Investigators have linked rare copy number variation (CNVs) to neuropsychiatric diseases, such as schizophrenia. One hypothesis is that CNV events cause disease by affecting genes with specific brain functions. Under these circumstances, we expect that CNV events in cases should impact brain-function genes more frequently than those events in controls. Previous publications have applied “pathway” analyses to genes within neuropsychiatric case CNVs to show enrichment for brain-functions. While such analyses have been suggestive, they often have not rigorously compared the rates of CNVs impacting genes with brain function in cases to controls, and therefore do not address important confounders such as the large size of brain genes and overall differences in rates and sizes of CNVs. To demonstrate the potential impact of confounders, we genotyped rare CNV events in 2,415 unaffected controls with Affymetrix 6.0; we then applied standard pathway analyses using four sets of brain-function genes and observed an apparently highly significant enrichment for each set. The enrichment is simply driven by the large size of brain-function genes. Instead, we propose a case-control statistical test, cnv-enrichment-test, to compare the rate of CNVs impacting specific gene sets in cases versus controls. With simulations, we demonstrate that cnv-enrichment-test is robust to case-control differences in CNV size, CNV rate, and systematic differences in gene size. Finally, we apply cnv-enrichment-test to rare CNV events published by the International Schizophrenia Consortium (ISC). This approach reveals nominal evidence of case-association in neuronal-activity and the learning gene sets, but not the other two examined gene sets. The neuronal-activity genes have been associated in a separate set of schizophrenia cases and controls; however, testing in independent samples is necessary to definitively confirm this association. Our method is implemented in the PLINK software package. Specific rare deletion and duplication events in the genome have now been shown to be associated with neuropsychiatric diseases such as 16p11.2 to autism and 22q11.21 to schizophrenia. However, controversy remains as to whether rare events impacting certain pathways as a group increase the risk of disease, and if so, what those pathways are. Other studies have used standard gene-set enrichment approaches to demonstrate that events discovered in cases contain more genes in neuro-developmental pathways than would be expected by chance. However, these analyses do not explicitly compare the relative enrichment in cases to any enrichment that may also be present in controls. Therefore, they can be confounded by the large size of brain genes or by larger size or frequency of CNVs in cases. Here we propose a case-control statistical test to assess whether a key pathway is differentially impacted by CNVs in cases compared to controls. Our approach is robust to skewed gene sizes and case-control differences in CNV rate and size.

Summary: We describe a software tool to perform haplotype-based association analysis, for quantitative and qualitative traits, in population and family samples, using single nucleotide polymorphism or multiallelic marker data. A range of tests is offered: omnibus and haplotype-specific tests; prospective and retrospective likelihoods; covariates and moderators; sliding window analyses; permutation P-values. We focus on the ability to flexibly impose constraints on haplotype effects, which allows for a range of conditional haplotype-based likelihood ratio tests: for example, whether an allele has an effect independent of its haplotypic background, or whether a single variant can explain the overall association at a locus. We illustrate using these tests to dissect a multi-locus association. Availability: WHAP is a C/C++ program, freely available from the author's website: Contact:shaun@pngu.mgh.harvard.edu

Summary: Over the past 2 years genome-wide association studies have made major contributions to understanding the genetic architecture of many common human diseases. This editorial outlines the development of such studies in psychiatry and highlights the opportunities for advancing understanding of the biological underpinnings and nosological structure of psychiatric disorders.

One in nine American women will meet criteria for the diagnosis of posttraumatic stress disorder (PTSD) in their lifetime. Although twin studies suggest genetic influences account for substantial variance in PTSD risk, little progress has been made in identifying variants in specific genes that influence liability to this common, debilitating disorder. We are using the unique resource of the Nurses Health Study II, a prospective epidemiologic cohort of 68,518 women, to conduct what promises to be the largest candidate gene association study of PTSD to date. The entire cohort will be screened for trauma exposure and PTSD; 3,000 women will be selected for PTSD diagnostic interviews based on the screening data. Our nested case-control study will genotype1000 women who developed PTSD following a history of trauma exposure; 1000 controls will be selected from women who experienced similar traumas but did not develop PTSD. The primary aim of this study is to detect genetic variants that predict the development of PTSD following trauma. We posit inherited vulnerability to PTSD is mediated by genetic variation in three specific neurobiological systems whose alterations are implicated in PTSD etiology: the hypothalamic-pituitary-adrenal axis, the locus coeruleus/noradrenergic system, and the limbic-frontal neuro-circuitry of fear. The secondary, exploratory aim of this study is to dissect genetic influences on PTSD in the broader genetic and environmental context for the candidate genes that show significant association with PTSD in detection analyses. This will involve: conducting conditional tests to identify the causal genetic variant among multiple correlated signals; testing whether the effect of PTSD genetic risk variants is moderated by age of first trauma, trauma type, and trauma severity; and exploring gene-gene interactions using a novel gene-based statistical approach. Identification of liability genes for PTSD would represent a major advance in understanding the pathophysiology of the disorder. Such understanding could advance the development of new pharmacological agents for PTSD treatment and prevention. Moreover, the addition of PTSD assessment data will make the NHSII cohort an unparalleled resource for future genetic studies of PTSD as well as provide the unique opportunity for the prospective examination of PTSD-disease associations.