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 ancestry 1-3. 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-specific 4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may Reprints and permissions information is available at http://www.nature.com/reprints.

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.

In clinical trials, missing data commonly arise through nonadherence to the randomized treatment or to study procedure. For trials in which recurrent event endpoints are of interests, conventional analyses using the proportional intensity model or the count model assume that the data are missing at random, which cannot be tested using the observed data alone. Thus, sensitivity analyses are recommended. We implement the control-based multiple imputation as sensitivity analyses for the recurrent event data. We model the recurrent event using a piecewise exponential proportional intensity model with frailty and sample the parameters from the posterior distribution. We impute the number of events after dropped out and correct the variance estimation using a bootstrap procedure. We apply the method to an application of sitagliptin study.

Dental caries is characterized by a dysbiotic shift at the biofilm–tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study’s analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography–tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)–machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10−3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose ( P = 3.0 × 10−6) and N-acetylneuraminate (p = 6.8 × 10−6) with higher ECC prevalence, as well as catechin ( P = 4.7 × 10−6) and epicatechin ( P = 2.9 × 10−6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.

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