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Studying developmental variation with Geometric Morphometric Image Analysis (GMIA).

ABSTRACT: The ways in which embryo development can vary across individuals of a population determine how genetic variation translates into adult phenotypic variation. The study of developmental variation has been hampered by the lack of quantitative methods for the joint analysis of embryo shape and the spatial distribution of cellular activity within the developing embryo geometry. By drawing from the strength of geometric morphometrics and pixel/voxel-based image analysis, we present a new approach for the biometric analysis of two-dimensional and three-dimensional embryonic images. Well-differentiated structures are described in terms of their shape, whereas structures with diffuse boundaries, such as emerging cell condensations or molecular gradients, are described as spatial patterns of intensities. We applied this approach to microscopic images of the tail fins of larval and juvenile rainbow trout. Inter-individual variation of shape and cell density was found highly spatially structured across the tail fin and temporally dynamic throughout the investigated period.

SUBMITTER: Mayer C

PROVIDER: S-EPMC4264869 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Studying developmental variation with Geometric Morphometric Image Analysis (GMIA).

Mayer Christine C Metscher Brian D BD Müller Gerd B GB Mitteroecker Philipp P

PloS one 20141212 12

The ways in which embryo development can vary across individuals of a population determine how genetic variation translates into adult phenotypic variation. The study of developmental variation has been hampered by the lack of quantitative methods for the joint analysis of embryo shape and the spatial distribution of cellular activity within the developing embryo geometry. By drawing from the strength of geometric morphometrics and pixel/voxel-based image analysis, we present a new approach for ...[more]

PMID: 25500820

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Project description:BackgroundFor centuries, morphology has been the most commonly selected trait in horses. A 3D video recording enabled us to obtain the coordinates of 43 anatomical landmarks of 2089 jumping horses. Generalized Procrustes analysis provided centered and scaled coordinates that were independent of volume, i.e., centroid size. Genetic analysis of these coordinates (mixed model; 17,994 horses in the pedigree) allowed us to estimate a variance-covariance matrix. New phenotypes were then defined: the "summarized shapes". They were obtained by linear combinations of Procrustes coordinates with, as coefficients, the eigenvectors of the genetic variance-covariance matrix. These new phenotypes were used in genome-wide association analyses (GWAS) and multitrait genetic analysis that included judges' scores and competition results of the horses.ResultsWe defined ten shapes that represented 86% of the variance, with heritabilities ranging from 0.14 to 0.42. Only one of the shapes was found to be genetically correlated with competition success (rg = - 0.12, standard error = 0.07). Positive and negative genetic correlations between judges' scores and shapes were found. This means that the breeding objective defined by judges involves improvement of anatomical parts of the body that are negatively correlated with each other. Known single nucleotide polymorphisms (SNPs) on chromosomes 1 and 3 for height at withers were significant for centroid size but not for any of the shapes. As these SNPs were not associated with the shape that distinguished rectangular horses from square horses (with height at withers greater than body length), we hypothesize that these SNPs play a role in the overall development of horses, i.e. in height, width, and length but not in height at withers when standardized to unit centroid size. Several other SNPs were found significant for other shapes.ConclusionsThe main application of 3D morphometric analysis is the ability to define the estimated breeding value (EBV) of a sire based on the shape of its potential progeny, which is easier for breeders to visualize in a single synthetic image than a full description based on linear profiling. However, the acceptance of these new phenotypes by breeders and the complex nature of summarized shapes may be challenging. Due to the low genetic correlations of the summarized shapes with jumping performance, the methodology did not allow indirect performance selection criteria to be defined.

Dataset Information

Studying developmental variation with Geometric Morphometric Image Analysis (GMIA).

Publications

Studying developmental variation with Geometric Morphometric Image Analysis (GMIA).

Similar Datasets

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