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Statistical driver genes as a means to uncover missing heritability for age-related macular degeneration

BACKGROUND: Age-related macular degeneration (AMD) is a progressive retinal disease contributing to blindness worldwide. Multiple estimates for AMD heritability (h(2)) exist; however, a substantial proportion of h(2) is not attributable to known genomic loci. The International AMD Genomics Consortiu...

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Autores principales: Waksmunski, Andrea R., Grunin, Michelle, Kinzy, Tyler G., Igo, Robert P., Haines, Jonathan L., Cooke Bailey, Jessica N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336430/
https://www.ncbi.nlm.nih.gov/pubmed/32631374
http://dx.doi.org/10.1186/s12920-020-00747-4
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author Waksmunski, Andrea R.
Grunin, Michelle
Kinzy, Tyler G.
Igo, Robert P.
Haines, Jonathan L.
Cooke Bailey, Jessica N.
author_facet Waksmunski, Andrea R.
Grunin, Michelle
Kinzy, Tyler G.
Igo, Robert P.
Haines, Jonathan L.
Cooke Bailey, Jessica N.
author_sort Waksmunski, Andrea R.
collection PubMed
description BACKGROUND: Age-related macular degeneration (AMD) is a progressive retinal disease contributing to blindness worldwide. Multiple estimates for AMD heritability (h(2)) exist; however, a substantial proportion of h(2) is not attributable to known genomic loci. The International AMD Genomics Consortium (IAMDGC) gathered the largest dataset of advanced AMD (ADV) cases and controls available and identified 34 loci containing 52 independent risk variants defining known AMD h(2). To better define AMD heterogeneity, we used Pathway Analysis by Randomization Incorporating Structure (PARIS) on the IAMDGC data and identified 8 statistical driver genes (SDGs), including 2 novel SDGs not discovered by the IAMDGC. We chose to further investigate these pathway-based risk genes and determine their contribution to ADV h(2), as well as the differential ADV subtype h(2). METHODS: We performed genomic-relatedness-based restricted maximum-likelihood (GREML) analyses on ADV, geographic atrophy (GA), and choroidal neovascularization (CNV) subtypes to investigate the h(2) of genotyped variants on the full DNA array chip, 34 risk loci (n = 2758 common variants), 52 variants from the IAMDGC 2016 GWAS, and the 8 SDGs, specifically the novel 2 SDGs, PPARA and PLCG2. RESULTS: Via GREML, full chip h(2) was 44.05% for ADV, 46.37% for GA, and 62.03% for CNV. The lead 52 variants’ h(2) (ADV: 14.52%, GA: 8.02%, CNV: 13.62%) and 34 loci h(2) (ADV: 13.73%, GA: 8.81%, CNV: 12.89%) indicate that known variants contribute ~ 14% to ADV h(2). SDG variants account for a small percentage of ADV, GA, and CNV heritability, but estimates based on the combination of SDGs and the 34 known loci are similar to those calculated for known loci alone. We identified modest epistatic interactions among variants in the 2 SDGs and the 52 IAMDGC variants, including modest interactions between variants in PPARA and PLCG2. CONCLUSIONS: Pathway analyses, which leverage biological relationships among genes in a pathway, may be useful in identifying additional loci that contribute to the heritability of complex disorders in a non-additive manner. Heritability analyses of these loci, especially amongst disease subtypes, may provide clues to the importance of specific genes to the genetic architecture of AMD.
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spelling pubmed-73364302020-07-07 Statistical driver genes as a means to uncover missing heritability for age-related macular degeneration Waksmunski, Andrea R. Grunin, Michelle Kinzy, Tyler G. Igo, Robert P. Haines, Jonathan L. Cooke Bailey, Jessica N. BMC Med Genomics Research Article BACKGROUND: Age-related macular degeneration (AMD) is a progressive retinal disease contributing to blindness worldwide. Multiple estimates for AMD heritability (h(2)) exist; however, a substantial proportion of h(2) is not attributable to known genomic loci. The International AMD Genomics Consortium (IAMDGC) gathered the largest dataset of advanced AMD (ADV) cases and controls available and identified 34 loci containing 52 independent risk variants defining known AMD h(2). To better define AMD heterogeneity, we used Pathway Analysis by Randomization Incorporating Structure (PARIS) on the IAMDGC data and identified 8 statistical driver genes (SDGs), including 2 novel SDGs not discovered by the IAMDGC. We chose to further investigate these pathway-based risk genes and determine their contribution to ADV h(2), as well as the differential ADV subtype h(2). METHODS: We performed genomic-relatedness-based restricted maximum-likelihood (GREML) analyses on ADV, geographic atrophy (GA), and choroidal neovascularization (CNV) subtypes to investigate the h(2) of genotyped variants on the full DNA array chip, 34 risk loci (n = 2758 common variants), 52 variants from the IAMDGC 2016 GWAS, and the 8 SDGs, specifically the novel 2 SDGs, PPARA and PLCG2. RESULTS: Via GREML, full chip h(2) was 44.05% for ADV, 46.37% for GA, and 62.03% for CNV. The lead 52 variants’ h(2) (ADV: 14.52%, GA: 8.02%, CNV: 13.62%) and 34 loci h(2) (ADV: 13.73%, GA: 8.81%, CNV: 12.89%) indicate that known variants contribute ~ 14% to ADV h(2). SDG variants account for a small percentage of ADV, GA, and CNV heritability, but estimates based on the combination of SDGs and the 34 known loci are similar to those calculated for known loci alone. We identified modest epistatic interactions among variants in the 2 SDGs and the 52 IAMDGC variants, including modest interactions between variants in PPARA and PLCG2. CONCLUSIONS: Pathway analyses, which leverage biological relationships among genes in a pathway, may be useful in identifying additional loci that contribute to the heritability of complex disorders in a non-additive manner. Heritability analyses of these loci, especially amongst disease subtypes, may provide clues to the importance of specific genes to the genetic architecture of AMD. BioMed Central 2020-07-06 /pmc/articles/PMC7336430/ /pubmed/32631374 http://dx.doi.org/10.1186/s12920-020-00747-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Waksmunski, Andrea R.
Grunin, Michelle
Kinzy, Tyler G.
Igo, Robert P.
Haines, Jonathan L.
Cooke Bailey, Jessica N.
Statistical driver genes as a means to uncover missing heritability for age-related macular degeneration
title Statistical driver genes as a means to uncover missing heritability for age-related macular degeneration
title_full Statistical driver genes as a means to uncover missing heritability for age-related macular degeneration
title_fullStr Statistical driver genes as a means to uncover missing heritability for age-related macular degeneration
title_full_unstemmed Statistical driver genes as a means to uncover missing heritability for age-related macular degeneration
title_short Statistical driver genes as a means to uncover missing heritability for age-related macular degeneration
title_sort statistical driver genes as a means to uncover missing heritability for age-related macular degeneration
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336430/
https://www.ncbi.nlm.nih.gov/pubmed/32631374
http://dx.doi.org/10.1186/s12920-020-00747-4
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