Beyond the Fourth Wave of Genome-wide Obesity Association Studies

Overview

Journal Nutr Diabetes

Specialties Endocrinology
Nutritional Sciences

Date 2012 Nov 22

PMID 23168490

Citations 32

Authors

C H Sandholt

T Hansen

O Pedersen

Affiliations

Soon will be listed here.

Abstract

Obesity and related complications are major health burdens. Almost 700 million adults are currently obese globally and the prevalence is predicted to rise towards 2030. The sudden change of lifestyle with physical inactivity and excessive calorie intake undoubtedly have a major part of the epidemic development; however, some individuals seem to be more prone to be affected by an unhealthy lifestyle than others. Hence, genetic predisposition also has an essential role in determining disease susceptibility and response to lifestyle factors. Since the introduction of genome-wide association studies (GWAS), the success of identifying obesity susceptibility variants have increased, and a total of 32 variants have been identified associating genome-wide significantly with body mass index (BMI) and 18 with measures of fat distribution during four overall obesity GWAS waves. However, the immediate success of the GWAS approach has eased off, but the proportion of explained variance for BMI by the identified obesity variants remains low. This review suggests and discusses new initiatives to take GWAS of obesity to the next level, including gene-environment interactions as modulating/masking factors, low-frequent or rare variants and ways to address such analyses, and finally reflections about the applicability of epigenetic modifications when elucidating the genetic background of obesity.

Citing Articles

Aging reveals a sex-dependent susceptibility of sarcospan-deficient mice to cardiometabolic disease.

Kahmini A, Valera I, Crawford R, Samarah L, Reis G, Elsheikh S Am J Physiol Heart Circ Physiol. 2024; 327(4):H1067-H1085.

PMID: 39120469 PMC: 11482229. DOI: 10.1152/ajpheart.00702.2023.

Mapping novel QTL and fine mapping of previously identified QTL associated with glucose tolerance using the collaborative cross mice.

Abu-Toamih-Atamni H, Lone I, Binenbaum I, Mott R, Pilalis E, Chatziioannou A Mamm Genome. 2023; 35(1):31-55.

PMID: 37978084 DOI: 10.1007/s00335-023-10025-0.

Chrononutrition-When We Eat Is of the Essence in Tackling Obesity.

Ahluwalia M Nutrients. 2022; 14(23).

PMID: 36501110 PMC: 9739590. DOI: 10.3390/nu14235080.

The regulatory role of AP-2β in monoaminergic neurotransmitter systems: insights on its signalling pathway, linked disorders and theragnostic potential.

Al-Sabri M, Nikpour M, Clemensson L, Attwood M, Williams M, Rask-Anderson M Cell Biosci. 2022; 12(1):151.

PMID: 36076256 PMC: 9461128. DOI: 10.1186/s13578-022-00891-7.

Stomatin modulates adipogenesis through the ERK pathway and regulates fatty acid uptake and lipid droplet growth.

Wu S, Lo Y, Lee J, Chen C, Chen T, Liu H Nat Commun. 2022; 13(1):4174.

PMID: 35854007 PMC: 9296665. DOI: 10.1038/s41467-022-31825-z.

References

Heid I, Jackson A, Randall J, Winkler T, Qi L, Steinthorsdottir V . Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet. 2010; 42(11):949-60. PMC: 3000924. DOI: 10.1038/ng.685. View

McCarthy M, Hirschhorn J . Genome-wide association studies: potential next steps on a genetic journey. Hum Mol Genet. 2008; 17(R2):R156-65. PMC: 2782356. DOI: 10.1093/hmg/ddn289. View

Oda M, Glass J, Thompson R, Mo Y, Olivier E, Figueroa M . High-resolution genome-wide cytosine methylation profiling with simultaneous copy number analysis and optimization for limited cell numbers. Nucleic Acids Res. 2009; 37(12):3829-39. PMC: 2709560. DOI: 10.1093/nar/gkp260. View

Heidema A, Feskens E, Doevendans P, Ruven H, van Houwelingen H, Mariman E . Analysis of multiple SNPs in genetic association studies: comparison of three multi-locus methods to prioritize and select SNPs. Genet Epidemiol. 2007; 31(8):910-21. DOI: 10.1002/gepi.20251. View

