Corrigendum to Evaluation of the lasso and the elastic net in genome-wide association studies [Front. Genet., 4, (2013), 270], DOI:10.3389/fgene.2013.00270;Evaluation of the lasso and the elastic net in genome-wide association studies

Frontiers in Genetics

Volumn 4, Issue DEC, 2013, Pages

  Waldmann, Patrik ^a,b   Mészáros, Gábor ^a   Gredler, Birgit ^c   Fuerst, Christian ^d   Sölkner, Johann ^a  

^a UNIVERSITY OF NATURAL RESOURCES AND LIFE SCIENCES   (Austria)

^b LINKÖPING UNIVERSITY   (Sweden)

^c QUALITAS AG   (Switzerland)

^d ZucbtData EDV Dienstleistungen GmbH   (Austria)

Author keywords

Cattle; Elastic net; GWAS; Lasso; Population structure; Simulation; LASSO; Elasticnet; Populationstructure

Indexed keywords

EID: 84892384237     PISSN: None     EISSN: 16648021     Source Type: Journal    
DOI: 10.3389/fgene.2014.00349     Document Type: Erratum

References (49)

1. Ayers, K. L., and Cordell, H. J. (2010). SNP selection in genome-wide and candidate gene studies via penalized logistic regression. Genet. Epidemiol. 34, 879-891. doi: 10.1002/gepi.20543.
2. Bornn, L., Gottardo, R., and Doucet, A. (2010). Grouping priors and the Bayesian elastic net. Technical report 254, Department of Statistics. University of British Columbia. arXiv:1001.4083.
3. Bühlmann, P., and van de Geer, S. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. New York, NY: Springer-Verlag. doi: 10.1007/978-3-642-20192-9.
4. Cantor, R. M., Lange, K., and Sinsheimer, J. S. (2010). Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 86, 6-22. doi: 10.1016/j.ajhg.2009.11.017.
5. Cho, S., Kim, K., Kim, Y. J., Lee, J. K., Cho, Y. S., Lee, J. Y., et al. (2010). Joint identification of multiple genetic variants via elastic-net variable selection in a genome-wide association analysis. Ann. Hum. Genet. 74, 416-428. doi: 10.1111/j.1469-1809.2010.00597.x.
6. Dasgupta, A., Sun, Y. V., König, I. R., Bailey-Wilson, J. E., and Malley, J. D. (2011). Brief review of regression-based and machine learning methods in genetic epidemiology: the Genetic Analysis Workshop 17 experience. Genet. Epidemiol. 35(Suppl. 1), S5-S11. doi: 10.1002/gepi.20642.
7. de Los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D., and Calus, M. P. (2013). Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327-345. doi: 10.1534/genetics.112.143313.
8. Efron, B. (2010). Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambrige, UK: Cambrige University Press. doi: 10.1017/CBO9780511761362.
9. Efron, B., Turnbull, B. B., and Narasimhan, B. (2011). Locfdr: Computes Local False Discovery Rates. R package Version 1.1-7. Available online at: http://CRAN.R-project.org/package=locfdr.
10. Fadista, J., and Bendixen, C. (2012). Genomic position mapping discrepancies of commercial SNP chips. PLoS ONE 7:e31025. doi: 10.1371/journal.pone.0031025.
11. Fan, J., and Lv, J. (2010). A selective overview of variable selection in high dimensional feature space. Stat. Sin. 20, 101-148.
12. Friedman, J., Hastie, T., Hoefling, H., and Tibshirani, R. (2007). Pathwise coordinate optimization. Ann. Appl. Stat. 2, 302-332. doi: 10.1214/07-AOAS131.
13. Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1-22.
14. Garrick, D. J., Taylor, J. F., and Fernando, R. L. (2009). Deregressing estimated breeding values and weighting information for genomic regression analyses. Genet. Sel. Evol. 41, 55. doi: 10.1186/1297-9686-41-55.
15. Gianola, G. (2013). Priors in whole-genome regression: the Bayesian alphabet returns. Genetics 194, 573-596. doi: 10.1534/genetics.113.151753.
16. Goddard, M. E., and Hayes, B. J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat. Rev. Genet. 10, 381-391. doi: 10.1038/nrg2575.
17. Grisart, B., Coppieters, W., Farnir, F., Karim, L., Ford, C., Berzi, P., et al. (2002). Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Res. 12, 222-231. doi: 10.1101/gr.224202.
18. Hans, C. (2010). Model uncertainty and variable selection in Bayesian lasso regression. Stat. Comput. 20, 221-229. doi: 10.1007/s11222-009-9160-9.
19. Hastie, T. R., Tibshirani, R., and Friedman, J. (2009). Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd Edn. New York, NY: Springer-Verlag.
20. Hayes, B. J., Pryce, J., Chamberlain, A. J., Bowman, P. J., and Goddard, M. E. (2010). Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits. PLoS Genet. 6:e1001139. doi: 10.1371/journal.pgen.1001139.
21. Heffner, E. L., Sorrels, M. E., and Jannink, J. L. (2009). Genomic selection for crop improvement. Crop. Sci. 49, 1-12. doi: 10.2135/cropsci2008.08.0512.
22. 1 penalized regression: a review. Stat. Surv. 2, 61-93. doi: 10.1214/08-SS035.
23. Hoerl, A. E., and Kennard, R. (1970). Ridge regression: biased estimation for non orthogonal problems. Technometrics 12, 55-67. doi: 10.1080/00401706.1970.10488634.
