Maturation and application of phenome-wide association studies.

In the past 10 years since its introduction, phenome-wide association studies (PheWAS) have uncovered novel genotype-phenotype relationships. Along the way, PheWAS have evolved in many aspects as a study design with the expanded availability of large data repositories with genome-wide data linked to detailed phenotypic data. Advancement in methods, including algorithms, software, and publicly available integrated resources, makes it feasible to more fully realize the potential of PheWAS, overcoming the previous computational and analytical limitations. We review here the most recent improvements and notable applications of PheWAS since the second half of the decade from its inception. We also note the challenges that remain embedded along the entire PheWAS analytical pipeline that necessitate further development of tools and resources to further advance the understanding of the complex genetic architecture underlying human diseases and traits.

An association test of the spatial distribution of rare missense variants within protein structures identifies Alzheimer's disease-related patterns.

More than 90% of genetic variants are rare in most modern sequencing studies, such as the Alzheimer's Disease Sequencing Project (ADSP) whole-exome sequencing (WES) data. Furthermore, 54% of the rare variants in ADSP WES are singletons. However, both single variant and unit-based tests are limited in their statistical power to detect an association between rare variants and phenotypes. To best use missense rare variants and investigate their biological effect, we examine their association with phenotypes in the context of protein structures. We developed a protein structure-based approach, protein optimized kernel evaluation of missense nucleotides (POKEMON), which evaluates rare missense variants based on their spatial distribution within a protein rather than their allele frequency. The hypothesis behind this test is that the three-dimensional spatial distribution of variants within a protein structure provides functional context to power an association test. POKEMON identified three candidate genes (TREM2, SORL1, and EXOC3L4) and another suggestive gene from the ADSP WES data. For TREM2 and SORL1, two known Alzheimer's disease (AD) genes, the signal from the spatial cluster is stable even if we exclude known AD risk variants, indicating the presence of additional low-frequency risk variants within these genes. EXOC3L4 is a novel AD risk gene that has a cluster of variants primarily shared by case subjects around the Sec6 domain. This cluster is also validated in an independent replication data set and a validation data set with a larger sample size.

A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program.

The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide Association Study (PheWAS) of genetic variants associated with critical illness (n = 35) or hospitalization (n = 42) due to severe COVID-19 using genome-wide association summary data from the Host Genetics Initiative. PheWAS analysis was performed using genotype-phenotype data from the Veterans Affairs Million Veteran Program (MVP). Phenotypes were defined by International Classification of Diseases (ICD) codes mapped to clinically relevant groups using published PheWAS methods. Among 658,582 Veterans, variants associated with severe COVID-19 were tested for association across 1,559 phenotypes. Variants at the ABO locus (rs495828, rs505922) associated with the largest number of phenotypes (nrs495828 = 53 and nrs505922 = 59); strongest association with venous embolism, odds ratio (ORrs495828 1.33 (p = 1.32 x 10-199), and thrombosis ORrs505922 1.33, p = 2.2 x10-265. Among 67 respiratory conditions tested, 11 had significant associations including MUC5B locus (rs35705950) with increased risk of idiopathic fibrosing alveolitis OR 2.83, p = 4.12 × 10-191; CRHR1 (rs61667602) associated with reduced risk of pulmonary fibrosis, OR 0.84, p = 2.26× 10-12. The TYK2 locus (rs11085727) associated with reduced risk for autoimmune conditions, e.g., psoriasis OR 0.88, p = 6.48 x10-23, lupus OR 0.84, p = 3.97 x 10-06. PheWAS stratified by ancestry demonstrated differences in genotype-phenotype associations. LMNA (rs581342) associated with neutropenia OR 1.29 p = 4.1 x 10-13 among Veterans of African and Hispanic ancestry but not European. Overall, we observed a shared genetic architecture between COVID-19 severity and conditions related to underlying risk factors for severe and poor COVID-19 outcomes. Differing associations between genotype-phenotype across ancestries may inform heterogenous outcomes observed with COVID-19. Divergent associations between risk for severe COVID-19 with autoimmune inflammatory conditions both respiratory and non-respiratory highlights the shared pathways and fine balance of immune host response and autoimmunity and caution required when considering treatment targets.

T-cell receptor diversity in minimal change disease in the NEPTUNE study.

