Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos

Type 2 diabetes and cause-specific mortality in Mexico City: a Mendelian randomisation analysis

Background

Observational epidemiological studies in Mexico have shown high mortality risks associated with type 2 diabetes (T2D). However, it is unclear whether these relationships are wholly causal. We aimed to assess the association of genetically-predicted T2D liability with risk of death in Mexico.

Methods

Between 1998 and 2004, 150,000 men and women were recruited from Mexico City and followed-up until September 2022 for cause-specific mortality. Mendelian randomisation analyses, using a genetic risk score (GRS) comprising 1055 established T2D-associated risk variants, estimated associations with risk of all-cause and cause-specific mortality at ages 35-74.

Findings

Among 121,433 included participants with a mean (standard deviation) age of 51 (11), 68% (n = 82,249) were women and 18% (n = 21,371) had T2D. The GRS explained 6.3% of T2D liability and was not associated with major potential confounders of the T2D-mortality relationship. During a median (interquartile range) of 20.2 (19.4-21.4) years' follow-up, 12,293 participants died. Genetically-predicted T2D liability was associated with a death rate ratio (RR) of 1.29 (95% confidence interval [CI] 1.23-1.36) per trebling in genetically-predicted odds of T2D. There were particularly strong associations with death from renal disease (n = 1696; RR 2.29 [95% CI 1.99-2.64]) and acute diabetic crises (n = 509; RR 2.27 [1.75-2.93]) and weaker, but still strong, associations with death from vascular disease (n = 3226; RR 1.31 [1.19-1.46]) and infection (n = 2437; RR 1.21 [1.07-1.36]). Genetically-predicted T2D liability was not clearly associated with death from cancer (n = 2016; RR 1.00 [95% CI 0.88-1.14]) or cirrhosis (n = 895; RR 0.90 [0.74-1.10]).

Interpretation

T2D is causally associated with death from vascular, renal and infectious diseases. Its prevention and effective management could substantially reduce premature deaths in Mexico, where T2D is common.

Funding

Wellcome Trust, the Mexican Health Ministry, the National Council for Science and Technology (CONACyT) for Mexico, Cancer Research UK, British Heart Foundation, Kidney Research UK, UK Medical Research Council, AstraZeneca, Regeneron.

Trans-ancestry genome-wide association study of childhood body mass index identifies novel loci and age-specific effects

Over the past 30 years, obesity prevalence has markedly increased globally, including among children. Although genome-wide association studies (GWASs) have identified over 1,000 genetic loci associated with obesity-related traits in adults, the genetic architecture of childhood obesity is less well characterized. Moreover, most childhood obesity GWASs have been restricted to severely obese children, in relatively small sample sizes, and in primarily European-ancestry populations. To identify genetic loci associated with early-childhood body mass index (BMI), we performed GWAS of BMI Z scores in eight ancestrally diverse cohorts: ZOE 2.0 cohort, the Santiago Longitudinal Study (SLS), the Vanderbilt University BioVU biobank, the Geisinger MyCode Health Initiative biobank, Study of Latino (SOL) Youth, Pelotas (Brazil) Birth Cohort, Cameron County Hispanic Cohort (CCHC), and Viva La Familia cohort. We subsequently performed inverse-variance-weighted fixed-effect meta-analysis of these results with previously published GWAS summary statistics of BMI Z scores of children in the Early Growth Genetics (EGG) Consortium and the Norwegian Mother and Child Cohort (MoBa), constituting a final total of 84,804 individuals. We identified 39 genome-wide significant loci associated with childhood BMI, including three putatively novel loci (EFNA5 and DTWD2, RP11-2N5.1 on chromosome 5, and LSM14A on chromosome 19). We also observed a dynamic nature of genetic loci-BMI associations across the life course, with distinct effects across childhood and adulthood, highlighting possible critical periods for early-childhood interventions. These findings strengthen calls for larger population-based studies of children across age strata and across diverse populations.

A comparison of genome-wide association analyses of persistent symptoms after Lyme disease, fibromyalgia, and myalgic encephalomyelitis - chronic fatigue syndrome

BACKGROUND

Up to 20% of Lyme disease cases experience post-treatment Lyme disease syndrome (PTLDS). The biological basis for PTLDS is poorly understood and no evidence-based treatment has been identified. Genetic studies have the potential to elucidate PTLDS pathophysiology and identify treatment targets.

METHODS

We used electronic health record data (EHR) and genetic data from a linked biorepository to conduct a genome-wide association study (GWAS) for PTLDS among patients from a Pennsylvania health system. We evaluated the validity of the GWAS results in two separate conditions that have hypothesized overlapping pathophysiology, fibromyalgia and myalgic encephalomyelitis - chronic fatigue syndrome (ME/CFS). GWAS analyses were performed using logistic regression in SUGEN, assuming an additive genetic model, and adjusting for age, sex, array, and the first 10 principal components calculated from whole genome genotyping to adjust for ancestry, and accounting for relatedness including all 1st degree relationships. The functional mapping and annotation analysis (FUMA) tool was used to explore top findings from our GWAS.

RESULTS

Among the 161,875 eligible MyCode participants with genotyping, there were 3,585 who met the criteria for treated Lyme disease. A subset of 695 (19.4%) of these patients met the criteria for PTLDS and the remaining 2890 were classified as controls. We identified two PTLDS loci that reached the suggestive significance threshold (P < 5 × 10- 7), with lead variants rs77857587, near IRX1, and rs10833979, near GAS2. Our top index single nucleotide polymorphism (SNP), rs77857587, is in high linkage disequilibrium with a long-range protein quantitative locus SNP, rs111774530, for the MARC2 (Mitochondrial Amidoxime Reducing Component 2) protein. We identified 5,041 cases of fibromyalgia (150,599 controls) and 2,268 cases of ME/CFS (151,594 controls) among the MyCode participants. Neither of the two suggestively significant loci were associated with fibromyalgia or ME/CFS.

CONCLUSION

We identified two PTLDS loci that reached a suggestive significance threshold. Our top index SNP is associated with the MARC2 protein, a protein that has been linked to multiple immune checkpoints. Further study is needed in a larger population to evaluate whether there is genetic evidence of the role of immune response in the occurrence of PTLDS.

