ORCID Profile
0000-0003-0667-2054
Current Organisations
Northern Arizona University
,
University of California Davis
,
USDA Forest Service Pacific Northwest Region
,
Michigan State University
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Publisher: Wiley
Date: 12-04-2022
DOI: 10.1111/BRV.12852
Abstract: Bio ersity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well‐being. Understanding how bio ersity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying bio ersity data. A major challenge is that bio ersity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Bio ersity Observation Network (GEO BON) has developed the Essential Bio ersity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret bio ersity observation data from erse sources. Mapping and analyzing EBVs can help to evaluate how aspects of bio ersity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of bio ersity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within‐species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic bio ersity monitoring with respect to theory, s ling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: ( i ) Genetic Diversity ( ii ) Genetic Differentiation ( iii ) Inbreeding and ( iv ) Effective Population Size ( N e ). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large‐scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for bio ersity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all bio ersity and species' long‐term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic bio ersity monitoring regionally and globally.
Publisher: Springer Science and Business Media LLC
Date: 12-2022
DOI: 10.1038/S41598-022-19710-7
Abstract: We searched a database of single-gene knockout (KO) mice produced by the International Mouse Phenotyping Consortium (IMPC) to identify candidate ciliopathy genes. We first screened for phenotypes in mouse lines with both ocular and renal or reproductive trait abnormalities. The STRING protein interaction tool was used to identify interactions between known cilia gene products and those encoded by the genes in in idual knockout mouse strains in order to generate a list of “candidate ciliopathy genes.” From this list, 32 genes encoded proteins predicted to interact with known ciliopathy proteins. Of these, 25 had no previously described roles in ciliary pathobiology. Histological and morphological evidence of phenotypes found in ciliopathies in knockout mouse lines are presented as ex les (genes Abi2, Wdr62, Ap4e1, Dync1li1, and Prkab1 ). Phenotyping data and descriptions generated on IMPC mouse line are useful for mechanistic studies, target discovery, rare disease diagnosis, and preclinical therapeutic development trials. Here we demonstrate the effective use of the IMPC phenotype data to uncover genes with no previous role in ciliary biology, which may be clinically relevant for identification of novel disease genes implicated in ciliopathies.
Location: United States of America
No related grants have been discovered for Denise Imai.