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Physicist, Startup Founder, Blogger, Dad

Thursday, February 26, 2015

Second-generation PLINK

"... these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM"  :-)

Interview with author Chris Chang. User Google group.

If one estimates a user population of ~1000, each saving of order $1000 in CPU/work time per year, then in the next few years PLINK 1.9 and its successors will deliver millions of dollars in value to the scientific community.
Second-generation PLINK: rising to the challenge of larger and richer datasets

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, 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.

2 comments:

Yan Shen said...

"... these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM" :-)

Can someone please set this man up on a date with Miranda Kerr? We need these genes to proliferate in the gene pool.

https://petitions.whitehouse.gov/



Can we petition Obama to make it happen?

Endre Bakken Stovner said...

Not only is it faster, but it actually works, which is as common as not in bioinformatics.

When I need to do something, I always try to find a way to make plink do it. This isn't just to keep the number of different programs used in pipelines low, but I can be pretty confident it works. The speed is a nice bonus, of course. And Chang has been incredibly quick to come up with patches for those edge cases that are off, less than 24 hours every single time afaicr.

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