Genome-wide association study reveals shared and distinct genetic architecture of fatty acids and oxylipins in the Hispanic Community Health Study/Study of Latinos

Bioactive fatty acid-derived oxylipin molecules play key roles mediating inflammation and oxidative stress. Circulating levels of fatty acids and oxylipins are influenced by environmental and genetic factors; characterizing the genetic architecture of bioactive lipids could yield new insights into underlying biology. We performed a genome-wide association study (GWAS) of 81 fatty acids and oxylipins in 11,584 Hispanic Community Health Study/Study of Latinos (HCHS/SOL) participants with genetic and lipidomic data measured at study baseline (58.6% female, mean age = 46.1 years (standard deviation 13.8)). Additionally, given the effects of central obesity on inflammation, we examined interactions with waist circumference using two-degree-of-freedom joint tests. Thirty-three of the 81 oxylipins and fatty acids were significantly heritable (heritability range: 0-32.7%). Forty (49.4%) oxylipins and fatty acids had at least one genome-wide significant (p < 6.94E-11) variant resulting in 19 independent genetic loci. Six loci (lead variant minor allele frequency [MAF] range: 0.08-0.50), including desaturase-encoding FADS and OATP1B1 transporter protein-encoding SLCO1B1, exhibited associations with two or more fatty acids and oxylipins. At several of these loci, there was evidence of colocalization of the top variant across fatty acids and oxylipins. The remaining loci were only associated with one oxylipin or fatty acid and included several CYP loci. We also identified an additional rare variant (MAF = 0.002) near CARS2 in two-degree-of-freedom tests. Our analyses revealed shared and distinct genetic architecture underlying fatty acids and oxylipins, providing insights into genetic factors and motivating work to characterize these compounds and elucidate their roles in disease.

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

Multivariable Mendelian randomization allows simultaneous estimation of direct causal effects of multiple exposure variables on an outcome. When the exposure variables of interest are quantitative omic features, obtaining complete data can be economically and technically challenging: the measurement cost is high, and the measurement devices may have inherent detection limits. In this paper, we propose a valid and efficient method to handle unmeasured and undetectable values of the exposure variables in a one-sample multivariable Mendelian randomization analysis with individual-level data. We estimate the direct causal effects with maximum likelihood estimation and develop an expectation-maximization algorithm to compute the estimators. We show the advantages of the proposed method through simulation studies and provide an application to the Hispanic Community Health Study/Study of Latinos, which has a large amount of unmeasured exposure data.

Genome-wide association study reveals shared and distinct genetic architecture underlying fatty acid and bioactive oxylipin metabolites in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Bioactive fatty acid-derived oxylipin molecules play key roles in mediating inflammation and oxidative stress, which underlie many chronic diseases. Circulating levels of fatty acids and oxylipins are influenced by both environmental and genetic factors; characterizing the genetic architecture of bioactive lipids could yield new insights into underlying biological pathways. Thus, we performed a genome wide association study (GWAS) of n=81 fatty acids and oxylipins in n=11,584 Hispanic Community Health Study/Study of Latinos (HCHS/SOL) participants with genetic and lipidomic data measured at study baseline (58.6% female, mean age = 46.1 years, standard deviation = 13.8 years). Additionally, given the effects of central obesity on inflammation, we examined interactions with waist circumference using two-degree-of-freedom joint tests. Heritability estimates ranged from 0% to 47.9%, and 48 of the 81oxylipins and fatty acids were significantly heritable. Moreover, 40 (49.4%) of the 81 oxylipins and fatty acids had at least one genome-wide significant (p< 6.94E-11) variant resulting in 19 independent genetic loci involved in fatty acid and oxylipin synthesis, as well as downstream pathways. Four loci (lead variant minor allele frequency [MAF] range: 0.08-0.50), including the desaturase-encoding FADS and the OATP1B1 transporter protein-encoding SLCO1B1, exhibited associations with four or more fatty acids and oxylipins. The majority of the 15 remaining loci (87.5%) (lead variant MAF range = 0.03-0.45, mean = 0.23) were only associated with one oxylipin or fatty acid, demonstrating evidence of distinct genetic effects. Finally, while most loci identified in two-degree-of-freedom tests were previously identified in our main effects analyses, we also identified an additional rare variant (MAF = 0.002) near CARS2, a locus previously implicated in inflammation. Our analyses revealed shared and distinct genetic architecture underlying fatty acids and oxylipins, providing insights into genetic factors and motivating future multi-omics work to characterize these compounds and elucidate their roles in disease pathways.

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.

Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies

MOTIVATION

statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics.

RESULTS

We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene-environment interaction studies.

AVAILABILITY AND IMPLEMENTATION

REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.

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.

Ancestral diversity in lipoprotein(a) studies helps address evidence gaps

INTRODUCTION

The independent and causal cardiovascular disease risk factor lipoprotein(a) (Lp(a)) is elevated in >1.5 billion individuals worldwide, but studies have prioritised European populations.

METHODS

Here, we examined how ancestrally diverse studies could clarify Lp(a)'s genetic architecture, inform efforts examining application of Lp(a) polygenic risk scores (PRS), enable causal inference and identify unexpected Lp(a) phenotypic effects using data from African (n=25 208), East Asian (n=2895), European (n=362 558), South Asian (n=8192) and Hispanic/Latino (n=8946) populations.

RESULTS

Fourteen genome-wide significant loci with numerous population specific signals of large effect were identified that enabled construction of Lp(a) PRS of moderate (R2=15% in East Asians) to high (R2=50% in Europeans) accuracy. For all populations, PRS showed promise as a 'rule out' for elevated Lp(a) because certainty of assignment to the low-risk threshold was high (88.0%-99.9%) across PRS thresholds (80th-99th percentile). Causal effects of increased Lp(a) with increased glycated haemoglobin were estimated for Europeans (p value =1.4×10-6), although inverse effects in Africans and East Asians suggested the potential for heterogeneous causal effects. Finally, Hispanic/Latinos were the only population in which known associations with coronary atherosclerosis and ischaemic heart disease were identified in external testing of Lp(a) PRS phenotypic effects.

CONCLUSIONS

Our results emphasise the merits of prioritising ancestral diversity when addressing Lp(a) evidence gaps.

Influence of alcohol consumption and alcohol metabolism variants on breast cancer risk among Black women: results from the AMBER consortium

BACKGROUND

Moderate to heavy alcohol consumption is associated with an increased risk of breast cancer. The etiologic role of genetic variation in genes involved in ethanol metabolism has not been established, with little information available among women of African ancestry.

METHODS

Our analysis from the African American Breast Cancer Epidemiology and Risk (AMBER) Consortium included 2889 U.S. Black women who were current drinkers at the time of breast cancer diagnosis (N cases = 715) and had available genetic data for four ethanol metabolism genomic regions (ADH, ALDH, CYP2E1, and ALDH2). We used generalized estimating equations to calculate genetic effects, gene* alcohol consumption (≥ 7drinks/week vs. < 7/week) interactions, and joint main plus interaction effects of up to 23,247 variants in ethanol metabolism genomic regions on odds of breast cancer.

