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A new tool called DISSECT for analysing large genomic data sets using a Big Data approach

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  • Oriol Canela-Xandri

    (The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Edinburgh EH25 9RG, UK)

  • Andy Law

    (The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Edinburgh EH25 9RG, UK)

  • Alan Gray

    (EPCC, The University of Edinburgh)

  • John A. Woolliams

    (The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Edinburgh EH25 9RG, UK)

  • Albert Tenesa

    (The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Edinburgh EH25 9RG, UK
    MRC HGU at the MRC IGMM, University of Edinburgh)

Abstract

Large-scale genetic and genomic data are increasingly available and the major bottleneck in their analysis is a lack of sufficiently scalable computational tools. To address this problem in the context of complex traits analysis, we present DISSECT. DISSECT is a new and freely available software that is able to exploit the distributed-memory parallel computational architectures of compute clusters, to perform a wide range of genomic and epidemiologic analyses, which currently can only be carried out on reduced sample sizes or under restricted conditions. We demonstrate the usefulness of our new tool by addressing the challenge of predicting phenotypes from genotype data in human populations using mixed-linear model analysis. We analyse simulated traits from 470,000 individuals genotyped for 590,004 SNPs in ∼4 h using the combined computational power of 8,400 processor cores. We find that prediction accuracies in excess of 80% of the theoretical maximum could be achieved with large sample sizes.

Suggested Citation

  • Oriol Canela-Xandri & Andy Law & Alan Gray & John A. Woolliams & Albert Tenesa, 2015. "A new tool called DISSECT for analysing large genomic data sets using a Big Data approach," Nature Communications, Nature, vol. 6(1), pages 1-6, December.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms10162
    DOI: 10.1038/ncomms10162
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    Cited by:

    1. Oriol Canela-Xandri & Konrad Rawlik & John A Woolliams & Albert Tenesa, 2016. "Improved Genetic Profiling of Anthropometric Traits Using a Big Data Approach," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-12, December.
    2. Aman Agrawal & Alec M Chiu & Minh Le & Eran Halperin & Sriram Sankararaman, 2020. "Scalable probabilistic PCA for large-scale genetic variation data," PLOS Genetics, Public Library of Science, vol. 16(5), pages 1-19, May.

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