Journal Article

Second-generation PLINK: rising to the challenge of larger and richer datasets

Purcell, Shaun M, Lee, James J, Tellier, Laurent CAM, Chang, Christopher C, Chow, Carson C, Vattikuti, Shashaank

2015

Overview

Abstract Background PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. Findings To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, (n) -time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0). Conclusions The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

• Publisher: Oxford University Press,BioMed Central
• Subject: Algorithms / Bioinformatics / Biomedical and Life Sciences / Computational Biology / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Data Mining and Knowledge Discovery / Datasets / Datasets as Topic / Format / Gene sequencing / Genetic analysis / Genetics, Population / Genome-wide association studies / Genome-Wide Association Study / Genomes / Genotypes / Genotyping Techniques / Human Genetics / Life Sciences / Likelihood Functions / Linkage Disequilibrium / Logistic Models / Polymorphism, Single Nucleotide / Population genetics / Population studies / Proteomics / Software / Technical Note / Whole genome sequencing