Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

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 t...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PLoS genetics Jg. 12; H. 2; S. e1005767
Hauptverfasser: Liu, Xiaolei, Huang, Meng, Fan, Bin, Buckler, Edward S., Zhang, Zhiwu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.02.2016
Public Library of Science (PLoS)
Schlagworte:
Arabidopsis - genetics
Big Data
Biology and Life Sciences
Datasets
Flowers - genetics
Flowers - physiology
Genes, Plant
Genetic Loci
Genome-wide association studies
Genome-Wide Association Study
Genomes
Genotype & phenotype
Humans
Linear models (Statistics)
Lung cancer
Medicine and Health Sciences
Models, Genetic
Physical Sciences
Population
Power
Quantitative Trait, Heritable
Research and Analysis Methods
Software
Species Specificity
Statistical methods
Studies
ISSN:1553-7404, 1553-7390, 1553-7404
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Conceived and designed the experiments: ZZ BF ESB. Performed the experiments: XL. Analyzed the data: XL MH. Contributed reagents/materials/analysis tools: XL ZZ. Wrote the paper: ZZ XL. Supervised the design of the study: ZZ BF ESB.
The authors have declared that no competing interests exist.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1005767