions. J. Dairy Sci. 98(8):5796-805.
  • White, D. S., K. J. Duberstein, J. L. Fain Bohlen, J. K. Bertrand, A. H. Nelson, M. A. Froetschel, B. E. Davidson, and W. M. Graves. 2015. Allometric comparison of Georgia dairy heifers on farms and at youth shows. J. Dairy. Sci. 98:1345-1353.
  • Zhang, X., I. Misztal, M. Heidaritabar, J. W. M. Bastiaansen, R. Borg, R. L. Sapp, T. Wing, R. R. Hawken, D. A. L. Lourenco, and Z. G. Vitezica. 2015. Prior genetic architecture impacting genomic regions under selection: an example using genomic selection in two poultry breeds. Livest. Sci. 171:1-11.

2014

Peer-reviewed paper

  • Aggrey, S. E., J. Lee, A. B. Karnuah, and R. Rekaya. 2014. Transcriptomic analysis of genes in the nitrogen recycling pathway of meat-type chickens divergently selected for feed efficiency. Anim. Genet. 45:215-222.
  • Dufrasne, M., I. Misztal, S. Tsuruta, N. Gengler, and K. A. Gray. 2014. Genetic analysis of pig survival up to commercial weight in a crossbred population. Livest. Sci. 167:19-24.
  • Fragomeni, B., I. Misztal, D. Lourenco, I. Aguilar, R. Okimoto, and W. Muir. 2014. Changes in variance of top SNP windows over generations for three traits in broiler chicken. Frontiers Genet. doi: 10.3389/fgene.2014.00332.
  • Froetschel, M. A., C. L. Ross, R. L. Stewart, M. J. Azain, P. Michot, and R. Rekaya. 2014. Nutritional value of ensiled grocery food waste for cattle. J. Anim. Sci. 92:5124–5133.
  • Legarra, A., O. F. Christensen, I. Aguilar, and I. Misztal. 2014. Single Step, A General Approach For Genomic Selection. Livest. Sci. 166:54-65.
  • Lourenco, D. A. L., I. Misztal, S. Tsuruta, I. Aguilar, E. Ezra, M. Ron, A. Shirak, and J. I. Weller. 2014. Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. J. Dairy Sci. 97:1742-1752.
  • Lourenco, D. A. L., I. Misztal, S. Tsuruta, I. Aguilar, T. J. Lawlor, S. Forni, and J. I. Weller. 2014. Are evaluations on young genotyped animals benefiting from the past generations? J. Dairy Sci. 97:3930-3942.
  • Misztal, I., A. Legarra, and I. Aguilar. 2014. Using recursion to compute the inverse of the genomic relationship matrix. J. Dairy Sci. 97:3943-3952.
  • Tokuhisa, K., S. Tsuruta, A. De Vries, J. K. Bertrand, and I. Misztal. 2014. Estimation of Regional Genetic Parameters for Mortality and 305-d Milk Yield of US Holsteins in the First Three Parities. J. Dairy Sci. 97:4497-4502.
  • Tsuruta, S., I. Misztal, D. A. L. Lourenco, and T. J. Lawlor. 2014. Assigning unknown parent groups to reduce bias in genomic evaluations of final score in US Holsteins. J. Dairy Sci.97: 5814-5821.
  • Wang, H., I. Misztal, I. Aguilar, A. Legarra, R. L. Fernando, Z. Vitezica, R. Okimoto, T. Wing, R. Hawken, and W. M. Muir. 2014. Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Frontiers Genet. DOI=10.3389/fgene.2014.00134.
  • Wang, H., I. Misztal and A. Legarra. 2014. Differences between genomic-based and pedigree-based relationships in a chicken population, as a function of quality control and pedigree links among individuals. J. Animal Breeding Genet. DOI: 10.1111/jbg.12109.

