Recent Publications

A list for publications before 2013 is also available. You can search the recent and previous publications at the BibSonomy website.

2017

Peer-reviewed paper

  • Andonov, S., D. A. L. Lourenco, B. O. Fragomeni, Y. Masuda, and I. Misztal. 2017. Accuracy of breeding values in small genotyped populations using different sources of external information — A simulation study. J. Dairy Sci. 100:395–401. https://doi.org/10.3168/jds.2016-11335
  • Bradford, H. L., I. Pocrnić, B. O. Fragomeni, D. A. L. Lourenco, I. Misztal. 2017. Selection of core animals in the Algorithm for Proven and Young using a simulation model. J. Anim. Breed. Genet. In Press. https://doi.org/10.1111/jbg.12276
  • Chang, L. -Y., S. Toghiani, A. Ling, E. H. Hay, S. E. Aggrey, R. Rekaya. 2017. Analysis of multiple binary responses using a threshold model. J. Agri. Biol. Env. Stat. https://doi.org/10.1007/s13253-017-0305-6
  • Cui, X., B. Marshall, N. Shi, S. -Y. Chen, R. Rekaya, H.-X. Liu. 2017. RNA-Seq analysis on chicken taste sensory organs: An ideal system to study organogenesis. Sci. Rep. 7:9131. https://dx.doi.org/10.1038%2Fs41598-017-09299-7
  • Fragomeni, B. O., D. A. L. Lourenco, Y. Masuda, A. Legarra, I. Misztal. 2017. Incorporation of causative quantitative trait nucleotides in single-step GBLUP. Genet. Sel. Evol. 49:59. https://doi.org/10.1186/s12711-017-0335-0
  • Habashy, W. S., M. C. Milfort, A. L. Fuller, Y. A. Attia, R. Rekaya, S. E. Aggrey. 2017. Effect of heat stress on protein utilization and nutrient transporters in meat-type chickens. International Journal of Biometeorology. https://doi.org/10.1007/s00484-017-1414-1
  • Masuda, Y., I. Misztal, A. Legarra, S. Tsuruta, D. A. L. Lourenco, B. O. Fragomeni, and I. Aguilar. 2017. Technical note: Avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic BLUP solved with preconditioned conjugate gradient. J. Anim. Sci. 95:49-52. https://doi.org/10.2527/jas.2016.0699
  • Misztal, I. and A. Legarra. 2017. Invited review: efficient computation strategies in genomic selection. Animal. 11:731-736https://doi.org/10.1017/S1751731116002366
  • Pocrnic, I., D. A. L. Lourenco, H. Bradford, C. Y. Chen, I. Misztal. 2017. Technical note: Impact of pedigree depth on convergence of single-step genomic blup in a purebred swine population. J. Anim. Sci. In Press. https://doi.org/10.2527/jas2017.1581
  • Toghiani, S., L. -Y. Chang, A. Ling, S. E. Aggrey, R. Rekaya. 2017. Genomic differentiation as a tool for single nucleotide polymorphism prioritization for genome wide association and phenotype prediction in livestock. Livest. Sci. 205:24-30. https://doi.org/10.1016/j.livsci.2017.09.007
  • Tsuruta, S., D. A. L. Lourenco, I. Misztal, T. J. Lawlor. 2017. Genomic analysis of cow mortality and milk production using a threshold-linear model. J. Dairy Sci. In Press. https://doi.org/10.3168/jds.2017-12665

Presentations

Workshop at PAG XXV in San Diego, CA

  • Lourenco, D. A. L., I. Misztal, B. O. Fragomeni, I. Pocrnic, H. L. Bradford, Y. Masuda, and S. Tsuruta. 2017. How large-scale genomic evaluations are possible: A look into the dimensionality of genomic Information.

Interbull Technical Workshop 2017 in Ljubljana, Slovenia

  • Masuda, Y., I. Misztal, P. M. VanRaden, T. J. Lawlor. 2017. Tests of single-step GBLUP for production traits in US Holsteins. (Slide)
  • Misztal, I., B. O. Fragomeni, Y. Masuda, D. A. L. Lourenco, S. Tsuruta, A. Legarra, I. Aguilar, T. J. Lawlor. 2017. Status of single-step and utility for Interbull / MACE. (Slide)

SIAM Conference on Computational Science & Engineering (CSE17) in Atlanta, GA

  • Masuda, Y. 2017. Selected inversion in quantitative genetics. In MS78: Selected inversion and its application. (Abstract)

Beef Improvement Federation 2017 (BIF) Research Symposium and Convention in Athens, GA

  • Lourenco, D. A. L. 2017. The promise of genomics for beef improvement.

ADSA 2017 Annual Meeting in Pittsburgh, PA

Citation: J. Dairy Sci. Vol. 100, Suppl. 2.

