Skip to main content
SpringerLink
Log in

Controlling population structure in the genomic prediction of tropical maize hybrids

  • Published:
Molecular Breeding Aims and scope Submit manuscript

Abstract

In tropical maize breeding programs where more than two heterotic groups are crossed, factors such as population structure (PS) can influence the achievement of reliable estimates of genomic breeding values (GEBVs) for complex traits. Hence, our objectives were (i) to investigate PS in a set of tropical maize inbreds and their derived hybrids, and (ii) to control PS in genomic predictions of single-crosses considering two scenarios: applying (1) the traditional GBLUP (GB) and four adjustment methods of PS in the whole group, and (2) homogeneous- (A-GB), within- (W-GB), multi- (MG-GB), and across-group (AC-GB) analysis in stratified groups. Three subpopulations were identified in the inbred lines and hybrids based on fineSTRUCTURE results. Adding four different sets of PS as covariates to the prediction model did not improve the predictive ability (r). However, using non-metric multidimensional scaling and fineSTRUCTURE group clustering increased the reliability of GEBV estimation for grain yield and plant height, respectively. The W-GB analysis in the stratified groups resulted in low r, mostly due to the reduction of training size. On the other hand, A-GB and MG-GB showed similar r for both traits. However, MG-GB presented higher broad sense genomic heritabilities compared to A-GB, efficiently controlling heterogeneity of marker effects between subpopulations. The r of the AC-GB method was low when predicting groups genetically distant. We conclude that predicting hybrid phenotypes by using PS covariates and multi-group analysis in stratified clusters may be an efficient method, increasing reliability and predictive ability, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Albrecht T, Auinger HJ, Wimmer V, Ogutu JO, Knaak C, Ouzunova M, Piepho HP, Schon CC (2014) Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years. Theor Appl Genet 127(6):1375–1386

    Article  Google Scholar 

  • Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19(9):1655–1664

    Article  CAS  Google Scholar 

  • Aschard H, Vilhjalmsson BJ, Joshi AD, Price AL, Kraft P (2015) Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am J Hum Genet 96(2):329–339

    Article  CAS  Google Scholar 

  • Azevedo CF, de Resende MDV, Silva FFE, Nascimento M, Viana JMS, Valente MSF (2017) Population structure correction for genomic selection through eigenvector covariates. Crop Breed Appl Biot 17(4):350–358

    Article  Google Scholar 

  • Bedoya CA, Dreisigacker S, Hearne S, Franco J, Mir C, Prasanna BM, Taba S, Charcosset A, Warburton ML (2017) Genetic diversity and population structure of native maize populations in Latin America and the Caribbean. PLoS One 12(4):e0173488

    Article  Google Scholar 

  • Bernardo R, Thompson AM (2016) Germplasm architecture revealed through chromosomal effects for quantitative traits in maize. Plant Genome 9(2):1–11

    Article  Google Scholar 

  • Braun HJ, Rajaram S, vanGinkel M (1996) CIMMYT's approach to breeding for wide adaptation. Euphytica 92(1–2):175–183

    Article  Google Scholar 

  • Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manual. Department of Primary Industries, Queensland

    Google Scholar 

  • Campoy JA, Lerigoleur-Balsemin E, Christmann H, Beauvieux R, Girollet N, Quero-Garcia J, Dirlewanger E, Barreneche T (2016) Genetic diversity, linkage disequilibrium, population structure and construction of a core collection of Prunus avium L. landraces and bred cultivars. BMC Plant Biol 16:49

    Article  Google Scholar 

  • Chen AH, Lipka AE (2016) The use of targeted marker subsets to account for population structure and relatedness in genome-wide association studies of maize (Zea mays L.). G3-Genes Genom Genet 6(8):2365–2374

    CAS  Google Scholar 

  • Chia JM, Song C, Bradbury PJ, Costich D, de Leon N, Doebley J, Elshire RJ, Gaut B, Geller L, Glaubitz JC, Gore M, Guill KE, Holland J, Hufford MB, Lai JS, Li M, Liu X, Lu YL, McCombie R, Nelson R, Poland J, Prasanna BM, Pyhajarvi T, Rong TZ, Sekhon RS, Sun Q, Tenaillon MI, Tian F, Wang J, Xu X, Zhang ZW, Kaeppler SM, Ross-Ibarra J, McMullen MD, Buckler ES, Zhang GY, Xu YB, Ware D (2012) Maize HapMap2 identifies extant variation from a genome in flux. Nat Genet 44(7):803–U238

