CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits
Genome-wide association study (GWAS) analyses have identified thousands of associations between genetic variants and complex traits. However, it is still a challenge to uncover the mechanisms underlying the association. With the growing availability of transcriptome data sets, it has become possible...
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ftdoajarticles:oai:doaj.org/article:9e3354128f024f49875224af87555164 2023-05-15T17:42:33+02:00 CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits Kar-Fu Yeung Yi Yang Can Yang Jin Liu 2019-10-01T00:00:00Z https://doi.org/10.1177/1177932219881435 https://doaj.org/article/9e3354128f024f49875224af87555164 EN eng SAGE Publishing https://doi.org/10.1177/1177932219881435 https://doaj.org/toc/1177-9322 1177-9322 doi:10.1177/1177932219881435 https://doaj.org/article/9e3354128f024f49875224af87555164 Bioinformatics and Biology Insights, Vol 13 (2019) Biology (General) QH301-705.5 article 2019 ftdoajarticles https://doi.org/10.1177/1177932219881435 2022-12-31T10:07:26Z Genome-wide association study (GWAS) analyses have identified thousands of associations between genetic variants and complex traits. However, it is still a challenge to uncover the mechanisms underlying the association. With the growing availability of transcriptome data sets, it has become possible to perform statistical analyses targeted at identifying influential genes whose expression levels correlate with the phenotype. Methods such as PrediXcan and transcriptome-wide association study (TWAS) use the transcriptome data set to fit a predictive model for gene expression, with genetic variants as covariates. The gene expression levels for the GWAS data set are then ‘imputed’ using the prediction model, and the imputed expression levels are tested for their association with the phenotype. These methods fail to account for the uncertainty in the GWAS imputation step, and we propose a collaborative mixed model (CoMM) that addresses this limitation by jointly modelling the multiple analysis steps. We illustrate CoMM’s ability to identify relevant genes in the Northern Finland Birth Cohort 1966 data set and extend the model to handle the more widely available GWAS summary statistics. Article in Journal/Newspaper Northern Finland Directory of Open Access Journals: DOAJ Articles Handle The ENVELOPE(161.983,161.983,-78.000,-78.000) Bioinformatics and Biology Insights 13 117793221988143 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Biology (General) QH301-705.5 |
spellingShingle |
Biology (General) QH301-705.5 Kar-Fu Yeung Yi Yang Can Yang Jin Liu CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits |
topic_facet |
Biology (General) QH301-705.5 |
description |
Genome-wide association study (GWAS) analyses have identified thousands of associations between genetic variants and complex traits. However, it is still a challenge to uncover the mechanisms underlying the association. With the growing availability of transcriptome data sets, it has become possible to perform statistical analyses targeted at identifying influential genes whose expression levels correlate with the phenotype. Methods such as PrediXcan and transcriptome-wide association study (TWAS) use the transcriptome data set to fit a predictive model for gene expression, with genetic variants as covariates. The gene expression levels for the GWAS data set are then ‘imputed’ using the prediction model, and the imputed expression levels are tested for their association with the phenotype. These methods fail to account for the uncertainty in the GWAS imputation step, and we propose a collaborative mixed model (CoMM) that addresses this limitation by jointly modelling the multiple analysis steps. We illustrate CoMM’s ability to identify relevant genes in the Northern Finland Birth Cohort 1966 data set and extend the model to handle the more widely available GWAS summary statistics. |
format |
Article in Journal/Newspaper |
author |
Kar-Fu Yeung Yi Yang Can Yang Jin Liu |
author_facet |
Kar-Fu Yeung Yi Yang Can Yang Jin Liu |
author_sort |
Kar-Fu Yeung |
title |
CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits |
title_short |
CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits |
title_full |
CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits |
title_fullStr |
CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits |
title_full_unstemmed |
CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits |
title_sort |
comm: a collaborative mixed model that integrates gwas and eqtl data sets to investigate the genetic architecture of complex traits |
publisher |
SAGE Publishing |
publishDate |
2019 |
url |
https://doi.org/10.1177/1177932219881435 https://doaj.org/article/9e3354128f024f49875224af87555164 |
long_lat |
ENVELOPE(161.983,161.983,-78.000,-78.000) |
geographic |
Handle The |
geographic_facet |
Handle The |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_source |
Bioinformatics and Biology Insights, Vol 13 (2019) |
op_relation |
https://doi.org/10.1177/1177932219881435 https://doaj.org/toc/1177-9322 1177-9322 doi:10.1177/1177932219881435 https://doaj.org/article/9e3354128f024f49875224af87555164 |
op_doi |
https://doi.org/10.1177/1177932219881435 |
container_title |
Bioinformatics and Biology Insights |
container_volume |
13 |
container_start_page |
117793221988143 |
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1766144429781942272 |