Leveraging eQTLs to identify individual-level tissue of interest for a complex trait.
Genetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed i...
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| Format: | Article |
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Public Library of Science (PLoS)
2021-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008915&type=printable |
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| author | Arunabha Majumdar Claudia Giambartolomei Claudia Giambartolomei Na Cai Tanushree Haldar Tommer Schwarz Michael Gandal Jonathan Flint Bogdan Pasaniuc |
| author_facet | Arunabha Majumdar Claudia Giambartolomei Claudia Giambartolomei Na Cai Tanushree Haldar Tommer Schwarz Michael Gandal Jonathan Flint Bogdan Pasaniuc |
| author_sort | Arunabha Majumdar |
| collection | DOAJ |
| description | Genetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed in the brain, or that control fat storage through dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) corresponding to tissue-specific genes to prioritize a relevant tissue underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize relevant tissues for the trait in the population, our approach probabilistically quantifies the tissue-wise genetic contribution to the trait for a given individual. We hypothesize that for a subgroup of individuals the genetic contribution to the trait can be mediated primarily through a specific tissue. Through simulations using the UK Biobank, we show that our approach can predict the relevant tissue accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI) in the UK Biobank to identify subgroups of individuals whose genetic predisposition act primarily through brain versus adipose tissue, and adipose versus muscle tissue, respectively. Notably, we find that these individuals have specific phenotypic features beyond BMI and WHRadjBMI that distinguish them from random individuals in the data, suggesting biological effects of tissue-specific genetic contribution for these traits. |
| format | Article |
| id | doaj-art-c1c77a8e2ad24712b5cb0d0acb5429fc |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2021-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-c1c77a8e2ad24712b5cb0d0acb5429fc2025-08-20T03:44:40ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-05-01175e100891510.1371/journal.pcbi.1008915Leveraging eQTLs to identify individual-level tissue of interest for a complex trait.Arunabha MajumdarClaudia GiambartolomeiClaudia GiambartolomeiNa CaiTanushree HaldarTommer SchwarzMichael GandalJonathan FlintBogdan PasaniucGenetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed in the brain, or that control fat storage through dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) corresponding to tissue-specific genes to prioritize a relevant tissue underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize relevant tissues for the trait in the population, our approach probabilistically quantifies the tissue-wise genetic contribution to the trait for a given individual. We hypothesize that for a subgroup of individuals the genetic contribution to the trait can be mediated primarily through a specific tissue. Through simulations using the UK Biobank, we show that our approach can predict the relevant tissue accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI) in the UK Biobank to identify subgroups of individuals whose genetic predisposition act primarily through brain versus adipose tissue, and adipose versus muscle tissue, respectively. Notably, we find that these individuals have specific phenotypic features beyond BMI and WHRadjBMI that distinguish them from random individuals in the data, suggesting biological effects of tissue-specific genetic contribution for these traits.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008915&type=printable |
| spellingShingle | Arunabha Majumdar Claudia Giambartolomei Claudia Giambartolomei Na Cai Tanushree Haldar Tommer Schwarz Michael Gandal Jonathan Flint Bogdan Pasaniuc Leveraging eQTLs to identify individual-level tissue of interest for a complex trait. PLoS Computational Biology |
| title | Leveraging eQTLs to identify individual-level tissue of interest for a complex trait. |
| title_full | Leveraging eQTLs to identify individual-level tissue of interest for a complex trait. |
| title_fullStr | Leveraging eQTLs to identify individual-level tissue of interest for a complex trait. |
| title_full_unstemmed | Leveraging eQTLs to identify individual-level tissue of interest for a complex trait. |
| title_short | Leveraging eQTLs to identify individual-level tissue of interest for a complex trait. |
| title_sort | leveraging eqtls to identify individual level tissue of interest for a complex trait |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008915&type=printable |
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