Common variants near ABCA1, AFAP1 and GMDS confer risk of primary open-angle glaucoma

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Abstract

Primary open-angle glaucoma (POAG) is a major cause of irreversible blindness worldwide. We performed a genome-wide association study in an Australian discovery cohort comprising 1,155 advanced POAG cases and 1,992 controls. Association of the top SNPs from the discovery stage was investigated in two Australian replication cohorts (total 932 cases, 6,862 controls) and two US replication cohorts (total 2,616 cases, 2,634 controls). Meta-analysis of all cohorts revealed three novel loci associated with development of POAG. These loci are located upstream of ABCA1 (rs2472493 [G] OR=1.31, P= 2.1 × 10−19), within AFAP1 (rs4619890 [G] OR=1.20, P= 7.0 × 10−10 ) and within GMDS (rs11969985 [G] OR=1.31, and P= 7.7 × 10−10). Using RT-PCR and immunolabelling, we also showed that these genes are expressed within human retina, optic nerve and trabecular meshwork and that ABCA1 and AFAP1 are also expressed in retinal ganglion cells.

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PLINK: a tool set for whole-genome association and population-based linkage analyses.

Shaun Purcell, Benjamin M. Neale, Kathe Todd-Brown … (2007)

Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

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Principal components analysis corrects for stratification in genome-wide association studies.

Alkes L. Price, Nick Patterson, Robert Plenge … (2006)

Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.

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METAL: fast and efficient meta-analysis of genomewide association scans

Cristen Willer, Yun Li, Gonçalo R Abecasis (2010)

Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu