Population Architecture Using Genomics And Epidemiology Research Articles

Abstract TP248: Genetic Predisposition to Diabetes and Risk of Stroke in Native Hawaiians

Background: Diabetes is a highly heritable stroke risk factor with numerous known genetic risk variants. However, the role of these genetic risk variants in the development of cerebrovascular disease in Native Hawaiians remains unknown. We sought to determine whether genetic predisposition to diabetes is associated with higher risk of stroke in a Native Hawaiian population. Methods: We conducted a genetic association case/control study using data from Native Hawaiians enrolled in the Population Architecture using Genomics and Epidemiology (PAGE) study. Stroke cases were ascertained by baseline questionnaires and matched by age and sex in a 1:10 ratio. We modeled genetic predisposition to diabetes through a polygenic risk score that contained independent genetic variants known to associate with diabetes at genome-wide levels (p<5x10 -8 ). This polygenic risk score became the independent variable of logistic regression models for both diabetes and stroke. Results: A total of 440 self-identified Native Hawaiians were included (15% < 50 years of age, 85% ages 50-69, 65% females), including 40 stroke patients and 400 age/sex-matched control subjects. Genetic predisposition to diabetes was associated with a 23% higher risk of diabetes (OR 1.23, CI 1.01-1.51; p=0.04) and with a 45% higher risk of stroke (OR 1.45, CI 1.03-2.05; p=0.04). Conclusions: Among Native Hawaiians, genetic predisposition to diabetes is associated with higher risk of stroke. As research aimed at evaluating the role of polygenic risk data in clinical practice grows rapidly, our results emphasize the importance of including Native Hawaiians in order to avoid health disparities exacerbated by incomplete representation.

Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics

The heritability explained by local ancestry markers in an admixed population (hγ2) provides crucial insight into the genetic architecture of a complex disease or trait. Estimation of hγ2 can be susceptible to biases due to population structure in ancestral populations. Here, we present heritability estimation from admixture mapping summary statistics (HAMSTA), an approach that uses summary statistics from admixture mapping to infer heritability explained by local ancestry while adjusting for biases due to ancestral stratification. Through extensive simulations, we demonstrate that HAMSTA hγ2 estimates are approximately unbiased and are robust to ancestral stratification compared to existing approaches. In the presence of ancestral stratification, we show a HAMSTA-derived sampling scheme provides a calibrated family-wise error rate (FWER) of ∼5% for admixture mapping, unlike existing FWER estimation approaches. We apply HAMSTA to 20 quantitative phenotypes of up to 15,988 self-reported African American individuals in the Population Architecture usingGenomics and Epidemiology (PAGE) study. We observe hˆγ2 in the 20 phenotypes range from 0.0025 to 0.033 (meanhˆγ2= 0.012± 9.2×10-4), which translates to hˆ2 ranging from 0.062 to 0.85 (mean hˆ2= 0.30± 0.023). Across these phenotypes we find little evidence of inflation due to ancestral population stratification in current admixture mapping studies (mean inflation factor of 0.99± 0.001). Overall, HAMSTA provides a fast and powerful approach to estimate genome-wide heritability and evaluate biases in test statistics of admixture mapping studies.

Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.

Assessment of multi-population polygenic risk scores for lipid traits in African Americans.

Polygenic risk scores (PRS) based on genome-wide discoveries are promising predictors or classifiers of disease development, severity, and/or progression for common clinical outcomes. A major limitation of most risk scores is the paucity of genome-wide discoveries in diverse populations, prompting an emphasis to generate these needed data for trans-population and population-specific PRS construction. Given diverse genome-wide discoveries are just now being completed, there has been little opportunity for PRS to be evaluated in diverse populations independent from the discovery efforts. To fill this gap, we leverage here summary data from a recent genome-wide discovery study of lipid traits (HDL-C, LDL-C, triglycerides, and total cholesterol) conducted in diverse populations represented by African Americans, Hispanics, Asians, Native Hawaiians, Native Americans, and others by the Population Architecture using Genomics and Epidemiology (PAGE) Study. We constructed lipid trait PRS using PAGE Study published genetic variants and weights in an independent African American adult patient population linked to de-identified electronic health records and genotypes from the Illumina Metabochip (n=3,254). Using multi-population lipid trait PRS, we assessed levels of association for their respective lipid traits, clinical outcomes (cardiovascular disease and type 2 diabetes), and common clinical labs. While none of the multi-population PRS were strongly associated with the tested trait or outcome, PRSLDL-Cwas nominally associated with cardiovascular disease. These data demonstrate the complexity in applying PRS to real-world clinical data even when data from multiple populations are available.

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

BackgroundConcurrent 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.MethodsUsing 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.ResultsOf 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.ConclusionsResults 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 Evidence Supporting the Causal Role of Homocysteine in Chronic Kidney Disease: A Mendelian Randomization Study.

