Guia Guffanti

Hippocampal Atrophy as a Quantitative Trait in a Genome-Wide Association Study Identifying Novel Susceptibility Genes for Alzheimer's Disease

Background With the exception of APOE ε4 allele, the common genetic risk factors for sporadic Alzheimers Disease (AD) are unknown. Methods and Findings We completed a genome-wide association study on 381 participants in the ADNI (Alzheimers Disease Neuroimaging Initiative) study. Samples were genotyped using the Illumina Human610-Quad BeadChip. 516,645 unique Single Nucleotide Polymorphisms (SNPs) were included in the analysis following quality control measures. The genotype data and raw genetic data are freely available for download (LONI, http://www.loni.ucla.edu/ADNI/Data/). Two analyses were completed: a standard case-control analysis, and a novel approach using hippocampal atrophy measured on MRI as an objectively defined, quantitative phenotype. A General Linear Model was applied to identify SNPs for which there was an interaction between the genotype and diagnosis on the quantitative trait. The case-control analysis identified APOE and a new risk gene, TOMM40 (translocase of outer mitochondrial membrane 40), at a genome-wide significance level of≤10−6 (10−11 for a haplotype). TOMM40 risk alleles were approximately twice as frequent in AD subjects as controls. The quantitative trait analysis identified 21 genes or chromosomal areas with at least one SNP with a p-value≤10−6, which can be considered potential “new” candidate loci to explore in the etiology of sporadic AD. These candidates included EFNA5, CAND1, MAGI2, ARSB, and PRUNE2, genes involved in the regulation of protein degradation, apoptosis, neuronal loss and neurodevelopment. Thus, we identified common genetic variants associated with the increased risk of developing AD in the ADNI cohort, and present publicly available genome-wide data. Supportive evidence based on case-control studies and biological plausibility by gene annotation is provided. Currently no available sample with both imaging and genetic data is available for replication. Conclusions Using hippocampal atrophy as a quantitative phenotype in a genome-wide scan, we have identified candidate risk genes for sporadic Alzheimers disease that merit further investigation.

A Genome-Wide Association Study of Schizophrenia Using Brain Activation as a Quantitative Phenotype

BACKGROUND Genome-wide association studies (GWASs) are increasingly used to identify risk genes for complex illnesses including schizophrenia. These studies may require thousands of subjects to obtain sufficient power. We present an alternative strategy with increased statistical power over a case-control study that uses brain imaging as a quantitative trait (QT) in the context of a GWAS in schizophrenia. METHODS Sixty-four subjects with chronic schizophrenia and 74 matched controls were recruited from the Functional Biomedical Informatics Research Network (FBIRN) consortium. Subjects were genotyped using the Illumina HumanHap300 BeadArray and were scanned while performing a Sternberg Item Recognition Paradigm in which they learned and then recognized target sets of digits in an functional magnetic resonance imaging protocol. The QT was the mean blood oxygen level-dependent signal in the dorsolateral prefrontal cortex during the probe condition for a memory load of 3 items. RESULTS Three genes or chromosomal regions were identified by having 2 single-nucleotide polymorphisms (SNPs) each significant at P < 10(-6) for the interaction between the imaging QT and the diagnosis (ROBO1-ROBO2, TNIK, and CTXN3-SLC12A2). Three other genes had a significant SNP at <10(-6) (POU3F2, TRAF, and GPC1). Together, these 6 genes/regions identified pathways involved in neurodevelopment and response to stress. CONCLUSION Combining imaging and genetic data from a GWAS identified genes related to forebrain development and stress response, already implicated in schizophrenic dysfunction, as affecting prefrontal efficiency. Although the identified genes require confirmation in an independent sample, our approach is a screening method over the whole genome to identify novel SNPs related to risk for schizophrenia.

A genome-wide association study of post-traumatic stress disorder identifies the retinoid-related orphan receptor alpha ( RORA ) gene as a significant risk locus

We describe the results of the first genome-wide association study (GWAS) of post-traumatic stress disorder (PTSD) performed using trauma-exposed white non-Hispanic participants from a cohort of veterans and their intimate partners (295 cases and 196 controls). Several single-nucleotide polymorphisms (SNPs) yielded evidence of association. One SNP (rs8042149), located in the retinoid-related orphan receptor alpha gene (RORA), reached genome-wide significance. Nominally significant associations were observed for other RORA SNPs in two African-American replication samples—one from the veteran cohort (43 cases and 41 controls) and another independent cohort (100 cases and 421 controls). However, only the associated SNP from the veteran African-American replication sample survived gene-level multiple-testing correction. RORA has been implicated in prior GWAS studies of psychiatric disorders and is known to have an important role in neuroprotection and other behaviorally relevant processes. This study represents an important step toward identifying the genetic underpinnings of PTSD.

