Source: https://dysci.wisc.edu/research/genetics/
Timestamp: 2019-04-19 22:58:23+00:00

Document:
Rathbun, F., R. Pralle, S. Bertics, L. Armentano, K-H. Cho, C. Do, K. Weigel, and H. White. 2017. Relationships between body condition score change, prior mid-lactation phenotypic residual feed intake, and hyperketonemia onset in transition dairy cows. Journal of Dairy Science 100:3685-3696.
Weigel, K. A., R. S. Pralle, H. Adams, K. Cho, C. Do, and H. M. White. 2017. Prediction of whole genome risk for selection and management of hyperketonemia in Holstein dairy cattle. Journal of Animal Breeding and Genetics 134:275-285.
Yao, C., G. de los Campos, M. VandeHaar, D. Spurlock, L. Armentano, M. Coffey, Y. de Haas, R. Veerkamp, C. Staples, E. Connor, Z. Wang, M. Hanigan, R. Tempelman, and K. Weigel. 2017. Use of genotype x environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle. Journal of Dairy Science 100:2007-2016.
Mikshowsky, A. A., D. Gianola, and K. A. Weigel. 2017. Assessing genomic prediction accuracy for Holstein sires using bootstrap aggregation sampling and leave-one-out cross validation. Journal of Dairy Science 100:453-464.
Lu, Y., M. J. VandeHaar, D. M. Spurlock, K. A. Weigel, L. E. Armentano, C. R. Staples, E. E. Connor, Z. Wang, M. Coffey, R. F. Veerkamp, Y. de Haas and R. J. Tempelman. 2017. Modeling genetic and nongenetic variation of feed efficiency and its partial relationships between component traits as a function of management and environmental factors. Journal of Dairy Science 100:412-427.
Abdalla, E. A., F. Peñagaricano, T. M. Byrem, K. A. Weigel, and G. J. M. Rosa. 2016. Genome-wide association mapping and pathway analysis of leukosis incidence in a US Holstein population. Animal Genetics 47:395-407.
Abdalla, E. A., K. A. Weigel, T. M. Byrem, and G. J. M. Rosa. 2016. Short communication: Genetic correlation of bovine leukosis incidence with somatic cell score and milk yield in a US Holstein population. Journal of Dairy Science 99:2005-2009.
Manzanilla-Pech, C. I. V., R. F. Veerkamp, R. J. Tempelman, M. L. van Pelt, K. A. Weigel, M. VandeHaar, T. J. Lawlor, D. M. Spurlock, L. E. Armentano, C. R. Staples, M. Hanigan, E. E. Connor, and Y. De Haas. 2016. Genetic parameters between feed-intake-related traits and conformation in 2 separate dairy populations – the Netherlands and United States. Journal of Dairy Science 99:443-457.
Mikshowsky, A. A., D. Gianola, and K. A. Weigel. 2016. Improving reliability of genomic predictions for Jersey sires using bootstrap aggregation sampling. Journal of Dairy Science 99:3632-3645.
VandeHaar, M. J., L. E. Armentano, K. Weigel, D. M. Spurlock, R. J. Tempelman, and R. Veerkamp. 2016. Harnessing the genetics of the modern dairy cow to continue improvement in feed efficiency. Journal of Dairy Science 99:4941-4954.
Yao, C., X. Zhu, and K. A. Weigel. 2016. Semi-supervised learning for genomic prediction of novel traits with small reference populations: An application to residual feed intake in dairy cattle. Genetics Selection Evolution 48:84-92.
Bjelland, D. W., K. A. Weigel, A. D. Coburn, and R. D. Wilson. 2015. Using a family-based structure to detect the effects of genomic inbreeding on embryo viability in Holstein cattle. Journal of Dairy Science 98:4934-4944.
de Haas, Y., J. E. Pryce, M. P. L. Calus, E. Wall, D. Berry, P. Lovendahl, N. Krattenmacher, F. Miglior, K. A. Weigel, D. M. Spurlock, K. Macdonald, B. Hulsegge, and R. F. Veerkamp. 2015. Genomic prediction of dry matter intake in dairy cattle from an international data set consisting of research herds in Europe, North America, and Australasia. Journal of Dairy Science 98:6522-6534.
