A fast algorithm for Bayesian multi-locus model in genome-wide association studies
Genome-wide association studies (GWAS) have identified a large amount of single-nucleotide polymorphisms (SNPs) associated with complex traits. A recently developed linear mixed model for estimating heritability by simultaneously fitting all SNPs suggests that common variants can explain a substanti...
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| Published in: | Molecular genetics and genomics : MGG Vol. 292; no. 4; pp. 923 - 934 |
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| Main Authors: | , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.08.2017
Springer Nature B.V |
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| ISSN: | 1617-4615, 1617-4623, 1617-4623 |
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| Abstract | Genome-wide association studies (GWAS) have identified a large amount of single-nucleotide polymorphisms (SNPs) associated with complex traits. A recently developed linear mixed model for estimating heritability by simultaneously fitting all SNPs suggests that common variants can explain a substantial fraction of heritability, which hints at the low power of single variant analysis typically used in GWAS. Consequently, many multi-locus shrinkage models have been proposed under a Bayesian framework. However, most use Markov Chain Monte Carlo (MCMC) algorithm, which are time-consuming and challenging to apply to GWAS data. Here, we propose a fast algorithm of Bayesian adaptive lasso using variational inference (BAL-VI). Extensive simulations and real data analysis indicate that our model outperforms the well-known Bayesian lasso and Bayesian adaptive lasso models in accuracy and speed. BAL-VI can complete a simultaneous analysis of a lung cancer GWAS data with ~3400 subjects and ~570,000 SNPs in about half a day. |
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| AbstractList | Genome-wide association studies (GWAS) have identified a large amount of single-nucleotide polymorphisms (SNPs) associated with complex traits. A recently developed linear mixed model for estimating heritability by simultaneously fitting all SNPs suggests that common variants can explain a substantial fraction of heritability, which hints at the low power of single variant analysis typically used in GWAS. Consequently, many multi-locus shrinkage models have been proposed under a Bayesian framework. However, most use Markov Chain Monte Carlo (MCMC) algorithm, which are time-consuming and challenging to apply to GWAS data. Here, we propose a fast algorithm of Bayesian adaptive lasso using variational inference (BAL-VI). Extensive simulations and real data analysis indicate that our model outperforms the well-known Bayesian lasso and Bayesian adaptive lasso models in accuracy and speed. BAL-VI can complete a simultaneous analysis of a lung cancer GWAS data with ~3400 subjects and ~570,000 SNPs in about half a day. Genome-wide association studies (GWAS) have identified a large amount of single-nucleotide polymorphisms (SNPs) associated with complex traits. A recently developed linear mixed model for estimating heritability by simultaneously fitting all SNPs suggests that common variants can explain a substantial fraction of heritability, which hints at the low power of single variant analysis typically used in GWAS. Consequently, many multi-locus shrinkage models have been proposed under a Bayesian framework. However, most use Markov Chain Monte Carlo (MCMC) algorithm, which are time-consuming and challenging to apply to GWAS data. Here, we propose a fast algorithm of Bayesian adaptive lasso using variational inference (BAL-VI). Extensive simulations and real data analysis indicate that our model outperforms the well-known Bayesian lasso and Bayesian adaptive lasso models in accuracy and speed. BAL-VI can complete a simultaneous analysis of a lung cancer GWAS data with ~3400 subjects and ~570,000 SNPs in about half a day. Genome-wide association studies (GWAS) have identified a large amount of single-nucleotide polymorphisms (SNPs) associated with complex traits. A recently developed linear mixed model for estimating heritability by simultaneously fitting all SNPs suggests that common variants can explain a substantial fraction of heritability, which hints at the low power of single variant analysis typically used in GWAS. Consequently, many multi-locus shrinkage models have been proposed under a Bayesian framework. However, most use Markov Chain Monte Carlo (MCMC) algorithm, which are time-consuming and challenging to apply to GWAS data. Here, we propose a fast algorithm of Bayesian adaptive lasso using variational inference (BAL-VI). Extensive simulations and real data analysis indicate that our model outperforms the well-known Bayesian lasso and Bayesian adaptive lasso models in accuracy and speed. BAL-VI can complete a simultaneous analysis of a lung cancer GWAS data with ~3400 subjects and ~570,000 SNPs in about half a day.Genome-wide association studies (GWAS) have identified a large amount of single-nucleotide polymorphisms (SNPs) associated with complex traits. A recently developed linear mixed model for estimating heritability by simultaneously fitting all SNPs suggests that common variants can explain a substantial fraction of heritability, which hints at the low power of single variant analysis typically used in GWAS. Consequently, many multi-locus shrinkage models have been proposed under a Bayesian framework. However, most use Markov Chain Monte Carlo (MCMC) algorithm, which are time-consuming and challenging to apply to GWAS data. Here, we propose a fast algorithm of Bayesian adaptive lasso using variational inference (BAL-VI). Extensive simulations and real data analysis indicate that our model outperforms the well-known Bayesian lasso and Bayesian adaptive lasso models in accuracy and speed. BAL-VI can complete a simultaneous analysis of a lung cancer GWAS data with ~3400 subjects and ~570,000 SNPs in about half a day. |