    Article  CAS  Google Scholar 

  • Cros D, Denis M, Sanchez L, Cochard B, Flori A, Durand-Gasselin T, Nouy B, Omore A, Pomies V, Riou V, Suryana E, Bouvet JM (2015) Genomic selection prediction accuracy in a perennial crop: case study of oil palm (Elaeis guineensis Jacq.). Theor Appl Genet 128(3):397–410

    Article  Google Scholar 

  • Crossa J, Burgueno J, Dreisigacker S, Vargas M, Herrera-Foessel SA, Lillemo M, Singh RP, Trethowan R, Warburton M, Franco J, Reynolds M, Crouch JH, Ortiz R (2007) Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics 177(3):1889–1913

    Article  CAS  Google Scholar 

  • Crossa J, Campos Gde L, Perez P, Gianola D, Burgueno J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J, Arief V, Banziger M, Braun HJ (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186(2):713–724

    Article  CAS  Google Scholar 

  • Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal 1695(5):1–9

    Google Scholar 

  • da Silva TA, Cantagalli LB, Saavedra J, Lopes AD, Mangolin CA, Machado MDPD, Scapim CA (2015) Population structure and genetic diversity of Brazilian popcorn germplasm inferred by microsatellite markers. Electron J Biotechnol 18(3):181–187

    Article  Google Scholar 

  • Da Y, Wang CK, Wang SW, Hu G (2014) Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers. PLoS One 9(1):e87666

    Article  Google Scholar 

  • Daetwyler HD, Kemper KE, van der Werf JHJ, Hayes BJ (2012) Components of the accuracy of genomic prediction in a multi-breed sheep population. J Anim Sci 90(10):3375–3384

    Article  CAS  Google Scholar 

  • de los Campos G, Sorensen D (2014) On the genomic analysis of data from structured populations. J Anim Breed Genet 131(3):163–164

    Article  Google Scholar 

  • Edriss V, Gao YX, Zhang XC, Jumbo MB, Makumbi D, Olsen MS, Crossa J, Packard KC, Jannink JL (2017) Genomic prediction in a large African maize population. Crop Sci 57(5):2361–2371

    Article  Google Scholar 

  • Endelman JB (2015) Ridge regression and other kernels for genomic selection. rrBLUP-package Version: 44

  • Ertiro BT, Semagn K, Das B, Olsen M, Labuschagne M, Worku M, Wegary D, Azmach G, Ogugo V, Keno T, Abebe B, Chibsa T, Menkir A (2017) Genetic variation and population structure of maize inbred lines adapted to the mid-altitude sub-humid maize agro-ecology of Ethiopia using single nucleotide polymorphic (SNP) markers. BMC Genomics 18(1):777

    Article  Google Scholar 

  • Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14(8):2611–2620

    Article  CAS  Google Scholar 

  • Fan XM, Bi YQ, Zhang YD, Jeffers DP, Yao WH, Chen HM, Zhao LQ, Kang MS (2015) Use of the suwan1 heterotic group in maize breeding programs in southwestern China. Agron J 107(6):2353–2362

    Article  CAS  Google Scholar 

  • Fernandes EH, Schuster I, Scapim CA, Vieira ESN, Coan MMD (2015) Genetic diversity in elite inbred lines of maize and its association with heterosis. Genet Mol Res 14(2):6509–6517

    Article  CAS  Google Scholar 

  • Frichot E, Francois O (2015) LEA: an R package for landscape and ecological association studies. Methods Ecol Evol 6(8):925–929

    Article  Google Scholar 

  • Galinsky KJ, Bhatia G, Loh PR, Georgiev S, Mukherjee S, Patterson NJ, Price AL (2016) Fast principal-component analysis reveals convergent evolution of ADH1B in Europe and East Asia. Am J Hum Genet 98(3):456–472

    Article  CAS  Google Scholar 

  • Gorjanc G, Bijma P, Hickey JM (2015) Reliability of pedigree-based and genomic evaluations in selected populations. Genet Sel Evol 47:65

    Article  Google Scholar 

  • Granato ISC, Galli G, Couto EGD, Souza MBE, Mendonca LF, Fritsche R (2018) snpReady: a tool to assist breeders in genomic analysis. Mol Breed 38(8):102

  • Guo Z, Tucker DM, Basten CJ, Gandhi H, Ersoz E, Guo B, Xu Z, Wang D, Gay G (2014) The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127(3):749–762