RESULTS

Among current drinkers, 21% of cases and 14% of controls reported consuming ≥ 7 drinks per week. We identified statistically significant genetic effects for rs79865122-C in CYP2E1 with odds of ER- breast cancer and odds of triple negative breast cancer, as well as a significant joint effect with odds of ER- breast cancer (≥ 7drinks per week OR = 3.92, < 7 drinks per week OR = 0.24, pjoint = 3.74 × 10-6). In addition, there was a statistically significant interaction of rs3858704-A in ALDH2 with consumption of ≥ 7 drinks/week on odds of triple negative breast cancer (≥ 7drinks per week OR = 4.41, < 7 drinks per week OR = 0.57, pint = 8.97 × 10-5).

CONCLUSIONS

There is a paucity of information on the impact of genetic variation in alcohol metabolism genes on odds of breast cancer among Black women. Our analysis of variants in four genomic regions harboring ethanol metabolism genes in a large consortium of U.S. Black women identified significant associations between rs79865122-C in CYP2E1 and odds of ER- and triple negative breast cancer. Replication of these findings is warranted.

A Gene-Acculturation Study of Obesity Among US Hispanic/Latinos: The Hispanic Community Health Study/Study of Latinos

OBJECTIVE

In the United States, Hispanic/Latino adults face a high burden of obesity; yet, not all individuals are equally affected, partly due in part to this ethnic group's marked sociocultural diversity. We sought to analyze the modification of body mass index (BMI) genetic effects in Hispanic/Latino adults by their level of acculturation, a complex biosocial phenomenon that remains understudied.

METHODS

Among 11,747 Hispanic/Latinos adults in the Hispanic Community Health Study/Study of Latinos aged 18 to 76 years from four urban communities (2008-2011), we a) tested our hypothesis that the effect of a genetic risk score (GRS) for increased BMI may be exacerbated by higher levels of acculturation and b) examined if GRS acculturation interactions varied by gender or Hispanic/Latino background group. All genetic modeling controlled for relatedness, age, gender, principal components of ancestry, center, and complex study design within a generalized estimated equation framework.

RESULTS

We observed a GRS increase of 0.34 kg/m 2 per risk allele in weighted mean BMI. The estimated main effect of GRS on BMI varied both across acculturation level and across gender. The difference between high and low acculturation ranged from 0.03 to 0.23 kg/m 2 per risk allele, but varied across acculturation measure and gender.

CONCLUSIONS

These results suggest the presence of effect modification by acculturation, with stronger effects on BMI among highly acculturated individuals and female immigrants. Future studies of obesity in the Hispanic/Latino community should account for sociocultural environments and consider their intersection with gender to better target obesity interventions.

Analysis of genetically determined gene expression suggests role of inflammatory processes in exfoliation syndrome

BACKGROUND

Exfoliation syndrome (XFS) is an age-related systemic disorder characterized by excessive production and progressive accumulation of abnormal extracellular material, with pathognomonic ocular manifestations. It is the most common cause of secondary glaucoma, resulting in widespread global blindness. The largest global meta-analysis of XFS in 123,457 multi-ethnic individuals from 24 countries identified seven loci with the strongest association signal in chr15q22-25 region near LOXL1. Expression analysis have so far correlated coding and a few non-coding variants in the region with LOXL1 expression levels, but functional effects of these variants is unclear. We hypothesize that analysis of the contribution of the genetically determined component of gene expression to XFS risk can provide a powerful method to elucidate potential roles of additional genes and clarify biology that underlie XFS.

RESULTS

Transcriptomic Wide Association Studies (TWAS) using PrediXcan models trained in 48 GTEx tissues leveraging on results from the multi-ethnic and European ancestry GWAS were performed. To eliminate the possibility of false-positive results due to Linkage Disequilibrium (LD) contamination, we i) performed PrediXcan analysis in reduced models removing variants in LD with LOXL1 missense variants associated with XFS, and variants in LOXL1 models in both multiethnic and European ancestry individuals, ii) conducted conditional analysis of the significant signals in European ancestry individuals, and iii) filtered signals based on correlated gene expression, LD and shared eQTLs, iv) conducted expression validation analysis in human iris tissues. We observed twenty-eight genes in chr15q22-25 region that showed statistically significant associations, which were whittled down to ten genes after statistical validations. In experimental analysis, mRNA transcript levels for ARID3B, CD276, LOXL1, NEO1, SCAMP2, and UBL7 were significantly decreased in iris tissues from XFS patients compared to control samples. TWAS genes for XFS were significantly enriched for genes associated with inflammatory conditions. We also observed a higher incidence of XFS comorbidity with inflammatory and connective tissue diseases.

CONCLUSION

Our results implicate a role for connective tissues and inflammation pathways in the etiology of XFS. Targeting the inflammatory pathway may be a potential therapeutic option to reduce progression in XFS.

Collectrin (Tmem27) deficiency in proximal tubules causes hypertension in mice and a TMEM27 variant associates with blood pressure in males in a Latino cohort

Collectrin (Tmem27), an angiotensin-converting enzyme 2 homologue, is a chaperone of amino acid transporters in the kidney and endothelium. Global collectrin knockout (KO) mice have hypertension, endothelial dysfunction, exaggerated salt sensitivity, and diminished renal blood flow. This phenotype is associated with altered nitric oxide and superoxide balance and increased proximal tubule (PT) Na+/H+ exchanger isoform 3 (NHE3) expression. Collectrin is located on the X chromosome where genome-wide association population studies have largely been excluded. In the present study, we generated PT-specific collectrin KO (PT KO) mice to determine the precise contribution of PT collectrin in blood pressure homeostasis. We also examined the association of human TMEM27 single-nucleotide polymorphisms with blood pressure traits in 11,926 Hispanic Community Health Study/Study of Latinos (HCHS/SOL) Hispanic/Latino participants. PT KO mice exhibited hypertension, and this was associated with increased baseline NHE3 expression and diminished lithium excretion. However, PT KO mice did not display exaggerated salt sensitivity or a reduction in renal blood flow compared with control mice. Furthermore, PT KO mice exhibited enhanced endothelium-mediated dilation, suggesting a compensatory response to systemic hypertension induced by deficiency of collectrin in the PT. In HCHS/SOL participants, we observed sex-specific single-nucleotide polymorphism associations with diastolic blood pressure. In conclusion, loss of collectrin in the PT is sufficient to induce hypertension, at least in part, through activation of NHE3. Importantly, our model supports the notion that altered renal blood flow may be a determining factor for salt sensitivity. Further studies are needed to investigate the role of the TMEM27 locus on blood pressure and salt sensitivity in humans.NEW & NOTEWORTHY The findings of our study are significant in several ways: 1) loss of an amino acid chaperone in the proximal tubule is sufficient to cause hypertension, 2) the results in global and proximal tubule-specific collectrin knockout mice support the notion that vascular dysfunction is required for salt sensitivity or that impaired renal tubule function causes hypertension but is not sufficient to cause salt sensitivity, and 3) our study is the first to implicate a role of collectrin in human hypertension.

