Article

A tool for selecting SNPs for association studies based on observed linkage disequilibrium patterns.

Applied Biosystems, 850 Lincoln Centre Dr., Foster City, CA 94404, USA.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 02/2006; pp.487-98
Source: PubMed

ABSTRACT The design of genetic association studies using single-nucleotide polymorphisms (SNPs) requires the selection of subsets of the variants providing high statistical power at a reasonable cost. SNPs must be selected to maximize the probability that a causative mutation is in linkage disequilibrium (LD) with at least one marker genotyped in the study. The HapMap project performed a genome-wide survey of genetic variation with about a million SNPs typed in four populations, providing a rich resource to inform the design of association studies. A number of strategies have been proposed for the selection of SNPs based on observed LD, including construction of metric LD maps and the selection of haplotype tagging SNPs. Power calculations are important at the study design stage to ensure successful results. Integrating these methods and annotations can be challenging: the algorithms required to implement these methods are complex to deploy, and all the necessary data and annotations are deposited in disparate databases. Here, we present the SNPbrowser Software, a freely available tool to assist in the LD-based selection of markers for association studies. This stand-alone application provides fast query capabilities and swift visualization of SNPs, gene annotations, power, haplotype blocks, and LD map coordinates. Wizards implement several common SNP selection workflows including the selection of optimal subsets of SNPs (e.g. tagging SNPs). Selected SNPs are screened for their conversion potential to either TaqMan SNP Genotyping Assays or the SNPlex Genotyping System, two commercially available genotyping platforms, expediting the set-up of genetic studies with an increased probability of success.

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    Article: Fine mapping of the GLC1K juvenile primary open-angle glaucoma locus and exclusion of candidate genes.
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    ABSTRACT: Primary open-angle glaucoma is a leading cause of blindness worldwide. We previously identified a region on chromosome 20p12 associated with juvenile-onset primary open-angle glaucoma (JOAG) that was designated GLC1K. The aim of this study is to refine the boundaries of the GLC1K region and to screen selected candidate genes located within the refined region for biologically significant mutations. Four JOAG families (44 individuals) with linkage to GLC1K were used for this study. Informative single nucleotide polymorphism (SNP) markers located throughout the previously defined region were used for haplotype analysis. Four candidate genes within the refined region were screened for biologically significant mutations using direct genomic sequencing: bone morphogenetic protein 2 (BMP2); phospholipase C beta 1 (PLCB1); phospholipase C beta 4 (PLCB4); and BTB POZ domain containing 3 (BTBD3). Haplotype analysis identified a new critical interval of 12.7 Mb using a combination of SNPs and microsatellite markers. This analysis extended the region of GLC1K from D20S846 to rs6081603 in affected individuals, and the region was further reduced to 9 Mb if unaffected recombinant individuals were included in the analysis. Biologically significant DNA sequence variants were not identified in the BMP2, PLCB1, PLCB4, or BTBD3 genes in these families. Using recombinant breakpoint mapping and haplotypes based on a combination of SNP and microsatellite markers, the GLC1K region has been reduced to a maximum of 12.7 Mb and a minimum of 9 Mb. Four genes that are located within the refined region with attractive ocular expression and function have been excluded as causative genes for JOAG.
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Keywords

causative mutation
 
common SNP selection workflows
 
gene annotations
 
genetic association studies
 
genome-wide survey
 
haplotype tagging SNPs
 
increased probability
 
LD map
 
LD-based selection
 
linkage disequilibrium
 
metric LD maps
 
million SNPs
 
one marker genotyped
 
optimal subsets
 
reasonable cost
 
rich resource
 
SNPlex Genotyping System
 
statistical power
 
tagging SNPs
 
TaqMan SNP Genotyping Assays
 

Francisco M De La Vega