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Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits

Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage ple...

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Autores principales: Chun, Sung, Akle, Sebastian, Teodosiadis, Athanasios, Cade, Brian E., Wang, Heming, Sofer, Tamar, Evans, Daniel S., Stone, Katie L., Gharib, Sina A., Mukherjee, Sutapa, Palmer, Lyle J., Hillman, David, Rotter, Jerome I., Hanis, Craig L., Stamatoyannopoulos, John A., Redline, Susan, Cotsapas, Chris, Sunyaev, Shamil R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9829185/
https://www.ncbi.nlm.nih.gov/pubmed/36574455
http://dx.doi.org/10.1371/journal.pgen.1010557
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author Chun, Sung
Akle, Sebastian
Teodosiadis, Athanasios
Cade, Brian E.
Wang, Heming
Sofer, Tamar
Evans, Daniel S.
Stone, Katie L.
Gharib, Sina A.
Mukherjee, Sutapa
Palmer, Lyle J.
Hillman, David
Rotter, Jerome I.
Hanis, Craig L.
Stamatoyannopoulos, John A.
Redline, Susan
Cotsapas, Chris
Sunyaev, Shamil R.
author_facet Chun, Sung
Akle, Sebastian
Teodosiadis, Athanasios
Cade, Brian E.
Wang, Heming
Sofer, Tamar
Evans, Daniel S.
Stone, Katie L.
Gharib, Sina A.
Mukherjee, Sutapa
Palmer, Lyle J.
Hillman, David
Rotter, Jerome I.
Hanis, Craig L.
Stamatoyannopoulos, John A.
Redline, Susan
Cotsapas, Chris
Sunyaev, Shamil R.
author_sort Chun, Sung
collection PubMed
description Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV(1)/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.
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spelling pubmed-98291852023-01-10 Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits Chun, Sung Akle, Sebastian Teodosiadis, Athanasios Cade, Brian E. Wang, Heming Sofer, Tamar Evans, Daniel S. Stone, Katie L. Gharib, Sina A. Mukherjee, Sutapa Palmer, Lyle J. Hillman, David Rotter, Jerome I. Hanis, Craig L. Stamatoyannopoulos, John A. Redline, Susan Cotsapas, Chris Sunyaev, Shamil R. PLoS Genet Research Article Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV(1)/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases. Public Library of Science 2022-12-27 /pmc/articles/PMC9829185/ /pubmed/36574455 http://dx.doi.org/10.1371/journal.pgen.1010557 Text en © 2022 Chun et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chun, Sung
Akle, Sebastian
Teodosiadis, Athanasios
Cade, Brian E.
Wang, Heming
Sofer, Tamar
Evans, Daniel S.
Stone, Katie L.
Gharib, Sina A.
Mukherjee, Sutapa
Palmer, Lyle J.
Hillman, David
Rotter, Jerome I.
Hanis, Craig L.
Stamatoyannopoulos, John A.
Redline, Susan
Cotsapas, Chris
Sunyaev, Shamil R.
Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits
title Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits
title_full Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits
title_fullStr Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits
title_full_unstemmed Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits
title_short Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits
title_sort leveraging pleiotropy to discover and interpret gwas results for sleep-associated traits
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9829185/
https://www.ncbi.nlm.nih.gov/pubmed/36574455
http://dx.doi.org/10.1371/journal.pgen.1010557
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