GenomicLand: Software for genome-wide association studies and genomic prediction
DOI:
https://doi.org/10.4025/actasciagron.v41i1.45361Palavras-chave:
statistical analysis, genomic analysis, molecular markers, biometrics.Resumo
GenomicLand is free software intended for prediction and genomic association studies based on the R software. This computational tool has an intuitive interface and supports large genomic databases, without requiring the user to use the command line. GenomicLand is available in English, can be downloaded from the Internet (https://licaeufv.wordpress.com/), and requires the Windows or Linux operating system. The software includes statistical procedures based on mixed models, Bayesian inference, dimensionality reduction and artificial intelligence. Examples of data files that can be processed by GenomicLand are available. The examples are useful to learn about the operation of the modules and statistical procedures.
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Referências
Azevedo, C. F., Resende, M. D. V., Silva, F. F., Lopes, P. S., & Guimarães, S. E. F. (2013). Regressão via componentes independentes aplicada à seleção genômica para características de carcaça em suínos. Pesquisa Agropecuária Brasileira, 48(6), 619-626. DOI: 10.1590/S0100-204X2013000600007
Azevedo, C. F., Silva, F. F., Resende, M. D. V., Lopes, M. S., Duijvesteijn, N., Guimarães, S.E.F., ... Knol, E. F. (2014). Supervised independent component analysis as an alternative method for genomic selection in pigs. Journal of Animal Breeding and Genetics, 131(6), 452-461. DOI: 10.1111/jbg.12104
Azevedo, C. F., Nascimento, M., Silva, F. F., Resende, M. D. V., Lopes, P. S., Guimarães, S. E. F., & Glória, L. S. (2015a). Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs. Genetics and Molecular Research, 14(4), 12217-12227. DOI: 10.4238/2015.October.9.10
Azevedo, C. F., Resende, M. D. V., Silva, F. F., Viana, J. M. S., Valente, M. S. F., Resende Jr, M. F. R., & Muñoz, P. (2015b). Ridge, Lasso and Bayesian additive-dominance genomic models. BMC Genetics, 16(105), 1-13. DOI: 10.1186/s12863-015-0264-2
Azevedo, C. F., Resende, M. D. V., Silva, F. F., Nascimento, M., Viana, J. M. S., & Valente, M. S. F. (2017). Population structure correction for genomic selection through eigenvector covariates. Crop Breeding and Applied Biotechnology, 17(4), 350-358. DOI: 10.1590/1984-70332017v17n4a53
Barroso, L. M. A., Nascimento, M., Nascimento, A. C. C., Silva, F. F., Serão, N. V. L., Cruz, C. D., ... Guimarães, S. E. F. (2017). Regularized quantile regression for SNP marker estimation of pig growth curves. Journal of Animal Science and Biotechnology, 8(59), 1-9. DOI: 10.1186/s40104-017-0187-z
de Los Campos, G., Naya, H., Gianola, D., Crossa, J., Legarra, A., Manfredi, E., ... Cotes, J. M. (2009). Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics, 182(1), 375-385. DOI: 10.1534/genetics.109.101501
Gianola, D., de Los Campos, G., Hill, W. G., Manfredi, E., & Fernando, R. (2009). Additive genetic variability and the Bayesian alphabet. Genetics, 183(1), 347-363. DOI: 10.1534/genetics.109.103952
Goddard, M. E. (2012). Uses of genomics in livestock agriculture. Animal Production Science, 52(3), 73-77. DOI: 10.1071/AN11180
González-Camacho, J. M., Ornella, L., Pérez-Rodríguez, P., Gianola, D., Dreisigacker, S., & Crossa, J. (2018). Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance. The Plant Genome, 11(2), 1-15. DOI: 10.3835/plantgenome2017.11.0104
Hayes, B. J., Chamberlain, A. J., Mcpartlan, H., Macleod, I., Sethuraman, L., & Goddard, M. E. (2007). Accuracy of marker assisted selection with single markers and marker haplotypes in cattle. Genetical Research, 89(4), 215-220. DOI: 10.1017/S0016672307008865
Henderson, C. R. (1975). Best linear estimation and prediction under a selection model. Biometrics, 31(2), 423-447. DOI: 10.2307/2529430
Macleod, I. M., Hayes, B. J., Savin, K., Chamberlain, A. J., Cpartlan, H., & Goddard, M. E. (2010). Power of a genome scan to detect and locate quantitative trait loci in cattle using dense single nucleotide polymorphisms. Journal of Animal Breeding and Genetics, 127(2), 133-142. DOI: 10.1111/j.1439-0388.2009.00831.x
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819-1829.
