Literature DB >> 11427178

A permutation procedure for the haplotype method for identification of disease-predisposing variants.

H Li1.   

Abstract

Once a genetic region involved in a complex disease has been localized through linkage or association studies, we need methods to help us identify the actual disease predisposing genetic variant(s) in the region. A large number of single nucleotide polymorphic (SNP) sites may exist in this region. It is important to identify genetic variants directly involved in disease from those in linkage disequilibrium, and thus associated with, the disease predisposing variant(s). A question of great interest is to test whether a SNP, or a combination of SNPs, that influence the trait under investigation have been identified. For many complex HLA-associated diseases, patterns of amino acid site variability raise the possibility that HLA-variation association with a disease may not be due to a given allele but rather one or more variable amino acid sites occurring on several alleles. Here the question is whether an amino acid variant or a combination of amino acid variants involved in disease are identified. To address this question, this paper proposes a permutation procedure for the haplotype method, to test whether all the sites involved in the disease have been identified using the haplotypic data of patients and controls. The method is based on the theoretical result of Valdes and Thomson, that, for each haplotype combination containing all the amino acid sites involved in the disease process, the relative frequencies of amino acid variants at sites not involved in disease, but in linkage disequilibrium with the disease-predisposing sites, are expected to be the same in patients and controls. This procedure takes into account the non-independence of the sites sampled and is robust to mode of inheritance and penetrance of the disease, and can definitely specify when all the disease predisposing sites have not been identified. Application to both simulated data and real data sets on type 1 diabetes and alcoholism indicates that the proposed procedure works well in testing the important null hypothesis of whether all the predisposing sites are identified.

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Year:  2001        PMID: 11427178     DOI: 10.1017/S0003480001008491

Source DB:  PubMed          Journal:  Ann Hum Genet        ISSN: 0003-4800            Impact factor:   1.670


  5 in total

1.  A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes.

Authors:  Heather J Cordell; David G Clayton
Journal:  Am J Hum Genet       Date:  2001-11-21       Impact factor: 11.025

2.  Incorporating genotyping uncertainty in haplotype inference for single-nucleotide polymorphisms.

Authors:  Hosung Kang; Zhaohui S Qin; Tianhua Niu; Jun S Liu
Journal:  Am J Hum Genet       Date:  2004-02-13       Impact factor: 11.025

3.  PTPN22 genetic variation: evidence for multiple variants associated with rheumatoid arthritis.

Authors:  Victoria E H Carlton; Xiaolan Hu; Anand P Chokkalingam; Steven J Schrodi; Rhonda Brandon; Heather C Alexander; Monica Chang; Joseph J Catanese; Diane U Leong; Kristin G Ardlie; Daniel L Kastner; Michael F Seldin; Lindsey A Criswell; Peter K Gregersen; Ellen Beasley; Glenys Thomson; Christopher I Amos; Ann B Begovich
Journal:  Am J Hum Genet       Date:  2005-08-10       Impact factor: 11.025

4.  The largest prospective warfarin-treated cohort supports genetic forecasting.

Authors:  Mia Wadelius; Leslie Y Chen; Jonatan D Lindh; Niclas Eriksson; Mohammed J R Ghori; Suzannah Bumpstead; Lennart Holm; Ralph McGinnis; Anders Rane; Panos Deloukas
Journal:  Blood       Date:  2008-06-23       Impact factor: 22.113

5.  Association of warfarin dose with genes involved in its action and metabolism.

Authors:  Mia Wadelius; Leslie Y Chen; Niclas Eriksson; Suzannah Bumpstead; Jilur Ghori; Claes Wadelius; David Bentley; Ralph McGinnis; Panos Deloukas
Journal:  Hum Genet       Date:  2006-10-18       Impact factor: 4.132

  5 in total

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