The impact of population structure on genomic prediction in stratified populations
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Impacts of population structure on the evaluation of genomic heritability and prediction were investigated and quantified using high-density markers in diverse panels in rice and maize.
Population structure is an important factor affecting estimation of genomic heritability and assessment of genomic prediction in stratified populations. In this study, our first objective was to assess effects of population structure on estimations of genomic heritability using the diversity panels in rice and maize. Results indicate population structure explained 33 and 7.5 % of genomic heritability for rice and maize, respectively, depending on traits, with the remaining heritability explained by within-subpopulation variation. Estimates of within-subpopulation heritability were higher than that derived from quantitative trait loci identified in genome-wide association studies, suggesting 65 % improvement in genetic gains. The second objective was to evaluate effects of population structure on genomic prediction using cross-validation experiments. When population structure exists in both training and validation sets, correcting for population structure led to a significant decrease in accuracy with genomic prediction. In contrast, when prediction was limited to a specific subpopulation, population structure showed little effect on accuracy and within-subpopulation genetic variance dominated predictions. Finally, effects of genomic heritability on genomic prediction were investigated. Accuracies with genomic prediction increased with genomic heritability in both training and validation sets, with the former showing a slightly greater impact. In summary, our results suggest that the population structure contribution to genomic prediction varies based on prediction strategies, and is also affected by the genetic architectures of traits and populations. In practical breeding, these conclusions may be helpful to better understand and utilize the different genetic resources in genomic prediction.
KeywordsQuantitative Trait Locus Population Structure Single Nucleotide Polymorphism Genetic Gain Genomic Prediction
The authors of the current manuscript would like to thank researchers and institutions who contributed to the development of the rice and maize diversity panels. In addition, the authors would like to express gratitude to the editor and three anonymous reviewers for their detailed input in assessment and improvement of the manuscript.
Conflict of interest
The authors declare that they have no conflict of interest.
- Beavis WD (1994) QTL analysis: power, precision and accuracy. In: Paterson AH (ed) Molecular dissection of complex traits. CRC Press, Boca Raton, pp 145–162Google Scholar
- Crossa J, de los Campos G, Pérez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J, Arief V, Banziger M, Braun HJ (2010) Predictions of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724PubMedCrossRefGoogle Scholar
- Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Prentice Hall, LondonGoogle Scholar
- Mujibi FDN, Nkumah JD, Durunna ON, Stothard P, Mah J, Wang Z, Basarab J, Plastow G, Crews DH Jr, Moore SS (2011) Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle. J Dairy Sci 89:3353–3361Google Scholar
- Windhausen VS, Atlin CN, 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 predictions of maize hybrid performance in different breeding populations and environments. G3 2:1427–1436PubMedCrossRefGoogle Scholar
- Wolc A, Stricker C, Arango J, Settar P, Fulton JE, O’Sullivan NP, Preisinger R, Habier D, Fernardo R, Garrick D, Lamont SJ, Dekkers JCM (2011) Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model. Genet Sel Evol 43:5PubMedCentralPubMedCrossRefGoogle Scholar
- Zhao KY, Tung CW, Eizenga GC, Wright MH, Ali L, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2:467PubMedCentralPubMedCrossRefGoogle Scholar