Dataset Information

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

ABSTRACT: BACKGROUND:Linkage disequilibrium is defined as the non-random associations of alleles at different loci, and it occurs when genotypes at the two loci depend on each other. The model of genetic hitchhiking predicts that strong positive selection affects the patterns of linkage disequilibrium around the site of a beneficial allele, resulting in specific motifs of correlation between neutral polymorphisms that surround the fixed beneficial allele. Increased levels of linkage disequilibrium are observed on the same side of a beneficial allele, and diminish between sites on different sides of a beneficial mutation. This specific pattern of linkage disequilibrium occurs more frequently when positive selection has acted on the population rather than under various neutral models. Thus, detecting such patterns could accurately reveal targets of positive selection along a recombining chromosome or a genome. Calculating linkage disequilibria in whole genomes is computationally expensive because allele correlations need to be evaluated for millions of pairs of sites. To analyze large datasets efficiently, algorithmic implementations used in modern population genetics need to exploit multiple cores of current workstations in a scalable way. However, population genomic datasets come in various types and shapes while typically showing SNP density heterogeneity, which makes the implementation of generally scalable parallel algorithms a challenging task. FINDINGS:Here we present a series of four parallelization strategies targeting shared-memory systems for the computationally intensive problem of detecting genomic regions that have contributed to the past adaptation of the species, also referred to as regions that have undergone a selective sweep, based on linkage disequilibrium patterns. We provide a thorough performance evaluation of the proposed parallel algorithms for computing linkage disequilibrium, and outline the benefits of each approach. Furthermore, we compare the accuracy of our open-source sweep-detection software OmegaPlus, which implements all four parallelization strategies presented here, with a variety of neutrality tests. CONCLUSIONS:The computational demands of selective sweep detection algorithms depend greatly on the SNP density heterogeneity and the data representation. Choosing the right parallel algorithm for the analysis can lead to significant processing time reduction and major energy savings. However, determining which parallel algorithm will execute more efficiently on a specific processor architecture and number of available cores for a particular dataset is not straightforward.

SUBMITTER: Alachiotis N

PROVIDER: S-EPMC4746822 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Alachiotis Nikolaos N Pavlidis Pavlos P

GigaScience 20160208

<h4>Background</h4>Linkage disequilibrium is defined as the non-random associations of alleles at different loci, and it occurs when genotypes at the two loci depend on each other. The model of genetic hitchhiking predicts that strong positive selection affects the patterns of linkage disequilibrium around the site of a beneficial allele, resulting in specific motifs of correlation between neutral polymorphisms that surround the fixed beneficial allele. Increased levels of linkage disequilibrium ...[more]

PMID: 26862394

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Project description:Single nucleotide polymorphisms (SNPs) may be correlated due to linkage disequilibrium (LD). Association studies look for both direct and indirect associations with disease loci. In a Random Forest (RF) analysis, correlation between a true risk SNP and SNPs in LD may lead to diminished variable importance for the true risk SNP. One approach to address this problem is to select SNPs in linkage equilibrium (LE) for analysis. Here, we explore alternative methods for dealing with SNPs in LD: change the tree-building algorithm by building each tree in an RF only with SNPs in LE, modify the importance measure (IM), and use haplotypes instead of SNPs to build a RF.We evaluated the performance of our alternative methods by simulation of a spectrum of complex genetics models. When a haplotype rather than an individual SNP is the risk factor, we find that the original Random Forest method performed on SNPs provides good performance. When individual, genotyped SNPs are the risk factors, we find that the stronger the genetic effect, the stronger the effect LD has on the performance of the original RF. A revised importance measure used with the original RF is relatively robust to LD among SNPs; this revised importance measure used with the revised RF is sometimes inflated. Overall, we find that the revised importance measure used with the original RF is the best choice when the genetic model and the number of SNPs in LD with risk SNPs are unknown. For the haplotype-based method, under a multiplicative heterogeneity model, we observed a decrease in the performance of RF with increasing LD among the SNPs in the haplotype.Our results suggest that by strategically revising the Random Forest method tree-building or importance measure calculation, power can increase when LD exists between SNPs. We conclude that the revised Random Forest method performed on SNPs offers an advantage of not requiring genotype phase, making it a viable tool for use in the context of thousands of SNPs, such as candidate gene studies and follow-up of top candidates from genome wide association studies.

Dataset Information

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

Publications

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

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