Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1783307959060660224 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5860197 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chatzinakoschris jepegmix2improvedgeneleveljointanalysisofeqtlsincosmopolitancohorts AT leedonghyung jepegmix2improvedgeneleveljointanalysisofeqtlsincosmopolitancohorts AT webbbradleyt jepegmix2improvedgeneleveljointanalysisofeqtlsincosmopolitancohorts AT vladimirovvladimiri jepegmix2improvedgeneleveljointanalysisofeqtlsincosmopolitancohorts AT kendlerkenneths jepegmix2improvedgeneleveljointanalysisofeqtlsincosmopolitancohorts AT bacanusilviualin jepegmix2improvedgeneleveljointanalysisofeqtlsincosmopolitancohorts |