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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
Published on: June 23, 2012
Detecting rare variant associations by identity-by-descent mapping in case-control studies
Sharon R Browning1, Elizabeth A Thompson
1Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA. sguy@uw.edu
Genetics
|January 24, 2012
Summary
Identity-by-descent (IBD) mapping may outperform SNP association testing for rare variants, especially when clustered within genes. However, large sample sizes are often needed for genome-wide significance in outbred populations.
Area of Science:
- Genetics
- Population Genetics
- Statistical Genetics
Background:
- Identity-by-descent (IBD) segments can be detected from genome-wide SNP data.
- IBD mapping assesses if cases share more IBD segments around causal variants than controls.
- Rare variants are of particular interest due to their recent ancestry.
Purpose of the Study:
- To compare the power of IBD mapping versus SNP association testing for genome-wide case-control data.
- To investigate the influence of population history and selection on the relative performance of these methods.
- To analyze type 1 diabetes data using IBD mapping.
Main Methods:
- Simulating genome-wide SNP data for case-control studies.
- Evaluating IBD mapping and SNP association testing power.
- Analyzing a type 1 diabetes dataset.
Main Results:
- The relative performance of IBD mapping and SNP association testing is contingent on population demographics and selection strength.
- IBD mapping detected association only in the HLA region for the type 1 diabetes dataset.
- IBD mapping may offer higher power than SNP association for rare causal variants clustered within a gene.
Conclusions:
- IBD mapping shows potential for detecting associations with rare variants, particularly when clustered.
- Population history and selection significantly impact the efficacy of IBD mapping versus SNP association.
- Genome-wide significance via IBD mapping in outbred populations may necessitate substantial sample sizes or strong causal variant effects.
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