A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to genes involved in neurological, thyroidal, bone metabolism, and hematopoietic pathways. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausibility of distinct influences of both sleep duration extremes in cardiovascular health. With several of our loci reflecting specificity towards population background or sex, our discovery sheds light on the importance of embracing granularity when addressing heterogeneity entangled in gene-environment interactions, and in therapeutic design approaches for blood pressure management.

Genome-Wide Gene-Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk.

BACKGROUND: High red meat and/or processed meat consumption are established colorectal cancer risk factors. We conducted a genome-wide gene-environment (GxE) interaction analysis to identify genetic variants that may modify these associations. METHODS: A pooled sample of 29,842 colorectal cancer cases and 39,635 controls of European ancestry from 27 studies were included. Quantiles for red meat and processed meat intake were constructed from harmonized questionnaire data. Genotyping arrays were imputed to the Haplotype Reference Consortium. Two-step EDGE and joint tests of GxE interaction were utilized in our genome-wide scan. RESULTS: Meta-analyses confirmed positive associations between increased consumption of red meat and processed meat with colorectal cancer risk [per quartile red meat OR = 1.30; 95% confidence interval (CI) = 1.21-1.41; processed meat OR = 1.40; 95% CI = 1.20-1.63]. Two significant genome-wide GxE interactions for red meat consumption were found. Joint GxE tests revealed the rs4871179 SNP in chromosome 8 (downstream of HAS2); greater than median of consumption ORs = 1.38 (95% CI = 1.29-1.46), 1.20 (95% CI = 1.12-1.27), and 1.07 (95% CI = 0.95-1.19) for CC, CG, and GG, respectively. The two-step EDGE method identified the rs35352860 SNP in chromosome 18 (SMAD7 intron); greater than median of consumption ORs = 1.18 (95% CI = 1.11-1.24), 1.35 (95% CI = 1.26-1.44), and 1.46 (95% CI = 1.26-1.69) for CC, CT, and TT, respectively. CONCLUSIONS: We propose two novel biomarkers that support the role of meat consumption with an increased risk of colorectal cancer. IMPACT: The reported GxE interactions may explain the increased risk of colorectal cancer in certain population subgroups.

Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response.

BACKGROUND: Tobacco smoking is an established risk factor for colorectal cancer. However, genetically defined population subgroups may have increased susceptibility to smoking-related effects on colorectal cancer. METHODS: A genome-wide interaction scan was performed including 33,756 colorectal cancer cases and 44,346 controls from three genetic consortia. RESULTS: Evidence of an interaction was observed between smoking status (ever vs. never smokers) and a locus on 3p12.1 (rs9880919, P = 4.58 × 10-8), with higher associated risk in subjects carrying the GG genotype [OR, 1.25; 95% confidence interval (CI), 1.20-1.30] compared with the other genotypes (OR <1.17 for GA and AA). Among ever smokers, we observed interactions between smoking intensity (increase in 10 cigarettes smoked per day) and two loci on 6p21.33 (rs4151657, P = 1.72 × 10-8) and 8q24.23 (rs7005722, P = 2.88 × 10-8). Subjects carrying the rs4151657 TT genotype showed higher risk (OR, 1.12; 95% CI, 1.09-1.16) compared with the other genotypes (OR <1.06 for TC and CC). Similarly, higher risk was observed among subjects carrying the rs7005722 AA genotype (OR, 1.17; 95% CI, 1.07-1.28) compared with the other genotypes (OR <1.13 for AC and CC). Functional annotation revealed that SNPs in 3p12.1 and 6p21.33 loci were located in regulatory regions, and were associated with expression levels of nearby genes. Genetic models predicting gene expression revealed that smoking parameters were associated with lower colorectal cancer risk with higher expression levels of CADM2 (3p12.1) and ATF6B (6p21.33). CONCLUSIONS: Our study identified novel genetic loci that may modulate the risk for colorectal cancer of smoking status and intensity, linked to tumor suppression and immune response. IMPACT: These findings can guide potential prevention treatments.

Beyond GWAS of Colorectal Cancer: Evidence of Interaction with Alcohol Consumption and Putative Causal Variant for the 10q24.2 Region.