A Guide to Genome-Wide Association Study Design for Diabetic Retinopathy

Diabetic retinopathy (DR) is the most common microvascular complication related to diabetes. There is evidence that genetics play an important role in DR pathogenesis, but the complexity of the disease makes genetic studies a challenge. This chapter is a practical overview of the basic steps for genome-wide association studies with respect to DR and its associated traits. Also described are approaches that can be adopted in future DR studies. This is intended to serve as a guide for beginners and to provide a framework for further in-depth analysis.

Including diverse and admixed populations in genetic epidemiology research

The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.

Genetic pleiotropy underpinning adiposity and inflammation in self-identified Hispanic/Latino populations

BACKGROUND

Concurrent variation in adiposity and inflammation suggests potential shared functional pathways and pleiotropic disease underpinning. Yet, exploration of pleiotropy in the context of adiposity-inflammation has been scarce, and none has included self-identified Hispanic/Latino populations. Given the high level of ancestral diversity in Hispanic American population, genetic studies may reveal variants that are infrequent/monomorphic in more homogeneous populations.

METHODS

Using multi-trait Adaptive Sum of Powered Score (aSPU) method, we examined individual and shared genetic effects underlying inflammatory (CRP) and adiposity-related traits (Body Mass Index [BMI]), and central adiposity (Waist to Hip Ratio [WHR]) in HLA participating in the Population Architecture Using Genomics and Epidemiology (PAGE) cohort (N = 35,871) with replication of effects in the Cameron County Hispanic Cohort (CCHC) which consists of Mexican American individuals.

RESULTS

Of the > 16 million SNPs tested, variants representing 7 independent loci were found to illustrate significant association with multiple traits. Two out of 7 variants were replicated at statistically significant level in multi-trait analyses in CCHC. The lead variant on APOE (rs439401) and rs11208712 were found to harbor multi-trait associations with adiposity and inflammation.

CONCLUSIONS

Results from this study demonstrate the importance of considering pleiotropy for improving our understanding of the etiology of the various metabolic pathways that regulate cardiovascular disease development.

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.

Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries

Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals because of various confounding challenges. Here, we demonstrate that enrichment analyses that aggregate SNP-level association statistics at multiple genomic scales-from genes to genomic regions and pathways-have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.

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.

Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits

One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.

Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study

AIMS/HYPOTHESIS

Type 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study.

METHODS

We conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci.

RESULTS

Four novel associations were identified (p < 5 × 10-9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis.

CONCLUSIONS/INTERPRETATION

Our findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations.

DATA AVAILABILITY

Full summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).

Population-based genetic effects for developmental stuttering

Despite a lifetime prevalence of at least 5%, developmental stuttering, characterized by prolongations, blocks, and repetitions of speech sounds, remains a largely idiopathic speech disorder. Family, twin, and segregation studies overwhelmingly support a strong genetic influence on stuttering risk; however, its complex mode of inheritance combined with thus-far underpowered genetic studies contribute to the challenge of identifying and reproducing genes implicated in developmental stuttering susceptibility. We conducted a trans-ancestry genome-wide association study (GWAS) and meta-analysis of developmental stuttering in two primary datasets: The International Stuttering Project comprising 1,345 clinically ascertained cases from multiple global sites and 6,759 matched population controls from the biobank at Vanderbilt University Medical Center (VUMC), and 785 self-reported stuttering cases and 7,572 controls ascertained from The National Longitudinal Study of Adolescent to Adult Health (Add Health). Meta-analysis of these genome-wide association studies identified a genome-wide significant (GWS) signal for clinically reported developmental stuttering in the general population: a protective variant in the intronic or genic upstream region of SSUH2 (rs113284510, protective allele frequency = 7.49%, Z = -5.576, p = 2.46 × 10-8) that acts as an expression quantitative trait locus (eQTL) in esophagus-muscularis tissue by reducing its gene expression. In addition, we identified 15 loci reaching suggestive significance (p < 5 × 10-6). This foundational population-based genetic study of a common speech disorder reports the findings of a clinically ascertained study of developmental stuttering and highlights the need for further research.

Phenome risk classification enables phenotypic imputation and gene discovery in developmental stuttering

Developmental stuttering is a speech disorder characterized by disruption in the forward movement of speech. This disruption includes part-word and single-syllable repetitions, prolongations, and involuntary tension that blocks syllables and words, and the disorder has a life-time prevalence of 6-12%. Within Vanderbilt's electronic health record (EHR)-linked biorepository (BioVU), only 142 individuals out of 92,762 participants (0.15%) are identified with diagnostic ICD9/10 codes, suggesting a large portion of people who stutter do not have a record of diagnosis within the EHR. To identify individuals affected by stuttering within our EHR, we built a PheCode-driven Gini impurity-based classification and regression tree model, PheML, by using comorbidities enriched in individuals affected by stuttering as predicting features and imputing stuttering status as the outcome variable. Applying PheML in BioVU identified 9,239 genotyped affected individuals (a clinical prevalence of ∼10%) for downstream genetic analysis. Ancestry-stratified GWAS of PheML-imputed affected individuals and matched control individuals identified rs12613255, a variant near CYRIA on chromosome 2 (B = 0.323; p value = 1.31 × 10-8) in European-ancestry analysis and rs7837758 (B = 0.518; p value = 5.07 × 10-8), an intronic variant found within the ZMAT4 gene on chromosome 8, in African-ancestry analysis. Polygenic-risk prediction and concordance analysis in an independent clinically ascertained sample of developmental stuttering cases validate our GWAS findings in PheML-imputed affected and control individuals and demonstrate the clinical relevance of our population-based analysis for stuttering risk.

Recommendations for Statistical Reporting in Cardiovascular Medicine: A Special Report From the American Heart Association

Statistical analyses are a crucial component of the biomedical research process and are necessary to draw inferences from biomedical research data. The application of sound statistical methodology is a prerequisite for publication in the American Heart Association (AHA) journal portfolio. The objective of this document is to summarize key aspects of statistical reporting that might be most relevant to the authors, reviewers, and readership of AHA journals. The AHA Scientific Publication Committee convened a task force to inventory existing statistical standards for publication in biomedical journals and to identify approaches suitable for the AHA journal portfolio. The experts on the task force were selected by the AHA Scientific Publication Committee, who identified 12 key topics that serve as the section headers for this document. For each topic, the members of the writing group identified relevant references and evaluated them as a resource to make the standards summarized herein. Each section was independently reviewed by an expert reviewer who was not part of the task force. Expert reviewers were also permitted to comment on other sections if they chose. Differences of opinion were adjudicated by consensus. The standards presented in this report are intended to serve as a guide for high-quality reporting of statistical analyses methods and results.

Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study

BACKGROUND

Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented.

RESULTS

We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance (P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance (P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations.

CONCLUSIONS

Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.

Leveraging genetic ancestry to study health disparities

Research to understand human genomic variation and its implications in health has great potential to contribute in the reduction of health disparities. Biological anthropology can play important roles in genomics and health disparities research using a biocultural approach. This paper argues that racial/ethnic categories should not be used as a surrogate for sociocultural factors or global genomic clusters in biomedical research or clinical settings, because of the high genetic heterogeneity that exists within traditional racial/ethnic groups. Genetic ancestry is used to show variation in ancestral genomic contributions to recently admixed populations in the United States, such as African Americans and Hispanic/Latino Americans. Genetic ancestry estimates are also used to examine the relationship between ancestry-related biological and sociocultural factors affecting health disparities. To localize areas of genomes that contribute to health disparities, admixture mapping and genome-wide association studies (GWAS) are often used. Recent GWAS have identified many genetic variants that are highly differentiated among human populations that are associated with disease risk. Some of these are population-specific variants. Many of these variants may impact disease risk and help explain a portion of the difference in disease burden among racial/ethnic groups. Genetic ancestry is also of particular interest in precision medicine and disparities in drug efficacy and outcomes. By using genetic ancestry, we can learn about potential biological differences that may contribute to the heterogeneity observed across self-reported racial groups.

CLUE: Exact maximal reduction of kinetic models by constrained lumping of differential equations

MOTIVATION

Detailed mechanistic models of biological processes can pose significant challenges for analysis and parameter estimations due to the large number of equations used to track the dynamics of all distinct configurations in which each involved biochemical species can be found. Model reduction can help tame such complexity by providing a lower-dimensional model in which each macro-variable can be directly related to the original variables.

RESULTS

We present CLUE, an algorithm for exact model reduction of systems of polynomial differential equations by constrained linear lumping. It computes the smallest dimensional reduction as a linear mapping of the state space such that the reduced model preserves the dynamics of user-specified linear combinations of the original variables. Even though CLUE works with nonlinear differential equations, it is based on linear algebra tools, which makes it applicable to high-dimensional models. Using case studies from the literature, we show how CLUE can substantially lower model dimensionality and help extract biologically intelligible insights from the reduction.

AVAILABILITY

An implementation of the algorithm and relevant resources to replicate the experiments herein reported are freely available for download at https://github.com/pogudingleb/CLUE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic discovery and risk characterization in type 2 diabetes across diverse populations

Genomic discovery and characterization of risk loci for type 2 diabetes (T2D) have been conducted primarily in individuals of European ancestry. We conducted a multiethnic genome-wide association study of T2D among 53,102 cases and 193,679 control subjects from African, Hispanic, Asian, Native Hawaiian, and European population groups in the Population Architecture Genomics and Epidemiology (PAGE) and Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortia. In individuals of African ancestry, we discovered a risk variant in the TGFB1 gene (rs11466334, risk allele frequency (RAF) = 6.8%, odds ratio [OR] = 1.27, p = 2.06 × 10-8), which replicated in independent studies of African ancestry (p = 6.26 × 10-23). We identified a multiethnic risk variant in the BACE2 gene (rs13052926, RAF = 14.1%, OR = 1.08, p = 5.75 × 10-9), which also replicated in independent studies (p = 3.45 × 10-4). We also observed a significant difference in the performance of a multiethnic genetic risk score (GRS) across population groups (pheterogeneity = 3.85 × 10-20). Comparing individuals in the top GRS risk category (40%-60%), the OR was highest in Asians (OR = 3.08) and European (OR = 2.94) ancestry populations, followed by Hispanic (OR = 2.39), Native Hawaiian (OR = 2.02), and African ancestry (OR = 1.57) populations. These findings underscore the importance of genetic discovery and risk characterization in diverse populations and the urgent need to further increase representation of non-European ancestry individuals in genetics research to improve genetic-based risk prediction across populations.

A novel age-informed approach for genetic association analysis in Alzheimer's disease

BACKGROUND

Many Alzheimer's disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery.

METHODS

Using simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced individuals (54% cases) and replicated suggestive associations in 21,631 genotype-imputed individuals (51% cases).

RESULTS

Modeling variable AD risk across age results in 5-10% statistical power gain compared to logistic regression without age adjustment, while incorrect age adjustment leads to critical power loss. Applying our novel AD-age score and/or Cox regression, we discovered and replicated novel variants associated with AD on KIF21B, USH2A, RAB10, RIN3, and TAOK2 genes.

CONCLUSION

Our AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.

Genetic variants and physical activity interact to affect bone density in Hispanic children

Background

Our aim was to investigate if moderate to vigorous physical activity (MVPA), calcium intake interacts with bone mineral density (BMD)-related single nucleotide polymorphisms (SNPs) to influence BMD in 750 Hispanic children (4-19y) of the cross-sectional Viva La Familia Study.

Methods

Physical activity and dietary intake were measured by accelerometers and multiple-pass 24 h dietary recalls, respectively. Total body and lumbar spine BMD were measured by dual energy X-ray absorptiometry. A polygenic risk score (PRS) was computed based on SNPs identified in published literature. Regression analysis was conducted with PRSs, MVPA and calcium intake with total body and lumbar spine BMD.

Results

We found evidence of statistically significant interaction effects between the PRS and MVPA on total body BMD and lumbar spine BMD (p < 0.05). Higher PRS was associated with a lower total body BMD (β = − 0.040 ± 0.009, p = 1.1 × 10^− 5) and lumbar spine BMD (β = − 0.042 ± 0.013, p = 0.0016) in low MVPA group, as compared to high MVPA group (β = − 0.015 ± 0.006, p = 0.02; β = 0.008 ± 0.01, p = 0.4, respectively).

Discussion

The study indicated that calcium intake does not modify the relationship between genetic variants and BMD, while it implied physical activity interacts with genetic variants to affect BMD in Hispanic children. Due to limited sample size of our study, future research on gene by environment interaction on bone health and functional studies to provide biological insights are needed.

