Population Genetic Study of the Brain-Derived Neurotrophic Factor (BDNF) Gene

Genetic variants in the brain-derived neurotrophic factor (BDNF) gene, predominantly the functional Val66Met polymorphism, have been associated with risk of bipolar disorder and other psychiatric disorders. However, not all studies support these findings, and overall the evidence for the association of BDNF with disease risk is weak. As differences in population genetic structure between patient samples could cause discrepant or spurious association results, we investigated this possibility by carrying out population genetic analyses of the BDNF genomic region. Substantial variation was detected in BDNF coding region single-nucleotide polymorphism (SNP) allele and haplotype frequencies between 58 global populations, with the derived Met allele of Val66Met ranging in frequency from 0 to 72% across populations. FST analyses to assess diversity in the HapMap populations determined that the Val66Met FST value was at the 99.8th percentile among all SNPs in the genome. As the BDNF population genetic differences may be due to local selection, we performed the long-range haplotype test for selection using 68 SNPs spanning the BDNF genomic region in 12 European-derived pedigrees. Evidence for positive selection was found for a high-frequency Val-carrying haplotype, with a relative extended haplotype homozygosity value above the 99th percentile compared with HapMap data $(P=4.6 \times 10^{−4})$. In conclusion, we observed considerable BDNF allele and haplotype diversity among global populations and evidence for positive selection at the BDNF locus. These phenomena can have a profound impact on the detection of disease susceptibility genes and must be considered in gene association studies of BDNF.Organismic and Evolutionary BiologyOther Research Uni

Hypothesis-driven candidate genes for schizophrenia compared to genome-wide association results

Candidate gene studies have been a key approach to the genetics of schizophrenia. Results of these studies have been confusing and no genes have been unequivocally implicated. The hypothesis-driven candidate gene literature can be appraised via comparison with the results of genome-wide association studies (GWAS)

De novo CNVs in bipolar affective disorder and schizophrenia

An increased rate of de novo copy number variants (CNVs) has been found in schizophrenia (SZ), autism and developmental delay. An increased rate has also been reported in bipolar affective disorder (BD). Here, in a larger BD sample, we aimed to replicate these findings and compare de novo CNVs between SZ and BD. We used Illumina microarrays to genotype 368 BD probands, 76 SZ probands and all their parents. Copy number variants were called by PennCNV and filtered for frequency (10 kb). Putative de novo CNVs were validated with the z-score algorithm, manual inspection of log R ratios (LRR) and qPCR probes. We found 15 de novo CNVs in BD (4.1% rate) and 6 in SZ (7.9% rate). Combining results with previous studies and using a cut-off of >100 kb, the rate of de novo CNVs in BD was intermediate between controls and SZ: 1.5% in controls, 2.2% in BD and 4.3% in SZ. Only the differences between SZ and BD and SZ and controls were significant. The median size of de novo CNVs in BD (448 kb) was also intermediate between SZ (613 kb) and controls (338 kb), but only the comparison between SZ and controls was significant. Only one de novo CNV in BD was in a confirmed SZ locus (16p11.2). Sporadic or early onset cases were not more likely to have de novo CNVs. We conclude that de novo CNVs play a smaller role in BD compared with SZ. Patients with a positive family history can also harbour de novo mutations

Cis-Acting Regulation of Brain-Specific ANK3 Gene Expression by a Genetic Variant Associated with Bipolar Disorder

Several genome-wide association studies (GWAS) for bipolar disorder (BD) have found a strong association of the Ankyrin3 (ANK3) gene. This association spans numerous linked single nucleotide polymorphisms (SNPs) in a ~250 kb genomic region overlapping ANK3. The associated region encompasses predicted regulatory elements as well as two of six validated alternative first exons, which encode distinct protein domains at the N-terminus of the protein also known as ankyrin-G (AnkG). Using RNA Ligase-Mediated Rapid Amplification of cDNA Ends (RLM-RACE) to identify novel transcripts in conjunction with a highly sensitive, exon-specific multiplexed mRNA expression assay, we detected differential regulation of distinct ANK3 transcription start sites (TSSs) and coupling of specific 5’ ends with 3’ mRNA splicing events in post-mortem human brain and human stem cell-derived neural progenitors and neurons. Furthermore, allelic variation at the BD–associated SNP rs1938526 correlated with a significant difference in cerebellar expression of a brain-specific ANK3 transcript. These findings suggest a brain-specific cis-regulatory transcriptional effect of ANK3 may be relevant to BD pathophysiology