| Author | Duan, Weiwei Yang, Sheng Huang, Lihong Du, Mulong Hu, Zhibin Zhao, Yang Chen, Feng Shen, Sipeng Wei, Yongyue Bai, Jianling |
| Author_xml | – sequence: 1 givenname: Weiwei surname: Duan fullname: Duan, Weiwei organization: Department of Biostatistics, School of Public Health, Nanjing Medical University, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Joint Laboratory of Health and Environmental Risk Assessment (HERA), Nanjing Medical University School of Public Health/Harvard School of Public Health, Key Laboratory of Biomedical Big Data, Nanjing Medical University – sequence: 2 givenname: Yang surname: Zhao fullname: Zhao, Yang organization: Department of Biostatistics, School of Public Health, Nanjing Medical University, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Joint Laboratory of Health and Environmental Risk Assessment (HERA), Nanjing Medical University School of Public Health/Harvard School of Public Health, Key Laboratory of Biomedical Big Data, Nanjing Medical University – sequence: 3 givenname: Yongyue surname: Wei fullname: Wei, Yongyue organization: Department of Biostatistics, School of Public Health, Nanjing Medical University, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Joint Laboratory of Health and Environmental Risk Assessment (HERA), Nanjing Medical University School of Public Health/Harvard School of Public Health, Key Laboratory of Biomedical Big Data, Nanjing Medical University – sequence: 4 givenname: Sheng surname: Yang fullname: Yang, Sheng organization: Department of Biostatistics, School of Public Health, Nanjing Medical University, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Joint Laboratory of Health and Environmental Risk Assessment (HERA), Nanjing Medical University School of Public Health/Harvard School of Public Health, Key Laboratory of Biomedical Big Data, Nanjing Medical University – sequence: 5 givenname: Jianling surname: Bai fullname: Bai, Jianling organization: Department of Biostatistics, School of Public Health, Nanjing Medical University, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Joint Laboratory of Health and Environmental Risk Assessment (HERA), Nanjing Medical University School of Public Health/Harvard School of Public Health, Key Laboratory of Biomedical Big Data, Nanjing Medical University – sequence: 6 givenname: Sipeng surname: Shen fullname: Shen, Sipeng organization: Department of Biostatistics, School of Public Health, Nanjing Medical University, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Joint Laboratory of Health and Environmental Risk Assessment (HERA), Nanjing Medical University School of Public Health/Harvard School of Public Health, Key Laboratory of Biomedical Big Data, Nanjing Medical University – sequence: 7 givenname: Mulong surname: Du fullname: Du, Mulong organization: Department of Biostatistics, School of Public Health, Nanjing Medical University, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Joint Laboratory of Health and Environmental Risk Assessment (HERA), Nanjing Medical University School of Public Health/Harvard School of Public Health, Key Laboratory of Biomedical Big Data, Nanjing Medical University – sequence: 8 givenname: Lihong surname: Huang fullname: Huang, Lihong organization: Department of Biostatistics, School of Public Health, Nanjing Medical University, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Joint Laboratory of Health and Environmental Risk Assessment (HERA), Nanjing Medical University School of Public Health/Harvard School of Public Health, Key Laboratory of Biomedical Big Data, Nanjing Medical University – sequence: 9 givenname: Zhibin surname: Hu fullname: Hu, Zhibin organization: The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Department of Epidemiology, School of Public Health, Nanjing Medical University, Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Nanjing Medical University – sequence: 10 givenname: Feng surname: Chen fullname: Chen, Feng email: fengchen@njmu.edu.cn organization: Department of Biostatistics, School of Public Health, Nanjing Medical University, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Joint Laboratory of Health and Environmental Risk Assessment (HERA), Nanjing Medical University School of Public Health/Harvard School of Public Health, Key Laboratory of Biomedical Big Data, Nanjing Medical University |
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| CitedBy_id | crossref_primary_10_1080_21655979_2020_1860479 crossref_primary_10_1002_ijc_32079 crossref_primary_10_3390_jpm9030038 crossref_primary_10_1186_s40246_018_0179_x |
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| Keywords | Bayesian adaptive lasso Genome-wide association studies Multi-locus model Variational inference Variable selection |
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| Title | A fast algorithm for Bayesian multi-locus model in genome-wide association studies |
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