Previous publications (-2018)

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

2018

Peer-reviewed paper

  • Garcia, A. L. S., B. Bosworth, G. Waldbieser, I. Misztal, S. Tsuruta, D. A. L. Lourenco. 2018. Development of genomic predictions for harvest and carcass weight in channel catfish. Genet. Sel. Evol. 50:66. https://doi.org/10.1186/s12711-018-0435-5
  • Guarini, A. R., D. A. L. Lourenco, L. F. Brito, M. Sargolzaei, C. F. Baes, F. Miglior, I. Misztal, and F. S. Schenkel. 2018. Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic best linear unbiased predictor in Holstein cattle. J. Dairy Sci. 101:8076-8086. https://doi.org/10.3168/jds.2017-14193
  • Junqueira, V.S., P.S. Lopes, M.D.V. Resende, F.F. Silva, D.A.L. Lourenco, M.J. Yokoo, F.F. Cardoso. 2018. Impact of embryo transfer phenotypic records on large-scale beef cattle genetic evaluations. R. Bras. Zootec. 47:e20170033. https://doi.org/10.1590/rbz4720170033
  • Maiorano, A.M., D.A.L. Lourenco, S. Tsuruta, A.M. Toro, N.B. Stafuzza, Y. Masuda, A. Vercesi Filho, J.N.S.G. Cyrillo, R.A. Curi, J.A.V. Silva. 2018. Assessing genetic architecture and signatures of selection of dual purpose Gir cattle populations using genomic information. PlosONE. 13(8):e0200694. https://doi.org/10.1371/journal.pone.0200694
  • Masuda, Y., P. M. VanRaden, I. Misztal, and T. J. Lawlor. 2018. Differing genetic trend estimates from traditional and genomic evaluations of genotyped animals as evidence of preselection bias in US Holsteins. J. Dairy Sci. 101:5194-5206. https://doi.org/10.3168/jds.2017-13310
  • Oliveira, D. P., D. A. L. Lourenco, S. Tsuruta, I. Misztal, D. J. A. Santos, F. R. de Araújo Neto, R. R. Aspilcueta-Borquis, F. Baldi, R. Carvalheiro, G. M. F. de Camargo, L. G. Albuquerque, and H. Tonhati. 2018. Reaction norm for yearling weight in beef cattle using single-step genomic evaluation. J. Anim. Sci. 96:27-34. https://doi.org/10.1093/jas/skx006
  • Vallejo R.L., Silva R.M.O., Evenhuis J.P., Gao G., Liu S., Parsons J.E., Martin K.E., Lourenco D.A.L., Leeds T.D. & Y. Palti. 2018. Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low-density SNP panels: Evidence that long-range LD is a major contributing factor. J. Anim. Breed. Genet. 135:263-274. https://doi.org/10.1111/jbg.12335
  • Zhang, X., S. Tsuruta, S. Andonov, D. A. L. Lourenco, R. L. Sapp, C. Wang, and I. Misztal. 2018. Relationships among mortality, performance, and disorder traits in broiler chickens: a genetic and genomic approach. Poultry Sci. 97:1511-1518. https://doi.org/10.3382/ps/pex431

Presentations

Workshop at PAG XXVI in San Diego, CA

  • Misztal, I.2018. Studies in genetics of heat stress in dairy, beef and pigs. [PDF]
  • Garcia, A., B. Bosworth, G. Waldbieser, S. Tsuruta, I. Misztal, and D.A.L. Lourenco. 2018. Genomic evaluation for harvest and residual carcass weight in channel catfish using ssGBLUP. [PDF]
  • Silva, R.M.O., J.P. Evenhuis, R.L. Vallejo, G. Gao, K.E. Martin, I. Misztal, T.D. Leeds, D.A.L. Lourenco, and Y. Palti. 2018. GWAS for detecting QTL associated with columnaris disease in two rainbow trout populations. [PDF]

2018 Interbull Meeting, Auckland, New Zealand

  • Lourenco, D. A. L, A. Legarra, S. Tsuruta, I. Misztal. 2018. Tuning indirect predictions based on SNP effects from single-step GBLUP.  [PDF]

The 11th World Congress on Genetics Applied to Livestock Production (WCGALP), Auckland, New Zealand

