Literature DB >> 18378114

LINKGEN: a new algorithm to process data in genetic linkage studies.

Rodrigo Secolin1, Cristiane S Rocha, Fábio R Torres, Marilza L Santos, Cláudia V Maurer-Morelli, Neide F Santos, Iscia Lopes-Cendes.   

Abstract

Genetic linkage studies using whole genome scans are useful approaches for identifying genes related to human diseases. In general, these studies require genotyping of a large number of markers, which are used in statistical analysis. Recent technology has allowed easy genotyping of a large number of markers in less time; therefore, interface programs are required for manipulation of these large data sets. We present a new algorithm, which processes input data in LINKAGE format from data analyzed by automated genotyping systems. The algorithm was implemented in PERL script and R environment. Validation was performed with genotyped data from 127 individuals and 720 microsatellite markers of two whole genome scans. Our results showed a significant decrease in data processing time. In addition, this algorithm provides unbiased allele frequency estimation used for linkage analysis. LINKGEN is a freely available online tool and allows easier, faster, and reliable manipulation of large genotyping data sets.

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Year:  2008        PMID: 18378114     DOI: 10.1016/j.ygeno.2008.02.001

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  2 in total

1.  A Locus Identified on Chromosome18P11.31 is Associated with Hippocampal Abnormalities in a Family with Mesial Temporal Lobe Epilepsy.

Authors:  Cláudia V Maurer-Morelli; Rodrigo Secolin; Márcia E Morita; Romênia R Domingues; Rafael B Marchesini; Neide F Santos; Eliane Kobayashi; Fernando Cendes; Iscia Lopes-Cendes
Journal:  Front Neurol       Date:  2012-08-10       Impact factor: 4.003

2.  GWAS analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis.

Authors:  Christine Fong; Dennis C Ko; Michael Wasnick; Matthew Radey; Samuel I Miller; Mitchell Brittnacher
Journal:  Bioinformatics       Date:  2010-01-06       Impact factor: 6.937

  2 in total

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