Improved two-step testing of genome-wide gene-environment interactions.

Two-step tests for gene-environment ( G × E $G\times E$ ) interactions exploit marginal single-nucleotide polymorphism (SNP) effects to improve the power of a genome-wide interaction scan. They combine a screening step based on marginal effects used to "bin" SNPs for weighted hypothesis testing in the second step to deliver greater power over single-step tests while preserving the genome-wide Type I error. However, the presence of many SNPs with detectable marginal effects on the trait of interest can reduce power by "displacing" true interactions with weaker marginal effects and by adding to the number of tests that need to be corrected for multiple testing. We introduce a new significance-based allocation into bins for Step-2 G × E $G\times E$ testing that overcomes the displacement issue and propose a computationally efficient approach to account for multiple testing within bins. Simulation results demonstrate that these simple improvements can provide substantially greater power than current methods under several scenarios. An application to a multistudy collaboration for understanding colorectal cancer reveals a G × Sex interaction located near the SMAD7 gene.

Air pollution and age-dependent changes in emotional behavior across early adolescence in the U.S.

Recent studies have linked air pollution to increased risk for behavioral problems during development, albeit with inconsistent findings. Additional longitudinal studies are needed that consider how emotional behaviors may be affected when exposure coincides with the transition to adolescence - a vulnerable time for developing mental health difficulties. This study investigates if annual average PM2.5 and NO2 exposure at ages 9-10 years moderates age-related changes in internalizing and externalizing behaviors over a 2-year follow-up period in a large, nationwide U.S. sample of participants from the Adolescent Brain Cognitive Development (ABCD) Study®. Air pollution exposure was estimated based on the residential address of each participant using an ensemble-based modeling approach. Caregivers answered questions from the Child Behavior Checklist (CBCL) at the baseline, 1-year follow-up, and 2-year follow-up visits, for a total of 3 waves of data; from the CBCL we obtained scores on internalizing and externalizing problems plus 5 syndrome scales (anxious/depressed, withdrawn/depressed, rule-breaking behavior, aggressive behavior, and attention problems). Zero-inflated negative binomial models were used to examine both the main effect of age as well as the interaction of age with each pollutant on behavior while adjusting for various socioeconomic and demographic characteristics. Against our hypothesis, there was no evidence that greater air pollution exposure was related to more behavioral problems with age over time.

A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes.

MOTIVATION: While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes. RESULTS: Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e. GxE pleiotropy), our approach offers substantial gain in power (18-43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two loci with an overall GxE effect on the vector of lipids, one of which was missed by the competing approaches. AVAILABILITY AND IMPLEMENTATION: We provide an R package MPGE implementing the proposed approach which is available from CRAN: https://cran.r-project.org/web/packages/MPGE/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint.

Defined by their genetic profile, individuals may exhibit differential clinical outcomes due to an environmental exposure. Identifying subgroups based on specific exposure-modifying genes can lead to targeted interventions and focused studies. Genome-wide interaction scans (GWIS) can be performed to identify such genes, but these scans typically suffer from low power due to the large multiple testing burden. We provide a novel framework for powerful two-step hypothesis tests for GWIS with a time-to-event endpoint under the Cox proportional hazards model. In the Cox regression setting, we develop an approach that prioritizes genes for Step-2 G×E testing based on a carefully constructed Step-1 screening procedure. Simulation results demonstrate this two-step approach can lead to substantially higher power for identifying gene-environment ( G×E ) interactions compared to the standard GWIS while preserving the family wise error rate over a range of scenarios. In a taxane-anthracycline chemotherapy study for breast cancer patients, the two-step approach identifies several gene expression by treatment interactions that would not be detected using the standard GWIS.

Mapping the 17q12-21.1 Locus for Variants Associated with Early-Onset Asthma in African Americans.

