Editorial: the applications of new multi-locus GWAS methodologies in the genetic dissection of complex traits
Zhang, Y.-M., Jia, Z. and Dunwell, J. M.
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.3389/fpls.2019.00100
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Barsh, G. S., Copenhaver, G. P., Gibson, G., and Williams, S. M. (2012). Guidelines for genome-wide association studies. PLoS Genet. 8:e1002812. doi: 10.1371/journal.pgen.1002812
PubMed Abstract | CrossRef Full Text | Google Scholar
Chang, M., He, L., and Cai, L. (2018). An overview of genome-wide association studies. Methods Mol. Biol. 1754, 97–108. doi: 10.1007/978-1-4939-7717-8_6
PubMed Abstract | CrossRef Full Text | Google Scholar
Eichler, E. E., Flint, J., Gibson, G., Kong, A., Leal, S. M., Moore, J. H., et al. (2010). Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446–450. doi: 10.1038/nrg2809
PubMed Abstract | CrossRef Full Text | Google Scholar
Fadista, J., Manning, A. K., Florez, J. C., and Groop, L. (2016). The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants. Eur J. Hum. Genet. 24, 1202–1205. doi: 10.1038/ejhg.2015.269
PubMed Abstract | CrossRef Full Text | Google Scholar
Feng, J. Y., Wen, Y. J., Zhang, J., and Zhang, Y. M. (2016). Advances on methodologies for genome-wide association studies in plants. Acta Agron. Sin. 42, 945–956. doi: 10.3724/SP.J.1006.2016.00945
CrossRef Full Text | Google Scholar
He, L., Xiao, J., Rashid, K. Y., Jia, G., Li, P., Yao, Z., et al. (2019). Evaluation of genomic prediction for pasmo resistance in flax. Int. J. Mol. Sci. 20:E359. doi: 10.3390/ijms20020359
PubMed Abstract | CrossRef Full Text | Google Scholar
Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S. Y., Freimer, N. B., et al. (2010). Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354. doi: 10.1038/ng.548
PubMed Abstract | CrossRef Full Text | Google Scholar
Khan, M. A., Tong, F., Wang, W., He, J., Zhao, T., and Gai, J. (2018). Analysis of QTL-allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [Glycine max (L.) Merr.] using a novel GWAS procedure. Planta 248, 947–962. doi: 10.1007/s00425-018-2952-4
CrossRef Full Text | Google Scholar
Li, C. X., Xu, W. G., Guo, R., Zhang, J. Z., Qi, X. L., Hu, L., et al. (2018). Molecular marker assisted breeding and genome composition analysis of Zhengmai 7698, an elite winter wheat cultivar. Sci. Rep. 8:322. doi: 10.1038/s41598-017-18726-8
PubMed Abstract | CrossRef Full Text | Google Scholar
Li, M., Liu, X., Bradbury, P., Yu, J., Zhang, Y. M., Todhunter, R. J., et al. (2014). Enrichment of statistical power for genome-wide association studies. BMC Biol. 12:73. doi: 10.1186/s12915-014-0073-5
PubMed Abstract | CrossRef Full Text | Google Scholar
Liu, X., Huang, M., Fan, B., Buckler, E. S., and Zhang, Z. (2016). Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet. 12:e1005767. doi: 10.1371/journal.pgen.1005767
PubMed Abstract | CrossRef Full Text | Google Scholar
Maher, B. (2008). Personal genomes: the case of the missing heritability. Nature 456, 18–21. doi: 10.1038/456018a
PubMed Abstract | CrossRef Full Text | Google Scholar
Ren, W. L., Wen, Y. J., Dunwell, J. M., and Zhang, Y. M. (2018). pKWmEB: integration of Kruskal-Wallis test with empirical bayes under polygenic background control for multi-locus genome-wide association study. Heredity 120, 208–218. doi: 10.1038/s41437-017-0007-4
PubMed Abstract | CrossRef Full Text | Google Scholar
