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.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Canela-Xandri O,Law A,Gray A,Woolliams JA,Tenesa Adoi
10.1038/ncomms10162subject
Has Abstractpub_date
2015-12-11 00:00:00pages
10162issn
2041-1723pii
ncomms10162journal_volume
6pub_type
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