Rationale: The 17q12-21.1 locus is one of the most highly replicated genetic associations with asthma. Individuals of African descent have lower linkage disequilibrium in this region, which could facilitate identifying causal variants.Objectives: To identify functional variants at 17q12-21.1 associated with early-onset asthma among African American individuals.Methods: We evaluated African American participants from SAPPHIRE (Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity) (n = 1,940), SAGE II (Study of African Americans, Asthma, Genes and Environment) (n = 885), and GCPD-A (Study of the Genetic Causes of Complex Pediatric Disorders-Asthma) (n = 2,805). Associations with asthma onset at ages under 5 years were meta-analyzed across cohorts. The lead signal was reevaluated considering haplotypes informed by genetic ancestry (i.e., African vs. European). Both an expression-quantitative trait locus analysis and a phenome-wide association study were performed on the lead variant.Measurements and Main Results: The meta-analyzed results from SAPPHIRE, SAGE II, and the GCPD-A identified rs11078928 as the top association for early-onset asthma. A haplotype analysis suggested that the asthma association partitioned most closely with the rs11078928 genotype. Genetic ancestry did not appear to influence the effect of this variant. In the expression-quantitative trait locus analysis, rs11078928 was related to alternative splicing of GSDMB (gasdermin-B) transcripts. The phenome-wide association study of rs11078928 suggested that this variant was predominantly associated with asthma and asthma-associated symptoms.Conclusions: A splice-acceptor polymorphism appears to be a causal variant for asthma at the 17q12-21.1 locus. This variant appears to have the same magnitude of effect in individuals of African and European descent.

Childhood traffic-related air pollution and adverse changes in subclinical atherosclerosis measures from childhood to adulthood.

BACKGROUND: Chronic exposure to air pollutants is associated with increased risk of cardiovascular disease (CVD) among adults. However, little is known about how air pollution may affect the development of subclinical atherosclerosis in younger populations. Carotid artery intima-media thickness (CIMT) is a measure of subclinical atherosclerosis that provides insight into early CVD pathogenesis. METHODS: In a pilot study of 70 participants from the Southern California Children's Health Study, we investigated CIMT progression from childhood to adulthood. Using carotid artery ultrasound images obtained at age 10 and follow-up images at age 21-22, we examined associations between childhood ambient and traffic-related air pollutants with changes in CIMT over time and attained adult CIMT using linear mixed-effects models adjusted for potential confounders. Average residential childhood exposures (i.e., birth to time of measurement at 10-11 years) were assigned for regional, ambient pollutants (ozone, nitrogen dioxide, particulate matter, interpolated from regulatory air monitoring data) and traffic-related nitrogen oxides (NOx) by road class (modeled using the CALINE4 line source dispersion model). Traffic density was calculated within a 300-m residential buffer. RESULTS: For each 1 standard deviation (SD) increase in childhood traffic-related total NOx exposure, we observed greater yearly rate of change in CIMT from childhood to adulthood (ß: 2.17 µm/yr, 95% CI: 0.78-3.56). Increases in annual rate of CIMT change from childhood to adulthood also were observed with freeway NOx exposure (ß: 2.24 µm/yr, 95% CI: 0.84-3.63) and traffic density (ß: 2.11 µm/yr, 95% CI: 0.79-3.43). Traffic exposures were also related to increases in attained CIMT in early adulthood. No associations of CIMT change or attained level were observed with ambient pollutants. CONCLUSIONS: Overall, we observed adverse changes in CIMT over time in relation to childhood traffic-related NOx exposure and traffic density in our study population. While these results must be cautiously interpreted given the limited sample size, the observed associations of traffic measures with CIMT suggest a need for future studies to more fully explore this relationship.

The Potential Effects of Policy-driven Air Pollution Interventions on Childhood Lung Development.

