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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Scalable linkage-disequilibrium-based selective sweep detection: a performance guide.

Nikolaos Alachiotis¹, Pavlos Pavlidis²

¹Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213 PA USA.

|February 11, 2016

Summary

This summary is machine-generated.

Detecting positive selection in genomes involves analyzing linkage disequilibrium patterns. New parallel algorithms offer efficient computation for selective sweep detection, aiding evolutionary studies.

Keywords:

High performance Linkage disequilibrium Omega statistic OmegaPlus

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Published on: August 21, 2016

Area of Science:

Population Genetics
Genomics
Evolutionary Biology

Background:

Linkage disequilibrium (LD) describes non-random allele associations at different loci.
Genetic hitchhiking models predict specific LD patterns around beneficial alleles under positive selection.
Detecting these LD patterns can identify genomic regions shaped by past adaptation.

Purpose of the Study:

To develop and evaluate parallel algorithms for efficient selective sweep detection using linkage disequilibrium.
To address the computational challenges of analyzing large population genomic datasets with heterogeneous SNP density.

Main Methods:

Developed four parallelization strategies for shared-memory systems to compute linkage disequilibrium.
Implemented these strategies in the open-source software OmegaPlus for selective sweep detection.
Evaluated algorithm performance and compared OmegaPlus accuracy against neutrality tests.

Main Results:

The proposed parallel algorithms effectively address the computational intensity of selective sweep detection.
Performance evaluation provides insights into the benefits of each parallelization strategy.
OmegaPlus demonstrates competitive accuracy when compared to various neutrality tests.

Conclusions:

Computational demands for selective sweep detection are influenced by SNP density and data representation.
Selecting appropriate parallel algorithms can significantly reduce processing time and energy consumption.
Optimal algorithm choice depends on specific processor architectures, core counts, and dataset characteristics.