Lu, Y., M. J. VandeHaar, D. M. Spurlock, K. A. Weigel, L. E. Armentano, C. R. Staples, E. E. Connor, Z. Wang, N. M. Bello, and R. J. Tempelman. 2015. An alternative approach to modeling genetic merit of feed efficiency in dairy cattle. Journal of Dairy Science 98:6535-6551.
Shahinfar, S., J. Guenther, C. D. Page, A. Kalantari, V. Cabrera, P. Fricke, and K. Weigel. 2015. Optimization of reproductive management programs using lift chart analysis and cost-sensitive evaluation of classification errors. Journal of Dairy Science 98:3717-3728.
Hardie, L., L.E. Armentano, R.D. Shaver, M.J. VandeHaar, D.M. Spurlock, C. Yao, S. Bertics, F. Contreras-Govea, and K.A. Weigel. 2015. Considerations when combining data from multiple nutrition experiments to estimate genetic parameters for feed efficiency. Journal of Dairy Science. 98:2727-2737.
Tempelman, R.J., D.M. Spurlock, M. Coffey, R.F. Veerkamp, L.E. Armentano, K.A. Weigel, Y. de Haas, C.R. Staples, M.D. Hanigan and M.J. VandeHaar. 2014. Heterogeneity in genetic and non-genetic variation and energy sink relationships for residual feed intake across research stations and countries. Journal of Dairy Science. 98:2013-2026.
Yao, C., L.E. Armentano, M.J. VandeHaar and K.A. Weigel. Short communication: Use of single nucleotide polymorphism genotypes and health history to predict future phenotypes for milk production, dry matter intake, body weight, and residual feed intake in dairy cattle. Journal of Dairy Science. 98:2027-2032.
Berry, D., M. Coffey, J. Pryce, Y. de Haas, P. Lovendahl, N. Krattenmacher, J. Crowley, Z. Wang, D. Spurlock, K. Weigel, K. Macdonald and R. Veerkamp. 2014. International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources. Journal of Dairy Science. 97:3894-3905.
Gianola, D., K.A. Weigel, N. Kramer, A. Stella and C.C. Schon. 2014. Enhancing genome-enabled prediction by Bagging Genomic BLUP. PLoS ONE 9(4):e91693.
Pryce, J.E., J. Johnston, B.J. Hayes, G. Sahana, K.A. Weigel, S. McParland, D. Spurlock, N. Krattenmacher, R.J. Spelman, E. Wall and M.P.L. Calus. 2014. Imputation of genotypes from low density (50,000 markers) to high density (700,000 markers) of cows from research herds in Europe, North America and Australasia using 2 reference populations. J. Dairy Sci. 97:1799-1811.
Shahinfar, S., A.S. Kalantari, V. Cabrera and K.A. Weigel. 2014. Short communication: Prediction of retention pay-off using a machine learning algorithm. J. Dairy Sci. 97:2949-2952.
Shahinfar, S., D. Page, J. Guenther, V. Cabrera, P. Fricke and K.A. Weigel. 2014. Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms. J. Dairy Sci. 97:731-742.
Yao, C., K.A. Weigel and J.B. Cole. 2014. Short communication: Genetic evaluation of stillbirth in US Brown Swiss and Jersey cattle. J. Dairy Sci. 97:2474-2480.
Valente, B. D., G. Morota, G.J.M. Rosa, D. Gianola, and K.A. Weigel. 2014. The causal meaning of genomic predictors and how it affects the construction of genome-enabled selection models. eprint arXiv:1401.1165.
