André Lacour

Genome-wide association study reveals two new risk loci for bipolar disorder

Bipolar disorder (BD) is a common and highly heritable mental illness and genome-wide association studies (GWAS) have robustly identified the first common genetic variants involved in disease aetiology. The data also provide strong evidence for the presence of multiple additional risk loci, each contributing a relatively small effect to BD susceptibility. Large samples are necessary to detect these risk loci. Here we present results from the largest BD GWAS to date by investigating 2.3 million single-nucleotide polymorphisms (SNPs) in a sample of 24,025 patients and controls. We detect 56 genome-wide significant SNPs in five chromosomal regions including previously reported risk loci ANK3, ODZ4 and TRANK1, as well as the risk locus ADCY2 (5p15.31) and a region between MIR2113 and POU3F2 (6q16.1). ADCY2 is a key enzyme in cAMP signalling and our finding provides new insights into the biological mechanisms involved in the development of BD.

Follow-up of loci from the International Genomics of Alzheimer's Disease Project identifies TRIP4 as a novel susceptibility gene

To follow-up loci discovered by the International Genomics of Alzheimer’s Disease Project, we attempted independent replication of 19 single nucleotide polymorphisms (SNPs) in a large Spanish sample (Fundació ACE data set; 1808 patients and 2564 controls). Our results corroborate association with four SNPs located in the genes INPP5D, MEF2C, ZCWPW1 and FERMT2, respectively. Of these, ZCWPW1 was the only SNP to withstand correction for multiple testing (P=0.000655). Furthermore, we identify TRIP4 (rs74615166) as a novel genome-wide significant locus for Alzheimer’s disease risk (odds ratio=1.31; confidence interval 95% (1.19–1.44); P=9.74 × 10−9).

SUCLG2 identified as both a determinator of CSF Aβ1–42 levels and an attenuator of cognitive decline in Alzheimer's disease

Cerebrospinal fluid amyloid-beta 1-42 (Aβ1-42) and phosphorylated Tau at position 181 (pTau181) are biomarkers of Alzheimers disease (AD). We performed an analysis and meta-analysis of genome-wide association study data on Aβ1-42 and pTau181 in AD dementia patients followed by independent replication. An association was found between Aβ1-42 level and a single-nucleotide polymorphism in SUCLG2 (rs62256378) (P = 2.5×10(-12)). An interaction between APOE genotype and rs62256378 was detected (P = 9.5 × 10(-5)), with the strongest effect being observed in APOE-ε4 noncarriers. Clinically, rs62256378 was associated with rate of cognitive decline in AD dementia patients (P = 3.1 × 10(-3)). Functional microglia experiments showed that SUCLG2 was involved in clearance of Aβ1-42.

Genome-wide significant risk factors for Alzheimer’s disease: role in progression to dementia due to Alzheimer's disease among subjects with mild cognitive impairment

Few data are available concerning the role of risk markers for Alzheimer’s disease (AD) in progression to AD dementia among subjects with mild cognitive impairment (MCI). We therefore investigated the role of well-known AD-associated single-nucleotide polymorphism (SNP) in the progression from MCI to AD dementia. Four independent MCI data sets were included in the analysis: (a) the German study on Aging, Cognition and Dementia in primary care patients (n=853); (b) the German Dementia Competence Network (n=812); (c) the Fundació ACE from Barcelona, Spain (n=1245); and (d) the MCI data set of the Amsterdam Dementia Cohort (n=306). The effects of single markers and combined polygenic scores were measured using Cox proportional hazards models and meta-analyses. The clusterin (CLU) locus was an independent genetic risk factor for MCI to AD progression (CLU rs9331888: hazard ratio (HR)=1.187 (1.054–1.32); P=0.0035). A polygenic score (PGS1) comprising nine established genome-wide AD risk loci predicted a small effect on the risk of MCI to AD progression in APOE-ɛ4 (apolipoprotein E-ɛ4) carriers (HR=1.746 (1.029–2.965); P=0.038). The novel AD loci reported by the International Genomics of Alzheimers Project were not implicated in MCI to AD dementia progression. SNP-based polygenic risk scores comprising currently available AD genetic markers did not predict MCI to AD progression. We conclude that SNPs in CLU are potential markers for MCI to AD progression.

