Understanding the genetic basis of evolution requires the identification of genes and mutations responsible for phenotypic variation between individuals and populations. A complete catalog of such variants will allow the identification of the genetic paths favored by evolution (Stern and Orgogozo, 2008; Martin and Orgogozo, 2013). Regarding morphological variation, the latest catalog, that includes animals, yeasts, and plants, listed 386 alleles (Martin and Orgogozo, 2013), most of them related to pigmentation. Genes associated with shape variation are fewer, and correspond to variation in wing shape in flies and butterflies, and body shape in fish. However, there have been many other studies exploring the genetic basis of shape variation (e.g. craniofacial shape), but given the highly polygenic nature of these traits it has been difficult to validate the extensive list of candidate genes identified through quantitative trait loci (QTL) and genome-wide association (GWAS) approaches.

The mouse tooth is one of the traits extensively studied regarding shape variation. In paleontology, it is a key character for phylogenetic and dietary inferences (Misonne, 1969; Gómez Cano et al., 2013). It has been a model for developmental genetics (Jernvall and Thesleff, 2012; Urdy et al., 2016). The genes and pathways involved in tooth morphogenesis are well known (Thesleff and Sharpe, 1997; Jernvall and Thesleff, 2000; Jernvall and Thesleff, 2012; Lan et al., 2014) and a few genes affecting cusp patterning in mice have been identified (Jernvall and Thesleff, 2012). Moreover, computational models have been able to recreate the morphological transition between species and individuals (Salazar-Ciudad and Jernvall, 2002, 2010; Harjunmaa et al., 2014; Urdy et al., 2016). As for many other traits, there is a wide gap between the macro- and micro-evolutionary understanding of tooth morphological variation (Nunes et al., 2013). The wide-spread morphological variation at the micro-evolutionary level in mice (i.e. within-species) has been repeatedly highlighted (Boell and Tautz, 2011; Ledevin et al., 2016), as well as its relevance for understanding the evolutionary history of the taxon (Macholán, 2006; Renaud et al., 2011; Renaud and Auffray, 2013; Renaud et al., 2013).

Surprisingly, only two studies have tried to map loci responsible for such morphological variation. Using F2 crosses between LG/J and SM/J inbred lines of mice, Workman et al., 2002 found three QTL for molar row size, and 18 QTL for 2D molar row shape. Using recombinant inbred lines between A/J and SM/J, Shimizu et al. (2004) found seven QTL affecting size of either the upper or lower molars. As is common for QTL approaches, hundreds of genes co-localized with the QTL, impeding the identification of clear candidate loci.

In this study, we focused on the first upper molar and used mice derived from a wild population, which allowed us to directly address the genetic basis of molar shape variation from a micro-evolutionary perspective. The mice used here are first-generation offspring of natural hybrids between Mus musculus musculus and Mus musculus domesticus collected in the Bavarian hybrid zone (Turner et al., 2012). As a result, phenotypic variation in the sample has a between-subspecies as well as a within-population component. This sample represents phenotypic and genetic variation segregating in nature, and therefore constitutes an evolutionarily relevant scenario in contrast to inbred lines traditionally used in mapping studies in mice. Previous studies successfully identified loci associated with cranial morphology and male sterility phenotypes in this mapping population (Pallares et al., 2014; Turner and Harr, 2014). We performed the first GWAS of 3D molar shape variation, and identified several candidate regions for naturally occurring variation. We also, for the first time, showed that mutations in the candidate gene Mitf can cause between-individual differences in molar shape in the mouse.

The mice used in this study were derived from wild-caught hybrid mice between M. m. musculus and M. m. domesticus and represent a hybridization continuum between the two subspecies (Figure 1, a). Molars of M. m. musculus are characterized by anterior elongation, expansion of the labial cusps, and reduction of the antero-lingual cusp compared to M. m. domesticus mice (Figure 1, b). Despite the hybrid character of the sample, the major axes of variation are not polarized by shape differences between the two subspecies (Figure 1, c). This indicates that phenotypic variation in the sample has a between-subspecies component, but axes of within-subspecies variation are more important, representing other directions of shape changes different from M. m. musculus – M. m. domesticus. This suggests that within-population variation in molar shape is larger than between-subspecies variation, and therefore species-specific alleles might not be playing any major role in tooth shape in this population. A similar pattern of between- vs within-population variation has been previously described for mandible shape (Boell and Tautz, 2011). Additional factors that might contribute to such a pattern are transgressive phenotypes and hybrid developmental instability, although the latter seems to be not very important in this population (see Pallares et al., 2016).

