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Review
. 2023 May 20;4(4):100444.
doi: 10.1016/j.xinn.2023.100444. eCollection 2023 Jul 10.

Understanding and modeling human traits and diseases: Insights from the comparative genomics resources of Zoonomia

Affiliations
Review

Understanding and modeling human traits and diseases: Insights from the comparative genomics resources of Zoonomia

Maosen Ye et al. Innovation (Camb). .
No abstract available

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Comparative genomics-based understanding and modeling of human traits and diseases Human and medical genomics focus on identifying genomic variants associated with human traits and diseases, with the functional implications of these variants often remaining unclear. To address this, multi-species genome alignment and annotation tools can be used to annotate and predict the function of genomic sequences and elements where variants are located. These annotated disease-relevant functional alleles and elements can then be targeted in appropriate animals using precise methodologies to model diseases and traits. For disease variants located in constrained coding alleles or conserved genes in a specific animal, constructing a gene knockin model becomes a straightforward process. For those disease variants located in noncoding regions, selecting the appropriate animal and modulating the correct CRE sequence or activity are critical. Starting from a comparative genomics perspective, assessing the sequence conservation and activity of single bases, functional elements, three-dimensional chromatin structures, and transcriptional regulation facilitates the extension of traditional “transgenic” or knockout models to “trans-element” and “trans-epigenic” models. ∗For details regarding the Zoonomia Project, please refer to https://zoonomiaproject.org/. CRE, cis-regulatory element; GWAS, genome-wide association study; MPRA, massively parallel reporter assay; TOGA, tools to infer orthologs from genome alignments; TACIT, tissue-aware conservation inference toolkit.

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