Genetic Data Analysis for Plant and Animal Breeding

Genetic Data Analysis for Plant and Animal Breeding

Volumn , Issue , 2017, Pages 1-400

  Isik, Fikret ^a   Holland, James ^a   Maltecca, Christian ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

Associate genetics; Genomic selection; Mixed models; Plant and animal breeding; Quantitative genetics

Indexed keywords

EID: 85149714057     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-319-55177-7     Document Type: Book

References (161)

1. Aguilar, I., Misztal, I., Johnson, D. L., Legarra, A., Tsuruta, S., & Lawlor, T. J. (2010). Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science, 93, 743–752. doi:10.3168/jds. 2009-2730.
2. Aguilar, I., Misztal, I., Legarra, A., & Tsuruta, S. (2011). Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation. Journal of Animal Breeding and Genetics, 128, 422–428. doi:10.1111/j.1439-0388.2010.00912.x.
3. Andrews, D. F., & Mallows, C. L. (1974). Scale mixtures of normal distributions. Journal of the Royal Statistical Society: Series B: Methodological, 36, 99–102.
4. Astle, W., & Balding, D. J. (2009). Population structure and cryptic relatedness in genetic association studies. Statistical Science, 24, 451–471. doi:10.1214/09-STS307.
5. Baker, R. J. (1978). Issues in diallel analysis. Crop Science, 18, 533. doi:10.2135/cropsci1978.0011183X001800040001x.
6. Bates, D., Maechler, M., Bolker, B., Walker, S. (2013). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48.
7. Botstein, D., White, R. L., Skolnick, M., & Davis, R. W. (1980). Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics, 32, 314–331.
8. Brooks, S. P., & Roberts, G. O. (1998). Convergence assessment techniques for Markov chain Monte Carlo. Statistics and Computing, 8, 319–335. doi:10.1023/A:1008820505350.
9. Brownie, C., & Gumpertz, M. L. (1997). Validity of spatial analyses for large field trials. Journal of Agricultural, Biological, and Environmental Statistics, 2, 1–23.
10. Brownie, C., Bowman, D. T., & Burton, J. W. (1993). Estimating spatial variation in analysis of data from yield trials: A comparison of methods. Agronomy Journal, 85, 1244–1253.
11. Browning, S. R. (2006). Multilocus association mapping using variable-length markov chains. The American Journal of Human Genetics, 78, 903–913. doi:10.1086/503876.
12. Browning, S. R., & Browning, B. L. (2007). Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. The American Journal of Human Genetics, 81, 1084–1097. doi:10.1086/521987.
13. Browning, S. R., & Browning, B. L. (2011). Haplotype phasing: Existing methods and new developments. Nature Reviews. Genetics, 12, 703–714. doi:10.1038/nrg3054.
14. Browning, B. L., & Browning, S. R. (2013). Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics, 194, 459–471. doi:10.1534/genetics.113.150029.
15. Browning, B. L., & Yu, Z. (2009). Simultaneous genotype calling and haplotype phasing improves genotype accuracy and reduces false-positive associations for genome-wide association studies. The American Journal of Human Genetics, 85, 847–861. doi:10.1016/j.ajhg.2009.11.004.
16. Buckler, E. S., Holland, J. B., Bradbury, P. J., Acharya, C. B., Brown, P. J., Browne, C., Ersoz, E., Flint-Garcia, S., Garcia, A., Glaubitz, J. C., Goodman, M. M., Harjes, C., Guill, K., Kroon, D. E., Larsson, S., Lepak, N. K., Li, H., Mitchell, S. E., Pressoir, G., Peiffer, J. A., Rosas, M. O., Rocheford, T. R., Romay, M. C., Romero, S., Salvo, S., Villeda, H. S., da Silva, H. S., Sun, Q., Tian, F., Upadyayula, N., Ware, D., Yates, H., Yu, J., Zhang, Z., Kresovich, S., & McMullen, M. D. (2009). The genetic architecture of maize flowering time. Science, 325, 714–718. doi:10.1126/science.1174276.
17. Burdon, R. D. (1977). Genetic correlation as a concept for studying genotype-environment interaction in forest tree breeding. Silvae Genetica, 26, 5–6.
