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Atlas of Variant Effects Alliance : Precision medicine at nucleotide resolution

Atlas of Variant Effects Alliance:
Precision medicine at nucleotide resolution

Atlas of Variant Effects Alliance

Art by Uta Mackensen (CC BY-ND) Image Description: Background: A world map and chromosome idiogram. Foreground: People moving amongst and inspecting larger than life Variant Effect Maps of clinically important genes BRCA1, HMBS, MTHFR and TDP-43.

The vision of the Alliance is to create comprehensive variant effect maps for important regions of human and human pathogen genomes that could ultimately assist in the diagnosis, prognosis and treatment of disease. The goal of our Alliance is to bring together data generators, curators and consumers, along with funders and other stakeholders, to set standards, share tools and develop strategy.

By describing the effects of variants in the genome, the atlas will accelerate and empower biological research, drug discovery and medical practice.

Graphic Credits: kjpargeter Freepik, Sayeh Gorjifard and Uta Mackensen

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The Alliance welcomes individuals from academia, industry, government or other entities anywhere in the world
Variant Effects Seminar Series
In this series, early-career scientists from around the globe share and discuss their research related to interpreting human genetic variation
Mutational Scanning Symposium
8th Annual Mutational Scanning Symposium in Barcelona, Spain

Latest Event

9th Annual Mutational Scanning Symposium 2026

25 March 2026, Melbourne, Australia.

Mutational Scanning Symposium 2026

Event jointly organized by: AVE and St Vincent's Institute of Medical Research

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Latest AVE Mention in the News

AVE Alliance Working Group Developing International Guidelines for Genetic Variant Classification

29 August 2025.

New working group developing more definitive guidelines for genetic variant classification.

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Latest Seminar

Equitable machine learning counteracts ancestral bias in cancer genomics

4 November 2025.

Leslie Smith is a 5th year PhD student in the Computer Science department at the University of Florida. Her research focuses on the development of methods for addressing ascertainment bias in genomic datasets and disease modeling

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