Fast Epistasis Detection in Large-Scale GWAS for Intel Xeon Phi Clusters

Luecke, Glenn R.; Weeks, Nathan T.; Groth, Brandon M.; Kraeva, Marina; Ma, Li; Kramer, Luke M.; Koltes, James E.; Reecy, James M.

doi:10.1109/trustcom.2015.637

Cited by 12 publications

(11 citation statements)

References 9 publications

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“…EpiSNPmpi was used to statistically analyze the fatty acid traits and identify epistatic interactions [17, 18]. Covariate of carcass contemporary group and lab sampling contemporary group, as well as pedigree and sex data were used in the model.…”

Section: Methodsmentioning

confidence: 99%

Epistatic interactions associated with fatty acid concentrations of beef from angus sired beef cattle

et al. 2016

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BackgroundConsumers are becoming increasingly conscientious about the nutritional value of their food. Consumption of some fatty acids has been associated with human health traits such as blood pressure and cardiovascular disease. Therefore, it is important to investigate genetic variation in content of fatty acids present in meat. Previously publications reported regions of the cattle genome that are additively associated with variation in fatty acid content. This study evaluated epistatic interactions, which could account for additional genetic variation in fatty acid content.ResultsEpistatic interactions for 44 fatty acid traits in a population of Angus beef cattle were evaluated with EpiSNPmpi. False discovery rate (FDR) was controlled at 5 % and was limited to well-represented genotypic combinations. Epistatic interactions were detected for 37 triacylglyceride (TAG), 36 phospholipid (PL) fatty acid traits, and three weight traits. A total of 6,181, 7,168, and 0 significant epistatic interactions (FDR < 0.05, 50-animals per genotype combination) were associated with Triacylglyceride fatty acids, Phospholipid fatty acids, and weight traits respectively and most were additive-by-additive interactions. A large number of interactions occurred in potential regions of regulatory control along the chromosomes where genes related to fatty acid metabolism reside.ConclusionsMany fatty acids were associated with epistatic interactions. Despite a large number of significant interactions, there are a limited number of genomic locations that harbored these interactions. While larger population sizes are needed to accurately validate and quantify these epistatic interactions, the current findings point towards additional genetic variance that can be accounted for within these fatty acid traits.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3235-8) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Epistatic interactions associated with fatty acid concentrations of beef from angus sired beef cattle

et al. 2016

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“…The approaches that operate at the lowest level take existing communication tools such as MPI and facilitate their integration with heterogeneous frameworks keeping the same level of abstraction. When the accelerator is a Xeon Phi, it is possible to only rely on MPI, potentially combining it within each node with traditional shared memory programming tools such as OpenMP . However, interesting efforts to program clusters of Xeon Phi only based on compiler directives such as OmpSs, discussed later, have also been made .…”

Section: Related Workmentioning

confidence: 99%

“…When the accelerator is a Xeon Phi, it is possible to only rely on MPI, potentially combining it within each node with traditional shared memory programming tools such as OpenMP. 13 However, interesting efforts to program clusters of Xeon Phi only based on compiler directives such as OmpSs, 5 discussed later, have also been made. 14 A more ambitious approach is to enable the execution of unaltered or very slightly modified heterogeneous applications written using well-known frameworks such as CUDA [15][16][17][18] or OpenCL 3,6,7,19-24 on distributed systems, so that they can exploit remote accelerators in clusters, grids, and the cloud, typically by virtualizing them.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous distributed computing based on high‐level abstractions

Viñas

Fraguela

Andrade

et al. 2018

Concurrency and Computation

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Summary The rise of heterogeneous systems has given place to great challenges for users as they involve new concepts, restrictions, and frameworks. Their exploitation is further complicated in the context of distributed memory systems, which require the usage of additional different programming paradigms and tools. In this paper, we propose a novel approach to program heterogeneous clusters that is based on high‐level abstractions such as tiles and hierarchical decomposition combined with the powerful APIs that data types and embedded languages can provide in languages such as C++. Rather than building our proposal from scratch, we have implemented it as a natural integration of the existing Hierarchically Tiled Arrays (HTA) and Heterogeneous Programming Library (HPL) projects, ie, the first one being focused on distributed computing and the second one on heterogeneous processing. The result, called Heterogeneous Hierarchically Tiled Arrays (H2TA), is very intuitive and easy to use thanks to the global view of the data and the single‐threaded view of the execution that it provides at cluster level together with the transparency it provides with respect to the management of the heterogeneous devices. An evaluation comparing our proposal with MPI‐based implementations shows its large programmability advantages and the reasonable overhead incurred.

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“…Goudey et al [15] performs k-way GWAS studies for arbitrary k with consideration of load balancing and elimination of redundancies on a 4096-node IBM Blue Gene/Q system; results for a single GPU are also presented. Luecke et al [16] performs 2-way analyses on up to 126 nodes of the Intel Phi-based Stampede system (cf. [17]).…”

Section: Introductionmentioning

confidence: 99%

Parallel accelerated Custom Correlation Coefficient calculations for genomics applications

et al. 2019

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The massive quantities of genomic data being made available through gene sequencing techniques are enabling breakthroughs in genomic science in many areas such as medical advances in the diagnosis and treatment of diseases. Analyzing this data, however, is a computational challenge insofar as the computational costs of the relevant algorithms can grow with quadratic, cubic or higher complexity-leading to the need for leadership scale computing. In this paper we describe a new approach to calculations of the Custom Correlation Coefficient (CCC) between Single Nucleotide Polymorphisms (SNPs) across a population, suitable for parallel systems equipped with graphics processing units (GPUs) or Intel Xeon Phi processors. We describe the mapping of the algorithms to accelerated processors, techniques used for eliminating redundant calculations due to symmetries, and strategies for efficient mapping of the calculations to many-node parallel systems. Results are presented demonstrating high per-node performance and near-ideal parallel scalability with rates of more than nine quadrillion (9 × 10 15 ) elementwise comparisons achieved per second with the latest optimized code on the ORNL Titan system, this being orders of magnitude faster than rates achieved using other codes and platforms as reported in the literature. Also it is estimated that as many as 90 quadrillion (90 × 10 15 ) comparisons per second may be achievable on the upcoming ORNL Summit system, an additional 10X performance increase. In a companion paper we describe corresponding techniques applied to calculations of the Proportional Similarity metric for comparative genomics applications.

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Fast Epistasis Detection in Large-Scale GWAS for Intel Xeon Phi Clusters

Cited by 12 publications

References 9 publications

Epistatic interactions associated with fatty acid concentrations of beef from angus sired beef cattle

Epistatic interactions associated with fatty acid concentrations of beef from angus sired beef cattle

Heterogeneous distributed computing based on high‐level abstractions

Parallel accelerated Custom Correlation Coefficient calculations for genomics applications

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