SNP-based pathway enrichment analysis for genome-wide association studies

BackgroundRecently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs.ResultsWe describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available at http://cbcl.ics.uci.edu/SSEA.ConclusionsThe SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.

Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: Methodological considerations

Introduction. Genes play a well-documented role in determining normal cognitive function. This paper focuses on reviewing strategies for the identification of common genetic variation in genes that modulate normal and abnormal cognition with a genome-wide association scan (GWAS). GWASs make it possible to survey the entire genome to discover important but unanticipated genetic influences. Methods. The use of a quantitative phenotype in combination with a GWAS provides many advantages over a case-control design, both in power and in physiological understanding of the underlying cognitive processes. We review the major features of this approach, and show how, using a General Linear Model method, the contribution of each Single Nucleotide Polymorphism (SNP) to the phenotype is determined, and adjustments then made for multiple tests. An example of the strategy is presented, in which fMRI measures of cortical inefficiency while performing a working memory task are used as the quantitative phenotype. We estimate power under different effect sizes (10–30%) and variations in allelic frequency for a Quantitative Trait (QT) (10–20%), and compare them to a case-control design with an Odds Ratio (OR) of 1.5, showing how a QT approach is superior to a traditional case-control. In the presented example, this method identifies putative susceptibility genes for schizophrenia which affect prefrontal efficiency and have functions related to cell migration, forebrain development and stress response. Conclusion. The use of QT as phenotypes provide increased statistical power over categorical association approaches and when combined with a GWAS creates a strategy for identification of unanticipated genes that modulate cognitive processes and cognitive disorders.

Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia

We have discovered two genes, RSRC1 and ARHGAP18, associated with schizophrenia and in an independent study provided additional support for this association. We have both discovered and verified the association of two genes, RSRC1 and ARHGAP18, with schizophrenia. We combined a genome-wide screening strategy with neuroimaging measures as the quantitative phenotype and identified the single nucleotide polymorphisms (SNPs) related to these genes as consistently associated with the phenotypic variation. To control for the risk of false positives, the empirical P-value for association significance was calculated using permutation testing. The quantitative phenotype was Blood-Oxygen-Level Dependent (BOLD) Contrast activation in the left dorsal lateral prefrontal cortex measured during a working memory task. The differential distribution of SNPs associated with these two genes in cases and controls was then corroborated in a larger, independent sample of patients with schizophrenia (n=82) and healthy controls (n=91), thus suggesting a putative etiological function for both genes in schizophrenia. Up until now these genes have not been linked to any neuropsychiatric illness, although both genes have a function in prenatal brain development. We introduce the use of functional magnetic resonance imaging activation as a quantitative phenotype in conjunction with genome-wide association as a gene discovery tool.

Identifying Gene Regulatory Networks in Schizophrenia

The imaging genetics approach to studying the genetic basis of disease leverages the individual strengths of both neuroimaging and genetic studies by visualizing and quantifying the brain activation patterns in the context of genetic background. Brain imaging as an intermediate phenotype can help clarify the functional link among genes, the molecular networks in which they participate, and brain circuitry and function. Integrating genetic data from a genome-wide association study (GWAS) with brain imaging as a quantitative trait (QT) phenotype can increase the statistical power to identify risk genes. A QT analysis using brain imaging (DLPFC activation during a working memory task) as a quantitative trait has identified unanticipated risk genes for schizophrenia. Several of these genes (RSRC1, ARHGAP18, ROBO1-ROBO2, GPC1, TNIK, and CTXN3-SLC12A2) have functions related to progenitor cell proliferation, migration, and differentiation, cytoskeleton reorganization, axonal connectivity, and development of forebrain structures. These genes, however, do not function in isolation but rather through gene regulatory networks. To obtain a deeper understanding how the GWAS-identified genes participate in larger gene regulatory networks, we measured correlations among transcript levels in the mouse and human postmortem tissue and performed a gene set enrichment analysis (GSEA) that identified several microRNA associated with schizophrenia (448, 218, 137). The results of such computational approaches can be further validated in animal experiments in which the networks are experimentally studied and perturbed with specific compounds. Glypican 1 and FGF17 mouse models for example, can be used to study such gene regulatory networks. The model demonstrates epistatic interactions between FGF and glypican on brain development and may be a useful model of negative symptom schizophrenia.

