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Published on: February 3, 2013
Obtaining accurate p values from a dense SNP linkage scan.
William C L Stewart1, Ryan L Subaran
1Battelle Center for Mathematical Medicine, Research Institute at Nationwide Children's Hospital, Columbus, Ohio 43215, USA. William.Stewart@nationwidechildrens.org
Ignoring linkage disequilibrium (LD) in genetic studies inflates errors, mistakenly identifying neutral mutations as pathogenic. Our simulation shows a 14% error rate, highlighting the need for accurate methods like Haplodrop to control false positives in variant discovery.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Predicting mutation pathogenicity in resequencing studies is challenging.
- Linkage analysis is used to refine candidate mutations but can be affected by linkage disequilibrium (LD).
- Ignoring LD can lead to inflated Type 1 error rates, misclassifying neutral mutations and reducing power in variant discovery.
Purpose of the Study:
- To address the issue of inflated Type 1 error in linkage analysis due to unaddressed linkage disequilibrium (LD).
- To develop a method that controls Type 1 error in linkage tests, particularly in the presence of LD.
- To improve the efficiency of follow-up resequencing studies by reducing the burden of multiple testing.
Main Methods:
- Simulated genetic data using a panel of single nucleotide polymorphisms (SNPs) with an average spacing of 1.27 cM.
- Incorporated an LD pattern estimated from real data into the simulations.
- Developed Haplodrop, a simulation program generating founder haplotypes with LD and simulating recombination to non-founders in pedigrees.
Main Results:
- Simulations demonstrated that ignoring LD can inflate the Type 1 error of maximum LOD scores up to 14%.
- Haplodrop effectively controls the Type 1 error of linkage tests.
- Haplodrop demonstrates good agreement with existing software and accommodates complex pedigree structures.
Conclusions:
- Haplodrop provides a robust method for controlling Type 1 error in linkage analysis, crucial for accurate variant pathogenicity prediction.
- By correctly excluding mutations in unlinked regions with high LD, Haplodrop aids in reducing the multiple testing burden in resequencing studies.
- This tool enhances the power and accuracy of identifying causal variants in genetic studies.
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