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JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts

MOTIVATION: To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make st...

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Autores principales: Chatzinakos, Chris, Lee, Donghyung, Webb, Bradley T, Vladimirov, Vladimir I, Kendler, Kenneth S, Bacanu, Silviu-Alin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860197/
https://www.ncbi.nlm.nih.gov/pubmed/28968763
http://dx.doi.org/10.1093/bioinformatics/btx509
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author Chatzinakos, Chris
Lee, Donghyung
Webb, Bradley T
Vladimirov, Vladimir I
Kendler, Kenneth S
Bacanu, Silviu-Alin
author_facet Chatzinakos, Chris
Lee, Donghyung
Webb, Bradley T
Vladimirov, Vladimir I
Kendler, Kenneth S
Bacanu, Silviu-Alin
author_sort Chatzinakos, Chris
collection PubMed
description MOTIVATION: To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on (i) summary statistics from genome-wide association studies (GWAS) and (ii) linkage disequilibrium patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals. RESULTS: We propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses (i) cis-eQTL SNPs from the latest expression studies and (ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also (i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and (ii) to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of P-values. AVAILABILITY AND IMPLEMENTATION: https://github.com/Chatzinakos/JEPEGMIX2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-58601972018-03-21 JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts Chatzinakos, Chris Lee, Donghyung Webb, Bradley T Vladimirov, Vladimir I Kendler, Kenneth S Bacanu, Silviu-Alin Bioinformatics Applications Notes MOTIVATION: To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on (i) summary statistics from genome-wide association studies (GWAS) and (ii) linkage disequilibrium patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals. RESULTS: We propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses (i) cis-eQTL SNPs from the latest expression studies and (ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also (i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and (ii) to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of P-values. AVAILABILITY AND IMPLEMENTATION: https://github.com/Chatzinakos/JEPEGMIX2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-01-15 2017-09-14 /pmc/articles/PMC5860197/ /pubmed/28968763 http://dx.doi.org/10.1093/bioinformatics/btx509 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Chatzinakos, Chris
Lee, Donghyung
Webb, Bradley T
Vladimirov, Vladimir I
Kendler, Kenneth S
Bacanu, Silviu-Alin
JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts
title JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts
title_full JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts
title_fullStr JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts
title_full_unstemmed JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts
title_short JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts
title_sort jepegmix2: improved gene-level joint analysis of eqtls in cosmopolitan cohorts
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860197/
https://www.ncbi.nlm.nih.gov/pubmed/28968763
http://dx.doi.org/10.1093/bioinformatics/btx509
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