Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression.

The hypothesis that the S allele of the 5-HTTLPR serotonin transporter promoter region is associated with increased risk of depression, but only in individuals exposed to stressful situations, has generated much interest, research and controversy since first proposed in 2003. Multiple meta-analyses combining results from heterogeneous analyses have not settled the issue. To determine the magnitude of the interaction and the conditions under which it might be observed, we performed new analyses on 31 data sets containing 38 802 European ancestry subjects genotyped for 5-HTTLPR and assessed for depression and childhood maltreatment or other stressful life events, and meta-analysed the results. Analyses targeted two stressors (narrow, broad) and two depression outcomes (current, lifetime). All groups that published on this topic prior to the initiation of our study and met the assessment and sample size criteria were invited to participate. Additional groups, identified by consortium members or self-identified in response to our protocol (published prior to the start of analysis) with qualifying unpublished data, were also invited to participate. A uniform data analysis script implementing the protocol was executed by each of the consortium members. Our findings do not support the interaction hypothesis. We found no subgroups or variable definitions for which an interaction between stress and 5-HTTLPR genotype was statistically significant. In contrast, our findings for the main effects of life stressors (strong risk factor) and 5-HTTLPR genotype (no impact on risk) are strikingly consistent across our contributing studies, the original study reporting the interaction and subsequent meta-analyses. Our conclusion is that if an interaction exists in which the S allele of 5-HTTLPR increases risk of depression only in stressed individuals, then it is not broadly generalisable, but must be of modest effect size and only observable in limited situations.

Rare, low frequency and common coding variants in CHRNA5 and their contribution to nicotine dependence in European and African Americans.

The common nonsynonymous variant rs16969968 in the α5 nicotinic receptor subunit gene (CHRNA5) is the strongest genetic risk factor for nicotine dependence in European Americans and contributes to risk in African Americans. To comprehensively examine whether other CHRNA5 coding variation influences nicotine dependence risk, we performed targeted sequencing on 1582 nicotine-dependent cases (Fagerström Test for Nicotine Dependence score⩾4) and 1238 non-dependent controls, with independent replication of common and low frequency variants using 12 studies with exome chip data. Nicotine dependence was examined using logistic regression with individual common variants (minor allele frequency (MAF)⩾0.05), aggregate low frequency variants (0.05>MAF⩾0.005) and aggregate rare variants (MAF<0.005). Meta-analysis of primary results was performed with replication studies containing 12 174 heavy and 11 290 light smokers. Next-generation sequencing with 180 × coverage identified 24 nonsynonymous variants and 2 frameshift deletions in CHRNA5, including 9 novel variants in the 2820 subjects. Meta-analysis confirmed the risk effect of the only common variant (rs16969968, European ancestry: odds ratio (OR)=1.3, P=3.5 × 10(-11); African ancestry: OR=1.3, P=0.01) and demonstrated that three low frequency variants contributed an independent risk (aggregate term, European ancestry: OR=1.3, P=0.005; African ancestry: OR=1.4, P=0.0006). The remaining 22 rare coding variants were associated with increased risk of nicotine dependence in the European American primary sample (OR=12.9, P=0.01) and in the same risk direction in African Americans (OR=1.5, P=0.37). Our results indicate that common, low frequency and rare CHRNA5 coding variants are independently associated with nicotine dependence risk. These newly identified variants likely influence the risk for smoking-related diseases such as lung cancer.

The age of necrotizing enterocolitis onset: an application of Sartwell's incubation period model.

OBJECTIVE: Model age of necrotizing enterocolitis (NEC) onset applying Sartwell's model of incubation periods, and examine its relationship to gestational age (GA). STUDY DESIGN: Retrospective chart review of St Louis Children's Hospital neonates diagnosed with NEC (≥Bell's stage II) from 2004 to 2008, inclusive. RESULT: The relationship between age of NEC (N=84 cases) onset and GA best fits a non-linear model, with infants ≤28 weeks having a disproportionately longer time to onset than older GA groups and explained 50.3% of the variability in age of NEC onset. Additional clinical variables provided no improvement in explaining age of NEC onset. Application of Sartwell's model to age of NEC onset proved a good fit, when birth is used as the common exposure episode, and age is equivalent of the incubation period. CONCLUSION: The relationship between day of NEC diagnosis and GA is non-linear, with lower GA infants having disproportionately longer time to onset. Despite these GA differences, the fit to Sartwell's model for incubation periods model is consistent with NEC being a consequence of an event that occurs at or soon after birth.

The use of the restricted partition method with case-control data.

BACKGROUND/AIMS: Many diseases important to public health are not due solely to a single mutation or environmental insult. Instead, complex interactions among multiple genes and environmental exposures likely play crucial roles in the etiology of diverse phenotypes from schizophrenia to chemotherapy response. The Restricted Partition Method (RPM) was designed to detect qualitative genetic and environmental factors contributing to a quantitative trait, even if the contribution is predominantly presented as an interaction (displaying little or no signal in univariate analyses). Although the RPM was developed with the expectation that trait values would be drawn from normal distributions, the algorithm will function if the quantitative trait values are replaced with 0's or 1's indicating control or case status. The aim of this study is to evaluate the performance of the RPM on case-control data. METHODS: Case-control data simulated for this study and data provided to the Pharmcogenetics Research Network Analysis Workshop 2005 were used to assess power and type I error for the RPM in this setting. RESULTS: For the tested data, the RPM displayed good power and type I error very close to nominal rates. CONCLUSIONS: The RPM is an appropriate method for the analysis of case-control data.

Exploiting linkage disequilibrium in population isolates.

A comparison of haplotype frequencies between unrelated cases and controls from population isolates identifies a strong signal for the presence/absence of the discrete phenotype in an interval on chromosome 6 bounded by markers 34 and 35. We define a dissimilarity index, D, which is sensitive to differences in allele distributions and differences in the patterns of linkage disequilibrium between cases and controls. We describe two statistical methods to utilize D: a method appropriate for a single moderately sized sample and a sequential approach appropriate for multiple small independent samples.

Covariates in linkage analysis.

We apply a novel technique to detect significant covariates in linkage analysis using a logistic regression approach. An overall test of linkage is first performed to determine whether there is significant perturbation from the expected 50% sharing under the hypothesis of no linkage; if the overall test is significant, the importance of the individual covariate is assessed. In addition, association analyses were performed. These methods were applied to simulated data from multiple populations, and detected correct marker linkages and associations. No population heterogeneity was detected. These methods have the advantages of using all sib pairs and of providing a formal test for heterogeneity across populations.