  • Aguilar, I, S. Tsuruta, Y. Masuda, D. A. L. Lourenco, A. Legarra, I. Misztal. 2018. BLUPF90 suite of programs for animal breeding with focus on genomics. No. 11.751. [PDF]
  • Bradford, H. L., B. O. Fragomeni, S. Tsuruta. J. K. Bertrand, K. Gray, Y. Huang, D. A. L. Lourenco, I. Misztal. 2018. Genetic evaluations for heat tolerance in meat animal species. No. 11.1. [PDF]
  • Fragomeni, B. O., D. A. L. Lourenco, M. E. Tooker, P. M. VanRaden, A. Legarra, I. Misztal. 2018. Use of causative variants and SNP weighting in a single-step GBLUP context. [PDF]
  • Garcia, A., B. Bosworth, G. Waldbieser, S. Tsuruta, I. Misztal, D. A. L. Lourenco. 2018. Genomic evaluation for harvest weight and residual carcass weight in channel catfish using single-step genomic BLUP. No. 11.188. [PDF]
  • Lourenco, D. A. L., S. Tsuruta, B. O. Fragomeni, Y. Masuda, I. Aguilar, A. Legarra, D. Moser, S. Miller, I. Misztal. 2018. Single-step genomic BLUP for national beef cattle evaluation in US: from initial developments to final implementation. No. 11.495. [PDF]
  • Masuda, Y. I. Misztal, P. M. VanRaden. T. J. Lawlor. 2018. Pre-selection bias and validation method in single-step GBLUP for production traits in US Holstein. No. 11.540. [PDF]
  • Misztal, I. 2018. Extension of single-step ssGBLUP to many genotyped individuals. No. 11.306. [PDF]
  • Pocrnic, I., D. A. L. Lourenco, Y. Masuda, A. Legarra, I. Misztal. 2018. Limited dimensionality of genomic information and effective population size. No. 11.32. [PDF]
  • Oliveira H.R., F.F. Silva, L.F. Brito, J. Jamrozik, D.A.L. Lourenco & F.S. Schenkel. 2018. Genome-wide association study for milk, fat and protein yields in different lactation stages in Canadian Holstein and Jersey cattle. No. 11.601. [PDF]
  • Stafuzza N.B., R.M.O. Silva, E. Peripolli1, R.B. Lôbo, C.U. Magnabosco, F.A. Di Croce, J.B. Osterstock, D.P. Munari, D.A.L. Lourenco & F. Baldi. 2018. Investigation of genes related with polledness in Nellore beef cattle by genome-wide association. No. 11.640. [PDF]
  • Tsuruta, S., D. A. L. Lourenco, I. Misztal, T. J. Lawlor. 2018. Possible causes of inflation in genomic evaluations for dairy cattle. [PDF]
  • Vallejo R.L., R.M. Silva, J.P. Evenhuis, G. Gao, S. Liu, J.E. Parsons, K.E. Martin, D.A.L. Lourenco, T.D. Leeds & Y. Palti. 2018. Accurate genomic predictions for bacterial cold water disease resistance using low-density SNP panels in rainbow trout.
  • Zhang, X., S. Tsuruta, S. Andonov, D. A. L. Lourenco, R. Sapp, C. Wang, I. Misztal. 2018. Genetics of performance and disorder traits of broiler chicken. No. 11.255. [PDF]

ADSA 2018 Annual Meeting in Knoxville, TN

Citation: J. Dairy Sci. Vol. 101, Suppl. 2.

  • Bradford, H. L., Y. Masuda, J. B. Cole, I. Misztal, and P. M. VanRaden. 2018. Modelling uncertain paternity to address differential pedigree accuracy. [PDF]
  • Fragomeni, B. O., D. A. L. Lourenco, Y. Masuda, and I. Misztal. 2018. Implementing SNP-level multiple-trait across country genomic evaluation without genotype sharing.
  • Lourenco, D. A. L.,  A. Legarra, S. Tsuruta, D. Moser, S. Miller, and I. Misztal. 2018. Indirect predictions based on SNP effects from single-step GBLUP in large genotyped populations. [PDF]
  • Masuda, Y., I. Misztal, P. M. VanRaden, and T. J. Lawlor. 2018. Genomic predictability of single-step GBLUP for production traits in US Holstein. [PDF]
  • Misztal, I., I. Pocrnic, and D. A. L. Lourenco. 2018. Implications of limited dimensionality of genomic information on persistency of genomic predictions and GWAS.
  • Tsuruta, S., D. A. L. Lourenco, I. Misztal, and T. J. Lawlor. 2018. Causes of inflation in genomic evaluations for young genotyped dairy bulls. [PDF]

