ArticleBMC proceedings2011
Enrichment analysis of genetic association in genes and pathways by aggregating signals from both rare and common variants.
Article in BMC proceedings, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- Pathway analysis with next-generation sequencing data.European journal of human genetics : EJHG · 2015Article
- Pathway analysis approaches for rare and common variants: insights from Genetic Analysis Workshop 18.Genetic epidemiology · 2014Article
- Inflated type I error rates when using aggregation methods to analyze rare variants in the 1000 Genomes Project exon sequencing data in unrelated individuals: summary results from Group 7 at Genetic Analysis Workshop 17.Genetic epidemiology · 2011Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New high-throughput sequencing technologies have brought forth opportunities for unbiased analysis of thousands of rare genomic variants in genome-wide association studies of complex diseases. Because it is hard to detect single rare variants with appreciable effect sizes at the population level, existing methods mostly aggregate effects of multiple markers by collapsing the rare variants in genes (or genomic regions). We hypothesize that a higher level of aggregation can further improve association signal strength. Using the Genetic Analysis Workshop 17 simulated data, we test a two-step strategy that first applies a collapsing method in a gene-level analysis and then aggregates the gene-level test results by performing an enrichment analysis in gene sets. We find that the gene set approach which combines signals across multiple genes outperforms testing individual genes separately and that the power of the gene set enrichment test is further improved by proper adjustment of statistics to account for gene-wise differences.
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Registered trials
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