Concurrent outcomes from multiple approaches of epistasis analysis for human body mass index associated loci provide insights into obesity biology.
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ABSTRACT:
SUBMITTER: D'Silva S
PROVIDER: S-EPMC9068779 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
| S-EPMC4382211 | biostudies-literature
Project description: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 × 10-15), rs6235 (PCSK1; p = 7.11 × 10-6), rs7903146 (TCF7L2; p = 9.60 × 10-6), rs11873305 (MC4R; p = 5.08 × 10-5), rs12617233 (FANCL; p = 5.30 × 10-5), rs11672660 (GIPR; p = 1.64 × 10-4), rs997295 (MAP2K5; p = 3.25 × 10-4), rs6499653 (FTO; p = 6.23 × 10-4), and rs3824755 (NT5C2; p = 7.90 × 10-4)-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 × 10-4), 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 × 10-37; 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.
| S-EPMC5812888 | biostudies-literature
Project description:We surveyed gene-gene interactions (epistasis) in human body mass index (BMI) in four European populations (n<1200) via exhaustive pair-wise genome scans where interactions were computed as F ratios by testing a linear regression model fitting two single-nucleotide polymorphisms (SNPs) with interactions against the one without. Before the association tests, BMI was corrected for sex and age, normalised and adjusted for relatedness. Neither single SNPs nor SNP interactions were genome-wide significant in either cohort based on the consensus threshold (P=5.0E-08) and a Bonferroni corrected threshold (P=1.1E-12), respectively. Next we compared sub genome-wide significant SNP interactions (P<5.0E-08) across cohorts to identify common epistatic signals, where SNPs were annotated to genes to test for gene ontology (GO) enrichment. Among the epistatic genes contributing to the commonly enriched GO terms, 19 were shared across study cohorts of which 15 are previously published genome-wide association loci, including CDH13 (cadherin 13) associated with height and SORCS2 (sortilin-related VPS10 domain containing receptor 2) associated with circulating insulin-like growth factor 1 and binding protein 3. Interactions between the 19 shared epistatic genes and those involving BMI candidate loci (P<5.0E-08) were tested across cohorts and found eight replicated at the SNP level (P<0.05) in at least one cohort, which were further tested and showed limited replication in a separate European population (n>5000). We conclude that genome-wide analysis of epistasis in multiple populations is an effective approach to provide new insights into the genetic regulation of BMI but requires additional efforts to confirm the findings.
| S-EPMC3400731 | biostudies-literature
Project description:BackgroundAccurate obesity classification is important so that appropriate intervention can be instituted to modify metabolic risk factors. Commonly utilized body mass index (BMI) and percentage body fat (PBF) are influenced by lean mass whereas fat mass index (FMI) measures only body fat. This study compares the prevalence of obesity and metabolic risk factors with FMI, BMI and PBF using DXA (dual-energy x-ray absorptiometry).Methods489 women randomly recruited from the electoral roll were stratified into 4 age groups; 40-49, 50-59, 60-69 and 70-79 years from 2000 to 2001. Clinical data and DXA body composition were obtained. Statistical analyses were performed using Medcalc v15 (Ostend, Belgium) with significance level at p = 0.05 (two-tailed).ResultsThere was higher prevalence of obesity using PBF compared to BMI and FMI (p<0.001). This difference was greater from age 50-59 (p<0.05) which may be explained by age-related lean mass loss. PBF over-classified obesity in over 35% of normal and 95% of overweight categories compared to FMI and BMI. BMI has a sensitivity of 78.9% and specificity of 98.3% for obesity using FMI as the standard. BMI under-classified obesity in the overweight category by 14.9% compared to FMI. There was no difference in diabetes, dyslipidemia, hypertension and metabolic syndrome prevalence within the BMI-obesity and FMI-obesity categories (p>0.05).ConclusionPBF classified more obesity than BMI and FMI because of its low pre-determined threshold. The greater difference with PBF compared to BMI and FMI from the 50-59 decade onwards can be attributed to age-related lean mass loss. BMI had the lowest sensitivity for obesity diagnosis. BMI under-classified obesity in the overweight category compared to FMI due to its inability to differentiate lean mass. However, there was no significant difference in the prevalence of metabolic risk factors between BMI and FMI-obesity categories indicating that fat location may influence metabolic dysregulation.
