Background Selection

Introduction

Background selection refers to selection applied to regions of the genome that are not known to be associated with desired traits. This type of selection is usually referred to in the context of trait introgression. Trait introgression, the process of integrating a trait from one species into another, is a common strategy used for crop improvement. View an example of introducing bacterial spot resistance to the cultivated tomato.

In the case of tomato, wild species are often used as a source of desirable traits, as there is limited genetic variation within the cultivated tomato. Making crosses (see video below) between cultivated and wild tomato species allows breeders to introduce desirable traits from the wild species. Along with the desired trait from the wild species comes undesired genetic material that the breeder wants to eliminate. This undesired genetic background may, for example, reduce yield or crop quality.

Background Selection Aided by Molecular Markers

Selection against background genomic regions can be greatly facilitated by knowledge of molecular markers. Genotypic data acquired from molecular marker data of mapping populations can be used to create genetic maps so that researchers know the relative distances between molecular markers. Learn more about genetic mapping. If the desired trait has also been mapped, this knowledge can be used to determine which molecular markers located in background regions are linked to the desired traits. This is of particular importance because it can be difficult to remove unwanted linked genetic background. If a genetic map and genotypic data for the population being studied are available, graphical genotypes, can be created. A graphical genotype allows the breeder to visualize the genotype data for each individual. Using graphical genotyping software, breeders can identify individuals with the least unwanted background contribution, as well as individuals with the least linked genetic background. With knowledge of the number of background regions that must be eliminated, breeders can estimate the minimum number of individuals that must be evaluated to obtain at least one individual with fewer (or no) unwanted background regions. Learn more about calculating the minimum number of individuals.

Selecting Against Linked Background

Linked genetic background is generally much more difficult to remove than genetic background on other chromosomes. The probability of removing a portion of linked genetic background depends on the probability of breaking the genetic linkage between the background and desired gene. The probability of breaking the genetic linkage depends on the genetic distance. The greater the genetic distance, the higher the probability that the linkage can be broken. It is therefore important to consider the minimum number of individuals that must be evaluated to help ensure that the linkage will be broken. See a sample calculation of the minimum population size required.

The phenomenon of undesired traits linked to the desired trait being introduced is referred to as linkage drag. Linkage drag has been reported in numerous economically important species, including bean (Miklas, 2007), canola (Cao et al., 2010), potato (Collins et al., 1999), soybean (Warrington et al., 2008), tobacco (Lewis and Rose, 2010), and tomato (Frary et al., 2004). For example, Brouwer et al. (2004) identified QTLs conferring resistance to late blight in tomato. However, they also identified alleles associated with undesirable traits in these regions. To make use of the QTLs conferring late blight resistance, they suggest employing a backcrossing scheme, but without molecular markers tightly linked to late blight resistance and the deleterious alleles, this would be a daunting process. This phenomenon has also been reported in other crop species in the literature. In potato, QTLs conferring late blight resistance have been identified on chromosome V in multiple studies. In all of these studies, the QTL region has also been associated with late maturity and low plant vigor, which are undesirable traits from a breeding perspective (Collins et al., 1999; Oberhagemann et al., 1999; Ewing et al., 2000).

Conclusion

When introgressing desired traits from wild species, background selection must often be applied to help recover the desired background genome. Selection based on molecular marker data and graphical genotypes can be useful to accelerate the background genome recovery process. This is particularly true when desired traits and undesired traits are linked.

References Cited

• Brouwer, D. J., and D. A. St. Clair. 2004. Fine mapping of three quantitative trait loci for late blight resistance in tomato using near isogenic lines (NILs) and sub-NILs. Theoretical and Applied Genetics 108: 628–638 (Available online at: http://dx.doi.org/10.1007/s00122-003-1469-8) (verified 1 June 2012).
• Cao, Z. Y., F. Tian, N. A. Wang, C. C. Jiang, B. Lin, W. Xia, J. Q. Shi, Y. Long, C. Y. Zhang, and J. L. Meng. 2010. Analysis of QTLs for erucic acid and oil content in seeds on A8 chromosome and the linkage drag between the alleles for the two traits in Brassica napus. Journal of Genetics and Genomics 37: 231–240 (Available online at: http://dx.doi.org/10.1016/S1673-8527(09)60041-2) (verified 1 June 2012).
• Collins, A., D. Milbourne, L. Ramsay, R. Meyer, C. Chatot-Balandras, P. Oberhagemann, W. De Jong, C. Gebhardt, E. Bonnel, and R. Waugh. 1999. QTL for field resistance to late blight in potato are strongly correlated with maturity and vigor. Molecular Breeding 5: 387–398. (Availavble online at: http://dx.doi.org/10.1023/A:1009601427062) (verified 1 june 2012).
• Ewing, E. E., I. Simko, C. D. Smart, M. W. Bonierbale, E.S.G. Mizubuti, G. D. May, and W. E. Fry. 2000. Genetic mapping from field tests of qualitative and quantitative resistance to Phytophthora infestans in a population derived from Solanum tuberosum and Solanum berthaultii. Molecular Breeding 6: 25–36. (Availavble online at: http://dx.doi.org/10.1023/A:1009648408198) (verified 1 June 2012).
• Frary, A., T. M. Fulton, D. Zamir, and S. D. Tanksley. 2004. Advanced backcross QTL analysis of a Lycopersicum esculentum x L. pennellii cross and identification of possible orthologs in the Solanaceae. Theoretical and Applied Genetics 108: 485–496 (Available onilne at: http://dx.doi.org/10.1007/s00122-003-1422-x) (verified 1 June 2012).
• Lewis, R. S.,and C. Rose. 2010. Agronomic performance of tobacco mosaic virus-resistant tobacco lines and hybrids possessing the resistance gene N introgressed on different chromosomes. Crop Science 50: 1339–1347 (Available online at: http://dx.doi.org/10.2135/cropsci2009.10.0615) (verified 1 June 2012).
• Miklas, P. N. 2007. Marker-assisted backcrossing QTL for partial resistance to sclerotinia white mold in dry bean. Crop Science 47: 935–942 (Available online at: http://dx.doi.org/10.2135/cropsci2006.08.0525) (verified 1 June 2012).
• Oberhagemann, P., C. Chatot-Balandras, R. Schafer-Pregl, D. Wegener, C. Palomino, F. Salamini, E. Bonnel, and C. Gebhardt. 1999. A genetic analysis of quantitative resistance to late blight in potato: Towards marker-assisted selection. Molecular Breeding 5: 399–415. (Availavble online at: http://dx.doi.org/10.1023/A:1009623212180) (verified 4 June 2012).
• Warrington, C. V., S. Zhu, W. A. Parrott, J. N. All, and H. R. Boerma. 2008. Seed yield of near-isogenic soybean lines with introgressed quantitative trait loci conditioning resistance to corn earworm (Lepidoptera: Noctuidae) and soybean looper (Lepidoptera: Noctuidae) from PI 229358. Journal of Economic Entomology 101: 1471–1477. (Availavble online at: http://dx.doi.org/10.1603/0022-0493(2008)101[1471:SYONSL]2.0.CO;2) (verified 1 June 2012).

External Links

• Shermin, J.,and D. Quinn. Genetic mapping [Online lesson]. Wheat CAP: Wheat Coordinated Agricultural Project. Available at: http://maswheat.ucdavis.edu/Education/animations/anim_mapping.htm (verified 1 June 2012).

Funding Statement

Development of this page was supported in part by the National Institute of Food and Agriculture (NIFA) Solanaceae Coordinated Agricultural Project, agreement 2009-85606-05673, administered by Michigan State University. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the United States Department of Agriculture.

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