Prenatal ambient air pollution exposure and child weight trajectories from the 3rd trimester of pregnancy to 2 years of age: a cohort study.

BACKGROUND: Prenatal air pollution exposure may increase risk for childhood obesity. However, few studies have evaluated in utero growth measures and infant weight trajectories. This study will evaluate the associations of prenatal exposure to ambient air pollutants with weight trajectories from the 3rd trimester through age 2 years. METHODS: We studied 490 pregnant women who were recruited from the Maternal and Development Risks from Environmental and Social Stressors (MADRES) cohort, which comprises a low-income, primarily Hispanic population in Los Angeles, California. Nitrogen dioxide (NO2), particulate matter < 10 µm (PM10), particulate matter < 2.5 µm (PM2.5), and ozone (O3) concentrations during pregnancy were estimated from regulatory air monitoring stations. Fetal weight was estimated from maternal ultrasound records. Infant/child weight measurements were extracted from medical records or measured during follow-up visits. Piecewise spline models were used to assess the effect of air pollutants on weight, overall growth, and growth during each period. RESULTS: The mean (SD) prenatal exposure concentrations for NO2, PM2.5, PM10, and O3 were 16.4 (2.9) ppb, 12.0 (1.1) µg/m3, 28.5 (4.7) µg/m3, and 26.2 (2.9) ppb, respectively. Comparing an increase in prenatal average air pollutants from the 10th to the 90th percentile, the growth rate from the 3rd trimester to age 3 months was significantly increased (1.55% [95%CI 1.20%, 1.99%] for PM2.5 and 1.64% [95%CI 1.27%, 2.13%] for NO2), the growth rate from age 6 months to age 2 years was significantly decreased (0.90% [95%CI 0.82%, 1.00%] for NO2), and the attained weight at age 2 years was significantly lower (- 7.50% [95% CI - 13.57%, - 1.02%] for PM10 and - 7.00% [95% CI - 11.86%, - 1.88%] for NO2). CONCLUSIONS: Prenatal ambient air pollution was associated with variable changes in growth rate and attained weight from the 3rd trimester to age 2 years. These results suggest continued public health benefits of reducing ambient air pollution levels, particularly in marginalized populations.

A trans-ancestral meta-analysis of genome-wide association studies reveals loci associated with childhood obesity.

Although hundreds of genome-wide association studies-implicated loci have been reported for adult obesity-related traits, less is known about the genetics specific for early-onset obesity and with only a few studies conducted in non-European populations to date. Searching for additional genetic variants associated with childhood obesity, we performed a trans-ancestral meta-analysis of 30 studies consisting of up to 13 005 cases (≥95th percentile of body mass index (BMI) achieved 2-18 years old) and 15 599 controls (consistently <50th percentile of BMI) of European, African, North/South American and East Asian ancestry. Suggestive loci were taken forward for replication in a sample of 1888 cases and 4689 controls from seven cohorts of European and North/South American ancestry. In addition to observing 18 previously implicated BMI or obesity loci, for both early and late onset, we uncovered one completely novel locus in this trans-ancestral analysis (nearest gene, METTL15). The variant was nominally associated with only the European subgroup analysis but had a consistent direction of effect in other ethnicities. We then utilized trans-ancestral Bayesian analysis to narrow down the location of the probable causal variant at each genome-wide significant signal. Of all the fine-mapped loci, we were able to narrow down the causative variant at four known loci to fewer than 10 single nucleotide polymorphisms (SNPs) (FAIM2, GNPDA2, MC4R and SEC16B loci). In conclusion, an ethnically diverse setting has enabled us to both identify an additional pediatric obesity locus and further fine-map existing loci.

Using Bayes model averaging to leverage both gene main effects and G × E interactions to identify genomic regions in genome-wide association studies.