24. Hulsen, T., de Vlieg, J., and Alkema, W. (2008). BioVenn-a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics 9:488. doi: 10.1186/1471-2164-9-488.
25. Klei, L., Kent, B. P., Melhem, N., Devlin, B., and Roeder, K. (2011). GemTools: a Fast and Efficient Approach to Estimating Genetic Ancestry. Available online at: http://arxiv.org/pdf/1104.1162.
26. Kooperberg, C., LeBlanc, M., and Obenchain, V. (2010). Risk prediction using genome-wide association studies. Genet. Epidemiol. 34, 643-652. doi: 10.1002/gepi.20509.
27. Kyung, M., Gill, J., Ghosh, M., and Casella, G. (2010). Penalized regression, standard errors, and Bayesian lassos. Bay. Anal. 5, 369-412. doi: 10.1214/10-BA607.
28. Lee, A. B., Luca, D., and Roeder, K. (2010). A spectral graph approach to discovering genetic ancestry. Ann. Appl. Stat. 4, 179-202. doi: 10.1214/09-AOAS281.
29. Li, Q., and Lin, N. (2010). The Bayesian elastic net. Bay. Anal. 5, 151-170. doi: 10.1214/10-BA506.
30. Malo, N., Libiger, O., and Schork, N. J. (2008). Accommodating linkage disequilibrium in genetic-association analyses via ridge regression. Am. J. Hum. Genet. 82, 375-385. doi: 10.1016/j.ajhg.2007.10.012.
31. McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P. A., et al. (2008). Genome-wide association studies for complex traits: consensus,uncertainty and challenges. Nat. Rev. Genet. 9, 356-369. doi: 10.1038/nrg2344.
32. Meredith, B. K., Kearney, F. J., Finlay, E. K., Bradley, D. G., Fahey, A. G., Berry, D. P., et al. (2012). Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland. BMC Genet. 13:21. doi: 10.1186/1471-2156-13-21.
33. Moore, J. H., Asselbergs, F. W., and Williams, S. M. (2010). Bioinformatics challenges for genome-wide association studies. Bioinformatics, 26, 445-455. doi: 10.1093/bioinformatics/btp713.
34. Mucha, S., Pszczoła, M., Strabel, T., Wolc, A., Paczyñska, P., and Szydlowski, M. (2011). Comparison of analyses of the QTLMAS XIV common dataset. II: QTL analysis. BMC Proc. 5(Suppl 3):S2. doi: 10.1186/1753-6561-5-S3-S2.
35. Ogutu, J. O., Schulz-Streeck, T., and Piepho, H. P. (2012). Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proc. 6(Suppl. 2):S10. doi: 10.1186/1753-6561-6-S2-S10.
36. Patterson, N., Price, A. L., and Reich, D. (2006). Population structure and eigenanalysis. PLoS Genet. 2:e190. doi: 10.1371/journal.pgen.0020190.
37. Price, A. L., Zaitlen, N. A., Reich, D., and Patterson, N. (2010). New approaches to population stratification in genome-wide association studies. Nat. Rev. Genet. 11, 459-463. doi: 10.1038/nrg2813.
38. Puniyani, K., Kim, S., and Xing, E. P. (2010). Multi-population GWA mapping via multi-task regularized regression. Bioinformatics 26, 208-216. doi: 10.1093/bioinformatics/btq191.
39. Rice, T. K., Schork, N. J., and Rao, D. C. (2008). Methods for handling multiple testing. Adv. Genet. 60, 293-308. doi: 10.1016/S0065-2660(07)00412-9.
40. Sillanpää, M. J. (2011). Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses. Heredity 106, 511-519. doi: 10.1038/hdy.2010.91.
41. Sung, Y. J., Rice, T. K., Shi, G., Gu, C. C., and Rao, D. C. (2009). Comparison between single-marker analysis using Merlin and multi-marker analysis using LASSO for Framingham simulated data. BMC Proc. 3(Suppl. 7):S27. doi: 10.1186/1753-6561-3-s7-s27.
42. Szydłowski, M., and Paczyñska, P. (2011). QTLMAS 2010: simulated dataset. BMC Proc. 5(Suppl. 3):S3. doi: 10.1186/1753-6561-5-S3-S3.
43. Szymczak, S., Biernacka, J. M., Cordell, H. J., González-Recio, O., König, I. R., Zhang, H., et al. (2009). Machine learning in genome-wide association studies. Genet. Epidemiol. 33, S51-S57. doi: 10.1002/gepi.20473.
44. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B 58, 267-288. doi: 10.1111/j.1467-9868.2011.00771.x.
45. Tutz, G., and Ulbricht, J. (2009). Penalized regression with correlation-based penalty. Stat. Comp. 19, 239-253. doi: 10.1007/s11222-008-9088-5.
46. Usai, M. G., Goddard, M. E., and Hayes, B. J. (2009). LASSO with cross-validation for genomic selection. Genet. Res. 91, 427-436. doi: 10.1017/S0016672309990334.
47. Waldron, L., Pintilie, M., Tsao, M. S., Shepherd, F. A., Huttenhower, C., and Jurisica, I. (2011). Optimized application of penalized regression methods to diverse genomic data. Bioinformatics 27, 3399-3406. doi: 10.1093/bioinformatics/btr591.
48. Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E., and Lange, K. (2009). Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25, 714-721. doi: 10.1093/bioinformatics/btp041.
49. Zou, H., and Hastie, T. (2005). Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. B 67, 301-320. doi: 10.1111/j.1467-9868.2005.00503.x.