Glossary
- Bayesian inference :
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Statistical inference approach based on the combination of prior information and evidence (i.e., observations) for estimation or hypothesis testing. In Bayesian analysis the prior information is updated with the experimental data to generate the posterior distribution of unknowns, such as model parameters. The name “Bayesian” comes from the use of the Bayes’ theorem in the updating process.
- Breeding value :
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A measure of the genetic merit of an individual for breeding purposes.
- Genetic correlation :
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The correlation between traits that is caused by genetic as opposed to environmental factors. Genetic correlations can be caused by pleiotropy (genes that affect multiple traits simultaneously) or by linkage disequilibrium between genes affecting the different traits.
- Genomic selection :
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Genomic selection is a form of marker-assisted selection in which genetic markers covering the whole genome are used such that all quantitative trait loci (QTL) are in linkage...
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Bibliography
Primary Literature
Lush JL (1994) The genetics of populations. Prepared for publication by A. B. Chapman and R. R. Shrode, with an addendum by J. F. Crow. Special Report 94, College of Agriculture, Iowa State University, Ames, IA
Bulmer MG (1985) The mathematical theory of quantitative genetics. Clarendon, Oxford
Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longmans Green, Harlow
Lynch M, Walsh B (1998) Genetic analysis of quantitative traits. Sinauer Associates, Sunderland
Hill WG (1969) On the theory of artificial selection in finite populations. Genet Res 13:143–163
Havenstein B, Ferket PR, Qureshi MA (2003) Growth, livability, and feed conversion of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poult Sci 82:1509–1518
Bourdon RM (2000) Understanding animal breeding, 2nd edn. Prentice Hall, Upper Saddle River
Crow J, Kimura M (1970) An introduction to populations genetics theory. Haraper and Row, New York
Shook GE (2006) Major advances in determining appropriate selection goals. J Dairy Sci:1349–1361
Henderson CR (1949) Estimation of changes in herd environment. J Dairy Sci 32:709
Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423–447
Henderson CR (1984) Applications of linear models in animal breeding. University of Guelph, Guelph
Gianola D, Rosa GJM (2015) One hundred years of statistical developments in animal breeding. Book Ser Annu Rev Anim Biosci 3:19–56
Fernando RL, Grossman M (1989) Marker-assisted selection using best linear unbiased prediction. Genet Sel Evol 21:467–477
Yu J et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208
Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS (2001) Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol 8:625–637
Rosa GJM, Steibel JP, Tempelman RJ (2005) Reassessing design and analysis of two-color microarray experiments using mixed effects models. Comp Funct Genomics 6:123–131
Steibel JP, Poletto R, Coussens PM, Rosa GJM (2009) A powerful and flexible linear mixed model framework for the analysis of relative quantification RT-PCR data. Genomics 94:146–152
Henderson CR (1950) Estimation of genetic parameters. Ann Math Stat 21:309
Henderson CR (1953) Estimation of variance and covariance components. Biometrics 9:226
Rao CR (1971) Estimation of variance and covariance components MINQUE theory. J Multivar Anal 1:257–275
Harville DA (1977) Maximum likelihood approaches to variance component estimation and to related problems. J Am Stat Assoc 72(358):320–338
Patterson HD, Thompson R (1971) Recovery of inter-block information when block sizes are unequal. Biometrika 58(3):545–554
Sorensen D, Gianola D (2002) Likelihood, Bayesian, and MCMC methods in quantitative genetics. Springer, New York
Littell RC, Miliken GA, Stroup WW, Wolfinger RD (2006) SAS system for mixed models, 2nd edn. SAS Institute Inc., Cary
Pinheiro JC, Bates DM (2000) Mixed-effects models in S and S-plus. Springer, New York
Searle SR, Casella G, McCulloch CE (1992) Variance components. Wiley, New York
Verbeke G, Molenberghs G (1997) Linear mixed models in practice: a SAS-oriented approach. Lecture notes in statistics 126. Springer, New York
Wright S (1921) Systems of mating. I. The biometric relations between parents and offspring. Genetics 6:111–123
Henderson CR (1976) A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32:69–83
Quaas RL (1976) Computing the diagonal elements of a large numerator relationship matrix. Biometrics 32:949–953