Conclusions

Bone health in Hispanic children with high genetic risk for low BMD is benefitted more by MVPA than children with low genetic risk. Our results may be useful to predict disease risk and tailor dietary and physical activity advice delivery to people, especially children.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-021-02537-y.

Psychiatric symptoms and syndromes transcending diagnostic boundaries in Indian multiplex families: The cohort of ADBS study

Syndromes of schizophrenia, bipolar disorder, obsessive-compulsive disorder, substance use disorders and Alzheimer's dementia are highly heritable. About 10-20% of subjects have another affected first degree relative (FDR), and thus represent a 'greater' genetic susceptibility. We screened 3583 families to identify 481 families with multiple affected members, assessed 1406 individuals in person, and collected information systematically about other relatives. Within the selected families, a third of all FDRs were affected with serious mental illness. Although similar diagnoses aggregated within families, 62% of the families also had members with other syndromes. Moreover, 15% of affected individuals met criteria for co-occurrence of two or more syndromes, across their lifetime. Using dimensional assessments, we detected a range of symptom clusters in both affected and unaffected individuals, and across diagnostic categories. Our findings suggest that in multiplex families, there is considerable heterogeneity of clinical syndromes, as well as sub-threshold symptoms. These families would help provide an opportunity for further research using both genetic analyses and biomarkers.

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.

Alternative Applications of Genotyping Array Data Using Multivariant Methods

One of the forerunners that pioneered the revolution of high-throughput genomic technologies is the genotyping microarray technology, which can genotype millions of single-nucleotide variants simultaneously. Owing to apparent benefits, such as high speed, low cost, and high throughput, the genotyping array has gained lasting applications in genome-wide association studies (GWAS) and thus accumulated an enormous amount of data. Empowered by continuous manufactural upgrades and analytical innovation, unconventional applications of genotyping array data have emerged to address more diverse genetic problems, holding promise of boosting genetic research into human diseases through the re-mining of the rich accumulated data. Here, we review several unconventional genotyping array analysis techniques that have been built on the idea of large-scale multivariant analysis and provide empirical application examples. These unconventional outcomes of genotyping arrays include polygenic score, runs of homozygosity (ROH)/heterozygosity ratio, distant pedigree computation, and mitochondrial DNA (mtDNA) copy number inference.

Cohort Profile: ZOE 2.0-A Community-Based Genetic Epidemiologic Study of Early Childhood Oral Health

Early childhood caries (ECC) is an aggressive form of dental caries occurring in the first five years of life. Despite its prevalence and consequences, little progress has been made in its prevention and even less is known about individuals' susceptibility or genomic risk factors. The genome-wide association study (GWAS) of ECC ("ZOE 2.0") is a community-based, multi-ethnic, cross-sectional, genetic epidemiologic study seeking to address this knowledge gap. This paper describes the study's design, the cohort's demographic profile, data domains, and key oral health outcomes. Between 2016 and 2019, the study enrolled 8059 3-5-year-old children attending public preschools in North Carolina, United States. Participants resided in 86 of the state's 100 counties and racial/ethnic minorities predominated-for example, 48% (n = 3872) were African American, 22% white, and 20% (n = 1611) were Hispanic/Latino. Seventy-nine percent (n = 6404) of participants underwent clinical dental examinations yielding ECC outcome measures-ECC (defined at the established caries lesion threshold) prevalence was 54% and the mean number of decayed, missing, filled surfaces due to caries was eight. Nearly all (98%) examined children provided sufficient DNA from saliva for genotyping. The cohort's community-based nature and rich data offer excellent opportunities for addressing important clinical, epidemiologic, and biological questions in early childhood.

Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis

We investigated type 2 diabetes (T2D) genetic susceptibility via multi-ethnic meta-analysis of 228,499 cases and 1,178,783 controls in the Million Veteran Program, DIAMANTE, Biobank Japan, and other studies. We report 568 associations, including 286 autosomal, 7 X chromosomal, and 25 identified in ancestry-specific analyses that were previously unreported. Transcriptome-wide association analysis detected 3,568 T2D-associations with genetically predicted gene expression in 687 novel genes; of these, 54 are known to interact with FDA-approved drugs. A polygenic risk score was strongly associated with increased risk of T2D-related retinopathy and modestly associated with chronic kidney disease (CKD), peripheral artery disease (PAD), and neuropathy. We investigated the genetic etiology of T2D-related vascular outcomes in MVP and observed statistical SNP-T2D interactions at 13 variants, including coronary heart disease, CKD, PAD, and neuropathy. These findings may help to identify potential therapeutic targets for T2D and genomic pathways that link T2D to vascular outcomes.

Importance of Genetic Studies of Cardiometabolic Disease in Diverse Populations

Genome-wide association studies have revolutionized our understanding of the genetic underpinnings of cardiometabolic disease. Yet, the inadequate representation of individuals of diverse ancestral backgrounds in these studies may undercut their ultimate potential for both public health and precision medicine. The goal of this review is to describe the imperativeness of studying the populations who are most affected by cardiometabolic disease, to the aim of better understanding the genetic underpinnings of the disease. We support this premise by describing the current variation in the global burden of cardiometabolic disease and emphasize the importance of building a globally and ancestrally representative genetics evidence base for the identification of population-specific variants, fine-mapping, and polygenic risk score estimation. We discuss the important ethical, legal, and social implications of increasing ancestral diversity in genetic studies of cardiometabolic disease and the challenges that arise from the (1) lack of diversity in current reference populations and available analytic samples and the (2) unequal generation of health-associated genomic data and their prediction accuracies. Despite these challenges, we conclude that additional, unprecedented opportunities lie ahead for public health genomics and the realization of precision medicine, provided that the gap in diversity can be systematically addressed. Achieving this goal will require concerted efforts by social, academic, professional and regulatory stakeholders and communities, and these efforts must be based on principles of equity and social justice.

Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics

BACKGROUND

Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population.

RESULTS

We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP.

CONCLUSION

This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.

Minority-centric meta-analyses of blood lipid levels identify novel loci in the Population Architecture using Genomics and Epidemiology (PAGE) study

Lipid levels are important markers for the development of cardio-metabolic diseases. Although hundreds of associated loci have been identified through genetic association studies, the contribution of genetic factors to variation in lipids is not fully understood, particularly in U.S. minority groups. We performed genome-wide association analyses for four lipid traits in over 45,000 ancestrally diverse participants from the Population Architecture using Genomics and Epidemiology (PAGE) Study, followed by a meta-analysis with several European ancestry studies. We identified nine novel lipid loci, five of which showed evidence of replication in independent studies. Furthermore, we discovered one novel gene in a PrediXcan analysis, minority-specific independent signals at eight previously reported loci, and potential functional variants at two known loci through fine-mapping. Systematic examination of known lipid loci revealed smaller effect estimates in African American and Hispanic ancestry populations than those in Europeans, and better performance of polygenic risk scores based on minority-specific effect estimates. Our findings provide new insight into the genetic architecture of lipid traits and highlight the importance of conducting genetic studies in diverse populations in the era of precision medicine.

