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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
Published on: June 23, 2012
DASH: a method for identical-by-descent haplotype mapping uncovers association with recent variation
Alexander Gusev1, Eimear E Kenny2, Jennifer K Lowe3
1Department of Computer Science, Columbia University, New York, NY 10027, USA.
American Journal of Human Genetics
|May 31, 2011
Summary
This study introduces DASH, a novel algorithm for identifying rare genetic variants associated with diseases by analyzing shared genomic segments. DASH enhances disease gene discovery in human genetics research.
Area of Science:
- Human Genetics
- Genomic Association Studies
- Bioinformatics
Background:
- Rare variants pose challenges for human genetics, often missed by genome-wide association studies (GWAS) due to low representation on SNP arrays.
- Detecting associations with rare or population-specific alleles requires methods beyond standard single-marker testing.
Purpose of the Study:
- To develop and validate a new algorithm, DASH, for detecting associations between rare genetic variants and phenotypic traits.
- To overcome the limitations of current GWAS in identifying rare causal variants using SNP array data.
Main Methods:
- Developed DASH (Detecting Association with Shared Haplotypes), an algorithm using pairwise identical-by-descent (IBD) segments to infer haplotype-sharing clusters.
- Constructed graphs where nodes are individuals and links represent shared segments, employing a minimum cut algorithm to identify clusters.
- Applied DASH to simulated data and diverse GWAS datasets, including isolated (Kosrae) and outbred (WTCCC) cohorts.
Main Results:
- DASH demonstrated significantly higher power than single-marker testing in simulations, particularly in isolated populations with abundant IBD.
- Identified multiple significant haplotype associations in both WTCCC (5 loci) and Kosrae (10 loci) data, exceeding the significance of nearby individual markers.
- Replicated one locus in an independent cohort and found evidence of structural changes in carriers via low-pass whole-genome sequencing.
Conclusions:
- DASH effectively identifies disease associations with rare alleles by leveraging long stretches of identical-by-descent genomic sharing.
- The algorithm provides a powerful approach to complement existing GWAS methods, enhancing the discovery of rare variants.
- Findings suggest DASH is valuable for human genetics research, particularly in populations with high IBD or for detecting recent rare variants.
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