Improving lodgepole pine genomic evaluation using spatial correlation structure and SNP selection with single-step GBLUP.
Journal
Heredity
ISSN: 1365-2540
Titre abrégé: Heredity (Edinb)
Pays: England
ID NLM: 0373007
Informations de publication
Date de publication:
04 2022
04 2022
Historique:
received:
17
05
2021
accepted:
28
01
2022
revised:
27
01
2022
pubmed:
20
2
2022
medline:
9
4
2022
entrez:
19
2
2022
Statut:
ppublish
Résumé
Modeling environmental spatial heterogeneity can improve the efficiency of forest tree genomic evaluation. Furthermore, genotyping costs can be lowered by reducing the number of markers needed. We investigated the impact on variance components, breeding value accuracy, and bias of two phenotypic data adjustments (experimental design and autoregressive spatial models), and a relationship matrix calculated from a subset of markers selected for their ability to infer ancestry. Using a multiple-trait multiple-site single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) approach, four scenarios (2 phenotype adjustments × 2 marker sets) were applied to diameter at breast height (DBH), height (HT), and resistance to western gall rust (WGR) in four open-pollinated progeny trials of lodgepole pine, with 1490 (out of 11,188) trees genotyped with 25,099 SNPs. As a control, we fitted the conventional ABLUP model using pedigree information. The highest heritability estimates were achieved for the ABLUP followed closely by the ssGBLUP with the full marker set and using the spatial phenotype adjustments. The highest predictive ability was obtained by using a reduced marker subset (8000 SNPs) when either the spatial (DBH: 0.429, and WGR: 0.513) or design (HT: 0.467) phenotype corrections were used. No significant difference was detected in prediction bias among the six fitted models, and all values were close to 1 (0.918-1.014). Results demonstrated that selecting informative markers, such as those capturing ancestry, can improve the predictive ability. The use of spatial correlation structure increased traits' heritability and reduced prediction bias, while increases in predictive ability were trait-dependent.
Identifiants
pubmed: 35181761
doi: 10.1038/s41437-022-00508-2
pii: 10.1038/s41437-022-00508-2
pmc: PMC8986842
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
209-224Informations de copyright
© 2022. The Author(s), under exclusive licence to The Genetics Society.
Références
Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, Lawlor TJ (2010) Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J Dairy Sci 93:743–752
pubmed: 20105546
doi: 10.3168/jds.2009-2730
Anderegg WRL, Hicke JA, Fisher RA, Allen CD, Aukema J, Bentz B et al. (2015) Tree mortality from drought, insects, and their interactions in a changing climate. N Phytol 208:674–683
doi: 10.1111/nph.13477
Arenas S, Cortés AJ, Mastretta-Yanes A, Jaramillo-Correa JP (2021) Evaluating the accuracy of genomic prediction for the management and conservation of relictual natural tree populations. Tree Genet Genomes 17:12
doi: 10.1007/s11295-020-01489-1
Bernal-Vasquez A-M, Möhring J, Schmidt M, Schönleben M, Schön C-C, Piepho H-P (2014) The importance of phenotypic data analysis for genomic prediction-a case study comparing different spatial models in rye. BMC Genomics 15:646
pubmed: 25087599
pmcid: 4133075
doi: 10.1186/1471-2164-15-646
Bernatzky R, Mulcahy DL (1992) Marker-aided selection in a backcross breeding program for resistance to chestnut blight in the American chestnut. Can J Res 22:1031–1035
doi: 10.1139/x92-137
Bohra A, Chand Jha U, Godwin ID, Kumar Varshney R (2020) Genomic interventions for sustainable agriculture. Plant Biotechnol J 18:2388–2405
pubmed: 32875704
pmcid: 7680532
doi: 10.1111/pbi.13472
Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320:1444–1449
pubmed: 18556546
doi: 10.1126/science.1155121
Calleja-Rodriguez A, Pan J, Funda T, Chen Z, Baison J, Isik F et al. (2020) Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine. BMC Genomics 21:796
pubmed: 33198692
pmcid: 7667760
doi: 10.1186/s12864-020-07188-4
Callister AN, Bradshaw BP, Elms S, Gillies RAW, Sasse JM, Brawner JT (2021) Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus globulus Labill. G3 Genes|Genomes|Genetics 11:jkab253
Cappa EP, Cantet RJC (2007) Bayesian estimation of a surface to account for a spatial trend using penalized splines in an individual-tree mixed model. Can J For Res 2677–2688
Cappa EP, El-Kassaby YA, Muñoz F, Garcia MN, Villalba PV, Klápště J et al. (2017) Improving accuracy of breeding values by incorporating genomic information in spatial-competition mixed models. Mol Breed 37:125
doi: 10.1007/s11032-017-0725-6
Cappa EP, El-Kassaby YA, Muñoz F, Garcia MN, Villalba PV, Klápště J, et al. (2018) Genomic-based multiple-trait evaluation in Eucalyptus grandis using dominant DArT markers. Plant Sci 271:27–33
Cappa EP, de Lima BM, da Silva-Junior OB, Garcia CC, Mansfield SD, Grattapaglia D (2019) Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP. Plant Sci 284:9–15
pubmed: 31084883
doi: 10.1016/j.plantsci.2019.03.017
Cappa EP, Muñoz F, Sanchez L (2019) Performance of alternative spatial models in empirical Douglas-fir and simulated datasets. Ann For Sci 76:16
Cappa EP, Yanchuk AD, Cartwright CV (2012) Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials. Ann For Sci 69:627–640
Chateigner A, Lesage-Descauses MC, Rogier O, Jorge V, Leplé JC, Brunaud V et al. (2020) Gene expression predictions and networks in natural populations supports the omnigenic theory. BMC Genomics 21:416
pubmed: 32571208
pmcid: 7310122
doi: 10.1186/s12864-020-06809-2
Chen ZQ, Baison J, Pan J, Karlsson B, Gull BA, Westin J, et al. (2018) Accuracy of genomic selection for growth and wood quality traits in two control - pollinated progeny trials using exome capture as genotyping platform in Norway spruce. BMC Genom 19:946
Chen C, Mitchell SE, Elshire RJ, Buckler ES, El-Kassaby YA (2013) Mining conifers’ mega-genome using rapid and efficient multiplexed high-throughput genotyping-by-sequencing (GBS) SNP discovery platform. Tree Genet Genomes 9:1537–1544
doi: 10.1007/s11295-013-0657-1
Chhetri HB, Macaya-Sanz D, Kainer D, Biswal AK, Evans LM, Chen J-G et al. (2019) Multitrait genome-wide association analysis of Populus trichocarpa identifies key polymorphisms controlling morphological and physiological traits. N Phytol 223:293–309
doi: 10.1111/nph.15777
Christensen OF, Lund MS (2010) Genomic prediction when some animals are not genotyped. Genet Sel Evol 42:2
pubmed: 20105297
pmcid: 2834608
doi: 10.1186/1297-9686-42-2
Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G (2012) Single-step methods for genomic evaluation in pigs. Animal 6:1565–1571
pubmed: 22717310
doi: 10.1017/S1751731112000742
Coops NC, Waring RH (2011) A process-based approach to estimate lodgepole pine (Pinus contorta Dougl.) distribution in the Pacific Northwest under climate change. Clim Change 105:313–328
doi: 10.1007/s10584-010-9861-2
Cortés AJ, Restrepo-Montoya M, Bedoya-Canas LE (2020) Modern strategies to assess and breed forest tree adaptation to changing climate. Front Plant Sci 11:1606
doi: 10.3389/fpls.2020.583323
Costa e Silva J, Dutkowski GW, Gilmour AR (2001) Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual. Can J Res 31:1887–1893
doi: 10.1139/x01-123
Dhir NK (1983) Development of genetically improved strains of lodgepole pine seed for reforestation in Alberta. In: USDA Forest Service (ed) Lodgepole pine: regeneration and management., General Technical Report, PNW-157: 20–22, p 20