ASAS 2018 Annual Meeting in Vancouver, BC

Citation: J. Anim Sci. Vol. 96, Suppl. ?.

  • Lourenco, D.A.L., B.O. Fragomeni, S. Tsuruta, and I. Misztal. 2018. Selection to mitigate heat stress in pigs. [PDF]
  • Pocrnic, I., D.A.L. Lourenco, S. Tsuruta, C.Y. Chen, and I. Misztal. 2018. Practical problems and solutions using unknown parent groups in combined commercial pig sub-lines. [PDF]
  • Steyn, Y., D.A.L. Lourenco, B.O. Fragomeni, I. Misztal. 2018. Accuracies of multi-breed genomic evaluations of breeds that share the same SNP effects [PDF]
  • Costa-Maia, F.M., D.A.L. Lourenco, S. Tsuruta, and E. N. Martins. 2018. Selection criteria for improving honey production in Africanized honey bees. [PDF]

EAAP 2018 in Dubrovnik, Croatia

  • Andonov, S., C. Costa, A. Uzunov, P. Bergomi, D. Lourenco and I. Misztal. 2018. Covariance estimation for yield and behavioural traits in Italian honey bee by linear-threshold model.
  • Lourenco, D.A.L., V.S. Junqueira, Y. Masuda and I. Misztal. 2018. Single-step genomic REML with algorithm for proven and young and reduced pedigree.
  • Masuda, Y., I. Misztal, P.M. Vanraden and T.J. Lawlor. 2018. Genomic predictability of single-step GBLUP for production traits in US Holstein.
  • Misztal, I., I. Pocrnic, and D.A.L. Lourenco. 2018. Possible implications of limited dimensionality of genomic information.
  • Oliveira, H.R, L.F. Brito, D.A.L. Lourenco, Y. Masuda, I. Misztal, S. Tsuruta, J. Jamrozik, F.F. Silva and F.S. Schenkel. 2018. Application of ssGBLUP using random regression models in the Ayrshire and Jersey breeds.
  • Oliveira, H.R., L.F. Brito, D.A.L. Lourenco, Y. Masuda, I. Misztal, S. Tsuruta, J. Jamrozik, F.F. Silva and F.S. Schenkel. 2018. Effect of including only genotype of animals with accurate proofs in ssGBLUP using random regression.
  • Pocrnic, I., D.A.L. Lourenco and I. Misztal. 2018. Deriving dimensionality of genomic information from limited SNP information.

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. 134:545-552. 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
  • Lourenco D. A. L., B. O. Fragomeni, H. L. Bradford, I. R. Menezes, J. B. Ferraz, I. Aguilar, S. Tsuruta, I. Misztal. 2017. Implications of SNP weighting on single‐step genomic predictions for different reference population sizes. J. Anim. Breed. Genet. 134:463-471. https://doi.org/10.1111/jbg.12288
  • 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. 11:731-736. https://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. 95:3391-3395. 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
  • Tonussi, R. L., R. M. de Oliveira Silva, A. F. Magalhães, R. Espigolan, E. Peripolli, B. F. Olivieri, F. L. Feitosa, M. V. Lemos, M. P. Berton, H. L. Chiaia, A. S. Pereira. R. B. Lôbo, L. A. F. Bezerra, C. U. Magnabosco, D. A. L. Lourenco, I. Aguilar. 2017. Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data. PLoS ONE. 12:e0181752. https://doi.org/10.1371/journal.pone.0181752
  • 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. 100:7295-7305. 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. Lourenco, Y. 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. Chen, I. 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 efficient 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. 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. 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. Rekaya. 2014. Nutritional value of ensiled grocery food waste for cattle. J. 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