Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P _meta<1×10⁻⁴, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available.     

Candidate-gene screening and association analysis at the autism-susceptibility locus on chromosome 16p: evidence of association at GRIN2A and ABAT

Autism is a highly heritable neurodevelopmental disorder whose underlying genetic causes have yet to be identified. To date, there have been eight genome screens for autism, two of which identified a putative susceptibility locus on chromosome 16p. In the present study, 10 positional candidate genes that map to 16p11-13 were examined for coding variants: A2BP1, ABAT, BFAR, CREBBP, EMP2, GRIN2A, MRTF-B, SSTR5, TBX6, and UBN1. Screening of all coding and regulatory regions by denaturing high-performance liquid chromatography identified seven nonsynonymous changes. Five of these mutations were found to cosegregate with autism, but the mutations are not predicted to have deleterious effects on protein structure and are unlikely to represent significant etiological variants. Selected variants from candidate genes were genotyped in the entire International Molecular Genetics Study of Autism Consortium collection of 239 multiplex families and were tested for association with autism by use of the pedigree disequilibrium test. Additionally, genotype frequencies were compared between 239 unrelated affected individuals and 192 controls. Patterns of linkage disequilibrium were investigated, and the transmission of haplotypes across candidate genes was tested for association. Evidence of single-marker association was found for variants in ABAT, CREBBP, and GRIN2A. Within these genes, 12 single-nucleotide polymorphisms (SNPs) were subsequently genotyped in 91 autism trios (one affected individual and two unaffected parents), and the association was replicated within GRIN2A (Fisher's exact test, P<.0001). Logistic regression analysis of SNP data across GRIN2A and ABAT showed a trend toward haplotypic differences between cases and controls.     

SIVagm Infection in Wild African Green Monkeys from South Africa: Epidemiology,Natural History,and Evolutionary Considerations

Pathogenesis studies of SIV infection have not been performed to date in wild monkeys due to difficulty in collecting and storing samples on site and the lack of analytical reagents covering the extensive SIV diversity. We performed a large scale study of molecular epidemiology and natural history of SIVagm infection in 225 free-ranging AGMs from multiple locations in South Africa. SIV prevalence (established by sequencing pol, env, and gag) varied dramatically between infant/juvenile (7%) and adult animals (68%) (p<0.0001), and between adult females (78%) and males (57%). Phylogenetic analyses revealed an extensive genetic diversity, including frequent recombination events. Some AGMs harbored epidemiologically linked viruses. Viruses infecting AGMs in the Free State, which are separated from those on the coastal side by the Drakensberg Mountains, formed a separate cluster in the phylogenetic trees; this observation supports a long standing presence of SIV in AGMs, at least from the time of their speciation to their Plio-Pleistocene migration. Specific primers/probes were synthesized based on the pol sequence data and viral loads (VLs) were quantified. VLs were of 10⁴–10⁶ RNA copies/ml, in the range of those observed in experimentally-infected monkeys, validating the experimental approaches in natural hosts. VLs were significantly higher (10⁷–10⁸ RNA copies/ml) in 10 AGMs diagnosed as acutely infected based on SIV seronegativity (Fiebig II), which suggests a very active transmission of SIVagm in the wild. Neither cytokine levels (as biomarkers of immune activation) nor sCD14 levels (a biomarker of microbial translocation) were different between SIV-infected and SIV-uninfected monkeys. This complex algorithm combining sequencing and phylogeny, VL quantification, serology, and testing of surrogate markers of microbial translocation and immune activation permits a systematic investigation of the epidemiology, viral diversity and natural history of SIV infection in wild African natural hosts.     

Antepartum Depression and Anxiety Associated with Disability in African Women: Cross-Sectional Results from the CDS Study in Ghana and C?te d'Ivoire

Background

Common mental disorders, particularly unipolar depressive disorders, rank among the top 5 with respect to the global burden of disease. As a major public health concern, antepartum depression and anxiety not only affects the individual woman, but also her offspring. Data on the prevalence of common mental disorders in pregnant women in sub-Saharan Africa are scarce. We provide results from Ghana and Côte d''Ivoire.

Methods

We subsequently recruited and screened n = 1030 women in the third trimester of their pregnancy for depressed mood, general anxiety, and perceived disability using the Patient Health Questionnaire depression module (PHQ-9), the 7-item Anxiety Scale (GAD-7), and the World Health Organisation Disability Assessment Schedule II (WHO-DAS 2.0, 12-item version). In addition to estimates of means and prevalence, a hierarchical linear regression model was calculated to determine the influence of antepartum depression and anxiety on disability.

Results

In Ghana, 26.6% of women showed substantially depressed mood. In Côte d''Ivoire, this figure was even higher (32.9%). Clear indications for a generalized anxiety disorder were observed in 11.4% and 17.4% of pregnant women, respectively. Comorbidity of both conditions was common, affecting about 7.7% of Ghanaian and 12.6% of Ivorian participants. Pregnant women in both countries reported a high degree of disability regarding everyday activity limitations and participation restrictions. Controlled for country and age, depression and anxiety accounted for 33% of variance in the disability score.

Conclusions

Antepartum depression and anxiety were highly prevalent in our sample and contributed substantially to perceived disability. These serious threats to health must be further investigated and more data are needed to comprehensively quantify the problem in sub-Saharan Africa.     

