Genetic diversity and trait genomic prediction in a pea diversity panel
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- 作者：Judith Burstin (1)
Pauline Salloignon (2)
Marianne Chabert-Martinello (1)
Jean-Bernard Magnin-Robert (1)
Mathieu Siol (1)
Fran莽oise Jacquin (1)
Aur茅lie Chauveau (1) (3)
Caroline Pont (4)
Gr茅goire Aubert (1)
Catherine Delaitre (1)
Caroline Truntzer (2)
G茅rard Duc (1)
1. UMR1347 ; Agroecology ; INRA ; 17 rue de Sully ; Dijon Cedex ; 21065 ; France
2. Clinical and Innovation Proteomic Platform (CLIPP) ; CHU Dijon ; Universit茅 de Bourgogne ; 1 rue du Professeur Marion ; Dijon ; 21000 ; France
3. Present address ; US EPGV ; IG-CEA ; Centre National de G茅notypage ; 2 rue Gaston Cr茅mieux ; Evry Cedex ; 91057 ; France
4. UMR GDEC ; Plateforme Gentyane ; Clermont Ferrand ; 63100 ; France - 刊名：BMC Genomics
- 出版年：2015
- 出版时间：December 2015
- 年：2015
- 卷：16
- 期：1
- 全文大小：3,086 KB
- 参考文献：1. Food and agriculture organization corporate statistical database. FAOSTAT. 2014. http://faostat.fao.org.
2. Sm媒kal, P, Aubert, G, Burstin, J, Coyne, CJ, Ellis, NTH, Flavell, AJ (2012) Pea (Pisum sativum L.,) in the genomic era. Agronomy 2: pp. 74-115 3390/agronomy2020074" target="_blank" title="It opens in new window">CrossRef
3. Smartt, J (1990) Grain Legumes: Evolution and Genetic Resources. Cambridge University Press, Cambridge 3" target="_blank" title="It opens in new window">CrossRef
4. Jing, R, Ambrose, MA, Knox, MR, Smykal, P, Hybl, M (2012) Genetic diversity in european Pisum germplasm collections. Theor Appl Genet. 125: pp. 367-80 39-1" target="_blank" title="It opens in new window">CrossRef
5. Baranger, A, Aubert, G, Arnau, G, Lain茅, AL, Deniot, G, Potier, J (2004) Genetic diversity within Pisum sativum using protein- and PCR-based markers. Theor Appl Genet. 108: pp. 1309-21 3-1540-5" target="_blank" title="It opens in new window">CrossRef
6. Tar鈥檃n, B, Zhang, C, Warkentin, T, Tullu, A, Vandenberg, A (2005) Genetic diversity among varieties and wild species accessions of pea (Pisum sativum L.,) based on molecular markers, and morphological and physiological characters. Genome 48: pp. 257-72 39/g04-114" target="_blank" title="It opens in new window">CrossRef
7. Burstin, J, Gallardo, K, Aubert, G, Bordat, A, Marget, P (2007) Diversity in Pisum sativum and comparative mapping with medicago truncatula. Proceedings of the 6th European Conference on Grain Legumes. AEP Association Europ茅enne des prot茅agineux, Avenue Georges V, Paris, France
8. Sm媒kal, P, H媒bl, M, Corander, J, Jarkovsk媒, J, Flavell, A, Griga, M (2008) Genetic diversity and population structure of pea (Pisum sativum L.,) varieties derived from combined retrotransposon, microsatellite and morphological marker analysis. Theor Appl Genet. 117: pp. 413-24 CrossRef
9. Zong, X, Redden, R, Liu, Q, Wang, S, Guan, J, Liu, J (2009) Analysis of a diverse global Pisum sp. collection and comparison to a chinese local P. sativum collection with microsatellite markers. Theor Appl Genet. 118: pp. 193-204 CrossRef
10. Kwon, S-J, Brown, A, Hu, J, McGee, R, Watt, C, Kisha, T (2012) Genetic diversity, population structure and genome-wide marker-trait association analysis emphasizing seed nutrients of the USDA pea (Pisum sativum L.,) core collection. Genes & Genomics 34: pp. 305-20 3258-011-0213-z" target="_blank" title="It opens in new window">CrossRef
11. Jing, R, Vershinin, A, Grzebyta, J, Shaw, P, Smykal, P, Marshall, D (2010) The genetic diversity and evolution of field pea (Pisum) studied by high throughput retrotransposon based insertion polymorphism (RBIP) marker analysis. BMC Evolutionary Biol. 10: pp. 44 CrossRef