Non-random mating, parent-of-origin, and maternal–fetal incompatibility effects in schizophrenia

Although the association of common genetic variation in the extended MHC region with schizophrenia is the most significant yet discovered, the MHC region is one of the more complex regions of the human genome, with unusually high gene density and long-range linkage disequilibrium. The statistical test on which the MHC association is based is a relatively simple, additive model which uses logistic regression of SNP genotypes to predict case-control status. However, it is plausible that more complex models underlie this association. Using a well-characterized sample of trios, we evaluated more complex models by looking for evidence for: (a) non-random mating for HLA alleles, schizophrenia risk profiles, and ancestry; (b) parent-of-origin effects for HLA alleles; and (c) maternal-fetal genotype incompatibility in the HLA. We found no evidence for non-random mating in the parents of individuals with schizophrenia in terms of MHC genotypes or schizophrenia risk profile scores. However, there was evidence of non-random mating that appeared mostly to be driven by ancestry. We did not detect over-transmission of HLA alleles to affected offspring via the general TDT test (without regard to parent of origin) or preferential transmission via paternal or maternal inheritance. We evaluated the hypothesis that maternal-fetal HLA incompatibility may increase risk for schizophrenia using eight classical HLA loci. The most significant alleles were in HLA-B, HLA-C, HLA-DQB1, and HLA-DRB1 but none was significant after accounting for multiple comparisons. We did not find evidence to support more complex models of gene action, but statistical power may have been limiting

Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach

Therapeutic treatments for schizophrenia do not alleviate symptoms for all patients and efficacy is limited by common, often severe, side-effects. Genetic studies of disease can identify novel drug targets, and drugs for which the mechanism has direct genetic support have increased likelihood of clinical success. Large-scale genetic studies of schizophrenia have increased the number of genes and gene sets associated with risk. We aimed to examine the overlap between schizophrenia risk loci and gene targets of a comprehensive set of medications to potentially inform and improve treatment of schizophrenia

Genetic validation of bipolar disorder identified by automated phenotyping using electronic health records

Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363–372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h2g) and genetic correlation (rg) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency—“coded-strict”, “coded-broad”, and “coded-broad based on a single clinical encounter” (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h2g were 0.24 (p = 0.015), 0.09 (p = 0.064), 0.13 (p = 0.003), 0.00 (p = 0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h2g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h2g) was 0.12 (p = 0.004). These h2g were lower or similar to the h2g observed by the ICCBD + PGCBD (0.23, p = 3.17E−80, total N = 33,181). However, the rg between ICCBD + PGCBD and the EHR-based cases were high for 95-NLP (0.66, p = 3.69 × 10–5), coded-strict (1.00, p = 2.40 × 10−4), and coded-broad (0.74, p = 8.11 × 10–7). The rg between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research

Translating genome-wide association findings into new therapeutics for psychiatry

Genome-wide association studies (GWAS) in psychiatry, once they reach sufficient sample size and power, have been enormously successful. The Psychiatric Genomics Consortium (PGC) aims for mega-analyses with sample sizes that will grow to (cumulatively) >1 million individuals in the next 5 years. This should lead to hundreds of new findings for common genetic variants across nine psychiatric disorders studied by the PGC. The new targets discovered by GWAS have the potential to restart largely stalled psychiatric drug development pipelines, and the translation of GWAS findings into the clinic is a key aim of the recently funded phase 3 of the PGC. This is not without considerable technical challenges. These approaches complement the other main aim of GWAS studies on risk prediction approaches for improving detection, differential diagnosis, and clinical trial design. This paper outlines the motivations, technical and analytical issues, and the plans for translating PGC3 findings into new therapeutics