BMI prediction within a Korean population

Identifying BMI-associated genes via a genome-wide multi-omics integrative approach using summary data

OBJECTIVE

This study aims to identify BMI-associated genes by integrating aggregated summary information from different omics data.

METHODS

We conducted a meta-analysis to leverage information from a genome-wide association study (n = 339 224), a transcriptome-wide association study (n = 5619), and an epigenome-wide association study (n = 3743). We prioritized the significant genes with a machine learning-based method, netWAS, which borrows information from adipose tissue-specific interaction networks. We also used the brain-specific network in netWAS to investigate genes potentially involved in brain-adipose interaction.

RESULTS

We identified 195 genes that were significantly associated with BMI through meta-analysis. The netWAS analysis narrowed down the list to 21 genes in adipose tissue. Among these 21 genes, six genes, including FUS, STX4, CCNT2, FUBP1, NDUFS3, and RAPSN, were not reported to be BMI-associated in PubMed or GWAS Catalog. We also identified 11 genes that were significantly associated with BMI in both adipose and whole brain tissues.

CONCLUSION

This study integrated three types of omics data and identified a group of genes that have not previously been reported to be associated with BMI. This strategy could provide new insights for future studies to identify molecular mechanisms contributing to BMI regulation.

3D genomic features across >50 diverse cell types reveal insights into the genomic architecture of childhood obesity

The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18 and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 - an inflammation-responsive gene in nerve nociceptors - was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.

Influence of polymorphisms in IRS1, IRS2, MC3R, and MC4R on metabolic and inflammatory status and food intake in Brazilian adults: An exploratory pilot study

Polymorphisms in genes of leptin-melanocortin and insulin pathways have been associated with obesity and type 2 diabetes. We hypothesized that polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory markers and food intake composition in Brazilian subjects. This exploratory pilot study included 358 adult subjects. Clinical, anthropometric, and laboratory data were obtained through interview and access to medical records. The variants IRS1 rs2943634 A˃C, IRS2 rs1865434 C>T, MC3R rs3746619 C>A, and MC4R rs17782313 T>C were analyzed by real-time polymerase chain reaction. Food intake composition was assessed in a group of subjects with obesity (n = 84) before and after a short-term nutritional counseling program (9 weeks). MC4R rs17782313 was associated with increased risk of obesity (P = .034). Multivariate linear regression analysis adjusted by covariates indicated associations of IRS2 rs1865434 with reduced low-density lipoprotein cholesterol and resistin, MC3R rs3746619 with high glycated hemoglobin, and IRS1 rs2943634 and MC4R rs17782313 with increased high-sensitivity C-reactive protein (P < .05). Energy intake and carbohydrate and total fat intakes were reduced after the diet-oriented program (P < .05). Multivariate linear regression analysis showed associations of IRS2 rs1865434 with high basal fiber intake, IRS1 rs2943634 with low postprogram carbohydrate intake, and MC4R rs17782313 with low postprogram total fat and saturated fatty acid intakes (P < .05). Although significant associations did not survive correction for multiple comparisons using the Benjamini-Hochberg method in this exploratory study, polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory status in Brazilian adults. IRS1 and MC4R variants may influence carbohydrate, total fat, and saturated fatty acid intakes in response to a diet-oriented program in subjects with obesity.

Fine Mapping of the MAP2K5 Region Identified rs7175517 as a Causal Variant Related to BMI in China and the United Kingdom Populations

Background: Genome-wide association studies (GWASs) have consistently identified MAP2K5 as an obesity susceptibility gene. To deepen our understanding of the potential causal genetic variants of this region, a fine-mapping study of MAP2K5 was conducted.

Methods and Results: SNPs rs7175517 (G > A) and rs4776970 (T > A) were identified as the leading SNPs associated with BMI in both Chinese and the United Kingdom populations. Second, colocalization of GWAS and expression quantitative trait loci (eQTL) analyses and bioinformatic analyses indicated that rs7175517 is the functionally leading variant in the MAP2K5 gene region. Dual-luciferase assays indicated that the G allele of rs7175517 reduced the mRNA expression of MAP2K5 in HEK293T cells. The possible mechanism was that the G allele interacted with more RNA repressors from nuclei extracts, which was evidenced by electrophoretic mobility shift assays (EMSAs). Furthermore, the pathway enrichment analyses of the products from DNA pull-down and protein mass spectrometry demonstrated that the G allele of rs7175517 might interact with RNA catabolic or splicing transcription factors, which consequentially increased adiposity deposition.

Conclusion: SNP rs7175517 of the MAP2K5 gene was the putative causal variant associated with BMI. More precisely designed in vitro or animal experiments are warranted to further delineate the function of MAP2K5 in adipogenesis.

Body mass index but not genetic risk is longitudinally associated with altered structural brain parameters

Evidence from previous studies suggests that elevated body mass index (BMI) and genetic risk for obesity is associated with reduced brain volume, particularly in areas of reward-related cognition, e.g. the medial prefrontal cortex (AC-MPFC), the orbitofrontal cortex (OFC), the striatum and the thalamus. However, only few studies examined the interplay between these factors in a joint approach. Moreover, previous findings are based on cross-sectional data. We investigated the longitudinal relationship between increased BMI, brain structural magnetic resonance imaging (MRI) parameters and genetic risk scores in a cohort of n = 502 community-dwelling participants from the Study of Health in Pomerania (SHIP) with a mean follow-up-time of 4.9 years. We found that (1) increased BMI values at baseline were associated with decreased brain parameters at follow-up. These effects were particularly pronounced for the OFC and AC-MPFC. (2) The genetic predisposition for BMI had no effect on brain parameters at baseline or follow-up. (3) The interaction between the genetic score for BMI and brain parameters had no effect on BMI at baseline. Finding a significant impact of overweight, but not genetic predisposition for obesity on altered brain structure suggests that metabolic mechanisms may underlie the relationship between obesity and altered brain structure.

