Background: Several methods have been used to account for measurement error inherent in using ambient concentration of particulate matter <2.5 μm/m³ (PM_2.5) as a proxy for personal exposure. Such methods commonly rely on the estimated correlation between ambient and personal PM_2.5 concentrations (r). However, studies of r have not been systematically and quantitatively assessed for publication bias or heterogeneity. Methods: We searched 7 electronic reference databases for studies of the within-participant correlation between ambient and personal PM_2.5. Results: We identified 567 candidate studies, 18 (3%) of which met inclusion criteria and were abstracted. The studies were published between 1999 and 2008, representing 619 nonsmoking participants aged 6–93 years in 17 European and North American cities. Correlation coefficients (median 0.54; range 0.09–0.83) were based on a median of 8 ambient-personal PM_2.5 pairs per participant (range 5–20) collected over 27–547 days. Overall, there was little evidence for publication bias (funnel plot symmetry tests: Begg's log-rank test, P = 0.9; Egger's regression asymmetry test, P = 0.2). However, strong evidence for heterogeneity was noted (Cochran's Q test for heterogeneity, P < 0.001). European locales, eastern longitudes in North America, higher ambient PM_2.5 concentrations, higher relative humidity, and lower between-participant variation in r were associated with increased r. Conclusions: Characteristics of participants, studies, and the environments in which they are conducted may affect the accuracy of ambient PM_2.5 as a proxy for personal exposure.

Despite evidence of the clustering of metabolic syndrome components, current approaches for identifying unifying genetic mechanisms typically evaluate clinical categories that do not provide adequate etiological information. Here, we used data from 19,486 European American and 6,287 African American Candidate Gene Association Resource Consortium participants to identify loci associated with the clustering of metabolic phenotypes. Six phenotype domains (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, central obesity, and elevated plasma glucose) encompassing 19 quantitative traits were examined. Principal components analysis was used to reduce the dimension of each domain such that >55% of the trait variance was represented within each domain. We then applied a statistically efficient and computational feasible multivariate approach that related eight principal components from the six domains to 250,000 imputed SNPs using an additive genetic model and including demographic covariates. In European Americans, we identified 606 genome-wide significant SNPs representing 19 loci. Many of these loci were associated with only one trait domain, were consistent with results in African Americans, and overlapped with published findings, for instance central obesity and FTO. However, our approach, which is applicable to any set of interval scale traits that is heritable and exhibits evidence of phenotypic clustering, identified three new loci in or near APOC1, BRAP, and PLCG1, which were associated with multiple phenotype domains. These pleiotropic loci may help characterize metabolic dysregulation and identify targets for intervention. The metabolic syndrome represents a clustering of metabolic phenotypes (e.g. elevated blood pressure, cholesterol levels, and plasma glucose, as well as abdominal obesity) and is associated with an increased risk of atherosclerosis and type 2 diabetes. Although multiple genes influencing the specific metabolic syndrome components have been reported, few studies have evaluated the genetic underpinnings of the syndrome as a whole. Here, we describe an approach to evaluate multiple clustered traits, which allows us to test whether common genetic variants influence the co-occurrence of one or more metabolic phenotypes. By examining approximately 20,000 European American and 6,200 African American participants from five studies, we show that three regions on chromosomes 12, 19, and 20 are associated with multiple metabolic phenotypes. These genetic variants are highly intriguing candidates that may increase our understanding of the biologic basis of the clustering of metabolic phenotypes and help identify targets for early intervention.

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women’s Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.