Henderson CR, Quaas RL (1976) Multiple trait evaluation using relatives’ records. J Anim Sci 43:1188–1197
Schaeffer LR (1984) Sire and cow evaluation under multiple trait models. J Dairy Sci 67:1567–1580
Thompson R (1977) Estimation of quantitative genetic parameters. In: Pollak E, Kempthorne O, Bailey TB (eds) Proceedings of the international conference on quantitative genetics. Iowa State University Press, Ames, pp 639–657
Meyer K (1985) Maximum-likelihood estimation of variance-components for a multivariate mixed model with equal design matrices. Biometrics 41(153):1985
Ducrocq V, Besbes B (1993) Solution of multiple trait animal models with missing data on some traits. J Anim Breed Genet 110:81–92
Quaas RL, Pollak EJ (1981) Modified equations for sire models with groups. J Dairy Sci 64:1868–1872
Quaas RL, Pollak EJ (1980) Mixed model methodology for farm and ranch beef cattle testing programs. J Anim Sci 51:1277–1287
Misztal I, Gianola D (1988) Indirect solution of mixed model equations. J Dairy Sci 77(Suppl. 2):99–106
Schaeffer LR, Kennedy BW (1986) Computing solutions to mixed model equations. In: 3rd world congr genet appl livest prod, vol XII, pp 382–393
Lander ES, Botstein D (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199
Haley CS, Knott SA (1992) A simple regression method to for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324
Haley CS, Knott SA, Elsen J-M (1994) Mapping quantitative trait loci in crosses between outbred lines using least squares. Genetics 136:1195–1207
Pérez-Enciso M, Misztal I (2004) Qxpak: a versatile mixed model application for genetical genomics and QTL analyses. Bioinformatics 20(16):2792–2798
Meuwissen THE, Goddard ME (2000) Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci. Genetics 155:421–430
Pérez-Enciso M (2003) Fine mapping of complex trait genes combining pedigree and linkage disequilibrium information: a Bayesian unified framework. Genetics 163:1497–1510
Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756
Dekkers JCM, Hospital F (2002) The use of molecular genetics in the improvement of agricultural populations. Nat Rev Genet 3(1):22–32
Dekkers JCM, van Arendonk JAM (1998) Optimizing selection for quantitative traits with information on an identified locus in outbred populations. Genet Res 71(3):257–275
Manfredi E, Barbieri M, Fournet F, Elsen JM (1998) A dynamic deterministic model to evaluate breeding strategies under mixed inheritance. Genet Selet Evol 30:127–148
Chakraborty R, Moreau L, Dekkers JCM (2002) A method to optimize selection on multiple identified quantitative trait loci. Genet Sel Evol 34(2):145–170
Goddard ME (1992) A mixed model for analyses of data on multiple genetic-markers. Theor Appl Genet 83:878–886
Goddard ME, Hayes BJ (2007) Genomic selection. J Anim Breed Genet 124(6):323–330
Schaeffer LR (2006) Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet 123:218–223
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829
Whittaker JC, Thompson R, Visscher PM (2000) Marker-assisted selection using ridge regression. Genet Res 75:249–252
Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B 58:267–288
Gianola D, Perez-Enciso M, Toro MA (2003) On marker-assisted prediction of genetic value: beyond the ridge. Genetics 163:347–365
Xu S (2003) Estimating polygenic effects using markers of the entire genome. Genetics 163(2):789–801
ter Braak CJF, Boer MP, Bink MCAM (2005) Extending Xu’s Bayesian model for estimating polygenic effects using markers of the entire genome. Genetics 170(3):1435–1438
Hastie T, Tibshirani R, Friedman JH (2001) The elements of statistical learning: data mining, inference, and predictions. Springer
VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423
Misztal I, Legarra A, Aguilar I (2009) Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci 92:4648–4655
Calus MPL, Veerkamp RF (2007) Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. J Anim Breed Genet 124:362–368
Muir WM (2007) Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J Anim Breed Genet 124:342–355
VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor J, Schenkel FS (2009) Reliability of genomic predictions for North American dairy bulls. J Dairy Sci 92:16–24
Weigel KA, de los Campos G, González-Recio O, Naya H, Wu XL, Long N, GJM R, Gianola D (2009) Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. J Dairy Sci 92:5248–5257
Henderson CR (1985) Best linear unbiased prediction of non-additive genetic merits in non-inbred populations. J Anim Sci 60:111–117
Hoeschele I, VanRaden PM (1991) Rapid inverse of dominance relationship matrices for noninbred populations by including sire and dam subclass effects. J Dairy Sci 74:557–569
Gianola D (1982) Theory and analysis of threshold characters. J Anim Sci 54:1079–1096
