Yungui Huang

Hashimoto's thyroiditis with papillary thyroid carcinoma (PTC)‐like nuclear alterations express molecular markers of PTC

Aims:  Focal papillary thyroid carcinoma (PTC)‐like nuclear alterations have been documented in Hashimotos thyroiditis; however, the molecular association between PTC and Hashimotos thyroiditis is poorly understood. The aim of this study was to determine whether molecular expression patterns of PTC are present in association with PTC‐like nuclear alterations in Hashimotos thyroiditis.

Identification of a Schizophrenia-Associated Functional Noncoding Variant in NOS1AP

OBJECTIVE The authors previously demonstrated significant association between markers within NOS1AP and schizophrenia in a set of Canadian families of European descent, as well as significantly increased expression in schizophrenia of NOS1AP in unrelated postmortem samples from the dorsolateral prefrontal cortex. In this study the authors sought to apply novel statistical methods and conduct additional biological experiments to isolate at least one risk allele within NOS1AP. METHOD Using the posterior probability of linkage disequilibrium (PPLD) to measure the probability that a single nucleotide polymorphism (SNP) is in linkage disequilibrium with schizophrenia, the authors evaluated 60 SNPs from NOS1AP in 24 Canadian families demonstrating linkage and association to this region. SNPs exhibiting strong evidence of linkage disequilibrium were tested for regulatory function by luciferase reporter assay. Two human neural cell lines (SK-N-MC and PFSK-1) were transfected with a vector containing each allelic variant of the SNP, the NOS1AP promoter, and a luciferase gene. Alleles altering expression were further assessed for binding of nuclear proteins by electrophoretic mobility shift assay. RESULTS Three SNPs produced PPLDs >40%. One of them, rs12742393, demonstrated significant allelic expression differences in both cell lines tested. The allelic variation at this SNP altered the affinity of nuclear protein binding to this region of DNA. CONCLUSIONS The A allele of rs12742393 appears to be a risk allele associated with schizophrenia that acts by enhancing transcription factor binding and increasing gene expression.

SRGAP1 Is a Candidate Gene for Papillary Thyroid Carcinoma Susceptibility

BACKGROUND Papillary thyroid carcinoma (PTC) shows high heritability, yet efforts to find predisposing genes have been largely negative. OBJECTIVES The objective of this study was to identify susceptibility genes for PTC. METHODS A genome-wide linkage analysis was performed in 38 families. Targeted association study and screening were performed in 2 large cohorts of PTC patients and controls. Candidate DNA variants were tested in functional studies. RESULTS Linkage analysis and association studies identified the Slit-Robo Rho GTPase activating protein 1 gene (SRGAP1) in the linkage peak as a candidate gene. Two missense variants, Q149H and A275T, localized in the Fes/CIP4 homology domain segregated with the disease in 1 family each. One missense variant, R617C, located in the RhoGAP domain occurred in 1 family. Biochemical assays demonstrated that the ability to inactivate CDC42, a key function of SRGAP1, was severely impaired by the Q149H and R617C variants. CONCLUSIONS Our findings suggest that SRGAP1 is a candidate gene in PTC susceptibility. SRGAP1 is likely a low-penetrant gene, possibly of a modifier type.

Exploiting gene × gene interaction in linkage analysis

When two genes interact to cause a clinically important phenotype, it would seem reasonable to expect that we could leverage genotypic information at one of the loci in order to improve our ability to detect the other. We were therefore interested in extending the posterior probability of linkage (PPL), a class of linkage statistics we have been developing over the past decade, in order to explicitly allow for gene × gene interaction. In this report we utilize a new implementation of the PPL incorporating liability classes (LCs), which provide a direct parameterization of gene × gene interaction by allowing the penetrances at the locus being evaluated to depend upon measured genotypes at a known locus. With knowledge of the generating model for the simulated rheumatoid arthritis (RA) data, we selected two loci for examination: Locus A, which in interaction with the HLA-DR antigen locus affects risk of the dichotomous RA phenotype; and Locus E, which in interaction with DR affects quantitative levels of the anti-CCP phenotype. The data comprised nuclear families of two parents and an affected sib pair (ASP). Our results confirm theoretical work suggesting that gene × gene interactions CANNOT be leveraged to improve linkage detection for dichotomous traits based on affecteds-only data structures. However, incorporation of DR-based LCs did lead to appreciably higher quantitative trait PPLs. This suggests that gene × gene interactions could be effectively used in quantitative trait analyses even when families have been ascertained as ASPs for a related dichotomous trait.

‘RE:fine drugs’: an interactive dashboard to access drug repurposing opportunities

The process of discovering new drugs has been extremely costly and slow in the last decades despite enormous investment in pharmaceutical research. Drug repurposing enables researchers to speed up the process of discovering other conditions that existing drugs can effectively treat, with low cost and fast FDA approval. Here, we introduce ‘RE:fine Drugs’, a freely available interactive website for integrated search and discovery of drug repurposing candidates from GWAS and PheWAS repurposing datasets constructed using previously reported methods in Nature Biotechnology. ‘RE:fine Drugs’ demonstrates the possibilities to identify and prioritize novelty of candidates for drug repurposing based on the theory of transitive Drug–Gene–Disease triads. This public website provides a starting point for research, industry, clinical and regulatory communities to accelerate the investigation and validation of new therapeutic use of old drugs. Database URL: http://drug-repurposing.nationwidechildrens.org

A Review on Genomics APIs.

