William J. Gauderman

A genome-wide association meta-analysis identifies new childhood obesity loci

Multiple genetic variants have been associated with adult obesity and a few with severe obesity in childhood; however, less progress has been made in establishing genetic influences on common early-onset obesity. We performed a North American, Australian and European collaborative meta-analysis of 14 studies consisting of 5,530 cases (≥95th percentile of body mass index (BMI)) and 8,318 controls (<50th percentile of BMI) of European ancestry. Taking forward the eight newly discovered signals yielding association with P < 5 × 10−6 in nine independent data sets (2,818 cases and 4,083 controls), we observed two loci that yielded genome-wide significant combined P values near OLFM4 at 13q14 (rs9568856; P = 1.82 × 10−9; odds ratio (OR) = 1.22) and within HOXB5 at 17q21 (rs9299; P = 3.54 × 10−9; OR = 1.14). Both loci continued to show association when two extreme childhood obesity cohorts were included (2,214 cases and 2,674 controls). These two loci also yielded directionally consistent associations in a previous meta-analysis of adult BMI.

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. The EXPOLIS study assessed workday time–microenvironment–activity patterns among a total of 1427 subjects (age 19–60 years) in Helsinki (Finland), Athens (Greece), Basel (Switzerland), Grenoble (France), Milan (Italy), Prague (Czech Republic), and Oxford (UK). Subjects completed time–microenvironment–activity diaries during two working days. We present time spent indoors — at home, at work, and elsewhere, and time exposed to tobacco smoke indoors for all cities. The contribution of sociodemographic factors has been assessed using regression models. More than 90% of the variance in indoor time–microenvironment–activity patterns originated from differences between and within subjects rather than between cities. The most common factors that were associated with indoor time–microenvironment–activity patterns, with similar contributions in all cities, were the specific work status, employment status, whether the participants were living alone, and whether the participants had children at home. Gender and season were associated with indoor time–microenvironment–activity patterns as well but the effects were rather heterogeneous across the seven cities. Exposure to second-hand tobacco smoke differed substantially across these cities. The heterogeneity of these factors across cities may reflect city-specific characteristics but selection biases in the sampled local populations may also explain part of the findings. Determinants of time–microenvironment–activity patterns need to be taken into account in exposure assessment, epidemiological analyses, exposure simulations, as well as in the development of preventive strategies that focus on time–microenvironment–activity patterns that ultimately determine exposures.

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. Genet. Epidemiol. 2011.

Genetic Variations in Nitric Oxide Synthase and Arginase Influence Exhaled Nitric Oxide Levels in Children

To cite this article: Salam MT, Bastain TM, Rappaport EB, Islam T, Berhane K, Gauderman WJ, Gilliland FD. Genetic variations in nitric oxide synthase and arginase influence exhaled nitric oxide levels in children. Allergy 2011; 66: 412–419.

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.

The ADRB3 Trp64Arg variant and obesity in African-American breast cancer cases.

Objective:To determine if a missense change at codon 64 of ADRB3 (Trp64Arg), a candidate obesity gene, is associated with obesity and levels of subcutaneous or visceral fat in African-American breast cancer cases. Several observational studies have found that women, who are overweight or obese at the time of diagnosis, as well as those who gain weight after diagnosis, are at greater risk for breast cancer recurrence and death than non-overweight women.Design:Prospective cohort of breast cancer cases.Subjects:219 African-American breast cancer patients participating in the Los Angeles component of the Health, Eating, Activity and Lifestyle Study.Measures:ADRB3 Trp64Arg genotype, measures of weight including body mass index (BMI), weight gain (weight 5 years before diagnosis compared with weight at 30 months after diagnosis), obesity (BMI⩾30 kg/m2), waist/hip circumference and visceral or subcutaneous fat were determined by magnetic resonance imaging.Results:African-American women who were homozygous for the ADRB3 wild-type allele had significantly higher mean visceral fat levels than women who carried the variant (P=0.04), and were significantly more likely to be obese (odd ratios (OR)=2.1, 95% confidence interval (CI)=1.1–4.2). The association with obesity was most pronounced among women who were premenopausal (OR=4.8, 95% CI=1.3–18), who received chemotherapy for their breast cancer (OR=6.1, 95% CI=1.8–20), or who were not physically active (OR=3.9, 95% CI=1.5–9.7).Conclusion:The wild-type allele of the ADRB3 missense change was associated with measures of obesity in our sample of African-American women. The association was modified by menopausal status, history of chemotherapy and modest levels of physical activity. These results will need to be confirmed in an independent sample.

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. In particular, several self‐contained set tests have been proposed to directly or indirectly “adapt” to the a priori unknown proportion and distribution of effects of the truly associated SNPs in the set, which is a major determinant of their power. A popular adaptive set‐based test is the adaptive rank truncated product (ARTP), which seeks the set of SNPs that yields the best‐combined evidence of association. We compared the standard ARTP, several ARTP variations we introduced, and other adaptive methods in a comprehensive simulation study to evaluate their performance. We used permutations to assess significance for all the methods and thus provide a level playing field for comparison. We found the standard ARTP test to have the highest power across our simulations followed closely by the global model of random effects (GMRE) and a least absolute shrinkage and selection operator (LASSO)‐based test.