Comparison of shape quantification methods for genomic prediction, and genome-wide association study of sorghum seed morphology
Autoři:
Lisa Sakamoto aff001; Hiromi Kajiya-Kanegae aff003; Koji Noshita aff004; Hideki Takanashi aff001; Masaaki Kobayashi aff006; Toru Kudo aff006; Kentaro Yano aff006; Tsuyoshi Tokunaga aff007; Nobuhiro Tsutsumi aff001; Hiroyoshi Iwata aff001
Působiště autorů:
Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan
aff001; JSPS Research Fellow, Tokyo, Japan
aff002; Research Center for Agricultural Information Technology, NARO, Ibaraki, Japan
aff003; Department of Biology, Kyushu University, Fukuoka, Japan
aff004; PRESTO, JST, Saitama, Japan
aff005; Faculty of Agriculture, Meiji University, Kanagawa, Japan
aff006; EARTHNOTE Co., Ltd., Okinawa, Japan
aff007
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0224695
Souhrn
Seed shape is an important agronomic trait with continuous variation among genotypes. Therefore, the quantitative evaluation of this variation is highly important. Among geometric morphometrics methods, elliptic Fourier analysis and semi-landmark analysis are often used for the quantification of biological shape variations. Elliptic Fourier analysis is an approximation method to treat contours as a waveform. Semi-landmark analysis is a method of superimposed points in which the differences of multiple contour positions are minimized. However, no detailed comparison of these methods has been undertaken. Moreover, these shape descriptors vary when the scale and direction of the contour and the starting point of the contour trace change. Thus, these methods should be compared with respect to the standardization of the scale and direction of the contour and the starting point of the contour trace. In the present study, we evaluated seed shape variations in a sorghum (Sorghum bicolor Moench) germplasm collection to analyze the association between shape variations and genome-wide single-nucleotide polymorphisms by genomic prediction (GP) and genome-wide association studies (GWAS). In our analysis, we used all possible combinations of three shape description methods and eight standardization procedures for the scale and direction of the contour as well as the starting point of the contour trace; these combinations were compared in terms of GP accuracy and the GWAS results. We compared the shape description methods (elliptic Fourier descriptors and the coordinates of superposed pseudo-landmark points) and found that principal component analysis of their quantitative descriptors yielded similar results. Different scaling and direction standardization procedures caused differences in the principal component scores, average shape, and the results of GP and GWAS.
Klíčová slova:
Principal component analysis – Genome-wide association studies – Genomic libraries – Seeds – Sorghum – Morphometry – Fourier analysis – Ellipsoids
Zdroje
1. Ayoub M, Symons SJ, Edney MJ, Mather DE. QTLs affecting kernel size and shape in a two-rowed by six-rowed barley cross. Theor Appl Genet. 2002;105: 237–247. doi: 10.1007/s00122-002-0941-1 12582525
2. Groh S, Kianian SF, Phillips RL, Rines HW, Stuthman DD, Wesenberg DM, et al. Analysis of factors influencing milling yield and their association to other traits by QTL analysis in two hexaploid oat populations. Theor Appl Genet. 2001;103: 9–18.
3. Williams K, Munkvold J, Sorrells M. Comparison of digital image analysis using elliptic Fourier descriptors and major dimensions to phenotype seed shape in hexaploid wheat (Triticum aestivum L.). Euphytica. 2013;190: 99–116.
4. Zheng TQ, Xu JL, Li ZK, Zhai HQ, Wan JM. Genomic regions associated with milling quality and grain shape identified in a set of random introgression lines of rice (Oryza sativa L.). Plant Breed. 2007;126: 158–163.
5. Ohsawa R, Tsutsumi T, Uehara H, Namai H, Ninomiya S. Quantitative evaluation of common buckwheat (Fagopyrum esculentum Moench) kernel shape by elliptic Fourier descriptor. Euphytica. 1998;101: 175–183.
6. Rabiei B, Valizadeh M, Ghareyazie B, Moghaddam M, Ali AJ. Identification of QTLs for rice grain size and shape of Iranian cultivars using SSR markers. Euphytica. 2004;137: 325–332. doi: 10.1023/B:EUPH.0000040452.76276.76
7. Rohlf FJ. MORPHOMETRICS. Annu Rev Ecol Syst. 1990;21: 299–316.
8. Iwata H, Ebana K, Uga Y, Hayashi T, Jannink JL. Genome-wide association study of grain shape variation among Oryza sativa L. germplasms based on elliptic Fourier analysis. Mol Breed. 2010;25: 203–215.
9. Iwata H, Ebana K, Uga Y, Hayashi T. Genomic prediction of biological shape: Elliptic Fourier analysis and kernel Partial Least Squares (PLS) regression applied to grain shape prediction in rice (Oryza sativa L.). PLoS One. 2015;10: 1–17.
10. Zhang D, Li J, Compton RO, Robertson J, Goff VH, Epps E, et al. Comparative genetics of seed size traits in divergent cereal lineages represented by Sorghum (Panicoidae) and Rice (Oryzoidae). G3 Genes|Genomes|Genetics. 2015;5: 1117–1128. doi: 10.1534/g3.115.017590 25834216
11. Tao Y, Mace ES, Tai S, Cruickshank A, Campbell BC, Zhao X, et al. Whole-genome analysis of candidate genes associated with seed size and weight in sorghum bicolor reveals signatures of artificial selection and insights into parallel domestication in cereal crops. Front Plant Sci. 2017;8: 1–14. doi: 10.3389/fpls.2017.00001 28220127
12. Tao Y, Mace E, George-Jaeggli B, Hunt C, Cruickshank A, Henzell R, et al. Novel grain weight loci revealed in a cross between cultivated and wild sorghum. Plant Genome. 2018;11: 0.
