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Improved Estimation of Phenotypic Correlations Using Summary Association Statistics

Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology and statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect single...

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Detalles Bibliográficos
Autores principales: Li, Ting, Ning, Zheng, Shen, Xia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421683/
https://www.ncbi.nlm.nih.gov/pubmed/34504513
http://dx.doi.org/10.3389/fgene.2021.665252
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author Li, Ting
Ning, Zheng
Shen, Xia
author_facet Li, Ting
Ning, Zheng
Shen, Xia
author_sort Li, Ting
collection PubMed
description Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology and statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect single nucleotide polymorphisms (SNPs) and LD score regression intercept, were widely applied to estimate phenotypic correlations. Here, we propose an improved Z-score correlation strategy based on SNPs with low minor allele frequencies (MAFs), and show how this simple strategy can correct the bias generated by the current methods. The low MAF estimator improves phenotypic correlation estimation, thus it is beneficial for methods and applications using phenotypic correlations inferred from summary association statistics.
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spelling pubmed-84216832021-09-08 Improved Estimation of Phenotypic Correlations Using Summary Association Statistics Li, Ting Ning, Zheng Shen, Xia Front Genet Genetics Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology and statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect single nucleotide polymorphisms (SNPs) and LD score regression intercept, were widely applied to estimate phenotypic correlations. Here, we propose an improved Z-score correlation strategy based on SNPs with low minor allele frequencies (MAFs), and show how this simple strategy can correct the bias generated by the current methods. The low MAF estimator improves phenotypic correlation estimation, thus it is beneficial for methods and applications using phenotypic correlations inferred from summary association statistics. Frontiers Media S.A. 2021-08-24 /pmc/articles/PMC8421683/ /pubmed/34504513 http://dx.doi.org/10.3389/fgene.2021.665252 Text en Copyright © 2021 Li, Ning and Shen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Li, Ting
Ning, Zheng
Shen, Xia
Improved Estimation of Phenotypic Correlations Using Summary Association Statistics
title Improved Estimation of Phenotypic Correlations Using Summary Association Statistics
title_full Improved Estimation of Phenotypic Correlations Using Summary Association Statistics
title_fullStr Improved Estimation of Phenotypic Correlations Using Summary Association Statistics
title_full_unstemmed Improved Estimation of Phenotypic Correlations Using Summary Association Statistics
title_short Improved Estimation of Phenotypic Correlations Using Summary Association Statistics
title_sort improved estimation of phenotypic correlations using summary association statistics
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421683/
https://www.ncbi.nlm.nih.gov/pubmed/34504513
http://dx.doi.org/10.3389/fgene.2021.665252
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