Penetrance of Polygenic Obesity Susceptibility Loci across the Body Mass Index Distribution

Abstract

A growing number of single-nucleotide polymorphisms (SNPs) have been associated with body mass index (BMI) and obesity, but whether the effects of these obesity-susceptibility loci are uniform across the BMI distribution remains unclear. We studied the effects of 37 BMI-associated SNPs in 75,230 adults of European ancestry across BMI percentiles by using conditional quantile regression (CQR) and meta-regression (MR) models. The effects of nine SNPs (24%)-rs1421085 (FTO; p = 8.69 × 10), rs6235 (PCSK1; p = 7.11 × 10), rs7903146 (TCF7L2; p = 9.60 × 10), rs11873305 (MC4R; p = 5.08 × 10), rs12617233 (FANCL; p = 5.30 × 10), rs11672660 (GIPR; p = 1.64 × 10), rs997295 (MAP2K5; p = 3.25 × 10), rs6499653 (FTO; p = 6.23 × 10), and rs3824755 (NT5C2; p = 7.90 × 10)-increased significantly across the sample BMI distribution. We showed that such increases stemmed from unadjusted gene interactions that enhanced the effects of SNPs in persons with a high BMI. When 125 height-associated SNPs were analyzed for comparison, only one (<1%), rs6219 (IGF1, p = 1.80 × 10), showed effects that varied significantly across height percentiles. Cumulative gene scores of these SNPs (GS-BMI and GS-height) showed that only GS-BMI had effects that increased significantly across the sample distribution (BMI: p = 7.03 × 10; height: p = 0.499). Overall, these findings underscore the importance of gene-gene and gene-environment interactions in shaping the genetic architecture of BMI and advance a method for detecting such interactions by using only the sample outcome distribution.

Non-uniform genetic effect sizes of variants associated with refractive error suggests gene-gene or gene-environment interactions are pervasive

“…Repeating our CQR-MR analyses for the variants most strongly associated with height identified by the GIANT consortium [45] revealed scant evidence for variants with nonuniform effect sizes across quantiles. This finding is consistent with previously-observed results for this trait [35,46]. Since height is known to have a very high narrow-sense heritability [45], our findings point to a lack of GxE or GxG interactions for height SNPs that have strong marginal associations.…”

“…The detection of non-uniform genetic effect sizes across quantiles of the trait distribution by CQR-MR has previously been proposed as a method for identifying SNPs involved in GxE or GxG interactions, without any prior knowledge of the identity of the interacting variant or environmental exposure [35]. Here, we studied changes in genetic effect size across the refractive error trait distribution in SNPs previously shown to be associated with this trait, and found evidence for non-uniform effects in 88% of the 146 variants tested.…”

“…However, recent studies using the conditional quantile regression (CQR) technique to assess the consistency of effect sizes in phenotypes as diverse as BMI [33][34][35], height [33,35], growth-rate of pigs [36], plant flowering time [37] and gene expression [38] have demonstrated that certain genetic variants exert differing effects depending on where an individual lies in the phenotypic distribution. For instance, using CQR, the BMI-susceptibility variant rs6235 in the PCSK1 gene was recently shown to confer a shift towards a lower BMI in slim individuals and a higher BMI in obese individuals [35]. Such a non-constant effect size across quantiles of the phenotype distribution is a 'signature' of a variant involved in either a gene-gene (GxG) or geneenvironment (GxE) interaction, or both.…”

Associations between the <b><i>LEP</i></b> -2548G/A Promoter and Baseline Weight and between <b><i>LEPR</i></b> Gln223Arg and Lys656Asn Variants and Change in BMI <b><i>z</i></b> Scores in Arab Children and Adolescents Treated with Risperidone

“…Although often difficult in practice, replication studies should ideally use protocols with antipsychotic monotherapy, especially for children and adolescents in their first psychotic episode, and, like our study, ideally commence data collection when patients are antipsychotic-naïve. Additionally, extending the analysis to other antipsychotics and to more genes, as well as conducting more complex analyses including consideration of gene-environment interactions including epigenetics and gene-gene interactions models, could shed further light on relevant biological mechanisms [39]. As patients and their caregivers are certainly interested in preventing and ameliorating antipsychotic-associated weight gain and predicting who will respond well to interventions for this and who will not, further collaborative research efforts in this area are indicated.…”

Obesity-related loci in TMEM18, CDKAL1 and FAIM2 are associated with obesity and type 2 diabetes in Chinese Han patients

“…To date, GWA analyses have identified more than thirty susceptibility loci robustly associated with obesity measured by body mass index (BMI). Those loci are in or near genes, including GNPDA2, SH2B1, TMEM18, MTCH2, CDKAL1, FAIM2, and MC4R [5][6][7][8]. It has been reported that the genotype-phenotype association varies in diverse groups of patients, and results need to be verified in a specific population [9].…”