Van Eenannaam, A.L., K.A. Weigel, A.E. Young, M.A. Cleveland and J.C.M. Dekkers. 2014. Applied animal genomics: Results from the field. Annual Reviews in Animal Biosciences. 2:105-139.
Weigel, K.A. 2014. Genetic improvement programs for dairy cattle. Molecular and Quantitative Animal Genetics, H. Khatib, editor. Wiley-Blackwell (in press).
Weigel, K.A. 2014. Genomic selection, inbreeding, and crossbreeding in dairy cattle. Molecular and Quantitative Animal Genetics, H. Khatib, editor. Wiley-Blackwell (in press).
Yao, C., N. Leng, K.A. Weigel, K.E. Lee, C.D. Engelman and K.J. Meyers. 2014. Prediction on genetic contributions of complex traits using whole genome sequencing data. BioMed Central (BMC) Proceedings from Genetic Analysis Workshop 18 (in press). 8(Suppl 1):S68.
Yao, C., K.A. Weigel and J.B. Cole. 2014. Short communication: Genetic evaluation of stillbirth in US Brown Swiss and Jersey cattle. Journal of Dairy Science. 97:2474-2480.
Tusell, L., P. Pérez‐Rodríguez, S. Forni and D. Gianola. 2014. Model averaging for genome‐enabled prediction with reproducing kernel Hilbert spaces: a case study with pig litter size and wheat yield. Journal of Animal Breeding and Genetics.131:105-115.
V.P.S. Felipe, H. Okut, D. Gianola, M.A. Silva and G.J. M Rosa. 2014. Effect of genotype imputation on genome-enabled prediction of complex traits: an empirical study with mice data. BMC Genetics. 15:149.
Abdalla, E.A., G.J.M. Rosa, K.A. Weigel and T. Byrem. 2013. Genetic analysis of leukosis incidence in United States Holstein and Jersey populations. J. Dairy Sci. 96:6022-6029.
Bjelland, D.W., K.A. Weigel, N. Vukasinovic and J.D. Nkrumah. 2013. Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding. J. Dairy Sci. 96:4697-4706.
Curran, R., K.A. Weigel, P. Hoffman, J. Marshall, K. Kuzdas and W. Coblentz. 2013. Relationships between age at first calving, herd management criteria, and lifetime milk, fat, and protein production in Holstein cattle. Professional Animal Scientist. 29:1-9.
Gambra, R., F. Peñagaricano, J. Kropp, K. Khateeb, K.A. Weigel, J. Lucey and H. Khatib. 2013. Genomic architecture of kappa-casein and beta-lactoglobulin. J. Dairy Sci. 96:5324-5332.
Jiménez-Montero, J.A., D. Gianola, K.A. Weigel, R. Alenda and O. González-Recio. 2013. Assets of imputation to ultra-high density for productive and functional traits. J. Dairy Sci. 96:6047-6058.
Lan, X. Y., F. Peñagaricano, L. DeJung, K.A. Weigel and H. Khatib. 2013. Short communication: A missense mutation in the PROP1 (prophet of Pit 1) gene affects male fertility and milk production traits in the US Holstein population. J. Dairy Sci. 96:1255-1257.
H. Okut, X-L Wu, G. J.M. Rosa, S. Bauck, B.W. Woodward, R.D. Schnabel, J.F. Taylor and D. Gianola. 2013. Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models. Genetics Selection Evolution. 45:34.
T.M. Beissinger, C.N. Hirsch, B. Vaillancourt, S. Deshpande, K. Barry, C.R. Buell, S.M. Kaeppler, D. Gianola and N. de Leon. 2014. Genome-Wide Scan for Selection Following Thirty Generations of Artificial Selection for Increased Number of Ears per Plant in the Golden Glow Maize Population. Genetics. 196: 829-840.
Morota, G., M. Koyama, G.J.M. Rosa, K.A. Weigel, and D. Gianola. 2013. Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat breeding. Genetics Selection Evolution. 45:17-31.