Gianola D, Foulley JL (1983) Sire evaluation for ordered categorical-data with a threshold-model. Genet Sel Evol 15(2):201–223
Tempelman RJ, Gianola D (1996) A mixed effects model for overdispersed count data in animal breeding. Biometrics 52:265–279
Strandén I, Gianola D (1998) Attenuating effects of preferential treatment with Student-t mixed linear models: a simulation study. Genet Sel Evol 31:25–42
Rosa GJM, Padovani CR, Gianola D (2003) Robust linear mixed models with normal/independent distributions and Bayesian MCMC implementation. Biom J 45(5):573–590
Ducrocq V, Casella G (1996) A Bayesian analysis of mixed survival models. Genet Sel Evol 28(6):505–529
Varona L (1997) Multiple trait genetic analysis of underlying biological variables of production functions. Livest Prod Sci 47:201–209
Forni S, Piles M, Blasco A et al (2009) Comparison of different nonlinear functions to describe Nelore cattle growth. J Anim Sci 87(2):496–506
Gianola D, Fernando RL (1986) Bayesian methods in animal breeding theory. J Anim Sci 63:217–244
Shoemaker JS, Painter IS, Weir BS (1999) Bayesian statistics in genetics – a guide for the uninitiated. Trends Genet 15:354–358
Blasco A (2001) The Bayesian controversy in animal breeding. J Anim Sci 79(8):2023–2046
Beaumont MA, Rannala B (2004) The Bayesian revolution in genetics. Nat Rev Genet 5:251–261
Yi N, Xu S (2008) Bayesian Lasso for quantitative trait loci mapping. Genetics 179:1045–1055
Gianola D, de los Campos G, Hill WG et al (2009) Additive genetic variability and the Bayesian alphabet. Genetics 183(1):347–363
De los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes J (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigrees. Genetics 182:375–385
Gianola D, Fernando RL, Stella A (2006) Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173:1761–1776
Gianola D, van Kaam JBCHM (2008) Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178:2289–2303
Long N, Gianola D, Rosa GJM, Weigel KA, Avendaño S (2007) Machine learning procedure for selecting SNPs in genomic selection: application to early mortality in broilers. J Anim Breed Genet 124(6):377–389
González-Recio O, Gianola D, Long N, Weigel KA, Rosa GJM, Avendano S (2008) Nonparametric methods for incorporating genomic information into genetic evaluations: an application to mortality in broilers. Genetics 178(4):2305–2313
De los Campos G, Gianola D, Rosa GJM (2009) The linear model of quantitative genetics is a reproducing kernel Hilbert spaces regression. J Anim Sci 87:1883–1887
Gianola D, Okut H, Weigel KA, Rosa GJM (2011) Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat. BMC Genet 12:87
Okut H, Gianola D, Rosa GJM, Weigel KA (2011) Prediction of body mass index in mice using dense molecular markers and a regularized neural network. Genet Res 93:189–201
Koltes JE, Cole JB, Clemmens R et al (2019) A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock. Front Genet 10:1197
Silva FF, Morota G, Rosa GJM (2021) High-throughput phenotyping in the genomic improvement of livestock. Front Genet 12:707343. https://doi.org/10.3389/fgene.2021.707343
Fernandes AFA, Dórea JRR, Rosa GJM (2020) Image analysis and computer vision applications in animal sciences: an overview. Front Vet Sci 7:551269
Bresolin T, Dórea JRR (2020) Infrared spectrometry as a high-throughput phenotyping technology to predict complex traits in livestock systems. Front Genet 11:923. https://doi.org/10.3389/fgene.2020.00923
Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
Kuhn M, Johnson K (2013) Applied predictive modeling. Springer, New York
Books and Reviews
Chapman AB (1980) General and quantitative genetics. World animal science series. Elsevier, Amsterdam
Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis, 2nd edn. Chapman & Hall, London
Gondro C, van der Werf J, Hayes B (2013) Genome-wide association studies. Springer, New York
Lange K (2002) Mathematical and statistical methods for genetic analysis, 2nd edn. Springer, New York
Liu BH (1998) Statistical genomics. CRC Press, Boca Raton
Mrode R (2005) Linear models for the prediction of animal breeding values, 2nd edn. CAB Int, New York
Ott J (1991) Analysis of human genetic linkage. Johns Hopkins
Sham P (1998) Statistics in human genetics. Arnold
Van Vleck LD (1993) Selection index and introduction to mixed model methods for genetic improvement of animals. CRC Press, Boca Raton
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Rosa, G.J.M. (2023). Quantitative Methods Applied to Animal Breeding. In: Spangler, M.L. (eds) Animal Breeding and Genetics. Encyclopedia of Sustainability Science and Technology Series. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2460-9_334
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