Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis

Two obsessive-compulsive disorder (OCD) genome-wide association studies (GWASs) have been published by independent OCD consortia, the International Obsessive-Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and the OCD Collaborative Genetics Association Study (OCGAS), but many of the top-ranked signals were supported in only one study. We therefore conducted a meta-analysis from the two consortia, investigating a total of 2688 individuals of European ancestry with OCD and 7037 genomically matched controls. No single-nucleotide polymorphisms (SNPs) reached genome-wide significance. However, in comparison with the two individual GWASs, the distribution of P-values shifted toward significance. The top haplotypic blocks were tagged with rs4733767 (P=7.1 × 10-7; odds ratio (OR)=1.21; confidence interval (CI): 1.12-1.31, CASC8/CASC11), rs1030757 (P=1.1 × 10-6; OR=1.18; CI: 1.10-1.26, GRID2) and rs12504244 (P=1.6 × 10-6; OR=1.18; CI: 1.11-1.27, KIT). Variants located in or near the genes ASB13, RSPO4, DLGAP1, PTPRD, GRIK2, FAIM2 and CDH20, identified in linkage peaks and the original GWASs, were among the top signals. Polygenic risk scores for each individual study predicted case-control status in the other by explaining 0.9% (P=0.003) and 0.3% (P=0.0009) of the phenotypic variance in OCGAS and the European IOCDF-GC target samples, respectively. The common SNP heritability in the combined OCGAS and IOCDF-GC sample was estimated to be 0.28 (s.e.=0.04). Strikingly, ∼65% of the SNP-based heritability in the OCGAS sample was accounted for by SNPs with minor allele frequencies of ⩾40%. This joint analysis constituting the largest single OCD genome-wide study to date represents a major integrative step in elucidating the genetic causes of OCD

A General Test for Gene-Environment Interaction in Sib Pair-based Association Analysis of Quantitative Traits

Several association studies support the hypothesis that genetic variants can modify the influence of environmental factors on behavioral outcomes, i.e., G 9 E interaction. The case-control design used in these studies is powerful, but population stratification with respect to allele frequencies can give rise to false positive or false negative associations. Stratification with respect to the environmental factors can lead to false positives or false negatives with respect to environmental main effects and G 9 E interaction effects as well. Here we present a model based on Fulker et al. (1999) and Purcell (2002) for the study of G 9 E interaction in family-based association designs, in which the effects of stratification can be controlled. Simulations illustrate the power to detect genetic and environmental main effects, and G 9 E interaction effects for the sib pair design. The power to detect interaction was studied in eight different situations, both with and without the presence of population stratification, and for categorical and continuous environmental factors. Results show that the power to detect genetic and environmental main effects, and G 9 E interaction effects, depends on the allele frequencies and the distribution of the environmental moderator. Admixture effects of realistic effect size lead only to very small stratification effects in the G 9 E component, so impractically large numbers of sib pairs are required to detect such stratification

Genetically-Informed Patient Selection for iPSC Studies of Complex Diseases May Aid in Reducing Cellular Heterogeneity

Induced pluripotent stem cell (iPSC) technology is more and more used for the study of genetically complex human disease but is challenged by variability, sample size and polygenicity. We discuss studies involving iPSC-derived neurons from patients with Schizophrenia (SCZ), to exemplify that heterogeneity in sampling strategy complicate the detection of disease mechanisms. We offer a solution to controlling variability within and between iPSC studies by using specific patient selection strategies

Terrace Standard, October, 22, 2003

Sex differences in the etiology of human trait variation are a major topic of interest in the social and medical sciences given its far-reaching implications. For example, in genetic research, the presence of sex-specific effects would require sex-stratified analysis, and in clinical practice sex-specific treatments would be warranted. Here, we present a study of 2,335,920 twin pairs, in which we tested sex differences in genetic and environmental contributions to variation in 2,608 reported human traits, clustered in 50 trait categories. Monozygotic and dizygotic male and female twin correlations were used to test whether the amount of genetic and environmental influences was equal between the sexes. By comparing dizygotic opposite sex twin correlations with dizygotic same sex twin correlations we could also test whether sex-specific genetic or environmental factors were involved. We observed for only 3% of all trait categories sex differences in the amount of etiological influences. Sex-specific genetic factors were observed for 25% of trait categories, often involving obviously sex-dependent trait categories such as puberty-related disorders. Our findings show that for most traits the number of sex-specific genetic variants will be small. For those traits where we do report sexual dimorphism, sex-specific approaches may aid in future gene-finding efforts

Understanding the assumptions underlying Mendelian randomization

With the rapidly increasing availability of large genetic data sets in recent years, Mendelian Randomization (MR) has quickly gained popularity as a novel secondary analysis method. Leveraging genetic variants as instrumental variables, MR can be used to estimate the causal effects of one phenotype on another even when experimental research is not feasible, and therefore has the potential to be highly informative. It is dependent on strong assumptions however, often producing biased results if these are not met. It is therefore imperative that these assumptions are well-understood by researchers aiming to use MR, in order to evaluate their validity in the context of their analyses and data. The aim of this perspective is therefore to further elucidate these assumptions and the role they play in MR, as well as how different kinds of data can be used to further support them

Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson's disease

Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson's disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson's disease

What Twin Studies Tell Us About the Heritability of Brain Development, Morphology, and Function: A Review

The development of brain structure and function shows large inter-individual variation. The extent to which this variation is due to genetic or environmental influences has been investigated in twin studies using structural and functional Magnetic Resonance Imaging (MRI). The current review presents an overview of twin studies using MRI in children, adults and elderly, and focuses on cross-sectional and longitudinal designs. The majority of the investigated brain measures are heritable to a large extent (60–80 %), although spatial differences in heritability are observed as well. Cross-sectional studies suggest that heritability estimates slightly increase from childhood to adulthood. Long-term longitudinal studies are better suited to study developmental changes in heritability, but these studies are limited. Results so far suggest that the heritability of change over time is relatively low or absent, but more studies are needed to confirm these findings. Compared to brain structure, twin studies of brain function are scarce, and show much lower heritability estimates (~40 %). The insights from heritability studies aid our understanding of individual differences in brain structure and function. With the recent start of large genetic MRI consortia, the chance of finding genes that explain the heritability of brain morphology increases. Gene identification may provide insight in biological mechanisms involved in brain processes, which in turn will learn us more about healthy and disturbed brain functioning

Terrace Standard, October, 22, 1997

A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations