Amélie Bonnefond

Amélie Bonnefond

orcid

Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 January 2010 in Nature Genetics
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JDJosée Dupuis
Josée Dupuis
CLClaudia Langenberg
Claudia Langenberg
IPInga Prokopenko
Inga Prokopenko
RSRicha Saxena
Richa Saxena
NSNicole Soranzo
Nicole Soranzo
IBInês Barroso
Inês Barroso
The MAGIC investigators report results of a large genome-wide association study meta-analysis to identify common variants influencing fasting glucose homeostasis. They further show that several of the newly discovered loci influencing glycemic traits are also associated with risk of type 2 diabetes. Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.

19 February 2012 in Nature
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AIAtsuhiko Ichimura
Atsuhiko Ichimura
AHAkira Hirasawa
Akira Hirasawa
OPOdile POULAIN-GODEFROY
Odile POULAIN-GODEFROY
ABAmélie Bonnefond
Amélie Bonnefond
THTakafumi Hara
Takafumi Hara
PFPhilippe Froguel
Philippe Froguel

17 January 2010 in Nature Genetics
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Journal Article
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GCGIANT consortium
GIANT consortium
RSRicha Saxena
Richa Saxena
TIthe MAGIC investigators
the MAGIC investigators
MHMarie-France Hivert
Marie-France Hivert
CLClaudia Langenberg
Claudia Langenberg
JZJing Hua Zhao
Jing Hua Zhao

7 December 2008 in Nature Genetics
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NBNabila Bouatia-Naji
Nabila Bouatia-Naji
ABAmélie Bonnefond
Amélie Bonnefond
CCChristine Cavalcanti-Proença
Christine Cavalcanti-Proença
TSThomas Sparsø
Thomas Sparsø
JHJohan Holmkvist
Johan Holmkvist
PFPhilippe Froguel
Philippe Froguel

6 June 2013 in PLOS Genetics
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JRJoshua C. Randall
Joshua C. Randall
TWThomas W. Winkler
Thomas W. Winkler
ZKZoltán Kutalik
Zoltán Kutalik
SBSonja I. Berndt
Sonja I. Berndt
AJAnne U. Jackson
Anne U. Jackson
IHIris M. Heid
Iris M. Heid
Given the anthropometric differences between men and women and previous evidence of sex-difference in genetic effects, we conducted a genome-wide search for sexually dimorphic associations with height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip-ratio (133,723 individuals) and took forward 348 SNPs into follow-up (additional 137,052 individuals) in a total of 94 studies. Seven loci displayed significant sex-difference (FDR<5%), including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were genome-wide significant in women (P<5×10−8), but not in men. Sex-differences were apparent only for waist phenotypes, not for height, weight, BMI, or hip circumference. Moreover, we found no evidence for genetic effects with opposite directions in men versus women. The PPARG locus is of specific interest due to its role in diabetes genetics and therapy. Our results demonstrate the value of sex-specific GWAS to unravel the sexually dimorphic genetic underpinning of complex traits. Men and women differ substantially regarding height, weight, and body fat. Interestingly, previous work detecting genetic effects for waist-to-hip ratio, to assess body fat distribution, has found that many of these showed sex-differences. However, systematic searches for sex-differences in genetic effects have not yet been conducted. Therefore, we undertook a genome-wide search for sexually dimorphic genetic effects for anthropometric traits including 133,723 individuals in a large meta-analysis and followed promising variants in further 137,052 individuals, including a total of 94 studies. We identified seven loci with significant sex-difference including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were significant in women, but not in men. Of interest is that sex-difference was only observed for waist phenotypes, but not for height or body-mass-index. We found no evidence for sex-differences with opposite effect direction for men and women. The PPARG locus is of specific interest due to its link to diabetes genetics and therapy. Our findings demonstrate the importance of investigating sex differences, which may lead to a better understanding of disease mechanisms with a potential relevance to treatment options.

1 October 2015 in PLOS Genetics
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TWThomas W. Winkler
Thomas W. Winkler
AJAnne E. Justice
Anne E. Justice
MGMariaelisa Graff
Mariaelisa Graff
LBLlilda Barata
Llilda Barata
MFMary Feitosa
Mary Feitosa
RLRuth J. F. Loos
Ruth J. F. Loos
Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR<5%) age-specific effects, of which 11 had larger effects in younger (320,000 individuals), to specifically identify genetic loci with age- and or sex-dependent effects on body size and shape. We identified 15 loci of which the effect on BMI was different in older compared to younger adults, whereas we found no evidence for loci with different effects in men compared to women. The opposite was seen for body shape as we identified 44 loci of which the effect on waist-to-hip ratio differed between men and women, but no difference between younger and older adults were observed. Our observations may provide new insights into the biology that underlies weight change with age or the sexual dimorphism of body shape.