Do genetic risk scores for childhood adiposity operate independent of BMI of their mothers?

OBJECTIVES: Genetic predisposition and maternal body mass index (BMI) are risk factors for childhood adiposity, defined by either BMI or overweight. We aimed to investigate whether childhood-specific genetic risk scores (GRSs) for adiposity-related traits are associated with childhood adiposity independent of maternal BMI, or whether the associations are modified by maternal BMI. METHODS: We constructed a weighted 26-SNP child BMI-GRS and a weighted 17-SNP child obesity-GRS in overall 1674 genotyped children within the Danish National Birth Cohort. We applied a case-cohort (N = 1261) and exposure-based cohort (N = 912) sampling design. Using logistic regression models we estimated associations of the GRSs and child overweight at age 7 years and examined if the GRSs influence child adiposity independent of maternal BMI (per standard deviation units). RESULTS: In the case-cohort design analysis, maternal BMI and the child GRSs were associated with increased odds for childhood overweight [OR for maternal BMI: 2.01 (95% CI: 1.86; 2.17), OR for child BMI-GRS: 1.56 (95% CI: 1.47; 1.66), and OR for child obesity-GRS 1.46 (95% CI: 1.37; 1.54)]. Adjustment for maternal BMI did not change the results, and there were no significant interactions between the GRSs and maternal BMI. However, in the exposure-based cohort design analysis, significant interactions between the child GRSs and maternal BMI on child overweight were observed, suggesting 0.85-0.87-fold attenuation on ORs of child overweight at higher values of maternal BMI and child GRS. CONCLUSION: GRSs for childhood adiposity are strongly associated with childhood adiposity even when adjusted for maternal BMI, suggesting that the child-specific GRSs and maternal BMI contribute to childhood overweight independent of each other. However, high maternal BMI may attenuate the effects of child GRSs in children.

Evidence of genetic predisposition for metabolically healthy obesity and metabolically obese normal weight.

Obesity has evolved into a global pandemic that constitutes a major threat to public health. The majority of obesity-related health care costs are due to cardiometabolic complications, such as insulin resistance, dyslipidemia, and hypertension, which are risk factors for Type 2 diabetes and cardiovascular disease. However, many obese individuals, often called metabolically healthy obese (MHO), seem to be protected from these cardiometabolic complications. Conversely, there is a group of individuals who suffer from cardiometabolic complications despite being of normal weight; a condition termed metabolically obese normal weight (MONW). Recent large-scale genomic studies have provided evidence that a number of genetic variants show an association with increased adiposity but a favorable cardiometabolic profile, an indicator for the genetic basis of the MHO and MONW phenotypes. Many of these loci are located in or near genes that implicate pathways involved in adipogenesis, fat distribution, insulin signaling, and insulin resistance. It has been suggested that a threshold for subcutaneous adipose tissue expandability may be at play in the manifestation of MHO and MONW, where expiry of adipose tissue storage capacity could lead to ectopic lipid accumulation in non-adipose tissues such as liver, muscle, heart, and pancreatic beta cells. Understanding the genetic aspects of the mechanisms that underpin MHO and MONW is crucial to define appropriate public health action points and to develop effective intervention measures.

Genome-wide discovery of genetic loci that uncouple excess adiposity from its comorbidities.

Obesity is a major risk factor for cardiometabolic diseases. Nevertheless, a substantial proportion of individuals with obesity do not suffer cardiometabolic comorbidities. The mechanisms that uncouple adiposity from its cardiometabolic complications are not fully understood. Here, we identify 62 loci of which the same allele is significantly associated with both higher adiposity and lower cardiometabolic risk. Functional analyses show that the 62 loci are enriched for genes expressed in adipose tissue, and for regulatory variants that influence nearby genes that affect adipocyte differentiation. Genes prioritized in each locus support a key role of fat distribution (FAM13A, IRS1 and PPARG) and adipocyte function (ALDH2, CCDC92, DNAH10, ESR1, FAM13A, MTOR, PIK3R1 and VEGFB). Several additional mechanisms are involved as well, such as insulin-glucose signalling (ADCY5, ARAP1, CREBBP, FAM13A, MTOR, PEPD, RAC1 and SH2B3), energy expenditure and fatty acid oxidation (IGF2BP2), browning of white adipose tissue (CSK, VEGFA, VEGFB and SLC22A3) and inflammation (SH2B3, DAGLB and ADCY9). Some of these genes may represent therapeutic targets to reduce cardiometabolic risk linked to excess adiposity.

Analysis of Case-Parent Trios Using a Loglinear Model with Adjustment for Transmission Ratio Distortion.

Transmission of the two parental alleles to offspring deviating from the Mendelian ratio is termed Transmission Ratio Distortion (TRD), occurs throughout gametic and embryonic development. TRD has been well-studied in animals, but remains largely unknown in humans. The Transmission Disequilibrium Test (TDT) was first proposed to test for association and linkage in case-trios (affected offspring and parents); adjusting for TRD using control-trios was recommended. However, the TDT does not provide risk parameter estimates for different genetic models. A loglinear model was later proposed to provide child and maternal relative risk (RR) estimates of disease, assuming Mendelian transmission. Results from our simulation study showed that case-trios RR estimates using this model are biased in the presence of TRD; power and Type 1 error are compromised. We propose an extended loglinear model adjusting for TRD. Under this extended model, RR estimates, power and Type 1 error are correctly restored. We applied this model to an intrauterine growth restriction dataset, and showed consistent results with a previous approach that adjusted for TRD using control-trios. Our findings suggested the need to adjust for TRD in avoiding spurious results. Documenting TRD in the population is therefore essential for the correct interpretation of genetic association studies.