    Article  Google Scholar 

  • Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177(4):2389–2397

    CAS  PubMed  PubMed Central  Google Scholar 

  • Hallauer AR, Carena MJ (2014) Adaptation of tropical maize germplasm to temperate environments. Euphytica 196(1):1–11

    Article  Google Scholar 

  • Hayes BJ, Bowman PJ, Chamberlain AC, Verbyla K, Goddard ME (2009) Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet Sel Evol 41:51

    Article  Google Scholar 

  • He S, Schulthess AW, Mirdita V, Zhao Y, Korzun V, Bothe R, Ebmeyer E, Reif JC, Jiang Y (2016) Genomic selection in a commercial winter wheat population. Theor Appl Genet 129(3):641–651

    Article  CAS  Google Scholar 

  • Huang M, Cabrera A, Hoffstetter A, Griffey C, Van Sanford D, Costa J, McKendry A, Chao S, Sneller C (2016) Genomic selection for wheat traits and trait stability. Theor Appl Genet 129(9):1697–1710

    Article  CAS  Google Scholar 

  • Iheshiulor OOM, Woolliams JA, Yu XJ, Wellmann R, Meuwissen THE (2016) Within- and across-breed genomic prediction using whole-genome sequence and single nucleotide polymorphism panels. Genet Sel Evol 48:15

    Article  Google Scholar 

  • Isidro J, Jannink JL, Akdemir D, Poland J, Heslot N, Sorrells ME (2015) Training set optimization under population structure in genomic selection. Theor Appl Genet 128(1):145–158

    Article  Google Scholar 

  • Jan HU, Abbadi A, Lucke S, Nichols RA, Snowdon RJ (2016) Genomic prediction of testcross performance in canola (Brassica napus). PLoS One 11(1):e0147769

    Article  Google Scholar 

  • Janss L, de Los Campos G, Sheehan N, Sorensen D (2012) Inferences from genomic models in stratified populations. Genetics 192(2):693–704

    Article  Google Scholar 

  • Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11:94

    Article  Google Scholar 

  • Jombart T, Collins C, Kamvar ZN, Lustrik R, Solymos P, Ahmed I, Jombart MT (2015) adegenet: exploratory analysis of genetic and genomic data. R Package Version 201

  • Karoui S, Carabano MJ, Diaz C, Legarra A (2012) Joint genomic evaluation of French dairy cattle breeds using multiple-trait models. Genet Sel Evol 44:39

    Article  Google Scholar 

  • Laborda PR, Oliveira KM, Garcia AAF, Paterniani MEA, de Souza AP (2005) Tropical maize germplasm: what can we say about its genetic diversity in the light of molecular markers? Theor Appl Genet 111(7):1288–1299

    Article  CAS  Google Scholar 

  • Lanes ECM, Viana JMS, Paes GP, Paula MFB, Maia C, Caixeta ET, Miranda GV (2014) Population structure and genetic diversity of maize inbreds derived from tropical hybrids. Genet Mol Res 13(3):7365–7376

    Article  CAS  Google Scholar 

  • Lawson DJ, Hellenthal G, Myers S, Falush D (2012) Inference of population structure using dense haplotype data. PLoS Genet 8(1):e1002453

    Article  CAS  Google Scholar 

  • Lehermeier C, Kramer N, Bauer E, Bauland C, Camisan C, Campo L, Flament P, Melchinger AE, Menz M, Meyer N, Moreau L, Moreno-Gonzalez J, Ouzunova M, Pausch H, Ranc N, Schipprack W, Schonleben M, Walter H, Charcosset A, Schon CC (2014) Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction. Genetics 198(1):3–16

    Article  Google Scholar 

  • Lehermeier C, Schon CC, de los Campos G (2015) Assessment of genetic heterogeneity in structured plant populations using multivariate whole-genome regression models. Genetics 201(1):323–337

    Article  CAS  Google Scholar 

  • Maenhout S, De Baets B, Haesaert G (2010) Prediction of maize single-cross hybrid performance: support vector machine regression versus best linear prediction. Theor Appl Genet 120(2):415–427

    Article  Google Scholar 

  • Massman JM, Gordillo A, Lorenzana RE, Bernardo R (2013) Genomewide predictions from maize single-cross data. Theor Appl Genet 126(1):13–22