Genome-wide association studies typically search for marginal associations between a single-nucleotide polymorphism (SNP) and a disease trait while gene-environment (G × E) interactions remain generally unexplored. More powerful methods beyond the simple case-control (CC) approach leverage either marginal effects or CC ascertainment to increase power. However, these potential gains depend on assumptions whose aptness is often unclear a priori. Here, we review G × E methods and use simulations to highlight performance as a function of main and interaction effects and the association of the two factors in the source population. Substantial variation in performance between methods leads to uncertainty as to which approach is most appropriate for any given analysis. We present a framework that (a) balances the robustness of a CC approach with the power of the case-only (CO) approach; (b) incorporates main SNP effects; (c) allows for incorporation of prior information; and (d) allows the data to determine the most appropriate model. Our framework is based on Bayes model averaging, which provides a principled statistical method for incorporating model uncertainty. We average over inclusion of parameters corresponding to the main and G × E interaction effects and the G-E association in controls. The resulting method exploits the joint evidence for main and interaction effects while gaining power from a CO equivalent analysis. Through simulations, we demonstrate that our approach detects SNPs within a wide range of scenarios with increased power over current methods. We illustrate the approach on a gene-environment scan in the USC Children's Health Study.

Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies.

Variants identified in recent genome-wide association studies based on the common-disease common-variant hypothesis are far from fully explaining the hereditability of complex traits. Rare variants may, in part, explain some of the missing hereditability. Here, we explored the advantage of the extreme phenotype sampling in rare-variant analysis and refined this design framework for future large-scale association studies on quantitative traits. We first proposed a power calculation approach for a likelihood-based analysis method. We then used this approach to demonstrate the potential advantages of extreme phenotype sampling for rare variants. Next, we discussed how this design can influence future sequencing-based association studies from a cost-efficiency (with the phenotyping cost included) perspective. Moreover, we discussed the potential of a two-stage design with the extreme sample as the first stage and the remaining nonextreme subjects as the second stage. We demonstrated that this two-stage design is a cost-efficient alternative to the one-stage cross-sectional design or traditional two-stage design. We then discussed the analysis strategies for this extreme two-stage design and proposed a corresponding design optimization procedure. To address many practical concerns, for example measurement error or phenotypic heterogeneity at the very extremes, we examined an approach in which individuals with very extreme phenotypes are discarded. We demonstrated that even with a substantial proportion of these extreme individuals discarded, an extreme-based sampling can still be more efficient. Finally, we expanded the current analysis and design framework to accommodate the CMC approach where multiple rare-variants in the same gene region are analyzed jointly.

Pediatric glioma and medulloblastoma risk and population demographics: a Poisson regression analysis.

BACKGROUND: The incidence of pediatric brain tumors varies by race and ethnicity, but these relationships may be confounded by socioeconomic status (SES). In this study, the Surveillance, Epidemiology, and End Results Program (SEER) database was evaluated for associations between race/ethnicity and pediatric glioma and medulloblastoma risk with adjustment for SES. METHODS: Pediatric glioma and medulloblastoma cases from the SEER database (years: 2000-2016) were included. Differences in incidence rates by ethnicity, sex, age, and SES-related factors were evaluated by calculation of age-adjusted incidence rates (AAIRs) and annual percent change (APC). SES-related factors (percentage without less than high school graduation, median household income, and percentage foreign-born) were derived from the census at the county-level (year: 2000). Multivariable Poisson regression models with adjustment for selected covariates were constructed to evaluate risk factors. RESULTS: The highest AAIRs of pediatric glioma were observed among non-Hispanic Whites (AAIR: 2.91 per 100 000, 95%-CI: 2.84-2.99). An increasing incidence of pediatric glioma by calendar time was observed among non-Hispanic Whites and non-Hispanic Blacks (APC: 0.97%, 95%-CI: 0.28-1.68 and APC: 1.59%, 95%-CI: 0.03-3.18, respectively). Hispanic and non-Hispanic Black race/ethnicity was associated with lower risk when compared with non-Hispanic White (incidence rate ratios [IRRs]: 0.66, 95%-CI: 0.63-0.70; and 0.69, 95%-CI: 0.65-0.74, respectively). For medulloblastoma, the highest AAIR was observed for non-Hispanic Whites with a positive APC (1.52%, 95%-CI: 0.15-2.91). Hispanics and non-Hispanic Blacks had statistically significant lower IRRs compared with non-Hispanic Whites (IRRs: 0.83, 95%-CI: 0.73-0.94; and 0.72, 95%-CI: 0.59-0.87, respectively). CONCLUSION: Non-Hispanic White race/ethnicity was associated with higher pediatric glioma and medulloblastoma IRRs in models with adjustments for SES.

