Screening for Mutations in Isolated Central Hypothyroidism Reveals a Novel Mutation in Insulin Receptor Substrate 4

Background Central hypothyroidism (CeH) is a rare condition affecting approximately 1:16 000- 100 000 individuals. Congenital forms can harm normal development if not detected and treated promptly. Clinical and biochemical diagnosis, especially of isolated CeH, can be challenging. Cases are not usually detected in neonatal screening, which, in most countries, is focused on detection of the more prevalent primary hypothyroidism. Until now, five genetic causes for isolated CeH have been identified. Here we aimed to identify the genetic cause in two brothers with impaired growth diagnosed with CeH at the age of 5 years. We further evaluated the candidate gene variants in a large genetic database. Methods Clinical and biochemical characterization together with targeted next-generation sequencing (NGS) was used to identify the genetic cause in a family of two brothers presenting with CeH. Screening of insulin receptor substrate 4 (IRS4) variants was carried out in the FinnGen database. Results A novel monoallelic frameshift mutation c.1712_1713insT, p.Gly572Trp fs*32 in the X-linked IRS4 gene was identified by NGS analysis in both affected males and confirmed using Sanger sequencing. Their mother was an unaffected carrier. In addition to the declined growth at presentation, central hypothyroidism and blunted TRH test, no other phenotypic alterations were found. Diagnostic tests included head MRI, thyroid imaging, bone age, and laboratory tests for thyroid autoantibodies, glucose, insulin and glycosylated hemoglobin levels. Examination of the IRS4 locus in FinnGen (R5) database revealed the strongest associations to a rare Finnish haplotype associated with thyroid disorders (p = 1.3e-7) and hypothyroidism (p = 8.3e-7). Conclusions Here, we identified a novel frameshift mutation in an X-linked IRS4 gene in two brothers with isolated CeH. Furthermore, we demonstrate an association of IRS4 gene locus to a general thyroid disease risk in the FinnGen database. Our findings confirm the role of IRS4 in isolated central hypothyroidism.Peer reviewe

Glyph-based visualization of health trajectories

Whenever a diagnosis is given, a procedure is performed, or a drug is prescribed, it leads to an entry into an electronic health record (EHR) system. Previously, this data was difficult to utilize because of rules regarding confidentiality, but new security approaches and pseudonymization have enabled us to work with this data. Health-related data is voluminous and complex, and it can be difficult to abstract a meaningful overview. One of the complexities is its longitudinality. Often medical research is cross-sectional - we often take a point in time for analysis, when instead, it might be more beneficial to see the trajectory that led to the point in time. We are currently developing a trajectory visualization tool for longitudinal electronic health data. It is a web-based tool that interfaces with the OHDSI data infrastructure and visualizes the cohorts and concept sets (groups of medical codes) defined via the OHDSI Atlas GUI. Currently, our tool is in user testing and it will be deployed to a wider user group during the spring. The user feedback has been positive. Users find the tool especially useful in understanding and debugging their OHDSI Atlas cohort definitions.acceptedVersionPeer reviewe

Screening for Mutations in Isolated Central Hypothyroidism Reveals a Novel Mutation in Insulin Receptor Substrate 4

Background: Central hypothyroidism (CeH) is a rare condition affecting approximately 1:16 000- 100 000 individuals. Congenital forms can harm normal development if not detected and treated promptly. Clinical and biochemical diagnosis, especially of isolated CeH, can be challenging. Cases are not usually detected in neonatal screening, which, in most countries, is focused on detection of the more prevalent primary hypothyroidism. Until now, five genetic causes for isolated CeH have been identified. Here we aimed to identify the genetic cause in two brothers with impaired growth diagnosed with CeH at the age of 5 years. We further evaluated the candidate gene variants in a large genetic database.MethodsClinical and biochemical characterization together with targeted next-generation sequencing (NGS) was used to identify the genetic cause in a family of two brothers presenting with CeH. Screening of insulin receptor substrate 4 (IRS4) variants was carried out in the FinnGen database.Results: A novel monoallelic frameshift mutation c.1712_1713insT, p.Gly572Trp fs*32 in the X-linked IRS4 gene was identified by NGS analysis in both affected males and confirmed using Sanger sequencing. Their mother was an unaffected carrier. In addition to the declined growth at presentation, central hypothyroidism and blunted TRH test, no other phenotypic alterations were found. Diagnostic tests included head MRI, thyroid imaging, bone age, and laboratory tests for thyroid autoantibodies, glucose, insulin and glycosylated hemoglobin levels. Examination of the IRS4 locus in FinnGen (R5) database revealed the strongest associations to a rare Finnish haplotype associated with thyroid disorders (p = 1.3e-7) and hypothyroidism (p = 8.3e-7).ConclusionsHere, we identified a novel frameshift mutation in an X-linked IRS4 gene in two brothers with isolated CeH. Furthermore, we demonstrate an association of IRS4 gene locus to a general thyroid disease risk in the FinnGen database. Our findings confirm the role of IRS4 in isolated central hypothyroidism</p