Figure 1

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Tooth shape, mouse age and wear

Once erupted, tooth shape does not change except by the effect of wear, but the degree of wear is correlated with the age of the mice, that in this study ranges from 9 to 12 weeks. The effect of age and wear on molar tooth shape was explored using the first 18 PCs derived from each of the two approaches: complete template and wear-free template. Age differences have a small but significant effect on molar shape variation when using the complete template (p-value=5.4×10⁻⁵, r²=0.01), and this is reflected in the significant correlation between age and some PCs (r²(PC1) = 2.1%, p-value=0.027; r²(PC4) = 2.2%, p-value=0.025; r²(PC5) = 3.3%, p-value=0.008; r²(PC14) = 1.8%, p-value=0.04; r²(PC18) = 4.9%, p-value=0.002). More importantly, a qualitative assessment indicates that wear-like patterns are present already in the first axis of variation (PC1) (Figure 2-a).

Figure 2

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Although age correlates with wear patterns, there are other factors, such as diet and behavior that also influence tooth morphology through wear (Renaud and Ledevin, 2017). The wear-free template lacks the tip of the cusps, mimicking the same level of wear in all individuals (Figure 2-b), and therefore addressing wear in all its complexity, being this related to age or any other factors. The effect of age on overall shape variation using this template was still significant but explained very little variance (p-value=0.01, r²=0.01). Only two PCs kept a very small age-related signal (r²(PC3) = 4.6%, p-value=0.002; r²(PC18) = 3.5, p-value=0.007).

Heritability

The heritability values estimated in this study correspond to the effect that all SNPs used in the mapping have on the phenotype; this value is also known as SNP or chip heritability, and it serves as a proxy of the additive genetic variance underlying phenotypic variation. Substantial genetic variance was found in all PCs (Table 2). A weighted average of all PCs genetic variance was used to summarize the total heritability of molar shape (see Materials and methods). This resulted in a value of 65.5%, indicating that more than half of shape variation is accounted for by additive genetic effects.

Table 2

PC	%var	Heritability per PC	Error	Molar herit
1	18.9	0.83	0.09	15.8
2	15.4	0.95	0.08	14.7
3	9.9	0.78	0.10	7.7
4	7.1	0.62	0.14	4.4
5	5.9	0.49	0.12	2.9
6	5.1	0.83	0.10	4.2
7	3.9	0.53	0.12	2.1
8	3.2	0.89	0.11	2.8
9	2.8	0.89	0.09	2.5
10	2.5	0.86	0.13	2.2
11	2.1	0.72	0.14	1.5
12	1.8	0.60	0.15	1.1
13	1.7	0.68	0.13	1.2
14	1.5	0.56	0.16	0.8
15	1.4	0.53	0.15	0.7
16	1.3	0.26	0.16	0.3
17	1.2	0.14	0.20	0.2
18	1	0.51	0.14	0.5
Total Var	86.7%
		pve for molar shape		65.50

When exploring heritability from a chromosomal point of view (Figure 3), instead of individual associations between SNPs and phenotype, a positive correlation is evident between the amount of phenotypic variation explained by each chromosome and chromosomal length (r = 0.67, p-value=0.001). This is the expected pattern when the effect of individual SNPs is small, and such SNPs are many and homogeneously distributed along the genome.

Figure 3

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Mapping of loci associated with molar shape variation

We decomposed variation in molar tooth shape in PCs. The mapping was performed using 183 mice and ~145,000 SNPs. Centroid size was used as proxy for tooth size, however, no genomic regions were significantly associated with size variation.

When shape data derived from the complete template was used in the mapping, no significant associations were found. In contrast, the data derived from the wear-free template resulted in nine SNPs significantly associated with molar shape variation, clustered in five genomic regions. Hereafter, we will thus focus on the results of the wear-free approach.

These five loci are found in chromosomes 1, 5, 6, and X, and were associated with PC7, PC11, PC16, and PC18 (Figure 4-a, Table 3). The name of each locus is defined by Mo (molar) and chromosomal location. Regions Mo.1, Mo.5, Mo.6, and Mo.X.1 have an arbitrary size of 500 Kb (see Materials and methods) and contain, together, 15 protein coding genes. Mo.X.2 is 63.8 Mb given the strong LD pattern around the best SNP and contains 306 protein coding genes. Together, the loci explain ~10% of molar shape variation, with individual effects ranging from 1% to 3% (Table 3). The shape changes associated with each region were estimated as the difference between the consensus shape of the two homozygous states for each SNP. The phenotypic effect of each SNP is therefore not restricted to the PC it was associated with, but represents the effect of the SNP on the complete shape dimensionality (Figure 4-b).

Figure 4

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Figure 4—source data 1

Shape data used in the association mapping.