An International Bioinformatics Infrastructure to Underpin the Arabidopsis Community

The future bioinformatics needs of the Arabidopsis community as well as those of other scientific communities that depend on Arabidopsis resources were discussed at a pair of recent meetings held by the Multinational Arabidopsis Steering Committee and the North American Arabidopsis Steering Committee. There are extensive tools and resources for information storage, curation, and retrieval of Arabidopsis data that have been developed over recent years primarily through the activities of The Arabidopsis Information Resource, the Nottingham Arabidopsis Stock Centre, and the Arabidopsis Biological Resource Center, among others. However, the rapid expansion in many data types, the international basis of the Arabidopsis community, and changing priorities of the funding agencies all suggest the need for changes in the way informatics infrastructure is developed and maintained. We propose that there is a need for a single core resource that is integrated into a larger international consortium of investigators. We envision this to consist of a distributed system of data, tools, and resources, accessed via a single information portal and funded by a variety of sources, under shared international management of an International Arabidopsis Informatics Consortium (IAIC). This article outlines the proposal for the development, management, operations, and continued funding for the IAIC.The Multinational Arabidopsis Steering Committee (MASC) and the North American Arabidopsis Steering Committee (NAASC) hosted workshops in Nottingham, UK (April 15 to 16, 2010) and Washington DC (May 10 to 11, 2010) to consider the future bioinformatics needs of the Arabidopsis community as well as other science communities that depend vitally on Arabidopsis resources. The outcomes of both workshops were presented and discussed at the International Conference on Arabidopsis Research (ICAR) in Yokohama, Japan. The focus of the workshops was on Arabidopsis because of its unique and essential role as a reference organism for all seed plant species. The development of the highly annotated “gold standard” Arabidopsis genome sequence has been an invaluable resource for plant and crop sciences. This platform provides important information and working practices for other species and for comparative genomic and evolutionary studies. Arabidopsis tools and resources for information storage, curation, and retrieval have been developed over recent years primarily through the activities of The Arabidopsis Information Resource (TAIR), the Nottingham Arabidopsis Stock Centre (NASC), and the Arabidopsis Biological Resource Center, among others. However, the Arabidopsis community and funding agencies recognize the need for a single data management infrastructure. The key challenge is to develop and fund this resource in a sustainable and transparent manner.Global challenges surrounding food and energy security require intelligent plant breeding strategies that will be dependent on a central Arabidopsis information resource to aid our understanding of gene function and associated phenotype in many different environments. The knowledge accrued in Arabidopsis informs our understanding of the genetic basis of plant processes and crop traits. To date, this has accumulated primarily through analysis of single genes. However, gene products do not act alone but rather in complex interacting networks. Thus, the challenge for the Arabidopsis community is to understand this higher level of complexity, to a significant extent through the application of new high volume, quantitative experimental techniques. The goals of these efforts are to develop gene/protein/metabolite networks that will enable systems-level modeling of plant processes and ultimately to translate these findings to crop plants. To achieve these goals, we must develop novel approaches to data management, integration, and access.The UK workshop addressed three principal issues: the types of data generated by the Arabidopsis community, the types of data used by the community, and future needs of the community. The objective was to produce recommendations for the type of infrastructure necessary to address the challenges and opportunities associated with the application of new technologies and recommendations for a sustainable funding model to support this infrastructure. These recommendations were considered and expanded upon at the US workshop with the ultimate goal of generating solutions to the issues discussed in the first meeting. It was recognized that cohesive, cooperative, and long-term international collaboration will be critical to successfully maintain an Arabidopsis database infrastructure that is essential for plant biology research worldwide.The workshop participants concluded that there is a continued need for a central Arabidopsis information resource, based on the productivity of the Arabidopsis community and the critical importance of the findings generated by this community. For example, ∼3000 Arabidopsis publications are currently published in peer-reviewed journals each year, a nearly 10-fold increase since the early 1990s; and in 2009, TAIR was accessed by 335,692 unique visitors and had nearly 20 million page views. Furthermore, the importance of a current, well-organized, and carefully curated Arabidopsis genome to researchers studying other plants, including crops, cannot be overstated. In the future, this resource should be part of a larger infrastructure that would be dynamic and responsive to new directions in plant biology research.     

Screening ethnically diverse human embryonic stem cells identifies a chromosome 20 minimal amplicon conferring growth advantage

The International Stem Cell Initiative analyzed 125 human embryonic stem (ES) cell lines and 11 induced pluripotent stem (iPS) cell lines, from 38 laboratories worldwide, for genetic changes occurring during culture. Most lines were analyzed at an early and late passage. Single-nucleotide polymorphism (SNP) analysis revealed that they included representatives of most major ethnic groups. Most lines remained karyotypically normal, but there was a progressive tendency to acquire changes on prolonged culture, commonly affecting chromosomes 1, 12, 17 and 20. DNA methylation patterns changed haphazardly with no link to time in culture. Structural variants, determined from the SNP arrays, also appeared sporadically. No common variants related to culture were observed on chromosomes 1, 12 and 17, but a minimal amplicon in chromosome 20q11.21, including three genes expressed in human ES cells, ID1, BCL2L1 and HM13, occurred in >20% of the lines. Of these genes, BCL2L1 is a strong candidate for driving culture adaptation of ES cells.     

Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein-protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease.