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The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index

Globally, more than 1.9 billion adults are overweight. Thus, obesity is a serious public health issue. Moreover, obesity is a major risk factor for diabetes mellitus, coronary heart disease, and cardiovascular disease. Recently, GWAS examining obesity and body mass index (BMI) have increasingly unve...

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Autores principales: Baek, Eun Ju, Jung, Hae-Un, Chung, Ju Yeon, Jung, Hye In, Kwon, Shin Young, Lim, Ji Eun, Kim, Han Kyul, Kang, Ji-One, Oh, Bermseok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676478/
https://www.ncbi.nlm.nih.gov/pubmed/36419825
http://dx.doi.org/10.3389/fgene.2022.1025568
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author Baek, Eun Ju
Jung, Hae-Un
Chung, Ju Yeon
Jung, Hye In
Kwon, Shin Young
Lim, Ji Eun
Kim, Han Kyul
Kang, Ji-One
Oh, Bermseok
author_facet Baek, Eun Ju
Jung, Hae-Un
Chung, Ju Yeon
Jung, Hye In
Kwon, Shin Young
Lim, Ji Eun
Kim, Han Kyul
Kang, Ji-One
Oh, Bermseok
author_sort Baek, Eun Ju
collection PubMed
description Globally, more than 1.9 billion adults are overweight. Thus, obesity is a serious public health issue. Moreover, obesity is a major risk factor for diabetes mellitus, coronary heart disease, and cardiovascular disease. Recently, GWAS examining obesity and body mass index (BMI) have increasingly unveiled many aspects of the genetic architecture of obesity and BMI. Information on genome-wide genetic variants has been used to estimate the genome-wide polygenic score (GPS) for a personalized prediction of obesity. However, the prediction power of GPS is affected by various factors, including the unequal variance in the distribution of a phenotype, known as heteroscedasticity. Here, we calculated a GPS for BMI using LDpred2, which was based on the BMI GWAS summary statistics from a European meta-analysis. Then, we tested the GPS in 354,761 European samples from the UK Biobank and found an effective prediction power of the GPS on BMI. To study a change in the variance of BMI, we investigated the heteroscedasticity of BMI across the GPS via graphical and statistical methods. We also studied the homoscedastic samples for BMI compared to the heteroscedastic sample, randomly selecting samples with various standard deviations of BMI residuals. Further, we examined the effect of the genetic interaction of GPS with environment (GPS×E) on the heteroscedasticity of BMI. We observed the changing variance (i.e., heteroscedasticity) of BMI along the GPS. The heteroscedasticity of BMI was confirmed by both the Breusch-Pagan test and the Score test. Compared to the heteroscedastic sample, the homoscedastic samples from small standard deviation of BMI residuals showed a decreased heteroscedasticity and an improved prediction accuracy, suggesting a quantitatively negative correlation between the phenotypic heteroscedasticity and the prediction accuracy of GPS. To further test the effects of the GPS×E on heteroscedasticity, first we tested the genetic interactions of the GPS with 21 environments and found 8 significant GPS×E interactions on BMI. However, the heteroscedasticity of BMI was not ameliorated after adjusting for the GPS×E interactions. Taken together, our findings suggest that the heteroscedasticity of BMI exists along the GPS and is not affected by the GPS×E interaction.
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spelling pubmed-96764782022-11-22 The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index Baek, Eun Ju Jung, Hae-Un Chung, Ju Yeon Jung, Hye In Kwon, Shin Young Lim, Ji Eun Kim, Han Kyul Kang, Ji-One Oh, Bermseok Front Genet Genetics Globally, more than 1.9 billion adults are overweight. Thus, obesity is a serious public health issue. Moreover, obesity is a major risk factor for diabetes mellitus, coronary heart disease, and cardiovascular disease. Recently, GWAS examining obesity and body mass index (BMI) have increasingly unveiled many aspects of the genetic architecture of obesity and BMI. Information on genome-wide genetic variants has been used to estimate the genome-wide polygenic score (GPS) for a personalized prediction of obesity. However, the prediction power of GPS is affected by various factors, including the unequal variance in the distribution of a phenotype, known as heteroscedasticity. Here, we calculated a GPS for BMI using LDpred2, which was based on the BMI GWAS summary statistics from a European meta-analysis. Then, we tested the GPS in 354,761 European samples from the UK Biobank and found an effective prediction power of the GPS on BMI. To study a change in the variance of BMI, we investigated the heteroscedasticity of BMI across the GPS via graphical and statistical methods. We also studied the homoscedastic samples for BMI compared to the heteroscedastic sample, randomly selecting samples with various standard deviations of BMI residuals. Further, we examined the effect of the genetic interaction of GPS with environment (GPS×E) on the heteroscedasticity of BMI. We observed the changing variance (i.e., heteroscedasticity) of BMI along the GPS. The heteroscedasticity of BMI was confirmed by both the Breusch-Pagan test and the Score test. Compared to the heteroscedastic sample, the homoscedastic samples from small standard deviation of BMI residuals showed a decreased heteroscedasticity and an improved prediction accuracy, suggesting a quantitatively negative correlation between the phenotypic heteroscedasticity and the prediction accuracy of GPS. To further test the effects of the GPS×E on heteroscedasticity, first we tested the genetic interactions of the GPS with 21 environments and found 8 significant GPS×E interactions on BMI. However, the heteroscedasticity of BMI was not ameliorated after adjusting for the GPS×E interactions. Taken together, our findings suggest that the heteroscedasticity of BMI exists along the GPS and is not affected by the GPS×E interaction. Frontiers Media S.A. 2022-11-07 /pmc/articles/PMC9676478/ /pubmed/36419825 http://dx.doi.org/10.3389/fgene.2022.1025568 Text en Copyright © 2022 Baek, Jung, Chung, Jung, Kwon, Lim, Kim, Kang and Oh. 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
Baek, Eun Ju
Jung, Hae-Un
Chung, Ju Yeon
Jung, Hye In
Kwon, Shin Young
Lim, Ji Eun
Kim, Han Kyul
Kang, Ji-One
Oh, Bermseok
The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index
title The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index
title_full The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index
title_fullStr The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index
title_full_unstemmed The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index
title_short The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index
title_sort effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676478/
https://www.ncbi.nlm.nih.gov/pubmed/36419825
http://dx.doi.org/10.3389/fgene.2022.1025568
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