2013

Peer-reviewed paper

  • Aggrey, S. E., and R. Rekaya. 2013. Dissection of Koch’s residual feed intake: Implications for selection. Poultry Sci. 92:2600-2605.
  • Elzo, M.A., C.A. Martinez, G.C. Lamb, D.D. Johnson, M.G. Thomas, I. Misztal, D.O. Rae, J.G. Wasdin, J.D. Driver. 2013. Genomic-polygenic evaluation for ultrasound and weight traits in Angus?Brahman multibreed cattle with the Illumina3k chip, Livest. Sci. 153:39-49.
  • Dufrasne, M., I. Misztal, S. Tsuruta, J. Holl, K. A. Gray, and N. Gengler. 2013. Estimation of genetic parameters for birth weight, preweaning mortality, and hot carcass weight of crossbred pigs. J. Anim. Sci. 5565-5571.
  • Lourenco, D. A. L., I. Misztal, H. Wang, I. Aguilar, S. Tsuruta, and J. K. Bertrand. 2013. Prediction accuracy for a simulated maternally affected trait of beef cattle using different genomic evaluation models. J. Anim. Sci. 4090-4098.
  • Misztal, I., S. Aggrey, and B. Muir. 2013. Experiences with a Single-Step Genome Evaluation (ssGBLUP)” . Poultry Sci. 92:2530-2534.
  • Misztal, I., S. Tsuruta, I. Aguilar, A. Legarra, P. M. VanRaden, and T. J. Lawlor. 2013. Methods to Approximate Reliabilities in Single-Step Genomic Evaluation. J. Dairy Sci. 96:647-654.
  • Misztal, I., Z.G. Vitezica, A. Legarra, I. Aguilar, and A.A. Swan. 2013. Unknown-parent groups in single-step genomic evaluation. J. Anim. Breed. Genet. 130:252-258.
  • Rekaya, R., R. L. Sapp, T. Wing, and S. E. Aggrey. 2013. Genetic evaluation for growth, body composition, feed efficiency, and leg soundness. Poultry Sci. 92:923-929.
  • Rekaya, R., S. Smith, H. El Hay, and S. E. Aggrey. 2013. Misclassification in binary responses and effect on genome-wide association studies. Poultry Sci. 92:2535-2540.
  • Smith, S., E. Hay, N. Farhat, and R. Rekaya. 2013. Genome wide association studies in presence of misclassified binary responses. BMC Genet. 14:124.
  • Tsuruta, S., I. Misztal, and T. J. Lawlor. 2013. Genomic evaluations of final score for US Holsteins benefit from the inclusion of genotypes on cows. J. Dairy Sci. 96:3332-3335.

2012

Peer-reviewed paper

  • Bloemhof, S.,A. Kause, E. Knol, J. van Arendonk, and I. Misztal. 2012. Heat stress effects on farrowing rate in sows: Genetic parameter estimation using within-line and crossbred models. J. Anim. Sci. 90:2109-2119.
  • Elzo,M. A., G. C. Lamb, D. D. Johnson, M. G. Thomas, I. Misztal, D. O. Rae, C. A. Martinez, J. G. Wasdin and J. D. Driver. 2012. Genomic-polygenic evaluation of Angus-Brahman multibreed cattle for feed efficiency and postweaning growth using the Illumina3k chip. J. Animal Sci. 90:2488-2497.
  • Faux, P., N. Gengler, and I. Misztal. 2012. A recursive algorithm for decomposition and creation of the inverse of the genomic relationship matrix. J. Dairy Sci. 95:6093-6102.
  • Liu, X., Y. Wang, R. Rekaya, and T. N. Sriram. 2012. Sample size determination for classifiers based on single-nucleotide polymorphisms. Biostatistics. 13:217-227.
  • London, M. L., J. K. Bernard, M. A. Froetschel, J. K. Bertrand, and W. M. Graves. 2012. The relationship between weight, age, and average daily gain to show performance of Georgia 4-H and Future Farmers of America (FFA) commercial dairy heifers. J. Dairy Sci. 95:986-996.
  • Simeone, R., I. Misztal, I. Aguilar, and Z. Vitezica. 2012. Evaluation of a multi-line broiler chicken population using a single-step genomic evaluation procedure. J, Anim. Breed. Genet. 129( 1):3-10.
  • Wang, H., I. Misztal, I. Aguilar, A. Legarra, and W. M. Muir. 2012. Genome-wide association mapping including phenotypes from relatives without genotypes. Genet. Res. 94:73-83.
  • Wang, H., B. Woodward, S. Bauck, and R. Rekaya. 2012. Imputation of missing SNP genotypes using low density panels. Livestock Science 146:80-83.
  • Wang, Y., S. J. Joseph, X. Liu, M. Kelley, and R. Rekaya. 2012. SNPxGE2: a database for human SNP–coexpression associations. Bioinformatics 28:403-410.
  • Williams, J. L., J.K. Bertrand, M. Lukaszewicz and I. Misztal. 2012. Genotype by environment interaction for growth due to high altitude disease in United States Angus cattle. J. Animal Sci. 90:2152-2158.
  • Williams, J. L., J.K. Bertrand, M. Lukaszewicz and I. Misztal. 2012. Genotype by region and season interactions on weaning weight in United States Angus cattle. J. Animal Sci. 90:3368-3374.

