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

Chang, Christopher CChow, Carson CTellier, Laurent CAMVattikuti, ShashaankPurcell, Shaun M.Lee, James J2015-03-022015Chang, Christopher C, Carson C Chow, Laurent CAM Tellier, Shashaank Vattikuti, Shaun M Purcell, and James J Lee. 2015. “Second-generation PLINK: rising to the challenge of larger and richer datasets.” GigaScience 4 (1): 7. doi:10.1186/s13742-015-0047-8. http://dx.doi.org/10.1186/s13742-015-0047-8.2047-217Xhttp://nrs.harvard.edu/urn-3:HUL.InstRepos:14065447Background: 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, O √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.en-USGWASPopulation geneticsWhole-genome sequencingHigh-density SNP genotypingComputational statisticsSecond-generation PLINK: rising to the challenge of larger and richer datasetsJournal Article2015-03-0210.1186/s13742-015-0047-8