Rationale: Although elevated air pollution exposure impairs lung-function development in childhood, it remains a challenge to use this information to estimate the potential public health benefits of air pollution interventions in exposed populations.Objectives: Apply G-computation to estimate hypothetical effects of several realistic scenarios for future air pollution reductions on lung growth.Methods: Mixed-effects linear regression was used to estimate FEV1 and FVC from age 11 to 15 years in 2,120 adolescents across 3 cohorts (1993-2001, 1997-2004, and 2007-2011). Models included regional pollutants (nitrogen dioxide [NO2] or particulate matter with an aerodynamic diameter ≤2.5 µm [PM2.5]) and other important covariates. Using G-computation, a causal inference-based method, we then estimated changes in mean lung growth in our population for hypothetical interventions on either NO2 or PM2.5. Confidence intervals (CIs) were computed by bootstrapping (N = 1,000).Measurements and Main Results: Compared with the effects of exposure from observed NO2 concentrations during the study period, had communities remained at 1994 to 1997 NO2 levels, FEV1 and FVC growth were estimated to have been reduced by 2.7% (95% CI, -3.6 to -1.8) and 4.2% (95% CI, -5.2 to -3.4), respectively. If NO2 concentrations had been reduced by 30%, we estimated a 4.4% increase in FEV1 growth (95% CI, 2.8-5.9) and a 7.1% increase in FVC growth (95% CI, 5.7-8.6). Comparable results were observed for PM2.5 interventions.Conclusions: We estimated that substantial increases in lung function would occur as a result of interventions that reduce NO2 or PM2.5 concentrations. These findings provide a quantification of potential health benefits of air quality improvement.

A genome-wide association study on medulloblastoma.

INTRODUCTION: Medulloblastoma is a malignant embryonal tumor of the cerebellum that occurs predominantly in children. To find germline genetic variants associated with medulloblastoma risk, we conducted a genome-wide association study (GWAS) including 244 medulloblastoma cases and 247 control subjects from Sweden and Denmark. METHODS: Genotyping was performed using Illumina BeadChips, and untyped variants were imputed using IMPUTE2. RESULTS: Fifty-nine variants in 11 loci were associated with increased medulloblastoma risk (p < 1 × 10-5), but none were statistically significant after adjusting for multiple testing (p < 5 × 10-8). Thirteen of these variants were genotyped, whereas 46 were imputed. Genotyped variants were further investigated in a validation study comprising 249 medulloblastoma cases and 629 control subjects. In the validation study, rs78021424 (18p11.23, PTPRM) was associated with medulloblastoma risk with OR in the same direction as in the discovery cohort (ORT = 1.59, pvalidation = 0.02). We also selected seven medulloblastoma predisposition genes for investigation using a candidate gene approach: APC, BRCA2, PALB2, PTCH1, SUFU, TP53, and GPR161. The strongest evidence for association was found for rs201458864 (PALB2, ORT = 3.76, p = 3.2 × 10-4) and rs79036813 (PTCH1, ORA = 0.42, p = 2.6 × 10-3). CONCLUSION: The results of this study, including a novel potential medulloblastoma risk loci at 18p11.23, are suggestive but need further validation in independent cohorts.

An admixture mapping meta-analysis implicates genetic variation at 18q21 with asthma susceptibility in Latinos.

BACKGROUND: Asthma is a common but complex disease with racial/ethnic differences in prevalence, morbidity, and response to therapies. OBJECTIVE: We sought to perform an analysis of genetic ancestry to identify new loci that contribute to asthma susceptibility. METHODS: We leveraged the mixed ancestry of 3902 Latinos and performed an admixture mapping meta-analysis for asthma susceptibility. We replicated associations in an independent study of 3774 Latinos, performed targeted sequencing for fine mapping, and tested for disease correlations with gene expression in the whole blood of more than 500 subjects from 3 racial/ethnic groups. RESULTS: We identified a genome-wide significant admixture mapping peak at 18q21 in Latinos (P = 6.8 × 10-6), where Native American ancestry was associated with increased risk of asthma (odds ratio [OR], 1.20; 95% CI, 1.07-1.34; P = .002) and European ancestry was associated with protection (OR, 0.86; 95% CI, 0.77-0.96; P = .008). Our findings were replicated in an independent childhood asthma study in Latinos (P = 5.3 × 10-3, combined P = 2.6 × 10-7). Fine mapping of 18q21 in 1978 Latinos identified a significant association with multiple variants 5' of SMAD family member 2 (SMAD2) in Mexicans, whereas a single rare variant in the same window was the top association in Puerto Ricans. Low versus high SMAD2 blood expression was correlated with case status (13.4% lower expression; OR, 3.93; 95% CI, 2.12-7.28; P < .001). In addition, lower expression of SMAD2 was associated with more frequent exacerbations among Puerto Ricans with asthma. CONCLUSION: Ancestry at 18q21 was significantly associated with asthma in Latinos and implicated multiple ancestry-informative noncoding variants upstream of SMAD2 with asthma susceptibility. Furthermore, decreased SMAD2 expression in blood was strongly associated with increased asthma risk and increased exacerbations.

