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Data released on December 23, 2016

Supporting data for "Stepwise Distributed Open Innovation Contests for Software Development - Acceleration of Genome-Wide Association Analysis"

Bharadwaj, R, B; Guinan, E; Hill, A; Jelinsky, S, A; Kilty, I; Lakhani, K; Loh, P; Pons, P; Shang, J (2016): Supporting data for "Stepwise Distributed Open Innovation Contests for Software Development - Acceleration of Genome-Wide Association Analysis" GigaScience Database. http://dx.doi.org/10.5524/100264 RIS BibTeX Text

The association of differing genotypes with disease related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low cost genotyping and sequencing has made collecting large scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies are being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies (GWAS) associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets.
Using open innovation (OI) and contest based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in less than 6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd based contest a combination of computational, numeric and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645863 variants, compared to PLINK 1.07’s logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project.

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Related manuscripts:

doi:10.1093/gigascience/gix009

Additional information:

https://github.com/hillan141/gwas-speedup

Keywords:

Open innovation Crowdsourcing Genome-wide association study PLINK Logistic regression 

Software

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File NameSample IDFile TypeFile FormatSizeRelease Date 
otherarchive28.51 MB2016-12-20
GitHub archivearchive12.01 MB2016-12-20
ReadmeTEXT2.41 KB2016-12-20
otherUNKNOWN15.45 MB2016-12-20
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