| S-EPMC7808627 | biostudies-literature
Project description:BackgroundDespite compelling links between excess body weight and cancer, body mass index (BMI) cut-points, or thresholds above which cancer incidence increased, have not been identified. The objective of this study was to determine if BMI cut-points exist for 14 obesity-related cancers.Subjects/methodsIn this retrospective cohort study, patients 18-75 years old were included if they had ≥2 clinical encounters with BMI measurements in the electronic health record (EHR) at a single academic medical center from 2008 to 2018. Patients who were pregnant, had a history of cancer, or had undergone bariatric surgery were excluded. Adjusted logistic regression was performed to identify cancers that were associated with increasing BMI. For those cancers, BMI cut-points were calculated using adjusted quantile regression for cancer incidence at 80% sensitivity. Logistic and quantile regression models were adjusted for age, sex, race/ethnicity, and smoking status.ResultsA total of 7079 cancer patients (mean age 58.5 years, mean BMI 30.5 kg/m2) and 270,441 non-cancer patients (mean age 43.8 years, mean BMI 28.8 kg/m2) were included in the study. In adjusted logistic regression analyses, statistically significant associations were identified between increasing BMI and the incidence of kidney, thyroid, and uterine cancer. BMI cut-points were identified for kidney (26.3 kg/m2) and uterine (26.9 kg/m2) cancer.ConclusionsBMI cut-points that accurately predicted development kidney and uterine cancer occurred in the overweight category. Analysis of multi-institutional EHR data may help determine if these relationships are generalizable to other health care settings. If they are, incorporation of BMI into the screening algorithms for these cancers may be warranted.
| S-EPMC9615027 | biostudies-literature
Project description:Little research has addressed the heterogeneity and mortality risk in body mass index (BMI) trajectories among older populations. Applying latent class trajectory models to 9,538 adults aged 51 to 77 years from the US Health and Retirement Study (1992-2008), we defined 6 latent BMI trajectories: normal weight downward, normal weight upward, overweight stable, overweight obesity, class I obese upward, and class II/III obese upward. Using survival analysis, we found that people in the overweight stable trajectory had the highest survival rate, followed by those in the overweight obesity, normal weight upward, class I obese upward, normal weight downward, and class II/III obese upward trajectories. The results were robust after controlling for baseline demographic and socioeconomic characteristics, smoking status, limitations in activities of daily living, a wide range of chronic illnesses, and self-rated health. Further analysis suggested that BMI trajectories were more predictive of mortality risk than was static BMI status. Using attributable risk analysis, we found that approximately 7.2% of deaths after 51 years of age among the 1931-1941 birth cohort were due to class I and class II/III obese upward trajectories. This suggests that trajectories of increasing obesity past 51 years of age pose a substantive threat to future gains in life expectancy.
| S-EPMC3842899 | biostudies-other
Project description:BackgroundGenome-wide association studies (GWASs) have identified hundreds of loci for body mass index (BMI), but functional variants in these loci are less known. The purpose of this study was to identify RNA modification-related SNPs (RNAm-SNPs) for BMI in GWAS loci. BMI-associated RNAm-SNPs were identified in a GWAS of approximately 700,000 individuals. Gene expression and circulating protein levels affected by the RNAm-SNPs were identified by QTL analyses. Mendelian randomization (MR) methods were applied to test whether the gene expression and protein levels were associated with BMI.ResultsA total of 78 RNAm-SNPs associated with BMI (P < 5.0 × 10-8) were identified, including 65 m6A-, 10 m1A-, 3 m7G- and 1 A-to-I-related SNPs. Two functional loss, high confidence level m6A-SNPs, rs6713978 (P = 6.4 × 10-60) and rs13410999 (P = 8.2 × 10-59), in the intron of ADCY3 were the top significant SNPs. These two RNAm-SNPs were associated with ADCY3 gene expression in adipose tissues, whole blood cells, the tibial nerve, the tibial artery and lymphocytes, and the expression levels in these tissues were associated with BMI. Proteins enriched in specific KEGG pathways, such as natural killer cell-mediated cytotoxicity, the Rap1 signaling pathway and the Ras signaling pathway, were affected by the RNAm-SNPs, and circulating levels of some of these proteins (ADH1B, DOCK9, MICB, PRDM1, STOM, TMPRSS11D and TXNDC12) were associated with BMI in MR analyses.ConclusionsOur study identified RNAm-SNPs in BMI-related genomic loci and suggested that RNA modification may affect BMI by affecting the expression levels of corresponding genes and proteins.