18. Butler, D. G., Cullis, B. R., Gilmour, A. R., & Gogel, B. J. (2009). Mixed models for S language environments ASReml-R reference manual. Brisbane: The State of Queensland, Department of Primary Industries and Fisheries.
19. Chancerel, E., Lepoittevin, C., Provost, G. L., Lin, Y.-C., Jaramillo-Correa, J. P., Eckert, A. J., Wegrzyn, J. L., Zelenika, D., Boland, A., Frigerio, J.-M., Chaumeil, P., Garnier-Géré, P., Boury, C., Grivet, D., González-Martínez, S. C., Rouzé, P., de Peer, Y. V., Neale, D. B., Cervera, M. T., Kremer, A., & Plomion, C. (2011). Development and implementation of a highly-multiplexed SNP array for genetic mapping in maritime pine and comparative mapping with loblolly pine. BMC Genomics, 12, 368. doi:10.1186/1471-2164-12-368.
20. Chancerel, E., Lamy, J.-B., Lesur, I., Noirot, C., Klopp, C., Ehrenmann, F., Boury, C., Le Provost, G., Label, P., & Lalanne, C. (2013). High-density linkage mapping in a pine tree reveals a genomic region associated with inbreeding depression and provides clues to the extent and distribution of meiotic recombination. BMC Biology, 11, 50.
21. Christensen, O. F., & Lund, M. S. (2010). Genomic prediction when some animals are not genotyped. Genetics Selection Evolution, 42(1), 2.
22. Cleveland, M., Hickey, J., & Forni, S. (2012). A common dataset for genomic analysis of livestock populations. G3 (Bethesda), 2, 429–435. doi:10.1534/g3.111.001453.
23. Cochran, W. G., & Cox, G. M. (1957). Experimental designs (2nd ed.). New York: Wiley.
24. Cockerham, C. C. (1963). Estimation of genetic variances. In W. D. Hanson & H. F. Robinson (Eds.), Statistical genetics and plant breeding (pp. 53–94). Washington, DC: National Research Council.
25. Cooper, M., & DeLacy, I. H. (1994). Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments. Theoretical and Applied Genetics, 88, 561–572. doi:10.1007/BF01240919.
26. Crossa, J., Burgueño, J., Cornelius, P. L., McLaren, G., Trethowan, R., & Krishnamachari, A. (2006). Modeling genotype × environment interaction using additive genetic covariances of relatives for predicting breeding values of wheat genotypes. Crop Science, 46, 1722. doi:10.2135/cropsci2005.11-0427.
27. Cullis, B., Gogel, B., Verbyla, A., & Thompson, R. (1998). Spatial analysis of multi-environment early generation variety trials. Biometrics, 54, 1–18.
28. Cullis, B. R., Smith, A. B., & Coombes, N. E. (2006). On the design of early generation variety trials with correlated data. JABES, 11, 381–393. doi:10.1198/108571106X154443.
29. Cullis, B. R., Smith, A. B., Beeck, C. P., & Cowling, W. A. (2010). Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysis. This article is one of a selection of papers from the conference “exploiting genome-wide association in oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”. Genome, 53, 1002–1016. doi:10.1139/G10-080.
30. Cullis, B. R., Jefferson, P., Thompson, R., & Smith, A. B. (2014). Factor analytic and reduced animal models for the investigation of additive genotype-by-environment interaction in outcrossing plant species with application to a Pinus radiata breeding programme. Theoretical and Applied Genetics, 127, 2193–2210. doi:10.1007/s00122-014-2373-0.
31. de los Campos, G., Naya, H., Gianola, D., Crossa, J., Legarra, A., Manfredi, E., Weigel, K., & Cotes, J. M. (2009). Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics, 182, 375–385. doi:10.1534/genetics.109.101501.
32. de los Campos, G., Gianola, D., Rosa, G. J., Weigel, K. A., & Crossa, J. (2010). Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genetics Research, 92, 295–308.
33. de los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D., & Calus, M. P. L. (2013). Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics, 193, 327–345. doi:10.1534/genetics.112.143313.
34. Dickerson, G. E. (1969). Techniques for research in quantitative animal genetics. In Techniques and procedures in animal science research (pp. 36–79).
35. Duchemin, S. I., Colombani, C., Legarra, A., Baloche, G., Larroque, H., Astruc, J. M., … & Manfredi, E. (2012). Genomic selection in the French Lacaune dairy sheep breed. Journal of dairy science, 95(5), 2723–2733.
