TY - JOUR
T1 - Genomic selection for genotype performance and stability using information on multiple traits and multiple environments
AU - Bančič, J.
AU - Ovenden, B.
AU - Gorjanc, G.
AU - Tolhurst, D. J.
N1 - Funding Information:
JB was funded by Scotland’s Rural College (SRUC) through a PhD studentship Research Excellence Grant. The Australian Rice Breeding Program is funded under the Australian Rice Partnership II project, a partnership between NSW Department of Primary Industries, AgriFutures and SunRice. GG was funded by the BBSRC ISP grant BBS/E/D/30002275 to The Roslin Institute and the BBSRC grant BB/R019940/1.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/4/7
Y1 - 2023/4/7
N2 - Key message: The inclusion of multiple traits and multiple environments within a partially separable factor analytic approach for genomic selection provides breeders with an informative framework to utilise genotype by environment by trait interaction for efficient selection. Abstract: This paper develops a single-stage genomic selection (GS) approach which incorporates information on multiple traits and multiple environments within a partially separable factor analytic framework. The factor analytic linear mixed model is an effective method for analysing multi-environment trial (MET) datasets, but has not been extended to GS for multiple traits and multiple environments. The advantage of using all information is that breeders can utilise genotype by environment by trait interaction (GETI) to obtain more accurate predictions across correlated traits and environments. The partially separable factor analytic linear mixed model (SFA-LMM) developed in this paper is based on a three-way separable structure, which includes a factor analytic matrix between traits, a factor analytic matrix between environments and a genomic relationship matrix between genotypes. A diagonal matrix is then added to enable a different genotype by environment interaction (GEI) pattern for each trait and a different genotype by trait interaction (GTI) pattern for each environment. The results show that the SFA-LMM provides a better fit than separable approaches and a comparable fit to non-separable and partially separable approaches. The distinguishing feature of the SFA-LMM is that it will include fewer parameters than all other approaches as the number of genotypes, traits and environments increases. Lastly, a selection index is used to demonstrate simultaneous selection for overall performance and stability. This research represents an important continuation in the advancement of plant breeding analyses, particularly with the advent of high-throughput datasets involving a very large number of genotypes, traits and environments.
AB - Key message: The inclusion of multiple traits and multiple environments within a partially separable factor analytic approach for genomic selection provides breeders with an informative framework to utilise genotype by environment by trait interaction for efficient selection. Abstract: This paper develops a single-stage genomic selection (GS) approach which incorporates information on multiple traits and multiple environments within a partially separable factor analytic framework. The factor analytic linear mixed model is an effective method for analysing multi-environment trial (MET) datasets, but has not been extended to GS for multiple traits and multiple environments. The advantage of using all information is that breeders can utilise genotype by environment by trait interaction (GETI) to obtain more accurate predictions across correlated traits and environments. The partially separable factor analytic linear mixed model (SFA-LMM) developed in this paper is based on a three-way separable structure, which includes a factor analytic matrix between traits, a factor analytic matrix between environments and a genomic relationship matrix between genotypes. A diagonal matrix is then added to enable a different genotype by environment interaction (GEI) pattern for each trait and a different genotype by trait interaction (GTI) pattern for each environment. The results show that the SFA-LMM provides a better fit than separable approaches and a comparable fit to non-separable and partially separable approaches. The distinguishing feature of the SFA-LMM is that it will include fewer parameters than all other approaches as the number of genotypes, traits and environments increases. Lastly, a selection index is used to demonstrate simultaneous selection for overall performance and stability. This research represents an important continuation in the advancement of plant breeding analyses, particularly with the advent of high-throughput datasets involving a very large number of genotypes, traits and environments.
UR - http://www.scopus.com/inward/record.url?scp=85152168660&partnerID=8YFLogxK
U2 - 10.1007/s00122-023-04305-1
DO - 10.1007/s00122-023-04305-1
M3 - Article
C2 - 37027029
AN - SCOPUS:85152168660
SN - 0040-5752
VL - 136
SP - 1
EP - 19
JO - Theoretical and Applied Genetics
JF - Theoretical and Applied Genetics
IS - 5
M1 - 104
ER -