Optimising Genomic Selection in Wheat: Effect of Marker Density, Population Size and Population Structure on Prediction Accuracy

Abstract

Genomic selection applied to plant breeding enables earlier estimates of a line’s performance and significant reductions in generation interval. Several factors affecting prediction accuracy should be well understood if breeders are to harness genomic selection to its full potential. We used a panel of 10,375 bread wheat (Triticum aestivum) lines genotyped with 18,101 SNP markers to investigate the effect and interaction of training set size, population structure and marker density on genomic prediction accuracy. Through assessing the effect of training set size we showed the rate at which prediction accuracy increases is slower beyond approximately 2,000 lines. The structure of the panel was assessed via principal component analysis and K-means clustering, and its effect on prediction accuracy was examined through a novel cross-validation analysis according to the K-means clusters and breeding cohorts. Here we showed that accuracy can be improved by increasing the diversity within the training set, particularly when relatedness between training and validation sets is low. The breeding cohort analysis revealed that traits with higher selection pressure (lower allelic diversity) can be more accurately predicted by including several previous cohorts in the training set. The effect of marker density and its interaction with population structure was assessed for marker subsets containing between 100 and 17,181 markers. This analysis showed that response to increased marker density is largest when using a diverse training set to predict between poorly related material. These findings represent a significant resource for plant breeders and contribute to the collective knowledge on the optimal structure of calibration panels for genomic prediction.

Shown for reading. Its publisher allows quoting it, not republishing it.

References 48

1. 1.

   Auinger H. Schӧnleben M. Lehermeier C. Schmidt M. Korzun V., 2016 Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.). Theor. Appl. Genet. 129: 2043–2053.

   Not in our library10.1007/s00122-016-2756-5

2. 2.

   Ben Hassen M. Cao T. Bartholomé J. Orasen G. Colombi C., 2018 Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel. Theor. Appl. Genet. 131: 417–435.

   Not in our library10.1007/s00122-017-3011-4

3. 3.

   Bennett D. Izanloo A. Edwards J. Kuchel H. Chalmers K., 2012 a Identification of novel quantitative trait loci for days to ear emergence and flag leaf glaucousness in a bread wheat (Triticum aestivum L.) population adapted to southern Australian conditions. Theor. Appl. Genet. 124: 697–711.

   Not in our library10.1007/s00122-011-1740-3

4. 4.

   Bennett D. Izanloo A. Reynolds M. Kuchel H. Langridge P., 2012 b Genetic dissection of grain yield and physical grain quality in bread wheat (Triticum aestivum L.) under water-limited environments. Theor. Appl. Genet. 125: 255–271.

   Not in our library10.1007/s00122-012-1831-9

5. 5.

   Bentley A. Scutari M. Gosman N. Faure S. Bedford F., 2014 Applying association mapping and genomic selection to the dissection of key traits in elite European wheat. Theor. Appl. Genet. 127: 2619–2633.

   Not in our library10.1007/s00122-014-2403-y

6. 6.

   Butler D. Cullis B. Gilmour A. Gogel B., 2009 ASReml-R reference manual, Queensland Department of Primary Industries, Queensland, Australia.

   Not in our library

7. 7.

   Cane K. Eagles H. Laurie D. Trevaskis B. Vallance N., 2013 Ppd-B1 and Ppd-D1 and their effects in southern Australian wheat. Crop Pasture Sci. 64: 100–114.

   Not in our library10.1071/cp13086

8. 8.
   Genomic Prediction of Gene Bank Wheat Landraces · 2016

   No notice
9. 9.

   Daetwyler H. Calus M. Pong-Wong R. de los Campos G. Hickey J., 2013 Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking. Genetics 193: 347–365.

   Not in our library10.1534/genetics.112.147983

10. 10.

    Daetwyler H. Bansal U. Bariana H. Hayden M. Hayes B., 2014 Genomic prediction for rust resistance in diverse wheat landraces. Theor. Appl. Genet. 127: 1795–1803.

