Exome sequencing followed by large-scale genotyping suggests a limited role for moderately rare risk factors of strong effect in schizophrenia.

Schizophrenia is a severe psychiatric disorder with strong heritability and marked heterogeneity in symptoms, course, and treatment response. There is strong interest in identifying genetic risk factors that can help to elucidate the pathophysiology and that might result in the development of improved treatments. Linkage and genome-wide association studies (GWASs) suggest that the genetic basis of schizophrenia is heterogeneous. However, it remains unclear whether the underlying genetic variants are mostly moderately rare and can be identified by the genotyping of variants observed in sequenced cases in large follow-up cohorts or whether they will typically be much rarer and therefore more effectively identified by gene-based methods that seek to combine candidate variants. Here, we consider 166 persons who have schizophrenia or schizoaffective disorder and who have had either their genomes or their exomes sequenced to high coverage. From these data, we selected 5,155 variants that were further evaluated in an independent cohort of 2,617 cases and 1,800 controls. No single variant showed a study-wide significant association in the initial or follow-up cohorts. However, we identified a number of case-specific variants, some of which might be real risk factors for schizophrenia, and these can be readily interrogated in other data sets. Our results indicate that schizophrenia risk is unlikely to be predominantly influenced by variants just outside the range detectable by GWASs. Rather, multiple rarer genetic variants must contribute substantially to the predisposition to schizophrenia, suggesting that both very large sample sizes and gene-based association tests will be required for securely identifying genetic risk factors. © 2012 The American Society of Human Genetics

Screening the human exome: a comparison of whole genome and whole transcriptome sequencing

BACKGROUND: There is considerable interest in the development of methods to efficiently identify all coding variants present in large sample sets of humans. There are three approaches possible: whole-genome sequencing, whole-exome sequencing using exon capture methods, and RNA-Seq. While whole-genome sequencing is the most complete, it remains sufficiently expensive that cost effective alternatives are important. RESULTS: Here we provide a systematic exploration of how well RNA-Seq can identify human coding variants by comparing variants identified through high coverage whole-genome sequencing to those identified by high coverage RNA-Seq in the same individual. This comparison allowed us to directly evaluate the sensitivity and specificity of RNA-Seq in identifying coding variants, and to evaluate how key parameters such as the degree of coverage and the expression levels of genes interact to influence performance. We find that although only 40% of exonic variants identified by whole genome sequencing were captured using RNA-Seq; this number rose to 81% when concentrating on genes known to be well-expressed in the source tissue. We also find that a high false positive rate can be problematic when working with RNA-Seq data, especially at higher levels of coverage. CONCLUSIONS: We conclude that as long as a tissue relevant to the trait under study is available and suitable quality control screens are implemented, RNA-Seq is a fast and inexpensive alternative approach for finding coding variants in genes with sufficiently high expression levels

A Genome-Wide Investigation of SNPs and CNVs in Schizophrenia

We report a genome-wide assessment of single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) in schizophrenia. We investigated SNPs using 871 patients and 863 controls, following up the top hits in four independent cohorts comprising 1,460 patients and 12,995 controls, all of European origin. We found no genome-wide significant associations, nor could we provide support for any previously reported candidate gene or genome-wide associations. We went on to examine CNVs using a subset of 1,013 cases and 1,084 controls of European ancestry, and a further set of 60 cases and 64 controls of African ancestry. We found that eight cases and zero controls carried deletions greater than 2 Mb, of which two, at 8p22 and 16p13.11-p12.4, are newly reported here. A further evaluation of 1,378 controls identified no deletions greater than 2 Mb, suggesting a high prior probability of disease involvement when such deletions are observed in cases. We also provide further evidence for some smaller, previously reported, schizophrenia-associated CNVs, such as those in NRXN1 and APBA2. We could not provide strong support for the hypothesis that schizophrenia patients have a significantly greater “load” of large (>100 kb), rare CNVs, nor could we find common CNVs that associate with schizophrenia. Finally, we did not provide support for the suggestion that schizophrenia-associated CNVs may preferentially disrupt genes in neurodevelopmental pathways. Collectively, these analyses provide the first integrated study of SNPs and CNVs in schizophrenia and support the emerging view that rare deleterious variants may be more important in schizophrenia predisposition than common polymorphisms. While our analyses do not suggest that implicated CNVs impinge on particular key pathways, we do support the contribution of specific genomic regions in schizophrenia, presumably due to recurrent mutation. On balance, these data suggest that very few schizophrenia patients share identical genomic causation, potentially complicating efforts to personalize treatment regimens

A Predictive Model of the Oxygen and Heme Regulatory Network in Yeast

Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included

A Sterol-Regulatory Element Binding Protein Is Required for Cell Polarity, Hypoxia Adaptation, Azole Drug Resistance, and Virulence in Aspergillus fumigatus