Peñagaricano, F., K.A. Weigel, G.J.M. Rosa, and H. Khatib. 2013. Inferring quantitative trait pathways associated with bull fertility from a genome-wide association study. Frontiers in Genetics. 3:307:1-7.
Pérez-Rodriguez, P., D. Gianola, K.A. Weigel, G.J.M. Rosa, and J. Crossa. 2013. Technical Note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding. Journal of Animal Science. 91:3522-3531.
Sun, C., P.M. VanRaden, J.R. O’Connell, K.A. Weigel, and D. Gianola. 2013. Mating programs including genomic relationships and dominance effects. J. Dairy Sci. 96:8014-8023.
Valente, B., K.A. Weigel, G.J.M. Rosa and D. Gianola. 2013. Is structural equation modeling advantageous for the genetic improvement of multiple traits? Genetics. 194:561-572.
Yao, C., D. Spurlock, K.A. Weigel, L.E. Armentano and M. VandeHaar. 2013. Random forests approach for identifying additive and epistatic SNPs associated with residual feed intake in dairy cattle. J. Dairy Sci. 96:6716-6729.
R. Abdollahi‐Arpanahi, A. Pakdel, A. Nejati‐Javaremi, M. Moradi Shahrbabak, G. Morota, B.D. Valente, A. Kranis, G.J.M. Rosa and D. Gianola. 2014. Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens. Journal of Animal Breeding and Genetics.131:123-133.
Gota Morota, Prashanth Boddhireddy, Natascha Vukasinovic, D. Gianola and Sue DeNise. 2014. Kernel-based variance component estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits. Frontiers in Genetics. 5:56.
Gota Morota, Rostam Abdollahi-Arpanahi, Andreas Kranis and Daniel Gianola. 2014. Genome-enabled prediction of quantitative traits in chickens using genomic annotation. BMC Genomics. 15:109.
J. Cuevas, S. Pérez-Elizalde, V. Soberanis, P. Pérez-Rodríguez, D. Gianola and J. Crossa. 2014. Bayesian Genomic-Enabled Prediction as an Inverse Problem. G3: Genes| Genomes| Genetics 4:1991-2001.
O. González-Recio, G.J.M. Rosa and D. Gianola. 2014. Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits. Livestock Science. 166: 217-231.
Gianola, D., K.A. Weigel, N. Krämer, A. Stella and C.C. Schön. 2014. Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP. PloS one 9:e91693.
E. Lopez de Maturana, S.J. Chanok, A.C. Picornell, Nathaniel Rothman, Jesus Herranz, M. Luz Calle, Montserrat Garcia -Closas, Gaelle Marenne, A. Brand, A. Tardon, Alfredo Carrato, D.T. Silverman, M. Kogevinas, D. Gianola, F.X. Real and Nuria Malats. 2014. Whole genome prediction of bladder cancer risk With the Bayesian LASSO. Genetic epidemiology.
Casellas, J. D. Gianola and J. F. Medrano. 2014. Bayesian analysis of additive epistasis arising from new mutations in mice. Genet. Res., Cambr., 96, e008.
Morota, G., and D. Gianola. 2014. Kernel-based whole-genome prediction of complex traits: a review. Frontiers in Genetics. 5.
X-L Wu, D. Gianola, G.J.M. Rosa and K.A. Weigel. 2013. Meta-analysis of candidate gene effects using Bayesian parametric and mon-Parametric approaches. Journal of Genomics. 2:1-19.
B.D. Valente, G. Morota, G.J.M. Rosa, D. Gianola and K.A. Weigel. 2013. The causal meaning of genomic predictors and how it affects the construction and comparison of genome-enabled selection model. arXiv preprint arXiv: 1401. 1165.
Gianola, D., F. Hospital and E. Verrier. 2013. On the contribution of an additive locus to genetic variance when inheritance is multifactorial with implications on the interpretation of GWAS. Theoretical and Applied Genetics. 6:1457-1472.