    Not in our library10.1007/s00122-014-2341-8

11. 11.

    de los Campos G. Hickey J. Pong-Wong R. Daetwyler H. Calus M., 2013 Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193: 327–345.

    Not in our library10.1534/genetics.112.143313

12. 12.

    Desta Z. Ortiz R., 2014 Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci. 19: 592–601.

    Not in our library10.1016/j.tplants.2014.05.006

13. 13.
    Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information · 2011

    No notice
14. 14.

    Gilmour A., 2007 Mixed model regression mapping for QTL detection in experimental crosses. Comput. Stat. Data Anal. 51: 3749–3764.

    Not in our library10.1016/j.csda.2006.12.031

15. 15.

    Gilmour A. Thompson R. Cullis B., 1995 Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 51: 1440–1450.

    Not in our library10.2307/2533274

16. 16.

    Gilmour A. Cullis B. Verbyla A., 1997 Accounting for natural and extraneous variation in the analysis of field experiments. J. Agric. Biol. Environ. Stat. 2: 269–293.

    Not in our library10.2307/1400446

17. 17.
    Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection · 2018

    No notice
18. 18.

    He S. Reif J. Korzun V. Bothe R. Ebmeyer E., 2017 Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to central europe. Theor. Appl. Genet. 130: 635–647.

    Not in our library10.1007/s00122-016-2840-x

19. 19.

    Heffner E. Sorrells M. Jannink J., 2009 Genomic selection for crop improvement. Crop Sci. 49: 1–12.

    Not in our library10.2135/cropsci2008.08.0512

20. 20.

    Heffner E. Jannink J. Iwata H. Souza E. Sorrells M., 2011 a Genomic selection accuracy for grain quality traits in biparental wheat populations. Crop Sci. 51: 2597–2606.

    Not in our library10.2135/cropsci2011.05.0253

21. 21.
    Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program · 2011

    Not checked
22. 22.

    Henderson C. R., 1953 Estimation of variance and covariance components. Biometrics 9: 226–252.

    Not in our library10.2307/3001853

23. 23.

    Heslot N. Yang H. Sorrells M. Jannink J., 2012 Genomic selection in plant breeding: a comparison of models. Crop Sci. 52: 146–160.

    Not in our library10.2135/cropsci2011.06.0297

24. 24.

    Hickey J. Dreisigacker S. Crossa J. Hearne S. Babu R., 2014 Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation. Crop Sci. 54: 1476–1488.

    Not in our library10.2135/cropsci2013.03.0195

25. 25.

    Huang B. George A. Forrest K. Kilian A. Hayden M., 2012 A multiparent advanced generation inter-cross population for genetic analysis in wheat. Plant Biotechnol. J. 10: 826–839.

    Not in our library10.1111/j.1467-7652.2012.00702.x

26. 26.

    Huang X. Cloutier S. Lycar L. Radovanovic N. Humphreys D., 2006 Molecular detection of QTLs for agronomic and quality traits in a doubled haploid population derived from two Canadian wheats (Triticum aestivum L.). Theor. Appl. Genet. 113: 753–766.

    Not in our library10.1007/s00122-006-0346-7

27. 27.
    Training set optimization under population structure in genomic selection · 2014

    No notice
28. 28.

    Jannink J. Lorenz A. Iwata H., 2010 Genomic selection in plant breeding: from theory to practice. Brief. Funct. Genomics 9: 166–177.

    Not in our library10.1093/bfgp/elq001

29. 29.

    Kuchel H. Williams K. Langridge P. Eagles H. Jefferies S., 2007 Genetic dissection of grain yield in bread wheat. I. QTL analysis. Theor. Appl. Genet. 115: 1029–1041.

    Not in our library10.1007/s00122-007-0629-7

30. 30.

    Liu Z. Seefried F. Reinhardt F. Rensing S. Thaller G., 2011 Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction. Genet. Sel. Evol. 43: 19

    Not in our library10.1186/1297-9686-43-19

31. 31.

    Lund M, Van den Berg I, Ma P, Brøndum R, Su G (2016) How to improve genomic predictions in small dairy cattle populations. Animal 10(6):1042–1049

    Not in our library10.1017/s1751731115003031

32. 32.

    Maphosa L. Langridge P. Taylor H. Parent B. Emebiri L., 2014 Genetic control of grain yield and grain physical characteristics in a bread wheat population grown under a range of environmental conditions. Theor. Appl. Genet. 127: 1607–1624.

    Not in our library10.1007/s00122-014-2322-y

33. 33.

    Meuwissen T. Hayes B. Goddard M., 2001 Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819–1829.

    Not in our library10.1093/genetics/157.4.1819

34. 34.

    Michel S. Ametz C. Gungor H. Akgöl B. Epure D., 2017 Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials. Theor. Appl. Genet. 130: 363–376.

    Not in our library10.1007/s00122-016-2818-8

35. 35.

    Muir W., 2007 Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J. Anim. Breed. Genet. 124: 342–355.

    Not in our library10.1111/j.1439-0388.2007.00700.x

36. 36.

    Nakaya A. Isobe S., 2012 Will genomic selection be a practical method for plant breeding? Ann. Bot. (Lond.) 110: 1303–1316.

    Not in our library10.1093/aob/mcs109

37. 37.

    Norman A. Taylor J. Tanaka E. Telfer P. Edwards J., 2017 Increased genomic prediction accuracy in wheat breeding using a large Australian panel. Theor. Appl. Genet. 130: 2543–2555.

    Not in our library10.1007/s00122-017-2975-4

38. 38.

    Patterson H. Thompson R., 1971 Recovery of inter-block information when block sizes are unequal. Biometrika 58: 545–554.

    Not in our library10.1093/biomet/58.3.545

39. 39.
    Population Structure and Eigenanalysis · 2006

    No notice
40. 40.

    Poland J. Endelman J. Dawson J. Rutkoski J. Wu S., 2012 Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5: 103–113.

    Not in our library10.3835/plantgenome2012.06.0006

41. 41.

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

    Not in our library10.1038/nrg2813

42. 42.

    R Core Team, (2017) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL https://www.R-project.org/

    Not in our library

43. 43.

    Snape J. Sarma R. Quarrie S. Fish L. Galiba G., 2001 Mapping genes for flowering time and frost tolerance in cereals using precise genetic stocks. Euphytica 120: 309–315.

    Not in our library10.1023/a:1017541505152

44. 44.

    Solberg T. R. Sonesson A. K. Woolliams J. A. Meuwissen T. H. 2008 Genomic selection using different marker types and densities. J. Anim. Sci. 86: 2447–2454.

    Not in our library10.2527/jas.2007-0010

45. 45.

    Sun X. Wu K. Zhao Y. Kong F. Han G., 2009 QTL analysis of kernel shape and weight using recombinant inbred lines in wheat. Euphytica 165: 615–624.

    Not in our library10.1007/s10681-008-9794-2

46. 46.

    VanRaden P., 2008 Efficient methods to compute genomic predictions. J. Dairy Sci. 91: 4414–4423.

    Not in our library10.3168/jds.2007-0980

47. 47.

    Zadoks J. Chang T. Konzak C. 1974 A decimal code for the growth stages of cereals. Weed Res. 14: 415–421.

    Not in our library10.1111/j.1365-3180.1974.tb01084.x

48. 48.

    Zhang Z. Erbe M. He J. Ober U. Gao N., 2015 Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix. G3: Genes, Genomes. Genetics 5: 615–627.

    Not in our library10.1534/g3.114.016261

Cited by papers we hold 3

• Chile Pepper (Capsicum) Breeding and Improvement in the “Multi-Omics” Era · 2022
  Not checked
• Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction · 2021
  Not checked
• Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits · 2019
  No notice

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.