Vector2Variant: Discovery of Genetic Associations Without Phenotype Engineering

Researchers at Calico Life Sciences introduced Vector2Variant (V2V), a framework that converts high-dimensional measurements such as machine learning embeddings into genome-wide association scans without predefined phenotypes, according to a preprint whose authors M.S., R.S., S.S., Z.C. and J.X. were Calico employees at the time of the study. Applied to imaging, timeseries and omics data in the UK Biobank, V2V recovered established biology including the role of CASP9 in renal failure and surfaced novel associations such as a frameshift variant in LRRIQ1 potentially protective for cardiovascular disease. The method finds the axis along which carriers and non-carriers maximally differ, producing projection phenotypes that correlated with orthogonal clinical biomarkers never seen during training, and the study received no external funding.

Abstract Genome-wide association studies GWAS have transformed our understanding of human biology, but are constrained by the need for predefined phenotypes. We introduce Vector2Variant V2V , a general-purpose framework that transforms any set of high-dimensional measurements such as machine learning embeddings into a genome-wide scan for associations, without requiring rigid specification of a phenotype. Rather than testing genetic variants against single traits, V2V finds the axis in multivariate space along which carriers and non-carriers maximally differ, and produces a continuous “projection phenotype” that can be interpreted by association with disease labels. The projection phenotypes correlate with orthogonal clinical biomarkers never seen during training, suggesting the learned axes capture biologically meaningful variation. We applied V2V to imaging, timeseries, and omics modalities in the UK Biobank and recovered established biology like the role of CASP9 in renal failure without the need for targeted measurements, alongside novel associations including a frameshift variant in LRRIQ1 potentially protective for cardiovascular disease . V2V is computationally efficient at genome-wide scale, producing summary statistics and disease associations that facilitate target prioritization without the need for phenotype engineering. Competing Interest Statement M.S., R.S., S.S., Z.C., and J.X. were employees of Calico Life Sciences LLC at the time of this study. Funding Statement This study did not receive any external funding. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The UK Biobank study was approved by the North West Multi-centre Research Ethics Committee reference 11/NW/0382 . This work was authorized by UK Biobank under application ID 44584. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone e.g., hospital staff, patients or participants themselves outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance . Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist s and other pertinent material, if applicable. Yes Footnotes - Listing order is random Data Availability The UK Biobank data used in this study are available via the UKB research application process. Derived summary statistics will be made available at the time of peer-reviewed publication. CC-BY-NC-ND 4.0 International license http://creativecommons.org/licenses/by-nc-nd/4.0/ .