FTO genotype is associated with phenotypic variability of body mass index

University of Queensland Diamantina Institute, The University of Queensland, Princess Alexandra Hospital, Brisbane, Queensland 4102, Australia.
Nature (Impact Factor: 41.46). 09/2012; 490(7419):267-72. DOI: 10.1038/nature11401
Source: PubMed


There is evidence across several species for genetic control of phenotypic variation of complex traits, such that the variance among phenotypes is genotype dependent. Understanding genetic control of variability is important in evolutionary biology, agricultural selection programmes and human medicine, yet for complex traits, no individual genetic variants associated with variance, as opposed to the mean, have been identified. Here we perform a meta-analysis of genome-wide association studies of phenotypic variation using ∼170,000 samples on height and body mass index (BMI) in human populations. We report evidence that the single nucleotide polymorphism (SNP) rs7202116 at the FTO gene locus, which is known to be associated with obesity (as measured by mean BMI for each rs7202116 genotype), is also associated with phenotypic variability. We show that the results are not due to scale effects or other artefacts, and find no other experiment-wise significant evidence for effects on variability, either at loci other than FTO for BMI or at any locus for height. The difference in variance for BMI among individuals with opposite homozygous genotypes at the FTO locus is approximately 7%, corresponding to a difference of ∼0.5 kilograms in the standard deviation of weight. Our results indicate that genetic variants can be discovered that are associated with variability, and that between-person variability in obesity can partly be explained by the genotype at the FTO locus. The results are consistent with reported FTO by environment interactions for BMI, possibly mediated by DNA methylation. Our BMI results for other SNPs and our height results for all SNPs suggest that most genetic variants, including those that influence mean height or mean BMI, are not associated with phenotypic variance, or that their effects on variability are too small to detect even with samples sizes greater than 100,000.

Download full-text


Available from: Mary F Feitosa, May 13, 2014
  • Source
    • "consistently the most prevalent genetic variant for human BMI (Speliotes et al. 2010, Yang et al. 2012) and, the latest GWAS meta-analysis on 249 796 adults, established an unambiguous effect size of 0.39 kg m À2 per FTO effect allele (P-value<10 À120 ) (Speliotes et al. 2010). The association between polymorphisms in the FTO gene and adult BMI is robust in both genders with consistent effect in almost all ethnic groups studied although differences exist in haplotype blocks and frequency of polymorphisms between populations [see (Jacobsson et al. 2013) for review]. "

  • Source
    • "For example, variants in RNA methylating as well as demethylating factors have been genetically associated with pathological phenotypes, such as obesity and intellectual impairment. Variants of the FTO gene, a nonheme FeII/α-KG-dependent dioxygenase that catalyzes the demethylation of m6A in RNA [61], have been associated with high body mass index, risk of obesity, and type 2 diabetes [62]. While the disease-associated genetic variants are characterized by Mendelian inheritance, these findings suggest that altered RNA methylation patterns can have a considerable pathophysiological relevance. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Genomic concepts are based on the assumption that phenotypes arise from the expression of genetic variants. However, the presence of non-Mendelian inheritance patterns provides a direct challenge to this view and suggests an important role for alternative mechanisms of gene regulation and inheritance. Over the past few years, a highly complex and diverse network of noncoding RNAs has been discovered. Research in animal models has shown that RNAs can be inherited and that RNA methyltransferases can be important for the transmission and expression of modified phenotypes in the next generation. We discuss possible mechanisms of RNA-mediated inheritance and the role of these mechanisms for human health and disease.
    PLoS Genetics 04/2014; 10(4):e1004296. DOI:10.1371/journal.pgen.1004296 · 7.53 Impact Factor
  • Source
    • "GWAS has had many successes. Many common polymorphisms have now been found that increase genetic risk for AD (Harold et al. 2009; Lambert et al. 2009; Naj et al. 2011), age-related cognitive decline (Davies et al. 2012), schizophrenia (Almasy et al. 2008; Stefansson et al. 2009; Ripke et al. 2011; Rietschel et al. 2012), bipolar disorder (Sklar et al. 2011; Cichon et al. 2011) as well as obesity (Yang et al. 2012), alcohol drinking (Schumann et al. 2011), tobacco smoking (Thorgeirsson et al. 2008), cardiovascular disease (CARDIoGRAMplusC4D Consortium et al. 2013), osteoporosis (Estrada et al. 2012), prevalent psychiatric disorders (Cross-Disorder Group of the Psychiatric Genomics Consortium et al. 2013) and for many other traits and diseases. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This article reviews work published by the ENIGMA Consortium and its Working Groups (http://enigma.ini.usc.edu). It was written collaboratively; P.T. wrote the first draft and all listed authors revised and edited the document for important intellectual content, using Google Docs for parallel editing, and approved it. Some ENIGMA investigators contributed to the design and implementation of ENIGMA or provided data but did not participate in the analysis or writing of this report. A complete listing of ENIGMA investigators is available at http://enigma.ini.usc.edu/publications/the-enigma-consortium-in-review/ For ADNI, some investigators contributed to the design and implementation of ADNI or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators is available at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ ADNI_Acknowledgement_List.pdf The work reviewed here was funded by a large number of federal and private agencies worldwide, listed in Stein et al. (2012); the funding for listed consortia is also itemized in Stein et al. (2012).
    Brain Imaging and Behavior 01/2014; 8(2). DOI:10.1007/s11682-013-9269-5 · 4.60 Impact Factor
Show more