Familial clustering of mice consistent to known pedigrees enabled by the genome profiling (GP) method

  • Harshita Sharma
  • , Fumihito Ohtani
  • , Parmila Kumari
  • , Deepti Diwan
  • , Naoko Ohara
  • , Tetsuya Kobayashi
  • , Miho Suzuki
  • , Naoto Nemoto
  • , Yoshibumi Matsushima
  • , Koichi Nishigaki

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Familial clustering without any prerequisite knowledge becomes often necessary in Behavioral Science, and forensic studies in case of great disasters like Tsunami and earthquake requiring body-identification without any usable information. However, there has been no well-established method for this purpose although conventional ones such as short tandem repeats (STR) and single nucleotide polymorphism (SNP), which might be applied with toil and moil to some extent. In this situation, we could find that the universal genome distance-measuring method genome profiling (GP), which is made up of three elemental techniques; random PCR, micro-temperature gradient gel electrophoresis (μTGGE), and computer processing for normalization, can do this purpose with ease when applied to mouse families. We also confirmed that the sequencing approach based on the ccgf (commonly conserved genetic fragment appearing in the genome profile) was not completely discriminative in this case. This is the first demonstration that the familial clustering can be attained without a priori sequence information to the level of discriminating strains and sibling relationships. This method can complement the conventional approaches in preliminary familial clustering.

Original languageEnglish
Article number8
Pages (from-to)55-62
Number of pages8
JournalBiophysics (Japan)
Volume10
DOIs
StatePublished - 2014

Keywords

  • Genome distance
  • Pedigree analysis
  • Universal genotyping method

Fingerprint

Dive into the research topics of 'Familial clustering of mice consistent to known pedigrees enabled by the genome profiling (GP) method'. Together they form a unique fingerprint.

Cite this