A Unified Model for the Analysis of Gene-Environment Interaction.

Gene-environment (G × E) interaction is important for many complex traits. In a case-control study of a disease trait, logistic regression is the standard approach used to model disease as a function of a gene (G), an environmental factor (E), G × E interaction, and adjustment covariates. We propose an alternative model with G as the outcome and show how it provides a unified framework for obtaining results from all of the common G × E tests. These include the 1-degree-of-freedom (df) test of G × E interaction, the 2-df joint test of G and G × E, the case-only and empirical Bayes tests, and several 2-step tests. In the context of this unified model, we propose a novel 3-df test and demonstrate that it provides robust power across a wide range of underlying G × E interaction models. We demonstrate the 3-df test in a genome-wide scan of G × sex interaction for childhood asthma using data from the Children's Health Study (Southern California, 1993-2001). This scan identified a strong G × sex interaction at the phosphodiesterase gene 4D locus (PDE4D), a known asthma-related locus, with a strong effect in males (per-allele odds ratio = 1.70; P = 3.8 × 10-8) and virtually no effect in females. We describe a software program, G×EScan (University of Southern California, Los Angeles, California), which can be used to fit standard and unified models for genome-wide G × E studies.

Genetic-Epigenetic Interactions in Asthma Revealed by a Genome-Wide Gene-Centric Search.

OBJECTIVES: There is evidence to suggest that asthma pathogenesis is affected by both genetic and epigenetic variation independently, and there is some evidence to suggest that genetic-epigenetic interactions affect risk of asthma. However, little research has been done to identify such interactions on a genome-wide scale. The aim of this studies was to identify genes with genetic-epigenetic interactions associated with asthma. METHODS: Using asthma case-control data, we applied a novel nonparametric gene-centric approach to test for interactions between multiple SNPs and CpG sites simultaneously in the vicinities of 18,178 genes across the genome. RESULTS: Twelve genes, PF4, ATF3, TPRA1, HOPX, SCARNA18, STC1, OR10K1, UPK1B, LOC101928523, LHX6, CHMP4B, and LANCL1, exhibited statistically significant SNP-CpG interactions (false discovery rate = 0.05). Of these, three have previously been implicated in asthma risk (PF4, ATF3, and TPRA1). Follow-up analysis revealed statistically significant pairwise SNP-CpG interactions for several of these genes, including SCARNA18, LHX6, and LOC101928523 (p = 1.33E-04, 8.21E-04, 1.11E-03, respectively). CONCLUSIONS: Joint effects of genetic and epigenetic variation may play an important role in asthma pathogenesis. Statistical methods that simultaneously account for multiple variations across chromosomal regions may be needed to detect these types of effects on a genome-wide scale.

Traffic-Related Pollutants: Exposure and Health Effects Among Hispanic Children.