    Article  Google Scholar 

  • Mendes MP, de Souza CL (2016) Genomewide prediction of tropical maize single-crosses. Euphytica 209(3):651–663

    Article  CAS  Google Scholar 

  • Molin D, Coelho CJ, Maximo DS, Ferreira FS, Gardingo JR, Matiello RR (2013) Genetic diversity in the germplasm of tropical maize landraces determined using molecular markers. Genet Mol Res 12(1):99–114

    Article  CAS  Google Scholar 

  • Mundim GB, Viana JMS, Maia C, Paes GP, DeLima RO, Valente MSF (2015) Inferring tropical popcorn gene pools based on molecular and phenotypic data. Euphytica 202(1):55–68

    Article  CAS  Google Scholar 

  • Navarro JAR, Wilcox M, Burgueno J, Romay C, Swarts K, Trachsel S, Preciado E, Terron A, Delgado HV, Vidal V, Ortega A, Banda AE, Montiel NOG, Ortiz-Monasterio I, Vicente FS, Espinoza AG, Atlin G, Wenzl P, Hearne S, Buckler ES (2017) A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nat Genet 49(6):970

    Article  CAS  Google Scholar 

  • Nelson PT, Krakowsky MD, Coles ND, Holland JB, Bubeck DM, Smith JSC, Goodman MM (2016) Genetic characterization of the North Carolina State University maize lines. Crop Sci 56(1):259–275

    Article  CAS  Google Scholar 

  • Newell MA, Jannink JL (2014) Genomic selection in plant breeding. In: Fleury D, Whitford R (eds) Crop breeding: methods and protocols. Humana Press/Springer, New Delhi, p 117–130

    Google Scholar 

  • Olson KM, VanRaden PM, Tooker ME (2012) Multibreed genomic evaluations using purebred Holsteins, jerseys, and Brown Swiss. J Dairy Sci 95(9):5378–5383

    Article  CAS  Google Scholar 

  • Orozco-Ramirez Q, Ross-Ibarra J, Santacruz-Varela A, Brush S (2016) Maize diversity associated with social origin and environmental variation in southern Mexico. Heredity 116(5):477–484

    Article  CAS  Google Scholar 

  • Oyekunle M, Badu-Apraku B, Hearne S, Franco J (2015) Genetic diversity of tropical early-maturing maize inbreds and their performance in hybrid combinations under drought and optimum growing conditions. Field Crop Res 170:55–65

    Article  Google Scholar 

  • Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2(12):e190

    Article  Google Scholar 

  • Perez P, de los Campos G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198(2):483–U463

    Article  Google Scholar 

  • Porto-Neto LR, Barendse W, Henshall JM, McWilliam SM, Lehnert SA, Reverter A (2015) Genomic correlation: harnessing the benefit of combining two unrelated populations for genomic selection. Genet Sel Evol 47:84

    Article  Google Scholar 

  • Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38(8):904–909

    Article  CAS  Google Scholar 

  • Price AL, Zaitlen NA, Reich D, Patterson N (2010) New approaches to population stratification in genome-wide association studies. Nat Rev Genet 11(7):459–463

    Article  CAS  Google Scholar 

  • Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575

    Article  CAS  Google Scholar 

  • Reif JC, Melchinger AE, Xia XC, Warburton ML, Hoisington DA, Vasal SK, Beck D, Bohn M, Frisch M (2003) Use of SSRs for establishing heterotic groups in subtropical maize. Theor Appl Genet 107(5):947–957

    Article  CAS  Google Scholar 

  • Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R, Altmann T, Stitt M, Willmitzer L, Melchinger AE (2012) Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet 44(2):217–220

    Article  CAS  Google Scholar 

  • Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink JL, Melchinger AE (2013) Genomic predictability of interconnected biparental maize populations. Genetics 194(2):493–503

    Article  Google Scholar 

  • Rincent R, Laloe D, Nicolas S, Altmann T, Brunel D, Revilla P, Rodriguez VM, Moreno-Gonzalez J, Melchinger A, Bauer E, Schoen CC, Meyer N, Giauffret C, Bauland C, Jamin P, Laborde J, Monod H, Flament P, Charcosset A, Moreau L (2012) Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics 192(2):715–728

    Article  CAS  Google Scholar 

  • Rincent R, Nicolas S, Bouchet S, Altmann T, Brunel D, Revilla P, Malvar RA, Moreno-Gonzalez J, Campo L, Melchinger AE, Schipprack W, Bauer E, Schoen CC, Meyer N, Ouzunova M, Dubreuil P, Giauffret C, Madur D, Combes V, Dumas F, Bauland C, Jamin P, Laborde J, Flament P, Moreau L, Charcosset A (2014) Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production. Theor Appl Genet 127(11):2313–2331

    Article  CAS  Google Scholar 

  • Rincent R, Charcosset A, Moreau L (2017) Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations. Theor Appl Genet 130(11):2231–2247

    Article  CAS  Google Scholar 

  • Roberts DW (2016) labdsv: ordination and multivariate analysis for ecology. R Package Version 16–1

  • Roorkiwal M, Rathore A, Das RR, Singh MK, Jain A, Srinivasan S, Gaur PM, Chellapilla B, Tripathi S, Li Y, Hickey JM, Lorenz A, Sutton T, Crossa J, Jannink JL, Varshney RK (2016) Genome-enabled prediction models for yield related traits in chickpea. Front Plant Sci 7:1666

    Article  Google Scholar 

  • Saatchi M, McClure MC, McKay SD, Rolf MM, Kim J, Decker JE, Taxis TM, Chapple RH, Ramey HR, Northcutt SL, Bauck S, Woodward B, Dekkers JCM, Fernando RL, Schnabel RD, Garrick DJ, Taylor JF (2011) Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genet Sel Evol 43:1–16

    Article  Google Scholar 

  • Santos JPR, Vasconcellos RCD, Pires LPM, Balestre M, Von Pinho RG (2016) Inclusion of dominance effects in the multivariate GBLUP model. PLoS One 11(4):1–21

    Google Scholar 

  • Schaefer CM, Bernardo R (2013) Population structure and single nucleotide polymorphism diversity of historical Minnesota maize inbreds. Crop Sci 53(4):1529

    Article  CAS  Google Scholar 

  • Schulz-Streeck T, Ogutu JO, Karaman Z, Knaak C, Piepho HP (2012) Genomic selection using multiple populations. Crop Sci 52(6):2453–2461

    Article  Google Scholar 

  • Sousa MBE, Cuevas J, Couto EGD, Perez-Rodriguez P, Jarquin D, Fritsche-Neto R, Burgueno J, Crossa J (2017) Genomic-enabled prediction in maize using kernel models with genotype x environment interaction. G3-Genes Genom Genet 7(6):1995–2014

    Google Scholar 

  • Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redona E, Atlin G, Jannink JL, McCouch SR (2015) Genomic selection and association mapping in Rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet 11(2):e1004982

    Article  Google Scholar 

  • Sukumaran S, Xiang W, Bean SR, Pedersen JF, Kresovich S, Tuinstra MR, Tesso TT, Hamblin MT, Yu J (2012) Association mapping for grain quality in a diverse sorghum collection. Plant Genome 5(3):126–135

    Article  CAS  Google Scholar 

  • Sukumaran S, Dreisigacker S, Lopes M, Chavez P, Reynolds MP (2015) Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theor Appl Genet 128(2):353–363

    Article  CAS  Google Scholar 

  • Technow F, Riedelsheimer C, Schrag TA, Melchinger AE (2012) Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects. Theor Appl Genet 125(6):1181–1194

    Article  Google Scholar 

  • Teixeira JEC, Weldekidan T, de Leon N, Flint-Garcia S, Holland JB, Lauter N, Murray SC, Xu W, Hessel DA, Kleintop AE, Hawk JA, Hallauer A, Wisser RJ (2015) Hallauer’s Tuson: a decade of selection for tropical-to-temperate phenological adaptation in maize. Heredity 114(2):229–240

    Article  CAS  Google Scholar 

  • Thomasen JR, Sorensen AC, Su G, Madsen P, Lund MS, Guldbrandtsen B (2013) The admixed population structure in Danish Jersey dairy cattle challenges accurate genomic predictions. J Anim Sci 91(7):3105–3112

    Article  CAS  Google Scholar 

  • Tucker G, Price AL, Berger B (2014) Improving the power of GWAS and avoiding confounding from population stratification with PC-Select. Genetics 197(3):1045–1049

    Article  Google Scholar 

  • Unterseer S, Bauer E, Haberer G, Seidel M, Knaak C, Ouzunova M, Meitinger T, Strom TM, Fries R, Pausch H, Bertani C, Davassi A, Mayer KFX, Schon CC (2014) A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array. BMC Genomics 15(823):1–15