Identification of shared risk loci and pathways for bipolar disorder and schizophrenia

Bipolar disorder (BD) is a highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. BD shows substantial clinical and genetic overlap with other psychiatric disorders, in particular schizophrenia (SCZ). The genes underlying this etiological overlap remain largely unknown. A recent SCZ genome wide association study (GWAS) by the Psychiatric Genomics Consortium identified 128 independent genome-wide significant single nucleotide polymorphisms (SNPs). The present study investigated whether these SCZ-associated SNPs also contribute to BD development through the performance of association testing in a large BD GWAS dataset (9747 patients, 14278 controls). After re-imputation and correction for sample overlap, 22 of 107 investigated SCZ SNPs showed nominal association with BD. The number of shared SCZ-BD SNPs was significantly higher than expected (p = 1.46x10-8). This provides further evidence that SCZ-associated loci contribute to the development of BD. Two SNPs remained significant after Bonferroni correction. The most strongly associated SNP was located near TRANK1, which is a reported genome-wide significant risk gene for BD. Pathway analyses for all shared SCZ-BD SNPs revealed 25 nominally enriched gene-sets, which showed partial overlap in terms of the underlying genes. The enriched gene-sets included calcium- and glutamate signaling, neuropathic pain signaling in dorsal horn neurons, and calmodulin binding. The present data provide further insights into shared risk loci and disease-associated pathways for BD and SCZ. This may suggest new research directions for the treatment and prevention of these two major psychiatric disorders.

The protocadherin 17 gene affects cognition, personality, amygdala structure and function, synapse development and risk of major mood disorders

Major mood disorders, which primarily include bipolar disorder and major depressive disorder, are the leading cause of disability worldwide and pose a major challenge in identifying robust risk genes. Here, we present data from independent large-scale clinical data sets (including 29 557 cases and 32 056 controls) revealing brain expressed protocadherin 17 (PCDH17) as a susceptibility gene for major mood disorders. Single-nucleotide polymorphisms (SNPs) spanning the PCDH17 region are significantly associated with major mood disorders; subjects carrying the risk allele showed impaired cognitive abilities, increased vulnerable personality features, decreased amygdala volume and altered amygdala function as compared with non-carriers. The risk allele predicted higher transcriptional levels of PCDH17 mRNA in postmortem brain samples, which is consistent with increased gene expression in patients with bipolar disorder compared with healthy subjects. Further, overexpression of PCDH17 in primary cortical neurons revealed significantly decreased spine density and abnormal dendritic morphology compared with control groups, which again is consistent with the clinical observations of reduced numbers of dendritic spines in the brains of patients with major mood disorders. Given that synaptic spines are dynamic structures which regulate neuronal plasticity and have crucial roles in myriad brain functions, this study reveals a potential underlying biological mechanism of a novel risk gene for major mood disorders involved in synaptic function and related intermediate phenotypes.

Alzheimer's disease risk variants modulate endophenotypes in mild cognitive impairment

We evaluated the effect of Alzheimers disease (AD) susceptibility loci on endophenotypes closely related with AD pathology in patients with mild cognitive impairment (MCI).

Integrated Genome-Wide Pathway Association Analysis with INTERSNP

Objectives: Pathway association analysis (PAA) tests for an excess of moderately significant SNPs in genes from a common pathway. Methods: We present a Monte-Carlo simulation framework that allows to formulate the main ideas of existing PAA approaches using a self-contained rather than a competitive null hypothesis. A stand-alone implementation in INTERSNP makes time-consuming communication with standard GWAS software redundant. By additional parallelization with the OpenMP API, we achieve a reduction in running time for PAA by orders of magnitude, making a power simulation study for PAA feasible. Our approach properly accounts for linkage disequilibrium and is robust with respect to residual λ inflation. Results: We demonstrate that under simple, realistic disease models, PAA can actually strongly outperform the GWAS single-marker approach. PAA methods that make use of the strength of the SNP association (GenGen, Fisher’s combination test), in general, perform better than ratio-based methods (ALIGATOR, SNP ratio), whereas the relative performance of gene-based scoring (ALIGATOR, GenGen) and pathway-based scoring (SNP ratio, Fisher’s combination test) depends on the architecture of the assumed disease model. Finally, we present a new PAA score that models independent signals from the same gene in a regression framework and show that it is a reasonable compromise that combines the advantages of existing ideas.

