Abstract
Key message
Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection to accelerate improvement in grain end-use quality traits of wheat.
Abstract
Grain end-use quality traits are among the most important in wheat breeding. These traits are difficult to breed for, as their assays require flour quantities only obtainable late in the breeding cycle, and are expensive. These traits are therefore an ideal target for genomic selection. However, large reference populations are required for accurate genomic predictions, which are challenging to assemble for these traits for the same reasons they are challenging to breed for. Here, we use predictions of end-use quality derived from near infrared (NIR) or nuclear magnetic resonance (NMR), that require very small amounts of flour, as well as end-use quality measured by industry standard assay in a subset of accessions, in a multi-trait approach for genomic prediction. The NIR and NMR predictions were derived for 19 end-use quality traits in 398 accessions, and were then assayed in 2420 diverse wheat accessions. The accessions were grown out in multiple locations and multiple years, and were genotyped for 51208 SNP. Incorporating NIR and NMR phenotypes in the multi-trait approach increased the accuracy of genomic prediction for most quality traits. The accuracy ranged from 0 to 0.47 before the addition of the NIR/NMR data, while after these data were added, it ranged from 0 to 0.69. Genomic predictions were reasonably robust across locations and years for most traits. Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection for grain end-use quality traits in wheat breeding.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
AACC-76-21 (2000) General pasting method for wheat or rye flour or starch using the Rapid Visco Analyser, AACC International. Approved methods of analysis, 10th edn. AACC International, St Paul
Baker RJ, Tipples KH, Campbell AB (1971) Heritabilities of and correlations among quality traits in wheat. Can J Plant Sci 51:441–448
Barnard A, Labuschagne M, van Niekerk H (2002) Heritability estimates of bread wheat quality traits in the Western Cape province of South Africa. Euphytica 127:115
Battenfield SD, Guzmán C, Gaynor RC, Singh RP, Peña RJ, Dreisigacker S, Fritz AK, Poland JA (2016) Genomic selection for processing and end-use quality traits in the cimmyt spring bread wheat breeding program. Plant Genome 9. doi:10.3835/plantgenome2016.01.0005
Browning BL, Browning SR (2009) A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 84:210–223
CCD 07-06 (2010) Yellow alkaline noodles. Supplement Official Testing Methods of the Cereal Chemistry Division, 4th edn. Royal Australian Chemical Institute, North Melbourne
Chambers J, McKevitt NJ, Scudamore KA, Bowman CE (1989) Non-destructive determination of the moisture content of individual wheat grains by nuclear magnetic resonance. J Sci Food Agric 49:211–224
Chao S, Dubcovsky J, Dvorak J, Luo MC, Baenziger SP, Matnyazov R, Clark DR, Talbert LE, Anderson JA, Dreisigacker S, Glover K, Chen J, Campbell K, Bruckner PL, Rudd JC, Haley S, Carver BF, Perry S, Sorrells ME, Akhunov ED (2010) Population-and genome-specific patterns of linkage disequilibrium and SNP variation in spring and winter wheat (Triticum aestivum L.). BMC Genom 11:727
Charmet G, Storlie E, Oury FX, Laurent V, Beghin D, Chevarin L, Lapierre A, Perretant MR, Rolland B, Heumez E, Duchalais L, Goudemand E, Bordes J, Robert O (2014) Genome-wide prediction of three important traits in bread wheat. Mol Breed 34:1843–1852
Daetwyler HD, Villanueva B, Woolliams JA. (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3:e3395
Daetwyler HD, Swan AA, van der Werf JH, Hayes BJ (2012) Accuracy of pedigree and genomic predictions of carcass and novel meat quality traits in multi-breed sheep data assessed by cross-validation. Genet Sel Evol 44:33
Daetwyler HD, Bansal UK, Bariana HS, Hayden MJ, Hayes BJ (2014) Genomic prediction for rust resistance in diverse wheat landraces. Theor Appl Genet 127:1795–1803
de los Campos G, Sorensen D, Gianola D (2015) Genomic heritability: what is it? PLoS Genet 11:e1005048
Delwiche SR, Graybosch RA, Peterson CJ (1998) Predicting protein composition, biochemical properties, and dough-handling properties of hard red winter wheat flour by near-infrared reflectance. Cereal Chem J 75:412–416
Dowell FE, Maghirang EB, Xie F, Lookhart GL, Pierce PO, Seabourn BW, Bean SR, Heffner EL, Jannink J, Sorrells ME (2011) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome J 4:65–75
Gartner T et al (2009) Improved heterosis prediction by combining information on DNA-and metabolic markers. PLoS One 4:e5220
Gilmour AR, Gogel B, Cullis B, Thompson R, Butler D (2009) ASReml user guide release 3.0. VSN International Ltd, Hemel Hempstead
