Biology subjects

Katz, D. C.

Publications and source records attributed to Katz, D. C..

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

The occasional perils of reflection (across the midline; in geometric morphometrics)

Manually collecting landmark data on a large biological sample takes a long time. Several options exist to speed data collection, though each strategy introduces problems or raises concerns of its own. For bilaterally symmetric structures (e.g., crania), some recent papers recommend limiting landmark collection to one side and the midline, then \"mirror-reflecting\" landmarks across the midline to produce an approximation of the true bilateral configuration. However, where the midline is narrow relative to the bilateral anatomy, net midline landmark deviations from the mid-sagittal axis or plane will distort the mirror-reflected configuration. Here, I test whether this is a substantive concern at the scale of real biology. To do so, I simulate small amounts of mediolateral error on the mean shape from a sample of human mandibles (n = 178), then compare the distribution of simulated forms to variation in the data. I also test how faithfully mirror-reflected configurations replicate bilateral shape and size relationships. In both analyses, midline deviations from symmetry create striking distortions. I go on to show that incorporating a small number of landmarks from the opposite side of the mandible produces far more accurate estimates of bilateral shape than does mirror reflection. Mirror reflection is clearly inappropriate for these data and is likely suspect in all cases of narrow midline morphology.

zoology↗