SAIR Related Consortium Program (SRCP)

AI and Human Diseases Consortium (AIHDC)

SAIR offers workshop and consortium opportunities for undergraduate and graduate students interested in gaining hands-on research experience in artificial intelligence and human genomics. Members will work closely with SAIR members on ongoing literature review publication at the frontiers of AI across areas like enhance our understanding the role of genomics/genetics on human diseases with the support from machine learning, computer vision, natural language processing, robotics, and more. The key mission for this workgroup will be publishing review papers to enhance the field.

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Mendelian Randomization Consortium (MRC)

The Mendelian Randomization Consortium (MRC) is a collaboration of international academics focused on promoting and facilitating Mendelian randomization research globally. Mendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to infer causal relationships between modifiable exposures and disease outcomes. The MRC was established in 2022 by a group of researchers interested in harnessing Mendelian randomization to understand causality in observational data. The consortium aims to bring together experts across disciplines to advance methods, provide training resources, share data, and disseminate research findings. Core goals include supporting novel Mendelian randomization methods, evolving guidelines and best practices, facilitating data sharing and meta-analyses, and translating findings to improve public health and clinical care. Key activities of the MRC include maintaining an online database of genetic variants suitable as instrumental variables, developing statistical software packages for Mendelian randomization analysis, hosting workshops and webinars, and publishing methodological and applied Mendelian randomization research. The consortium also engages with scientific journals to improve the appraisal and publication of Mendelian randomization studies. With over 50 members spanning population health sciences, genetics, statistics, and clinical medicine, the MRC represents a leading effort to harness Mendelian randomization for causal inference from observational data. The consortium provides an important platform to enhance methods, build capacity, and translate findings in this rapidly evolving field.

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Therapeutic Target Discovery Consortium (TTDC)

The identification of new therapeutic targets is a crucial step in the development of innovative treatments for human disease. The Therapeutic Target Discovery Consortium (TTDC) brings together leading genetics researchers from academia and industry with the shared goal of leveraging insights from genome-wide association studies (GWAS) to elucidate potential new drug targets and therapeutic hypotheses. GWAS have helped uncover thousands of genetic variants associated with increased disease risk across a wide spectrum of common, complex diseases. However, the translation of these findings into clinically actionable targets has been slow. The TTDC aims to help close this gap through collaborative efforts to comprehensively analyze and summarize GWAS findings, while also considering complimentary genomic and experimental datasets, to highlight and prioritize promising genes, pathways, and mechanisms for therapeutic intervention. Representing a diversity of expertise and perspectives, TTDC members will work closely together to interpret genetic signals from GWAS using pathway analysis, annotate potential biological mechanisms, evaluate links to druggable genes and pathways, and publish high-quality consensus reviews summarizing insights and therapeutic hypotheses. By bringing the right people together in a coordinated fashion, the TTDC seeks to overcome many of the challenges and knowledge gaps that have hampered target identification from GWAS results until now. The consortium’s open, collaborative structure is designed to generate robust target hypotheses and accelerate the translation of human genetics into new medicines that improve health outcomes.