  • Bradford, H. L., I. Pocrnic, B. O. Fragomeni, D. A. L. Lourenco, I. Misztal. 2017. Optimum selection of core animals in the efficient inversion of the genomic relationship matrix.
  • Fragomeni B. O., D. A. L. LourencoY. Masuda, A. Legarra, I. Misztal. 2017. Including causative variants into single-step genomic BLUP.
  • Lourenco, D. A. L., I. R. Menezes, B. O. Fragomeni, H. L. Bradford, S. Tsuruta, I. Misztal. 2017. Impact of SNP selection on genomic prediction for different reference population sizes.
  • Masuda, Y., I. Misztal, P. M. VanRaden, T. J. Lawlor. 2017. Genetic trends from single-step GBLUP and traditional BLUP for production traits in US Holstein.
  • Pocrnic, I., D. A. L. Lourenco, H. L. Bradford, C. Y. ChenI. Misztal. 2017. Impact of pedigree truncation on accuracy and convergence of ssGBLUP in a population with long pedigree when only a fraction of animals are phenotyped.
  • Tsuruta, S., T. J. Lawlor, D. A. L. Lourenco, Y. Masuda, I. Misztal. 2017. Genetic trends of linear type traits for validation of genomic evaluation in US Holsteins.

ASAS 2017 Annual Meeting in Baltimore, MD

Citation: J. Anim. Sci. Vol. 95, Suppl. 4.

  • Bradford H. L., I. Pocrnic, B. O. Fragomeni, D. A. L. Lourenco, I. Misztal. 2017. Optimum selection of core animals in the efficient inversion of the genomic relationship matrix.
  • Chang, L. Y., S. Toghiani, S. E. Aggrey, R. Rekaya. 2017. Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms.
  • Fragomeni, B. O., D. A. L. Lourenco, Y. Masuda, A. Legarra, I. Misztal. 2017. Including causative variants into single step genomic BLUP.
  • Garcia, A. L., C. Sary, H. M. Karin, R. P. Ribeiro, D. A. L. Lourenco, S. Tsuruta, C. A. Oliveira. 2017. Fillet yield and quality traits as selection criteria for Nile tilapia (Oreochromis niloticus) breeding.
  • Ling, A., P. Sumreddee, E. H. A. Hay, R. Rekaya, S. E. Aggrey. 2017. Analysis of misclassified categorical responses.
  • Lourenco D. A. L., B. O. Fragomeni, H. L. Bradford, I. Menezes, S. Tsuruta, I. Misztal. 2017. Impact of SNP selection on genomic prediction for different reference population sizes.
  • Silva, R. M. O., R. L. Vallejo, J. P. Evenhuis, T. D. Leeds, G. Gao, J. E. Parsons, K. E. Martin, D. A. L. Lourenco, Y. Palti. 2017. Prospecting genomic regions associated with columnaris disease in two rainbow trout breeding populations.
  • Sumreddee, P., S. Toghiani, S. E. Aggrey, R. Rekaya. 2017. Joint genome-wide association analysis of continuous and discrete traits.
  • Toghiani, S., L. Y. Chang, S. E. Aggrey, R. Rekaya. 2017. A hybrid of prioritized SNP and polygenetic effect method for implementation of genomic selection.

EAAP 2017 in Tallinn, Estonia

  • Bradford, H. L., I. Pocrnic, B. O. Fragomeni, D. A. L. Lourenco, I. Misztal. 2017. Optimum selection of core animals in the e!cient inversion of the genomic relationship matrix.
  • Lourenco, D. A. L., B. O. Fragomeni, Y. Masuda, A. Legarra, I. Misztal. 2017. Can single-step genomic BLUP account for causative variants?
  • Lourenco, D. A. L., X. Zhang, S. Tsuruta, S. Andonov, R. L. Sapp, C. Wang, I. Misztal. 2017. Relationships among mortality, performance, and disorder traits in broiler chickens.
  • Misztal, I., Y. Masuda, P.M. VanRaden, T.J. Lawlor. 2017. Genetic trends from single-step GBLUP and traditional BLUP for production traits in US Holstein. (joint session with Interbull)

Interbull Annual Meeting 2017 in Tallinn, Estonia

  • Misztal, I., S. Tsuruta, Y. Masuda, D. A. L. Lourenco, T. J. Lawlor, 2017. Studies on inflation of GEBV in single-step GBLUP for type.