36. Dudley, J. W., & Moll, R. H. (1969). Interpretation and use of estimates of heritability and genetic variances in plant breeding1. Crop Science, 9, 257. doi:10.2135/cropsci1969.0011183X000900030001x.
37. Endelman, J. B. (2011). Ridge regression and other kernels for genomic selection with R package rrBLUP. The Plant Genome Journal, 4, 250. doi:10.3835/plantgenome2011.08.0024.
38. Endelman, J. B., & Jannink, J.-L. (2012). Shrinkage estimation of the realized relationship matrix. G3, 2, 1405–1413. doi:10.1534/g3.112.004259.
39. Falconer, D. S, & Mackay, T. F. (1996). Introduction to Quantitative Genetics (4th edition). 480 p. Longman Group Ltd.
40. Falconer, D. S., Mackay, T. F., & Frankham, R. (1996). Introduction to quantitative genetics. (4th edn). Trends in Genetics, 12, 280.
41. Federer, W. T., & Raghavarao, D. (1975). On augmented designs. Biometrics, 31, 29–35. doi:10.2307/2529707.
42. Fisher, R. A. (1918). 009: The Correlation Between Relatives on the Supposition of Mendelian Inheritance.
43. Flint-Garcia, S. A., Thornsberry, J. M., & Buckler IV, E. S. (2003). Structure of linkage disequilibrium in plants. Annual Review of Plant Biology, 54, 357–374. doi:10.1146/annurev.arplant.54.031902.134907.
44. Forni, S., Aguilar, I., & Misztal, I. (2011). Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genetics Selection Evolution, 43, 1.
45. Garrick, D. J., Taylor, J. F., Fernando, R. L., et al. (2009). Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics, Selection, Evolution, 41, 10–1186.
46. Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiplesequences. Statistical Science, 7, 457–472.
47. Gianola, D. (2013). Priors in whole-genome regression: The Bayesian alphabet returns. Genetics, 194, 573–596.
48. Gianola, D., de los Campos, G., Hill, W. G., Manfredi, E., & Fernando, R. (2009). Additive genetic variability and the Bayesian alphabet. Genetics, 183, 347–363. doi:10.1534/genetics.109.103952.
49. Gilmour, A. R., Cullis, B. R., & Verbyla, A. P. (1997). Accounting for natural and extraneous variation in the analysis of field experiments. Journal of Agricultural, Biological, and Environmental Statistics, 2, 269–293.
50. Gilmour, A. R., Gogel, B., Cullis, B., & Thompson, R. (2009). ASReml user’s guide. Release 3.0. Hemel Hempstead: VSN International, Ltd.
51. Gilmour, A. R., Gogel, B. J., Cullis, B. R., Welham, C. J., & Thompson, R. (2014). ASReml user guide release 4.1, functional specification. Hemel Hempstead: VSN International Ltd. www.vsni.co.uk.
52. Grattapaglia, D., & Resende, M. D. (2011). Genomic selection in forest tree breeding. Tree Genetics & Genomes, 7, 241–255.
53. Grattapaglia, D., Ribeiro, V. J., & Rezende, G. D. S. P. (2004). Retrospective selection of elite parent trees using paternity testing with microsatellite markers: An alternative short term breeding tactic for Eucalyptus. Theoretical and Applied Genetics, 109, 192–199. doi:10.1007/s00122-004-1617-9.
54. Habier, D., Fernando, R. L., & Dekkers, J. C. M. (2007). The impact of genetic relationship information on genome-assisted breeding values. Genetics, 177, 2389–2397. doi:10.1534/genetics.107.081190.
55. Habier, D., Fernando, R. L., & Dekkers, J. C. M. (2009). Genomic selection using low-density marker panels. Genetics, 182, 343–353. doi:10.1534/genetics.108.100289.
56. Habier, D., Fernando, R. L., Kizilkaya, K., & Garrick, D. J. (2011). Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics, 12, 186. doi:10.1186/1471-2105-12-186.
57. Habier, D., Fernando, R. L., & Garrick, D. J. (2013). Genomic BLUP decoded: A look into the black box of genomic prediction. Genetics, 194, 597–607. doi:10.1534/genetics.113.152207.