Nascimento, M., Nascimento, A. C. C., Silva, F. F., Barili, L. D., Vale, N. M., Carneiro, J. E., ... Serão, N. V. L. (2018). Quantile regression for genome-wide association study of flowering time-related traits in common bean. PLoS ONE, 13(1), e0190303. DOI: 10.1371/journal.pone.0190303
Polejaeva, I. A., Broek, D. M., Walker, S. C., Zhou, W., Walton, M., Benninghoff, A. D., & Faber, D. C. (2013). Longitudinal Study of Reproductive Performance of Female Cattle Produced by Somatic Cell Nuclear Transfer. PLoS ONE, 8(12), e84283. DOI: 10.1371/journal.pone.0084283
Resende, M. D. V., Resende Jr, M. F. R., Sansaloni, C. P., Petroli, C. D., Missiaggia, A. A., Aguiar, A. M., ... Grattapaglia, D. (2012a). Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytologist, 194(1), 116-128. DOI: 10.1111/j.1469-8137.2011.04038.x
Resende, M. D. V., Silva, F. F., Lopes, P. S., & Azevedo, C. F. (2012b). Seleção Genômica Ampla (GWS) via Modelos Mistos (REML/BLUP), Inferência Bayesiana (MCMC), Regressão Aleatória Multivariada (RRM) e Estatística Espacial. Viçosa: Universidade Federal de Viçosa/Departamento de Estatística. Retrieved on September 15, 2018 from http://www.ppestbio.ufv.br/?page_id=448.
Su, G., Christensen, O. F., Ostersen, T., Henryon, M., & Lund, M. S. (2012). Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers. PLoS ONE, 7(9), e45293. DOI: 10.1371/journal.pone.0045293
Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B (Methodological), 58(1), 267-288.
Vandenplas, J., Calus, M. P. L., & Gorjanc, G. (2018). Genomic Prediction Using Individual-Level Data and Summary Statistics from Multiple Populations. Genetics, 210(1), 53-69. DOI: 10.1534/genetics.118.301109
Vanraden, P. M. (2008). Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science, 91(11), 4414-4423. DOI: 10.3168/jds.2007-0980
Vitezica, Z. G., Varona, L., & Legarra, A. (2013). On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics, 195(4), 1223-1230. DOI: 10.1534/genetics.113.155176
Wellmann, R., & Bennewitz, J. (2012). Bayesian models with dominance effects for genomic evaluation of quantitative traits. Genetics Research, 94(1), 21-37. DOI: 10.1017/S0016672312000018
Zhang, Z., Ersoz, E., Lai, C. Q., Todhunter, R. J., Tiwari, H. K., Gore, M. A., … Buckler, E. S. (2010). Mixed linear model approach adapted for genome-wide association studies. Nature Genetics, 42(4), 355-360. DOI: 10.1038/ng.546
Azevedo, C. F., Silva, F. F., Resende, M. D. V., Lopes, M. S., Duijvesteijn, N., Guimarães, S.E.F., ... Knol, E. F. (2014). Supervised independent component analysis as an alternative method for genomic selection in pigs. Journal of Animal Breeding and Genetics, 131(6), 452-461. DOI: 10.1111/jbg.12104
Azevedo, C. F., Nascimento, M., Silva, F. F., Resende, M. D. V., Lopes, P. S., Guimarães, S. E. F., & Glória, L. S. (2015a). Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs. Genetics and Molecular Research, 14(4), 12217-12227. DOI: 10.4238/2015.October.9.10
Azevedo, C. F., Resende, M. D. V., Silva, F. F., Viana, J. M. S., Valente, M. S. F., Resende Jr, M. F. R., & Muñoz, P. (2015b). Ridge, Lasso and Bayesian additive-dominance genomic models. BMC Genetics, 16(105), 1-13. DOI: 10.1186/s12863-015-0264-2
Azevedo, C. F., Resende, M. D. V., Silva, F. F., Nascimento, M., Viana, J. M. S., & Valente, M. S. F. (2017). Population structure correction for genomic selection through eigenvector covariates. Crop Breeding and Applied Biotechnology, 17(4), 350-358. DOI: 10.1590/1984-70332017v17n4a53
Barroso, L. M. A., Nascimento, M., Nascimento, A. C. C., Silva, F. F., Serão, N. V. L., Cruz, C. D., ... Guimarães, S. E. F. (2017). Regularized quantile regression for SNP marker estimation of pig growth curves. Journal of Animal Science and Biotechnology, 8(59), 1-9. DOI: 10.1186/s40104-017-0187-z
de Los Campos, G., Naya, H., Gianola, D., Crossa, J., Legarra, A., Manfredi, E., ... Cotes, J. M. (2009). Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics, 182(1), 375-385. DOI: 10.1534/genetics.109.101501