BackgroundThe causal relationship between homocysteine (Hcy) levels and chronic kidney disease (CKD) remains unclear. This study was performed to estimate the potential causal effects of Hcy on the estimated glomerular filtration rate (eGFR) and CKD.Materials and MethodsThe single nucleotide polymorphisms (SNPs) associated with one standard deviation (SD) Hcy increase were identified using the genome-wide association study (GWAS). The summary statistics of the eGFR and CKD were from the CKDGen project in the European ancestry and the Population Architecture using Genomics and Epidemiology (PAGE) project in the non-European ancestry. Two-sample Mendelian randomization (MR) analyses were used in this study to verify the causal effects among Hcy, eGFR, and CKD.ResultsThe results showed that 1-SD Hcy increase was causally associated with eGFR decline in the CKDGen project (β = −0.027 log ml.min–1/1.73 m2, p < 0.01 for the overall cohort; β = −0.028 log ml.min–1/1.73 m2, p < 0.01 after excluding the patients with diabetes). In addition, 1-SD Hcy increase was associated with a 1.32-fold risk of CKD in the PAGE project (95% CI = 1.06–1.64, p < 0.05). The association was directionally similar in the CKDGen project [odds ratio (OR) = 1.08, 95% CI = 0.97–1.44, p = 0.098]. The pooled OR of CKD was 1.24 (95% CI = 1.07–1.44, p < 0.05) per 1-SD Hcy increase.ConclusionUsing genetic data, Hcy increase is causally associated with renal function injury and further CKD.

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.

Abstract P181: The Genetic Epidemiology Of Longitudinal Blood Pressure Traits From Adolescence To Early Adulthood In Latinos

Introduction: Blood pressure (BP) is known to rise during developmental stages from childhood to adulthood, and that those with higher BP trajectories are more likely to develop hypertension and CVD. Yet, there is a paucity of studies that have evaluated trajectories of BP change or the genetic factors involved in the BP changes from childhood to adulthood. Epidemiological and genetic epidemiological studies of longitudinal BP changes through the life-course may enable a better understanding of the pathogenesis of hypertension and may lead to the identification of loci associated with BP trajectories and CVD risk during this critical period. Methods: We leverage multiple self-identified Hispanic/Latino populations with longitudinal BP and genomic data across the lifecourse, in particular between adolescence and young adulthood. We present herein preliminary results from the Santiago longitudinal study (SLS) that enrolled infants of Hispanic ancestry and measured BP at late adolescence (average age of 16 years) and early adulthood (average age of 22 years) to identify genetic variants associated with trajectories of BP traits (systolic BP, diastolic BP, mean arterial pressure and pulse pressure). We conducted a complete case analysis excluding participants taking antihypertensive medications and those whose BP measures were statistical outliers. Linear mixed models were used to assess the trajectories of individual BP traits between late adolescence and early adulthood (n = 1236), adjusting for age, sex and BMI. We assessed the relationship between residuals and genetic variants adjusting for two ancestral principal components using SUGEN software. Results: Males constituted 51% of the total SLS cohort. Interestingly, with each year increase in age, we observed downward trajectories of both mean systolic BP (-0.3 mmHg, 95% CI (-0.4, -0.2)) and mean diastolic BP (-0.1 mmHg, 95% CI (-0.2, -0.04)) adjusted for BMI and sex. We identified evidence for five known BP loci and two suggestive new signals. Our most significant finding is rs10894989 [beta (SE) = 6.57 (1.26), p =1.86x10 -7 , minor allele frequency (EAF = 0.94)] near the ARHGAP42 gene, an established locus for hypertension. However, this variant is not in linkage disequilibrium with the commonly reported SNP rs604723 (r 2 = 0.04). Ancestry-specific allele frequencies differed with rs604723 more common in East Asians and Europeans and rs10894989 commonest in Africans. Conclusion: Our longitudinal analyses of BP from late adolescence to early adulthood replicated BP loci and identified novel suggestive loci associated with BP trajectories supporting a need for further GWAS of longitudinal BP. We are currently conducting a meta-analysis of longitudinal BP trajectories in 3,000 young adults self-identified as Hispanic Latino in the Population Architecture using Genomics and Epidemiology (PAGE) study.

Protein prediction for trait mapping in diverse populations.

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.

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

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

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

BackgroundCirculating 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.ResultsWe 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.ConclusionsOur 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.