A newly identified group of adolescents at “invisible” risk for psychopathology and suicidal behavior: findings from the SEYLE study

This study explored the prevalence of risk behaviors (excessive alcohol use, illegal drug use, heavy smoking, reduced sleep, overweight, underweight, sedentary behavior, high use of Internet/TV/videogames for reasons not related to school or work, and truancy), and their association with psychopathology and self‐destructive behaviors, in a sample of 12,395 adolescents recruited in randomly selected schools across 11 European countries. Latent class analysis identified three groups of adolescents: a low‐risk group (57.8%) including pupils with low or very low frequency of risk behaviors; a high‐risk group (13.2%) including pupils who scored high on all risk behaviors, and a third group (“invisible” risk, 29%) including pupils who were positive for high use of Internet/TV/videogames for reasons not related to school or work, sedentary behavior and reduced sleep. Pupils in the “invisible” risk group, compared with the high‐risk group, had a similar prevalence of suicidal thoughts (42.2% vs. 44%), anxiety (8% vs. 9.2%), subthreshold depression (33.2% vs. 34%) and depression (13.4% vs. 14.7%). The prevalence of suicide attempts was 5.9% in the “invisible” group, 10.1% in the high‐risk group and 1.7% in the low‐risk group. The prevalence of all risk behaviors increased with age and most of them were significantly more frequent among boys. Girls were significantly more likely to experience internalizing (emotional) psychiatric symptoms. The “invisible” group may represent an important new intervention target group for potentially reducing psychopathology and other untoward outcomes in adolescence, including suicidal behavior.

Lack of replication of genetic associations with human longevity

The exceptional longevity of centenarians is due in part to inherited genetic factors, as deduced from data that show that first degree relatives of centenarians live longer and have reduced overall mortality. In recent years, a number of groups have performed genetic association studies on long-living individuals (LLI) and young controls to identify alleles that are either positively or negatively selected in the centenarian population as consequence of a demographic pressure. Many of the reported studies have shown genetic loci associated with longevity. Of these, with the exception of APOE, none have been convincingly reproduced. We validated our populations by typing the APOE locus. In addition, we used 749 American Caucasian LLI, organized in two independent tiers and 355 American Caucasian controls in the attempt to replicate previously published findings. We tested Klotho (KL)-VS variant (rs952706), Cholesteryl Ester Transfer Protein (CETP) I405V (rs5882), Paraoxonase 1 (PON1) Q192R (rs662), Apolipoprotein C-III (APOC3) -641C/A (rs2542052), Microsomal Transfer Protein (MTP) -493G/T (rs2866164) and apolipoprotein E (APOE) ε2 and ε4 isoforms, (rs7412 and rs429358) haplotypes respectively. Our results show that, at present, except for APOE, none of the selected genes show association with longevity if carefully tested in a large cohort of LLI and their controls, pointing to the need of larger populations for case–control studies in extreme longevity.

Genome-wide association study implicates a novel RNA gene, the lincRNA AC068718.1, as a risk factor for post-traumatic stress disorder in women

Posttraumatic stress disorder (PTSD) is a common and debilitating mental disorder with a particularly high burden for women. Emerging evidence suggests PTSD may be more heritable among women and evidence from animal models and human correlational studies suggest connections between sex-linked biology and PTSD vulnerability, which may extend to the disorders genetic architecture. We conducted a genome-wide association study (GWAS) of PTSD in a primarily African American sample of women from the Detroit Neighborhood Health Study (DNHS) and tested for replication in an independent cohort of primarily European American women from the Nurses Health Study II (NHSII). We genotyped 413 DNHS women - 94 PTSD cases and 319 controls exposed to at least one traumatic event - on the Illumina HumanOmniExpress BeadChip for >700,000 markers and tested 578 PTSD cases and 1963 controls from NHSII for replication. We performed a network-based analysis integrating data from GWAS-derived independent regions of association and the Reactome database of functional interactions. We found genome-wide significant association for one marker mapping to a novel RNA gene, lincRNA AC068718.1, for which we found suggestive evidence of replication in NHSII. Our network-based analysis indicates that our top GWAS results were enriched for pathways related to telomere maintenance and immune function. Our findings implicate a novel RNA gene, lincRNA AC068718.1, as risk factor for PTSD in women and add to emerging evidence that non-coding RNA genes may play a crucial role in shaping the landscape of gene regulation with putative pathological effects that lead to phenotypic differences.