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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #290194

Title: Combined arms: a full spectrum approach to structural variant discovery in livestock populations

Author
item Bickhart, Derek
item Liu, Ge - George

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/11/2013
Publication Date: 1/11/2013
Citation: Bickhart, D.M., Liu, G. 2013. Combined arms: a full spectrum approach to structural variant discovery in livestock populations. Meeting Abstract. W145.

Interpretive Summary:

Technical Abstract: The glut of next-generation sequencing (NGS) data from several economically important livestock species has opened the door for more advanced analysis of structural variants (SVs). Using modified forms of open-source variant detection software packages, we have created a high throughput pipeline that detects SVs from paired-end NGS data in an efficient manner. Our pipeline uses previously reported SV detection strategies evaluated in the 1000 genomes pilot project such as read depth (RD), read pair (RP), split read (SR) and local assembly (AS). We also use an algorithm called "Precision Aware Merger" (PAM) to combine rare overlapping events and resolve conflicts in the output. As a demonstration of the power of this combined approach to variant discovery, we present preliminary data obtained from a survey of 112 cattle individuals from eight breeds comprising nearly 2 Terabases of sequence data. Over 4000 CNVs were identified in each animal and approximately 2-3% of the total genome sequence of each animal was found to be structurally variable. We show that our SV detection methods are complementary and do not share a significant overlap. We also provide extensive experimental validation using CGH arrays, qPCR experiments and SNP arrays. This pipeline and the results it produces will serve as resources for the livestock genetics community to assist in future genome resequencing studies and animal selection surveys, respectively.