Genome-wide linkage analysis of 972 bipolar pedigrees using single-nucleotide polymorphisms.

Because of the high costs associated with ascertainment of families, most linkage studies of Bipolar I disorder (BPI) have used relatively small samples. Moreover, the genetic information content reported in most studies has been less than 0.6. Although microsatellite markers spaced every 10 cM typically extract most of the genetic information content for larger multiplex families, they can be less informative for smaller pedigrees especially for affected sib pair kindreds. For these reasons we collaborated to pool family resources and carried out higher density genotyping. Approximately 1100 pedigrees of European ancestry were initially selected for study and were genotyped by the Center for Inherited Disease Research using the Illumina Linkage Panel 12 set of 6090 single-nucleotide polymorphisms. Of the ~1100 families, 972 were informative for further analyses, and mean information content was 0.86 after pruning for linkage disequilibrium. The 972 kindreds include 2284 cases of BPI disorder, 498 individuals with bipolar II disorder (BPII) and 702 subjects with recurrent major depression. Three affection status models (ASMs) were considered: ASM1 (BPI and schizoaffective disorder, BP cases (SABP) only), ASM2 (ASM1 cases plus BPII) and ASM3 (ASM2 cases plus recurrent major depression). Both parametric and non-parametric linkage methods were carried out. The strongest findings occurred at 6q21 (non-parametric pairs LOD 3.4 for rs1046943 at 119 cM) and 9q21 (non-parametric pairs logarithm of odds (LOD) 3.4 for rs722642 at 78 cM) using only BPI and schizoaffective (SA), BP cases. Both results met genome-wide significant criteria, although neither was significant after correction for multiple analyses. We also inspected parametric scores for the larger multiplex families to identify possible rare susceptibility loci. In this analysis, we observed 59 parametric LODs of 2 or greater, many of which are likely to be close to maximum possible scores. Although some linkage findings may be false positives, the results could help prioritize the search for rare variants using whole exome or genome sequencing

Genetic Background Strongly Modifies the Severity of Symptoms of Hirschsprung Disease, but Not Hearing Loss in Rats Carrying Ednrbsl Mutations

Hirschsprung disease (HSCR) is thought to result as a consequence of multiple gene interactions that modulate the ability of enteric neural crest cells to populate the developing gut. However, it remains unknown whether the single complete deletion of important HSCR-associated genes is sufficient to result in HSCR disease. In this study, we found that the null mutation of the Ednrb gene, thought indispensable for enteric neuron development, is insufficient to result in HSCR disease when bred onto a different genetic background in rats carrying Ednrbsl mutations. Moreover, we found that this mutation results in serious congenital sensorineural deafness, and these strains may be used as ideal models of Waardenburg Syndrome Type 4 (WS4). Furthermore, we evaluated how the same changed genetic background modifies three features of WS4 syndrome, aganglionosis, hearing loss, and pigment disorder in these congenic strains. We found that the same genetic background markedly changed the aganglionosis, but resulted in only slight changes to hearing loss and pigment disorder. This provided the important evidence, in support of previous studies, that different lineages of neural crest-derived cells migrating along with various pathways are regulated by different signal molecules. This study will help us to better understand complicated diseases such as HSCR and WS4 syndrome

Genome-Wide Association of Bipolar Disorder Suggests an Enrichment of Replicable Associations in Regions near Genes

Although a highly heritable and disabling disease, bipolar disorder's (BD) genetic variants have been challenging to identify. We present new genotype data for 1,190 cases and 401 controls and perform a genome-wide association study including additional samples for a total of 2,191 cases and 1,434 controls. We do not detect genome-wide significant associations for individual loci; however, across all SNPs, we show an association between the power to detect effects calculated from a previous genome-wide association study and evidence for replication (P = 1.5×10−7). To demonstrate that this result is not likely to be a false positive, we analyze replication rates in a large meta-analysis of height and show that, in a large enough study, associations replicate as a function of power, approaching a linear relationship. Within BD, SNPs near exons exhibit a greater probability of replication, supporting an enrichment of reproducible associations near functional regions of genes. These results indicate that there is likely common genetic variation associated with BD near exons (±10 kb) that could be identified in larger studies and, further, provide a framework for assessing the potential for replication when combining results from multiple studies

Comparative Linkage Meta-Analysis Reveals Regionally-Distinct, Disparate Genetic Architectures: Application to Bipolar Disorder and Schizophrenia

New high-throughput, population-based methods and next-generation sequencing capabilities hold great promise in the quest for common and rare variant discovery and in the search for ”missing heritability.” However, the optimal analytic strategies for approaching such data are still actively debated, representing the latest rate-limiting step in genetic progress. Since it is likely a majority of common variants of modest effect have been identified through the application of tagSNP-based microarray platforms (i.e., GWAS), alternative approaches robust to detection of low-frequency (1–5% MAF) and rare (<1%) variants are of great importance. Of direct relevance, we have available an accumulated wealth of linkage data collected through traditional genetic methods over several decades, the full value of which has not been exhausted. To that end, we compare results from two different linkage meta-analysis methods—GSMA and MSP—applied to the same set of 13 bipolar disorder and 16 schizophrenia GWLS datasets. Interestingly, we find that the two methods implicate distinct, largely non-overlapping, genomic regions. Furthermore, based on the statistical methods themselves and our contextualization of these results within the larger genetic literatures, our findings suggest, for each disorder, distinct genetic architectures may reside within disparate genomic regions. Thus, comparative linkage meta-analysis (CLMA) may be used to optimize low-frequency and rare variant discovery in the modern genomic era

