The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Methodological implementation of mixed linear models in multi-locus genome-wide association studies
Open accessAbstract
The mixed linear model has been widely used in genome-wide association studies (GWAS), but its application to multi-locus GWAS analysis has not been explored and assessed. Here, we implemented a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA) model for GWAS. The model is built on random single nucleotide polymorphism (SNP) effects and a new algorithm. This algorithm whitens the covariance matrix of the polygenic matrix K and environmental noise, and specifies the number of nonzero eigenvalues as one. The model first chooses all putative quantitative trait nucleotides (QTNs) with ≤ 0.005 P-values and then includes them in a multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection and model fit, has less bias in QTN effect estimation and requires a less running time than existing single- and multi-locus methods, such as empirical Bayes, settlement of mixed linear model under progressively exclusive relationship (SUPER), efficient mixed model association (EMMA), compressed MLM (CMLM) and enriched CMLM (ECMLM). FASTmrEMMA provides an alternative for multi-locus GWAS.
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Cites
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559
Genome-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 5×
“[3] proposed an efficient mixed model association (EMMA), and other authors suggested alternatives, such as EMMA eXpedited (EMMAX) [20], FaST-LMM [21], FaST-LMM-Select [22], genome-wide EMMA [4] and genome-wide rapid association using mixed model and regression-Gamma (GRAMMAR-Gamma) [23].”
MethodInfluentialVariance 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
Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines
Atwell, S., Huang, Y. S., Vilhjálmsson, B. J., Willems, G., Horton, M., Li, Y., Meng, D., Platt, A., Tarone, A. M., Hu, T. T., et al. (2010) · Nature · doi:10.1038/nature08800 · Cited by 1,780
FaST linear mixed models for genome-wide association studies
Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011) · Nature Methods · doi:10.1038/nmeth.1681 · Cited by 1,209
Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies
Hoggart, C. J., Whittaker, J. C., De Iorio, M., & Balding, D. J. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000130 · Cited by 321
Metadata
- Authors
- Wen, Y.-J., Zhang, H., Ni, Y.-L., Huang, B., Zhang, J., Feng, J.-Y., Wang, S.-B., Dunwell, J. M., Zhang, Y.-M., & Wu, R.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-02-01
- Volume
- 19
- Issue
- 4
- Pages
- 700-712
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W2585681007
- Cited by
- 394
- References
- 6
- FWCI
- 12.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Wen, Y.-J., Zhang, H., Ni, Y.-L., Huang, B., Zhang, J., Feng, J.-Y., Wang, S.-B., Dunwell, J. M., Zhang, Y.-M., & Wu, R. (2018). Methodological implementation of mixed linear models in multi-locus genome-wide association studies. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbw145
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