Genome-wide association study identifies host genetic variants influencing oral microbiota diversity and metabolic health.

The microbial communities of the oral cavity are important elements of oral and systemic health. With emerging evidence highlighting the heritability of oral bacterial microbiota, this study aimed to identify host genome variants that influence oral microbial traits. Using data from 16S rRNA gene amplicon sequencing, we performed genome-wide association studies with univariate and multivariate traits of the salivary microbiota from 610 unrelated adults from the Danish ADDITION-PRO cohort. We identified six single nucleotide polymorphisms (SNPs) in human genomes that showed associations with abundance of bacterial taxa at different taxonomical tiers (P < 5 × 10-8). Notably, SNP rs17793860 surpassed our study-wide significance threshold (P < 1.19 × 10-9). Additionally, rs4530093 was linked to bacterial beta diversity (P < 5 × 10-8). Out of these seven SNPs identified, six exerted effects on metabolic traits, including glycated hemoglobin A1c, triglyceride and high-density lipoprotein cholesterol levels, the risk of type 2 diabetes and stroke. Our findings highlight the impact of specific host SNPs on the composition and diversity of the oral bacterial community. Importantly, our results indicate an intricate interplay between host genetics, the oral microbiota, and metabolic health. We emphasize the need for integrative approaches considering genetic, microbial, and metabolic factors.

Non-linear interaction between physical activity and polygenic risk score of body mass index in Danish and Russian populations.

Body mass index (BMI) is a highly heritable polygenic trait. It is also affected by various environmental and behavioral risk factors. We used a BMI polygenic risk score (PRS) to study the interplay between the genetic and environmental factors defining BMI. First, we generated a BMI PRS that explained more variance than a BMI genetic risk score (GRS), which was using only genome-wide significant BMI-associated variants (R2 = 13.1% compared to 6.1%). Second, we analyzed interactions between BMI PRS and seven environmental factors. We found a significant interaction between physical activity and BMI PRS, even when the well-known effect of the FTO region was excluded from the PRS, using a small dataset of 6,179 samples. Third, we stratified the study population into two risk groups using BMI PRS. The top 22% of the studied populations were included in a high PRS risk group. Engagement in self-reported physical activity was associated with a 1.66 kg/m2 decrease in BMI in this group, compared to a 0.84 kg/m2 decrease in BMI in the rest of the population. Our results (i) confirm that genetic background strongly affects adult BMI in the general population, (ii) show a non-linear interaction between BMI genetics and physical activity, and (iii) provide a standardized framework for future gene-environment interaction analyses.

Abdominal and gluteofemoral fat depots show opposing associations with postprandial lipemia.

BACKGROUND: High postprandial lipemia is associated with increased risk of cardiovascular disease, independently of fasting lipid concentrations. Abdominal and gluteofemoral fat depots handle lipoproteins differently, which could affect postprandial lipemia and contribute to the relation between abdominal fat distribution and cardiovascular disease risk. OBJECTIVES: We aimed to study the influences of higher abdominal compared with gluteofemoral fat on postprandial lipemia after a high-fat meal in individuals with obesity. METHODS: A total of 755 adults with obesity from a randomized controlled trial in 7 European countries consumed a liquid high-fat meal. Concentrations of triglycerides (TG), glycerol, free fatty acids, and the cholesterol component of remnant-like particles (RLP), LDL, and HDL were measured postprandially for 3 h. Associations of waist circumference (WC), hip circumference (HC), and waist-hip ratio (WHR) with changes in postprandial lipid concentrations, adjusted for fasting concentrations and BMI, were examined using linear regression models. To assess whether the association of WHR with postprandial lipemia could be causal, we performed instrumental variable analyses using a genetic score of 442 variants known to be associated with WHR adjusted for BMI in 2-stage least-squares regression models. RESULTS: WHR was associated with higher TG and RLP cholesterol concentrations, independent of fasting lipid concentrations and BMI. Instrumental variable analyses suggested that the associations of WHR with postprandial TG (ß = 0.038 µmol/L*min, SE = 0.019 µmol/L*min, P = 0.044) and RLP cholesterol concentrations (ß = 0.059 mmol/L, SE = 0.025 mmol/L, P = 0.020) may be causal. WC and HC showed opposite effects: higher WC was associated with higher TG and RLP cholesterol concentrations whereas higher HC was associated with lower concentrations. CONCLUSIONS: Our results suggest that higher fat deposition abdominally versus gluteofemorally may be causally associated with elevated postprandial lipemia after a high-fat meal, independent of fasting lipid concentrations and BMI. Furthermore, higher abdominal and gluteofemoral fat depots show opposing effects on postprandial lipemia.This trial was registered at www.isrctn.com as ISRCTN25867281.