    Google Scholar 

  • Unterseer S, Pophaly SD, Peis R, Westermeier P, Mayer M, Seidel MA, Haberer G, Mayer KFX, Ordas B, Pausch H, Tellier A, Bauer E, Schon CC (2016) A comprehensive study of the genomic differentiation between temperate Dent and Flint maize. Genome Biol 17:137

    Article  Google Scholar 

  • VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91(11):4414–4423

    Article  CAS  Google Scholar 

  • Ventura R, Larmer S, Schenkel FS, Miller SP, Sullivan P (2016) Genomic clustering helps to improve prediction in a multibreed population. J Anim Sci 94(5):1844–1856

    Article  CAS  Google Scholar 

  • Wang ML, Sukumaran S, Barkley NA, Chen ZB, Chen CY, Guo BZ, Pittman RN, Stalker HT, Holbrook CC, Pederson GA, Yu JM (2011) Population structure and marker-trait association analysis of the US peanut (Arachis hypogaea L.) mini-core collection. Theor Appl Genet 123(8):1307–1317

    Article  Google Scholar 

  • Wientjes YCJ, Bijma P, Vandenplas J, Calus MPL (2017) Multi-population genomic relationships for estimating current genetic variances within and genetic correlations between populations. Genetics 207(2):503–515

    PubMed  PubMed Central  Google Scholar 

  • Windhausen VS, Atlin GN, Hickey JM, Crossa J, Jannink JL, Sorrells ME, Raman B, Cairns JE, Tarekegne A, Semagn K, Beyene Y, Grudloyma P, Technow F, Riedelsheimer C, Melchinger AE (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3-Genes Genom Genet 2(11):1427–1436

    Google Scholar 

  • Wu YS, Vicente FS, Huang KJ, Dhliwayo T, Costich DE, Semagn K, Sudha N, Olsen M, Prasanna BM, Zhang XC, Babu R (2016) Molecular characterization of CIMMYT maize inbred lines with genotyping-by-sequencing SNPs. Theor Appl Genet 129(4):753–765

    Article  CAS  Google Scholar 

  • Yan JB, Shah T, Warburton ML, Buckler ES, McMullen MD, Crouch J (2009) Genetic characterization and linkage disequilibrium estimation of a global maize collection using SNP markers. PLoS One 4(12):e8451

    Article  Google Scholar 

  • Yang XH, Gao SB, Xu ST, Zhang ZX, Prasanna BM, Li L, Li JS, Yan JB (2011) Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize. Mol Breed 28(4):511–526

    Article  Google Scholar 

  • Yu JM, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38(2):203–208

    Article  CAS  Google Scholar 

  • Yu X, Li X, Guo T, Zhu C, Wu Y, Mitchell SE, Roozeboom KL, Wang D, Wang ML, Pederson GA, Tesso TT, Schnable PS, Bernardo R, Yu J (2016) Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nat Plants 2:16150

    Article  CAS  Google Scholar 

  • Zhang J, Song Q, Cregan PB, Jiang GL (2016) Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max). Theor Appl Genet 129(1):117–130

    Article  CAS  Google Scholar 

  • Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012) A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28(24):3326–3328

    Article  CAS  Google Scholar 

  • Zhou L, Lund MS, Wang Y, Su G (2014) Genomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices. J Anim Breed Genet 131(4):249–257

    Article  CAS  Google Scholar 

  • Zhu C, Yu J (2009) Nonmetric multidimensional scaling corrects for population structure in association mapping with different sample types. Genetics 182(3):875–888

    Article  Google Scholar 

Download references

Funding

This project was supported by the São Paulo Research Foundation-FAPESP (Process: 2013/24135-2; 2014/26326-2; 2015/14376-8) and Coordination for the Improvement of Higher Level Personnel (CAPES).

Author information

Authors and Affiliations

  1. Luiz de Queiroz College of Agriculture, Department of Genetics, University of São Paulo, Piracicaba, São Paulo, Brazil

    Danilo Hottis Lyra, Ítalo Stefanine Correia Granato, Pedro Patric Pinho Morais, Filipe Couto Alves, Anna Rita Marcondes dos Santos & Roberto Fritsche-Neto

  2. Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK

    Danilo Hottis Lyra

  3. Department of Agronomy, Iowa State University, Ames, IA, 50011, USA

    Xiaoqing Yu, Tingting Guo & Jianming Yu

Authors
  1. Danilo Hottis Lyra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ítalo Stefanine Correia Granato
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pedro Patric Pinho Morais
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Filipe Couto Alves
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anna Rita Marcondes dos Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaoqing Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tingting Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jianming Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Roberto Fritsche-Neto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Danilo Hottis Lyra.