Lehermeier, C., V. Wimmer, T. Albrecht, H.J. Auinger, D. Gianola, V.J. Schmid and C.C. Schön. 2013. Sensitivity to prior specification in Bayesian genome-based prediction models. Statistical applications in genetics and molecular biology. 12:375-391.
Morota, G. and D. Gianola. 2013. Evaluation of linkage disequilibrium in wheat with an L1-regularized sparse Markov network. Theoretical and Applied Genetics.126: 1991-2002.
Gianola, D. 2013. Priors in whole genome regression: the Bayesian alphabet returns. Genetics. 194: 573-596.
Gianola, D., S. Qanbari and H. Simianer. 2013. An evaluation of a novel estimator of linkage disequilibrium. Heredity. 111: 275-285.
Tusell, L., P. Pérez-Rodríguez, S. Forni, X-L Wu, and D. Gianola. 2013. Genome-enabled methods for predicting litter size in pigs: a comparison. Animal. 7:1739-1749.
Haugaard, K., L. Tusell, P. Perez, D. Gianola, A.C. Whist and B. Heringstad. 2013. Prediction of clinical mastitis outcomes within and between environments using whole-genome markers. Journal of Dairy Science. 96:3986-3993.
Angeles Pérez-Cabal, M., A.I. Vazquez, D. Gianola, G.J.M. Rosa and K.A. Weigel. 2012. Accuracy of genome-enabled prediction in a dairy cattle population using different cross-validation layouts. Frontiers in Livestock Genomics. 3:27.
Morota, G., B.D. Valente, G.J.M. Rosa, K.A. Weigel and D. Gianola. 2012. An assessment of linkage disequilibrium in Holstein cattle using a Bayesian network. Journal of Animal Breeding and Genetics. 129:474-487.
González-Camacho, J. M., G. de los Campos, P. Pérez, D. Gianola, J.E. Cairns, G. Mahuku, R. Babu and J. Crossa. 2012. Genome-enabled prediction of genetic values using radial basis Function neural networks. Theoretical and Applied Genetics. 125:759-771.
Boligon, A. A., N. Long, L.G. Albuquerque, K.A. Weigel, D. Gianola and G.J.M. Rosa. 2012. Comparison of selective genotyping strategies for prediction of breeding values in a population undergoing selection. Journal of Animal Science. 90:4716-4722.
Burgueño, J., G. de los Campos, K.A. Weigel and J. Crossa. 2012. Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Science. 52:707-719.
Huang, W., F. Peñagaricano, K.R. Ahmad, J.A. Lucey, K.A. Weigel and H. Khatib. 2012. Association between milk protein gene variants and protein composition traits in dairy cattle. Journal of Dairy Science. 95:440-449.
Li, G., F. Peñagaricano, K. A. Weigel, Y. Zhang, G. Rosa and H. Khatib. 2012. Comparative genomics between fly, mouse, and cattle identifies genes associated with sire conception rate. Journal of Dairy Science. 95:6122-6129.
Peñagaricano, F., K. A. Weigel, and H. Khatib. 2012. Genome-wide association study identifies candidate markers for bull fertility in Holstein dairy cattle. Animal Genetics. 43:65-71.
Vazquez, A.I., G. de los Campos, Y.C. Klimentidis, G.J.M. Rosa, D. Gianola, N.Yi and D.B. Allison. 2012. A Comprehensive genetic approach for improving prediction of skin cancer risk in humans. Genetics. 192:1493-1502.
Pérez-Cabal, M., A.I. Vazquez, D. Gianola, G.J.M. Rosa, and K.A. Weigel. 2012. Accuracy of genome-enabled prediction of quantitative traits in dairy cattle and wheat populations using different cross-validation layouts. Frontiers in Genetics. 3:27.
Schefers, J.M., and K.A. Weigel. 2012. Genomic selection in dairy cattle: Integration of DNA testing into breeding programs. Animal Frontiers. 2:4-9.