Quick, “Imputation-free” meta-analysis with proxy-SNPs

BackgroundMeta-analysis (MA) is widely used to pool genome-wide association studies (GWASes) in order to a) increase the power to detect strong or weak genotype effects or b) as a result verification method. As a consequence of differing SNP panels among genotyping chips, imputation is the method of choice within GWAS consortia to avoid losing too many SNPs in a MA. YAMAS (Yet Another Meta Analysis Software), however, enables cross-GWAS conclusions prior to finished and polished imputation runs, which eventually are time-consuming.ResultsHere we present a fast method to avoid forfeiting SNPs present in only a subset of studies, without relying on imputation. This is accomplished by using reference linkage disequilibrium data from 1,000 Genomes/HapMap projects to find proxy-SNPs together with in-phase alleles for SNPs missing in at least one study. MA is conducted by combining association effect estimates of a SNP and those of its proxy-SNPs. Our algorithm is implemented in the MA software YAMAS. Association results from GWAS analysis applications can be used as input files for MA, tremendously speeding up MA compared to the conventional imputation approach. We show that our proxy algorithm is well-powered and yields valuable ad hoc results, possibly providing an incentive for follow-up studies. We propose our method as a quick screening step prior to imputation-based MA, as well as an additional main approach for studies without available reference data matching the ethnicities of study participants. As a proof of principle, we analyzed six dbGaP Type II Diabetes GWAS and found that the proxy algorithm clearly outperforms naïve MA on the p-value level: for 17 out of 23 we observe an improvement on the p-value level by a factor of more than two, and a maximum improvement by a factor of 2127.ConclusionsYAMAS is an efficient and fast meta-analysis program which offers various methods, including conventional MA as well as inserting proxy-SNPs for missing markers to avoid unnecessary power loss. MA with YAMAS can be readily conducted as YAMAS provides a generic parser for heterogeneous tabulated file formats within the GWAS field and avoids cumbersome setups. In this way, it supplements the meta-analysis process.

Gene set enrichment analysis and expression pattern exploration implicate an involvement of neurodevelopmental processes in bipolar disorder.

BACKGROUND Bipolar disorder (BD) is a common and highly heritable disorder of mood. Genome-wide association studies (GWAS) have identified several independent susceptibility loci. In order to extract more biological information from GWAS data, multi-locus approaches represent powerful tools since they utilize knowledge about biological processes to integrate functional sets of genes at strongly to moderately associated loci. METHODS We conducted gene set enrichment analyses (GSEA) using 2.3 million single-nucleotide polymorphisms, 397 Reactome pathways and 24,025 patients with BD and controls. RNA expression of implicated individual genes and gene sets were examined in post-mortem brains across lifespan. RESULTS Two pathways showed a significant enrichment after correction for multiple comparisons in the GSEA: GRB2 events in ERBB2 signaling, for which 6 of 21 genes were BD associated (PFDR = 0.0377), and NCAM signaling for neurite out-growth, for which 11 out of 62 genes were BD associated (PFDR = 0.0451). Most pathway genes showed peaks of RNA co-expression during fetal development and infancy and mapped to neocortical areas and parts of the limbic system. LIMITATIONS Pathway associations were technically reproduced by two methods, although they were not formally replicated in independent samples. Gene expression was explored in controls but not in patients. CONCLUSIONS Pathway analysis in large GWAS data of BD and follow-up of gene expression patterns in healthy brains provide support for an involvement of neurodevelopmental processes in the etiology of this neuropsychiatric disease. Future studies are required to further evaluate the relevance of the implicated genes on pathway functioning and clinical aspects of BD.