Kasper is the managing director of the new Novo Nordisk Foundation Center for Genomic Mechanisms of Disease at Broad Institute

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The Novo Nordisk Foundation and Broad Institute of MIT and Harvard are launching a new research Center for Genomic Mechanisms of Disease, an initiative that will accelerate efforts to mine genetic data for insights into disease mechanisms – and eventually rationally designed treatments.

Supported by a $47.5 million commitment from the Novo Nordisk Foundation, the Center will facilitate close collaborations between the Broad Institute and Danish researchers investigating the genetics and gene regulation of common complex disease, with an initial focus on type 2 diabetes and obesity. The Center will align with existing international efforts, data sharing, methodology, and tools to contribute to the roadmap of the International Common Disease Alliance, and working in common cause with investigators from other large-scale efforts such as the Accelerating Medicines Partnership in Common Metabolic Diseases and the Impact of Genomic Variation on Function consortia.

The research collaboration of the Center aims to advance patient-centered research and precision medicine. The Center will establish an exchange program to provide opportunities for Danish scientists to study genomic technologies at the Broad Institute. In turn, these collaborations will catalyze and contribute to expanding biomedical research in Denmark. The new Center will be directed by Kasper, who has played an important role in the planning and development of the initiative since it was conceived in 2019. His whole team is particularly excited and proud to be part of this exceptional research opportunity!

We are hiring!

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We are hiring for several positions. Email lage.kasper@mgh.harvard.edu with a CV and cover letter if you are interested in hearing about our openings.

Polygenic risk score paper published in Nature Communications

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Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction’ has been published in Nature Communications. This paper describes a machine learning method (MCCP) that can be used to estimate personalized confidence levels of disease risk prediction based on polygenic risk scores, thereby enhancing their utility and interpretability in clinical settings.

Coexpression network paper published in Nature Neuroscience

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Coexpression network architecture reveals the brain-wide and multiregional bias of disease susceptibility’, a collaborative project with the Geschwind Lab at UCLA and the Battle Lab at Johns Hopkins, has been published in Nature Neuroscience. In this project, we created an atlas of human brain coexpression networks using RNA-seq data from the GTEx project, and used this resource to understand convergent pathways and brain regions affected by disease-associated variation in the adult brain. The generated networks can be explored in a web browser hosted by the Geschwind Lab.

Julia Biagini is joining the Lage team!

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We are happy to welcome Julia Biagini as a new research associate II on our team. Julia will be joining @gretapinta in the lab to work on the differentiation of human neurons and the many downstream experiments that help us understand the biological mechanisms that are dysfunctional in neuropsychiatric disorders. Welcome to the Lage family, Julia!

Genoppi is published!

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Genoppi is an open-source software for robust and standardized integration of proteomic and genetic data’ is accepted for publication at Nature Communications! The Lage team produced a user-friendly, open-source software that allows its users to see beyond their proteomic datasets by identifying cell/tissue-specific patterns and importantly, converging proteomic and genetic data in order to identify a vast diversity of disease signatures. Fantastic work by @gretapinta, @flassen_, @yuhanhsu, @mjapkim, @JacquiMMartn1 and colleagues.

Lage Lab presents at the Stanley Center Program Meeting

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During the Stanley Center Program Meeting this week, Yu-Han and Frederik presented an overview of Genoppi and gave a live demo to illustrate how it can facilitate QC and analysis of quantitative proteomic data. Greta gave an update on the BINe-ASD project, in which we generated protein-protein interaction networks of autism risk genes in human induced neurons to uncover biological mechanisms and pathways underlying these genes.

Yu-Han completes her T32 post-doctoral fellowship

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Yu-Han completed her two-year post-doctoral fellowship in the Harvard T32 Training Program in Bioinformatics Applied to Diabetes, Obesity and Metabolism. For the first part of her fellowship, she worked on a project that combined human genetics and untargeted metaoblomics to infer causal relationships in the obesity metabolome in the Hirschhorn Lab. After joining Lage Lab, she contributed to our efforts in using cell-type-specific protein-protein interaction data to study complex diseases such as schizophrenia, autism, type 2 diabetes, and myocardial infarction. Yu-Han is looking forward to continuing these exciting projects in our group after the end of her fellowship!