Gianola, D., de Los Campos, G., Hill, W. G., Manfredi, E., & Fernando, R. (2009). Additive genetic variability and the Bayesian alphabet. Genetics, 183(1), 347-363. DOI: 10.1534/genetics.109.103952
Goddard, M. E. (2012). Uses of genomics in livestock agriculture. Animal Production Science, 52(3), 73-77. DOI: 10.1071/AN11180
González-Camacho, J. M., Ornella, L., Pérez-Rodríguez, P., Gianola, D., Dreisigacker, S., & Crossa, J. (2018). Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance. The Plant Genome, 11(2), 1-15. DOI: 10.3835/plantgenome2017.11.0104
Hayes, B. J., Chamberlain, A. J., Mcpartlan, H., Macleod, I., Sethuraman, L., & Goddard, M. E. (2007). Accuracy of marker assisted selection with single markers and marker haplotypes in cattle. Genetical Research, 89(4), 215-220. DOI: 10.1017/S0016672307008865
Henderson, C. R. (1975). Best linear estimation and prediction under a selection model. Biometrics, 31(2), 423-447. DOI: 10.2307/2529430
Macleod, I. M., Hayes, B. J., Savin, K., Chamberlain, A. J., Cpartlan, H., & Goddard, M. E. (2010). Power of a genome scan to detect and locate quantitative trait loci in cattle using dense single nucleotide polymorphisms. Journal of Animal Breeding and Genetics, 127(2), 133-142. DOI: 10.1111/j.1439-0388.2009.00831.x
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819-1829.
Nascimento, M., Nascimento, A. C. C., Silva, F. F., Barili, L. D., Vale, N. M., Carneiro, J. E., ... Serão, N. V. L. (2018). Quantile regression for genome-wide association study of flowering time-related traits in common bean. PLoS ONE, 13(1), e0190303. DOI: 10.1371/journal.pone.0190303
Polejaeva, I. A., Broek, D. M., Walker, S. C., Zhou, W., Walton, M., Benninghoff, A. D., & Faber, D. C. (2013). Longitudinal Study of Reproductive Performance of Female Cattle Produced by Somatic Cell Nuclear Transfer. PLoS ONE, 8(12), e84283. DOI: 10.1371/journal.pone.0084283
Resende, M. D. V., Resende Jr, M. F. R., Sansaloni, C. P., Petroli, C. D., Missiaggia, A. A., Aguiar, A. M., ... Grattapaglia, D. (2012a). Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytologist, 194(1), 116-128. DOI: 10.1111/j.1469-8137.2011.04038.x
Resende, M. D. V., Silva, F. F., Lopes, P. S., & Azevedo, C. F. (2012b). Seleção Genômica Ampla (GWS) via Modelos Mistos (REML/BLUP), Inferência Bayesiana (MCMC), Regressão Aleatória Multivariada (RRM) e Estatística Espacial. Viçosa: Universidade Federal de Viçosa/Departamento de Estatística. Retrieved on September 15, 2018 from http://www.ppestbio.ufv.br/?page_id=448.
Su, G., Christensen, O. F., Ostersen, T., Henryon, M., & Lund, M. S. (2012). Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers. PLoS ONE, 7(9), e45293. DOI: 10.1371/journal.pone.0045293
Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B (Methodological), 58(1), 267-288.
Vandenplas, J., Calus, M. P. L., & Gorjanc, G. (2018). Genomic Prediction Using Individual-Level Data and Summary Statistics from Multiple Populations. Genetics, 210(1), 53-69. DOI: 10.1534/genetics.118.301109
Vanraden, P. M. (2008). Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science, 91(11), 4414-4423. DOI: 10.3168/jds.2007-0980
Vitezica, Z. G., Varona, L., & Legarra, A. (2013). On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics, 195(4), 1223-1230. DOI: 10.1534/genetics.113.155176
Wellmann, R., & Bennewitz, J. (2012). Bayesian models with dominance effects for genomic evaluation of quantitative traits. Genetics Research, 94(1), 21-37. DOI: 10.1017/S0016672312000018
Zhang, Z., Ersoz, E., Lai, C. Q., Todhunter, R. J., Tiwari, H. K., Gore, M. A., … Buckler, E. S. (2010). Mixed linear model approach adapted for genome-wide association studies. Nature Genetics, 42(4), 355-360. DOI: 10.1038/ng.546
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Publicado
2019-03-13
Edição
Seção
Biometria, Modelagem e Estatística
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Como Citar
Azevedo, C. F., Nascimento, M., Fontes, V. C., Silva, F. F. e, Resende, M. D. V. de, & Cruz, C. D. (2019). GenomicLand: Software for genome-wide association studies and genomic prediction. Acta Scientiarum. Agronomy, 41(1), e45361. https://doi.org/10.4025/actasciagron.v41i1.45361