Segura, V., Vilhjálmsson, B. J., Platt, A., Korte, A., Seren, Ü., Long, Q., et al. (2012). An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat. Genet. 44, 825–830. doi: 10.1038/ng.2314
PubMed Abstract | CrossRef Full Text | Google Scholar
Tamba, C. L., Ni, Y. L., and Zhang, Y. M. (2017). Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies. PLoS Comput. Biol. 13:e1005357. doi: 10.1371/journal.pcbi.1005357
PubMed Abstract | CrossRef Full Text | Google Scholar
Wang, S. B., Feng, J. Y., Ren, W. L., Huang, B., Zhou, L., Wen, Y. J., et al. (2016). Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Sci. Rep. 6:19444. doi: 10.1038/srep19444
PubMed Abstract | CrossRef Full Text | Google Scholar
Wen, Y. J., Zhang, H., Ni, Y. L., Huang, B., Zhang, J., Feng, J. Y., et al. (2018a). Methodological implementation of mixed linear models in multi-locus genome-wide association studies. Brief. Bioinform. 19, 700–712. doi: 10.1093/bib/bbw145
PubMed Abstract | CrossRef Full Text | Google Scholar
Wen, Y. J., Zhang, Y. W., Zhang, J., Feng, J. Y., Dunwell, J. M., and Zhang, Y. M. (2018b). An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2. Brief. Bioinform. doi: 10.1093/bib/bby058. [Epub ahead of print].
PubMed Abstract | CrossRef Full Text | Google Scholar
Yang, J., Bakshi, A., Zhu, Z., Hemani, G., Vinkhuyzen, A. A., Lee, S. H., et al. (2015). Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120. doi: 10.1038/ng.3390
PubMed Abstract | CrossRef Full Text | Google Scholar
Yang, J., Lee, S. H., Goddard, M. E., and Visscher, P. M. (2011). GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82. doi: 10.1016/j.ajhg.2010.11.011
PubMed Abstract | CrossRef Full Text | Google Scholar
Yu, J., Pressoir, G., Briggs, W. H., Bi, I. V., Yamasaki, M., Doebley, J. F., et al. (2006). A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208. doi: 10.1038/ng1702
PubMed Abstract | CrossRef Full Text | Google Scholar
Zhang, J., Feng, J. Y., Ni, Y. L., Wen, Y. J., Niu, Y., Tamba, C. L., et al. (2017). pLARmEB: integration of least angle regression with empirical bayes for multi-locus genome-wide association studies. Heredity 118, 517–524. doi: 10.1038/hdy.2017.8
CrossRef Full Text | Google Scholar
Zhang, Y., Qi, G., Park, J. H., and Chatterjee, N. (2018). Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat. Genet. 50, 1318–1326. doi: 10.1038/s41588-018-0193-x
PubMed Abstract | CrossRef Full Text | Google Scholar
Zhang, Y. M., Mao, Y., Xie, C., Smith, H., Luo, L., and Xu, S. (2005). Mapping quantitative trait loci using naturally occurring genetic variance among commercial inbred lines of maize (Zea mays L.). Genetics 169, 2267–2275. doi: 10.1534/genetics.104.033217
PubMed Abstract | CrossRef Full Text | Google Scholar
Zhang, Y. M., and Tamba, C. L. (2018). A fast mrMLM algorithm for multi-locus genome-wide association studies. bioRxiv [Preprint]. doi: 10.1101/341784
CrossRef Full Text | Google Scholar
Zhou, X., and Stephens, M. (2012). Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821–824. doi: 10.1038/ng.2310
PubMed Abstract | CrossRef Full Text | Google Scholar University Staff: Request a correction | Centaur Editors: Update this record |
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Editorial: the applications of new multi-locus GWAS methodologies in the genetic dissection of complex traits
Zhang, Y.-M., Jia, Z. and Dunwell, J. M.
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.3389/fpls.2019.00100
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