Dutkowski GW, Costa E, Silva J, Gilmour AR, Wellendorf H, Aguiar A (2006) Spatial analysis enhances modelling of a wide variety of traits in forest genetic trials. Can J Res 36:1851–1870
doi: 10.1139/x06-059
Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES et al. (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6:1–10
doi: 10.1371/journal.pone.0019379
FGRMS (2016) Alberta forest genetic resource management and conservation standards. Alberta Agriculture and Forestry, Government of Alberta, Edmonton, Alberta, 158
Gamal El-Dien O, Ratcliffe B, Klapste J, Chen C, Porth I, El-Kassaby YA et al. (2015) Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing. BMC Genomics 16:370
pubmed: 25956247
pmcid: 4424896
doi: 10.1186/s12864-015-1597-y
Gamal El-Dien O, Ratcliffe B, Klápště J, Porth I, Chen C, El-Kassaby YA (2016) Implementation of the realized genomic relationship matrix to open-pollinated white spruce family testing for disentangling additive from nonadditive genetic effects. G3; Genes|Genomes|Genet 6:743–753
pubmed: 26801647
pmcid: 4777135
doi: 10.1534/g3.115.025957
Gianola D, Norton HW (1981) Scaling threshold characters. Genetics 99:357–364
pubmed: 17249122
pmcid: 1214506
doi: 10.1093/genetics/99.2.357
Gilmour AR, Cullis BR, Verbyla AP, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2:269
doi: 10.2307/1400446
Grattapaglia D, Resende MDV (2011) Genomic selection in forest tree breeding. Tree Genet Genomes 7:241–255
doi: 10.1007/s11295-010-0328-4
Grattapaglia D, Silva-Junior OB, Resende RT, Cappa EP, Müller BSF, Tan B et al. (2018) Quantitative genetics and genomics converge to accelerate forest tree breeding. Front Plant Sci 9:1–10
doi: 10.3389/fpls.2018.01693
Habier D, Fernando RL, Garrick DJ (2013) Genomic BLUP decoded: a look into the black box of genomic prediction. Genetics 194:597–607
pubmed: 23640517
pmcid: 3697966
doi: 10.1534/genetics.113.152207
Henderson CR (1984) Applications of linear models in animal breeding. University of Guelph, Guelph
Holliday JA, Wang T, Aitken S (2013) Predicting adaptive phenotypes from multilocus genotypes in sitka spruce (Picea sitchensis) using random forest. G3#58; Genes|Genomes|Genet 2:1085–1093
doi: 10.1534/g3.112.002733
John S, Sadoway S (2019) Region C lodgepole pine controlled parentage program plan seed orchards G284 and G827. West Fraser Mills Ltd, Blue Ridge Lumber Inc. Alberta, Canada
Klápště J, Dungey HS, Graham NJ, Telfer EJ (2020) Effect of trait’s expression level on single-step genomic evaluation of resistance to Dothistroma needle blight. BMC Plant Biol 20:205
pubmed: 32393229
pmcid: 7216529
doi: 10.1186/s12870-020-02403-6
Klápště J, Dungey HS, Telfer EJ, Suontama M, Graham NJ, Li Y et al. (2020) Marker selection in multivariate genomic prediction improves accuracy of low heritability traits. Front Genet 11:1240
doi: 10.3389/fgene.2020.499094
Klápště J, Suontama M, Dungey HS, Telfer EJ, Graham NJ, Low CB et al. (2018) Effect of hidden relatedness on single-step genetic evaluation in an advanced open-pollinated breeding program. J Hered 109:802–810
pubmed: 30285150
pmcid: 6208454
Lado B, Matus I, Rodriguez A, Inostroza L, Poland J, Belzile F et al. (2013) Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data. G3 Genes, Genomes, Genet 3:2105–2114
Legarra A, Aguilar I, Misztal I (2009) A relationship matrix including full pedigree and genomic information. J Dairy Sci 92:4656–4663
pubmed: 19700729
doi: 10.3168/jds.2009-2061
Legarra A, Christensen OF, Aguilar I, Misztal I (2014) Single Step, a general approach for genomic selection. Livest Sci 166:54–65
doi: 10.1016/j.livsci.2014.04.029
Legarra A, Robert-Granié C, Manfredi E, Elsen JM (2008) Performance of genomic selection in mice. Genetics 180:611–618
