new statistical genetics of molar cusp patterning in pedigreed … · 2017. 7. 2. · statistical...

Statistical Genetics of Molar Cusp Patterning inPedigreed Baboons: Implications for Primate DentalDevelopment and Evolution+

LESLEA J. HLUSKO1n, MARY-LOUISE MAAS1, and MICHAEL C. MAHANEY2

1Department of Anthropology, University of Illinois, Urbana, Illinois 618012Department of Genetics, Southwest Foundation for Biomedical Research, SanAntonio, Texas 78245

ABSTRACT Gene expression and knock-out studies provide considerable information about thegenetic mechanisms required for tooth organogenesis. Quantitative genetic studies of normalphenotypic variation are complementary to these developmental studies and may help elucidate thegenes and mechanisms that contribute to the normal population-level phenotypic variation uponwhich selection acts. Here we present the first quantitative genetic analysis of molar cusp positioningin mammals. We analyzed quantitative measures of molar cusp position in a captive pedigreedbaboon breeding colony housed at the Southwest National Primate Research Center in San Antonio,Texas. Our results reveal complete pleiotropy between antimeric pairs of traits – i.e., they areinfluenced by the same gene or suite of genes. Mandibular morphological homologues in the molarseries also exhibit complete pleiotropy. In contrast, morphological homologues in maxillary molarseries appear to be influenced by partial, incomplete pleiotropic effects. Variation in the mandibularmesial and distal molar loph orientation on the same molar crown is estimated to be geneticallyindependent, whereas the maxillary molar mesial and distal loph orientation is estimated to havepartially overlapping genetic affects. The differences between the maxillary and mandibular molarpatterning, and the degree of genetic independence found between lophs on the same molar crown,may be indicative of previously unrecognized levels of modularity in the primate dentition. J. Exp.Zool. (Mol. Dev. Evol.) 302B:268–283, 2004. r 2004 Wiley-Liss, Inc.

INTRODUCTION

Developmental research over the last 15 yearshas changed the way we think about morphologi-cal evolution (Raff, ’96). For example, knowledgeof the genetics underlying limb developmentelucidates the origins and evolution of the tetra-pod limb (Shubin, 2002; Tickle, 2002; Shubin,et al., ’97). The combination of fossil and geneticdata also sheds light on the radiation of metazoansand their body plans in the Cambrian ‘explosion’(Raff, ’96). Similarly, as our understanding of themechanisms underlying dental development im-proves, so does our appreciation for how evolutionof these processes may have produced the toothmorphologies recorded in the fossil record(Keranen et al., ’98; Weiss et al., ’98; Jernvall,2000; Jernvall et al., 2000; Stock, 2001).There are two fundamental research directions

concerning the development of the mammaliandentition, and advances have been made towardsour understanding of both, primarily through

gene expression and knock-out studies of mice(Maas and Bei, ’97; Weiss et al., ’98; Jernvall andThesleff, 2000; Stock, 2001). The aim of the firstmajor direction is to decipher the mechanisms thatdetermine tooth row patterning; i.e., how incisorsare produced in one region of the mouth andmolars in another, with canines and premolars inbetween. This overall dental patterning may bethe result of a combinatorial genetic code, muchlike is seen for Hox genes and the vertebralcolumn (Kessel and Gruss, ’91; Condie andCapecchi, ’93).

However, Hox genes are not expressed in thetissues from which the dentition develops, so they

Grant sponsor: National Science Foundation; Grant number: BCS-0130277; Grant sponsor: NIH/NCRR; Grant number: P51 RR013986supports the Southwest National Primate Research Center.

nCorrespondence to: Leslea J. Hlusko, Department of Anthropol-ogy, University of Illinois, 109 Davenport Hall, MC-148, Urbana,IL 61801. E-mail: [email protected]

Received 17 June 2003; Accepted 1 April 2004Published online in Wiley Interscience (www.interscience.wiley.

com). DOI: 10.1002/jez.b.21

r 2004 WILEY-LISS, INC.

JOURNAL OF EXPERIMENTAL ZOOLOGY (MOL DEV EVOL) 302B:268–283 (2004)

do not seem to regulate the patterning of thedentition. Rather, several other homeobox genesfrom the Barx, Dlx, and Msx families have beenimplicated in an odontogenetic code (Sharpe, ’95;Thomas and Sharpe, ’98; Tucker and Sharpe, ’99).A different model has also been proposed based onthe concept of reaction-diffusion or Bateson-Tur-ing processes (Turing, ’52; Kieser, ’84; Jernvall,’95; Jernvall et al., ’98; Weiss et al., ’98). In thismodel, morphogens interact in differential wave-like patterns to produce spatial variation inchemical reactions, such as inhibition, production,autocatalysis, etc. Cells respond to these spatialmorphogenetic variations, resulting in spatiallypatterned morphology, such as in pigmentation,mineralization, location of scales or feathers, etc.Though there is experimental and syndromicevidence supporting the combinatorial code model(Vastardis et al., ’96; Thomas et al., ’97; Fergusonet al., ’98; Vanden Boogaard et al., 2000), theseresults can also be interpreted in terms of thresh-old models that comply with the reaction-diffusionmodel (Thesleff, ’96; Stock, 2001). Some patternsof population morphological variation have alsobeen interpreted to better fit this latter model(Jernvall, 2000).The second major direction within dental devel-

opmental research focuses on the formation ofindividual teeth and the mechanism(s) that de-termine the number, size, shape, and placement ofcusps (Jernvall and Thesleff, 2000; Stock, 2001).By mouse embryonic day 13.5 (E13.5) tooth budshave formed as outgrowths of the dental lamina, athickened band of epithelial tissue, possiblyspecified by antagonistic fibroblast growth factor(FGF) and bone morphogenetic protein (BMP)signaling (Fig. 1). The tooth bud invaginates intothe mesenchyme as the inductive potential shiftsfrom the epithelium to the mesenchyme. Theepithelial tissue then enfolds a mass of mesench-yme forming a cap-like structure, initiating whatis known as the cap stage.At this point in odontogenesis, a condensation of

non-proliferating epithelial cells forms at the tip ofthe tooth bud. This condensation, known as anenamel knot, does not proliferate, expresses manyof the same signaling molecules as other embryo-nic signaling centers, and is surrounded by rapidlydividing epithelial cells (Jernvall et al., ’98;Thesleff et al., 2001). The initiation of the enamelknot may be a critical moment in morphogenesis,as it potentially indicates the start of the mole-cular cascades that determine species-specific cusppatterns (Keranen et al., ’98; Jernvall et al., 2000).