Molecular modelling of novel ADCY3 variant predicts a molecular target for tackling obesity

Severe early-onset obesity is mainly attributed to single gene variations of the hypothalamic leptin-melanocortin system, which is critical for controlling the balance between appetite and energy expenditure. Adenylate cyclase 3 (ADCY3), a transmembrane enzyme localized in primary neuronal cilia, is a key genetic candidate, which appears to have an essential role in regulating body weight. The present study aimed to identify ADCY3 genetic variants in severely obese young patients of Greek-Cypriot origin by genomic sequencing. Apart from previously reported variants, the novel and probably pathogenic variant c.349T>A, causing a p.Leu117Met substitution within one of the two pseudo-symmetric halves of the transmembrane part of the protein, was reported. Molecular modelling analysis used to delineate bonding interactions within the mutated protein structure strongly suggested a change in interactive forces and energy levels affecting the pseudo-twofold symmetry of the transmembrane domain of the protein and probably its catalytic function. These results support the involvement of ADCY3 in the pathology of the disease and point towards the requirement of defining protein function and evaluating the clinical significance of the detected variants.

Genotype-expression interactions for BDNF across human brain regions

Background

Genetic variations in brain-derived neurotrophic factor (BDNF) are associated with various psychiatric disorders including depression, obsessive-compulsive disorder, substance use disorders, and schizophrenia; altered gene expression triggered by these genetic variants may serve to create these phenotypes. But genotype-expression interactions for this gene have not been well-studied across brain regions relevant for psychiatric disorders.

Results

At false discovery rate (FDR) of 10% (q < 0.1), a total of 61 SNPs were associated with BDNF expression in cerebellum (n = 209), 55 SNPs in cortex (n = 205), 48 SNPs in nucleus accumbens (n = 202), 47 SNPs in caudate (n = 194), and 58 SNPs in cerebellar hemisphere (n = 175). We identified a set of 30 SNPs in 2 haplotype blocks that were associated with alterations in expression for each of these 5 regions. The first haplotype block included variants associated in the literature with panic disorders (rs16917204), addiction (rs11030104), bipolar disorder (rs16917237/rs2049045), and obsessive-compulsive disorder (rs6265). Likewise, variants in the second haplotype block have been previously associated with disorders such as nicotine addiction, major depressive disorder (rs988748), and epilepsy (rs6484320/rs7103411).

Conclusions

This work supports the association of variants within BDNF for expression changes in these key brain regions that may contribute to common behavioral phenotypes for disorders of compulsion, impulsivity, and addiction. These SNPs should be further investigated as possible therapeutic and diagnostic targets to aid in management of these and other psychiatric disorders.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07525-1.

BDNF Gene as a Precision Skill of Obesity Management

The scarcity of the results obtained for the treatment of obesity leads us to consider new strategies, contemplating all the factors involved in the development of the disease. One of the key molecules for controlling body weight and energy homeostasis is the brain-derived neurotrophic factor (BDNF). This work summarizes the mechanisms in which BDNF gene regulates this multifactorial disease. In addition, we discuss the role of other BDNF polymorphisms as genetic determinants of obesity. In this context, a total of 14 SNPs near or inside BDNF/BDNF-AS related to BMI were identified in various GWASs. Finally, we assess gene-diet interaction as a novel tool to prevent obesity and formulate solid and personalized nutritional management. Our research group has performed the first study on the association of BDNF-AS rs925946 polymorphism and calcium intake as potential modulators of the nutritional status. Although these results should be confirmed in future studies, they open the path for new prevention opportunities.

Prediction of cholesterol ratios within a Korean population

Cholesterol ratios (total cholesterol (TC)/high-density lipoprotein cholesterol (HDL-c) and triglyceride (TG)/HDL-c) have been suggested as better indicators to predict various clinical features such as insulin resistance and heart disease. Therefore, we aimed to build a single nucleotide polymorphism (SNP) set to predict constitutional lipid metabolism. The genotype data of 7795 samples were obtained from the Korea Association Resource. Among the total of 7795 samples, 7016 subjects were used to perform 10-fold cross-validation. We selected the SNPs that showed significance constantly throughout all 10 cross-validation sets; another 779 samples were used as the final validation set. After performing the 10-fold cross-validation, the six SNPs (rs4420638 (APOC1), rs12421652 (BUD13), rs17411126 (LPL), rs6589566 (ZPR1), rs16940212 (LOC101928635) and rs10852765 (ABCA8)) were finally selected for predicting cholesterol ratios. The weighted genetic risk scores (wGRS) were calculated based on the regression slopes of the six selected SNPs. Our results showed upward trends of wGRS for both the TC/HDL-c and TG/HDL-c ratios within the 10-fold cross-validation. Similarly, the wGRS of the six SNPs also showed upward trends in analyses using the SNP selection set and final validation set. The selected six SNPs can be used to explain both the TC/HDL-c and TG/HDL-c ratios. Our results may be useful for the prospective predictions of cholesterol-related diseases.