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      Modeling multifactorial diseases using mouse subspecies strains

      Impact

      Science Impact, Ltd.

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          Abstract

          As DNA sequencing becomes cheaper it becomes ever more realistic that analysing a person’s genome may be an effective method of diagnosis and treatment. New and more accurate methods of measuring other important factors now exist, such as the state of the epigenome and the proteome of a person. Together, these technologies will form the cornerstone of personalised medicine that, at its best, could offer treatments that are precisely tuned to a person’s needs. However, there are still many foundations that need to be laid before personalisation becomes an everyday reality. One such area is that of understanding currently unknown diseases and the link between genomics and the disease phenotype. There are many pathologies about which we know very little and for which it is difficult to find a treatment. At the same time, whilst we can document minute changes in the DNA sequence of an individual, we often cannot easily assign a particular downstream affect caused by a given change. Dr Takanori Amano of the RIKEN BioResource Research Center, Tsukuba, Japan, is currently leading a research team to better understand these unknown genotypes and diseases through the use of mouse models. The team is using mouse models to create realistic genomic representations of different, uncharacterised genomic changes, the focus is in the exploration of changes beyond those present within genes.

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          Author and article information

          Journal
          Impact
          impact
          Science Impact, Ltd.
          2398-7073
          November 30 2020
          November 30 2020
          : 2020
          : 7
          : 48-49
          Article
          10.21820/23987073.2020.7.48
          © 2020

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Earth & Environmental sciences, Medicine, Computer science, Agriculture, Engineering

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