BACKGROUND: Minimal change disease (MCD) is the major cause of childhood idiopathic nephrotic syndrome, which is characterized by massive proteinuria and debilitating edema. Proteinuria in MCD is typically rapidly reversible with corticosteroid therapy, but relapses are common, and children often have many adverse events from the repeated courses of immunosuppressive therapy. The pathobiology of MCD remains poorly understood. Prior clinical observations suggest that abnormal T-cell function may play a central role in MCD pathogenesis. Based on these observations, we hypothesized that T-cell responses to specific exposures or antigens lead to a clonal expansion of T-cell subsets, a restriction in the T-cell repertoire, and an elaboration of specific circulating factors that trigger disease onset and relapses. METHODS: To test these hypotheses, we sequenced T-cell receptors in fourteen MCD, four focal segmental glomerulosclerosis (FSGS), and four membranous nephropathy (MN) patients with clinical data and blood samples drawn during active disease and during remission collected by the Nephrotic Syndrome Study Network (NEPTUNE). We calculated several T-cell receptor diversity metrics to assess possible differences between active disease and remission states in paired samples. RESULTS: Median productive clonality did not differ between MCD active disease (0.0083; range: 0.0042, 0.0397) and remission (0.0088; range: 0.0038, 0.0369). We did not identify dominant clonotypes in MCD active disease, and few clonotypes were shared with FSGS and MN patients. CONCLUSIONS: While these data do not support an obvious role of the adaptive immune system T-cells in MCD pathogenesis, further study is warranted given the limited sample size. A higher resolution version of the Graphical abstract is available as Supplementary information.

Glaucoma Genetic Risk Scores in the Million Veteran Program.

PURPOSE: Primary open-angle glaucoma (POAG) is a degenerative eye disease for which early treatment is critical to mitigate visual impairment and irreversible blindness. POAG-associated loci individually confer incremental risk. Genetic risk score(s) (GRS) could enable POAG risk stratification. Despite significantly higher POAG burden among individuals of African ancestry (AFR), GRS are limited in this population. A recent large-scale, multi-ancestry meta-analysis identified 127 POAG-associated loci and calculated cross-ancestry and ancestry-specific effect estimates, including in European ancestry (EUR) and AFR individuals. We assessed the utility of the 127-variant GRS for POAG risk stratification in EUR and AFR Veterans in the Million Veteran Program (MVP). We also explored the association between GRS and documented invasive glaucoma surgery (IGS). DESIGN: Cross-sectional study. PARTICIPANTS: MVP Veterans with imputed genetic data, including 5830 POAG cases (445 with IGS documented in the electronic health record) and 64 476 controls. METHODS: We tested unweighted and weighted GRS of 127 published risk variants in EUR (3382 cases and 58 811 controls) and AFR (2448 cases and 5665 controls) Veterans in the MVP. Weighted GRS were calculated using effect estimates from the most recently published report of cross-ancestry and ancestry-specific meta-analyses. We also evaluated GRS in POAG cases with documented IGS. MAIN OUTCOME MEASURES: Performance of 127-variant GRS in EUR and AFR Veterans for POAG risk stratification and association with documented IGS. RESULTS: GRS were significantly associated with POAG (P < 5 × 10-5) in both groups; a higher proportion of EUR compared with AFR were consistently categorized in the top GRS decile (21.9%-23.6% and 12.9%-14.5%, respectively). Only GRS weighted by ancestry-specific effect estimates were associated with IGS documentation in AFR cases; all GRS types were associated with IGS in EUR cases. CONCLUSIONS: Varied performance of the GRS for POAG risk stratification and documented IGS association in EUR and AFR Veterans highlights (1) the complex risk architecture of POAG, (2) the importance of diverse representation in genomics studies that inform GRS construction and evaluation, and (3) the necessity of expanding diverse POAG-related genomic data so that GRS can equitably aid in screening individuals at high risk of POAG and who may require more aggressive treatment.

Unravelling the human genome-phenome relationship using phenome-wide association studies.

Advances in genotyping technology have, over the past decade, enabled the focused search for common genetic variation associated with human diseases and traits. With the recently increased availability of detailed phenotypic data from electronic health records and epidemiological studies, the impact of one or more genetic variants on the phenome is starting to be characterized both in clinical and population-based settings using phenome-wide association studies (PheWAS). These studies reveal a number of challenges that will need to be overcome to unlock the full potential of PheWAS for the characterization of the complex human genome-phenome relationship.