The constant improvement and falling prices of whole human genome Next Generation Sequencing (NGS) has resulted in rapid adoption of genomic information at both clinics and research institutions. Considered together, the complexity of genomics data, due to its large volume and diversity along with the need for genomic data sharing, has resulted in the creation of Application Programming Interface (API) for secure, modular, interoperable access to genomic data from different applications, platforms, and even organizations. The Genomics APIs are a set of special protocols that assist software developers in dealing with multiple genomic data sources for building seamless, interoperable applications leading to the advancement of both genomic and clinical research. These APIs help define a standard for retrieval of genomic data from multiple sources as well as to better package genomic information for integration with Electronic Health Records. This review covers three currently available Genomics APIs: a) Google Genomics, b) SMART Genomics, and c) 23andMe. The functionalities, reference implementations (if available) and authentication protocols of each API are reviewed. A comparative analysis of the different features across the three APIs is provided in the Discussion section. Though Genomics APIs are still under active development and have yet to reach widespread adoption, they hold the promise to make building of complicated genomics applications easier with downstream constructive effects on healthcare.

Revisiting schizophrenia linkage data in the NIMH Repository: reanalysis of regularized data across multiple studies.

OBJECTIVE The Combined Analysis of Psychiatric Studies (CAPS) project conducted extensive review and regularization across studies of all schizophrenia linkage data available as of 2011 from the National Institute of Mental Health-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI). The authors reanalyzed the data using statistical methods tailored to accumulation of evidence across multiple, potentially highly heterogeneous, sets of data. METHOD Data were subdivided based on contributing study, major population group, and presence or absence within families of schizophrenia with a substantial affective component. The posterior probability of linkage (PPL) statistical framework was used to sequentially update linkage evidence across these data subsets (omnibus results). RESULTS While some loci previously implicated using the HGI data were also identified in the present omnibus analysis (2q36.1, 15q23), others were not. Several loci were found that had not previously been reported in the HGI samples but are supported by independent linkage or association studies (3q28, 12q23.1, 11p11.2, Xq26.1). Not surprisingly, differences were seen across population groups. Of particular interest are signals on 11p15.3, 11p11.2, and Xq26.1, for which data from families with a substantial affective component support linkage while data from the remaining families provide evidence against linkage. All three of these loci overlap with loci reported in independent studies of bipolar disorder or mixed bipolar-schizophrenia samples. CONCLUSIONS Public data repositories provide the opportunity to leverage large multisite data sets for studying complex disorders. Analysis with a statistical method specifically designed for such data enables us to extract new information from an existing data resource.

Employing MCMC under the PPL framework to analyze sequence data in large pedigrees

The increased feasibility of whole-genome (or whole-exome) sequencing has led to renewed interest in using family data to find disease mutations. For clinical phenotypes that lend themselves to study in large families, this approach can be particularly effective, because it may be possible to obtain strong evidence of a causal mutation segregating in a single pedigree even under conditions of extreme locus and/or allelic heterogeneity at the population level. In this paper, we extend our capacity to carry out positional mapping in large pedigrees, using a combination of linkage analysis and within-pedigree linkage trait-variant disequilibrium analysis to fine map down to the level of individual sequence variants. To do this, we develop a novel hybrid approach to the linkage portion, combining the non-stochastic approach to integration over the trait model implemented in the software package Kelvin, with Markov chain Monte Carlo-based approximation of the marker likelihood using blocked Gibbs sampling as implemented in the McSample program in the JPSGCS package. We illustrate both the positional mapping template, as well as the efficacy of the hybrid algorithm, in application to a single large pedigree with phenotypes simulated under a two-locus trait model.

Clinical Exome Sequencing Reports: Current Informatics Practice and Future Opportunities

The increased adoption of clinical whole exome sequencing (WES) has improved the diagnostic yield for patients with complex genetic conditions. However, the informatics practice for handling information contained in whole exome reports is still in its infancy, as evidenced by the lack of a common vocabulary within clinical sequencing reports generated across genetic laboratories. Genetic testing results are mostly transmitted using portable document format, which can make secondary analysis and data extraction challenging. This paper reviews a sample of clinical exome reports generated by Clinical Laboratory Improvement Amendments-certified genetic testing laboratories at tertiary-care facilities to assess and identify common data elements. Like structured radiology reports, which enable faster information retrieval and reuse, structuring genetic information within clinical WES reports would help facilitate integration of genetic information into electronic health records and enable retrospective research on the clinical utility of WES. We identify elements listed as mandatory according to practice guidelines but are currently missing from some of the clinical reports, which might help to organize the data when stored within structured databases. We also highlight elements, such as patient consent, that, although they do not appear within any of the current reports, may help in interpreting some of the information within the reports. Integrating genetic and clinical information would assist the adoption of personalized medicine for improved patient care and outcomes.

Meta-Analysis of Repository Data: Impact of Data Regularization on NIMH Schizophrenia Linkage Results

Human geneticists are increasingly turning to study designs based on very large sample sizes to overcome difficulties in studying complex disorders. This in turn almost always requires multi-site data collection and processing of data through centralized repositories. While such repositories offer many advantages, including the ability to return to previously collected data to apply new analytic techniques, they also have some limitations. To illustrate, we reviewed data from seven older schizophrenia studies available from the NIMH-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI), and assessed the impact of data cleaning and regularization on linkage analyses. Extensive data regularization protocols were developed and applied to both genotypic and phenotypic data. Genome-wide nonparametric linkage (NPL) statistics were computed for each study, over various stages of data processing. To assess the impact of data processing on aggregate results, Genome-Scan Meta-Analysis (GSMA) was performed. Examples of increased, reduced and shifted linkage peaks were found when comparing linkage results based on original HGI data to results using post-processed data within the same set of pedigrees. Interestingly, reducing the number of affected individuals tended to increase rather than decrease linkage peaks. But most importantly, while the effects of data regularization within individual data sets were small, GSMA applied to the data in aggregate yielded a substantially different picture after data regularization. These results have implications for analyses based on other types of data (e.g., case-control GWAS or sequencing data) as well as data obtained from other repositories.