Literature DB >> 11793738

Maximum identity length contrast: a powerful method for susceptibility gene detection in isolated populations.

C Bourgain1, E Génin, P Margaritte-Jeannin, F Clerget-Darpoux.   

Abstract

We report the results of our analysis of the Genetic Analysis Workshop 12 simulated data set. Focusing on the isolated populations, we compare the efficiency of a new method, the maximum identity length contrast statistic (MILC) with the maximum likelihood score (MLS) in a genome screen strategy. MILC is a method based on the contrast of haplotype identity between transmitted and nontransmitted haplotypes in trios. It uses information on linkage and association. We found that MILC allows the detection of a risk factor corresponding to candidate gene 1 where the MLS fails, though the same population replicates were used. Interestingly, the association between this risk factor and the disease could not have been detected with the TDT at a genome-wide level.

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Year:  2001        PMID: 11793738     DOI: 10.1002/gepi.2001.21.s1.s560

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  5 in total

1.  An entropy-based statistic for genomewide association studies.

Authors:  Jinying Zhao; Eric Boerwinkle; Momiao Xiong
Journal:  Am J Hum Genet       Date:  2005-05-09       Impact factor: 11.025

2.  Nonlinear tests for genomewide association studies.

Authors:  Jinying Zhao; Li Jin; Momiao Xiong
Journal:  Genetics       Date:  2006-07-02       Impact factor: 4.562

3.  An entropy-based genome-wide transmission/disequilibrium test.

Authors:  Jinying Zhao; Eric Boerwinkle; Momiao Xiong
Journal:  Hum Genet       Date:  2007-02-13       Impact factor: 4.132

4.  A Bayesian approach using covariance of single nucleotide polymorphism data to detect differences in linkage disequilibrium patterns between groups of individuals.

Authors:  Taane G Clark; Susana G Campino; Elisa Anastasi; Sarah Auburn; Yik Y Teo; Kerrin Small; Kirk A Rockett; Dominic P Kwiatkowski; Christopher C Holmes
Journal:  Bioinformatics       Date:  2010-06-16       Impact factor: 6.937

5.  Imputation without doing imputation: a new method for the detection of non-genotyped causal variants.

Authors:  Richard Howey; Heather J Cordell
Journal:  Genet Epidemiol       Date:  2014-02-17       Impact factor: 2.135

  5 in total

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