Evaluating the Potential of Younger Cases and Older Controls Cohorts to Improve Discovery Power in Genome-Wide Association Studies of Late-Onset Diseases

For more than a decade, genome-wide association studies have been making steady progress in discovering the causal gene variants that contribute to late-onset human diseases. Polygenic late-onset diseases in an aging population display a risk allele frequency decrease at older ages, caused by individuals with higher polygenic risk scores becoming ill proportionately earlier and bringing about a change in the distribution of risk alleles between new cases and the as-yet-unaffected population. This phenomenon is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer’s disease, coronary artery disease, cerebral stroke, and type 2 diabetes, while for late-onset diseases with relatively lower prevalence and heritability, exemplified by cancers, the effect is significantly lower. In this research, computer simulations have demonstrated that genome-wide association studies of late-onset polygenic diseases showing high cumulative incidence together with high initial heritability will benefit from using the youngest possible age-matched cohorts. Moreover, rather than using age-matched cohorts, study cohorts combining the youngest possible cases with the oldest possible controls may significantly improve the discovery power of genome-wide association studies.

Age-related late-onset disease heritability patterns and implications for genome-wide association studies

Genome-wide association studies (GWASs) and other computational biology techniques are gradually discovering the causal gene variants that contribute to late-onset human diseases. After more than a decade of genome-wide association study efforts, these can account for only a fraction of the heritability implied by familial studies, the so-called "missing heritability" problem. Computer simulations of polygenic late-onset diseases (LODs) in an aging population have quantified the risk allele frequency decrease at older ages caused by individuals with higher polygenic risk scores (PRSs) becoming ill proportionately earlier. This effect is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer's disease, coronary artery disease, cerebral stroke, and type 2 diabetes. The incidence rate for LODs grows exponentially for decades after early onset ages, guaranteeing that the cohorts used for GWASs overrepresent older individuals with lower PRSs, whose disease cases are disproportionately due to environmental causes such as old age itself. This mechanism explains the decline in clinical predictive power with age and the lower discovery power of familial studies of heritability and GWASs. It also explains the relatively constant-with-age heritability found for LODs of lower prevalence, exemplified by cancers.

Genetic analyses of diverse populations improves discovery for complex traits

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.

Genetics of Chronic Kidney Disease Stages Across Ancestries: The PAGE Study

BACKGROUND

Chronic kidney disease (CKD) is common and disproportionally burdens United States ethnic minorities. Its genetic determinants may differ by disease severity and clinical stages. To uncover genetic factors associated CKD severity among high-risk ethnic groups, we performed genome-wide association studies (GWAS) in diverse populations within the Population Architecture using Genomics and Epidemiology (PAGE) study.

METHODS

We assembled multi-ethnic genome-wide imputed data on CKD non-overlapping cases [4,150 mild to moderate CKD, 1,105 end-stage kidney disease (ESKD)] and non-CKD controls for up to 41,041 PAGE participants (African Americans, Hispanics/Latinos, East Asian, Native Hawaiian, and American Indians). We implemented a generalized estimating equation approach for GWAS using ancestry combined data while adjusting for age, sex, principal components, study, and ethnicity.

RESULTS

The GWAS identified a novel genome-wide associated locus for mild to moderate CKD nearby NMT2 (rs10906850, p = 3.7 × 10-8) that replicated in the United Kingdom Biobank white British (p = 0.008). Several variants at the APOL1 locus were associated with ESKD including the APOL1 G1 rs73885319 (p = 1.2 × 10-9). There was no overlap among associated loci for CKD and ESKD traits, even at the previously reported APOL1 locus (p = 0.76 for CKD). Several additional loci were associated with CKD or ESKD at p-values below the genome-wide threshold. These loci were often driven by variants more common in non-European ancestry.

CONCLUSION

Our genetic study identified a novel association at NMT2 for CKD and showed for the first time strong associations of the APOL1 variants with ESKD across multi-ethnic populations. Our findings suggest differences in genetic effects across CKD severity and provide information for study design of genetic studies of CKD in diverse populations.

The Future of Genomic Studies Must Be Globally Representative: Perspectives from PAGE

The past decade has seen a technological revolution in human genetics that has empowered population-level investigations into genetic associations with phenotypes. Although these discoveries rely on genetic variation across individuals, association studies have overwhelmingly been performed in populations of European descent. In this review, we describe limitations faced by single-population studies and provide an overview of strategies to improve global representation in existing data sets and future human genomics research via diversity-focused, multiethnic studies. We highlight the successes of individual studies and meta-analysis consortia that have provided unique knowledge. Additionally, we outline the approach taken by the Population Architecture Using Genomics and Epidemiology (PAGE) study to develop best practices for performing genetic epidemiology in multiethnic contexts. Finally, we discuss how limiting investigations to single populations impairs findings in the clinical domain for both rare-variant identification and genetic risk prediction.

Extended methods for gene-environment-wide interaction scans in studies of admixed individuals with varying degrees of relationships

The etiology of many complex diseases involves both environmental exposures and inherited genetic predisposition as well as interactions between them. Gene-environment-wide interaction studies (GEWIS) provide a means to identify the interactions between genetic variation and environmental exposures that underlie disease risk. However, current GEWIS methods lack the capability to adjust for the potentially complex correlations in studies with varying degrees of relationships (both known and unknown) among individuals in admixed populations. We developed novel generalized estimating equation (GEE) based methods-GEE-adaptive and GEE-joint-to account for phenotypic correlations due to kinship while accounting for covariates, including, measures of genome-wide ancestry. In simulation studies of admixed individuals, both methods controlled family-wise error rates, an advantage over the case-only approach. They demonstrated higher power than traditional case-control methods across a wide range of underlying alternative hypotheses, especially where both marginal and interaction effects were present. We applied the proposed method to conduct a GEWIS of a known sarcoidosis risk factor (insecticide exposure) and risk of sarcoidosis in African Americans and identified two novel loci with suggestive evidence of G × E interaction.

