Dan Gianola

Emeritus Professor – Breeding

Professor Gianola teaches advanced linear and nonlinear models, statistical genetics and Bayesian methods in animal breeding. He also conducts research in the theory of quantitative genetics, statistical genetics and international aspects of animal breeding. His species of interest include dairy and beef cattle, swine and sheep. He holds joint appointments in the Department of Animal Sciences and in the Department of Biostatistics.

dgianola@wisc.edu

(608) 263-3308

1675 Observatory Dr
247 Animal Sciences
Madison, WI 53706

Type: Emeritus Faculty

Recent Publications

Gianola D. 2017. It is unlikely that genomic selection will ever be 100% accurate. J Anim Breed Genet. 134(6):438-440.
Momen M, Mehrgardi AA, Sheikhy A, Esmailizadeh A, Fozi MA, Kranis A, Valente BD, Rosa GJ, Gianola D. 2017. A predictive assessment of genetic correlations between traits in chickens using markers.Genet Sel Evol. 49(1):16.
Hu Y, Rosa GJ, Gianola D. 2016. Incorporating parent-of-origin effects in whole-genome prediction of complex traits.Genet Sel Evol. 48:34.
Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D. 2016. Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens.Genet Sel Evol. 48:10.
Beissinger TM, Gholami M, Erbe M, Weigend S, Weigend A, de Leon N, Gianola D, Using the variability of linkage disequilibrium between subpopulations to infer sweeps and epistatic selection in a diverse panel of chickens.Simianer H. Heredity (Edinb). 116(2):158-66.
Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D. Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens. Genet Sel Evol. 2016 Feb 3;48(1):10.
Cuyabano BC, Su G, Rosa GJ, Lund MS, Gianola D .Bootstrap study of genome-enabled prediction reliabilities using haplotype blocks across Nordic Red cattle breeds.Journal of dairy science. 2015; 98(10):7351-63.
Hu Y, Morota G, Rosa GJ, Gianola D.Prediction of Plant Height in Arabidopsis thaliana Using DNA Methylation Data. Genetics. 2015; 201(2):779-93.
Beissinger TM, Gholami M, Erbe M, Weigend S, Weigend A, et al.Using the variability of linkage disequilibrium between subpopulations to infer sweeps and epistatic selection in a diverse panel of chickens. Heredity. 2015;
Gianola D, de los Campos G, Toro MA, Naya H, Schön CC, Sorensen D. Do Molecular Markers Inform About Pleiotropy? Genetics. 2015; 201(1):23-9.
Hu Y, Rosa GJ, Gianola D. A GWAS assessment of the contribution of genomic imprinting to the variation of body mass index in mice. BMC genomics. 2015; 16:576.
Valente BD, Morota G, Peñagaricano F, Gianola D, Weigel K, Rosa GJ. The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models. Genetics. 2015; 200(2):483-94.
Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D. Assessment of bagging GBLUP for whole-genome prediction of broiler chicken traits.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie. 2015; 132(3):218-28.
de Los Campos G, Sorensen D, Gianola D. Genomic heritability: what is it? PLoS genetics. 2015; 11(5):e1005048.
Beissinger TM, Rosa GJ, Kaeppler SM, Gianola D, de Leon N. Defining window-boundaries for genomic analyses using smoothing spline techniques. Genetics, selection, evolution : GSE. 2015; 47:30.
Ehret A, Hochstuhl D, Gianola D, Thaller G. Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle. Genetics, selection, evolution : GSE. 2015; 47:22.
Gianola D, Rosa GJ. One hundred years of statistical developments in animal breeding. Annual review of animal biosciences. 2015; 3:19-56.
Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D. Assessment of bagging GBLUP for whole-genome prediction of broiler chicken traits. J Anim Breed Genet. 2015 Mar 1. doi: 10.1111/jbg.12131. [Epub ahead of print]
Casellas J, Gianola D, Medrano JF. Bayesian analysis of additive epistasis arising from new mutations in mice.Genet Res (Camb). 2014 Aug 13;96:e008.
Morota G, Gianola D. Kernel-based whole-genome prediction of complex traits: a review. Front Genet. 2014 Oct 16;5:363.
Cuevas J, Pérez-Elizalde S, Soberanis V, Pérez-Rodríguez P, Gianola D, Crossa J. Bayesian genomic-enabled prediction as an inverse problem. G3 (Bethesda). 2014 Aug 25;4(10):1991-2001.
Wu XL, Gianola D, Rosa GJ, Weigel KA. Meta-analysis of candidate gene effects using bayesian parametric and non-parametric approaches. J Genomics. 2014 Jan 2;2:1-19.
