Cargando…
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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1784833605194743808 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9676478 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT baekeunju theeffectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT junghaeun theeffectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT chungjuyeon theeffectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT junghyein theeffectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT kwonshinyoung theeffectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT limjieun theeffectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT kimhankyul theeffectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT kangjione theeffectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT ohbermseok theeffectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT baekeunju effectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT junghaeun effectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT chungjuyeon effectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT junghyein effectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT kwonshinyoung effectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT limjieun effectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT kimhankyul effectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT kangjione effectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex AT ohbermseok effectofheteroscedasticityonthepredictionefficiencyofgenomewidepolygenicscoreforbodymassindex |