Abstract
To increase the predictive accuracy of genomic selection, large-scale phenotypic and genomic data is needed, which results in enormous consumption of computational resources, hampering the feasibility of genomic selection in return. In order to address computational limitation, a rapid genomic selection method HEAPY|A is developed in this study, combining Haseman-Elston (HE) model and algorithm for proven and young (APY). The proposed approach utilizes (i) HE regression to estimate the heritability and then (ii) APY to solve the inverse of the large matrix in best linear prediction (BLP); both HE and APY can reduce the computational cost compared with conventional techniques. When the size of a core population is half of that of a large training population, GBLUP|A, HEAPY|A, and HEBLP|A have similar performance in simulation studies (the core population can be further reduced if the training population size is further increased). When the size of a core population is half of that of a small training population, the predictive accuracy of HEAPY|A is a little lower than that of GBLUP|A and HEBLP|A in simulation study and empirical data—an Arabidopsis thaliana F2 population. HEAPY|A helps in predicting large genomic selection dataset with comparable accuracy without significant expense of time seen in traditional genomic selection algorithm.
Change history
13 February 2020
In the original version of this article the co-corresponding author name is missing.
13 February 2020
In the original version of this article the co-corresponding author name is missing.
References
Chen GB (2014) Estimating heritability of complex traits from genome-wide association studies using IBS-based Haseman-Elston regression. Front Genet 5:107
Chen GB (2016) On the reconciliation of missing heritability for genome-wide association studies. Eur J Hum Genet 24:1810–1816
Goddard ME (2017) Can we make genomic selection 100% accurate? J Anim Breed Genet 134:287–288
Liu H, Chen GB (2017) A fast genomic selection approach for large genomic data. Theor Appl Genet 130:1277–1284
Masuda Y, Misztal I, Tsuruta S, Legarra A, Aguilar I, Lourenco DAL, Fragomeni BO, Lawlor TJ (2016) Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals. J Dairy Sci 99:1968–1974
Misztal I (2016) Inexpensive computation of the inverse of the genomic relationship matrix in populations with small effective population size. Genetics 202:401–409
Misztal I, Legarra A, Aguilar I (2014) Using recursion to compute the inverse of the genomic relationship matrix. J Dairy Sci 97:3943–3952
Pocrnic I, Lourenco DAL, Masuda Y, Misztal I (2016) Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species. Genet Sel Evol 48:82
Salomé PA, Bomblies K, Laitinen RAE, Yant L, Mott R, Weigel D (2011) Genetic architecture of flowering-time variation in Arabidopsis thaliana. Genetics 188:421–433
Spindel JE, McCouch SR (2016) When more is better: how data sharing would accelerate genomic selection of crop plants. New Phytol 212:814–826
Xu S, Zhu D, Zhang Q (2014) Predicting hybrid performance in rice using genomic best linear unbiased prediction. PNAS 111:12456–12461
Funding
This study was supported by the National Natural Science Foundation of China (31771392 to GBC) and Zhejiang Provincial People’s Hospital Research Startup (ZRY2018A004 to GBC).
Author information
Authors and Affiliations
Contributions
HL and GBC conceived and performed the study as well as wrote the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, H., Chen, GB. A rapid genomic selection method combining Haseman-Elston (HE) model and algorithm for proven and young (APY). Mol Breeding 40, 12 (2020). https://doi.org/10.1007/s11032-019-1095-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11032-019-1095-z