Genetic Variation and Obesity

Publikation: Bidrag til bog/antologi/rapportBidrag til bog/antologiForskningfagfællebedømt

Standard

Genetic Variation and Obesity. / Loos, Ruth J.F.

Handbook of Obesity: Epidemiology, Etiology, and Physiopathology. red. / George A. Bray; Claude Bouchard. Bind 1 4. udg. CRC Press, 2024. s. 113-122.

Publikation: Bidrag til bog/antologi/rapportBidrag til bog/antologiForskningfagfællebedømt

Harvard

Loos, RJF 2024, Genetic Variation and Obesity. i GA Bray & C Bouchard (red), Handbook of Obesity: Epidemiology, Etiology, and Physiopathology. 4 udg, bind 1, CRC Press, s. 113-122. https://doi.org/10.1201/9781003437673-14

APA

Loos, R. J. F. (2024). Genetic Variation and Obesity. I G. A. Bray, & C. Bouchard (red.), Handbook of Obesity: Epidemiology, Etiology, and Physiopathology (4 udg., Bind 1, s. 113-122). CRC Press. https://doi.org/10.1201/9781003437673-14

Vancouver

Loos RJF. Genetic Variation and Obesity. I Bray GA, Bouchard C, red., Handbook of Obesity: Epidemiology, Etiology, and Physiopathology. 4 udg. Bind 1. CRC Press. 2024. s. 113-122 https://doi.org/10.1201/9781003437673-14

Author

Loos, Ruth J.F. / Genetic Variation and Obesity. Handbook of Obesity: Epidemiology, Etiology, and Physiopathology. red. / George A. Bray ; Claude Bouchard. Bind 1 4. udg. CRC Press, 2024. s. 113-122

Bibtex

@inbook{becab203f4374e1c947644fc604bce2e,
title = "Genetic Variation and Obesity",
abstract = "Over the past 15 years, genome-wide association studies (GWASs) have identified more than 2,900 genetic loci associated with adiposity traits. The majority of these loci were discovered through large-scale GWAS meta-analyses for body mass index (BMI) and waist-to-hip ratio (WHR), mostly in adults and in European ancestry populations. However, some gene discovery efforts have focused on non-European ancestry populations, more refined adiposity traits, childhood and adolescent populations, and rare coding variants. Loci associated with overall obesity implicate pathways that act in the brain, whereas loci associated with body fat distribution point to pathways involved in adipocyte biology, lipolysis, glucose metabolism, and insulin resistance. In-depth functional follow-up analyses have been performed for a fraction of GWAS loci, and we are slowly expanding our understanding of the biology that underlies obesity and fat distribution. However, many more obesity-associated loci are waiting to be translated. With the increasing number of discoveries, there is a growing interest in using genetic information to predict who is at risk for developing obesity. However, prediction models that are solely based on genetic information show poor predictive accuracy. Given that both genetic and nongenetic factors contribute to body weight, effective models to predict obesity will have to include genetic and nongenetic factors.",
author = "Loos, {Ruth J.F.}",
note = "Publisher Copyright: {\textcopyright} 2024 selection and editorial matter, George A. Bray, Claude Bouchard, John Kirwan, Peter Katzmarzyk, Leanne Redman, Philip Schauer; individual chapters, the contributors.",
year = "2024",
doi = "10.1201/9781003437673-14",
language = "English",
isbn = "9781032558622",
volume = "1",
pages = "113--122",
editor = "Bray, {George A.} and Claude Bouchard",
booktitle = "Handbook of Obesity",
publisher = "CRC Press",
edition = "4",

}

RIS

TY - CHAP

T1 - Genetic Variation and Obesity

AU - Loos, Ruth J.F.

N1 - Publisher Copyright: © 2024 selection and editorial matter, George A. Bray, Claude Bouchard, John Kirwan, Peter Katzmarzyk, Leanne Redman, Philip Schauer; individual chapters, the contributors.

PY - 2024

Y1 - 2024

N2 - Over the past 15 years, genome-wide association studies (GWASs) have identified more than 2,900 genetic loci associated with adiposity traits. The majority of these loci were discovered through large-scale GWAS meta-analyses for body mass index (BMI) and waist-to-hip ratio (WHR), mostly in adults and in European ancestry populations. However, some gene discovery efforts have focused on non-European ancestry populations, more refined adiposity traits, childhood and adolescent populations, and rare coding variants. Loci associated with overall obesity implicate pathways that act in the brain, whereas loci associated with body fat distribution point to pathways involved in adipocyte biology, lipolysis, glucose metabolism, and insulin resistance. In-depth functional follow-up analyses have been performed for a fraction of GWAS loci, and we are slowly expanding our understanding of the biology that underlies obesity and fat distribution. However, many more obesity-associated loci are waiting to be translated. With the increasing number of discoveries, there is a growing interest in using genetic information to predict who is at risk for developing obesity. However, prediction models that are solely based on genetic information show poor predictive accuracy. Given that both genetic and nongenetic factors contribute to body weight, effective models to predict obesity will have to include genetic and nongenetic factors.

AB - Over the past 15 years, genome-wide association studies (GWASs) have identified more than 2,900 genetic loci associated with adiposity traits. The majority of these loci were discovered through large-scale GWAS meta-analyses for body mass index (BMI) and waist-to-hip ratio (WHR), mostly in adults and in European ancestry populations. However, some gene discovery efforts have focused on non-European ancestry populations, more refined adiposity traits, childhood and adolescent populations, and rare coding variants. Loci associated with overall obesity implicate pathways that act in the brain, whereas loci associated with body fat distribution point to pathways involved in adipocyte biology, lipolysis, glucose metabolism, and insulin resistance. In-depth functional follow-up analyses have been performed for a fraction of GWAS loci, and we are slowly expanding our understanding of the biology that underlies obesity and fat distribution. However, many more obesity-associated loci are waiting to be translated. With the increasing number of discoveries, there is a growing interest in using genetic information to predict who is at risk for developing obesity. However, prediction models that are solely based on genetic information show poor predictive accuracy. Given that both genetic and nongenetic factors contribute to body weight, effective models to predict obesity will have to include genetic and nongenetic factors.

U2 - 10.1201/9781003437673-14

DO - 10.1201/9781003437673-14

M3 - Book chapter

AN - SCOPUS:85179298805

SN - 9781032558622

SN - 9781032558646

VL - 1

SP - 113

EP - 122

BT - Handbook of Obesity

A2 - Bray, George A.

A2 - Bouchard, Claude

PB - CRC Press

ER -

ID: 390194168