Mendelian randomization with incomplete measurements on the exposure in the Hispanic Community Health Study/Study of Latinos

Mendelian randomization has been widely used to assess the causal effect of a heritable exposure variable on an outcome of interest, using genetic variants as instrumental variables. In practice, data on the exposure variable can be incomplete due to high cost of measurement and technical limits of detection. In this paper, we propose a valid and efficient method to handle both unmeasured and undetectable values of the exposure variable in one-sample Mendelian randomization analysis with individual-level data. We estimate the causal effect of the exposure variable on the outcome using maximum likelihood estimation and develop an expectation maximization algorithm for the computation of the estimator. Simulation studies show that the proposed method performs well in making inference on the causal effect. We apply our method to the Hispanic Community Health Study/Study of Latinos, a community-based prospective cohort study, and estimate the causal effect of several metabolites on phenotypes of interest.

Assessing efficiency of fine-mapping obesity-associated variants through leveraging ancestry architecture and functional annotation using PAGE and UKBB cohorts

Inadequate representation of non-European ancestry populations in genome-wide association studies (GWAS) has limited opportunities to isolate functional variants. Fine-mapping in multi-ancestry populations should improve the efficiency of prioritizing variants for functional interrogation. To evaluate this hypothesis, we leveraged ancestry architecture to perform comparative GWAS and fine-mapping of obesity-related phenotypes in European ancestry populations from the UK Biobank (UKBB) and multi-ancestry samples from the Population Architecture for Genetic Epidemiology (PAGE) consortium with comparable sample sizes. In the investigated regions with genome-wide significant associations for obesity-related traits, fine-mapping in our ancestrally diverse sample led to 95% and 99% credible sets (CS) with fewer variants than in the European ancestry sample. Lead fine-mapped variants in PAGE regions had higher average coding scores, and higher average posterior probabilities for causality compared to UKBB. Importantly, 99% CS in PAGE loci contained strong expression quantitative trait loci (eQTLs) in adipose tissues or harbored more variants in tighter linkage disequilibrium (LD) with eQTLs. Leveraging ancestrally diverse populations with heterogeneous ancestry architectures, coupled with functional annotation, increased fine-mapping efficiency and performance, and reduced the set of candidate variants for consideration for future functional studies. Significant overlap in genetic causal variants across populations suggests generalizability of genetic mechanisms underpinning obesity-related traits across populations.

Abstract P552: Mendelian Randomization Analysis of Metabolites Associated With Severe Obesity in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Obesity remains a global public health burden, with 4.7 million premature deaths globally attributed to obesity. Severe obesity (SevO: defined as body mass index (BMI)≥40) is a major risk factor for other comorbidities, including heart disease and Type 2 Diabetes, which disproportionately impact historically marginalized populations, including Hispanic/Latinos. Based on BRFSS data, 24.5% of US Hispanic/Latino adults are projected to have severe obesity by 2030. However, the etiology of the underlying metabolic dysfunction remains unknown.

To address this gap, we identified metabolites associated with SevO in genotyped participants aged ≥20 with 25 < BMI ≥ 40 and metabolic data in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We investigated cross-sectional associations between Blom-transformed metabolites and baseline SevO, adjusting for age, study center, background group, smoking status, and principal components of ancestry, stratified by sex (SUGEN), and meta-analyzed (SAS 9.4). For the top 20 metabolites, we extracted publicly available HCHS/SOL metabolite GWAS (mGWAS) summary statistics to derive metaboQTLs, and summary statistics from a multi-population meta-analysis of SevO (N>70,000) and implemented forward MR analysis using the MendelianRandomization R package (v0.3.0), which provides various robust causal estimation methods for summary data.

Anthropometry and data for 640 known Metabolon metabolites were available for 551 females (mean age: 43.3 years, 27% SevO) and 371 males (mean age 43.4 years, 15% SevO). We identified Bonferroni-corrected significant SevO associations (p<0.05/640) for 224 metabolites in the meta-analysis. Of the top 20 metabolites associated with SevO, we excluded six with no genome-wide significant SNPs in the mGWAS. For the remaining 14, we included independent SNPs with p<1E-6 as instrumental variables in the MR for each metabolite. After excluding SNPs ±500kb from known BMI loci, two metabolites showed causal associations with SevO, cytidine (penalized robust MR-Egger: ß (SE) =0.121 (0.051), p=0.018) and indoleproprionate (penalized weighted median-MR: ß (SE) =-0.138 (0.043), p=0.001).

