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.

Discovery and fine-mapping of height loci via high-density imputation of GWASs in individuals of African ancestry.

Although many loci have been associated with height in European ancestry populations, very few have been identified in African ancestry individuals. Furthermore, many of the known loci have yet to be generalized to and fine-mapped within a large-scale African ancestry sample. We performed sex-combined and sex-stratified meta-analyses in up to 52,764 individuals with height and genome-wide genotyping data from the African Ancestry Anthropometry Genetics Consortium (AAAGC). We additionally combined our African ancestry meta-analysis results with published European genome-wide association study (GWAS) data. In the African ancestry analyses, we identified three novel loci (SLC4A3, NCOA2, ECD/FAM149B1) in sex-combined results and two loci (CRB1, KLF6) in women only. In the African plus European sex-combined GWAS, we identified an additional three novel loci (RCCD1, G6PC3, CEP95) which were equally driven by AAAGC and European results. Among 39 genome-wide significant signals at known loci, conditioning index SNPs from European studies identified 20 secondary signals. Two of the 20 new secondary signals and none of the 8 novel loci had minor allele frequencies (MAF) < 5%. Of 802 known European height signals, 643 displayed directionally consistent associations with height, of which 205 were nominally significant (p < 0.05) in the African ancestry sex-combined sample. Furthermore, 148 of 241 loci contained ≤20 variants in the credible sets that jointly account for 99% of the posterior probability of driving the associations. In summary, trans-ethnic meta-analyses revealed novel signals and further improved fine-mapping of putative causal variants in loci shared between African and European ancestry populations.

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.

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.

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.

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.].

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.

Findings from the Hispanic Community Health Study/Study of Latinos on the Importance of Sociocultural Environmental Interactors: Polygenic Risk Score-by-Immigration and Dietary Interactions.

Introduction: Hispanic/Latinos experience a disproportionate burden of obesity. Acculturation to US obesogenic diet and practices may lead to an exacerbation of innate genetic susceptibility. We examined the role of gene-environment interactions to better characterize the sociocultural environmental determinants and their genome-scale interactions, which may contribute to missing heritability of obesity. We utilized polygenic risk scores (PRSs) for body mass index (BMI) to perform analyses of PRS-by-acculturation and other environmental interactors among self-identified Hispanic/Latino adults from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Methods: PRSs were derived using genome-wide association study (GWAS) weights from a publicly available, large meta-analysis of European ancestry samples. Generalized linear models were run using a set of a priori acculturation-related and environmental factors measured at visit 1 (2008-2011) and visit 2 (2014-2016) in an analytic subsample of 8,109 unrelated individuals with genotypic, phenotypic, and complete case data at both visits. We evaluated continuous measures of BMI and waist-to-hip ratio. All models were weighted for complex sampling design, combined, and sex-stratified. Results: Overall, we observed a consistent increase of BMI with greater PRS across both visits. We found the best-fitting model adjusted for top five principal components of ancestry, sex, age, study site, Hispanic/Latino background genetic ancestry group, sociocultural factors and PRS interactions with age at immigration, years since first arrival to the United States (p < 0.0104), and healthy diet (p < 0.0036) and explained 16% of the variation in BMI. For every 1-SD increase in PRS, there was a corresponding 1.10 kg/m2 increase in BMI (p < 0.001). When these results were stratified by sex, we observed that this 1-SD effect of PRS on BMI was greater for women than men (1.45 vs. 0.79 kg/m2, p < 0.001). Discussion: We observe that age at immigration and the adoption of certain dietary patterns may play a significant role in modifying the effect of genetic risk on obesity. Careful consideration of sociocultural and immigration-related factors should be evaluated. The role of nongenetic factors, including the social environment, should not be overlooked when describing the performance of PRS or for promoting population health in understudied populations in genomics.

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 (ß = -0.299, SE = 0.054, p = 2.72×10-8 ) and was only slightly attenuated after adjusting for body mass index z-scores (ß = -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.

Access to Recovery and Recidivism Among Former Prison Inmates.

Access to Recovery (ATR) is a Substance Abuse and Mental Health Services Administration (SAMHSA)-funded initiative that offers a mix of clinical and supportive services for substance abuse. ATR clients choose which services will help to overcome barriers in their road to recovery, and a recovery consultant provides vouchers and helps link the client to these community resources. One of ATR's goals was to provide services to those involved in the criminal justice system in the hopes that addressing substance abuse issues could reduce subsequent criminal behaviors. This study examines this goal by looking at recidivism among a sample of clients in one state's ATR program who returned to the community after incarceration. Results suggest that there were few differential effects of service selections on subsequent recidivism. However, there are significant differences in recidivism rates among the agencies that provided ATR services. Agencies with more resources and a focus on prisoner reentry had better recidivism outcomes than those that focus only on substance abuse services.