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

Oriol Canela-Xandri¹, Andy Law¹, Alan Gray²

¹The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Campus, Edinburgh EH25 9RG, UK.

Nature Communications

|December 15, 2015

Summary

New software, DISSECT, enables scalable analysis of large genetic datasets for complex traits. It achieves high phenotype prediction accuracy from genotype data in large human populations.

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Area of Science:

Genomics
Computational Biology
Statistical Genetics

Background:

Large-scale genetic and genomic data analysis is hindered by a lack of scalable computational tools.
Analyzing complex traits requires efficient methods for handling vast datasets.

Purpose of the Study:

To present DISSECT, a novel, freely available software for large-scale genomic and epidemiologic analyses.
To enable complex traits analysis on distributed-memory parallel architectures.

Main Methods:

Developed DISSECT software to leverage compute clusters for genomic analysis.
Utilized mixed-linear model analysis for phenotype prediction from genotype data.
Analyzed simulated traits from 470,000 individuals genotyped for 590,004 SNPs.

Main Results:

DISSECT successfully performed complex analyses on a large simulated dataset.
Analysis of 470,000 individuals was completed in approximately 4 hours using 8,400 processor cores.
Achieved prediction accuracies exceeding 80% of the theoretical maximum.

Conclusions:

DISSECT provides a scalable solution for analyzing large-scale genetic data.
The software facilitates robust phenotype prediction from genotype data in human populations.
Enables previously restricted analyses on reduced sample sizes or under limited conditions.