Heteronemin and Tetrac Induce Anti-Proliferation by Blocking EGFR-Mediated Signaling in Colorectal Cancer Cells.

Overexpressed EGFR and mutant K-Ras play vital roles in therapeutic resistance in colorectal cancer patients. To search for an effective therapeutic protocol is an urgent task. A secondary metabolite in the sponge Hippospongia sp., Heteronemin, has been shown to induce anti-proliferation in several types of cancers. A thyroxine-deaminated analogue, tetrac, binds to integrin αvß3 to induce anti-proliferation in different cancers. Heteronemin- and in combination with tetrac-induced antiproliferative effects were evaluated. Tetrac enhanced heteronemin-induced anti-proliferation in HT-29 cells (KRAS WT CRC) and HCT-116 cells (KRAS MT CRC). Heteronemin and tetrac arrested cell cycle in different phases. Combined treatment increased the cell accumulation in sub-G1 and S phases. The combined treatment also induced the inactivation of EGFR signaling and downregulated the phosphorylated ERK1/2 protein in both cell lines. Heteronemin and the combination showed the downregulation of the phosphorylated and total PI3K protein in HT-29 cells (KRAS WT CRC). Results by NanoString technology and RT-qPCR revealed that heteronemin and combined treatment suppressed the expression of EGFR and downstream genes in HCT-116 cells (KRAS MT CRC). Heteronemin or combined treatment downregulated genes associated with cancer progression and decreased cell motility. Heteronemin or the combined treatment suppressed PD-L1 expression in both cancer cell lines. However, only tetrac and the combined treatment inhibited PD-L1 protein accumulation in HT-29 cells (KRAS WT CRC) and HCT-116 cells (KRAS MT CRC), respectively. In summary, heteronemin induced anti-proliferation in colorectal cancer cells by blocking the EGFR-dependent signal transduction pathway. The combined treatment further enhanced the anti-proliferative effect via PD-L1 suppression. It can be an alternative strategy to suppress mutant KRAS resistance for anti-EGFR therapy.

African-specific improvement of a polygenic hazard score for age at diagnosis of prostate cancer.

Polygenic hazard score (PHS) models are associated with age at diagnosis of prostate cancer. Our model developed in Europeans (PHS46) showed reduced performance in men with African genetic ancestry. We used a cross-validated search to identify single nucleotide polymorphisms (SNPs) that might improve performance in this population. Anonymized genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Ten iterations of a 10-fold cross-validation search were conducted to select SNPs that would be included in the final PHS46+African model. The coefficients of PHS46+African were estimated in a Cox proportional hazards framework using age at diagnosis as the dependent variable and PHS46, and selected SNPs as predictors. The performance of PHS46 and PHS46+African was compared using the same cross-validated approach. Three SNPs (rs76229939, rs74421890 and rs5013678) were selected for inclusion in PHS46+African. All three SNPs are located on chromosome 8q24. PHS46+African showed substantial improvements in all performance metrics measured, including a 75% increase in the relative hazard of those in the upper 20% compared to the bottom 20% (2.47-4.34) and a 20% reduction in the relative hazard of those in the bottom 20% compared to the middle 40% (0.65-0.53). In conclusion, we identified three SNPs that substantially improved the association of PHS46 with age at diagnosis of prostate cancer in men with African genetic ancestry to levels comparable to Europeans.

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.

Optimizing identification of resistant hypertension: Computable phenotype development and validation.

PURPOSE: Computable phenotypes are constructed to utilize data within the electronic health record (EHR) to identify patients with specific characteristics; a necessary step for researching a complex disease state. We developed computable phenotypes for resistant hypertension (RHTN) and stable controlled hypertension (HTN) based on the National Patient-Centered Clinical Research Network (PCORnet) common data model (CDM). The computable phenotypes were validated through manual chart review. METHODS: We adapted and refined existing computable phenotype algorithms for RHTN and stable controlled HTN to the PCORnet CDM in an adult HTN population from the OneFlorida Clinical Research Consortium (2015-2017). Two independent reviewers validated the computable phenotypes through manual chart review of 425 patient records. We assessed precision of our computable phenotypes through positive predictive value (PPV) and test validity through interrater reliability (IRR). RESULTS: Among the 156 730 HTN patients in our final dataset, the final computable phenotype algorithms identified 24 926 patients with RHTN and 19 100 with stable controlled HTN. The PPV for RHTN in patients randomly selected for validation of the final algorithm was 99.1% (n = 113, CI: 95.2%-99.9%). The PPV for stable controlled HTN in patients randomly selected for validation of the final algorithm was 96.5% (n = 113, CI: 91.2%-99.0%). IRR analysis revealed a raw percent agreement of 91% (152/167) with Cohen's kappa statistic = 0.87. CONCLUSIONS: We constructed and validated a RHTN computable phenotype algorithm and a stable controlled HTN computable phenotype algorithm. Both algorithms are based on the PCORnet CDM, allowing for future application to epidemiological and drug utilization based research.

