GWAS-NR (Genome-wide Association Studies noise reduction) utilizes a linear filter to identify genomic regions demonstrating correlation among association signals in multiple datasets. GWAS-NR accepts multiple sets of p-values generated from GWAS data and allows correlation between sets of p-values. GWAS-NR utilizes a linear filter to identify genomic regions demonstrating correlation among association signals in multiple datasets. We used computer simulations to assess the ability of GWAS-NR to detect association against the commonly used joint analysis and Fisher’s methods. Furthermore, we applied GWAS-NR to a family-based autism GWAS of 597 families and a second existing autism GWAS of 696 families from the Autism Genetic Resource Exchange (AGRE) to arrive at a compendium of autism candidate genes. These genes were manually annotated and classified by a literature review and functional grouping in order to reveal biological pathways which might contribute to autism aetiology. GWAS-NR offers a powerful method for prioritizing regions for follow-up studies.
Hussman Institute for Human Genomics, University of Miami
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Hussman JP, et al.
A noise-reduction GWAS analysis implicates altered regulation of neurite outgrowth and guidance in autism.
Mol Autism. 2011 Jan 19;2(1):1.