Electronic supplementary material

Supplementary Fig. S1

Inference of group number in 128 tropical maize inbred lines. (A) 5-fold cross-validation error of ADMIXTURE, and (B) BIC values of k-means clustering. The dashed black line shows the number of groups inferred in each method. (PNG 1527 kb)

High Resolution (TIFF 235 kb)

Supplementary Fig. S2

ADMIXTURE results in 128 tropical maize inbred lines. Clustering assignments inferred in L7 (A), L6 (B), and L5 (C) groups. Individuals are represented by a single vertical line divided into L colored segments. White color separates the groups (L). (PNG 15804 kb)

High Resolution (TIFF 2488 kb)

Supplementary Fig. S3

Genomic analysis in 128 tropical maize inbred lines. (a) First two dimensions of nMDS and (b) pattern of linkage disequilibrium (LD) within 70 kb of distance among all pairs of markers (32 K) for 128 tropical maize inbreds, colored by fineSTRUCTURE group-clustering. (PNG 1360 kb)

High Resolution (TIFF 1716 kb)

Supplementary Fig. S4

Genomic analysis in 452 tropical maize single-cross hybrids. A) 3-D PCA score plot for the first three principal components. B) First two principal components of the Discriminant Analysis of Principal Components (DAPC). (PNG 191 kb)

High Resolution (TIFF 1123 kb)

Supplementary Fig. S5

Comparison of reliability (REL) for (a) grain yield and (b) plant height. GBLUP (GB) model and GB with four fixed covariates: principal components (GB + PC), nonmetric multidimensional scaling dimensions (GB + nMDS), admixture coefficients (GB + ADM), and fineSTRUCTURE group clustering (GB + FINE). Data are mean ± standard deviation (SD) estimated from fifty replications in independent validation. (PNG 1662 kb)

High Resolution (TIFF 391 kb)

Supplementary Fig. S6

Comparison of genomic heritability (\( {H}_g^2 \)) for grain yield. (a) GBLUP (GB) model and GB with four fixed covariates: principal components (GB + PC), nonmetric multidimensional scaling dimensions (GB + nMDS), admixture coefficients (GB + ADM), and fineSTRUCTURE group clustering (GB + FINE). (b) GB, homogeneous- (A-GBLUP), within- (W-GBLUP), and multi-group (MG-GBLUP) analysis for K1, K2, K3, K1 K2, K1 K3, and K2 K3 groups. (c) across-group (AC-GBLUP) analysis for nine prediction schemes. Data are mean ± standard deviation (SD) estimated from fifty replications in independent validation. (PNG 609 kb)

High Resolution (TIFF 260 kb)

Supplementary Fig. S7

Comparison of genomic heritability (\( {H}_g^2 \)) for plant height. (a) GBLUP (GB) model and GB with four fixed covariates: principal components (GB + PC), nonmetric multidimensional scaling dimensions (GB + nMDS), admixture coefficients (GB + ADM), and fineSTRUCTURE group clustering (GB + FINE). (b) GB, homogeneous- (A-GBLUP), within- (W-GBLUP), and multi-group (MG-GBLUP) analysis for K1, K2, K3, K1 K2, K1 K3, and K2 K3 groups. (c) across-group (AC-GBLUP) analysis for nine prediction schemes. Data are mean ± standard deviation (SD) estimated from fifty replications in independent validation. (PNG 629 kb)

High Resolution (TIFF 1541 kb)

Supplementary Fig. S8

Top principal components and comparison of prediction accuracy. (a) Percentage of variance explained by the principal components. The number of statistically significant (p < 0.05) principal components, measured by the Tracy-Widom statistic, is shown in the black region. (b) Barplot (mean ± SD) of prediction accuracy from GBLUP with 3, 5, 10, and 14 PCs as fixed covariates. (PNG 6327 kb)

High Resolution (TIFF 1634 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lyra, D.H., Granato, Í.S.C., Morais, P.P.P. et al. Controlling population structure in the genomic prediction of tropical maize hybrids. Mol Breeding 38, 126 (2018). https://doi.org/10.1007/s11032-018-0882-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11032-018-0882-2

Keywords

Search

Navigation