58. Hallauer, A. R., & Miranda, J. B. (1988). Quantitative genetics in maize breeding (2nd ed.). Ames: Iowa State Univ. Press.
59. Hallauer, A.R., Carena, M.J., Filho, J.B.M. (2010). Testers and combining ability. In Quantitative genetics in maize breeding, handbook of plant breeding (pp. 383–423). New York: Springer. doi:10.1007/978-1-4419-0766-0_8.
60. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning data mining, inference, and prediction (2nd ed.). New York: Springer.
61. Hayes, B. J., Bowman, P. J., Chamberlain, A. J., & Goddard, M. E. (2009). Invited review: Genomic selection in dairy cattle: Progress and challenges. Journal of Dairy Science, 92, 433–443. doi:10.3168/jds.2008-1646.
62. Hayes, B., & Goddard, M. (2010). Genome-wide association and genomic selection in animal breeding This article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”. Genome, 53(11), 876–883.
63. Henderson, C. R. (1949). Estimation of changes in herd environment. Journal of Dairy Science, 32, 706.
64. Henderson, C. R. (1990). Statistical methods in animal improvement: Historical overview. In P. D. D. Gianola & D. K. Hammond (Eds.), Advances in statistical methods for genetic improvement of livestock, advanced series in agricultural sciences (pp. 2–14). Berlin: Springer. doi:10.1007/978-3-642-74487-7_1.
65. Heslot, N., Yang, H.-P., Sorrells, M. E., & Jannink, J.-L. (2012). Genomic selection in plant breeding: A comparison of models. Crop Science, 52, 146. doi:10.2135/cropsci2011.06.0297.
66. Hickey, J. M., Kinghorn, B. P., Tier, B., van der Werf, J. H., & Cleveland, M. A. (2012). A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation. Genetics, Selection, Evolution, 44, 10–1186.
67. Hill, W. G. (1984). On selection among groups with heterogeneous variance. Animal Science, 39, 473–477. doi:10.1017/S0003356100032220.
68. Hill, W. G., Goddard, M. E., & Visscher, P. M. (2008). Data and theory point to mainly additive genetic variance for complex traits. PLoS Genetics, 4, e1000008. doi:10.1371/journal.pgen.1000008.
69. Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12, 55–67. doi:10.1080/00401706.1970.10488634.
70. Holland, J. B. (2006). Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Science, 46, 642–654.
71. Holland, J. B., Nyquist, W. E., & Cervantes-Martinez, C. T. (2003). Estimating and interpreting heritability for plant breeding: An update. Plant Breeding Reviews, 22, 9–111.
72. Horne, D. W., Eller, M. S., & Holland, J. B. (2016). Responses to recurrent index selection for reduced fusarium ear rot and lodging and for increased yield in maize. Crop Science, 56, 85–94. doi:10.2135/cropsci2015.06.0333.
73. Howie, B. N., Donnelly, P., & Marchini, J. (2009). A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genetics, 5, e1000529. doi:10.1371/journal.pgen.1000529.
74. Huang, Y., Hickey, J. M., Cleveland, M. A., & Maltecca, C. (2012). Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost. Genetics, Selection, Evolution, 44, 25.
75. Isik, F., Isik, K., Lee, S. (1999). Genetic variation in Pinus brutia ten. In Turkey: I. Growth, biomass and stem quality trats. International Journal of Forest Genetics, 6, 89–99.
76. Isik, F., Li, B., Frampton, J., & Goldfarb, B. (2004). Efficiency of seedlings and rooted cuttings for testing and selection in Pinus taeda. Forest Science, 50, 44–53.
77. Isik, F., Boos, D. D., & Li, B. (2005). The distribution of genetic parameter estimates and confidence intervals from small disconnected diallels. Theoretical and Applied Genetics, 110, 1236–1243. doi:10.1007/s00122-005-1957-0.
78. Isik, F., Bartholomé, J., Farjat, A., Chancerel, E., Raffin, A., Sanchez, L., Plomion, C., & Bouffier, L. (2016). Genomic selection in maritime pine. Plant Science, 242, 108–119.
79. John, J. A., & Eccleston, J. A. (1986). Row-column α-designs. Biometrika, 73, 301–306. doi:10.1093/biomet/73.2.301.
80. Kehel, Z., Habash, D. Z., Gezan, S. A., Welham, S. J., & Nachit, M. M. (2010). Estimation of spatial trend and automatic model selection in augmented designs. Agronomy Journal, 102, 1542. doi:10.2134/agronj2010.0175.
81. Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983–997. doi:10.2307/2533558.
82. Kenward, M. G., & Roger, J. H. (2009). An improved approximation to the precision of fixed effects from restricted maximum likelihood. Computational Statistics and Data Analysis, 53, 2583–2595. doi:10.1016/j.csda.2008.12.013.
83. Legarra, A., & Ducrocq, V. (2012). Computational strategies for national integration of phenotypic, genomic, and pedigree data in a single-step best linear unbiased prediction. Journal of Dairy Science, 95, 4629–4645. doi:10.3168/jds.2011-4982.
84. Legarra, A., Robert-Granié, C., Manfredi, E., & Elsen, J.-M. (2008). Performance of genomic selection in mice. Genetics, 180, 611–618. doi:10.1534/genetics.108.088575.
85. Legarra, A., Aguilar, I., & Misztal, I. (2009). A relationship matrix including full pedigree and genomic information. Journal of Dairy Science, 92, 4656–4663. doi:10.3168/jds.2009-2061.
86. Lewontin, R. C. (1964). The interaction of selection and linkage. I. General considerations; heterotic models. Genetics, 49, 49–67.
87. Li, Y., Willer, C. J., Ding, J., Scheet, P., & Abecasis, G. R. (2010). MaCH: Using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genetic Epidemiology, 34, 816–834. doi:10.1002/gepi.20533.
88. Lynch, M., & Walsh, B. (1998). Genetics and analysis of quantitative traits (1st ed.). Sunderland: Sinauer Associates.
89. Maltecca, C., Parker, K. L., & Cassady, J. P. (2012). Application of multiple shrinkage methods to genomic predictions. Journal of Animal Science, 90, 1777–1787. doi:10.2527/jas.2011-4350.
90. Mangin, B., Siberchicot, A., Nicolas, S., Doligez, A., This, P., & Cierco-Ayrolles, C. (2012). Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Heredity, 108, 285–291. doi:10.1038/hdy.2011.73.
91. Marchini, J., & Howie, B. (2010). Genotype imputation for genome-wide association studies. Nature Reviews. Genetics, 11, 499–511. doi:10.1038/nrg2796.
92. McKeand, S. E., & Bridgwater, F. E. (1998). A strategy for the third breeding cycle of loblolly pine in the southeastern US. Silvae Genetica, 47, 223–234.
93. Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819–1829.
94. Meyer, K. (2009). Factor-analytic models for genotype x environment type problems and structured covariance matrices. Genetics Selection Evolution, 41, 21.
95. Milliken, G. A., & Johnson, D. E. (2004). Analysis of messy data, Designed Experiments (Vol. 1). Boca Raton: Chapman & Hall/CRC.
96. Misztal, I., Legarra, A., & Aguilar, I. (2009). Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. Journal of Dairy Science, 92, 4648–4655. doi:10.3168/jds.2009-2064.
97. Mohring, J., & Piepho, H.-P. (2009). Comparison of weighting in two-stage analysis of plant breeding trials. Crop Science, 49, 1977. doi:10.2135/cropsci2009.02.0083.
98. Mohring, J., Melchinger, A. E., & Piepho, H. P. (2011). REML-based diallel analysis. Crop Science, 51, 470. doi:10.2135/cropsci2010.05.0272.
99. Money, D., Gardner, K., Migicovsky, Z., Schwaninger, H., Zhong, G.-Y., & Myles, S. (2015). LinkImpute: Fast and accurate genotype imputation for nonmodel organisms. G3: Genes|Genomes|Genetics, 5, 2383–2390.
100. Mrode, R. A. (2014). Linear models for the prediction of animal breeding values (3rd ed.). Boston: CABI.
101. Müller, B. U., Schützenmeister, A., & Piepho, H.-P. (2010). Arrangement of check plots in augmented block designs when spatial analysis is used. Plant Breeding, 129, 581–589. doi:10.1111/j.1439-0523.2010.01803.x.
