KinSNP software for homozygosity mapping of disease genes using SNP microarrays

El Ad David Amir, Ofer Bartal, Efrat Morad, Tal Nagar, Jony Sheynin, Ruti Parvari, Vered Chalifa-Caspi

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Consanguineous families affected with a recessive genetic disease caused by homozygotisation of a mutation offer a unique advantage for positional cloning of rare diseases. Homozygosity mapping of patient genotypes is a powerful technique for the identification of the genomic locus harbouring the causing mutation. This strategy relies on the observation that in these patients a large region spanning the disease locus is also homozygous with high probability. The high marker density in single nucleotide polymorphism (SNP) arrays is extremely advantageous for homozygosity mapping. We present KinSNP, a user-friendly software tool for homozygosity mapping using SNP arrays. The software searches for stretches of SNPs which are homozygous to the same allele in all ascertained sick individuals. User-specified parameters control the number of allowed genotyping 'errors' within homozygous blocks. Candidate disease regions are then reported in a detailed, coloured Excel file, along with genotypes of family members and healthy controls. An interactive genome browser has been included which shows homozygous blocks, individual genotypes, genes and further annotations along the chromosomes, with zooming and scrolling capabilities. The software has been used to identify the location of a mutated gene causing insensitivity to pain in a large Bedouin family. KinSNP is freely available from.

Original languageEnglish
Pages (from-to)394-401
Number of pages8
JournalHuman Genomics
Volume4
Issue number6
DOIs
StatePublished - 1 Aug 2010

Keywords

  • SNP microarrays
  • autozygosity
  • consanguinity
  • genetic mapping
  • homozygosity
  • software

Fingerprint

Dive into the research topics of 'KinSNP software for homozygosity mapping of disease genes using SNP microarrays'. Together they form a unique fingerprint.

Cite this