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Preeclampsia, a pregnancy complication characterized by hypertension after 20 gestational weeks, is a major cause of maternal and neonatal morbidity and mortality. Mechanisms leading to preeclampsia are unclear; however, there is evidence of high heritability. We evaluated the association of polygenic scores (PGS) for blood pressure traits and preeclampsia to assess whether there is shared genetic architecture. Non-Hispanic Black and White reproductive age females with pregnancy indications and genotypes were obtained from Vanderbilt University’s BioVU, Electronic Medical Records and Genomics network, and Penn Medicine Biobank. Preeclampsia was defined by ICD codes. Summary statistics for diastolic blood pressure (DBP), systolic blood pressure (SBP), and pulse pressure (PP) PGS were acquired from Giri et al. Associations between preeclampsia and each PGS were evaluated separately by race and data source before subsequent meta-analysis. Ten-fold cross validation was used for prediction modeling. In 3504 Black and 5009 White included individuals, the rate of preeclampsia was 15.49%. In cross-ancestry meta-analysis, all PGSs were associated with preeclampsia (OR DBP  = 1.10, 95% CI 1.02–1.17, p  = 7.68 × 10 −3 ; OR SBP  = 1.16, 95% CI 1.09–1.23, p  = 2.23 × 10 −6 ; OR PP  = 1.14, 95% CI 1.07–1.27, p  = 9.86 × 10 −5 ). Addition of PGSs to clinical prediction models did not improve predictive performance. Genetic factors contributing to blood pressure regulation in the general population also predispose to preeclampsia.

Acanthosis nigricans (AN) is a strong correlate of obesity and is considered a marker of insulin resistance (IR). AN is associated with various other cardiometabolic risk factors (CMRFs). However, the direct causal relationship of IR with AN in obesity has been debated. Therefore, we aimed to examine the complex causal relationships among the troika of AN, obesity, and IR in Mexican Americans (MAs). We used data from 670 non-diabetic MA children, aged 6-17 years (49% girls). AN (prevalence 33%) severity scores (range 0-5) were used as a quasi-quantitative trait (AN-q) for analysis. We used the program SOLAR for determining phenotypic, genetic, and environmental correlations between AN-q and CMRFs (e.g., BMI, HOMA-IR, lipids, blood pressure, hs-C-reactive protein (CRP), and Harvard physical fitness score (PFS)). The genetic and environmental correlations were subsequently used in mediation analysis (AMOS program). Model comparisons were made using goodness-of-fit indexes. Heritability of AN-q was 0.75 (p<0.0001). It was positively/significantly (p<0.05) correlated with traits such as BMI, HOMA-IR, and CRP, and negatively with HDL-C and PFS. Of the models tested, indirect mediation analysis of BMI→HOMA-IR→AN-q yielded lower goodness-of-fit than a partial mediation model where BMI explained the relationship with both HOMA-IR and AN-q simultaneously. Using complex models, BMI was associated with AN-q and IR mediating most of the CMRFs; but no relationship between IR and AN-q. Our study suggests that obesity explains the association of IR with AN, but no causal relationship between IR and AN in Mexican American children.

Background Reduced renal excretion of uric acid plays a significant role in the development of hyperuricemia and gout in adults. Hyperuricemia has been associated with chronic kidney disease and cardiovascular disease in children and adults. There are limited genome-wide association studies associating genetic polymorphisms with renal urate excretion measures. Therefore, we investigated the genetic factors that influence the excretion of uric acid and related indices in 768 Hispanic children of the Viva La Familia Study. Methods We performed a genome-wide association analysis for 24-h urinary excretion measures such as urinary uric acid/urinary creatinine ratio, uric acid clearance, fractional excretion of uric acid, and glomerular load of uric acid in SOLAR, while accounting for non-independence among family members. Results All renal urate excretion measures were significantly heritable ( p <2 × 10 −6 ) and ranged from 0.41 to 0.74. Empirical threshold for genome-wide significance was set at p <1 × 10 −7 . We observed a strong association ( p  < 8 × 10 −8 ) of uric acid clearance with a single nucleotide polymorphism (SNP) in zinc finger protein 446 ( ZNF446 ) (rs2033711 (A/G), MAF: 0.30). The minor allele (G) was associated with increased uric acid clearance. Also, we found suggestive associations of uric acid clearance with SNPs in ZNF324 , ZNF584 , and ZNF132 (in a 72 kb region of 19q13; p <1 × 10 −6 , MAFs: 0.28–0.31). Conclusion For the first time, we showed the importance of 19q13 region in the regulation of renal urate excretion in Hispanic children. Our findings indicate differences in inherent genetic architecture and shared environmental risk factors between our cohort and other pediatric and adult populations.

The clinical impact of rare loss-of-function variants has yet to be determined for most genes. Integration of DNA sequencing data with electronic health records (EHRs) could enhance our understanding of the contribution of rare genetic variation to human disease 1 . By leveraging 10,900 whole-exome sequences linked to EHR data in the Penn Medicine Biobank, we addressed the association of the cumulative effects of rare predicted loss-of-function variants for each individual gene on human disease on an exome-wide scale, as assessed using a set of diverse EHR phenotypes. After discovering 97 genes with exome-by-phenome-wide significant phenotype associations ( P  < 10 −6 ), we replicated 26 of these in the Penn Medicine Biobank, as well as in three other medical biobanks and the population-based UK Biobank. Of these 26 genes, five had associations that have been previously reported and represented positive controls, whereas 21 had phenotype associations not previously reported, among which were genes implicated in glaucoma, aortic ectasia, diabetes mellitus, muscular dystrophy and hearing loss. These findings show the value of aggregating rare predicted loss-of-function variants into ‘gene burdens’ for identifying new gene–disease associations using EHR phenotypes in a medical biobank. We suggest that application of this approach to even larger numbers of individuals will provide the statistical power required to uncover unexplored relationships between rare genetic variation and disease phenotypes. Analysis of 10,900 whole-exome sequences linked to electronic health care records in the Penn Medicine Biobank enabled an exome-wide study of the phenotypic effects of rare loss-of-function gene variants, identifying new gene–disease associations that replicated across other biobanks.