We examined traffic-related pollution (TRP) exposure and respiratory health effects in Hispanic white (HW) children, both compared with non-Hispanic white (NHW) children and according to genetically determined Native American (NA) ancestry. The sample included over 5,000 children from the Children's Health Study in California, followed during 1993-2014. HW children were 1.47 (95% confidence interval (CI): 1.24, 1.73) times more likely to live close (<500 m) to a freeway and 1.54 (95% CI: 1.26, 1.87) times more likely to live close (<75 m) to a major nonfreeway road compared with NHW children. Among HW children, those with >50% NA ancestry were >40% more likely to live close to a freeway or to a major nonfreeway road, compared with those with ≤50% NA ancestry. The association of TRP with ever having been diagnosed by a doctor as having asthma differed between HW and NHW children (P < 0.05), with the strongest association among HW children with >50% NA ancestry. Within this subgroup, those close to a major nonfreeway road were 2.16 (95% CI: 1.26, 3.69) times more likely to have ever reported asthma compared with those living further away. This paper provides evidence that HW children in southern California, especially those with greater NA ancestry, are more exposed to TRP and are potentially at greater risk for TRP-related respiratory health effects.

Genome-wide association study identifies WNT7B as a novel locus for central corneal thickness in Latinos.

The cornea is the outermost layer of the eye and is a vital component of focusing incoming light on the retina. Central corneal thickness (CCT) is now recognized to have a significant role in ocular health and is a risk factor for various ocular diseases, such as keratoconus and primary open angle glaucoma. Most previous genetic studies utilized European and Asian subjects to identify genetic loci associated with CCT. Minority populations, such as Latinos, may aid in identifying additional loci and improve our understanding of the genetic architecture of CCT. In this study, we conducted a genome-wide association study (GWAS) in Latinos, a traditionally understudied population in genetic research, to further identify loci contributing to CCT. Study participants were genotyped using either the Illumina OmniExpress BeadChip (∼730K markers) or the Illumina Hispanic/SOL BeadChip (∼2.5 million markers). All study participants were 40 years of age and older. We assessed the association between individual single nucleotide polymorphisms (SNPs) and CCT using linear regression, adjusting for age, gender and principal components of genetic ancestry. To expand genomic coverage and to interrogate additional SNPs, we imputed SNPs from the 1000 Genomes Project reference panels. We identified a novel SNP, rs10453441 (P = 6.01E-09), in an intron of WNT7B that is associated with CCT. Furthermore, WNT7B is expressed in the human cornea. We also replicated 11 previously reported loci, including IBTK, RXRA-COL5A1, COL5A1, FOXO1, LRRK1 and ZNF469 (P < 1.25E-3). These findings provide further insight into the genetic architecture of CCT and illustrate that the use of minority groups in GWAS will help identify additional loci.

Association of improved air quality with lung development in children.

BACKGROUND: Air-pollution levels have been trending downward progressively over the past several decades in southern California, as a result of the implementation of air quality-control policies. We assessed whether long-term reductions in pollution were associated with improvements in respiratory health among children. METHODS: As part of the Children's Health Study, we measured lung function annually in 2120 children from three separate cohorts corresponding to three separate calendar periods: 1994-1998, 1997-2001, and 2007-2011. Mean ages of the children within each cohort were 11 years at the beginning of the period and 15 years at the end. Linear-regression models were used to examine the relationship between declining pollution levels over time and lung-function development from 11 to 15 years of age, measured as the increases in forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC) during that period (referred to as 4-year growth in FEV1 and FVC). RESULTS: Over the 13 years spanned by the three cohorts, improvements in 4-year growth of both FEV1 and FVC were associated with declining levels of nitrogen dioxide (P<0.001 for FEV1 and FVC) and of particulate matter with an aerodynamic diameter of less than 2.5 µm (P= 0.008 for FEV1 and P<0.001 for FVC) and less than 10 µm (P<0.001 for FEV1 and FVC). These associations persisted after adjustment for several potential confounders. Significant improvements in lung-function development were observed in both boys and girls and in children with asthma and children without asthma. The proportions of children with clinically low FEV1 (defined as <80% of the predicted value) at 15 years of age declined significantly, from 7.9% to 6.3% to 3.6% across the three periods, as the air quality improved (P = 0.001). CONCLUSIONS: We found that long-term improvements in air quality were associated with statistically and clinically significant positive effects on lung-function growth in children. (Funded by the Health Effects Institute and others.).