Goddard M (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136:245–257
Guo ZG, Magwire MM, Basten CJ, Xu ZY, Wang DL (2016) Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize. Theor Appl Genet 129:2413–2427
Guzman C, Peña RJ, Singh R, Autrique E, Dreisigacker S, Crossa J, Rutkoski J, Poland J, Battenfield S (2016) Wheat quality improvement at CIMMYT and the use of genomic selection on it. Appl Transl Genom 11:3–8
Habier D, Fernando RL, Garrick DJ (2013) Genomic BLUP decoded: a look into the black box of genomic prediction. Genetics 194:597–607
Holland JB, Nyquist WE, Cervantes-Martinez CT (2003) Estimating and interpreting heritability for plant breeding: an update. Plant Breed Rev 22:9–112
Lado N, Matus I, Rodríguez A, Inostroza L, Poland J, Belzile F, del Pozo A, Quincke M, Castro M, von Zitzewitz J (2013) Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data. G3 3:2105–2143
Ly D, Hamblin M, Rabbi I, Melaku G, Bakare M, Gauch HG et al (2013) Relatedness and genotype × environment interaction affect prediction accuracies in genomic selection: a study in Cassava. Crop Sci 53:1312–1325
Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829
O’Brien L, Ronalds JA (1987) Heritabilities of small-scale and standard measures of wheat quality for early generation selection. Aust J Agric Res 38:801–808
Panozzo JF, Walker CK, Partington DL, Neumann NC, Tausz M, Seneweera S, Fitzgerald GJ (2014) Elevated carbon dioxide changes grain protein concentration and composition and compromises baking quality. A FACE study. J Cereal Sci 60:461–470
Pearson DC, Rosielle AA, Boyd WJR (1981) Heritabilities of five wheat quality traits for early generation selection. Aust J Exp Agric 21:512–515
Poland J, Endelman J, Dawson J, Rutkoski J, Wu S et al (2012) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome J 5:1–11
Resende MF Jr, Muñoz P, Resende MD, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M (2012) Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 190:1503–1510
Riedelsheimer C et al (2012) Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet 44:217–220
Rutkoski JE, Poland J, Jannink JL, Sorrells ME (2013) Imputation of unordered markers and the impact on genomic selection accuracy. G3 3:427–439
Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña 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:e1004982
Van Ginkel M, Ogbonnaya F (2006) Invited paper: using synthetic wheats to breed cultivars better adapted to changing production conditions “Groundbreaking Stuff”. Edited by Neil Turner and Tina Acuna. In: Proceedings of the 13th Australian Agronomy Conference. 10–14 September 2006 Perth, Western Australia
VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423
Vazquez AI (2016) Increased proportion of variance explained and prediction accuracy of survival of breast cancer patients with use of whole-genome multiomic profiles. Genetics 203:1425–1438
Wang S, Wong D, Forrest K, Allen A, Chao S, Huang BE, Maccaferri M, Salvi S, Milner SG, Cattivelli L, Mastrangelo AM, Whan A, Stephen S, Barker G, Wieseke R, Plieske J, International Wheat Genome Sequencing Consortium, Lillemo M, Mather D, Appels R, Dolferus R, Brown-Guedira G, Korol A, Akhunova AR, Feuillet C, Salse J, Morgante M, Pozniak C, Luo MC, Dvorak J, Morell M, Dubcovsky J, Ganal M, Tuberosa R, Lawley C, Mikoulitch I, Cavanagh C, Edwards KJ, Hayden M, Akhunov E (2014) Characterization of polyploid wheat genomic diversity using a high-density 90,000 single nucleotide polymorphism array. Plant Biotechnol J 12(6):787–796
Ward J, Rakszegi M, Bedo Z, Shewry PR, Mackay I (2015) Differentially penalized regression to predict agronomic traits from metabolites and markers in wheat. BMC Genet 16:19
Wiggans GR, Vanraden PM, Cooper TA (2011) The genomic evaluation system in the United States: past, present, future. J Dairy Sci 94:3202–3211
Xu SZ, Xu Y, Gong L, Zhang QF (2016) Metabolomic prediction of yield in hybrid rice. Plant J 88:219–227
Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565–569
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest.
Funding
The authors are grateful for funding from Dow AgroSciences for some of the research in this study. The authors are grateful for funding from Department of Economic Development, Jobs, Transport and Resources for some of the research in this study.
Additional information
Communicated by Dr. Ian Mackay.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Hayes, B.J., Panozzo, J., Walker, C.K. et al. Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes. Theor Appl Genet 130, 2505–2519 (2017). https://doi.org/10.1007/s00122-017-2972-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00122-017-2972-7