The dimensionality of the molar shape data was reduced using a PCA. The centroid size and PC scores for the first 18 PCs for each mouse are shown. These PC scores were used as phenotypes in the association mapping implemented in GEMMAX.

https://doi.org/10.7554/eLife.29510.008

Download elife-29510-fig4-data1-v2.txt

Table 3

QTL	Chr	Position	Best SNP	p-value	Effect size	PC axis	Genes*
Mo.1	chr1	84306638	JAX00006079	5.11E-07	1.1%	PC11	Pid1, Dner
Mo.5	chr5	36723779	JAX00128837	5.79E-07	3.2%	PC18	Psapl1, Tada2b, Ccdc96, Grpel1, Tbc1d14, D5Ertd579e, Sorcs2
Mo.6	chr6	97980057	JAX00619074	2.71E-08	2.8%	PC7	Gm765, Mitf
Mo.X.1	chrX	92638616	JAX00715960	1.18E-07	1.6%	PC16	Fam123b, Zc4h2, Asb12, Arhgef9
Mo.X.2**	chrX	104533418	JAX00183055	1.28E-10	2.2%	PC7	Rps6ka3, Dach2, Ap1s2, Itm2a and 301 other genes

Mitf gene affects molar shape in mice

From the genes located in the QTL regions, two have been reported to affect tooth development directly or indirectly (MGI database queried 22.03.16), namely Rps6ka3 (Laugel-Haushalter et al., 2014) and the microphthalmia-associated transcription factor Mitf (Al-Douri and Johnson, 1987). The Rps6ka3 gene is located in Mo.X.2 that contains 305 additional genes, making it difficult to assess its relevance. However, Mitf is one of two genes found in Mo.6, and since many different mutations are known in this gene, it is possible to determine its relevance in tooth development.

Mitf is mainly known for its role in melanocyte development and proliferation in mice and humans. Mice carrying Mitf mutations show reduced or absent pigmentation, reduced eye size (microphthalmia), and deafness (Steingrimsson et al., 2004). Some mutations result in defective bone resorption due to defects in osteoclast development (Hodgkinson et al., 1993; Moore, 1995; Hershey and Fisher, 2004); the Mitf^mi allele leads to lethality at 3 weeks of age due to severe osteopetrosis. Abnormal morphology and eruption of incisor and molar teeth has been reported in some mutants (Al-Douri and Johnson, 1987; Steingrimsson et al., 2002), although this is thought to be linked to severe osteopetrosis in the mandible of these mutants.

Interestingly, the large Mo.X.2 region, which is associated with the same PC as Mitf, includes Ap1s2. This gene is a target of the Mitf-regulated miRNA miR-211, and its function has been validated in the context of melanoma (Margue et al., 2013) and osteosclerosis in humans (Saillour et al., 2007).

To further evaluate a possible role of Mitf in molar shape variation, we examined molar morphology in mice carrying four mutant Mitf alleles. To avoid indirect effects on molar morphogenesis, we selected alleles for which no, or very mild osteopetrosis has been described (Table 1). Mitf^mi induces severe osteopetrosis when homozygous (Steingrimsson et al., 2002). However, we have only used it in the heterozygous state. The other mutant alleles do not exhibit osteopetrosis in homozygotes (Steingrimsson et al., 2002; Steingrimsson et al., 2004).

As shown in Figure 5, all Mitf alleles affected the shape of the upper first molar. Pairwise tests between wild type B6 mice and each mutant group showed significant differences in mean shape (Supplementary file 1A). In terms of Procrustes distances, Mitf^{mi-enu22(398)} is closest to wild type (0.0237), and Mitf^mi-vga9/Mitfmi^mi-vga9 is most distant (0.0466) (Supplementary file 1A).

Figure 5

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Mitf^Mi-wh/Mitf^mi compound heterozygotes have a similar molar shape as Mitf^Mi-wh/+ heterozygotes, suggesting that the Mitf^mi allele alone has no additional effect on molar shape (Figure 5d,g). Interestingly, in homozygous condition, Mitf^mi does result in severe pigmentation and eye phenotypes (Steingrimsson et al., 2004). However, given that Mitf^mi/+ mice were not examined in this study, we are not able to rule out the role of this allele in tooth shape. The molar shape generated by Mitf^mi-vga9/+ is very similar to Mitf^Mi-wh/Mitf^Mi-wh, although the intensity of the effect seems stronger in the latter (Figure 5b,e). For the Mitf^mi-vga9 and Mitf^Mi-wh alleles, three genetic combinations were available. The Mitf^mi-vga9 mutation behaves additively regarding molar shape (Figure 5). This contrasts with reports on other phenotypes where its effect is clearly recessive (Hodgkinson et al., 1993).

