PSVIII-26 Gene editing of complex traits. (5th December 2019)

• Record Type: Journal Article
• Authors: Ling, Ashley; Rekaya, Romdhane
• Is Part Of: Journal of animal science. Volume 97(2019)Supplement 3
• Page Start: 259
• Page End: 260
• Publication Date: 2019-12-05
• Subjects: gene editing -- genomic selection -- QTN assisted selection; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/skz258.529
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: Gene editing (GE) is a form of genetic engineering in which DNA is removed, inserted or replaced. For simple monogenic traits, the technology has been successfully implemented to create heritable modifications in animals and plants. The benefits of these niche applications are undeniable. For quantitative traits the benefits of GE are hard to quantify mainly because these traits are not genetic enough (low to moderate heritability) and their genetic architecture is often complex. Because its impact on the gene pool through the introduction of heritable modifications, the potential gain from GE must be evaluated within reasonable production parameters and in comparison, with available tools used in animal selection. A simulation was performed to compare GE with genomic selection (GS) and QTN-assisted selection (QAS) under four experimental factors: 1) heritability (0.1 or 0.4), 2) number of QTN affecting the trait (1000 or 10000) and their effect distribution (Gamma or uniform); 3) Percentage of selected females (100% or 33%); and 4) fixed or variable number of edited QTNs. Three models GS (M1), GS and GE (M2), and GS and QAS (M3) were implemented and compared. When the QTN effects were sampled from a Gamma distribution, all females were selected, and non-segregating QTNs were replaced, M2 clearly outperformed M1 and M3, with superiority ranging from 19 to 61%. Under the same scenario, M3 was 7 to 23% superior to M1. As the complexity of the genetic model increased (10000 QTN; uniform distribution), only one third of the females were selected, and the number of edited QTNs was fixed, the superiority of M2 was significantly reduced. In fact, M2 was only slightly better than M3 (2 to 6%). In all cases, M2 and M3 were better than M1. These results indicate that under realistic scenarios, GE for complex traits might have only limited advantages.