Association between Catechol-O-Methyltrasferase Val108/158Met Genotype and Prefrontal Hemodynamic Response in Schizophrenia

BACKGROUND:"Imaging genetics" studies have shown that brain function by neuroimaging is a sensitive intermediate phenotype that bridges the gap between genes and psychiatric conditions. Although the evidence of association between functional val108/158met polymorphism of the catechol-O-methyltransferase gene (COMT) and increasing risk for developing schizophrenia from genetic association studies remains to be elucidated, one of the most topical findings from imaging genetics studies is the association between COMT genotype and prefrontal function in schizophrenia. The next important step in the translational approach is to establish a useful neuroimaging tool in clinical settings that is sensitive to COMT variation, so that the clinician could use the index to predict clinical response such as improvement in cognitive dysfunction by medication. Here, we investigated spatiotemporal characteristics of the association between prefrontal hemodynamic activation and the COMT genotype using a noninvasive neuroimaging technique, near-infrared spectroscopy (NIRS). METHODOLOGY/PRINCIPAL FINDINGS:Study participants included 45 patients with schizophrenia and 60 healthy controls matched for age and gender. Signals that are assumed to reflect regional cerebral blood volume were monitored over prefrontal regions from 52-channel NIRS and compared between two COMT genotype subgroups (Met carriers and Val/Val individuals) matched for age, gender, premorbid IQ, and task performance. The [oxy-Hb] increase in the Met carriers during the verbal fluency task was significantly greater than that in the Val/Val individuals in the frontopolar prefrontal cortex of patients with schizophrenia, although neither medication nor clinical symptoms differed significantly between the two subgroups. These differences were not found to be significant in healthy controls. CONCLUSIONS/SIGNIFICANCE:These data suggest that the prefrontal NIRS signals can noninvasively detect the impact of COMT variation in patients with schizophrenia. NIRS may be a promising candidate translational approach in psychiatric neuroimaging

Fine mapping of the 9q31 Hirschsprung’s disease locus

Hirschsprung’s disease (HSCR) is a congenital disorder characterised by the absence of ganglia along variable lengths of the intestine. The RET gene is the major HSCR gene. Reduced penetrance of RET mutations and phenotypic variability suggest the involvement of additional modifying genes in the disease. A RET-dependent modifier locus was mapped to 9q31 in families bearing no coding sequence (CDS) RET mutations. Yet, the 9q31 causative locus is to be identified. To fine-map the 9q31 region, we genotyped 301 tag-SNPs spanning 7 Mb on 137 HSCR Dutch trios. This revealed two HSCR-associated regions that were further investigated in 173 Chinese HSCR patients and 436 controls using the genotype data obtained from a genome-wide association study recently conducted. Within one of the two identified regions SVEP1 SNPs were found associated with Dutch HSCR patients in the absence of RET mutations. This ratifies the reported linkage to the 9q31 region in HSCR families with no RET CDS mutations. However, this finding could not be replicated. In Chinese, HSCR was found associated with IKBKAP. In contrast, this association was stronger in patients carrying RET CDS mutations with p = 5.10 × 10−6 [OR = 3.32 (1.99, 5.59)] after replication. The HSCR-association found for IKBKAP in Chinese suggests population specificity and implies that RET mutation carriers may have an additional risk. Our finding is supported by the role of IKBKAP in the development of the nervous system

Applying polygenic risk scoring for psychiatric disorders to a large family with bipolar disorder and major depressive disorder

Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n similar to 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders

The glial growth factors deficiency and synaptic destabilization hypothesis of schizophrenia

BACKGROUND: A systems approach to understanding the etiology of schizophrenia requires a theory which is able to integrate genetic as well as neurodevelopmental factors. PRESENTATION OF THE HYPOTHESIS: Based on a co-localization of loci approach and a large amount of circumstantial evidence, we here propose that a functional deficiency of glial growth factors and of growth factors produced by glial cells are among the distal causes in the genotype-to-phenotype chain leading to the development of schizophrenia. These factors include neuregulin, insulin-like growth factor I, insulin, epidermal growth factor, neurotrophic growth factors, erbB receptors, phosphatidylinositol-3 kinase, growth arrest specific genes, neuritin, tumor necrosis factor alpha, glutamate, NMDA and cholinergic receptors. A genetically and epigenetically determined low baseline of glial growth factor signaling and synaptic strength is expected to increase the vulnerability for additional reductions (e.g., by viruses such as HHV-6 and JC virus infecting glial cells). This should lead to a weakening of the positive feedback loop between the presynaptic neuron and its targets, and below a certain threshold to synaptic destabilization and schizophrenia. TESTING THE HYPOTHESIS: Supported by informed conjectures and empirical facts, the hypothesis makes an attractive case for a large number of further investigations. IMPLICATIONS OF THE HYPOTHESIS: The hypothesis suggests glial cells as the locus of the genes-environment interactions in schizophrenia, with glial asthenia as an important factor for the genetic liability to the disorder, and an increase of prolactin and/or insulin as possible working mechanisms of traditional and atypical neuroleptic treatments

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)