Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: Recommendations based on a systematic evaluation.

Genome-wide association meta-analyses (GWAMAs) conducted separately by two strata have identified differences in genetic effects between strata, such as sex-differences for body fat distribution. However, there are several approaches to identify such differences and an uncertainty which approach to use. Assuming the availability of stratified GWAMA results, we compare various approaches to identify between-strata differences in genetic effects. We evaluate type I error and power via simulations and analytical comparisons for different scenarios of strata designs and for different types of between-strata differences. For strata of equal size, we find that the genome-wide test for difference without any filtering is the best approach to detect stratum-specific genetic effects with opposite directions, while filtering for overall association followed by the difference test is best to identify effects that are predominant in one stratum. When there is no a priori hypothesis on the type of difference, a combination of both approaches can be recommended. Some approaches violate type I error control when conducted in the same data set. For strata of unequal size, the best approach depends on whether the genetic effect is predominant in the larger or in the smaller stratum. Based on real data from GIANT (>175 000 individuals), we exemplify the impact of the approaches on the detection of sex-differences for body fat distribution (identifying up to 10 loci). Our recommendations provide tangible guidelines for future GWAMAs that aim at identifying between-strata differences. A better understanding of such effects will help pinpoint the underlying mechanisms

Genetic determinants of metabolic biomarkers and their associations with cardiometabolic traits in Hispanic/Latino adolescents

Background: Metabolic regulation plays a significant role in energy homeostasis, and adolescence is a crucial life stage for the development of cardiometabolic disease (CMD). This study aims to investigate the genetic determinants of metabolic biomarkers-adiponectin, leptin, ghrelin, and orexin-and their associations with CMD risk factors. Methods: We characterized the genetic determinants of the biomarkers among Hispanic/Latino adolescents of the Santiago Longitudinal Study (SLS) and identified the cumulative effects of genetic variants on adiponectin and leptin using biomarker polygenic risk scores (PRS). We further investigated the direct and indirect effect of the biomarker PRS on downstream body fat percent (BF%) and glycemic traits using structural equation modeling. Results: We identified putatively novel genetic variants associated with the metabolic biomarkers. A substantial amount of biomarker variance was explained by SLS-specific PRS, and the prediction was improved by including the putatively novel loci. Fasting blood insulin and insulin resistance were associated with PRS for adiponectin, leptin, and ghrelin, and BF% was associated with PRS for adiponectin and leptin. We found evidence of substantial mediation of these associations by the biomarker levels. Conclusions: The genetic underpinnings of metabolic biomarkers can affect the early development of CMD, partly mediated by the biomarkers. Impact: This study characterized the genetic underpinnings of four metabolic hormones and investigated their potential influence on adiposity and insulin biology among Hispanic/Latino adolescents. Fasting blood insulin and insulin resistance were associated with polygenic risk score (PRS) for adiponectin, leptin, and ghrelin, with evidence of some degree of mediation by the biomarker levels. Body fat percent (BF%) was also associated with PRS for adiponectin and leptin. This provides important insight on biological mechanisms underlying early metabolic dysfunction and reveals candidates for prevention efforts. Our findings also highlight the importance of ancestrally diverse populations to facilitate valid studies of the genetic architecture of metabolic biomarker levels

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution

Direct and indirect genetic effects on triglycerides through omics and correlated phenotypes

Even though there has been great success in identifying lipid-associated single-nucleotide polymorphisms (SNPs), the mechanisms through which the SNPs act on each trait are poorly understood. The emergence of large, complex biological data sets in well-characterized cohort studies offers an opportunity to investigate the genetic effects on trait variability as a way of informing the causal genes and biochemical pathways that are involved in lipoprotein metabolism. However, methods for simultaneously analyzing multiple omics, environmental exposures, and longitudinally measured, correlated phenotypes are lacking. The purpose of our study was to demonstrate the utility of the structural equation modeling (SEM) approach to inform our understanding of the pathways by which genetic variants lead to disease risk. With the SEM method, we examine multiple pathways directly and indirectly through previously identified triglyceride (TG)-associated SNPs, methylation, and high-density lipoprotein (HDL), including sex, age, and smoking behavior, while adding in biologically plausible direct and indirect pathways. We observed significant SNP effects (P < 0.05 and directionally consistent) on TGs at visit 4 (TG4) for five loci, including rs645040 (DOCK7), rs964184 (ZPR1/ZNF259), rs4765127 (ZNF664), rs1121980 (FTO), and rs10401969 (SUGP1). Across these loci, we identify three with strong evidence of an indirect genetic effect on TG4 through HDL, one with evidence of pleiotropic effect on HDL and TG4, and one variant that acts on TG4 indirectly through a nearby methylation site. Such information can be used to prioritize candidate genes in regions of interest, inform mechanisms of action of methylation effects, and highlight possible genes with pleiotropic effects