Cytidine is a pyrimidine nucleoside consisting of D-ribose and cytosine, which is a precursor of cytidine triphosphate required in the one-carbon metabolism pathway to convert phosphatidylcholine (PC) to phosphatidylethanolamine (PE). PC biosynthesis is higher in adipose tissue macrophages in obese mice and humans. Indoleproprionate is a microbial metabolite of tryptophan produced by gut bacteria. Indoleproprionate levels have been shown to be associated with higher microbiome diversity and lower incidence of T2D. Our work points to future efforts to validate findings in other cohorts, including reverse MR to further elucidate the causal relationship between metabolites and severe obesity.

Erratum: Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits-The Hispanic/Latino Anthropometry Consortium

[This corrects the article DOI: 10.1016/j.xhgg.2022.100099.].

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.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits-The Hispanic/Latino Anthropometry Consortium

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (stage 1, n = 59,771) and generalized our findings in 9 additional studies (stage 2, n = 10,538). We conducted a trans-ancestral GWAS with summary statistics from HISLA stage 1 and existing consortia of European and African ancestries. In our HISLA stage 1 + 2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified 3 secondary signals for BMI, 28 for height, and 2 for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability (nominal significance and effect estimate directional consistency) in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.

Genome-wide association study identifying novel variant for fasting insulin and allelic heterogeneity in known glycemic loci in Chilean adolescents: The Santiago Longitudinal Study

BACKGROUND

The genetic underpinnings of glycemic traits have been understudied in adolescent and Hispanic/Latino (H/L) populations in comparison to adults and populations of European ancestry.

OBJECTIVE

To identify genetic factors underlying glycemic traits in an adolescent H/L population.

METHODS

We conducted a genome-wide association study (GWAS) of fasting glucose (FG) and fasting insulin (FI) in H/L adolescents from the Santiago Longitudinal Study.

RESULTS

We identified one novel variant positioned in the CSMD1 gene on chromosome 8 (rs77465890, effect allele frequency = 0.10) that was associated with FI (β = -0.299, SE = 0.054, p = 2.72×10-8 ) and was only slightly attenuated after adjusting for body mass index z-scores (β = -0.252, SE = 0.047, p = 1.03×10-7 ). We demonstrated directionally consistent, but not statistically significant results in African and Hispanic adults of the Population Architecture Using Genomics and Epidemiology Consortium. We also identified secondary signals for two FG loci after conditioning on known variants, which demonstrate allelic heterogeneity in well-known glucose loci.

CONCLUSION

Our results exemplify the importance of including populations with diverse ancestral origin and adolescent participants in GWAS of glycemic traits to uncover novel risk loci and expand our understanding of disease aetiology.

Host genetic effects in pneumonia

Given the coronavirus disease 2019 (COVID-19) pandemic, investigations into host susceptibility to infectious diseases and downstream sequelae have never been more relevant. Pneumonia is a lung disease that can cause respiratory failure and hypoxia and is a common complication of infectious diseases, including COVID-19. Few genome-wide association studies (GWASs) of host susceptibility and severity of pneumonia have been conducted. We performed GWASs of pneumonia susceptibility and severity in the Vanderbilt University biobank (BioVU) with linked electronic health records (EHRs), including Illumina Expanded Multi-Ethnic Global Array (MEGAEX)-genotyped European ancestry (EA, n= 69,819) and African ancestry (AA, n = 15,603) individuals. Two regions of large effect were identified: the CFTR locus in EA (rs113827944; OR = 1.84, p value = 1.2 × 10-36) and HBB in AA (rs334 [p.Glu7Val]; OR = 1.63, p value = 3.5 × 10-13). Mutations in these genes cause cystic fibrosis (CF) and sickle cell disease (SCD), respectively. After removing individuals diagnosed with CF and SCD, we assessed heterozygosity effects at our lead variants. Further GWASs after removing individuals with CF uncovered an additional association in R3HCC1L (rs10786398; OR = 1.22, p value = 3.5 × 10-8), which was replicated in two independent datasets: UK Biobank (n = 459,741) and 7,985 non-overlapping BioVU subjects, who are genotyped on arrays other than MEGAEX. This variant was also validated in GWASs of COVID-19 hospitalization and lung function. Our results highlight the importance of the host genome in infectious disease susceptibility and severity and offer crucial insight into genetic effects that could potentially influence severity of COVID-19 sequelae.