Literature DB >> 16917817

A comparison of methods for intermediate fine mapping.

Charalampos Papachristou1, Shili Lin.   

Abstract

The arrival of highly dense genetic maps at low cost has geared the focus of linkage analysis studies toward developing methods for placing putative trait loci in narrow regions with high confidence. This shift has led to a new analytic scheme that expands the traditional two-stage protocol of preliminary genome scan followed by fine mapping through inserting a new stage in between the two. The goal of this new "intermediate" fine mapping stage is to isolate disease loci to narrow intervals with high confidence so that association studies can be more focused, efficient, and cost-effective. In this paper, we compared and contrasted five methods that can be used for performing this intermediate step. These methods are: the lod support approach, the generalized estimating equations (GEE) method, the confidence set inference (CSI) procedure, and two bootstrap methods. We compared these methods in terms of the coverage probability and precision of localization of the resulting intervals. Results from a simulation study considering several two-locus models demonstrated that the two bootstrap methods yield intervals with approximately correct coverage. On the other hand, the 1-lod support intervals, and those produced by the GEE method, tend to significantly undercover the trait locus, while the regions obtained by the CSI incline to overcover the gene position. When the observed coverage of the confidence intervals produced by all the methods was held to be the same, those obtained through the CSI procedure displayed a higher ability to localize loci, especially when these loci have a minor contribution to the trait and when the amount of data available for the analysis is relatively small. However, with very large sample sizes, lod support intervals emerged as a winner. Application of the methods to the data from the Arthritis Research Campaign National Repository led to intervals containing the position of a known trait locus for all methods, with the greatest precision achieved by the CSI.

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Year:  2006        PMID: 16917817     DOI: 10.1002/gepi.20179

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


  5 in total

1.  Agreement among type 2 diabetes linkage studies but a poor correlation with results from genome-wide association studies.

Authors:  S Lillioja; A Wilton
Journal:  Diabetologia       Date:  2009-03-19       Impact factor: 10.122

2.  The future is now - will the real disease gene please stand up?

Authors:  E R Martin; M A Schmidt
Journal:  Hum Hered       Date:  2008-03-31       Impact factor: 0.444

3.  Bayesian intervals for linkage locations.

Authors:  Ritwik Sinha; Robert P Igo; Shiv K Saini; Robert C Elston; Yuqun Luo
Journal:  Genet Epidemiol       Date:  2009-11       Impact factor: 2.135

4.  Two-step intermediate fine mapping with likelihood ratio test statistics: applications to Problems 2 and 3 data of GAW15.

Authors:  Ritwik Sinha; Yuqun Luo
Journal:  BMC Proc       Date:  2007-12-18

5.  Confidence intervals for putative quantitative trait loci - development and applications of new linkage methods.

Authors:  Charalampos Papachristou; Mark Abney; Shili Lin
Journal:  BMC Proc       Date:  2007-12-18
  5 in total

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