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

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

Diversity in genome studies

Human Genetics Genome-wide association studies have been used to identify genetic variants associated with specific phenotypes. However, such studies have mostly focused on individuals of European descent. Hu et al. examined the genome architecture of blood lipid traits associated with heart and metabolic disease in >45,000 ancestrally diverse participants from the Population Architecture using Genomics and Epidemiology (PAGE) study. This study identified previously uncharacterized loci and variants affecting lipid metabolism in non-European populations. By combining these results with other cohorts, some of which were primarily composed of individuals of European descent, disease-causative variants could be mapped, the magnitude of effects in loci of Europeans was confirmed, and new variants of potential interest were identified, including a gene associated with blood lipid concentrations in individuals from non-European populations. PLOS Genet. 16 , e1008684 (2020).

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.

A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans.

We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.

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 49,839non-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

BackgroundChronic 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.MethodsWe 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.ResultsThe 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.ConclusionOur 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.

Populations from under‐represented backgrounds are not adequately represented in clinical databases

Personalized medicine (PM) is a field in medicine that leverages information about a patient's genes, environment and behavior to prevent, diagnose, and treat disease. Health care providers rely on publicly available genetic databases for recommendations for implementing PM. For example, the Clinical Pharmacogenetics Implementation Consortium (CPIC) makes recommendations for clinical implementation of pharmacogenetics tests by creating, curating, and sharing peer‐reviewed, evidence‐based and updated drug‐gene pair clinical practice guidelines. While most drugs with CPIC guidelines have at least some evidence based off of non‐European data, none have been exclusively developed for a specific ancestral group. Similarly, ClinVar, an NIH supported, freely accessible archive, reports relationships between genetic variation and disease phenotypes. However, these databases reflect a measurable bias toward genetic data based on European ancestry, owed in large part to the underrepresentation of non‐white populations in federally funded and published research programs, including genome wide association (GWAS) and whole genome sequencing studies, which in turn limits the ability to identify variants associated with disease and response to certain therapeutics. Inadequate representation further exacerbates existing health disparities. After over a decade of GWAS, non‐European, non‐Asian groups combined account for &lt;4% of individuals represented in the international GWAS catalog. Previously the role that ancestry plays in variant prioritization approaches often implemented in genomic medicine (e.g., Human Gene Mutation Database, ClinVar) was explored and it was determined these databases are missing considerable African‐specific pathogenicity data. Recent developments through national and international initiatives have provided an opportunity to classify new genetic variants and reassess prior variant classifications and deposit this information into public databases. Approximately half of the NHLBI supported Trans‐Omics for Precision Medicine (TOPMed) program's samples represent non‐European samples. Similarly, the NHGRI Population Architecture using Genomics and Epidemiology (PAGE) study aims to better characterize how genetic factors influence susceptibility to disease in a multi‐ethnic dataset. Researchers representing both TOPMed and PAGE previously designed a commercially available multi‐ethnic genotyping array that was enriched with genetic variants representing underrepresented minority populations, which is being widely used, including institutional biobanks. Together, these developments promise to improve genomic diversity and subsequently benefit underrepresented minorities through personalized medicine.Support or Funding InformationNIH grant #HL104608This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Abstract MP57: Genetics of Chronic Kidney Disease Stages Across Ancestries: The PAGE Study

Background: Chronic kidney disease (CKD) is common, disproportionally burdens ethnic minorities in the US, and is associated with increased cardiovascular mortality. Our prior trans-ethnic genetic research has identified approximately 100 loci for kidney function. We now extend this research to CKD stages in studies of diverse populations. To uncover genetic factors associated CKD stages, we performed genome-wide association studies in diverse populations within the Population Architecture using Genomics and Epidemiology (PAGE) study. Methods: We assembled multi-ethnic data on CKD non-overlapping cases (4,690 mild to moderate CKD, 1,105 advanced CKD [stage 5] including end-stage kidney disease) and non-CKD controls for up to 46,263 PAGE participants (African Americans, Hispanics/Latinos, East Asian, Native Hawaiian and American Indians). We implemented a generalized estimating equation approach for single test genome-wide association analyses using ancestry combined data and adjusting for age, sex, principal components, study and ethnicity. Results: The analyses identified two associated loci at p &lt; 5.3 x 10 -8 , including a novel association with mild to moderate CKD on chromosome 9, which is driven by an intronic variant of TYRP1 (rs186208070, allele frequency 0.01 in our combined sample, 1000 Genomes: 0.02 in AFR, 0.003 AMR, 0.001 EUR, p=4.4 x 10 -8 ). Several genome-wide associated variants at the APOL1 locus were associated with advanced CKD. The most significant finding was for APOL1 G1 rs73885319 (p=1.19 x 10 -9 ). Additional variants with suggestive associations at p&lt;10 -6 for advanced CKD included an intronic low frequency variant in FTO (rs7189997) and a variant nearby IRX3 (rs8050506). Both variants are more common in African ancestry and these loci were previously associated with obesity traits. Conclusions: Our study on the genetics of CKD identified a novel locus for mild to moderate CKD and showed for the first time strong associations of African-ancestry APOL1 variants with advanced CKD across multi-ethnic populations.