Canela-Xandri Oriol, Law Andy, Gray Alan, Woolliams John A, Tenesa Albert
The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Edinburgh EH25 9RG, UK.
EPCC, The University of Edinburgh, Edinburgh EH9 3FD, UK.
Nat Commun. 2015 Dec 11;6:10162. doi: 10.1038/ncomms10162.
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.
大规模的遗传和基因组数据越来越容易获取，而对其进行分析的主要瓶颈是缺乏足够可扩展的计算工具。为了在复杂性状分析的背景下解决这个问题，我们推出了DISSECT。DISSECT是一款全新的免费软件，它能够利用计算集群的分布式内存并行计算架构，进行广泛的基因组和流行病学分析，而这些分析目前只能在样本量减少或条件受限的情况下进行。我们通过使用混合线性模型分析应对从人类群体的基因型数据预测表型这一挑战，展示了我们新工具的实用性。我们利用8400个处理器核心的综合计算能力，在约4小时内分析了470,000名个体针对590,004个单核苷酸多态性（SNP）进行基因分型的模拟性状。我们发现，在大样本量的情况下，可以实现超过理论最大值80%的预测准确率。