| S-EPMC9316745 | biostudies-literature
Project description:To investigate the genetic architecture of severe obesity, we performed a genome-wide association study of 775 cases and 3197 unascertained controls at approximately 550,000 markers across the autosomal genome. We found convincing association to the previously described locus including the FTO gene. We also found evidence of association at a further six of 12 other loci previously reported to influence body mass index (BMI) in the general population and one of three associations to severe childhood and adult obesity and that cases have a higher proportion of risk-conferring alleles than controls. We found no evidence of homozygosity at any locus due to identity-by-descent associating with phenotype which would be indicative of rare, penetrant alleles, nor was there excess genome-wide homozygosity in cases relative to controls. Our results suggest that variants influencing BMI also contribute to severe obesity, a condition at the extreme of the phenotypic spectrum rather than a distinct condition.
| S-EPMC2729668 | biostudies-literature
Project description:Obesity-associated differences in hedonic hunger, while consistently reported, have not been adequately quantified, with most studies failing to demonstrate strong correlations between Body Mass Index (BMI) and hedonic hunger indicators. Here, we quantified and assessed the nature of the relationship between hedonic hunger and BMI, in a cross-sectional study using the Portuguese version of the PFS (P-PFS) to measure hedonic hunger. Data were collected from 1266 participants belonging to non-clinical, clinical (candidates for weight-loss surgery) and population samples. Across samples, significant but weak positive associations were found between P-PFS scores and BMI, in adjusted linear regression models. However, in logistic regression models of data from the clinical and non-clinical samples, the P-PFS Food Available domain score was significantly and robustly associated with belonging to the clinical sample (OR?=?1.8, 95%CI: 1.2-2.8; p?=?0.008), while in the population sample it was associated to being obese (OR?=?2.1, 95%CI: 1.6-2.7; p?<?0.001). Thus, hedonic hunger levels are associated with obesity status with the odds of being obese approximately doubling for each unit increase in the P-PFS Food Available score.
| S-EPMC5895788 | biostudies-literature
Project description:BackgroundAlthough sleep duration is a risk factor for obesity in young children, less is known about other aspects of sleep health, including bedtime, on obesity risk.ObjectiveTo determine whether bedtime is associated with body mass index (BMI) z-score or obesity risk in children ages 2 to 5 years, and to determine if associations are independent of sleep duration.MethodsCohort analyses were undertaken using three early life obesity prevention trials (POI, INSIGHT, Healthy Beginnings) and a longitudinal cohort study (HOME). Bedtime was assessed by questionnaire and BMI through clinical measurement between 2 and 5 years in 1642 children. Adjusted regression models examined whether BMI z-score and obesity (BMI z-score ≥ 2) were associated with bedtime, nocturnal sleep time and 24-hour sleep time. A discrete mixture model categorized children into bedtime trajectory groups across time points.ResultsBedtime was inconsistently associated with BMI z-score. Although each hour later of bedtime was associated with greater odds of obesity at ages 3 (OR; 95% CI: 1.05; 1.003, 1.10) and 5 (1.35; 1.08, 1.69) years, odds were attenuated after adjustment for nocturnal or 24-hour sleep time. Longer nocturnal sleep duration at 2 years was associated with lower odds of obesity (OR 0.90; 0.86, 0.94), as was longer 24-hour sleep duration at 3 years in girls (0.70; 0.62, 0.78). BMI z-score and odds of obesity were not significantly different between 'early to bed' and 'late to bed' trajectory groups.ConclusionsTiming of bedtime appears inconsistently related to obesity in young children, possibly via influencing overall sleep duration.
| S-EPMC7745736 | biostudies-literature