12. Deulvot, C, Charrel, H, Marty, A, Jacquin, F, Donnadieu, C, Lejeune-H茅naut, I (2010) Highly-multiplexed SNP genotyping for genetic mapping and germplasm diversity studies in pea. BMC Genomics 11: pp. 468 CrossRef
13. Duarte, J, Riviere, N, Baranger, A, Aubert, G, Burstin, J, Cornet, L (2014) Transcriptome sequencing for high throughput SNP development and genetic mapping in pea. BMC Genomics 15: pp. 126 CrossRef
14. Leonforte, A, Sudheesh, S, Cogan, N, Salisbury, P, Nicolas, M, Materne, M (2013) SNP marker discovery, linkage map construction and identification of QTLs for enhanced salinity tolerance in field pea (Pisum sativum L.). BMC Plant Biol. 13: pp. 161 3-161" target="_blank" title="It opens in new window">CrossRef
15. Sindhu, A, Ramsay, L, Sanderson, L-A, Stonehouse, R, Li, R, Condie, J (2014) Gene-based SNP discovery and genetic mapping in pea. Theor Appl Genet. 127: pp. 2225-41 375-y" target="_blank" title="It opens in new window">CrossRef
16. Meuwissen, THE, Hayes, BJ, Goddard, ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: pp. 1819-29
17. Jannink, J-L, Lorenz, AJ, Iwata, H (2010) Genomic selection in plant breeding: from theory to practice. Briefings Funct Genomics 9: pp. 166-77 3/bfgp/elq001" target="_blank" title="It opens in new window">CrossRef
18. de los Campos, G, Hickey, JM, Pong-Wong, R, Daetwyler, HD, Calus, MPL (2013) Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193: pp. 327-45 34/genetics.112.143313" target="_blank" title="It opens in new window">CrossRef
19. Hamblin, MT, Buckler, ES, Jannink, J-L (2011) Population genetics of genomics-based crop improvement methods. Trends Genet. 27: pp. 98-106 3" target="_blank" title="It opens in new window">CrossRef
20. Pritchard, JK, Stephens, M, Donnelly, P (2000) Inference of population structure using multilocus genotype data. Genetics 155: pp. 945-59
21. Gao, H, Williamson, S, Bustamante, CD (2007) A markov chain monte carlo approach for joint inference of population structure and inbreeding rates from multilocus genotype data. Genetics 176: pp. 1635-51 34/genetics.107.072371" target="_blank" title="It opens in new window">CrossRef
22. Huelsenbeck, JP, Andolfatto, P (2007) Inference of population structure under a dirichlet process model. Genetics 175: pp. 1787-802 34/genetics.106.061317" target="_blank" title="It opens in new window">CrossRef
23. 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: pp. 94 CrossRef
24. Jestin, L, Ravel, C, Auroy, S, Laubin, B, Perretant, M-R, Pont, C (2008) Inheritance of the number and thickness of cell layers in barley aleurone tissue (Hordeum vulgare L.): an approach using f2鈥揻3 progeny. Theor Appl Genet. 116: pp. 991-1002 30-6" target="_blank" title="It opens in new window">CrossRef
25. Bordat, A, Savois, V, Nicolas, M, Salse, J, Chauveau, A, Bourgeois, M (2011) Translational genomics in legumes allowed placing in silico 5460 unigenes on the pea functional map and identified candidate genes in Pisum sativum L.. G3: Genes, Genomes, Genet. 1: pp. 93-103 34/g3.111.000349" target="_blank" title="It opens in new window">CrossRef
26. Burstin, J, Marget, P, Huart, M, Moessner, A, Mangin, B, Duchene, C (2007) Developmental genes have pleiotropic effects on plant morphology and source capacity, eventually impacting on seed protein content and productivity in pea. Plant Physiol. 144: pp. 768-81 CrossRef
27. Rogers, JS (1972) Measures of genetic similarity and genetic distances. Stud Genet. 7213: pp. 145-53
28. Chessel, D, Dufour, AB, Thioulouse, J (2004) The ade4 package-I- One-table methods. R News 4: pp. 5-10
R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria
29. Warnes G, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, et al.Gplots: various R programming tools for plotting data. 2012. R package version 2.11.0. http://CRAN.R-project.org/package=gplots.