2016

Peer-reviewed paper

  • Bradford, H. L., B. O. Fragomeni, D. A. L. Lourenco, and I. Misztal. 2016. Genetic evaluations for growth heat tolerance in Angus beef cattle. J. Anim. Sci. 94: 4143–4150. https://doi.org/10.2527/jas.2016-0707
  • Bradford, H. L., B. O. Fragomeni, D. A. L. Lourenco, and I. Misztal. 2016. Regional and seasonal analysis of weight in growing Angus cattle. J. Anim. Sci. 94:4369–4375. https://doi.org/10.2527/jas.2016-0683
  • Fragomeni, B. O., D. A. L. Lourenco, S. Tsuruta, S. Andonov, K. Gray, Y. Huang, and I. Misztal. 2016. Modeling response to heat stress in pigs from nucleus and commercial farms in different locations. J. Anim. Sci. 94:4789-4798. https://doi.org/10.2527/jas.2016-0536
  • Fragomeni, B.O., D. A. L. Lourenco, S. Tsuruta, K. Gray, Y. Huang, and I. Misztal. 2016. Using single step genomic BLUP to enhance the mitigation of seasonal losses due to heat stress in pigs. J. Anim. Sci. 94:5004-5013. https://doi.org/10.2527/jas.2016-0820
  • 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. https://doi.org/10.2527/jas.2015-9748
  • 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. https://doi.org/10.3168/jds.2015-10540
  • Misztal, I. 2016. Inexpensive computation of the inverse of the genomic relationship matrix in populations with small effective population size. Genetics 202:411-409. https://doi.org/10.1534/genetics.115.182089
  • 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. https://doi.org/10.1534/genetics.116.187013
  • Pocrnic, I., D. A. L. Lourenco, Y. Masuda, and I. Misztal. 2016. Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species. Genet. Sel. Evol. 48:82. https://doi.org/10.1186/s12711-016-0261-6
  • Silva, R. M. O., B. O. Fragomeni, D. A. L. Lourenco, F. B. Magalhaes, N. Irano, R.  Carvalheiro, A. A. Boligon, M. E. Z. Mercadante, R. C. Canesin, F. S. Baldi, I. Misztal, and L. G. Albuquerque. 2016. Accuracies of genomic prediction of feed efficiency traits using different prediction and validation methods in an experimental Nellore cattle population. J. Anim. Sci. 94:3613–3623. https://doi.org/10.2527/jas.2016-0401
  • Toghiani, S., Aggrey, S. E., and Rekaya, R. 2016. Multi-generational imputation of single nucleotide polymorphism marker genotypes and accuracy of genomic selection. Animal. 10:1077-1085. https://doi.org/10.1017/S1751731115002906
  • Van der Heide, E.M.M., D. A. L. Lourenco, C. Y. Chen, W. O. Herring, R. L. Sapp, D. W. Moser, S. Tsuruta, Y. Masuda, B. J. Ducro, and I. Misztal. 2016. Sexual dimorphism in livestock species selected for economically important traits. J. Anim. Sci. 94:3684-3692. https://doi.org/10.2527/jas.2016-0393
  • Vallejo, R. L., T. D. Leeds, B. O. Fragomeni, G. Gao, A. G. Hernandez, I. Misztal, T. J. Welch, G. D. Wiens, and Y. Palti. 2016. Evaluation of Genome-enabled selection for bacterial cold water disease resistance using progeny performance data in rainbow trout: insights on genotyping methods and genomic prediction models. Frontiers Genet. 7:96. https://doi.org/10.3389/fgene.2016.00096
  • 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. https://doi.org/10.1186/s12711-016-0185-1
  • Zhang, X., D. A. L. Lourenco, I. Aguilar, A. Legarra, and I. Misztal. 2016. Weighting strategies for single-step genomic BLUP: an iterative approach for accurate calculation of GEBV and GWAS. Frontiers in Genet. 7:151. https://doi.org/10.3389/fgene.2016.00151

Presentations

Joint Annual Meeting 2016 (JAM) in Salt Lake City, UT

Citation: J. Anim. Sci Vol. 94, E-Suppl. 5 or J. Dairy Sci. Vol. 99, E-Suppl. 1.

  • Bradford, H. L., B. O. Fragomeni, D. A. L. Lourenco, and I. Misztal. 2016. Genetic evaluation for heat tolerance in growing Angus cattle. (Slide)
  • Chang, L. Y., S. Toghiani, S. E. Aggrey, and R. Rekaya. 2016. High density marker panels, SNPs prioritizing and accuracy of genomic selection.
  • Fragomeni, B. O., D. A. L. Lourenco, S. Tsuruta, K. A. Gray, Y. Huang, and I. Misztal. 2016. Genetics of heat stress in purebred and crossbred pigs from different states using BLUP or ssGBLUP.
  • Lourenco, D. A. L.,S. Tsuruta, B. O. Fragomeni, Y. Masuda, I. Pocrnic, I. Aguilar, J. K. Bertrand, D. W. Moser, and I. Misztal. 2016. Issues in commercial application of single-step genomic BLUP for genetic evaluation in American Angus. (Slide)
  • Masuda, Y., I. Misztal, and P. M. VanRaden. 2016. Single-step GBLUP using APY inverse for protein yield in U.S. Holstein with a large number of genotyped animals. (Slide)
  • Misztal, I., I. Pocrnic, D. A. L. Lourenco, and Y. Masuda. 2016. APY inverse of genomic relationship matrix– theory, analyses and questions. (Slide)
  • Misztal, I. 2016. Resilience and lessons from studies in genetics of heat stress. (Slide)
  • Pocrnic, I., D. A. L. Lourenco, Y. Masuda, A. Legarra, and I. Misztal. 2016.  Dimensionality of genomic information and APY inverse of genomic relationship matrix.
  • Toghiani, S., L. Y. Chang, S. E. Aggrey, and R. Rekaya. 2016. SNP filtering using Fst and implications for genome wide association and phenotype prediction.
  • Tsuruta, S., D. A. L. Lourenco, Y. Masuda, D. W. Moser, and I. Misztal. 2016. Practical approximation of accuracy in genomic breeding values for a large number of genotyped animals.

