Biology subjects

Hallgrimsson, B.

Publications and source records attributed to Hallgrimsson, B..

3 recordsLinked to original sources

bioRxiv2019

A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics

O_LIGeometric morphometrics is the statistical analysis of landmark-based shape variation and its covariation with other variables. Over the past two decades, the gold standard of landmark data acquisition has been manual detection by a single observer. This approach has proven accurate and reliable in small-scale investigations. However, big data initiatives are increasingly common in biology and morphometrics. This requires fast, automated, and standardized data collection. Image registration, or the spatial alignment of images, is a fundamental technique in automatic image analysis that is well-poised for such purposes. Yet, in the few studies that have explored the utility of registration-based landmarks for geometric morphometrics, relatively high or catastrophic labelling errors around anatomical extrema are common. Such errors can result in misleading representations of the mean shape, an underestimation of biological signal, and altered variance-covariance patterns. C_LIO_LIWe combine image registration with a deep and domain-specific neural network to automate and optimize anatomical landmark detection for geometric morphometrics. Using micro-computed tomography images of genetically and morphologically variable mouse skulls, we test our landmarking approach under a variety of registration conditions, including different non-linear deformation frameworks (small vs. large) and atlas strategies (single vs. multi). C_LIO_LICompared to landmarks derived from conventional image registration workflows, our optimized landmark data show significant reductions in error at problematic locations (up to 0.63 mm), a 36.4% reduction in average landmark coordinate error, and up to a 45.1% reduction in total landmark distribution error. We achieve significant improvements in estimates of the sample mean shape and variance-covariance structure. C_LIO_LIFor biological imaging datasets and morphometric research questions, our method can eliminate the time and subjectivity of manual landmark detection whilst retaining the biological integrity of these expert annotations. C_LI

evolutionary biology↗

bioRxiv2019

Facial shape and allometry quantitative trait locus intervals in the Diversity Outbred mouse are enriched for known skeletal and facial development genes

The biology of how faces are built and come to differ from one another is complex. Discovering the genes that contribute to differences in facial morphology is one key to untangling this complexity, with important implications for medicine and evolutionary biology. This study maps quantitative trait loci (QTL) for skeletal facial shape using Diversity Outbred (DO) mice. The DO is a randomly outcrossed population with high heterozygosity that captures the allelic diversity of eight inbred mouse lines from three subspecies. The study uses a sample of 1147 DO animals (the largest sample yet employed for a shape QTL study in mouse), each characterized by 22 three-dimensional landmarks, 56,885 autosomal and X-chromosome markers, and sex and age classifiers. We identified 37 facial shape QTL across 20 shape principal components (PCs) using a mixed effects regression that accounts for kinship among observations. The QTL include some previously identified intervals as well as new regions that expand the list of potential targets for future experimental study. Three QTL characterized shape associations with size (allometry). Median support interval size was 3.5 Mb. Narrowing additional analysis to QTL for the five largest magnitude shape PCs, we found significant overrepresentation of genes with known roles in growth, skeletal development, and sensory organ development. For most intervals, one or more of these genes lies within 0.25 Mb of the QTLs peak. QTL effect sizes were small, with none explaining more than 0.5% of facial shape variation. Thus, our results are consistent with a model of facial diversity that is influenced by key genes in skeletal and facial development and, simultaneously, is highly polygenic.\n\nAuthor SummaryThe mammalian face is a complex structure serving many functions. We studied the genetic basis for facial skeletal diversity in a large sample of mice from an experimental population designed for the study of complex traits. We quantified the contribution of genetic variation to variation in three-dimensional facial shape across more than 55,000 genetic markers spread throughout the mouse genome. We found 37 genetic regions which are very likely to contribute to differences in facial shape. We then conducted a more detailed analysis of the genetic regions associated with the most variable aspects of facial shape. For these regions, a disproportionately large number of genes are known to be important to growth and to skeletal and facial development. The magnitude of these genetic contributions to differences in facial shape are consistently small. Our results therefore support the notion that facial skeletal diversity is influenced by many genes of small effect, but that some of these small effects may be related to genes that are fundamental to skeletal and facial development.

genetics↗

bioRxiv2019

A landmark-free morphometric pipeline: Application to characterise a Down syndrome model skull phenotype

Characterising phenotypes often requires quantification of anatomical shapes. Quantitative shape comparison (morphometrics) traditionally uses anatomical landmarks and is therefore limited by the number of landmarks and operator accuracy when landmarks are located manually. Here we apply a landmark-free method to characterise the craniofacial skeletal phenotype of the Dp1Tyb mouse model of Down syndrome (DS), validating it against a landmark-based approach. We identify cranial dysmorphologies in Dp1Tyb mice, especially smaller size and brachycephaly (front-back shortening) homologous to the human phenotype. The landmark-free phenotyping was less labour-intensive and required less user training than the landmark-based method. It also enabled mapping of local differences as planar expansion or shrinkage. This higher resolution and local mapping pinpointed reductions in interior mid-snout structures and occipital bones in this DS model that were not as apparent using a traditional landmark-based method. This approach could make morphometrics widely-accessible beyond traditional niches in zoology and palaeontology, especially in characterising mutant phenotypes.

genetics↗