PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data

doi:10.1371/journal.pcbi.1003101

PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data

Figure 7

Local manhattan plots illustrating individual examples of associations identified by PUMA analysis of the Wellcome Trust Case Control Consortium (WTCCC) data.

The top panel shows p-values (left axis, all methods except VBAY) and posterior probabilities for VBAY (right axis) for markers in the local genomic region, gene models are shown below in orange with the names of the associated gene indicated, the middle panel shows recombination rates and genetic distance from where the associated marker is indicated with an asterisk and the bottom panel shows a linkage disequilibrium plot among markers in the region using D. a) A region identified only by 2D-MCP replicates an association from a non-independent studies (which included WTCCC data) of Crohn's disease, b) a novel association identified for type 1 diabetes only by a PUMA method (2D-MCP) that implicates the etiologically relevant SLC30A1 gene, and c) an association identified only by a PUMA method (2D-MCP) for type 1 diabetes that implicated the LPHN2, a gene previously identified but not replicated as a risk locus for type 1 diabetes. Although the associations from the independent studies do not tag the same linkage disequilibrium block as the association identified by 2D-MCP, all three likely affect LPHN2 as they are located either in or directly upstream of this gene and next closest gene is 1.8 Mb (1.7 cM) away.

doi: https://doi.org/10.1371/journal.pcbi.1003101.g007