A comparison between each mutant group and Mo.6, the region where Mitf gene is found, shows that mice homozygous for the Mitf^Mi-wh allele are most similar to Mo.6 with respect to tooth shape. Mitf^mi-vga9/Mitfmi^mi-vga9 follows second in correlation strength (Figure 5h). The magnitude of shape variation generated by the mutant alleles falls within the range of shape variation observed in the mapping population (Figure 5i). However, it should be noted that the shape variation represented by the hybrid mice used here is determined by many genes, while the shape of mutant mice is the result of a mutation in a single gene.

Using genome-wide association mapping and 3D surface morphometrics we were able to map genomic regions underlying shape variation in the first upper molar of wild mice. The high mapping resolution achieved enabled identification of individual candidate genes making it feasible to functionally evaluate such candidates. Using a panel of mice with different mutant genotypes, we showed that one of these candidates, the transcription factor Mitf, has significant effects on molar shape.

The mapping population and the phenotyping approach used here played a definitive role in the ability to identify the genetic basis of molar shape variation. The mice were derived from wild-caught parents collected in the Bavarian hybrid zone in Germany (Turner et al., 2012) and therefore are representative of wild genomic and phenotypic variation. The phenotypic changes driving the first axis of variation do not correspond to the shape changes between M.m.musculus and M.m.domesticus, indicating that this sample contains not only between-species patterns of variation but also strong within-subspecies variation. Such variation can be the result of transgressive segregation or developmental instability, often associated with hybridization. However, the latter seem to play a very small role in this population (Pallares et al., 2016). This is the first time the genetics of molar shape variation in mice has been studied using wild mice, offering an evolutionarily relevant perspective for the understanding of phenotypic variation. The technical advantage comes from the fact that these mice are hybrids between M.m.musculus and M.m.domesticus. These subspecies have been hybridizing in the Bavarian region for around 3000 years (reviewed in Baird and Macholán, 2012) resulting in high mapping resolution as a consequence of a genomic landscape where LD blocks are much smaller compared to traditional QTL-mapping approaches which usually correspond to two generations of crossing (Workman et al., 2002). The second technical advantage comes from the reduction of environmental variation; the mice were raised in the laboratory under controlled conditions and therefore the relative effect of genetic variation is enhanced at the expense of environmental variation. The suitability of this population for mapping loci associated with complex traits has been demonstrated previously for craniofacial shape (Pallares et al., 2014) and sterility phenotypes (Turner and Harr, 2014).

The phenotyping approach used here made use of 3D surface morphometrics, allowing us to quantify additional dimensions of variation compared to the two-dimensional approach (Workman et al., 2002; Shimizu et al., 2004). By measuring the surface of the tooth, this approach captures variation generated by differential wear between individuals, a confounding factor that is not present in 2D studies. Following Ledevin et al. (2016), we used a wear-free template that allowed us to preserve the additional shape information captured by 3D methods and exclude wear-related variation. In this way, we were able to identify genetic loci associated with shape variation that otherwise would have been obscured by strong wear effects (see Results). Our findings are, however, a subset of the possible associations between molar shape and genomic regions. Between-individual differences in cusp shape remain to be explored at the genomic level.

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Author details

1. Luisa F Pallares

   Department of Evolutionary Genetics, Max-Planck Institute for Evolutionary Biology, Plön, Germany

   Present address

   Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Writing—original draft, Writing—review and editing

   Contributed equally with

   Ronan Ledevin

   For correspondence

   pallares@princeton.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6547-1901

2. Ronan Ledevin

   Laboratoire de Biométrie et Biologie Evolutive, UMR5558, CNRS, University Lyon 1, Campus de la Doua, Villeurbanne, France

   Present address

   Université de Bordeaux, PACEA, UMR5199, 33615 Pessac, France

   Contribution

   Conceptualization, Formal analysis, Investigation, Writing—review and editing

   Contributed equally with

   Luisa F Pallares

   Competing interests

   No competing interests declared

3. Sophie Pantalacci

   ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratoire de Biologie et Modélisation de la Cellule, 15 parvis Descartes, F-69007, UnivLyon, Lyon, France

   Contribution

   Conceptualization, Writing—review and editing

   Competing interests

   No competing interests declared

4. Leslie M Turner

   1. Department of Evolutionary Genetics, Max-Planck Institute for Evolutionary Biology, Plön, Germany
   2. Department of Biology and Biochemistry, Milner Centre for Evolution, University of Bath, Bath, Unites States

   Contribution

   Conceptualization, Resources, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5105-3546

5. Eirikur Steingrimsson

   Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Reykjavik, Iceland

   Contribution

   Conceptualization, Resources, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5826-7486

6. Sabrina Renaud

   Laboratoire de Biométrie et Biologie Evolutive, UMR5558, CNRS, University Lyon 1, Campus de la Doua, Villeurbanne, France

   Contribution

   Conceptualization, Supervision, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8730-3113

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