Patterns of genic intolerance of rare copy number variation in 59,898 human exomes.

Copy number variation (CNV) affecting protein-coding genes contributes substantially to human diversity and disease. Here we characterized the rates and properties of rare genic CNVs (<0.5% frequency) in exome sequencing data from nearly 60,000 individuals in the Exome Aggregation Consortium (ExAC) database. On average, individuals possessed 0.81 deleted and 1.75 duplicated genes, and most (70%) carried at least one rare genic CNV. For every gene, we empirically estimated an index of relative intolerance to CNVs that demonstrated moderate correlation with measures of genic constraint based on single-nucleotide variation (SNV) and was independently correlated with measures of evolutionary conservation. For individuals with schizophrenia, genes affected by CNVs were more intolerant than in controls. The ExAC CNV data constitute a critical component of an integrated database spanning the spectrum of human genetic variation, aiding in the interpretation of personal genomes as well as population-based disease studies. These data are freely available for download and visualization online

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

De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia

A small number of rare, recurrent genomic copy number variants (CNVs) are known to substantially increase susceptibility to schizophrenia. As a consequence of the low fecundity in people with schizophrenia and other neurodevelopmental phenotypes to which these CNVs contribute, CNVs with large effects on risk are likely to be rapidly removed from the population by natural selection. Accordingly, such CNVs must frequently occur as recurrent de novo mutations. In a sample of 662 schizophrenia proband–parent trios, we found that rare de novo CNV mutations were significantly more frequent in cases (5.1% all cases, 5.5% family history negative) compared with 2.2% among 2623 controls, confirming the involvement of de novo CNVs in the pathogenesis of schizophrenia. Eight de novo CNVs occurred at four known schizophrenia loci (3q29, 15q11.2, 15q13.3 and 16p11.2). De novo CNVs of known pathogenic significance in other genomic disorders were also observed, including deletion at the TAR (thrombocytopenia absent radius) region on 1q21.1 and duplication at the WBS (Williams–Beuren syndrome) region at 7q11.23. Multiple de novos spanned genes encoding members of the DLG (discs large) family of membrane-associated guanylate kinases (MAGUKs) that are components of the postsynaptic density (PSD). Two de novos also affected EHMT1, a histone methyl transferase known to directly regulate DLG family members. Using a systems biology approach and merging novel CNV and proteomics data sets, systematic analysis of synaptic protein complexes showed that, compared with control CNVs, case de novos were significantly enriched for the PSD proteome (P=1.72 × 10−6). This was largely explained by enrichment for members of the N-methyl-D-aspartate receptor (NMDAR) (P=4.24 × 10−6) and neuronal activity-regulated cytoskeleton-associated protein (ARC) (P=3.78 × 10−8) postsynaptic signalling complexes. In an analysis of 18 492 subjects (7907 cases and 10 585 controls), case CNVs were enriched for members of the NMDAR complex (P=0.0015) but not ARC (P=0.14). Our data indicate that defects in NMDAR postsynaptic signalling and, possibly, ARC complexes, which are known to be important in synaptic plasticity and cognition, play a significant role in the pathogenesis of schizophrenia

Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets

<p>Abstract</p> <p>Background</p> <p>The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.</p> <p>Results</p> <p>Using established protocols to synthesize eQTL networks and corresponding data, we show that our method achieves improved performance over existing leading methods. For the goal of gene network reconstruction, our method achieves improvements in recall ranging from 20% to 90% across a broad range of precision levels and for datasets of varying sample sizes. Additionally, we show that the learned networks can be utilized for expression quantitative trait loci mapping, resulting in upwards of 10-fold increases in recall over traditional univariate mapping.</p> <p>Conclusions</p> <p>Using the information from our method as a prior for Bayesian network structure learning yields large improvements in accuracy for the tasks of gene network reconstruction and expression quantitative trait loci mapping. In particular, our method is effective for establishing causal relationships between transcripts located both proximally and distally from genomic loci.</p

Genetic Architecture of Highly Complex Chemical Resistance Traits across Four Yeast Strains

Many questions about the genetic basis of complex traits remain unanswered. This is in part due to the low statistical power of traditional genetic mapping studies. We used a statistically powerful approach, extreme QTL mapping (X-QTL), to identify the genetic basis of resistance to 13 chemicals in all 6 pairwise crosses of four ecologically and genetically diverse yeast strains, and we detected a total of more than 800 loci. We found that the number of loci detected in each experiment was primarily a function of the trait (explaining 46% of the variance) rather than the cross (11%), suggesting that the level of genetic complexity is a consistent property of a trait across different genetic backgrounds. Further, we observed that most loci had trait-specific effects, although a small number of loci with effects in many conditions were identified. We used the patterns of resistance and susceptibility alleles in the four parent strains to make inferences about the allele frequency spectrum of functional variants. We also observed evidence of more complex allelic series at a number of loci, as well as strain-specific signatures of selection. These results improve our understanding of complex traits in yeast and have implications for study design in other organisms

Genome-Wide Analysis of Nucleotide-Level Variation in Commonly Used Saccharomyces cerevisiae Strains

Ten years have passed since the genome of Saccharomyces cerevisiae–more precisely, the S288c strain–was completely sequenced. However, experimental work in yeast is commonly performed using strains that are of unknown genetic relationship to S288c. Here, we characterized the nucleotide-level similarity between S288c and seven commonly used lab strains (A364A, W303, FL100, CEN.PK, ∑1278b, SK1 and BY4716) using 25mer oligonucleotide microarrays that provide complete and redundant coverage of the ∼12 Mb Saccharomyces cerevisiae genome. Using these data, we assessed the frequency and distribution of nucleotide variation in comparison to the sequenced reference genome. These data allow us to infer the relationships between experimentally important strains of yeast and provide insight for experimental designs that are sensitive to sequence variation. We propose a rational approach for near complete sequencing of strains related to the reference using these data and directed re-sequencing. These data and new visualization tools are accessible online in a new resource: the Yeast SNPs Browser (YSB; http://gbrowse.princeton.edu/cgi-bin/gbrowse/yeast_strains_snps) that is available to all researchers