BACKGROUND: Currently known associations between common genetic variants and colorectal cancer explain less than half of its heritability of 25%. As alcohol consumption has a J-shape association with colorectal cancer risk, nondrinking and heavy drinking are both risk factors for colorectal cancer. METHODS: Individual-level data was pooled from the Colon Cancer Family Registry, Colorectal Transdisciplinary Study, and Genetics and Epidemiology of Colorectal Cancer Consortium to compare nondrinkers (≤1 g/day) and heavy drinkers (>28 g/day) with light-to-moderate drinkers (1-28 g/day) in GxE analyses. To improve power, we implemented joint 2df and 3df tests and a novel two-step method that modifies the weighted hypothesis testing framework. We prioritized putative causal variants by predicting allelic effects using support vector machine models. RESULTS: For nondrinking as compared with light-to-moderate drinking, the hybrid two-step approach identified 13 significant SNPs with pairwise r2 > 0.9 in the 10q24.2/COX15 region. When stratified by alcohol intake, the A allele of lead SNP rs2300985 has a dose-response increase in risk of colorectal cancer as compared with the G allele in light-to-moderate drinkers [OR for GA genotype = 1.11; 95% confidence interval (CI), 1.06-1.17; OR for AA genotype = 1.22; 95% CI, 1.14-1.31], but not in nondrinkers or heavy drinkers. Among the correlated candidate SNPs in the 10q24.2/COX15 region, rs1318920 was predicted to disrupt an HNF4 transcription factor binding motif. CONCLUSIONS: Our study suggests that the association with colorectal cancer in 10q24.2/COX15 observed in genome-wide association study is strongest in nondrinkers. We also identified rs1318920 as the putative causal regulatory variant for the region. IMPACT: The study identifies multifaceted evidence of a possible functional effect for rs1318920.

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.

Finding novel genes by testing G × E interactions in a genome-wide association study.

In a genome-wide association study (GWAS), investigators typically focus their primary analysis on the direct (marginal) associations of each single nucleotide polymorphism (SNP) with the trait. Some SNPs that are truly associated with the trait may not be identified in this scan if they have a weak marginal effect and thus low power to be detected. However, these SNPs may be quite important in subgroups of the population defined by an environmental or personal factor, and may be detectable if such a factor is carefully considered in a gene-environment (G × E) interaction analysis. We address the question "Using a genome wide interaction scan (GWIS), can we find new genes that were not found in the primary GWAS scan?" We review commonly used approaches for conducting a GWIS in case-control studies, and propose a new two-step screening and testing method (EDG×E) that is optimized to find genes with a weak marginal effect. We simulate several scenarios in which our two-step method provides 70-80% power to detect a disease locus while a marginal scan provides less than 5% power. We also provide simulations demonstrating that the EDG×E method outperforms other GWIS approaches (including case only and previously proposed two-step methods) for finding genes with a weak marginal effect. Application of this method to a G × Sex scan for childhood asthma reveals two potentially interesting SNPs that were not identified in the marginal-association scan. We distribute a new software program (G×Escan, available at http://biostats.usc.edu/software) that implements this new method as well as several other GWIS approaches.

Efficient two-step testing of gene-gene interactions in genome-wide association studies.

Exhaustive testing of all possible SNP pairs in a genome-wide association study (GWAS) generally yields low power to detect gene-gene (G × G) interactions because of small effect sizes and stringent requirements for multiple-testing correction. We introduce a new two-step procedure for testing G × G interactions in case-control GWAS to detect interacting single nucleotide polymorphisms (SNPs) regardless of their marginal effects. In an initial screening step, all SNP pairs are tested for gene-gene association in the combined sample of cases and controls. In the second step, the pairs that pass the screening are followed up with a traditional test for G × G interaction. We show that the two-step method is substantially more powerful to detect G × G interactions than the exhaustive testing approach. For example, with 2,000 cases and 2,000 controls, the two-step method can have more than 90% power to detect an interaction odds ratio of 2.0 compared to less than 50% power for the exhaustive testing approach. Moreover, we show that a hybrid two-step approach that combines our newly proposed two-step test and the two-step test that screens for marginal effects retains the best power properties of both. The two-step procedures we introduce have the potential to uncover genetic signals that have not been previously identified in an initial single-SNP GWAS. We demonstrate the computational feasibility of the two-step G × G procedure by performing a G × G scan in the asthma GWAS of the University of Southern California Children's Health Study.

Limitations of direct estradiol and testosterone immunoassay kits.