EAAP 2016 in Belfast, UK

  • Masuda, Y., I. Misztal, and T. J. Lawlor. 2016 Single-step GBLUP using APY inverse for protein yield in US Holstein.
  • Misztal, I., I. Pocrnic, D. Lourenco, and Y. Masuda. 2016. APY inverse of genomic relationship matrix: theory, analyses and questions.
  • Pocrnic, I., I. Misztal, D.A.L. Lourenco, Y. Masuda, and A. Legarra. 2016. Dimensionality of genomic information and APY inverse of the genomic relationship matrix. (Slide)

Interbull meeting 2016 (with ICAR) in Puerto Varas, Chile

  • Lawlor, T, J., S. Tsuruta, D. A. L. Lourenco, B. O. Fragomeni, I. Aguilar, and I. Misztal. 2016. Reliabilities in single-step evaluation for udder depth in US Holsteins with different numbers of genotyped animals and external information from Interbull evaluations.

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. https://doi.org/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. https://doi.org/10.3168/jds.2014-9125
  • 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. https://doi.org/10.1111/jbg.12161
  • 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. https://doi.org/10.3382/ps/peu052
  • 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. https://doi.org/10.3382/ps/pev056
  • 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. https://doi.org/10.3382/ps/pev159
  • Hay, E. H., and R. Rekaya. 2015. A multi-compartment model for genomic selection in multi-breed populations. Livest. Sci. 177:1-7. https://doi.org/10.1016/j.livsci.2015.03.027
  • Hay, E. H., and R. Rekaya. 2015. A structural model for genetic similarity in genomic selection of admixed populations. Livest. Sci. 181: 72-76. https://doi.org/10.1016/j.livsci.2015.10.009
  • 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, 290:1673-1682. https://doi.org/10.1007/s00438-015-1025-7
  • 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. https://doi.org/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. https://doi.org/10.1186/s12711-015-0137-1
  • 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. https://doi.org/10.2527/jas.2014-8836
  • 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. https://doi.org/10.2527/jas.2014-8285
  • 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. https://doi.org/10.2527/jas.2015-9395
  • 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. https://doi.org/10.2527/jas.2014-8061
  • 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. 98:5796-805. https://doi.org/10.3168/jds.2014-9242
  • 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. https://doi.org/10.3168/jds.2014-8529
  • 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. https://doi.org/10.1016/j.livsci.2014.11.003

2014

Peer-reviewed paper

  • Aggrey, S. E., J. Lee, A. B. Karnuah, and R. Rekaya2014Transcriptomic analysis of genes in the nitrogen recycling pathway of meat-type chickens divergently selected for feed efficiencyAnim. Genet. 45:215-222. https://doi.org/10.1111/age.12098
  • 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.  https://doi.org/10.1016/j.livsci.2014.05.001
  • 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. https://doi.org/10.3389/fgene.2014.00332
  • Froetschel, M. A., C. L. Ross, R. L. Stewart, M. J. Azain, P. Michot, and R. Rekaya2014Nutritional value of ensiled grocery food waste for cattleJ. Anim. Sci. 92:5124-5133. https://doi.org/10.2527/jas.2014-8126
  • Legarra, A., O. F. Christensen, I. Aguilar, and I. Misztal. 2014. Single step, a general approach for genomic selection. Livest. Sci. 166:54-65. https://doi.org/10.1016/j.livsci.2014.04.029
  • 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. https://doi.org/10.3168/jds.2013-6916
  • 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. https://doi.org/10.3168/jds.2013-7769
  • 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. https://doi.org/10.3168/jds.2013-7752
  • 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. https://doi.org/10.3168/jds.2013-7697
  • 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. https://doi.org/10.3168/jds.2013-7821
  • 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. https://doi.org/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. Anim. Breed. Genet. https://doi.org/10.1111/jbg.12109