At the site of microbial infections, the significant influx of immune effector cells and the necrosis of tissue by the invading pathogen generate hypoxic microenvironments in which both the pathogen and host cells must survive. Currently, whether hypoxia adaptation is an important virulence attribute of opportunistic pathogenic molds is unknown. Here we report the characterization of a sterol-regulatory element binding protein, SrbA, in the opportunistic pathogenic mold, Aspergillus fumigatus. Loss of SrbA results in a mutant strain of the fungus that is incapable of growth in a hypoxic environment and consequently incapable of causing disease in two distinct murine models of invasive pulmonary aspergillosis (IPA). Transcriptional profiling revealed 87 genes that are affected by loss of SrbA function. Annotation of these genes implicated SrbA in maintaining sterol biosynthesis and hyphal morphology. Further examination of the SrbA null mutant consequently revealed that SrbA plays a critical role in ergosterol biosynthesis, resistance to the azole class of antifungal drugs, and in maintenance of cell polarity in A. fumigatus. Significantly, the SrbA null mutant was highly susceptible to fluconazole and voriconazole. Thus, these findings present a new function of SREBP proteins in filamentous fungi, and demonstrate for the first time that hypoxia adaptation is likely an important virulence attribute of pathogenic molds

SVA: software for annotating and visualizing sequenced human genomes

SUMMARY: Here we present Sequence Variant Analyzer (SVA), a software tool that assigns a predicted biological function to variants identified in next-generation sequencing studies and provides a browser to visualize the variants in their genomic contexts. SVA also provides for flexible interaction with software implementing variant association tests allowing users to consider both the bioinformatic annotation of identified variants and the strength of their associations with studied traits. We illustrate the annotation features of SVA using two simple examples of sequenced genomes that harbor Mendelian mutations. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://www.svaproject.org

Common human genetic variants and HIV-1 susceptibility: a genome-wide survey in a homogeneous African population.

OBJECTIVE: To date, CCR5 variants remain the only human genetic factors to be confirmed to impact HIV-1 acquisition. However, protective CCR5 variants are largely absent in African populations, in which sporadic resistance to HIV-1 infection is still unexplained. We investigated whether common genetic variants associate with HIV-1 susceptibility in Africans. METHODS: We performed a genome-wide association study (GWAS) in a population of 1532 individuals from Malawi, a country with high prevalence of HIV-1 infection. Using single-nucleotide polymorphisms (SNPs) present on the genome-wide chip, we also investigated previously reported associations with HIV-1 susceptibility or acquisition. Recruitment was coordinated by the Center for HIV/AIDS Vaccine Immunology at two sexually transmitted infection clinics. HIV status was determined by HIV rapid tests and nucleic acid testing. RESULTS: After quality control, the population consisted of 848 high-risk seronegative and 531 HIV-1 seropositive individuals. Logistic regression testing in an additive genetic model was performed for SNPs that passed quality control. No single SNP yielded a significant P value after correction for multiple testing. The study was sufficiently powered to detect markers with genotype relative risk 2.0 or more and minor allele frequencies 12% or more. CONCLUSION: This is the first GWAS of host determinants of HIV-1 susceptibility, performed in an African population. The absence of any significant association can have many possible explanations: rarer genetic variants or common variants with weaker effect could be responsible for the resistance phenotype; alternatively, resistance to HIV-1 infection might be due to nongenetic parameters or to complex interactions between genes, immunity and environment

Tibetans living at sea level have a hyporesponsive hypoxia-inducible factor system and blunted physiological responses to hypoxia.

Tibetan natives have lived on the Tibetan plateau (altitude ∼ 4,000 m) for at least 25,000 years, and as such they are adapted to life and reproduction in a hypoxic environment. Recent studies have identified two genetic loci, EGLN1 and EPAS1, that have undergone natural selection in Tibetans, and further demonstrated an association of EGLN1/EPAS1 genotype with hemoglobin concentration. Both genes encode major components of the hypoxia-inducible factor (HIF) transcriptional pathway, which coordinates an organism's response to hypoxia. Patients living at sea level with genetic disease of the HIF pathway have characteristic phenotypes at both the integrative-physiology and cellular level. We sought to test the hypothesis that natural selection to hypoxia within Tibetans results in related phenotypic differences. We compared Tibetans living at sea level with Han Chinese, who are Tibetans' most closely related major ethnic group. We found that Tibetans had a lower hemoglobin concentration, a higher pulmonary ventilation relative to metabolism, and blunted pulmonary vascular responses to both acute (minutes) and sustained (8 h) hypoxia. At the cellular level, the relative expression and hypoxic induction of HIF-regulated genes were significantly lower in peripheral blood lymphocytes from Tibetans compared with Han Chinese. Within the Tibetans, we found a significant correlation between both EPAS1 and EGLN1 genotype and the induction of erythropoietin by hypoxia. In conclusion, this study provides further evidence that Tibetans respond less vigorously to hypoxic challenge. This is evident at sea level and, at least in part, appears to arise from a hyporesponsive HIF transcriptional system