13. Boyles RE, Cooper EA, Myers MT, Brenton Z, Rauh BL, Morris GP, et al. Genome-wide association studies of grain yield components in diverse sorghum germplasm. Plant Genome. 2016;9: 0.
14. Wang J, Hu Z, Upadhyaya HD, Morris GP. Genomic signatures of seed mass adaptation to global precipitation gradients in sorghum. Heredity (Edinb). Springer US; 2019.
15. Kuhl FP, Giardina CR. Elliptic Fourier features of a closed contour. Comput Graph Image Process. 1982;18: 236–258.
16. Bookstein FL. Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Med Image Anal. 1997;1: 225–243. doi: 10.1016/s1361-8415(97)85012-8 9873908
17. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157: 1819–1829. 11290733
18. Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006;38: 203–208. doi: 10.1038/ng1702 16380716
19. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One. 2008;3: 1–7.
20. Ohyanagi H, Takano T, Terashima S, Kobayashi M, Kanno M, Morimoto K, et al. Plant omics data center: An integrated web repository for interspecies gene expression networks with NLP-based curation. Plant Cell Physiol. 2015;56: e9. doi: 10.1093/pcp/pcu188 25505034
21. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010;26: 589–595. doi: 10.1093/bioinformatics/btp698 20080505
22. Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH. Stacks: building and genotyping Loci de novo from short-read sequences. G3 (Bethesda). 2011;1: 171–82.
23. Kobayashi M, Ohyanagi H, Takanashi H, Asano S, Kudo T, Kajiya-Kanegae H, et al. Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data. DNA Res. 2017;24: 397–405. doi: 10.1093/dnares/dsx012 28498906
24. Mace ES, Tai S, Gilding EK, Li Y, Prentis PJ, Bian L, et al. Whole-genome sequencing reveals untapped genetic potential in Africa’s indigenous cereal crop sorghum. Nat Commun. 2013;4.
25. Zheng LY, Guo X Sen, He B, Sun LJ, Peng Y, Dong SS, et al. Genome-wide patterns of genetic variation in sweet and grain sorghum (Sorghum bicolor). Genome Biol. 2011;12: R114. doi: 10.1186/gb-2011-12-11-r114 22104744
26. Browning BL, Browning SR. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet. 2008;84: 210–223.
27. Paradis E, Claude J, Strimmer K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20: 289–290. doi: 10.1093/bioinformatics/btg412 14734327
28. R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
29. Gower JC. Generalized procrustes analysis. Psychometrika. 1975;40: 33–51.
30. Dryden IL, Mardia KV. Statistical Shape Analysis: With Applications in R. 2nd ed. Hoboken, New Jersey: Wiley; 2016.
31. Iwata H, Ukai Y. SHAPE: A computer program package for quantitative evaluation of biological shapes based on Elliptic Fourier Descriptors. J Hered. 2002;93: 384–385. doi: 10.1093/jhered/93.5.384 12547931
32. Furuta N, Ninomiya S, Takahashi N, Ohmori H, Ukai Y. Quantitative evaluation of soybean (Glycine max L. Merr.) leaflet shape by principal component scores based on elliptic Fourier descriptor. Breed Sci. 1995; 315–320.
33. Pérez P, De Los Campos G. Genome-wide regression and prediction with the BGLR statistical package. Genetics. 2014;198: 483–495. doi: 10.1534/genetics.114.164442 25009151
34. Endelman J.B. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 2011; 4:250–255.
35. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57: 289–300.
36. Rohlf FJ. Relationships among eigenshape analysis, Fourier analysis, and analysis of coordinates. Math Geol. 1986;18: 845–854.
37. Martin A, Lee J, Kichey T, Gerentes D, Zivy M, Tatout C, et al. Two cytosolic glutamine synthetase isoforms of maize are specifically involved in the control of grain production. Plant Cell Online. 2006;18: 3252–3274.
38. Riefler M, Novak O, Strnad M, Schmülling T. Arabidopsis cytokinin receptor mutants reveal functions in shoot growth, leaf senescence, seed size, germination, root development, and cytokinin metabolism. Plant Cell. 2006;18: 40–54. doi: 10.1105/tpc.105.037796 16361392
Článok vyšiel v časopise
PLOS One
2019 Číslo 11
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Somatizace stresu – typické projevy a možnosti řešení
- Profylaxe infekční endokarditidy ve stomatologii
- Masturbační chování žen v ČR − dotazníková studie
Najčítanejšie v tomto čísle
- A daily diary study on maladaptive daydreaming, mind wandering, and sleep disturbances: Examining within-person and between-persons relations
- Molecular validation of clinical Pantoea isolates identified by MALDI-TOF
- Pathways to conspiracy: The social and linguistic precursors of involvement in Reddit’s conspiracy theory forum
- Urticating setae of tarantulas (Araneae: Theraphosidae): Morphology, revision of typology and terminology and implications for taxonomy
Zvýšte si kvalifikáciu online z pohodlia domova
Všetky kurzy