Stunkard A, Harris J, Pedersen N, McClearn G . The body-mass index of twins who have been reared apart. N Engl J Med. 1990; 322(21):1483-7. DOI: 10.1056/NEJM199005243222102. View

Murcray C, Lewinger J, Conti D, Thomas D, Gauderman W . Sample size requirements to detect gene-environment interactions in genome-wide association studies. Genet Epidemiol. 2011; 35(3):201-10. PMC: 3076801. DOI: 10.1002/gepi.20569. View

Scott R, Bailey M, Moran C, Wilson R, Fuku N, Tanaka M . FTO genotype and adiposity in children: physical activity levels influence the effect of the risk genotype in adolescent males. Eur J Hum Genet. 2010; 18(12):1339-43. PMC: 3002848. DOI: 10.1038/ejhg.2010.131. View

Loos R . Recent progress in the genetics of common obesity. Br J Clin Pharmacol. 2009; 68(6):811-29. PMC: 2810793. DOI: 10.1111/j.1365-2125.2009.03523.x. View

Mukherjee B, Ahn J, Gruber S, Ghosh M, Chatterjee N . Case-control studies of gene-environment interaction: Bayesian design and analysis. Biometrics. 2009; 66(3):934-48. PMC: 3103064. DOI: 10.1111/j.1541-0420.2009.01357.x. View

10.

Hinney A, Nguyen T, Scherag A, Friedel S, Bronner G, Muller T . Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants. PLoS One. 2007; 2(12):e1361. PMC: 2137937. DOI: 10.1371/journal.pone.0001361. View

11.

Holzapfel C, Grallert H, Huth C, Wahl S, Fischer B, Doring A . Genes and lifestyle factors in obesity: results from 12,462 subjects from MONICA/KORA. Int J Obes (Lond). 2010; 34(10):1538-45. PMC: 3251754. DOI: 10.1038/ijo.2010.79. View

12.

McCarroll S, Kuruvilla F, Korn J, Cawley S, Nemesh J, Wysoker A . Integrated detection and population-genetic analysis of SNPs and copy number variation. Nat Genet. 2008; 40(10):1166-74. DOI: 10.1038/ng.238. View

13.

Stoger R . The thrifty epigenotype: an acquired and heritable predisposition for obesity and diabetes?. Bioessays. 2008; 30(2):156-66. DOI: 10.1002/bies.20700. View

14.

Benzinou M, Creemers J, Choquet H, Lobbens S, Dina C, Durand E . Common nonsynonymous variants in PCSK1 confer risk of obesity. Nat Genet. 2008; 40(8):943-5. DOI: 10.1038/ng.177. View

15.

Maes H, Neale M, Eaves L . Genetic and environmental factors in relative body weight and human adiposity. Behav Genet. 1997; 27(4):325-51. DOI: 10.1023/a:1025635913927. View

16.

Jiao H, Arner P, Hoffstedt J, Brodin D, Dubern B, Czernichow S . Genome wide association study identifies KCNMA1 contributing to human obesity. BMC Med Genomics. 2011; 4:51. PMC: 3148553. DOI: 10.1186/1755-8794-4-51. View

17.

Andreasen C, Stender-Petersen K, Mogensen M, Torekov S, Wegner L, Andersen G . Low physical activity accentuates the effect of the FTO rs9939609 polymorphism on body fat accumulation. Diabetes. 2007; 57(1):95-101. DOI: 10.2337/db07-0910. View

18.

Scuteri A, Sanna S, Chen W, Uda M, Albai G, Strait J . Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007; 3(7):e115. PMC: 1934391. DOI: 10.1371/journal.pgen.0030115. View

19.

Andreasen C, Mogensen M, Borch-Johnsen K, Sandbaek A, Lauritzen T, Almind K . Lack of association between PKLR rs3020781 and NOS1AP rs7538490 and type 2 diabetes, overweight, obesity and related metabolic phenotypes in a Danish large-scale study: case-control studies and analyses of quantitative traits. BMC Med Genet. 2008; 9:118. PMC: 2654670. DOI: 10.1186/1471-2350-9-118. View

20.

Murcray C, Lewinger J, Gauderman W . Gene-environment interaction in genome-wide association studies. Am J Epidemiol. 2008; 169(2):219-26. PMC: 2732981. DOI: 10.1093/aje/kwn353. View