Gianola D, Weigel KA, Krämer N, Stella A, Schön CC. Enhancing genome-enabled prediction by bagging genomic BLUP. PLoS One. 2014 Apr 10;9(4):e91693.
Abdollahi-Arpanahi R, Pakdel A, Nejati-Javaremi A, Moradi Shahrbabak M, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D. Dissection of additive genetic variability for quantitative traits in chickens using SNP markers. J Anim Breed Genet. 2014 Jun;131(3):183-93. doi: 10.1111/jbg.12079. Epub 2014 Jan 25.
Abdollahi-Arpanahi R, Nejati-Javaremi A, Pakdel A, Moradi-Shahrbabak M, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D. Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens. J Anim Breed Genet. 2014 Apr;131(2):123-33.
Morota G, Boddhireddy P, Vukasinovic N, Gianola D, Denise S. 2014. Kernel-based variance component estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits.Front Genet 5:56.
Tusell L, Pérez-Rodríguez P, Forni S, Gianola D. 2014. Model averaging for genome-enabled prediction with reproducing kernel Hilbert spaces: a case study with pig litter size and wheat yield. J Anim Breed Genet 131(2):105-15. Epub 2014 Jan 8.
Okut H, Wu XL, Rosa GJ, Bauck S, Woodward BW, Schnabel RD, Taylor JF, Gianola D. 2013. Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models. Genet Sel Evol45:34.
Gianola D, Qanbari S, Simianer H. 2013. An evaluation of a novel estimator of linkage disequilibrium. Heredity (Edinb) 111(4):275-85.
Tusell L, Pérez-Rodríguez P, Forni S, Wu XL, Gianola D. 2013. Genome-enabled methods for predicting litter size in pigs: a comparison. Animal 7(11):1739-49.
Morota G, Koyama M, Rosa GJ, Weigel KA, Gianola D. 2013. Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data. Genet Sel Evol 45:17.
Morota G, Gianola D. 2013. Evaluation of linkage disequilibrium in wheat with an L1-regularized sparse Markov network. Theor Appl Genet 126(8):1991-2002. Epub 2013 May 10.
Gianola D, Hospital F, Verrier E. 2013. Contribution of an additive locus to genetic variance when inheritance is multi-factorial with implications on interpretation of GWAS. Theor Appl Genet 126(6):1457-72. Epub 2013 Mar 19. Erratum in: Theor Appl Genet. 2013 Jun;126(6):1473-5.
Morota G, Valente BD, Rosa GJ, Weigel KA, Gianola D. 2012. An assessment of linkage disequilibrium in Holstein cattle using a Bayesian network. J Anim Breed Genet 129(6):474-87. Epub 2012 Sep 13.
Wu XL, Sun C, Beissinger TM, Rosa GJ, Weigel KA, Gatti Nde L, Gianola D.Parallel Markov chain Monte Carlo – bridging the gap to high-performance Bayesian computation in animal breeding and genetics. Genet Sel Evol 44:29.
Sun C, Wu XL, Weigel KA, Rosa GJ, Bauck S, Woodward BW, Schnabel RD, Taylor JF, Gianola D. 2012. An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle. Genet Res (Camb) 94(3):133-50.Epub 2012 Jul 18.
Gianola D, Manfredi E, Simianer H. 2012. On measures of association among genetic variables. Anim Genet 43 Suppl 1:19-35.
Gianola D, Rosa GJ, Allison DB. 2012. Humble Thanks to a Gentle Giant (an Obituary for James F. Crow). Front Genet 3:93.
Wu XL, Beissinger TM, Bauck S, Woodward B, Rosa GJ, Weigel KA, Gatti Nde L, Gianola D. 2011. A primer on high-throughput computing for genomic selection.Front Genet 2:4.
Gianola D, Okut H, Weigel KA, Rosa GJ. 2011. Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat. BMC Genet 12:87.
Long N, Gianola D, Rosa GJ, Weigel KA. 2011. Dimension reduction and variable selection for genomic selection: application to predicting milk yield in Holsteins. J Anim Breed Genet 128(4):247-57.
Long N, Gianola D, Rosa GJ, Weigel KA. 2011. Application of support vector regression to genome-assisted prediction of quantitative traits.Theor Appl Genet 123(7):1065-74. Epub 2011 Jul 8.
Long N, Gianola D, Rosa GJ, Weigel KA. 2011.Marker-assisted prediction of non-additive genetic values. Genetica 139(7):843-54. Epub 2011 Jun 15.
Long N, Gianola D, Rosa GJ, Weigel KA. 2011. Long-term impacts of genome-enabled selection. J Appl Genet 52(4):467-80. Epub 2011 May 17.
Okut H, Gianola D, Rosa GJ, Weigel KA. 2011. Prediction of body mass index in mice using dense molecular markers and a regularized neural network. Genet Res (Camb) 93(3):189-201. Epub 2011 Apr 12.
Rosa GJ, Valente BD, de los Campos G, Wu XL, Gianola D, Silva MA. 2011. Inferring causal phenotype networks using structural equation models. Genet Sel Evol 43:6.