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals ( P  < 5 × 10 − 9 ), including two secondary signals. Notably, we identified and replicated a novel low-frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine. Here, the authors leverage whole-genome sequencing from >88,000 diverse individuals to uncover 18 BMI-related genetic signals, including novel variants in participants of African genetic ancestry, highlighting the value of diversity in genetic discovery.

BackgroundMetabolic 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.MethodsWe 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.ResultsWe 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.ConclusionsThe genetic underpinnings of metabolic biomarkers can affect the early development of CMD, partly mediated by the biomarkers.ImpactThis 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.

Background Although the effect of the fat mass and obesity-associated (FTO) gene on adiposity is well established, there is a lack of evidence whether physical activity (PA) modifies the effect of FTO variants on obesity in Latino populations. Therefore, the purpose of this study was to examine PA influences and interactive effects between FTO variants and PA on measures of adiposity in Latinos. Results After controlling for age and sex, participants who did not engage in regular PA exhibited higher BMI, fat mass, HC, and WC with statistical significance (P < 0.001). Although significant associations between the three FTO genotypes and adiposity measures were found, none of the FTO genotype by PA interaction assessments revealed nominally significant associations. However, several of such interactive influences exhibited considerable trend towards association. Conclusions These data suggest that adiposity measures are associated with PA and FTO variants in Latinos, but the impact of their interactive influences on these obesity measures appear to be minimal. Future studies with large sample sizes may help to determine whether individuals with specific FTO variants exhibit differential responses to PA interventions.

While studies have reported genetic loci affecting serum urate (SU) concentrations, few studies have been conducted in minority populations. Our objective for this study was to identify genetic loci regulating SU in a multigenerational family-based cohort of American Indians, the Strong Heart Family Study (SHFS). We genotyped 162,718 single nucleotide polymorphisms (SNPs) in 2000 SHFS participants using an Illumina MetaboChip array. A genome-wide association analysis of SU was conducted using measured genotype analysis approach accounting for kinships in SOLAR, and meta-analysis in METAL. Our results showed strong association of SU with rs4481233, rs9998811, rs7696092 and rs13145758 (minor allele frequency (MAF) = 25–44%; P  < 3 × 10 −14 ) of solute carrier family 2, member 9 ( SLC2A9 ) and rs41481455, rs2231142 and rs1481012 (MAF = 29%; p < 3 × 10 −9 ) of ATP-binding cassette protein, subfamily G, member 2 ( ABCG2 ). Carriers of G alleles of rs9998811, rs4148155 and rs1481012 and A alleles of rs4481233, rs7696092 and rs13145758 and rs2231142 had lower SU concentrations as compared to non-carriers. Genetic analysis of SU conditional on significant SLC2A9 and ABCG2 SNPs revealed new loci, nucleobindin 1 ( NUCB1 ) and neuronal PAS domain protein 4 ( NPAS4 ) (p <6× 10 −6 ). To identify American Indian-specific SNPs, we conducted targeted sequencing of key regions of SLC2A9 . A total of 233 SNPs were identified of which 89 were strongly associated with SU (p < 7.1 × 10 −10 ) and 117 were American Indian specific. Analysis of key SNPs in cohorts of Mexican-mestizos, European, Indian and East Asian ancestries showed replication of common SNPs, including our lead SNPs. Our results demonstrate the association of SU with uric acid transporters in a minority population of American Indians and potential novel associations of SU with neuronal-related genes which warrant further investigation.

Type 2 diabetes (T2D) is a complex metabolic disease that is more prevalent in ethnic groups such as Mexican Americans, and is strongly associated with the risk factors obesity and insulin resistance. The goal of this study was to perform whole genome gene expression profiling in adipose tissue to detect common patterns of gene regulation associated with obesity and insulin resistance. We used phenotypic and genotypic data from 308 Mexican American participants from the Veterans Administration Genetic Epidemiology Study (VAGES). Basal fasting RNA was extracted from adipose tissue biopsies from a subset of 75 unrelated individuals, and gene expression data generated on the Illumina BeadArray platform. The number of gene probes with significant expression above baseline was approximately 31,000. We performed multiple regression analysis of all probes with 15 metabolic traits. Adipose tissue had 3,012 genes significantly associated with the traits of interest (false discovery rate, FDR ≤ 0.05). The significance of gene expression changes was used to select 52 genes with significant (FDR ≤ 10(-4)) gene expression changes across multiple traits. Gene sets/Pathways analysis identified one gene, alcohol dehydrogenase 1B (ADH1B) that was significantly enriched (P < 10(-60)) as a prime candidate for involvement in multiple relevant metabolic pathways. Illumina BeadChip derived ADH1B expression data was consistent with quantitative real time PCR data. We observed significant inverse correlations with waist circumference (2.8 x 10(-9)), BMI (5.4 x 10(-6)), and fasting plasma insulin (P < 0.001). These findings are consistent with a central role for ADH1B in obesity and insulin resistance and provide evidence for a novel genetic regulatory mechanism for human metabolic diseases related to these traits.