Gene Expression Profiling in Blood Provides Reproducible Molecular Insights into Asthma Control.

RATIONALE: Maintaining optimal symptom control remains the primary objective of asthma treatment. Better understanding of the biologic underpinnings of asthma control may lead to the development of improved clinical and pharmaceutical approaches. OBJECTIVES: To identify molecular pathways and interrelated genes whose differential expression was associated with asthma control. METHODS: We performed gene set enrichment analyses of asthma control in 1,170 adults with asthma, each with gene expression data derived from either whole blood (WB) or unstimulated CD4+ T lymphocytes (CD4), and a self-reported asthma control score representing either the preceding 6 months (chronic) or 7 days (acute). Our study comprised a discovery WB cohort (n = 245, chronic) and three independent, nonoverlapping replication cohorts: a second WB set (n = 448, acute) and two CD4 sets (n = 300, chronic; n = 77, acute). MEASUREMENTS AND MAIN RESULTS: In the WB discovery cohort, we found significant overrepresentation of genes associated with asthma control in 1,106 gene sets from the Molecular Signatures Database (false discovery rate, <5%). Of these, 583 (53%) replicated in at least one replication cohort (false discovery rate, <25%). Suboptimal control was associated with signatures of eosinophilic and granulocytic inflammatory signals, whereas optimal control signatures were enriched for immature lymphocytic patterns. These signatures included two related biologic processes related to activation by TREM-1 (triggering receptor expressed on myeloid cells 1) and lipopolysaccharide. CONCLUSIONS: Together, these results demonstrate the existence of specific, reproducible transcriptomic components in blood that vary with degree of asthma control and implicate a novel biologic target (TREM-1).

Genome-Wide Interaction Analysis of Air Pollution Exposure and Childhood Asthma with Functional Follow-up.

RATIONALE: The evidence supporting an association between traffic-related air pollution exposure and incident childhood asthma is inconsistent and may depend on genetic factors. OBJECTIVES: To identify gene-environment interaction effects on childhood asthma using genome-wide single-nucleotide polymorphism (SNP) data and air pollution exposure. Identified loci were further analyzed at epigenetic and transcriptomic levels. METHODS: We used land use regression models to estimate individual air pollution exposure (represented by outdoor NO2 levels) at the birth address and performed a genome-wide interaction study for doctors' diagnoses of asthma up to 8 years in three European birth cohorts (n = 1,534) with look-up for interaction in two separate North American cohorts, CHS (Children's Health Study) and CAPPS/SAGE (Canadian Asthma Primary Prevention Study/Study of Asthma, Genetics and Environment) (n = 1,602 and 186 subjects, respectively). We assessed expression quantitative trait locus effects in human lung specimens and blood, as well as associations among air pollution exposure, methylation, and transcriptomic patterns. MEASUREMENTS AND MAIN RESULTS: In the European cohorts, 186 SNPs had an interaction P < 1 × 10-4 and a look-up evaluation of these disclosed 8 SNPs in 4 loci, with an interaction P < 0.05 in the large CHS study, but not in CAPPS/SAGE. Three SNPs within adenylate cyclase 2 (ADCY2) showed the same direction of the interaction effect and were found to influence ADCY2 gene expression in peripheral blood (P = 4.50 × 10-4). One other SNP with P < 0.05 for interaction in CHS, rs686237, strongly influenced UDP-Gal:betaGlcNAc ß-1,4-galactosyltransferase, polypeptide 5 (B4GALT5) expression in lung tissue (P = 1.18 × 10-17). Air pollution exposure was associated with differential discs, large homolog 2 (DLG2) methylation and expression. CONCLUSIONS: Our results indicated that gene-environment interactions are important for asthma development and provided supportive evidence for interaction with air pollution for ADCY2, B4GALT5, and DLG2.

Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association Study.

A genome-wide association study (GWAS) typically is focused on detecting marginal genetic effects. However, many complex traits are likely to be the result of the interplay of genes and environmental factors. These SNPs may have a weak marginal effect and thus unlikely to be detected from a scan of marginal effects, but may be detectable in a gene-environment (G × E) interaction analysis. However, a genome-wide interaction scan (GWIS) using a standard test of G × E interaction is known to have low power, particularly when one corrects for testing multiple SNPs. Two 2-step methods for GWIS have been previously proposed, aimed at improving efficiency by prioritizing SNPs most likely to be involved in a G × E interaction using a screening step. For a quantitative trait, these include a method that screens on marginal effects [Kooperberg and Leblanc, 2008] and a method that screens on variance heterogeneity by genotype [Paré et al., 2010] In this paper, we show that the Paré et al. approach has an inflated false-positive rate in the presence of an environmental marginal effect, and we propose an alternative that remains valid. We also propose a novel 2-step approach that combines the two screening approaches, and provide simulations demonstrating that the new method can outperform other GWIS approaches. Application of this method to a G × Hispanic-ethnicity scan for childhood lung function reveals a SNP near the MARCO locus that was not identified by previous marginal-effect scans.

Trends in childhood leukemia incidence over two decades from 1992 to 2013.

Incidence rates of childhood leukemia in the United States have steadily increased over the last several decades, but only recently have disparities in the increase in incidence been recognized. In the current analysis, Surveillance, Epidemiology and End Results (SEER) data were used to evaluate recent trends in the incidence of childhood leukemia diagnosed at age 0-19 years from 1992 to 2013, overall and by age, race/ethnicity, gender and histologic subtype. Hispanic White children were more likely than non-Hispanic White, non-Hispanic Black or non-Hispanic Asian children to be diagnosed with acute lymphocytic leukemia (ALL) from 2009 to 2013. From 1992 to 2013, a significant increase in ALL incidence was observed for Hispanic White children [annual percent change (APC)Hispanic = 1.08, 95% CI: 0.59, 1.58]; no significant increase was observed for non-Hispanic White, Black or Asian children. ALL incidence increased by about 3% per year from 1992 to 2013 for Hispanic White children diagnosed from 15 to 19 years (APC = 2.67; 95% CI: 0.88, 4.49) and by 2% for those 10-14 years (APC = 2.09; 95% CI: 0.57, 3.63), while no significant increases in incidence were observed in non-Hispanic White, Black, or Asian children of the same age. Acute myeloid leukemia (AML) incidence increased among non-Hispanic White children under 1 year at diagnosis, and among Hispanic White children diagnosed at age 1-4. The increase in incidence rates of childhood ALL appears to be driven by rising rates in older Hispanic children (10-14, and 15-19 years). Future studies are needed to evaluate reasons for the increase in ALL among older Hispanic children.

Lessons Learned From Past Gene-Environment Interaction Successes.

Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G×E) has been a focus of research for several years. In this article, select examples of G×E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G×E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G×Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G×E in several examples. Future studies may benefit from these lessons learned.

Update on the State of the Science for Analytical Methods for Gene-Environment Interactions.

The analysis of gene-environment interaction (G×E) may hold the key for further understanding the etiology of many complex traits. The current availability of high-volume genetic data, the wide range in types of environmental data that can be measured, and the formation of consortiums of multiple studies provide new opportunities to identify G×E but also new analytical challenges. In this article, we summarize several statistical approaches that can be used to test for G×E in a genome-wide association study. These include traditional models of G×E in a case-control or quantitative trait study as well as alternative approaches that can provide substantially greater power. The latest methods for analyzing G×E with gene sets and with data in a consortium setting are summarized, as are issues that arise due to the complexity of environmental data. We provide some speculation on why detecting G×E in a genome-wide association study has thus far been difficult. We conclude with a description of software programs that can be used to implement most of the methods described in the paper.