A Primer on High-Throughput Computing for Genomic Selection

Frontiers in Genetics

Stewart eBauck¹  Brent eWoodwart¹  Xiao-Lin eWu²  Natalia eDe Leon Gatti²  Daniel eGianola²  Timothy M Beissinger²  Kent A Weigel²  Guilherme J M Rosa² 

[1] Merial Limited;University of Wisconsin - Madison;

关键词: Bayesian Models;    genomic selection;    General purpose computing;    High-throughput computing;    Parallel programming;    Pipelining;   

DOI  :  10.3389/fgene.2011.00004

来源: DOAJ

【 摘 要 】

High-throughput computing (HTC) uses computer clusters to solve advanced computational problems, with the goal of accomplishing high throughput over relatively long periods of time. In genomic selection, for example, a set of markers covering the entire genome is used to train a model based on known data, and the resulting model is used to predict the genetic merit of selection candidates. Sophisticated models are very computationally demanding and, with several traits to be evaluated sequentially, computing time is long and output is low. In this paper, we present scenarios and basic principles of how HTC can be used in genomic selection, implemented using various techniques from simple batch processing to pipelining in distributed computer clusters. Various scripting languages, such as shell scripting, Perl and R, are also very useful to devise pipelines. By pipelining, we can reduce total computing time and consequently increase throughput. In comparison to the traditional data processing pipeline residing on the central processors, performing general purpose computation on a graphics processing unit (GPU) provide a new-generation approach to massive parallel computing in genomic selection. While the concept of HTC may still be new to many researchers in animal breeding, plant breeding, and genetics, HTC infrastructures have already been built in many institutions, such as the University of Wisconsin – Madison, which can be leveraged for genomic selection, in terms of central processing unit (CPU) capacity, network connectivity, storage availability, and middleware connectivity. Exploring existing HTC infrastructures as well as general purpose computing environments will further expand our capability to meet increasing computing demands posed by unprecedented genomic data that we have today. We anticipate that HTC will impact genomic selection via better statistical models, faster solutions, and more competitive products (e.g., from design of marker panels to rea

【 授权许可】

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