102. Mullin, T., Andersson, B., Bastien, J.-C., Beaulieu, J., Burdon, R. D., Dvorak, W. S., King, J. N., Kondo, T., Krakowski, J., Lee, S. J., McKeand, S. E., Pâques, L., Raffin, A., Russell, J. H., Skr?ppa, T., Stoehr, M., Yanchuk, A. (2011). Economic importance, breeding objectives and achievements. In Genetics, genomics and breeding of conifers (pp. 40–127). Science Publishers.
103. Nyquist, W. E. (1991). Estimation of heritability and prediction of selection response in plant populations. Critical Reviews in Plant Sciences, 10, 235–322.
104. Ogut, F., Maltecca, C., Whetten, R., McKeand, S., & Isik, F. (2014). Genetic analysis of diallel progeny test data using factor analytic linear mixed models. Forest Science, 60(1), 119–127.
105. Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103, 681–686. doi:10.1198/016214508000000337.
106. Patterson, H. D., & Williams, E. R. (1976). A new class of resolvable incomplete block designs. Biometrika, 63, 83–92. doi:10.1093/biomet/63.1.83.
107. Patterson, B., Vaillancourt, R. E., Pilbeam, D. J., & Potts, B. M. (2004). Factors affecting variation in outcrossing rate in Eucalyptus globulus. Australian Journal of Botany, 52, 773–780.
108. Pérez, P., & de Los Campos, G. (2014). Genome-wide regression & prediction with the BGLR statistical package. Genetics, 198(2), 483–495.
109. Pérez, P., de los Campos, G., Crossa, J., & Gianola, D. (2010). Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. The Plant Genome Journal, 3, 106. doi:10.3835/plantgenome2010.04.0005.
110. Piepho, H.-P., & Mohring, J. (2007). Computing heritability and selection response from unbalanced plant breeding trials. Genetics, 177, 1881–1888. doi:10.1534/genetics.107.074229.
111. Piepho, H.-P., Mohring, J., Melchinger, A. E., & Buscher, A. (2007). BLUP for phenotypic selection in plant breeding and variety testing. Euphytica, 161, 209–228.
112. Plomion, C., Chancerel, E., Endelman, J., Lamy, J.-B., Mandrou, E., Lesur, I., Ehrenmann, F., Isik, F., Bink, M. C., Bouffier, L., et al. (2014). Genome-wide distribution of genetic diversity and linkage disequilibrium in a mass-selected population of maritime pine. BMC Genomics, 15, 171.
113. Plummer, M., Best, N., Cowles, K., & Vines, K. (2006). CODA: Convergence diagnosis and output analysis for MCMC. R News, 6, 7–11.
114. Powell, J. E., Visscher, P. M., & Goddard, M. E. (2010). Reconciling the analysis of IBD and IBS in complex trait studies. Nature Reviews Genetics, 11, 800–805. doi:10.1038/nrg2865.
115. Qiao, C. G., Basford, K. E., DeLacy, I. H., & Cooper, M. (2000). Evaluation of experimental designs and spatial analyses in wheat breeding trials. TAG Theoretical and Applied Genetics, 100, 9–16. doi:10.1007/s001220050002.
116. Qiao, C. G., Basford, K. E., DeLacy, I. H., & Cooper, M. (2004). Advantage of single-trial models for response to selection in wheat breeding multi-environment trials. Theoretical and Applied Genetics, 108, 1256–1264.
117. Quaas, R. L., & Pollak, E. J. (1981). Modified equations for sire models with groups. Journal of Dairy Science, 64, 1868–1872. doi:10.3168/jds. S0022-0302(81)82778-6.
118. Rawlings, J. O., Pantula, S., & Dickey, D. A. (2001). Applied regression analysis: A research tool. New York: Springer.
119. Reich, D. E., Cargill, M., Bolk, S., Ireland, J., Sabeti, P. C., Richter, D. J., Lavery, T., Kouyoumjian, R., Farhadian, S. F., Ward, R., & Lander, E. S. (2001). Linkage disequilibrium in the human genome. Nature, 411, 199–204. doi:10.1038/35075590.
120. Ripke, S., Isaacs, A., & van Duijn, C. M. (2007). GenABEL: An R library for genome-wide association analysis. Bioinformatics, 23, 1294–1296. doi:10.1093/bioinformatics/btm108.