Increased yield of actionable mutations using multi-gene panels to assess hereditary cancer susceptibility in an ethnically diverse clinical cohort.

This study aims to assess multi-gene panel testing in an ethnically diverse clinical cancer genetics practice. We conducted a retrospective study of individuals with a personal or family history of cancer undergoing clinically indicated multi-gene panel tests of 6-110 genes, from six commercial laboratories. The 475 patients in the study included 228 Hispanics (47.6%), 166 non-Hispanic Whites (35.4%), 55 Asians (11.6%), 19 Blacks (4.0%), and seven others (1.5%). Panel testing found that 15.6% (74/475) of patients carried deleterious mutations for a total of 79 mutations identified. This included 7.4% (35/475) of patients who had a mutation identified that would not have been tested with a gene-by-gene approach. The identification of a panel-added mutation impacted clinical management for most of cases (69%, 24/35), and genetic testing was recommended for the first degree relatives of nearly all of them (91%, 32/35). Variants of uncertain significance (VUSs) were identified in a higher proportion of tests performed in ethnic minorities. Multi-gene panel testing increases the yield of mutations detected and adds to the capability of providing individualized cancer risk assessment. VUSs represent an interpretive challenge due to less data available outside of White, non-Hispanic populations. Further studies are necessary to expand understanding of the implementation and utilization of panels across broad clinical settings and patient populations.

Genotype Imputation for Latinos Using the HapMap and 1000 Genomes Project Reference Panels.

Genotype imputation is a vital tool in genome-wide association studies (GWAS) and meta-analyses of multiple GWAS results. Imputation enables researchers to increase genomic coverage and to pool data generated using different genotyping platforms. HapMap samples are often employed as the reference panel. More recently, the 1000 Genomes Project resource is becoming the primary source for reference panels. Multiple GWAS and meta-analyses are targeting Latinos, the most populous, and fastest growing minority group in the US. However, genotype imputation resources for Latinos are rather limited compared to individuals of European ancestry at present, largely because of the lack of good reference data. One choice of reference panel for Latinos is one derived from the population of Mexican individuals in Los Angeles contained in the HapMap Phase 3 project and the 1000 Genomes Project. However, a detailed evaluation of the quality of the imputed genotypes derived from the public reference panels has not yet been reported. Using simulation studies, the Illumina OmniExpress GWAS data from the Los Angles Latino Eye Study and the MACH software package, we evaluated the accuracy of genotype imputation in Latinos. Our results show that the 1000 Genomes Project AMR + CEU + YRI reference panel provides the highest imputation accuracy for Latinos, and that also including Asian samples in the panel can reduce imputation accuracy. We also provide the imputation accuracy for each autosomal chromosome using the 1000 Genomes Project panel for Latinos. Our results serve as a guide to future imputation based analysis in Latinos.

Effects of glutathione S-transferase P1, M1, and T1 on acute respiratory illness in school children.

The relationships between glutathione S-transferase (GST) M1, GSTT1, and GSTP1 genotypes and acute respiratory illness were investigated in a cohort of fourth grade school children aged 9-11 years who resided in 12 southern California communities. We used respiratory illness-related absences as a measure of respiratory illness occurrence. We ascertained respiratory illness-related school absences using an active surveillance system from January 1996 through June 1996. Genotypes for GSTM1 (null versus present), GSTT1 (null versus present), and GSTP1 (Ile105Val) were determined using genomic DNA from buccal cell specimens. The effects of GST genotypes on respiratory illness were assessed using stratified absence incidence rates and Poisson regression models. GSTP1 genotype was associated with risk for respiratory illness severe enough to result in a school absence. Children who were homozygous for the Val105 variant allele had lower incidence rates of upper and lower respiratory illnesses than did children who were homozygous for the Val105 allele. Children inheriting at least one Val105 allele were protected from respiratory illnesses (relative risk, 0.80; 95% confidence interval, 0.65-0.99). GSTM1 and T1 genotypes were not associated with respiratory illness. We conclude that GSTP1 genotype influences the risk or severity of respiratory infections in school-aged children.