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...
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| Veröffentlicht in: | PLoS genetics Jg. 12; H. 2; S. e1005767 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
Public Library of Science
01.02.2016
Public Library of Science (PLoS) |
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| ISSN: | 1553-7404, 1553-7390, 1553-7404 |
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| Abstract | 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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| AbstractList |
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. 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. 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.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. 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. Genome-Wide Association Studies (GWAS) can reveal genetic-phenotypic relationships, but have limitations. To control false positives, population structure and kinship are incorporated in a fixed and random effect Mixed Linear Model (MLM). However, because of the confounding between population structure, kinship, and quantitative trait nucleotides (QTNs), MLM leads to false negatives, missing some potentially important discoveries. Here, we present a new method, Fixed and random model Circulating Probability Unification (FarmCPU). FarmCPU performs marker tests with associated markers as covariates in a fixed effect model and optimization on the associated covariate markers in a random effect model separately. This process enables efficient computation, removes the confounding, prevents model over-fitting, and controls false positives simultaneously. FarmCPU controls false positives as well as MLM with reductions in both false negatives and computing times. Researchers will not only be able to analyze big data, but will also have greater success with fewer mistakes when mapping genes of interest. |
| Audience | Academic |
| Author | Buckler, Edward S. Huang, Meng Zhang, Zhiwu Liu, Xiaolei Fan, Bin |
| AuthorAffiliation | 5 Department of Animal Sciences, Northeast Agricultural University, Harbin, Heilongjiang, China 4 United States Department of Agriculture (USDA)–Agricultural Research Service (ARS), Ithaca, New York, United States of America Microsoft Research, UNITED STATES 2 Institute for Genomic Diversity, Cornell University, Ithaca, New York, United States of America 3 Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, United States of America 1 Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China |
| AuthorAffiliation_xml | – name: 4 United States Department of Agriculture (USDA)–Agricultural Research Service (ARS), Ithaca, New York, United States of America – name: Microsoft Research, UNITED STATES – name: 5 Department of Animal Sciences, Northeast Agricultural University, Harbin, Heilongjiang, China – name: 2 Institute for Genomic Diversity, Cornell University, Ithaca, New York, United States of America – name: 3 Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, United States of America – name: 1 Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China |
| Author_xml | – sequence: 1 givenname: Xiaolei surname: Liu fullname: Liu, Xiaolei – sequence: 2 givenname: Meng surname: Huang fullname: Huang, Meng – sequence: 3 givenname: Bin surname: Fan fullname: Fan, Bin – sequence: 4 givenname: Edward S. surname: Buckler fullname: Buckler, Edward S. – sequence: 5 givenname: Zhiwu surname: Zhang fullname: Zhang, Zhiwu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26828793$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2016 Public Library of Science 2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Liu X, Huang M, Fan B, Buckler ES, Zhang Z (2016) Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies. PLoS Genet 12(2): e1005767. doi:10.1371/journal.pgen.1005767 |
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| Notes | 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. |
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