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.

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries.

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues.

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.

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.

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.

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.

Adaptive Set-Based Methods for Association Testing.

With a typical sample size of a few thousand subjects, a single genome-wide association study (GWAS) using traditional one single nucleotide polymorphism (SNP)-at-a-time methods can only detect genetic variants conferring a sizable effect on disease risk. Set-based methods, which analyze sets of SNPs jointly, can detect variants with smaller effects acting within a gene, a pathway, or other biologically relevant sets. Although self-contained set-based methods (those that test sets of variants without regard to variants not in the set) are generally more powerful than competitive set-based approaches (those that rely on comparison of variants in the set of interest with variants not in the set), there is no consensus as to which self-contained methods are best.

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.

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.

Indoor time-microenvironment-activity patterns in seven regions of Europe.

Personal exposure to environmental substances is largely determined by time-microenvironment-activity patterns while moving across locations or microenvironments. Therefore, time-microenvironment-activity data are particularly useful in modeling exposure. We investigated determinants of workday time-microenvironment-activity patterns of the adult urban population in seven European cities.

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.