Background: Genomic testing has reached the point where, technically at least, it can be cheaper to undertake panel-, exome- or whole genome testing than it is to sequence a single gene. An attribute of these approaches is that information gleaned will often have uncertain significance. In addition to the challenges this presents for pre-test counseling and informed consent, a further consideration emerges over how - ethically - we should conceive of and respond to this uncertainty. To date, the ethical aspects of uncertainty in genomics have remained under-explored.
Discussion: In this paper, we draft a conceptual and ethical response to the question of how to conceive of and respond to uncertainty in genomic medicine. After introducing the problem, we articulate a concept of 'genomic uncertainty'. Drawing on this, together with exemplar clinical cases and related empirical literature, we then critique the presumption that uncertainty is always problematic and something to be avoided, or eradicated. We conclude by outlining an 'ethics of genomic uncertainty'; describing how we might handle uncertainty in genomic medicine. This involves fostering resilience, welfare, autonomy and solidarity.
Conclusions: Uncertainty will be an inherent aspect of clinical practice in genomics for some time to come. Genomic testing should not be offered with the explicit aim to reduce uncertainty. Rather, uncertainty should be appraised, adapted to and communicated about as part of the process of offering and providing genomic information.
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http://dx.doi.org/10.1186/s12920-016-0219-0 | DOI Listing |
Nat Commun
January 2025
Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden.
Comparative glycomics data are compositional data, where measured glycans are parts of a whole, indicated by relative abundances. Applying traditional statistical analyses to these data often results in misleading conclusions, such as spurious "decreases" of glycans when other structures increase in abundance, or high false-positive rates for differential abundance. Our work introduces a compositional data analysis framework, tailored to comparative glycomics, to account for these data dependencies.
View Article and Find Full Text PDFTheor Appl Genet
January 2025
Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA.
In tetraploid F1 populations, traditional segregation distortion tests often inaccurately flag SNPs due to ignoring polyploid meiosis processes and genotype uncertainty. We develop tests that account for these factors. Genotype data from tetraploid F1 populations are often collected in breeding programs for mapping and genomic selection purposes.
View Article and Find Full Text PDFJ Exp Bot
January 2025
DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France.
Phenotypic plasticity can contribute to crop adaptation to challenging environments. Plasticity indices are potentially useful to identify the genetic basis of crop phenotypic plasticity. Numerous methods exist to measure phenotypic plasticity.
View Article and Find Full Text PDFJMIR Public Health Surveill
January 2025
Buehler Center for Health Policy and Economics, Robert J. Havey, MD Institute for Global Health, Northwestern University, 420 E. Superior, Chicago, US.
Background: This study updates the COVID-19 pandemic surveillance in East Asia and the Pacific we first conducted in 2020 with two additional years of data for the region.
Objective: First, we measure whether there was an expansion or contraction of the pandemic in East Asia and the Pacific region when the World Health Organization (WHO) declared the end of the COVID-19 public health emergency of international concern on May 5, 2023. Second, we use dynamic and genomic surveillance methods to describe the dynamic history of the pandemic in the region and situate the window of the WHO declaration within the broader history.
PLoS Genet
January 2025
Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
Recent statistical approaches have shown that the set of all available genetic variants explains considerably more phenotypic variance of complex traits and diseases than the individual variants that are robustly associated with these phenotypes. However, rapidly increasing sample sizes constantly improve detection and prioritization of individual variants driving the associations between genomic regions and phenotypes. Therefore, it is useful to routinely estimate how much phenotypic variance the detected variants explain for each region by taking into account the correlation structure of variants and the uncertainty in their causal status.
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