A trajectory modeling investigation of the biomass burning-tropical ozone relationship

The hypothesis that tropical total O3 maxima seen by the TOMS satellite derive from African biomass burning has been tested using isentropic trajectory analyses with global meteorological data fields. Two case studies from the 1989 biomass burning season demonstrate that a large fraction of the air arriving at the location of TOMS O3 maxima passed over regions of intense burning. Other trajectories initiated at a series of points over Africa and the Atlantic suggest flight strategies for field studies to be conducted in September 1992

Characterization of the contribution of shared environmental and genetic factors to metabolic syndrome methylation heritability and familial correlations

Abstract Background Transgenerational epigenetic inheritance has been posited as a possible contributor to the observed heritability of metabolic syndrome (MetS). Yet the extent to which estimates of epigenetic inheritance for DNA methylation sites are inflated by environmental and genetic covariance within families is still unclear. We applied current methods to quantify the environmental and genetic contributors to the observed heritability and familial correlations of four previously associated MetS methylation sites at three genes (CPT1A, SOCS3 and ABCG1) using real data made available through the GAW20. Results Our findings support the role of both shared environment and genetic variation in explaining the heritability of MetS and the four MetS cytosine-phosphate-guanine (CpG) sites, although the resulting heritability estimates were indistinguishable from one another. Familial correlations by type of relative pair generally followed our expectation based on relatedness, but in the case of sister and parent pairs we observed nonsignificant trends toward greater correlation than expected, as would be consistent with the role of shared environmental factors in the inflation of our estimated correlations. Conclusions Our work provides an interesting and flexible statistical framework for testing models of epigenetic inheritance in the context of human family studies. Future work should endeavor to replicate our findings and advance these methods to more robustly describe epigenetic inheritance patterns in human populations

Natural Selection of Immune and Metabolic Genes Associated with Health in Two Lowland Bolivian Populations

A growing body of work has addressed human adaptations to diverse environments using genomic data, but few studies have connected putatively selected alleles to phenotypes, much less among underrepresented populations such as Amerindians. Studies of natural selection and genotype–phenotype relationships in underrepresented populations hold potential to uncover previously undescribed loci underlying evolutionarily and biomedically relevant traits. Here, we worked with the Tsimane and the Moseten, two Amerindian populations inhabiting the Bolivian lowlands. We focused most intensively on the Tsimane, because long-term anthropological work with this group has shown that they have a high burden of both macro and microparasites, as well as minimal cardiometabolic disease or dementia. We therefore generated genome-wide genotype data for Tsimane individuals to study natural selection, and paired this with blood mRNA-seq as well as cardiometabolic and immune biomarker data generated from a larger sample that included both populations. In the Tsimane, we identified 21 regions that are candidates for selective sweeps, as well as 5 immune traits that show evidence for polygenic selection (e.g., C-reactive protein levels and the response to coronaviruses). Genes overlapping candidate regions were strongly enriched for known involvement in immune-related traits, such as abundance of lymphocytes and eosinophils. Importantly, we were also able to draw on extensive phenotype information for the Tsimane and Moseten and link five regions (containing PSD4, MUC21 and MUC22, TOX2, ANXA6, and ABCA1) with biomarkers of immune and metabolic function. Together, our work highlights the utility of pairing evolutionary analyses with anthropological and biomedical data to gain insight into the genetic basis of health-related traits