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Use of a gene score of multiple low-modest effect size variants can predict the risk of obesity better than the individual SNPs
BACKGROUND: Obesity is a complex disorder, the development of which is modulated by a multitude of environmental, behavioral and genetic factors. The common forms of obesity are polygenic in nature which means that many variants in the same or different genes act synergistically and affect the body...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6052513/ https://www.ncbi.nlm.nih.gov/pubmed/30021629 http://dx.doi.org/10.1186/s12944-018-0806-5 |
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author | Shabana Shahid, Saleem Ullah Hasnain, Shahida |
author_facet | Shabana Shahid, Saleem Ullah Hasnain, Shahida |
author_sort | Shabana |
collection | PubMed |
description | BACKGROUND: Obesity is a complex disorder, the development of which is modulated by a multitude of environmental, behavioral and genetic factors. The common forms of obesity are polygenic in nature which means that many variants in the same or different genes act synergistically and affect the body weight quantitatively. The aim of the current study was to use information from many common variants previously identified to affect body weight to construct a gene score and observe whether it improves the associations observed. The SNPs selected were G2548A in leptin (LEP) gene, Gln223Arg in leptin receptor (LEPR) gene, Ala54Thr in fatty acid binding protein 2 (FABP2) gene, rs1121980 in fat mass and obesity associated (FTO) gene, rs3923113 in Growth Factor Receptor Bound Protein 14 (GRB14), rs16861329 in Beta-galactoside alpha-2,6-sialyltransferase 1 (ST6GAL1), rs1802295 in Vacuolar protein sorting-associated protein 26A (VPS26A), rs7178572 in high mobility group 20A (HMG20A), rs2028299 in adaptor-related protein complex 3 (AP3S2), and rs4812829 in Hepatocyte Nuclear Factor 4 Alpha (HNF4A). METHODS: A total of 475 subjects were genotyped for the selected SNPs in different genes using different genotyping techniques. The study subjects’ age, weight, height, BMI, waist and hip circumference, serum total cholesterol, triglycerides, LDL and HDL were measured. A summation term, genetic risk score (GRS), was calculated using SPSS. RESULTS: The results showed a significantly higher mean gene score in obese cases than in non-obese controls (9.1 ± 2.26 vs 8.35 ± 2.07, p = 2 × 10(− 4)). Among the traits tested for association, gene score appeared to significantly affect BMI, waist circumference, and all lipid traits. CONCLUSION: In conclusion, the use of gene score is a better way to calculate the overall genetic risk from common variants rather than individual risk variants. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12944-018-0806-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6052513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-60525132018-07-20 Use of a gene score of multiple low-modest effect size variants can predict the risk of obesity better than the individual SNPs Shabana Shahid, Saleem Ullah Hasnain, Shahida Lipids Health Dis Research BACKGROUND: Obesity is a complex disorder, the development of which is modulated by a multitude of environmental, behavioral and genetic factors. The common forms of obesity are polygenic in nature which means that many variants in the same or different genes act synergistically and affect the body weight quantitatively. The aim of the current study was to use information from many common variants previously identified to affect body weight to construct a gene score and observe whether it improves the associations observed. The SNPs selected were G2548A in leptin (LEP) gene, Gln223Arg in leptin receptor (LEPR) gene, Ala54Thr in fatty acid binding protein 2 (FABP2) gene, rs1121980 in fat mass and obesity associated (FTO) gene, rs3923113 in Growth Factor Receptor Bound Protein 14 (GRB14), rs16861329 in Beta-galactoside alpha-2,6-sialyltransferase 1 (ST6GAL1), rs1802295 in Vacuolar protein sorting-associated protein 26A (VPS26A), rs7178572 in high mobility group 20A (HMG20A), rs2028299 in adaptor-related protein complex 3 (AP3S2), and rs4812829 in Hepatocyte Nuclear Factor 4 Alpha (HNF4A). METHODS: A total of 475 subjects were genotyped for the selected SNPs in different genes using different genotyping techniques. The study subjects’ age, weight, height, BMI, waist and hip circumference, serum total cholesterol, triglycerides, LDL and HDL were measured. A summation term, genetic risk score (GRS), was calculated using SPSS. RESULTS: The results showed a significantly higher mean gene score in obese cases than in non-obese controls (9.1 ± 2.26 vs 8.35 ± 2.07, p = 2 × 10(− 4)). Among the traits tested for association, gene score appeared to significantly affect BMI, waist circumference, and all lipid traits. CONCLUSION: In conclusion, the use of gene score is a better way to calculate the overall genetic risk from common variants rather than individual risk variants. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12944-018-0806-5) contains supplementary material, which is available to authorized users. BioMed Central 2018-07-18 /pmc/articles/PMC6052513/ /pubmed/30021629 http://dx.doi.org/10.1186/s12944-018-0806-5 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Research Shabana Shahid, Saleem Ullah Hasnain, Shahida Use of a gene score of multiple low-modest effect size variants can predict the risk of obesity better than the individual SNPs |
title | Use of a gene score of multiple low-modest effect size variants can predict the risk of obesity better than the individual SNPs |
title_full | Use of a gene score of multiple low-modest effect size variants can predict the risk of obesity better than the individual SNPs |
title_fullStr | Use of a gene score of multiple low-modest effect size variants can predict the risk of obesity better than the individual SNPs |
title_full_unstemmed | Use of a gene score of multiple low-modest effect size variants can predict the risk of obesity better than the individual SNPs |
title_short | Use of a gene score of multiple low-modest effect size variants can predict the risk of obesity better than the individual SNPs |
title_sort | use of a gene score of multiple low-modest effect size variants can predict the risk of obesity better than the individual snps |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6052513/ https://www.ncbi.nlm.nih.gov/pubmed/30021629 http://dx.doi.org/10.1186/s12944-018-0806-5 |
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