2011

Peer-reviewed paper

  • Aguilar, I., I. Misztal , A. Legarra , S. Tsuruta. 2011. Efficient computation of genomic relationship matrix and other matrices used in single-step evaluation. J. Anim. Breed. Genet. 128(6):422-428.
  • Aguilar, I., I. Misztal, S. Tsuruta, G. R. Wiggans and T. J. Lawlor. 2011. Multiple trait genomic evaluation of conception rate in Holsteins. J. Dairy Sci. 94:2621-2624.
  • Boonkum, W., I. Misztal, M. Duangjinda, V. Pattarajinda, S. Tumwasorn, and S. Buaban. 2011. Short communication: Genetic effects of heat stress on days open for Thai Holstein crossbreds. J. Dairy Sci. 94:1592-1596.
  • Boonkum, W., I. Misztal, M. Duangjinda, V. Pattarajinda, S. Tumwasorn, and J. Sanpote. 2011. Genetic effects of heat stress on milk yield of Thai Holstein crossbreds. J. Dairy Sci. 94:487-492.
  • Chen, C. Y., I. Misztal, I. Aguilar, A. Legarra, and B. Muir. 2011. Effect of different genomic relationship matrix on accuracy and scale. J. Anim. Sci. 89:2673-2679.
  • Chen, C. Y., I. Misztal, I. Aguilar, S. Tsuruta, T. H. E. Meuwissen, S. E. Aggrey, T. Wing, and W. M. Muir. 2011. Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: an example using broiler chickens. J. Anim. Sci. 89:23-28.
  • Costa, R. B., I. Misztal, M. A. Elzo, J. K. Bertrand, L. O. C. Silva, and M. Lukaszewicz. 2011. Estimation of genetic parameters for mature weight in Angus cattle. J. Animal Sci. 89:2680-2686.
  • Forni, S., I. Aguilar, and I. Misztal. 2011. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genet. Sel. Evol. 43:1.
  • Johanson, J. M., P. J. Berger, S. Tsuruta, and I. Misztal. 2011. A Bayesian Threshold-Linear Model Evaluation of Perinatal Mortality, Dystocia, Birth Weight, and Gestation Length in a Holstein Herd. J. Dairy Sci. 94:450-460.
  • Koduru, V. K. R., S. Tsuruta, M. Łukaszewicz, I. Misztal, and T. J. Lawlor. 2011. Studies on changes of estimated breeding values of U.S. Holstein bulls for final score from the first to second crop of daughters. J. Appl. Genet. 52:81-88.
  • Perez-Enciso, M., and I. Misztal. 2011. Qxpak.5: Old mixed model solutions for new genomics problems. BMC Bioinformatics.12:202.
  • Rekaya, R., R. L. Sapp, E. H. Hay, R. Davis, and J. K. Bertrand. 2011. Simulation study for analysis of binary responses in the presence of extreme case problems. Genet. Sel. Evol. 43:41.
  • Simeone, R., I. Misztal, I. Aguilar, and A. Legarra. 2011. Evaluation of the utility of genomic relationship matrix as a diagnostic tool to detect mislabeled genotyped animals in a broiler chicken population. J, Anim. Breed. Genet. 128(5):386-393.
  • Tsuruta, S., I. Aguilar, I. Misztal, and T. J. Lawlor. 2011. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J. Dairy Sci. 94:4198-4204.
  • Vitezica, Z. G., I. Aguilar, I. Misztal, and A. Legarra. 2011. Bias in Genomic Predictions for Populations Under Selection. Genet. Res. Camb. 93:357-366.

