The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies
Open accessAbstract
False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.
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Cites
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
Efficient Methods to Compute Genomic Predictions
VanRaden, P. M. (2008) · Journal of Dairy Science · doi:10.3168/jds.2007-0980 · Cited by 6,050
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
Population Structure and Eigenanalysis
Patterson, N., Price, A. L., & Reich, D. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020190 · Cited by 5,280
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 2×
“Similar to previous comparisons[6,18,20], we decomposed the total computational time into three components: building the K, attaining optimization on variance components, and performing association tests.”
MethodVariance 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
The NHGRI GWAS Catalog, a curated resource of SNP-trait associations
Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707
GenABEL: an R library for genome-wide association analysis
Aulchenko, Y. S., Ripke, S., Isaacs, A., & van Duijn, C. M. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm108 · Cited by 1,836 · cited 5×
“GRAMMAR-Gamma's (implemented in GenABEL) time for association tests is linear with n, but its times for building the Kmatrix and optimization on variance components are the square and the cube of n, respectively.”
MethodInfluential
Cited by
Huang, M., Liu, X., Zhou, Y., Summers, R. M., & Zhang, Z. (2019) · GigaScience · doi:10.1093/gigascience/giy154 · Cited by 855
Self-citationVarshney, R. K., Thudi, M., Roorkiwal, M., He, W., Upadhyaya, H. D., Yang, W., Bajaj, P., Cubry, P., Rathore, A., Jian, J., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0401-3 · Cited by 312
Self-citation(2022) · doi:10.1038/s41467-022-35621-7 · Cited by 34
Metadata
- Authors
- Liu, X., Huang, M., Fan, B., Buckler, E. S., & Zhang, Z.
- Venue
- PLOS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-02-01
- Volume
- 12
- Issue
- 2
- Pages
- e1005767
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- OpenAlex
- W2252512272
- Cited by
- 1,573
- References
- 15
- FWCI
- 44.52
- Type
- journal article
- Language
- en
- Open access
- gold
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- Liu, X., Huang, M., Fan, B., Buckler, E. S., & Zhang, Z. (2016). Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies. PLOS Genetics. https://doi.org/10.1371/journal.pgen.1005767
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