Limits...
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.).

Iwata H, Ebana K, Uga Y, Hayashi T - PLoS ONE (2015)

Bottom Line: Shape is an important morphological characteristic both in animals and plants.Datasets with larger sample size and higher marker density showed higher accuracy.Rice grain shape can be predicted accurately based on genome-wide SNP genotypes.

View Article: PubMed Central - PubMed

Affiliation: Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, University of Tokyo, Bunkyo, Tokyo, Japan.

ABSTRACT
Shape is an important morphological characteristic both in animals and plants. In the present study, we examined a method for predicting biological contour shapes based on genome-wide marker polymorphisms. The method is expected to contribute to the acceleration of genetic improvement of biological shape via genomic selection. Grain shape variation observed in rice (Oryza sativa L.) germplasms was delineated using elliptic Fourier descriptors (EFDs), and was predicted based on genome-wide single nucleotide polymorphism (SNP) genotypes. We applied four methods including kernel PLS (KPLS) regression for building a prediction model of grain shape, and compared the accuracy of the methods via cross-validation. We analyzed multiple datasets that differed in marker density and sample size. Datasets with larger sample size and higher marker density showed higher accuracy. Among the four methods, KPLS showed the highest accuracy. Although KPLS and ridge regression (RR) had equivalent accuracy in a single dataset, the result suggested the potential of KPLS for the prediction of high-dimensional EFDs. Ordinary PLS, however, was less accurate than RR in all datasets, suggesting that the use of a non-linear kernel was necessary for accurate prediction using the PLS method. Rice grain shape can be predicted accurately based on genome-wide SNP genotypes. The proposed method is expected to be useful for genomic selection in biological shape.

No MeSH data available.


Related in: MedlinePlus

Grain shape variation observed in datasets A (a) and B (b).Average grain shapes of all accessions were overlaid. Thick contour lines represent the grain shape averaged over all accessions.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4380318&req=5

pone.0120610.g002: Grain shape variation observed in datasets A (a) and B (b).Average grain shapes of all accessions were overlaid. Thick contour lines represent the grain shape averaged over all accessions.

Mentions: In Fig 2, the average grain shape of each accession was overlaid to visualize a grain shape variation among accessions. The among-accession variation was large in both datasets, and the length-to-width ratio of the grain was the major variation. A wider range of the variation was observed in dataset B than in dataset A (Fig 2). Especially, accessions having slender grain shape were included more frequently in dataset B than in dataset A. The MANOVA of EFDs revealed that the among-cultivar variation was significantly larger than the within-cultivar variation in both datasets (F = 1.52, p < 2.2 × 10−16 for dataset A and F = 2.05, p < 2.2 × 10−16 for dataset B), suggesting that variations in the average values of EFDs mostly reflect varietal differences of grain shapes.


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.).

Iwata H, Ebana K, Uga Y, Hayashi T - PLoS ONE (2015)

Grain shape variation observed in datasets A (a) and B (b).Average grain shapes of all accessions were overlaid. Thick contour lines represent the grain shape averaged over all accessions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4380318&req=5

pone.0120610.g002: Grain shape variation observed in datasets A (a) and B (b).Average grain shapes of all accessions were overlaid. Thick contour lines represent the grain shape averaged over all accessions.
Mentions: In Fig 2, the average grain shape of each accession was overlaid to visualize a grain shape variation among accessions. The among-accession variation was large in both datasets, and the length-to-width ratio of the grain was the major variation. A wider range of the variation was observed in dataset B than in dataset A (Fig 2). Especially, accessions having slender grain shape were included more frequently in dataset B than in dataset A. The MANOVA of EFDs revealed that the among-cultivar variation was significantly larger than the within-cultivar variation in both datasets (F = 1.52, p < 2.2 × 10−16 for dataset A and F = 2.05, p < 2.2 × 10−16 for dataset B), suggesting that variations in the average values of EFDs mostly reflect varietal differences of grain shapes.

Bottom Line: Shape is an important morphological characteristic both in animals and plants.Datasets with larger sample size and higher marker density showed higher accuracy.Rice grain shape can be predicted accurately based on genome-wide SNP genotypes.

View Article: PubMed Central - PubMed

Affiliation: Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, University of Tokyo, Bunkyo, Tokyo, Japan.

ABSTRACT
Shape is an important morphological characteristic both in animals and plants. In the present study, we examined a method for predicting biological contour shapes based on genome-wide marker polymorphisms. The method is expected to contribute to the acceleration of genetic improvement of biological shape via genomic selection. Grain shape variation observed in rice (Oryza sativa L.) germplasms was delineated using elliptic Fourier descriptors (EFDs), and was predicted based on genome-wide single nucleotide polymorphism (SNP) genotypes. We applied four methods including kernel PLS (KPLS) regression for building a prediction model of grain shape, and compared the accuracy of the methods via cross-validation. We analyzed multiple datasets that differed in marker density and sample size. Datasets with larger sample size and higher marker density showed higher accuracy. Among the four methods, KPLS showed the highest accuracy. Although KPLS and ridge regression (RR) had equivalent accuracy in a single dataset, the result suggested the potential of KPLS for the prediction of high-dimensional EFDs. Ordinary PLS, however, was less accurate than RR in all datasets, suggesting that the use of a non-linear kernel was necessary for accurate prediction using the PLS method. Rice grain shape can be predicted accurately based on genome-wide SNP genotypes. The proposed method is expected to be useful for genomic selection in biological shape.

No MeSH data available.


Related in: MedlinePlus