Shahinfar, S., H. Mehrabani-Yeganeh, C. Lucas, A. Kalhor, M. Kazemian and K.A. Weigel. 2012. Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems. Computational and Mathematical Models in Medicine. Article ID 127130.
Soyeurt, H., C. Bastin, F.G. Colinet, V.M.R. Arnould, D.P. Berry, E. Wall, F. Dehareng, H.N. Nguyen, P. Dardenne, J. Schefers, J. Vandenplas, K.A. Weigel, M. Coffey, L. Théron, J. Detilleux, E. Reding, N. Gengler and S. McParland. 2012. Mid-Infrared prediction of lactoferrin content in bovine milk: Potential indicator of mastitis. Animal. 6:1830-1838.
Gianola, D., E. Manfredi and H. Simianer. 2012. On measures of association among genetic variables. Animal Genetics. 43 (Suppl. 1) 10, 19-35.
Wu, X.L., C. Sun, T.M. Beissinger, G.J.M. Rosa, K.A. Weigel, N. de Leon Gatti and D. Gianola. 2012. Parallel Markov chain Monte Carlo – bridging the gap to high-performance bayesian computation in animal breeding and genetics. Genetics, Selection, Evolution. 44:29.
Sun, C., X. L. Wu, K.A. Weigel, G.J.M. Rosa, S. Bauck, B.W. Woodward, R.D. Schnabel, J.F. Taylor and D. Gianola. 2012. An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle. Genetics Research. 94:133-150.
Weigel, K.A., P.C. Hoffman, W. Herring and T.J. Lawlor, Jr. 2012. Potential gains in lifetime net merit from genomic testing of cows, heifers, and calves on commercial dairy farms. Journal of Dairy Science. 95:2215-2225.
Wu, X. L., T. M. Beissinger, G. J. M. Rosa, K. A. Weigel, N. de Leon Gatti, and D. Gianola. 2012. Parallel Markov chain Monte Carlo – Bridging the gap to high performance Bayesian computation in animal breeding and genetics. Genetics Selection Evolution 44:29.
Ober U., J. F. Ayroles, E.A. Stone, S. Richards, D.i Zhu, R.A. Gibbs, C. Stricker, D. Gianola, M. Schlather, Trudy F.C. Mackay and H. Simianer. 2012. Using whole-genome sequence data to predict quantitative trait phenotypes in Drosophila melanogaster. PLoS Genet. 8 (5): e1002685.
Vazquez A.I., Perez-Cabal M.A., Heringstad B., Rodrigues-Motta M. , Rosa G.J.M., Gianola D. and Weigel K.A.. 2012. Predictive ability of alternative models for genetic analysis of clinical mastitis. Journal of Animal Breeding and Genetics.129:120-128.
Gianola, D., G.J.M. Rosa and D.B. Allison. 2012. Humble Thanks to a Gentle Giant (an Obituary for James F. Crow). Frontiers in Genetics. 3:93.
Qanbari, S., T.M. Strom, G. Haberer, S. Weigend, A.A. Gheyas, F. Turner, D W. Burt, R. Preisinger, D. Gianola and H. Simianer. 2012. A high resolution genome-wide scan for significant selective sweeps: an application to pooled sequence data in laying chickens. PLoS ONE. 7(11): e49525. doi:10.1371/journal.pone.0049525.
Perez-Rodriguez, P., D. Gianola, J.M. Gonzalez-Camacho, J. Crossa, Y. Manes and S. Dreisigacker. 2012. Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3 (Genes, Genomes, Genetics). 12:1595-1605.
Weigel, K.A. 2017. Breeding for feed efficiency: Yes we can! Western Dairy Management Conference, Reno, NV.
Weigel, K.A. 2015. Effective use of genomics in sire selection and replacement heifer management. Western Dairy Management Conference, Reno, NV.
