INTERPRETING THE LANDSCAPE OF HUMAN GENETIC VARIATION
Bibliographic record
Abstract
Poster presented at the 2024 NHGRI Research Training and Career Development Annual Meeting April 8th, 2024 in Seattle, Washington USA Abstract: INTERPRETING THE LANDSCAPE OF HUMAN GENETIC VARIATION Our plan to measure the functional consequences of every single nucleotide variant in every disease-related gene and regulatory region of the human genome Genetic variation – changes in DNA sequence – can underlie disease risk. Sequence-based genetic testing can identify individuals with harmful, pathogenic variants. However, many variants identified by genetic testing are classified as variants of uncertain significance (VUS) because there is not enough information about these variants to know if they are pathogenic or if they are benign. These inconclusive test results are vexing to both providers and patients and are a major barrier to the practice of precision medicine. Functional assays that measure the effect of a variant on protein or cellular function in vitro or in vivo can provide information to reclassify VUS as pathogenic or benign. However, because clinical genetics databases already hold millions of variants, functional data are needed on a massive scale that cannot be achieved using traditional, one-variant-at-a-time methods. Our team is overcoming this problem by developing technologies called Multiplexed Assays of Variant Effect (MAVEs). MAVEs assess the function of thousands of variants in a single experiment, providing functional data that empower clinicians to make optimal use of genetic information. The University of Washington Department of Genome Sciences, along with the Brotman Baty Institute for Precision Medicine, is home to initiatives aimed at developing new MAVE technologies, deploying these technologies at scale and resolving variants of uncertain significance. These include the Center for the Multiplex Assessment of Phenotype (CMAP), the Center for Actionable Variant Analysis (CAVA) and the Atlas of Variant Effects (AVE) Alliance. CMAP is a Center of Excellence in Genomic Science (CEGS) and CAVA is part of the Impact of Genomic Variation on Function (IGVF) consortium. Here, we describe each of our initiatives in detail and show how our teams are using multiplexed functional data along with AI to map variant effects in clinically actionable genes and using the resulting data to reclassify VUS! Keywords: technology development, functional genomics, multiplex assays of variant effect, outreach, training and education, open access, production scale, computational, bold prediction, global community, variant effect map, equity, CEGS, IGVF, clinical relevance. Acknowledgments CMAP , CAVA, Atlas of Variant Effect Alliance members and leadership Graphics credits: Sayeh Gorjifard, Uta Mackensen Award number(s): HG010461 (NHGRI_Centers of Excellence in Genomic Science (CEGS) HG011969 (NHGRI_Impact of Genomic Variation on Function (IGVF) Consortium
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".