Article

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: 42.35). 09/2012; 490(7419):267-72. DOI: 10.1038/nature11401
Source: PubMed

ABSTRACT 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.

2 Bookmarks
 · 
516 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Traditional quantitative trait locus (QTL) analysis focuses on identifying loci associated with mean heterogeneity. Recent research has discovered loci associated with phenotype variance heterogeneity (vQTL), which is important in studying genetic association with complex traits, especially for identifying gene-gene and gene-environment interactions. While several tests have been proposed to detect vQTL for unrelated individuals, there are no tests for related individuals, commonly seen in family-based genetic studies. Here we introduce a likelihood ratio test (LRT) for identifying mean and variance heterogeneity simultaneously or for either effect alone, adjusting for covariates and family relatedness using a linear mixed effect model approach. The LRT test statistic for normally distributed quantitative traits approximately follows χ(2) -distributions. To correct for inflated Type I error for non-normally distributed quantitative traits, we propose a parametric bootstrap-based LRT that removes the best linear unbiased prediction (BLUP) of family random effect. Simulation studies show that our family-based test controls Type I error and has good power, while Type I error inflation is observed when family relatedness is ignored. We demonstrate the utility and efficiency gains of the proposed method using data from the Framingham Heart Study to detect loci associated with body mass index (BMI) variability.
    Annals of Human Genetics 11/2014; 79(1). DOI:10.1111/ahg.12089 · 1.93 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Recent studies are starting to show that genetic control over stochastic variation is a key evolutionary solution of single celled organisms in the face of unpredictable environments. This has been expanded to show that genetic variation can alter stochastic variation in transcriptional processes within multi-cellular eukaryotes. However, little is known about how genetic diversity can control stochastic variation within more non-cell autonomous phenotypes. Using an Arabidopsis reciprocal RIL population, we showed that there is significant genetic diversity influencing stochastic variation in the plant metabolome, defense chemistry, and growth. This genetic diversity included loci specific for the stochastic variation of each phenotypic class that did not affect the other phenotypic classes or the average phenotype. This suggests that the organism's networks are established so that noise can exist in one phenotypic level like metabolism and not permeate up or down to different phenotypic levels. Further, the genomic variation within the plastid and mitochondria also had significant effects on the stochastic variation of all phenotypic classes. The genetic influence over stochastic variation within the metabolome was highly metabolite specific, with neighboring metabolites in the same metabolic pathway frequently showing different levels of noise. As expected from bet-hedging theory, there was more genetic diversity and a wider range of stochastic variation for defense chemistry than found for primary metabolism. Thus, it is possible to begin dissecting the stochastic variation of whole organismal phenotypes in multi-cellular organisms. Further, there are loci that modulate stochastic variation at different phenotypic levels. Finding the identity of these genes will be key to developing complete models linking genotype to phenotype.
    PLoS Genetics 01/2015; 11(1):e1004779. DOI:10.1371/journal.pgen.1004779 · 8.17 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Tetra-primer amplification refractory mutation system PCR (T-ARMS-PCR) offers fast detection and extreme simplicity at a negligible cost for SNP genotyping. SIRT2, the family member (sirtuins, SIRT1-7) with the greatest homology to the silent information regulator 2 (Sir2), is the most abundantly expressed sirtuins in adipocytes and has been implicated in promoting fatty acid oxidation (FAO) by deacetylating various substrates. In the current study, we have successfully genotyped a new identified bovine SIRT2 SNP g.4140A > G by T-ARMS-PCR method and validated the accuracy by PCR-RFLP assay using 1255 animals representing the five main Chinese breeds. The concordance between the two different methods was 98.8%. Individuals with discordant genotypes were retyped by direct DNA sequencing. 40% of the discrepancies could be attributed to incomplete digestion in the PCR-RFLP assay. 60% of discordant genotypes resulted from allele failure in the T-ARMS-PCR assay. Chi-square test shows that the frequencies of SNP g.4140A > G are in Hardy-Weinberg equilibrium in all the samples (P > 0.05), which suggested that the five populations are almost a dynamic equilibrium even in artificial selection. Association analysis showed that the g.4140A > G polymorphism is significantly related to 24 months-old body weight in Nanyang cattle. Our results provide direct evidence that T-ARMS-PCR is a rapid, reliable, and cost-effective method for SNP genotyping and g.4140A > G polymorphism in bovine SIRT2 is associated with growth efficiency traits. These findings may be used for marker-assisted selection and management in feedlot cattle.
    Analytical methods 01/2014; 6(6):1835. DOI:10.1039/c3ay41370e · 1.94 Impact Factor

Full-text

Download
29 Downloads
Available from
May 20, 2014