Large-scale association analyses identify host factors influencing human gut microbiome composition.

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10-8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10-20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10-10 < P < 5 × 10-8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.

The influence of transmitted and non-transmitted parental BMI-associated alleles on the risk of overweight in childhood.

Overweight in children is strongly associated with parental body mass index (BMI) and overweight. We assessed parental transmitted and non-transmitted genetic contributions to overweight in children from the Danish National Birth Cohort by constructing genetic risk scores (GRSs) from 941 common genetic variants associated with adult BMI and estimating associations of transmitted maternal/paternal and non-transmitted maternal GRS with child overweight. Maternal and paternal BMI (standard deviation (SD) units) had a strong association with childhood overweight [Odds ratio (OR): 2.01 (95% confidence interval (CI) 1.74; 2.34) and 1.64 (95% CI 1.43; 1.89)]. Maternal and paternal transmitted GRSs (SD-units) increased odds for child overweight equally [OR: 1.30 (95% CI 1.16; 1.46) and 1.30 (95% CI 1.16; 1.47)]. However, both the parental phenotypic and the GRS associations may depend on maternal BMI, being weaker among mothers with overweight. Maternal non-transmitted GRS was not associated with child overweight [OR 0.98 (95% CI 0.88; 1.10)] suggesting no specific influence of maternal adiposity as such. In conclusion, parental transmitted GRSs, based on adult BMI, contribute to child overweight, but in overweight mothers other genetic and environmental factors may play a greater role.

Screening of 31 genes involved in monogenic forms of obesity in 23 Pakistani probands with early-onset childhood obesity: a case report.

BACKGROUND: Consanguine families display a high degree of homozygosity which increases the risk of family members suffering from autosomal recessive disorders. Thus, homozygous mutations in monogenic obesity genes may be a more frequent cause of childhood obesity in a consanguineous population. METHODS: We identified 23 probands from 23 Pakistani families displaying autosomal recessive obesity. We have previously excluded mutations in MC4R, LEP and LEPR in all probands. Using a chip-based, target-region capture array, 31 genes involved in monogenic forms of obesity, were screened in all probands. RESULTS: We identified 31 rare non-synonymous possibly pathogenic variants (28 missense and three nonsense) within the 31 selected genes. All variants were heterozygous, thus no homozygous pathogenic variants were found. Two of the rare heterozygous nonsense variants identified (p.R75X and p.R481X) were found in BBS9 within one proband, suggesting that obesity is caused by compound heterozygosity. Sequencing of the parents supported the compound heterozygous nature of obesity as each parent was carrying one of the variants. Subsequent clinical investigation strongly indicated that the proband had Bardet-Biedl syndrome. CONCLUSIONS: Mutation screening in 31 genes among probands with severe early-onset obesity from Pakistani families did not reveal the presence of homozygous obesity causing variants. However, a compound heterozygote carrier of BBS9 mutations was identified, indicating that compound heterozygosity must not be overlooked when investigating the genetic etiology of severe childhood obesity in populations with a high degree of consanguinity.

Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer.

Gene coexpression network analysis is a powerful "data-driven" approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise "meta-analysis" framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types. The analysis was conducted using an elaborate weighted gene coexpression network (WGCNA) methodology and identified over 3,000 robust gene coexpression modules. The modules covered a range of known tumor features, such as proliferation, extracellular matrix remodeling, hypoxia, inflammation, angiogenesis, tumor differentiation programs, specific signaling pathways, genomic alterations, and biomarkers of individual tumor subtypes. To prioritize genes with respect to those tumor features, we ranked genes within each module by connectivity, leading to identification of module-specific functionally prominent hub genes. To showcase the utility of this network information, we positioned known cancer drug targets within the coexpression networks and predicted that Anakinra, an anti-rheumatoid therapeutic agent, may be promising for development in colorectal cancer. We offer a comprehensive, normalized and well documented collection of >3000 gene coexpression modules in a variety of cancers as a rich data resource to facilitate further progress in cancer research.