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Accepted Paper
Paper short abstract
Genomic medicine often produces uncertain results whose clinical meaning remains unresolved. Drawing on an ethnography of medical genetics in India, this paper examines how clinicians manage such data, sustaining possibilities of diagnosis through practices of waiting, reinterpretation, and care.
Paper long abstract
Genomic medicine is often framed as a data-driven revolution promising precise diagnosis and personalised care. Yet in everyday clinical practice, genomic data frequently generate uncertainties by producing results whose clinical significance remains unclear or provisional. A large proportion of genetic tests produce Variants of Uncertain Significance (VUS), findings whose clinical significance cannot yet be confidently classified as either pathogenic or benign. Rather than delivering definite answers, these results place clinicians, laboratory scientists, and families in a suspended reality of prolonged waiting.
In Indian clinical settings, this uncertainty is intensified by structural conditions shaping genomic knowledge production in the region. Global genomic databases on which variant interpretation heavily relies remain disproportionately populated with Euro-American genomic data, while South Asian populations are significantly underrepresented. As a result, clinicians and laboratory scientists frequently encounter variants whose significance cannot be determined using existing reference datasets.
Drawing on a multi-sited ethnographic fieldwork in clinical genetics laboratories, medical conferences and hospitals in India, this paper examines how uncertain genomic variants are managed within the everyday practice of genomic medicine. By approaching VUS not simply as gaps in knowledge but as forms of data held in the “meantime”, this paper attempts to make sense of clinical decision-making in contexts where genomic evidence remains provisional and open to future reinterpretation.
Keywords: Uncertainty, VUS, genomic medicine, suspended time
Caring for the possible: In the meantime of healthcare’s data-driven futures
Session 1