2010

Peer-reviewed paper

  • Aguilar, I., I. Misztal, D. L. Johnson, A. Legarra, S. Tsuruta, and T. J. Lawlor. 2010. A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci. 93:743:752.
  • Aguilar, I., I. Misztal, and S. Tsuruta. 2010. Short Communication: Genetic trends of milk yield under heat stress for US Holsteins. J. Dairy Sci. 93:1754-1758.
  • Aguilar, I. S. Tsuruta, and I. Misztal. 2010. Computing options for multiple-trait test-day random regression models while accounting for heat tolerance. J. Anim. Breed. Genet. 127:235-241.
  • Barb, C. R., G. J. Hausman, R. Rekaya, C. A. Lents, S. Lkhagvadorj, L. Qu, W. Cai, O. P. Couture, L. L. Anderson, J. C. M. Dekkers, and C. K. Tuggle. 2010. Gene expression in hypothalamus, liver, and adipose tissues and food intake response to melanocortin-4 receptor agonist in pigs expressing melanocortin-4 receptor mutations. Physiological Genomics 41:254-268.
  • Chen, C. Y., I. Misztal, S. Tsuruta, W. O. Herring, J. Holl, and M. Culbertson. 2010. Influence of heritable social status on daily gain and feeding pattern in pigs. J. Anim. Breed. Genet. 127:107-112.
  • Chen, C. Y., I. Misztal, S. Tsuruta, B. Zumbach, W. O. Herring, T. Long, and M. Culbertson. 2010. Estimation of genetic parameters of feed intake and daily gain in Durocs using data from electronic swine feeders. J. Anim. Breed. Genet. 27:230-234.
  • Chen, C. Y., I. Misztal, S. Tsuruta, B. Zumbach, W. O. Herring, T. Long, and M. Culbertson. 2010. Genetic analyses of stillbirth in relation to litter size using random regression models. J. Anim. Sci. 88:3800-3808.
  • Joseph, S., S. L. Pratt, E. Pavan, R. Rekaya, and S. K. Duckett. 2010. Omega-6 Fat Supplementation Alters Lipogenic Gene Expression in Bovine Subcutaneous Adipose Tissue. Gene Regul. Syst. Biol. 4:91-101.
  • Joseph, S. J., K. R. Robbins, E. Pavan, S. I. Pratt, S. K. Duckett, and R. Rekaya. 2010. Effect of diet supplementation on the expression of bovine genes associated with fatty acid synthesis and metabolism. Bioinform. Biol. Insights. 4:19-31.
  • Joseph, S. J., K. R. Robbins, W. Zhang, and R. Rekaya. 2010. Comparison of two output-coding strategies for multi-class tumor classification using gene expression data and latent variable model as binary classifier. Cancer Inform. 9:39-48.
  • Scholtz. A. J., S.W.P. Cloete, J.B. van Wyk, I. Misztal, E. du Toit, and T.C. de K. van der Linde. 2010. Genetic (co)variances between wrinkle score and absence of breech strike in mulesed and unmulesed Merino sheep using a threshold model. Animal Production Science. 50:210-218.
  • Soyeurt, H, I. Misztal and N. Gengler. 2010. Genetic Variability of Milk Components Based on Mid-Infrared (MIR) Spectral Data. J. Dairy Sci. 93:1722-1728.
  • Tusell, L., M. Garcia-Tomas, M. Baselga, R. Rekaya, O. Rafel, J. Ramon, M. Lopez-Bejar, and M. Piles. 2010. Interaction of genotype x artificial insemination conditions for male effect on fertility and prolificacy. J. Anim. Sci. 88:3475-3485.
  • Wang, Y., X. Liu, K. Robbins, and R. Rekaya. 2010. AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm. BMC Research Notes 3:117.
  • Wang, Y., and R. Rekaya. 2010. LSOSS: Detection of cancer outlier differential gene expression. Biomark. Insights 5:69-78.
  • Wang, Y., K. R. Robbins, and R. Rekaya. 2010. Comparison of Computational Models for Assessing Conservation of Gene Expression across Species. PLoS ONE. 5:e13239.
  • Williams, J. L., I. Aguilar, R. Rekaya, and J. K. Bertrand. 2010. Estimation of breed and heterosis effects for growth and carcass traits in cattle using published crossbreeding studies. J. Anim. Sci. 88:460-466.
  • Zhang, W., K. Robbins, Y. Wang, K. Bertrand, and R. Rekaya. 2010. A jackknife-like method for classification and uncertainty assessment of multi-category tumor samples using gene expression information. BMC Genomics 11:273.
  • Zumbach, B., I. Misztal, C.Y. Chen, S. Tsuruta, M. Lukaszewicz, W.O. Herring, and M. Culbertson. 2010. Use of serial pig body weights for genetic evaluation of daily gain. J. Anim. Breed. Genet. 126:93-99.