30. Jombart, T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24: pp. 1403-5 3/bioinformatics/btn129" target="_blank" title="It opens in new window">CrossRef
31. Evanno, G, Regnaut, S, Goudet, J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol. 14: pp. 2611-20 365-294X.2005.02553.x" target="_blank" title="It opens in new window">CrossRef
32. De Mita, S, Siol, M (2012) Egglib: processing, analysis and simulation tools for population genetics and genomics. BMC Genet. 13: pp. 27 3-27" target="_blank" title="It opens in new window">CrossRef
33. Stacklies, W, Redestig, H, Scholz, M, Walther, D, Selbig, J (2007) pcamethods鈥揳 bioconductor package providing PCA methods for incomplete data. Bioinformatics 23: pp. 1164-7 3/bioinformatics/btm069" target="_blank" title="It opens in new window">CrossRef
34. Gentleman, RC, Carey, VJ, Bates, DM, Bolstad, B, Dettling, M (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5: pp. 80 CrossRef
35. Tibshirani, R (1994) Regression shrinkage and selection via the LASSO. J R Stat Soc Ser B 58: pp. 267-88
36. Wold, H (1966) Estimation of principal components and related models by iterative least squares. Academic Press, New York
37. L锚 Cao, K-A, Rossouw, D, Robert-Grani茅, C, Besse, P (2008) A sparse PLS for variable selection when integrating omics data. Stat Appl Genet Mol Biol. 7: pp. 35
38. VanRaden, PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci. 91: pp. 4414-23 3168/jds.2007-0980" target="_blank" title="It opens in new window">CrossRef
39. Johnson, WE, Li, C, Rabinovic, A (2007) Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8: pp. 118-27 3/biostatistics/kxj037" target="_blank" title="It opens in new window">CrossRef
40. Leek, JT, Johnson, WE, Parker, HS, Jaffe, AE, Storey, JD (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28: pp. 15 3/bioinformatics/bts034" target="_blank" title="It opens in new window">CrossRef
41. Hastie T, Efron B. Lars: Least Angle Regression, Lasso and Forward Stagewise. 2012. R package version 1.1.
42. Gonz谩lez I, L锚 Cao K, D茅jean S. Mixomics: Omics data integration project. 2011. http://www.math.univ-toulouse.fr/~biostat/mixOmics .
43. Endelman, JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4: pp. 250-5 3835/plantgenome2011.08.0024" target="_blank" title="It opens in new window">CrossRef
44. P茅rez, P, de los Campos, G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198: pp. 483-95 34/genetics.114.164442" target="_blank" title="It opens in new window">CrossRef
45. Loridon, K, McPhee, K, Morin, J, Dubreuil, P, Pilet-Nayel, ML (2005) Microsatellite marker polymorphism and mapping in pea (Pisum sativum L.). Theor Appl Genet. 111: pp. 1022-31 3" target="_blank" title="It opens in new window">CrossRef
46. Vershinin, AV, Allnutt, TR, Knox, MR, Ambrose, MJ, Ellis, THN (2003) Transposable elements reveal the impact of introgression, rather than transposition, in Pisum diversity, evolution, and domestication. Mol Biol Evol. 20: pp. 2067-75 3/molbev/msg220" target="_blank" title="It opens in new window">CrossRef
47. Jing, R, Knox, MR, Lee, JM, Vershinin, AV, Ambrose, M, Ellis, THN (2005) Insertional polymorphism and antiquity of PDR 1 retrotransposon insertions in Pisum species. Genetics 171: pp. 741-52 34/genetics.105.045112" target="_blank" title="It opens in new window">CrossRef
48. Jing, R, Johnson, R, Seres, A, Kiss, G, Ambrose, MJ, Knox, MR (2007) Gene-based sequence diversity analysis of field pea (Pisum). Genetics 177: pp. 2263-75 34/genetics.107.081323" target="_blank" title="It opens in new window">CrossRef
49. Litt, M, Luty, J (1989) A hypervariable microsatellite revealed by in vitro amplification of a dinucleotide repeat within the cardiac muscle actin gene. Am J Human Genet. 44: pp. 397-401
50. Cieslarov谩, J, Han谩膷ek, P, Fialov谩, E, H媒bl, M, Sm媒kal, P (2011) Estimation of pea (Pisum sativum L.) microsatellite mutation rate based on pedigree and single-seed descent analyses. J Appl Genet. 52: pp. 391-401 3353-011-0058-9" target="_blank" title="It opens in new window">CrossRef
51. Sm媒kal, P, Valledor, L, Rodr铆guez, R, Griga, M (2007) Assessment of genetic and epigenetic stability in long-term in vitro shoot culture of pea (Pisum sativum L.). Plant Cell Rep. 26: pp. 1985-98 3-9" target="_blank" title="It opens in new window">CrossRef
52. Le Clerc, V, Cadot, V, Canadas, M, Lallemand, J, Gu猫rin, D, Boulineau, F (2006) Indicators to assess temporal genetic diversity in the french catalogue: no losses for maize and peas. Theor Appl Genet. 113: pp. 1197-209 368-1" target="_blank" title="It opens in new window">CrossRef
