The vertebrate cranium is a prime example of the high evolvability of complex traits. While evidence of genes and developmental pathways underlying craniofacial shape determination is accumulating, we are still far from understanding how such variation at the genetic level is translated into craniofacial shape variation. Here we used 3D geometric morphometrics to map genes involved in shape determination in a population of outbred mice (Carworth Farms White, or CFW). We defined shape traits via principal component analysis of 3D skull and mandible measurements. We mapped genetic loci associated with shape traits at ~80,000 candidate single nucleotide polymorphisms in ~700 male mice. We found that craniofacial shape and size are highly heritable, polygenic traits. Despite the polygenic nature of the traits, we identified 17 loci that explain variation in skull shape, and 8 loci associated with variation in mandible shape. Together, the associated variants account for 11.4% of skull and 4.4% of mandible shape variation, however, the total additive genetic variance associated with phenotypic variation was estimated in ~45%. Candidate genes within the associated loci have known roles in craniofacial development; this includes 6 transcription factors and several regulators of bone developmental pathways. One gene, Mn1, has an unusually large effect on shape variation in our study. A knockout of this gene was previously shown to affect negatively the development of membranous bones of the cranial skeleton, and evolutionary analysis shows that the gene has arisen at the base of the bony vertebrates (Eutelostomi), where the ossified head first appeared. Therefore, Mn1 emerges as a key gene for both skull formation and within-population shape variation. Our study shows that it is possible to identify important developmental genes through genome-wide mapping of high-dimensional shape features in an outbred population.

The identification of the genes involved in morphological variation in nature is still a major challenge. Here, we explore a new approach: we combine 178 samples from a natural hybrid zone between two subspecies of the house mouse (Mus musculus domesticus and Mus musculus musculus), and high coverage of the genome (~145K SNPs) to identify loci underlying craniofacial shape variation. Due to the long history of recombination in the hybrid zone, high mapping resolution is anticipated. The combination of genomes from subspecies allows the mapping of both, variation within subspecies and inter-subspecific differences, thereby increasing the overall amount of causal genetic variation that can be detected. Skull and mandible shape were measured using 3D landmarks and geometric morphometrics. Using principal component axes as phenotypes, and a linear mixed model accounting for genetic relatedness in the mapping populations, we identified nine genomic regions associated with skull shape and 10 with mandible shape. High mapping resolution (median size of significant regions = 148 kb) enabled identification of single or few candidate genes in most cases. Some of the genes act as regulators or modifiers of signalling pathways relevant for morphological development and bone formation, including several with known craniofacial phenotypes in mice and humans. The significant associations combined explain 13% and 7% of the skull and mandible shape variation, respectively. In addition, a positive correlation was found between chromosomal length and proportion of variation explained. Our results suggest a complex genetic architecture for shape traits and support a polygenic model.

RNA-seq has become the standard tool for collecting genome-wide expression data in diverse fields, from quantitative genetics and medical genomics to ecology and developmental biology. However, RNA-seq library preparation is still prohibitive for many laboratories. Recently, the field of single-cell transcriptomics has reduced costs and increased throughput by adopting early barcoding and pooling of individual samples —producing a single final library containing all samples. In contrast, RNA-seq protocols where each sample is processed individually are significantly more expensive and lower throughput than single-cell approaches. Yet, many projects depend on individual library generation to preserve important samples or for follow-up re-sequencing experiments. Improving on currently available RNA-seq methods we have developed TM3′seq, a 3′-enriched library preparation protocol that uses Tn5 transposase and preserves sample identity at each step. TM3′seq is designed for high-throughput processing of individual samples (96 samples in 6h, with only 3h hands-on time) at a fraction of the cost of commercial kits ($1.5 per sample). The protocol was tested in a range of human and Drosophila melanogaster RNA samples, recovering transcriptomes of the same quality and reliability than the commercial NEBNext kit. We expect that the cost- and time-efficient features of TM3′seq make large-scale RNA-seq experiments more permissive for the entire scientific community.

Individual animals vary in their behaviors. This is true even when they share the same genotype and were reared in the same environment. Clusters of covarying behaviors constitute behavioral syndromes, and an individual’s position along such axes of covariation is a representation of their personality. Despite these conceptual frameworks, the structure of behavioral covariation within a genotype is essentially uncharacterized and its mechanistic origins unknown. Passing hundreds of inbred Drosophila individuals through an experimental pipeline that captured hundreds of behavioral measures, we found sparse but significant correlations among small sets of behaviors. Thus, the space of behavioral variation has many independent dimensions. Manipulating the physiology of the brain, and specific neural populations, altered specific correlations. We also observed that variation in gene expression can predict an individual’s position on some behavioral axes. This work represents the first steps in understanding the biological mechanisms determining the structure of behavioral variation within a genotype.