Protocols, Methods, and Tools for Genome-Wide Association Studies (GWAS) of Dental Traits

Oral health and disease are known to be influenced by complex interactions between environmental (e.g., social and behavioral) factors and innate susceptibility. Although the exact contribution of genomics and other layers of "omics" to oral health is an area of active research, it is well established that the susceptibility to dental caries, periodontal disease, and other oral and craniofacial traits is substantially influenced by the human genome. A comprehensive understanding of these genomic factors is necessary for the realization of precision medicine in the oral health domain. To aid in this direction, the advent and increasing affordability of high-throughput genotyping has enabled the simultaneous interrogation of millions of genetic polymorphisms for association with oral and craniofacial traits. Specifically, genome-wide association studies (GWAS) of dental caries and periodontal disease have provided initial insights into novel loci and biological processes plausibly implicated in these two common, complex, biofilm-mediated diseases. This paper presents a summary of protocols, methods, tools, and pipelines for the conduct of GWAS of dental caries, periodontal disease, and related traits. The protocol begins with the consideration of different traits for both diseases and outlines procedures for genotyping, quality control, adjustment for population stratification, heritability and association analyses, annotation, reporting, and interpretation. Methods and tools available for GWAS are being constantly updated and improved; with this in mind, the presented approaches have been successfully applied in numerous GWAS and meta-analyses among tens of thousands of individuals, including dental traits such as dental caries and periodontal disease. As such, they can serve as a guide or template for future genomic investigations of these and other traits.

Genetics of Obesity in Diverse Populations

PURPOSE OF REVIEW

The prevalence of obesity continues to rise, fueling a global public health crisis characterized by dramatic increases in type 2 diabetes, cardiovascular disease, and many cancers. In the USA, several minority populations, who bear much of the obesity burden (47% in African Americans and Hispanic/Latinos, compared to 38% in European descent groups), are particularly at risk of downstream chronic disease. Compounding these disparities, most genome-wide association studies (GWAS)-including those of obesity-have largely been conducted in populations of European or East Asian ancestry. In fact, analysis of the GWAS Catalog found that while the proportion of participants of non-European or non-Asian descent had risen from 4% in 2009 to 19% in 2016, African-ancestry participants are still just 3% of GWAS, Hispanic/Latinos are < 0.5%, and other ancestries are < 0.3% or not represented at all. This review summarizes recent developments in obesity genomics in US minority populations, with the goal of reducing obesity health disparities and improving public health programs and access to precision medicine.

RECENT FINDINGS

GWAS of populations with the highest burden of obesity are essential to narrow candidate variants for functional follow-up, to identify additional ancestry-specific variants that contribute to individual genetic susceptibility, and to advance both public health and precision medicine approaches to obesity. Given the global public health burden posed by obesity and downstream chronic conditions which disproportionately affect non-European populations, GWAS of obesity-related traits in diverse populations is essential to (1) locate causal variants in GWAS-identified regions through fine mapping, (2) identify variants which influence obesity across ancestries through generalization, and (3) discover novel ancestry-specific variants which may be low frequency in European populations but common in other groups. Recent efforts to expand obesity genomic studies to understudied and underserved populations, including AAAGC, PAGE, and HISLA, are working to reduce obesity health disparities, improve public health, and bring the promise of precision medicine to all.

Complex patterns of direct and indirect association between the transcription Factor-7 like 2 gene, body mass index and type 2 diabetes diagnosis in adulthood in the Hispanic Community Health Study/Study of Latinos

BACKGROUND

Genome-wide association studies have implicated the transcription factor 7-like 2 (TCF7L2) gene in type 2 diabetes risk, and more recently, in decreased body mass index. Given the contrary direction of genetic effects on these two traits, it has been suggested that the observed association with body mass index may reflect either selection bias or a complex underlying biology at TCF7L2.

METHODS

Using 9031 Hispanic/Latino adults (21-76 years) with complete weight history and genetic data from the community-based Hispanic Community Health Study/Study of Latinos (HCHS/SOL, Baseline 2008-2011), we estimated the multivariable association between the additive number of type 2 diabetes increasing-alleles at TCF7L2 (rs7903146-T) and body mass index. We then used structural equation models to simultaneously model the genetic association on changes in body mass index across the life course and estimate the odds of type 2 diabetes per TCF7L2 risk allele.

RESULTS

We observed both significant increases in type 2 diabetes prevalence at examination (independent of body mass index) and decreases in mean body mass index and waist circumference across genotypes at rs7903146. We observed a significant multivariable association between the additive number of type 2 diabetes-risk alleles and lower body mass index at examination. In our structured modeling, we observed non-significant inverse direct associations between rs7903146-T and body mass index at ages 21 and 45 years, and a significant positive association between rs7903146-T and type 2 diabetes onset in both middle and late adulthood.

CONCLUSIONS

Herein, we replicated the protective effect of rs7930146-T on body mass index at multiple time points in the life course, and observed that these effects were not explained by past type 2 diabetes status in our structured modeling. The robust replication of the negative effects of TCF7L2 on body mass index in multiple samples, including in our diverse Hispanic/Latino community-based sample, supports a growing body of literature on the complex biologic mechanism underlying the functional consequences of TCF7L2 on obesity and type 2 diabetes across the life course.

Discovery, fine-mapping, and conditional analyses of genetic variants associated with C-reactive protein in multiethnic populations using the Metabochip in the Population Architecture using Genomics and Epidemiology (PAGE) study

C-reactive protein (CRP) is a circulating biomarker indicative of systemic inflammation. We aimed to evaluate genetic associations with CRP levels among non-European-ancestry populations through discovery, fine-mapping and conditional analyses. A total of 30 503 non-European-ancestry participants from 6 studies participating in the Population Architecture using Genomics and Epidemiology study had serum high-sensitivity CRP measurements and ∼200 000 single nucleotide polymorphisms (SNPs) genotyped on the Metabochip. We evaluated the association between each SNP and log-transformed CRP levels using multivariate linear regression, with additive genetic models adjusted for age, sex, the first four principal components of genetic ancestry, and study-specific factors. Differential linkage disequilibrium patterns between race/ethnicity groups were used to fine-map regions associated with CRP levels. Conditional analyses evaluated for multiple independent signals within genetic regions. One hundred and sixty-three unique variants in 12 loci in overall or race/ethnicity-stratified Metabochip-wide scans reached a Bonferroni-corrected P-value <2.5E-7. Three loci have no (HACL1, OLFML2B) or only limited (PLA2G6) previous associations with CRP levels. Six loci had different top hits in race/ethnicity-specific versus overall analyses. Fine-mapping refined the signal in six loci, particularly in HNF1A. Conditional analyses provided evidence for secondary signals in LEPR, IL1RN and HNF1A, and for multiple independent signals in CRP and APOE. We identified novel variants and loci associated with CRP levels, generalized known CRP associations to a multiethnic study population, refined association signals at several loci and found evidence for multiple independent signals at several well-known loci. This study demonstrates the benefit of conducting inclusive genetic association studies in large multiethnic populations.