pubmed: 18757934
pmcid: 2535710
doi: 10.1534/genetics.108.088575
Lenz PRN, Beaulieu J, Mansfield SD, Clément S, Desponts M, Bousquet J (2017) Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana). BMC Genomics 18:335
pubmed: 28454519
pmcid: 5410046
doi: 10.1186/s12864-017-3715-5
Lenz PRN, Nadeau S, Mottet MJ, Perron M, Isabel N, Beaulieu J et al. (2020) Multi-trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce. Evol Appl 13:76–94
pubmed: 31892945
doi: 10.1111/eva.12823
Ling AS, Hay EH, Aggrey SE, Rekaya R (2021) Dissection of the impact of prioritized QTL-linked and -unlinked SNP markers on the accuracy of genomic selection(1). BMC Genom data 22:26
pubmed: 34380418
pmcid: 8356450
doi: 10.1186/s12863-021-00979-y
de los Campos G, Vazquez AI, Fernando R, Klimentidis YC, Sorensen D (2013) Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genet 9:e1003608
Lourenco D, Legarra A, Tsuruta S, Masuda Y, Aguilar I, Misztal I (2020) Single-step genomic evaluations from theory to practice: using snp chips and sequence data in blupf90. Genes (Basel) 11:1–32
doi: 10.3390/genes11070790
Mao X, Dutta S, Wong RKW, Nettleton D (2020) Adjusting for spatial effects in genomic prediction. J Agric Biol Environ Stat 25:699–718
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829
pubmed: 11290733
pmcid: 1461589
doi: 10.1093/genetics/157.4.1819
Meuwissen T, Hayes B, Goddard M (2013) Accelerating improvement of livestock with genomic selection. Annu Rev Anim Biosci 1:221–237
pubmed: 25387018
doi: 10.1146/annurev-animal-031412-103705
Misztal I, Legarra A, Aguilar I (2009) Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci 92:4648–4655
pubmed: 19700728
doi: 10.3168/jds.2009-2064
Misztal I, Tsuruta S, Lourenco D, Aguilar I, Legarra A, Vitezica Z (2018) Manual for BLUPF90 family of programs. University of Georgia, Athens, USA, 125
Mphahlele MM, Isik F, Hodge GR, Myburg AA (2021) Genomic breeding for diameter growth and tolerance to leptocybe gall wasp and botryosphaeria/teratosphaeria fungal disease complex in Eucalyptus grandis. Front Plant Sci 12:228
doi: 10.3389/fpls.2021.638969
Mphahlele MM, Isik F, Mostert-O’Neill MM, Reynolds SM, Hodge GR, Myburg AA (2020) Expected benefits of genomic selection for growth and wood quality traits in Eucalyptus grandis. Tree Genet Genomes 16:1–12
doi: 10.1007/s11295-020-01443-1
Muñoz F, Sanchez L (2020) breedR: Statistical methods for forest genetic resources analysts. R package version 0.12–4. https://github.com/famuvie/breedR
Muranty H, Jorge V, Bastien C, Lepoittevin C, Bouffier L, Sanchez L (2014) Potential for marker-assisted selection for forest tree breeding: lessons from 20 years of MAS in crops. Tree Genet Genomes 10:1491–1510
doi: 10.1007/s11295-014-0790-5
Nagano S, Hirao T, Takashima Y, Matsushita M, Mishima K, Takahashi M, et al. (2020) SNP genotyping with target amplicon sequencing using a multiplexed primer panel and its application to genomic prediction in Japanese cedar, Cryptomeria japonica (L.f.) D.Don. Forests 11:898
Paludeto JGZ, Grattapaglia D, Estopa RA, Tambarussi EV (2021) Genomic relationship–based genetic parameters and prospects of genomic selection for growth and wood quality traits in Eucalyptus benthamii. Tree Genet Genomes 17:38
doi: 10.1007/s11295-021-01516-9
Qaim M (2020) Role of new plant breeding technologies for food security and sustainable agricultural development. Appl Econ Perspect Policy 42:129–150
doi: 10.1002/aepp.13044
Ratcliffe B, El-Dien OG, Klápště J, Porth I, Chen C, Jaquish B et al. (2015) A comparison of genomic selection models across time in interior spruce (Picea engelmannii × glauca) using unordered SNP imputation methods. Heredity (Edinb) 115:547–555
doi: 10.1038/hdy.2015.57