121. Robinson, G. K. (1991). That BLUP is a good thing: The estimation of random effects. Statistical Science, 6, 15–32.
122. Rodríguez-Álvarez, M. X., Boer, M. P., van Eeuwijk, F. A., Eilers, P. H. C. (2016). Spatial models for field trials. arXiv:1607.08255 [stat].
123. Ron, D., Singer, Y., Tishby, N. (1995). On the learnability and usage of acyclic probabilistic finite automata. In Journal of computer and system sciences (pp. 31–40). ACM Press.
124. Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric regression. Cambridge, UK: Cambridge University Press.
125. Rutkoski, J. E., Poland, J., Jannink, J.-L., & Sorrells, M. E. (2013). Imputation of unordered markers and the impact on genomic selection accuracy. G3, 3, 427–439. doi:10.1534/g3.112.005363.
126. Saatchi, M., McClure, M. C., McKay, S. D., Rolf, M. M., Kim, J., Decker, J. E., Taxis, T. M., Chapple, R. H., Ramey, H. R., Northcutt, S. L., Bauck, S., Woodward, B., Dekkers, J. C., Fernando, R. L., Schnabel, R. D., Garrick, D. J., & Taylor, J. F. (2011). Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genetics Selection Evolution, 43, 40. doi:10.1186/1297-9686-43-40.
127. Sargolzaei, M., Chesnais, J. P., & Schenkel, F. S. (2014). A new approach for efficient genotype imputation using information from relatives. BMC Genomics, 15, 478. doi:10.1186/1471-2164-15-478.
128. SAS Institute, Inc. (2011a). The MIXED procedure. In SAS/STAT(R) 9.3 user’s guide.
129. SAS Institute, Inc. (2011b). The HPMIXED procedure. In SAS/STAT(R) 9.3 user’s guide.
130. Scheet, P., & Stephens, M. (2006). A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. American Journal of Human Genetics, 78, 629–644. doi:10.1086/502802.
131. Self, S. G., & Liang, K.-Y. (1987). Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. Journal of the American Statistical Association, 82, 605–610. doi:10.2307/2289471.
132. Shin, J.-H., Blay, S., McNeney, B., & Graham, J. (2006). LDheatmap: An R function for graphical display of pairwise linkage disequilibria between single nucleotide polymorphisms. Journal of Statistical Software, 16, 1–10.
133. Smith, A., Cullis, B., & Gilmour, A. (2001). The analysis of crop variety evaluation data in Australia. Australian & New Zealand Journal of Statistics, 43, 129–145.
134. Smith, A., Cullis, B., & Thompson, R. (2002). Exploring variety-environment data using random effects AMMI models with adjustments for spatial field trend: Part 1: Theory. In M. S. Kang (Ed.), Quantitative genetics, genomics, and plant breeding (pp. 323–335). Wallingford: CABI.
135. Smith, A. B., Cullis, B. R., & Thompson, R. (2005). The analysis of crop cultivar breeding and evaluation trials: An overview of current mixed model approaches. The Journal of Agricultural Science, 143, 449–462. doi:10.1017/S0021859605005587.
136. Smith, A. B., Ganesalingam, A., Kuchel, H., & Cullis, B. R. (2014). Factor analytic mixed models for the provision of grower information from national crop variety testing programs. Theoretical and Applied Genetics, 128, 55–72. doi:10.1007/s00122-014-2412-x.
137. Sorensen, D., & Gianola, D. (2007). Likelihood, Bayesian, and MCMC methods in quantitative genetics. New York: Springer Science & Business Media.
138. Speed, D., & Balding, D. J. (2015). Relatedness in the post-genomic era: Is it still useful? Nature Reviews Genetics, 16, 33–44. doi:10.1038/nrg3821.
139. Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B: Statistical Methodology, 64, 583–639. doi:10.1111/1467-9868.00353.
140. Steel, R. G. D., Torrie, J. H., & Dickey, D. A. (1997). Principles and procedures of statistics. A biometrical approach. New York: McGraw-Hill.
141. Thompson, R., Cullis, B., Smith, A., & Gilmour, A. (2003). A sparse implementation of the average information algorithm for factor analytic and reduced rank variance models. Australian & New Zealand Journal of Statistics, 45, 445–459. doi:10.1111/1467-842X.00297.
142. Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society: Series B: Methodological, 58, 267–288.
