Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space.
Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel.
C.T. de Wit Graduate School for Production Ecology and Resource Conservation (PE&RC), Wageningen, The Netherlands.|Biometris, Wageningen University and Research, The Netherlands.
Daniela
http://orcid.org/0000-0003-3827-6726
D
Bustos-Korts
Biometris, Wageningen University and Research, The Netherlands.
Marcos
http://orcid.org/0000-0002-8150-1397
M
Malosetti
Commonwealth Scientific and Industrial Research Organisation (CSIRO) Agriculture, Queensland Bioscience Precinct, St. Lucia, Queensland 4067, Australia.
Scott
S
Chapman
Department of Agriculture and Food, Western Australia, South Perth, Western Australia 6151, Australia.
Ben
B
Biddulph
Biometris, Wageningen University and Research, The Netherlands fred.vaneeuwijk@wur.nl.
Fred
http://orcid.org/0000-0003-3672-2921
F
van Eeuwijk
GenPred; Genomic Selection; RKHS model; Shared Data Resources; genetic space; genomic prediction; population structure; training set
Genet Sel Evol. 2010 Feb 19;42:5
20170500
Genetics. 2008 Apr;178(4):2289-303
18430950
Theor Appl Genet. 2011 Jul;123(2):339-50
21505832
Genome Res. 2014 Sep;24(9):1550-7
24963154
Genetics. 2010 Jul;185(3):1021-31
20407128
J Anim Sci. 2012 Oct;90(10):3375-84
23038744
PLoS Genet. 2015 Feb 17;11(2):e1004982
25689273
Nat Commun. 2011 Sep 13;2:467
21915109
J Dairy Sci. 2012 Sep;95(9):5378-5383
22916944
Theor Appl Genet. 2011 Jul;123(2):283-92
21476040
Genetics. 2012 Oct;192(2):715-28
22865733
PLoS One. 2008;3(10):e3395
18852893
Genetics. 2012 Oct;192(2):693-704
22813891
Genetics. 2013 Feb;193(2):347-65
23222650
Front Plant Sci. 2014 Oct 09;5:537
25346745
Science. 2009 Nov 20;326(5956):1115-7
19965431
Theor Appl Genet. 2014 Nov;127(11):2313-31
25301321
Theor Appl Genet. 2012 Mar;124(4):769-76
22075809
Theor Appl Genet. 2006 Jul;113(2):206-24
16791688
J Agric Biol Environ Stat. 2015;20(4):467-490
26660276
Heredity (Edinb). 2005 Sep;95(3):221-7
16077740
PLoS One. 2011;6(12):e28334
22174790
J Dairy Sci. 2012 Jan;95(1):389-400
22192218
Genetics. 2016 Feb;202(2):799-823
26637542
Theor Appl Genet. 2011 May;122(8):1605-16
21373796
J Anim Sci. 2009 Jun;87(6):1883-7
19213705
Theor Appl Genet. 2013 Jan;126(1):133-41
22945268
Mol Genet Genomics. 2014 Feb;289(1):47-62
24213553
Theor Appl Genet. 2016 Nov;129(11):2043-2053
27480157
Theor Appl Genet. 2015 Jan;128(1):145-58
25367380
Genetics. 2014 May;197(1):375-87
24532779
J Exp Bot. 2014 Nov;65(20):5849-65
25148833
Bioinformatics. 2012 Aug 1;28(15):2086-7
22689388
Nat Genet. 2012 Jan 15;44(2):217-20
22246502
J Exp Bot. 2014 Nov;65(21):6191-204
24596174
Genet Res (Camb). 2009 Feb;91(1):47-60
19220931
Genetics. 2001 Apr;157(4):1819-29
11290733
J Exp Bot. 2013 Sep;64(12):3747-61
23873997
Science. 2009 Aug 7;325(5941):714-8
19661422
Genet Res (Camb). 2010 Aug;92(4):295-308
20943010
PLoS Genet. 2006 Dec;2(12):e190
17194218
G3 (Bethesda). 2012 Nov;2(11):1427-36
23173094
Theor Appl Genet. 2007 Feb;114(3):421-8
17180377
Theor Appl Genet. 2014 Mar;127(3):749-62
24452438
Genet Sel Evol. 2011 Nov 28;43:40
22122853
Theor Appl Genet. 2013 Feb;126(2):289-305
22983567
Theor Appl Genet. 2015 Jul;128(7):1297-306
25877519
Front Plant Sci. 2015 Nov 17;6:941
26635819
Genetics. 2000 Jun;155(2):945-59
10835412
Theor Appl Genet. 2006 Sep;113(5):809-19
16896718
Genetics. 2015 Oct;201(2):759-68
26219298
Theor Appl Genet. 2014 Jun;127(6):1375-86
24723140
Heredity (Edinb). 2014 Jan;112(1):48-60
23572121
Genetics. 2015 Sep;201(1):323-37
26122758
Genetics. 2010 Oct;186(2):713-24
20813882