Ratcliffe B, Gamal El-Dien O, Cappa EP, Porth I, Klapste J, Chen C, et al. (2017) Single-step BLUP with varying genotyping effort in open-pollinated picea glauca. G3 Genes|Genomes|Genetics 7:935–942
Resende MFR, Munoz P, Resende MD, Garrick DJ, Fernando RL, Davis JM et al. (2012) Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 190:1503–1510
pubmed: 22271763
pmcid: 3316659
doi: 10.1534/genetics.111.137026
Resende RT, Resende MDV, Silva FF, Azevedo CF, Takahashi EK, Silva-Junior OB, et al. (2017) Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model. Heredity 119:245–255
Rosenberg NA, Li LM, Ward R, Pritchard JK (2003) Informativeness of genetic markers for inference of ancestry. Am J Hum Genet 73:1402–1422
pubmed: 14631557
pmcid: 1180403
doi: 10.1086/380416
Rweyongeza DM (2016) A new approach to prediction of the age-age correlation for use in tree breeding. Ann Sci 73:1099–1111
doi: 10.1007/s13595-016-0570-5
Shalizi MN, Cumbie WP, Isik F (2021) Genomic prediction for fusiform rust disease incidence in a large cloned population of Pinus taeda. G3 Genes|Genomes|Genetics 11:jkab235
Tan B, Grattapaglia D, Martins GS, Ferreira KZ, Sundberg B, Ingvarsson PK (2017) Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids. BMC Plant Biol 17:110
pubmed: 28662679
pmcid: 5492818
doi: 10.1186/s12870-017-1059-6
Thavamanikumar S, Arnold RJ, Luo J, Thumma BR (2020) Genomic studies reveal substantial dominant effects and improved genomic predictions in an open-pollinated breeding population of Eucalyptus pellita. Genes|Genomes|Genet 10:g3.401601.2020
Thistlethwaite FR, El-Dien OG, Ratcliffe B, Klápště J, Porth I, Chen C et al. (2020) Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species. PLoS One 15:1–14
doi: 10.1371/journal.pone.0232201
Thomas B, El-Kassaby Y, Cappa E, Klutsch J, Ullah A, Erbilgin N (2019) Genome Canada’s RES-FOR project: Genomic selection for white spruce and lodgepole pine – linking phenotypes and genotypes. In: Genomic Selection for white spruce and lodgepole pine - linking phenotypes and genotypes, Quebec, Canada
Tsai HY, Cericola F, Edriss V, Andersen JR, Orabi J, Jensen JD et al. (2020) Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data. PLoS One 15:1–14
doi: 10.1371/journal.pone.0232665
Ukrainetz NK, Mansfield SD (2020a) Prediction accuracy of single-step BLUP for growth and wood quality traits in the lodgepole pine breeding program in British Columbia. Tree Genet Genomes 16:1–13
doi: 10.1007/s11295-020-01456-w
Ukrainetz NK, Mansfield SD (2020b) Assessing the sensitivities of genomic selection for growth and wood quality traits in lodgepole pine using Bayesian models. Tree Genet Genomes 16:14
VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423
doi: 10.3168/jds.2007-0980
Varshney RK, Bohra A, Yu J, Graner A, Zhang Q, Sorrells ME (2021) Designing future crops: genomics-assisted breeding comes of age. Trends Plant Sci 26:631–649
pubmed: 33893045
doi: 10.1016/j.tplants.2021.03.010
Ward BP, Brown-Guedira G, Tyagi P, Kolb FL, van Sanford DA, Sneller CH et al. (2019) Multienvironment and multitrait genomic selection models in unbalanced early-generation wheat yield trials. Crop Sci 59:491–507
doi: 10.2135/cropsci2018.03.0189
Westfall J, Ebata T (2012) Summary of forest health conditions in British Columbia. Victoria, British Columbia
Ye TZ, Jayawickrama KJS (2008) Efficiency of using spatial analysis in first-generation coastal Douglas-fir progeny tests in the US Pacific Northwest. Tree Genet Genomes 4:677–692
doi: 10.1007/s11295-008-0142-4
Zhang J, Yang J, Zhang L, Luo J, Zhao H, Zhang J et al. (2020) A new SNP genotyping technology target SNP-seq and its application in genetic analysis of cucumber varieties. Sci Rep 10:5623
pubmed: 32221398
pmcid: 7101363
doi: 10.1038/s41598-020-62518-6