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

Abstract

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.

Figure A1.

Example transmissions and corresponding IBD states. For two haploid genomes, C₁ and C₂, the figure illustrates four (of many) possible patterns of transmission and the corresponding IBD states at two positions, U and V. The text describes how consideration of these possible scenarios leads to the specification of transition matrices for IBD state along the chromosome.

Figure 1.

MDS and classification of Asian HapMap individuals. MDS reveals in each panel two clear clusters that correspond to CHB (left) and JPT (right) HapMap populations. The figure’s three panels differ only in the color scheme, which represents classification according to PPC thresholds of 0.01 (A), 0.001 (B), and 0.0001 (C).

Figure 2.

Example segment shared IBD between two HapMap CEU offspring individuals and their parents. The main set of plots show the multipoint estimate of IBD sharing, P(Z=1), for a 25-Mb region of chromosome 9, for the pairs of individuals between two families (CEPH1375 and CEPH1341). The region was selected because the two offspring (NA10863 and NA06991) showed sharing in this region, shown in plot a. The three other segments shared between seemingly unrelated individuals are shown—that is, between the offspring in one family and a parent in the other family (two plots labeled b and c) and between those two parents (plot d). The lower-left diagram illustrates the region shared; this extended haplotype spans multiple haplotype blocks and recombination hotspots in the full phase II data. The lower-right diagram depicts the pattern of gene flow for this particular region—that is, a segment of the original common chromosome (dark rectangles) appears in the two families as shown.

Figure 3.

Schema of integration of PLINK, gPLINK, and Haploview. PLINK is the main C/C++ WGAS analytic engine that can run either as a stand-alone tool (from the command line or via shell scripting) or in conjunction with gPLINK, a Java-based graphical user interface (GUI). gPLINK also offers a simple project management framework to track PLINK analyses and facilitates integration with Haploview. It is easy to configure these tools, such that the whole-genome data and PLINK analyses (i.e., the computationally expensive aspects of this process) can reside on a remote server, but all initiation and viewing of results is done locally—for example, on a user’s laptop, connected to the whole-genome data via the Internet, by use of gPLINK’s secure shell networking.