A coordinate descent approach for sparse Bayesian learning in high dimensional QTL mapping and genome-wide association studies
Abstract Motivation Genomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies and quantitative trait locus mapping. The problems include large matrix inversion, over-conservativeness for tests after Bonferroni correction and difficult...
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Published in | Bioinformatics Vol. 35; no. 21; pp. 4327 - 4335 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
01.11.2019
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Online Access | Get full text |
ISSN | 1367-4803 1367-4811 1460-2059 1367-4811 |
DOI | 10.1093/bioinformatics/btz244 |
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Abstract | Abstract
Motivation
Genomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies and quantitative trait locus mapping. The problems include large matrix inversion, over-conservativeness for tests after Bonferroni correction and difficulty in evaluation of the total genetic contribution to a trait’s variance. Targeting these problems, we take a further step and investigate a multiple locus model that detects all markers simultaneously in a single model.
Results
We developed a sparse Bayesian learning (SBL) method for quantitative trait locus mapping and genome-wide association studies. This new method adopts a coordinate descent algorithm to estimate parameters (marker effects) by updating one parameter at a time conditional on current values of all other parameters. It uses an L2 type of penalty that allows the method to handle extremely large sample sizes (>100 000). Simulation studies show that SBL often has higher statistical powers and the simulated true loci are often detected with extremely small P-values, indicating that SBL is insensitive to stringent thresholds in significance testing.
Availability and implementation
An R package (sbl) is available on the comprehensive R archive network (CRAN) and https://github.com/MeiyueComputBio/sbl/tree/master/R%20packge.
Supplementary information
Supplementary data are available at Bioinformatics online. |
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AbstractList | Abstract
Motivation
Genomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies and quantitative trait locus mapping. The problems include large matrix inversion, over-conservativeness for tests after Bonferroni correction and difficulty in evaluation of the total genetic contribution to a trait’s variance. Targeting these problems, we take a further step and investigate a multiple locus model that detects all markers simultaneously in a single model.
Results
We developed a sparse Bayesian learning (SBL) method for quantitative trait locus mapping and genome-wide association studies. This new method adopts a coordinate descent algorithm to estimate parameters (marker effects) by updating one parameter at a time conditional on current values of all other parameters. It uses an L2 type of penalty that allows the method to handle extremely large sample sizes (>100 000). Simulation studies show that SBL often has higher statistical powers and the simulated true loci are often detected with extremely small P-values, indicating that SBL is insensitive to stringent thresholds in significance testing.
Availability and implementation
An R package (sbl) is available on the comprehensive R archive network (CRAN) and https://github.com/MeiyueComputBio/sbl/tree/master/R%20packge.
Supplementary information
Supplementary data are available at Bioinformatics online. Genomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies (GWAS) and quantitative trait locus (QTL) mapping. The problems include large matrix inversion, over-conservativeness for tests after Bonferroni correction and difficulty in evaluation of the total genetic contribution to a trait's variance. Targeting these problems, we take a further step and investigate a multiple locus model that detects all markers simultaneously in a single model. We developed a sparse Bayesian learning (SBL) method for QTL mapping and GWAS. This new method adopts a coordinate descent algorithm to estimate parameters (marker effects) by updating one parameter at a time conditional on current values of all other parameters. It uses an L2 type of penalty that allows the method to handle extremely large sample sizes (>100,000). Simulation studies show that SBL often has higher statistical powers and the simulated true loci are often detected with extremely small p-values, indicating that SBL is insensitive to stringent thresholds in significance testing. An R package (sbl) is available on the comprehensive R archive network (CRAN) and https://github.com/MeiyueComputBio/sbl/tree/master/R%20packge. Supplementary data are available at Bioinformatics online. Genomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies and quantitative trait locus mapping. The problems include large matrix inversion, over-conservativeness for tests after Bonferroni correction and difficulty in evaluation of the total genetic contribution to a trait's variance. Targeting these problems, we take a further step and investigate a multiple locus model that detects all markers simultaneously in a single model.MOTIVATIONGenomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies and quantitative trait locus mapping. The problems include large matrix inversion, over-conservativeness for tests after Bonferroni correction and difficulty in evaluation of the total genetic contribution to a trait's variance. Targeting these problems, we take a further step and investigate a multiple locus model that detects all markers simultaneously in a single model.We developed a sparse Bayesian learning (SBL) method for quantitative trait locus mapping and genome-wide association studies. This new method adopts a coordinate descent algorithm to estimate parameters (marker effects) by updating one parameter at a time conditional on current values of all other parameters. It uses an L2 type of penalty that allows the method to handle extremely large sample sizes (>100 000). Simulation studies show that SBL often has higher statistical powers and the simulated true loci are often detected with extremely small P-values, indicating that SBL is insensitive to stringent thresholds in significance testing.RESULTSWe developed a sparse Bayesian learning (SBL) method for quantitative trait locus mapping and genome-wide association studies. This new method adopts a coordinate descent algorithm to estimate parameters (marker effects) by updating one parameter at a time conditional on current values of all other parameters. It uses an L2 type of penalty that allows the method to handle extremely large sample sizes (>100 000). Simulation studies show that SBL often has higher statistical powers and the simulated true loci are often detected with extremely small P-values, indicating that SBL is insensitive to stringent thresholds in significance testing.An R package (sbl) is available on the comprehensive R archive network (CRAN) and https://github.com/MeiyueComputBio/sbl/tree/master/R%20packge.AVAILABILITY AND IMPLEMENTATIONAn R package (sbl) is available on the comprehensive R archive network (CRAN) and https://github.com/MeiyueComputBio/sbl/tree/master/R%20packge.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. |
Author | Wang, Meiyue Xu, Shizhong |
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Motivation
Genomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies and... Genomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies (GWAS) and quantitative trait locus... Genomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies and quantitative trait locus... |
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Title | A coordinate descent approach for sparse Bayesian learning in high dimensional QTL mapping and genome-wide association studies |
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