Weigel, K.A. 2014.Will genomic selection be the key to improving feed efficiency in dairy cattle? Four-State Dairy Nutrition and Management Conference, June 11-12, Dubuque, IA.
Weigel, K.A. 2014. Using genomics to improve the genetic potential and management of your herd. Four-State Dairy Nutrition and Management Conference, June 11-12, Dubuque, IA.
Weigel, K.A. 2014.The Cooperative Dairy DNA Repository: How it has helped the AI industry. National Association of Animal Breeders Technical Conference, Sept 25-26, Green Bay, WI.
de Haas, Y., J.E. Pryce, M.P.L. Calus, I. Hulsegge, D.M. Spurlock, D. Berry, E. Wall, P. Lovendahl, K.A. Weigel, K. Macdonald, F. Miglior, N. Krattenmacher, and R.F. Veerkamp. 2014. Genomic predictions for dry matter intake using the international reference population gDMI. Proc. 10th World Congress on Genetics Applied to Livestock Production, August 17-23, Vancouver, BC.
Spurlock, D.M., R.J. Tempelman, K.A. Weigel, L.E. Armentano, G.R. Wiggans, R.F. Veerkamp, Y. de Haas, M.P. Coffey, M.D. Hanigan, C. Staples and M.J. VandeHaar. 2014. Genetic architecture and biological basis of feed efficiency in dairy cattle. Proc. 10th World Congress on Genetics Applied to Livestock Production, August 17-23, Vancouver, BC.
Valente, B.D., G. Morota, G.J.M. Rosa, D. Gianola and K.A. Weigel. 2014. Causal meaning of genomic predictors: implication on genome-enabled selection modeling. Proc. 10th World Congress on Genetics Applied to Livestock Production, Vancouver.
Weigel, K.A., C. Yao, P.C. Hoffman, L.E. Armentano, D.M. Spurlock, R.J. Tempelman and M. J. VandeHaar. 2014. Improving biological and economic aspects of feed efficiency through genetic selection and genome-guided replacement management. Proc. 10th World Congress on Genetics Applied to Livestock Production, August 17-23, Vancouver, BC.
D. Gianola, G. Morota and J. Crossa. 2014. Genome-enabled prediction of complex traits with kernel methods: What have we learned? Proc. 10th World Congress on Genetics Applied to Livestock Production, Vancouver.
B. C. D. Cuyabano, M. S. Lund, G. J. M. Rosa, D. Gianola and G. Su. 2014. Haplotype based genome-enabled prediction of traits across Nordic Red Cattle breeds. Proc. 10th World Congress on Genetics Applied to Livestock Production, Vancouver.
G. de los Campos, Daniel Sorensen and D. Gianola. 2014. Genomic heritability: what is it? Proc. 10th World Congress on Genetics Applied to Livestock Production, Vancouver.
Y. Hu, G.J.M. Rosa and D. Gianola. 2014. Genomic imprinting as a potential source of missing heritability of mouse body mass index. Proc. 10th World Congress on Genetics Applied to Livestock Production, Vancouver.
Armentano, L.E. and K.A. Weigel. 2013. Considerations for improving feed efficiency in dairy cattle. Florida Animal Nutrition Conference, Gainesville, FL (February 5).
Cabrera, V. and K.A. Weigel. 2013. Development of a genomic testing decision support tool for Jersey dairy calves. American Jersey Cattle Association Annual Meeting, Amarillo, TX (June 26).
Armentano, L.E., K.A. Weigel, M.J. VandeHaar and D.M. Spurlock. 2013. Considerations for improving feed efficiency in dairy cattle. Cornell Animal Nutrition Conference, Ithaca, NY (October 23).
Weigel, K.A. 2014. Will genomic selection be the key to improving feed efficiency in dairy cattle? Progressive Dairyman, June issue.
Weigel, K.A. and A.A. Mikshowsky. 2014. To test or not to test? Hoard’s Dairyman, October 25 issue.

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