Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies
By Xiaolei Liu, Meng Huang, Bin Fan, Edward S. Buckler, Zhiwu Zhang. PLoS Genetics, 2016
1,338 citations recorded by the source corpus.
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.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/9dc3b72ecdeedfece817b3dec98dda1e4253f09b
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In Genetics
- Jiabo Wang
- Zhiwu Zhang
- K. Semagn
- Boddupalli M. Prasanna
- Xiaolei Liu
- Edward S. Buckler
- M. Olsen
- Jason G. Wallace
Citations in the corpus
- rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated Tool for Genome-wide Association Study (2020, methodology): “The difference in statistical power between GLM and MLM is 58 negligible in some scenarios, such as mapping genes under the same false discovery rate in 59 populations with strong population structure[19].”
- GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction (2020, methodology): “The method was named as Fixed and random model 101 Circulating Probability Unification (FarmCPU) [15].”
- Genome‐wide Association for Plant Height and Flowering Time across 15 Tropical Maize Populations under Managed Drought Stress and Well‐Watered Conditions in Sub‐Saharan Africa (2016, methodology): “Because of the relatively low resolution of the initial analyses, an additional analysis using bootstrapped phenotypes and model selection was performed using FarmCPU (Liu et al. 2016) to refine the location of associated SNPs (Fig.”
- BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions (2018, neutral): “S17); however, this might reduce the true positives as documented in a previous study on increasing number of PCs in GLM [29].”