Integrating genetic, transcriptional, and biological information provides insights into obesity

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (P BMI and P SNP , respectively) and then a correlated meta-analysis between the full summary data sets (P META ). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes ( NT5C2 , GSTM3 , SNAPC3 , SPNS1 , TMEM245 , YPEL3 , and ZNF646 ) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3 . We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (P META <0.05 & P META

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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.

Obesity - Standards, trends and advances

Obesity continues to be a significant global health concern, giving rise to various complications. This review article explores the current standards and emerging innovations in diagnosing and treating obesity, including recent disease name change, staging system or therapeutic goals. This narrative review has been based on recent scientific articles from PubMed database, limiting the scope of topics to current standards and upcoming developments and breakthroughs in the diagnosis and treatment of obesity. The educational and informative nature of the review has been maintained in order to make the information presented accessible to both researchers and clinical practitioners. The recognition of diverse obesity phenotypes has prompted a paradigm shift towards a complex and patient-centered approach to diagnosis and therapy. Pharmacotherapy for obesity is evolving rapidly, with ongoing research focusing on novel molecular targets and metabolic pathways. Promising developments include dual or triple incretin analogs, oral incretin drugs, neurotransmitter-based therapies, muscle mass-increasing treatments, and therapies targeting visceral adipose tissue browning. Despite current evidence-based international standards, the field of obesity diagnosis and treatment continues to expand, with new diagnostic tools and pharmacotherapies potentially replacing current practices. Therapeutic management should be tailored to individual patients, considering obesity phenotype, health status, lifestyle, and preferences. Looking ahead, the future holds promising opportunities for obesity management, but further research is required to assess the efficacy and safety of emerging therapies. A multifactorial and personalized approach will be pivotal in addressing the diverse challenges posed by obesity.

Identifying important gene signatures of BMI using network structure-aided nonparametric quantile regression

We focus on identifying genomics risk factors of higher body mass index (BMI) incorporating a priori information, such as biological pathways. However, the commonly used methods to incorporate prior information provide a model for the mean function of the outcome and rely on unmet assumptions. To address these concerns, we propose a method for nonparametric additive quantile regression with network regularization to incorporate the information encoded by known networks. To account for nonlinear associations, we approximate the unknown additive functional effect of each predictor with the expansion of a B-spline basis. We implement the group Lasso penalty to obtain a sparse model. We define the network-constrained penalty by the totalℓ 2 $$ {\ell}_2 $$ norm of the difference between the effect functions of any two linked genes in the known network. We further propose an efficient computation procedure to solve the optimization problem that arises in our model. Simulation studies show that our proposed method performs well in identifying more truly associated genes and less falsely associated genes than alternative approaches. We apply the proposed method to analyze the microarray gene-expression dataset in the Framingham Heart Study and identify several 75 percentile BMI associated genes. In conclusion, our proposed approach efficiently identifies the outcome-associated variables in a nonparametric additive quantile regression framework by leveraging known network information.

Obesity-related genomic instability and altered xenobiotic metabolism: possible consequences for cancer risk and chemotherapy

The increase in the prevalence of obesity has led to an elevated risk for several associated diseases including cancer. Several studies have investigated the DNA damage in human blood samples and showed a clear trend towards increased DNA damage in obesity. Reduced genomic stability is thus one of the consequences of obesity, which may contribute to the related cancer risk. Whether this is influenced by compromised DNA repair has not been elucidated sufficiently yet. On the other hand, obesity has also been linked to reduced therapy survival and increased adverse effects during chemotherapy, although the available data are controversial. Despite some indications that obesity might alter hepatic metabolism, current literature in humans is insufficient, and results from animal studies are inconclusive. Here we have summarised published data on hepatic drug metabolism to understand the impact of obesity on cancer therapy better. Furthermore, we highlight knowledge gaps in the interrelationship between obesity and drug metabolism from a toxicological perspective.

Genetics of Obesity: What We Have Learned Over Decades of Research

There is a genetic component to human obesity that accounts for 40% to 50% of the variability in body weight status but that is lower among normal weight individuals (about 30%) and substantially higher in the subpopulation of individuals with obesity and severe obesity (about 60%-80%). The appreciation that heritability varies across classes of BMI represents an important advance. After controlling for BMI, ectopic fat and fat distribution traits are characterized by heritability levels ranging from 30% to 55%. Defects in at least 15 genes are the cause of monogenic obesity cases, resulting mostly from deficiencies in the leptin-melanocortin signaling pathway. Approximately two-thirds of the BMI heritability can be imputed to common DNA variants, whereas low-frequency and rare variants explain the remaining fraction. Diminishing allele effect size is observed as the number of obesity-associated variants expands, with most BMI-increasing or -decreasing alleles contributing only a few grams or less to body weight. Obesity-promoting alleles exert minimal effects in normal weight individuals but have larger effects in individuals with a proneness to obesity, suggesting a higher penetrance; however, it is not known whether these larger effect sizes precede obesity or are caused by an obese state. The obesity genetic risk is conditioned by thousands of DNA variants that make genetically based obesity prevention and treatment a major challenge.