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Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects

Perinatal traits are influenced by genetic variants from both fetal and maternal genomes. Genome-wide association studies (GWAS) of these phenotypes have typically involved separate fetal and maternal scans, however, this approach may be inefficient as it does not utilize the information shared acro...

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Autores principales: Hwang, Liang-Dar, Cuellar-Partida, Gabriel, Yengo, Loic, Zeng, Jian, Beaumont, Robin N., Freathy, Rachel M., Moen, Gunn-Helen, Warrington, Nicole M., Evans, David M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491281/
https://www.ncbi.nlm.nih.gov/pubmed/37693475
http://dx.doi.org/10.1101/2023.08.22.23294446
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author Hwang, Liang-Dar
Cuellar-Partida, Gabriel
Yengo, Loic
Zeng, Jian
Beaumont, Robin N.
Freathy, Rachel M.
Moen, Gunn-Helen
Warrington, Nicole M.
Evans, David M.
author_facet Hwang, Liang-Dar
Cuellar-Partida, Gabriel
Yengo, Loic
Zeng, Jian
Beaumont, Robin N.
Freathy, Rachel M.
Moen, Gunn-Helen
Warrington, Nicole M.
Evans, David M.
author_sort Hwang, Liang-Dar
collection PubMed
description Perinatal traits are influenced by genetic variants from both fetal and maternal genomes. Genome-wide association studies (GWAS) of these phenotypes have typically involved separate fetal and maternal scans, however, this approach may be inefficient as it does not utilize the information shared across the individual GWAS. In this manuscript we investigate the performance of three strategies to detect loci in maternal and fetal GWAS of the same trait: (i) the traditional strategy of analysing maternal and fetal GWAS separately; (ii) a novel two degree of freedom test which combines information from maternal and fetal GWAS; and (iii) a novel one degree of freedom test where signals from maternal and fetal GWAS are meta-analysed together conditional on the estimated sample overlap. We demonstrate through a combination of analytical formulae and data simulation that the optimal strategy depends on the extent of sample overlap/relatedness between the maternal and fetal GWAS, the correlation between own and offspring phenotypes, whether loci jointly exhibit fetal and maternal effects, and if so, whether these effects are directionally concordant. We apply our methods to summary results statistics from a recent GWAS meta-analysis of birth weight from deCODE, the UK Biobank and the Early Growth Genetics (EGG) consortium. Both the two degree of freedom (213 loci) and meta-analytic approach (226 loci) dramatically increase the number of robustly associated genetic loci for birth weight relative to separately analysing the scans (183 loci). Our best strategy identifies an additional 62 novel loci compared to the most recent published meta-analysis of birth weight and implicates both known and new biological pathways in the aetiology of the trait. We implement our methods in the online DINGO (Direct and INdirect effects analysis of Genetic lOci) software package, which allows users to perform one and/or two degree of freedom tests easily and computationally efficiently across the genome. We conclude that whilst the novel two degree of freedom test may be particularly useful for the analysis of certain perinatal phenotypes where many loci exhibit discordant maternal and fetal genetic effects, for most phenotypes, a simple meta-analytic strategy is likely to perform best, particularly in situations where maternal and fetal GWAS only partially overlap.
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spelling pubmed-104912812023-09-09 Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects Hwang, Liang-Dar Cuellar-Partida, Gabriel Yengo, Loic Zeng, Jian Beaumont, Robin N. Freathy, Rachel M. Moen, Gunn-Helen Warrington, Nicole M. Evans, David M. medRxiv Article Perinatal traits are influenced by genetic variants from both fetal and maternal genomes. Genome-wide association studies (GWAS) of these phenotypes have typically involved separate fetal and maternal scans, however, this approach may be inefficient as it does not utilize the information shared across the individual GWAS. In this manuscript we investigate the performance of three strategies to detect loci in maternal and fetal GWAS of the same trait: (i) the traditional strategy of analysing maternal and fetal GWAS separately; (ii) a novel two degree of freedom test which combines information from maternal and fetal GWAS; and (iii) a novel one degree of freedom test where signals from maternal and fetal GWAS are meta-analysed together conditional on the estimated sample overlap. We demonstrate through a combination of analytical formulae and data simulation that the optimal strategy depends on the extent of sample overlap/relatedness between the maternal and fetal GWAS, the correlation between own and offspring phenotypes, whether loci jointly exhibit fetal and maternal effects, and if so, whether these effects are directionally concordant. We apply our methods to summary results statistics from a recent GWAS meta-analysis of birth weight from deCODE, the UK Biobank and the Early Growth Genetics (EGG) consortium. Both the two degree of freedom (213 loci) and meta-analytic approach (226 loci) dramatically increase the number of robustly associated genetic loci for birth weight relative to separately analysing the scans (183 loci). Our best strategy identifies an additional 62 novel loci compared to the most recent published meta-analysis of birth weight and implicates both known and new biological pathways in the aetiology of the trait. We implement our methods in the online DINGO (Direct and INdirect effects analysis of Genetic lOci) software package, which allows users to perform one and/or two degree of freedom tests easily and computationally efficiently across the genome. We conclude that whilst the novel two degree of freedom test may be particularly useful for the analysis of certain perinatal phenotypes where many loci exhibit discordant maternal and fetal genetic effects, for most phenotypes, a simple meta-analytic strategy is likely to perform best, particularly in situations where maternal and fetal GWAS only partially overlap. Cold Spring Harbor Laboratory 2023-08-28 /pmc/articles/PMC10491281/ /pubmed/37693475 http://dx.doi.org/10.1101/2023.08.22.23294446 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Hwang, Liang-Dar
Cuellar-Partida, Gabriel
Yengo, Loic
Zeng, Jian
Beaumont, Robin N.
Freathy, Rachel M.
Moen, Gunn-Helen
Warrington, Nicole M.
Evans, David M.
Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects
title Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects
title_full Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects
title_fullStr Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects
title_full_unstemmed Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects
title_short Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects
title_sort direct and indirect effects analysis of genetic loci (dingo): a software package to increase the power of locus discovery in gwas meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491281/
https://www.ncbi.nlm.nih.gov/pubmed/37693475
http://dx.doi.org/10.1101/2023.08.22.23294446
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