53. Sm媒kal, P, Kenicer, G, Flavell, AJ, Corander, J, Kosterin, O (2011) Phylogeny, phylogeography and genetic diversity of the Pisum genus. Plant Genet Resour. 9: pp. 4-18 33X" target="_blank" title="It opens in new window">CrossRef
54. Roullier, C, Benoit, L, McKey, DB, Lebot, V (2013) Historical collections reveal patterns of diffusion of sweet potato in oceania obscured by modern plant movements and recombination. Proc Nat Acad Sci. 110: pp. 2205-10 3/pnas.1211049110" target="_blank" title="It opens in new window">CrossRef
55. Lorenz, AJ, Chao, S, Asoro, FG, Heffner, EL, Hayashi, H, Smith, KP (2011) Genomic selection in plant breeding: Knowledge and prospects. Adv Agronomy 110: pp. 1309-21
56. Hayes, BJ, Visscher, PM, Goddard, ME (2009) Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res. 91: pp. 47-60 308009981" target="_blank" title="It opens in new window">CrossRef
57. Iwata, H, Jannink, J-L (2011) Accuracy of genomic selection prediction in barley breeding programs: a simulation study based on the real single nucleotide polymorphism data of barley breeding lines. Crop Science 4: pp. 1915-27 35/cropsci2010.12.0732" target="_blank" title="It opens in new window">CrossRef
58. Gouy, M, Rousselle, Y, Bastianelli, D, Lecomte, P, Bonnal, L, Roques, D (2013) Experimental assessment of the accuracy of genomic selection in sugarcane. Theor Appl Genet. 126: pp. 2575-86 3-2156-z" target="_blank" title="It opens in new window">CrossRef
59. Resende, MFR, Mu帽oz, P, Resende, MDV, Garrick, DJ, Fernando, RL, Davis, JM (2012) Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 190: pp. 1503-10 34/genetics.111.137026" target="_blank" title="It opens in new window">CrossRef
60. Larmure, A, Munier-Jolain, NG (2004) A crop model component simulating N partitioning during seed filling in pea. Field Crops Res. 85: pp. 135-48 378-4290(03)00158-8" target="_blank" title="It opens in new window">CrossRef
61. Bhattacharyya, M, Martin, C, Smith, A (1993) The importance of starch biosynthesis in the wrinkled seed shape character of peas studied by Mendel. Plant Mol Biol. 22: pp. 525-31 CrossRef - 刊物主题：Life Sciences, general; Microarrays; Proteomics; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics;
- 出版者：BioMed Central
- ISSN：1471-2164
文摘
Background Pea (Pisum sativum L.), a major pulse crop grown for its protein-rich seeds, is an important component of agroecological cropping systems in diverse regions of the world. New breeding challenges imposed by global climate change and new regulations urge pea breeders to undertake more efficient methods of selection and better take advantage of the large genetic diversity present in the Pisum sativum genepool. Diversity studies conducted so far in pea used Simple Sequence Repeat (SSR) and Retrotransposon Based Insertion Polymorphism (RBIP) markers. Recently, SNP marker panels have been developed that will be useful for genetic diversity assessment and marker-assisted selection. Results A collection of diverse pea accessions, including landraces and cultivars of garden, field or fodder peas as well as wild peas was characterised at the molecular level using newly developed SNP markers, as well as SSR markers and RBIP markers. The three types of markers were used to describe the structure of the collection and revealed different pictures of the genetic diversity among the collection. SSR showed the fastest rate of evolution and RBIP the slowest rate of evolution, pointing to their contrasted mode of evolution. SNP markers were then used to predict phenotypes -the date of flowering (BegFlo), the number of seeds per plant (Nseed) and thousand seed weight (TSW)- that were recorded for the collection. Different statistical methods were tested including the LASSO (Least Absolute Shrinkage ans Selection Operator), PLS (Partial Least Squares), SPLS (Sparse Partial Least Squares), Bayes A, Bayes B and GBLUP (Genomic Best Linear Unbiased Prediction) methods and the structure of the collection was taken into account in the prediction. Despite a limited number of 331 markers used for prediction, TSW was reliably predicted. Conclusion The development of marker assisted selection has not reached its full potential in pea until now. This paper shows that the high-throughput SNP arrays that are being developed will most probably allow for a more efficient selection in this species.