2009

Peer-reviewed paper

  • Aguilar, I., I. Misztal, and S. Tsuruta. 2009. Genetic components of heat stress for dairy cattle with multiple lactations. J. Dairy Sci. 92: 5702-5711.
  • Bertrand, J. K. 2009. Using actual and ultrasound carcass information in beef genetic evaluation programs. Revista Brasileira de Zootecnia. 38:58-63.
  • Cloete,S.W.P., I. Misztal, and J.J. Olivier. 2009. Genetic parameters and trends for lamb survival and birth weight in a Merino flock divergently selected for multiple rearing ability. J. Anim. Sci. 87:2196?2208.
  • Huang, C., S. Tsuruta, J. K. Bertrand, I. Misztal, T. J. Lawlor, and J. S. Clay. 2009. Trends for conception rate of Holsteins over time in Southeastern USA. J. Dairy Sci. 92:4641-4647.
  • Legarra, A., I. Aguilar, and I. Misztal. 2009. A relationship matrix including full pedigree and genomic information. J. Dairy Sci. 92:4656-4663.
  • Misztal, I., A. Legarra, and I. Aguilar. 2009. Computing procedures for genetic evaluation including phenotypic, full pedigree and genomic information. J. Dairy Sci. 92:4648-4655.
  • Pszczola, M., I. Aguilar, and I. Misztal. 2009. Short communication: Trends for monthly changes in days open in Holsteins. J. Dairy Sci. 92:4689-4696.
  • Rekaya, R. and K. Robbins. Ant colony algorithm for analysis of gene interaction in high-dimensional association data. Revista Brasileira de Zootecnia. 38:93-97.
  • Sanchez, J. P., I. Misztal , I. Aguilar, B. Zumbach, and R. Rekaya. 2009. Genetic determination of the onset of heat stress on daily milk production in the US Holstein cattle. J. Dairy Sci. 92: 4035-4045.
  • Sanchez, J. P., R. Rekaya and I. Misztal. 2009. Reaction norm models subject to threshold response. Genet. Sel. Evol.41:10.
  • Spangler, M. L., K. R. Robbins, J. K. Bertrand, M. MacNeil, and R. Rekaya. 2009. Ant colony optimization as a method for strategic genotype sampling. Anim. Genet. 40:308-314
  • Tsuruta, S., I. Misztal, C. Huang, and T. J. Lawlor. 2009. Bivariate analysis of conception rates and test-day milk yields using a threshold-linear model with random regressions. J. Dairy Sci. 92: 2922-2930.
  • Wang, Y., and R. Rekaya. 2009. A comprehensive analysis of gene expression evolution between humans and mice. Evolutionary Bioinformatics 5:81.

2008

Peer-reviewed paper

  • Aguilar, I., and I. Misztal. 2008. Recursive algorithm for inbreeding coefficients assuming non-zero inbreeding of unknown parents. J. Dairy Sci. 91:1669-1672.
  • Bohmanova,J., F. Miglior, J. Jamrozik, I. Misztal and P.G. Sullivan. 2008. Comparison of random regression models with Legendre polynomials and linear splines for production traits and somatic cell score of Canadian Holstein cows. J. Dairy Sci. 91:3627-3638.
  • Bohmanova, J., I. Misztal, S. Tsuruta, H.D. Norman, and T.J. Lawlor. 2008. Heat Stress as a Factor in Genotype x Environment Interaction in U.S. Holsteins. J. Dairy Science. 91:840-846.
  • Huang, C., S. Tsuruta, J. K. Bertrand, I. Misztal, T. J. Lawlor, and J. S. Clay. 2008. Environmental Effects on Conception Rate of Holsteins in New York and Georgia. J. Dairy Sci. 92:818-825.
  • Joseph, S. J., K. Robbins, W. Zhang, and R. Rekaya. 2008. Effects of misdiagnosis in input data on the identification of differential expression genes in incipient Alzheimer patients. In Silico Biology 8:545-554.
  • Legarra, A., and I. Misztal. 2008. Computing strategies in genome-wide selection. J. Dairy Sci. 91:360-366.
  • Misztal, I. 2008. Reliable Computing in Estimation of Variance Components. J. Anim. Breed. Genet. 125:363-370.
  • Pribyl, J., H. Krejcova, J. Pribylova, I. Misztal, S. Tsuruta, and N. Mielenz. 2008. Models for evaluation of growth of performance tested bulls. Czech J. Anim. Sci. 53:45-54.
  • Sanchez, J. P., I. Misztal, I. Aguilar, and J. K. Bertrand. 2008. Genetic evaluation of growth in a multibreed beef cattle population using random regression linear spline models. J. Anim. Sci. 86:267-277.
  • Sanchez, J. P., I. Misztal, and J. K. Bertrand. 2008. Evaluation of methods for computing approximate accuracies in maternal random regression models for growth trait in beef. J. Anim. Sci. 86:1057-1066.
  • Spangler, M. L., R. L. Sapp, J. K. Bertrand, M. D. MacNeil, and R. Rekaya. 2008. Different methods of selecting animals for genotyping to maximize the amount of genetic information known in the population. J. Anim. Sci. 86:2471-2479.
  • Tsuruta, S. and I. Misztal. 2008. Computing options for genetic evaluation with a large number of genetic markers. J. Anim. Sci. 86:1514-1518.
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