Estradiol (E2) and testosterone (T) are biologically active hormones that serve as important diagnostic markers in serum of premenopausal and postmenopausal women and in men. These hormones are measured frequently by immunoassay in clinical laboratories and the test results are used in the diagnosis and treatment of patients. For measuring the hormones by immunoassay, most laboratories utilize commercially available reagents that are packaged in the form of a kit and are used either in an automated instrument or manually. However, both the diagnostic kit manufacturer and testing laboratory seldom thoroughly validate the assay methods generated with these kits. This deficiency may lead to unreliable test results that could affect clinical evaluation and treatment of patients. The purpose of the present study was to assess the reliability of immunoassays that quantify serum E2 and T levels with commercial diagnostic kits. The data generally show wide differences in the apparent levels of each hormone in a given sample obtained with kits from different manufacturers. This was especially true when measuring postmenopausal E2 and T levels. However, a purification step, which included organic solvent extraction, prior to radioimmunoassay (RIA) of E2 gave values that compared well with those obtained by conventional RIA (with preceding extraction/chromatographic steps). Our results point out the importance of more thoroughly validating assays performed with commercial immunoassay kits, especially with respect to sensitivity and specificity, prior to their use for measuring hormone levels in patient samples.

Segregation and linkage analysis for longitudinal measurements of a quantitative trait.

We present a method for using slopes and intercepts from a linear regression of a quantitative trait as outcomes in segregation and linkage analyses. We apply the method to the analysis of longitudinal systolic blood pressure (SBP) data from the Framingham Heart Study. A first-stage linear model was fit to each subject's SBP measurements to estimate both their slope over time and an intercept, the latter scaled to represent the mean SBP at the average observed age (53.7 years). The subject-specific intercepts and slopes were then analyzed using segregation and linkage analysis. We describe a method for using the standard errors of the first-stage intercepts and slopes as weights in the genetic analyses. For the intercepts, we found significant evidence of a Mendelian gene in segregation analysis and suggestive linkage results (with LOD scores >or= 1.5) for specific markers on chromosomes 1, 3, 5, 9, 10, and 17. For the slopes, however, the data did not support a Mendelian model, and thus no formal linkage analyses were conducted.

Initial effect of controlled release chlorhexidine on subgingival microorganisms.

BACKGROUND: Little or no data exist on the ability of subgingival application of PerioChip (2.5 mg chlorhexidine gluconate in a biodegradable chip; Astra Pharmaceuticals, Westborough, MA, USA) to suppress periodontopathic microorganisms. The present study compared the subgingival microbiota of periodontitis sites receiving the chlorhexidine chip plus scaling and root planing (Sc/Rp) or Sc/Rp alone. METHODS: Seven males and six females, mean age 49 years, with moderate to advanced periodontitis participated in the study. In each patient, two bilateral pockets probing 6-7 mm were randomly assigned to treatment by chlorhexidine chip + Sc/Rp, or by Sc/Rp alone. Subgingival placement of chlorhexidine chips was carried out according to the manufacturer's instructions. Sc/Rp was performed with hand instruments for at least 10 min in each study tooth. Subgingival samples were collected by paper-points at baseline, at 2 weeks and at 4 weeks post-treatment. Anaerobic culture methods were used for microbial isolation and identification. The microbiologic examination was carried out blindly. Microbiological data were evaluated by a repeated measures analysis of variance. RESULTS: No statistical difference was found in total colony counts between subgingival sites treated with chlorhexidine chip + Sc/Rp and those treated with Sc/Rp alone. Also, the percentage of major periodontal pathogens (Actinobacillus actinomycetemcomitans, Porphyromonas gingivalis and Bacteroides forsythus) and the percentage of total periodontal pathogens (A. actinomycetemcomitans, P. gingivalis, B. forsythus, Prevotella intermedia-group, Fusobacterium, Eubacterium, Campylobacter rectus, Peptostreptococcus micros, Eikenella corrodens, enteric rods) were not significantly different between the chlorhexidine chip + Sc/Rp group and the Sc/Rp group. At baseline, A. actinomycetemcomitans was recovered from 4 chlorhexidine chip + Sc/Rp sites and 2 Sc/Rp sites, P. gingivalis from 5 chlorhexidine chip + Sc/Rp sites and 4 Sc/Rp sites, and B. forsythus from 9 chlorhexidine chip + Sc/Rp and 7 Sc/Rp sites. At 4 weeks, A. actinomycetemcomitans was detected in 2 chlorhexidine chip + Sc/Rp sites but not in any site receiving Sc/Rp, P. gingivalis in 2 chlorhexidine chip + Sc/Rp sites but not in any Sc/Rp site, and B. forsythus in 1 chlorhexidine chip + Sc/Rp and in 2 Sc/Rp sites. CONCLUSION: The present data obtained from bilateral periodontitis lesions of 13 adults suggest that chlorhexidine chip treatment of adult periodontitis lesions provides little or no additional antimicrobial benefits compared to thorough Sc/Rp alone.