The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
A SUPER Powerful Method for Genome Wide Association Study
Open accessAbstract
Genome-Wide Association Studies shed light on the identification of genes underlying human diseases and agriculturally important traits. This potential has been shadowed by false positive findings. The Mixed Linear Model (MLM) method is flexible enough to simultaneously incorporate population structure and cryptic relationships to reduce false positives. However, its intensive computational burden is prohibitive in practice, especially for large samples. The newly developed algorithm, FaST-LMM, solved the computational problem, but requires that the number of SNPs be less than the number of individuals to derive a rank-reduced relationship. This restriction potentially leads to less statistical power when compared to using all SNPs. We developed a method to extract a small subset of SNPs and use them in FaST-LMM. This method not only retains the computational advantage of FaST-LMM, but also remarkably increases statistical power even when compared to using the entire set of SNPs. We named the method SUPER (Settlement of MLM Under Progressively Exclusive Relationship) and made it available within an implementation of the GAPIT software package.
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Cites
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
TASSEL: software for association mapping of complex traits in diverse samples
Bradbury, P. J., Zhang, Z., Kroon, D. E., Casstevens, T. M., Ramdoss, Y., & Buckler, E. S. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm308 · Cited by 7,759
Self-citationEfficient Methods to Compute Genomic Predictions
VanRaden, P. M. (2008) · Journal of Dairy Science · doi:10.3168/jds.2007-0980 · Cited by 6,050
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · CHANCE · doi:10.1080/09332480.2005.10722754 · Cited by 4,702 · cited 1×
“Inflation of type I error (false positives) leads to more false discoveries than expected [4,5].”
BackgroundCommon SNPs explain a large proportion of the heritability for human height
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., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446
Yu, J., Pressoir, G., Briggs, W. H., Vroh Bi, I., Yamasaki, M., Doebley, J. F., McMullen, M. D., Gaut, B. S., Nielsen, D. M., Holland, J. B., et al. (2006) · Nature Genetics · doi:10.1038/ng1702 · Cited by 4,133
Self-citationGenome-wide efficient mixed-model analysis for association studies
Zhou, X., & Stephens, M. (2012) · Nature Genetics · doi:10.1038/ng.2310 · Cited by 3,627 · cited 13×
“FaST-LMM-Select performs better than EMMAX and GLM. SUPER performs better than FaST-LMM-Select (Figure 2b).”
MethodInfluentialHardy, O. J., & Vekemans, X. (2002) · Molecular Ecology Notes · doi:10.1046/j.1471-8286.2002.00305.x · Cited by 3,557
Variance component model to account for sample structure in genome-wide association studies
Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S.-Y., Freimer, N. B., Sabatti, C., & Eskin, E. (2010) · Nature Genetics · doi:10.1038/ng.548 · Cited by 2,817
Cited by
Huang, M., Liu, X., Zhou, Y., Summers, R. M., & Zhang, Z. (2019) · GigaScience · doi:10.1093/gigascience/giy154 · Cited by 855
Self-citationChopra, R., Burow, G., Burke, J. J., Gladman, N., & Xin, Z. (2017) · BMC Plant Biology · doi:10.1186/s12870-016-0966-2 · Cited by 113
Metadata
- Authors
- Wang, Q., Tian, F., Pan, Y., Buckler, E. S., & Zhang, Z.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-09-23
- Volume
- 9
- Issue
- 9
- Pages
- e107684
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2083967026
- Cited by
- 286
- References
- 39
- FWCI
- 5.35
- Type
- journal article
- Language
- en
- Open access
- gold
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- Wang, Q., Tian, F., Pan, Y., Buckler, E. S., & Zhang, Z. (2014). A SUPER Powerful Method for Genome Wide Association Study. PLoS ONE. https://doi.org/10.1371/journal.pone.0107684
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
