Importance of correcting genomic relationships in single-locus QTL mapping model with an advanced backcross population
Mathew, Boby; Léon, Jens; Dadshani, Said; Pillen, Klaus; Sillanpää, Mikko J.; Naz, Ali Ahmad (2021-04-05)
Boby Mathew, Jens Léon, Said Dadshani, Klaus Pillen, Mikko J Sillanpää, Ali Ahmad Naz, Importance of correcting genomic relationships in single-locus QTL mapping model with an advanced backcross population, G3 Genes|Genomes|Genetics, Volume 11, Issue 6, June 2021, jkab105, https://doi.org/10.1093/g3journal/jkab105
© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe2021092146723
Tiivistelmä
Abstract
Advanced backcross (AB) populations have been widely used to identify and utilize beneficial alleles in various crops such as rice, tomato, wheat, and barley. For the development of an AB population, a controlled crossing scheme is used and this controlled crossing along with the selection (both natural and artificial) of agronomically adapted alleles during the development of AB population may lead to unbalanced allele frequencies in the population. However, it is commonly believed that interval mapping of traits in experimental crosses such as AB populations is immune to the deviations from the expected frequencies under Mendelian segregation. Using two AB populations and simulated data sets as examples, we describe the severity of the problem caused by unbalanced allele frequencies in quantitative trait loci mapping and demonstrate how it can be corrected using the linear mixed model having a polygenic effect with the covariance structure (genomic relationship matrix) calculated from molecular markers.
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