Mind the gap: resources required to receive, process and interpret research-returned whole genome data.

Most genotype-phenotype studies have historically lacked population diversity, impacting the generalizability of findings and thereby limiting the ability to equitably implement precision medicine. This well-documented problem has generated much interest in the ascertainment of new cohorts with an emphasis on multiple dimensions of diversity, including race/ethnicity, gender, age, socioeconomic status, disability, and geography. The most well known of these new cohort efforts is arguably All of Us, formerly known as the Precision Medicine Cohort Initiative Program. All of Us intends to ascertain at least one million participants in the United States representative of the multiple dimensions of diversity. As an incentive to participate, All of Us is offering the return of research results, including whole genome sequencing data, as well as the opportunity to contribute to the scientific process as non-scientists. The scale and scope of the proposed return of research results are unprecedented. Here, we briefly review possible return of genetic data models, including the likely data file formats and modes of data transfer or access. We also review the resources required to access and interpret the genetic or genomic data once received by the average participant, highlighting the nuanced anticipated barriers that will challenge both the digitally, computationally literate and illiterate participant alike. This inventory of resources required to receive, process, and interpret return of research results exposes the potential for access disparities and warns the scientific community to mind the gap so that all participants have equal access and understanding of the benefits of human genetic research.

Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin.

Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loci.

Genome-wide association analysis of common genetic variants of resistant hypertension.

Resistant hypertension (RHTN), defined as uncontrolled blood pressure (BP) ≥ 140/90 using three or more drugs or controlled BP (<140/90) using four or more drugs, is associated with adverse outcomes, including decline in kidney function. We conducted a genome-wide association analysis in 1194 White and Hispanic participants with hypertension and coronary artery disease from the INternational VErapamil-SR Trandolapril STudy-GENEtic Substudy (INVEST-GENES). Top variants associated with RHTN at p < 10-4 were tested for replication in 585 White and Hispanic participants with hypertension and subcortical strokes from the Secondary Prevention of Subcortical Strokes GENEtic Substudy (SPS3-GENES). A genetic risk score for RHTN was created by summing the risk alleles of replicated RHTN signals. rs11749255 in MSX2 was associated with RHTN in INVEST (odds ratio (OR) (95% CI) = 1.50 (1.2-1.8), p = 7.3 × 10-5) and replicated in SPS3 (OR = 2.0 (1.4-2.8), p = 4.3 × 10-5), with genome-wide significance in meta-analysis (OR = 1.60 (1.3-1.9), p = 3.8 × 10-8). Other replicated signals were in IFLTD1 and PTPRD. IFLTD1 rs6487504 was associated with RHTN in INVEST (OR = 1.90 (1.4-2.5), p = 1.1 × 10-5) and SPS3 (OR = 1.70 (1.2-2.5), p = 4 × 10-3). PTPRD rs324498, a previously reported RHTN signal, was among the top signals in INVEST (OR = 1.60 (1.3-2.0), p = 3.4 × 10-5) and replicated in SPS3 (OR = 1.60 (1.1-2.4), one-sided p = 0.005). Participants with the highest number of risk alleles were at increased risk of RHTN compared to participants with a lower number (p-trend = 1.8 × 10-15). Overall, we identified and replicated associations with RHTN in the MSX2, IFLTD1, and PTPRD regions, and combined these associations to create a genetic risk score.

Meta-Analysis of Genome-Wide Association Studies for Abdominal Aortic Aneurysm Identifies Four New Disease-Specific Risk Loci.