An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease

Multivariate methods are known to increase the statistical power to detect associations in the case of shared genetic basis between phenotypes. They have, however, lacked essential analytic tools to follow-up and understand the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses. We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 representative variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p <1 x 10(-4)). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the representative variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics.Peer reviewe

Mapping the Colocalization Network: A Wayfinding Approach to Interacting with Complex Network Diagrams

Although network visualizations are becoming increasingly common, designing such visualizations can be challenging due to the number of visual elements and non-linear relations that they need to display. The main design challenge faced is finding the right trade-off between providing a sufficient level of information detail while keeping the visual complexity of the visualization as low as possible. One way of overcoming this challenge is to rely on the use of mental models that are familiar to the users of network visualizations. In this paper, we propose the use of a mental interaction model similar to that of map visualizations – generally based on geographical maps – as the basis for visual design of network diagrams. We argue that such a mental model would foster a set of network interaction tasks that can be defined broadly as wayfinding. We present the process of wayfinding from a semiotic standpoint, and match its main key points to those of interaction tasks with network diagrams. As a case study forthis analysis, we also present a prototype network diagram visualization tool, called Colocalization Network Explorer, which we have developed to support the exploration of the relationships between various diseases and the portion of the human genome that is potentially involved in their onset. Additionally, we describe how the design process has benefited from the adoption of the wayfinding mental model.Peer reviewe

Genome-map: Real-world test data and queries for logic databases

In the process of trying to nd a logic query language to use with our genomemapping database we extracted test data from our database and developed a set of representative queries over the data. We alsosynthesized data for a project that does not yet have released data. This data is available to those developing or testing logic databases and logic programming languages.

Molecular genetic advances in tuberous sclerosis

Over the past decade, there has been considerable progress in understanding the molecular genetics of tuberous sclerosis, a disorder characterised by hamartomatous growths in numerous organs. We review this progress, from cloning and characterising TSC1 and TSC2, the genes responsible for the disorder, through to gaining insights into the functions of their protein products hamartin and tuberin, and the identification and engineering of animal models. We also present the first comprehensive compilation and analysis of all reported TSC1 and TSC2 mutations, consider their diagnostic implications and review genotype/phenotype relationships

Splicing UNIX into a Genome Mapping Laboratory

The Whitehead Institute/MIT Center for Genome Research is responsible for a number of large genome mapping efforts, the scale of which create problems of data and workflow management that dictate reliance on computer support. Two years ago, when we started to design the informatics support for the laboratory, we realized that the fluid and everchanging nature of the experimental protocols precluded any effort to create a single monolithic piece of software. Instead we designed a system that relied on multiple distributed data analysis and processing tools knit together by a centralized database. The obvious choice of operating systems was UNIX. In order to make this choice palatable to the laboratory biologists---who rightly consider it their job to do experiments rather than to interact with computers, and who have come to expect all software to be as intuitive and responsive as the Apple Macintoshes on their desks---we designed a system that runs automatically and essentially invisibly. Whenever it is necessary for the informatics system to interact with a member of the laboratory we have carefully chosen a user interface paradigm that best balances the user&apos;s expectations against the system&apos;s capabilities. When possible we have chosen to adapt familiar software to our user interface needs rather than to write user interfaces from scratch. We&apos;ve managed to hide the power of UNIX behind the innocuous personal computer-based front ends our users know and love, using techniques that should be applicable in other environments as well. 1

LAVAA: A lightweight association viewer across ailments

We have created LAVAA, a visualization web-application to generate genetic volcano plots for simultaneously considering the p-value, effect size, case counts, trait class and fine-mapping posterior probability at a single SNP across a range of traits from a large set of GWAS. We find that user interaction with association results in LAVAA can enrich and enhance the biological interpretation of individual loci.Peer reviewe