143. Tiezzi, F., Maltecca, C., Cecchinato, A., & Bittante, G. (2015). Comparison between different statistical models for the prediction of direct genetic component on embryo establishment and survival in Italian Brown Swiss dairy cattle. Livestock Science, 180, 6–13.
144. Toft, N., Innocent, G. T., Gettinby, G., & Reid, S. W. J. (2007). Assessing the convergence of Markov Chain Monte Carlo methods: An example from evaluation of diagnostic tests in absence of a gold standard. Preventive Veterinary Medicine, 79, 244–256. doi:10.1016/j.prevetmed.2007. 01.003.
145. VanRaden, P. M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science, 91, 4414–4423.
146. VanRaden, P. M., Van Tassell, C. P., Wiggans, G. R., Sonstegard, T. S., Schnabel, R. D., Taylor, J. F., & Schenkel, F. S. (2009). Invited review: Reliability of genomic predictions for North American Holstein bulls. Journal of dairy science, 92(1), 16–24.
147. VanRaden, P. M., Null, D. J., Sargolzaei, M., Wiggans, G. R., Tooker, M. E., Cole, J. B., Sonstegard, T. S., Connor, E. E., Winters, M., van Kaam, J. B. C. H. M., Valentini, A., Van Doormaal, B. J., Faust, M. A., & Doak, G. A. (2013). Genomic imputation and evaluation using high-density Holstein genotypes. Journal of Dairy Science, 96, 668–678. doi:10.3168/jds.2012-5702.
148. Warnes, G., Gorjanc, G., Leisch, F., & Man, M. (2013). Genetics: a package for population genetics. R Package (Version 1.3. 8.1). R Foundation. Accessed 2016 Oct 1.
149. Welham, S. J., Gogel, B. J., Smith, A. B., Thompson, R., & Cullis, B. R. (2010). A comparison of analysis methods for late-stage variety evaluation trials. Australian & New Zealand Journal of Statistics, 52, 125–149. doi:10.1111/j.1467-842X.2010.00570.x.
150. White, T. L., Adams, T. W., & Neale, D. B. (2007). Forest genetics. Cambridge, MA: CABI.
151. Wickham, H. (Ed.). (2010). ggplot2: Elegant graphics for data analysis (1. 2009. Corr. 3rd printing 2010 edition. ed.). New York: Springer.
152. Wimmer, V., Albrecht, T., Auinger, H.-J., & Schon, C.-C. (2012). Synbreed: A framework for the analysis of genomic prediction data using R. Bioinformatics, 28, 2086–2087. doi:10.1093/bioinformatics/bts335.
153. Wolc, A., Stricker, C., Arango, J., Settar, P., Fulton, J. E., O’Sullivan, N. P., … & Lamont, S. J. (2011). Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model. Genetics Selection Evolution, 43(1), 5.
154. Wright, S. (1922). Coefficients of inbreeding and relationship. The American Naturalist, 56, 330–338.
155. Yamada, Y. (1962). Genotype by environment interaction and genetic correlation of the same trait under different environments. The Japanese Journal of Genetics, 37, 498–509. doi:10.1266/jjg.37.498.
156. Yang, R.-C., Crossa, J., Cornelius, P. L., & Burgueño, J. (2009). Biplot analysis of genotype × environment interaction: Proceed with caution. Crop Science, 49, 1564–1576. doi:10.2135/cropsci2008.11.0665.
157. Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., Goddard, M. E., & Visscher, P. M. (2010). Common SNPs explain a large proportion of the heritability for human height. Nature Genetics, 42, 565–569. doi:10.1038/ng.608.
158. Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: A tool for genome-wide complex trait analysis. The American Journal of Human Genetics, 88, 76–82. doi:10.1016/j.ajhg.2010.11.011.
159. Yi, N., & Xu, S. (2008). Bayesian LASSO for quantitative trait loci mapping. Genetics, 179, 1045–1055.
160. Yu, J., Holland, J. B., McMullen, M. D., & Buckler, E. S. (2008). Genetic design and statistical power of nested association mapping in maize. Genetics, 178, 539–551. doi:10.1534/genetics.107.074245.
161. Zapata-Valenzuela, J., Whetten, R. W., Neale, D., McKeand, S., & Isik, F. (2013). Genomic estimated breeding values using genomic relationship matrices in a cloned population of loblolly pine. G3, 3, 909–916. doi:10.1534/g3.113.005975.