RATIONALE: Abdominal aortic aneurysm (AAA) is a complex disease with both genetic and environmental risk factors. Together, 6 previously identified risk loci only explain a small proportion of the heritability of AAA. OBJECTIVE: To identify additional AAA risk loci using data from all available genome-wide association studies. METHODS AND RESULTS: Through a meta-analysis of 6 genome-wide association study data sets and a validation study totaling 10 204 cases and 107 766 controls, we identified 4 new AAA risk loci: 1q32.3 (SMYD2), 13q12.11 (LINC00540), 20q13.12 (near PCIF1/MMP9/ZNF335), and 21q22.2 (ERG). In various database searches, we observed no new associations between the lead AAA single nucleotide polymorphisms and coronary artery disease, blood pressure, lipids, or diabetes mellitus. Network analyses identified ERG, IL6R, and LDLR as modifiers of MMP9, with a direct interaction between ERG and MMP9. CONCLUSIONS: The 4 new risk loci for AAA seem to be specific for AAA compared with other cardiovascular diseases and related traits suggesting that traditional cardiovascular risk factor management may only have limited value in preventing the progression of aneurysmal disease.

The detection and characterization of pleiotropy: discovery, progress, and promise.

The impact of a single genetic locus on multiple phenotypes, or pleiotropy, is an important area of research. Biological systems are dynamic complex networks, and these networks exist within and between cells. In humans, the consideration of multiple phenotypes such as physiological traits, clinical outcomes and drug response, in the context of genetic variation, can provide ways of developing a more complete understanding of the complex relationships between genetic architecture and how biological systems function in health and disease. In this article, we describe recent studies exploring the relationships between genetic loci and more than one phenotype. We also cover methodological developments incorporating pleiotropy applied to model organisms as well as humans, and discuss how stepping beyond the analysis of a single phenotype leads to a deeper understanding of complex genetic architecture.

Participation of liver stem cells in cholangiocarcinogenesis after aflatoxin B1 exposure of glutathione S-transferase A3 knockout mice.

Aflatoxin B1, arguably the most potent human carcinogen, induces liver cancer in humans, rats, trout, ducks, and so on, but adult mice are totally resistant. This resistance is because of a detoxifying enzyme, mouse glutathione S-transferase A3, which binds to and inactivates aflatoxin B1 epoxide, preventing the epoxide from binding to DNA and causing mutations. Glutathione S-transferase A3 or its analog has not been detected in any of the sensitive species, including humans. The generation of a glutathione S-transferase A3 knockout (represented as KO or -/-) mice has allowed us to study the induction of liver cancer in mice by aflatoxin B1. In contrast to the induction of hepatocellular carcinomas in other species, aflatoxin B1 induces cholangiocarcinomas in GSTA3-/- mice. In other species and in knockout mice, the induction of liver cancer is preceded by extensive proliferation of small oval cells, providing additional evidence that oval cells are bipolar stem cells and may give rise to either hepatocellular carcinoma or cholangiocarcinoma depending on the nature of the hepatocarcinogen and the species of animal. The recent development of mouse oval cell lines in our laboratory from aflatoxin B1-treated GSTA3-/- mice should provide a new venue for study of the properties and potential of putative mouse liver stem cells.

Characterization of liver injury, oval cell proliferation and cholangiocarcinogenesis in glutathione S-transferase A3 knockout mice.

We recently generated glutathione S-transferase (GST) A3 knockout (KO) mice as a novel model to study the risk factors for liver cancer. GSTA3 KO mice are sensitive to the acute cytotoxic and genotoxic effects of aflatoxin B1 (AFB1), confirming the crucial role of GSTA3 in resistance to AFB1. We now report histopathological changes, tumor formation, biochemical changes and gender response following AFB1 treatment as well as the contribution of oxidative stress. Using a protocol of weekly 0.5 mg AFB1/kg administration, we observed extensive oval (liver stem) cell (OC) proliferation within 1-3 weeks followed by microvesicular lipidosis, megahepatocytes, nuclear inclusions, cholangiomas and small nodules. Male and female GSTA3 KO mice treated with 12 and 24 weekly AFB1 injections followed by a rest period of 12 and 6 months, respectively, all had grossly distorted livers with macro- and microscopic cysts, hepatocellular nodules, cholangiomas and cholangiocarcinomas and OC proliferation. We postulate that the prolonged AFB1 treatment leads to inhibition of hepatocyte proliferation, which is compensated by OC proliferation and eventually formation of cholangiocarcinoma (CCA). At low-dose AFB1, male KO mice showed less extensive acute liver injury, OC proliferation and AFB1-DNA adducts than female KO mice. There were no significant compensatory changes in KO mice GST subunits, GST enzymatic activity, epoxide hydrolase, or CYP1A2 and CYP3A11 levels. Finally, there was a modest increase in F2-isoprostane and isofuran in KO mice that confirmed putative GSTA3 hydroperoxidase activity in vivo for the first time.

Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture using Genomics and Epidemiology (PAGE) Study reveals evidence for multiple signals at established loci.

Most body mass index (BMI) genetic loci have been identified in studies of primarily European ancestries. The effect of these loci in other racial/ethnic groups is less clear. Thus, we aimed to characterize the generalizability of 170 established BMI variants, or their proxies, to diverse US populations and trans-ethnically fine-map 36 BMI loci using a sample of >102,000 adults of African, Hispanic/Latino, Asian, European and American Indian/Alaskan Native descent from the Population Architecture using Genomics and Epidemiology Study. We performed linear regression of the natural log of BMI (18.5-70 kg/m2) on the additive single nucleotide polymorphisms (SNPs) at BMI loci on the MetaboChip (Illumina, Inc.), adjusting for age, sex, population stratification, study site, or relatedness. We then performed fixed-effect meta-analyses and a Bayesian trans-ethnic meta-analysis to empirically cluster by allele frequency differences. Finally, we approximated conditional and joint associations to test for the presence of secondary signals. We noted directional consistency with the previously reported risk alleles beyond what would have been expected by chance (binomial p < 0.05). Nearly, a quarter of the previously described BMI index SNPs and 29 of 36 densely-genotyped BMI loci on the MetaboChip replicated/generalized in trans-ethnic analyses. We observed multiple signals at nine loci, including the description of seven loci with novel multiple signals. This study supports the generalization of most common genetic loci to diverse ancestral populations and emphasizes the importance of dense multiethnic genomic data in refining the functional variation at genetic loci of interest and describing several loci with multiple underlying genetic variants.

Detection of pleiotropy through a Phenome-wide association study (PheWAS) of epidemiologic data as part of the Environmental Architecture for Genes Linked to Environment (EAGLE) study.

We performed a Phenome-wide association study (PheWAS) utilizing diverse genotypic and phenotypic data existing across multiple populations in the National Health and Nutrition Examination Surveys (NHANES), conducted by the Centers for Disease Control and Prevention (CDC), and accessed by the Epidemiological Architecture for Genes Linked to Environment (EAGLE) study. We calculated comprehensive tests of association in Genetic NHANES using 80 SNPs and 1,008 phenotypes (grouped into 184 phenotype classes), stratified by race-ethnicity. Genetic NHANES includes three surveys (NHANES III, 1999-2000, and 2001-2002) and three race-ethnicities: non-Hispanic whites (n = 6,634), non-Hispanic blacks (n = 3,458), and Mexican Americans (n = 3,950). We identified 69 PheWAS associations replicating across surveys for the same SNP, phenotype-class, direction of effect, and race-ethnicity at p<0.01, allele frequency >0.01, and sample size >200. Of these 69 PheWAS associations, 39 replicated previously reported SNP-phenotype associations, 9 were related to previously reported associations, and 21 were novel associations. Fourteen results had the same direction of effect across more than one race-ethnicity: one result was novel, 11 replicated previously reported associations, and two were related to previously reported results. Thirteen SNPs showed evidence of pleiotropy. We further explored results with gene-based biological networks, contrasting the direction of effect for pleiotropic associations across phenotypes. One PheWAS result was ABCG2 missense SNP rs2231142, associated with uric acid levels in both non-Hispanic whites and Mexican Americans, protoporphyrin levels in non-Hispanic whites and Mexican Americans, and blood pressure levels in Mexican Americans. Another example was SNP rs1800588 near LIPC, significantly associated with the novel phenotypes of folate levels (Mexican Americans), vitamin E levels (non-Hispanic whites) and triglyceride levels (non-Hispanic whites), and replication for cholesterol levels. The results of this PheWAS show the utility of this approach for exposing more of the complex genetic architecture underlying multiple traits, through generating novel hypotheses for future research.