In mice, the enamel knot grows distally from themesial aspect of the tooth bud into a bullet-shapedstructure (Jernvall et al., ’94) that then undergoesapoptosis in reverse order from its originalgrowth; i.e. the most distal region dies off first(Vaahtokari et al., ’96).

This primary enamel knot gives rise to second-ary enamel knots (E15). These secondary enamelknots are located at what becomes the tip of eachcusp and express virtually all the same knownregulatory genes as the primary enamel knot,except for BMP2 (Jernvall, 2000). Additionally, nodifferences in homeobox gene expression have

Fig. 1. Representation of the early stages of mammaliantooth development. Panel 1: embryonic day 13.5 (E13.5), thebud stage; Panel 2: E14.5, cap stage, primary enamel knot(EK) shown in gray; Panel 3: formation of secondary EKs,shown in gray; Panel 4: E16, bell stage when mineralizationstarts. See text for more details.

STATISTICAL GENETICS OF MOLAR CUSP PATTERNING 269

been found between the various cusps (Zhao et al.,2000b). Therefore, the secondary enamel knotsappear to be identical in terms of their molecularsignaling, suggesting that the patterning of cusppositions may be the product of an overall dynamicprogram. Consequently, pleiotropy among cusps isassumed to be high (Jernvall and Jung, 2000).The next stage of odontogenesis is the bell stage

(E16) during which enamel-forming ameloblastsderive from the epithelium and the mesenchymegives rise to odontoblasts that form dentine. Withthe start of mineralization, bunodont molar crownmorphology is largely determined and is finalizedat the time of eruption. Teeth do not continue togrow after this point because the enamel-formingcells are external to the crown and are shed ateruption. Only wear and breakage alter crownmorphology after this point.Species-specific cusp arrangements first appear

with the development of the secondary enamelknots (Keranen et al., ’98; Jernvall and Thesleff,2000; Jernvall, et al., 2000). Keranen et al. (’98)and Jernvall et al. (2000) studied the expressionpatterns of numerous regulatory genes in tworodent species, mouse and vole. These two speciesare similar in size, gestation time, and dentalpattern. However, mice have roughly parallel andlow-crowned mandibular molar cusps, whereasvoles have asymmetrical and continuously grow-ing high-crowned cusps. Despite the morphologi-cal differences in their teeth and an evolutionarydivergence 20–25 million years ago (Nikoletopou-los et al., ’92), they are found to have similarmolecular cascades throughout tooth organogen-esis. This further demonstrates the conservednature of dental developmental mechanisms, asis also seen in such phylogenetically distant andmorphologically dissimilar taxa as mice and hu-mans (Davideau et al., ’99).Species differences do appear just prior to the

formation of the secondary enamel knots. Mor-phological differences between mouse and volesecondary enamel knots were closely correlatedwith immediately preceding Fgf4, Shh, Lef1, andp21 spatial expression patterns (Jernvall et al.,2000). Therefore all four genes appear to beinvolved in cusp patterning, though the interac-tions between these signaling pathways are cur-rently unknown (Jernvall, et al., 2000).It is widely recognized that the mechanisms

required to make an organ are not necessarily thesame mechanisms that result in its normalpopulation level variation. As selection operateson populations, knowledge of the mechanisms that

produce normal variation is needed in order toreconstruct an integrated geno- and phenotypicevolutionary history (Jernvall, 2000). The mouse/vole study represents a critical break-though inunderstanding molar morphological variation andevolution in terms of underlying variation in geneexpression (Polly, 2000), and draws attention toFgf4, Shh, Lef1, and p21 and their regulators aspotential candidate genes for determining popula-tion level molar morphological variation.

A mouse and human genetic mutation bolsterthis interpretation. Tabby mice and humans withX-linked anhidrotic (hypohidrotic) ectodermaldysplasia share a genetic mutation of the syntenicectodysplasin A gene (Eda) on the X chromosomethat causes buccal/labial and lingual molar cuspsto be compressed or fused along with otherepithelial disorders (Ferguson et al., ’97; Srivas-tava et al., ’97). Pipsa et al. (’99) found that Tabbymolars cultured in vitro with FGF4 and –10 havepartially corrected cusp development. A morerecent study (Gaide and Schneider, 2003) demon-strated that Tabby mouse embryos treated with arecombinant form of EDA1 in utero have analmost completely and permantly rescued pheno-type. Further research is needed to discern therelationship between Eda and Fgf4 and –10 andtheir potential role(s) in determining molar cusppositioning.

If Fgf4, Shh, Lef1, and p21 and/or the mechan-isms that regulate them underlie population levelvariation in cusp patterning, then the patterningcascade interpreted from their expression suggestssignificant pleiotropy between molar cusps, andthat distal cusp pairs are essentially geneticreiterations of the first cusp pair. Both morpholo-gical (Marshall and Butler, ’66; Van Valen, ’94;Jernvall and Jung, 2000) and developmentalstudies provide evidence that characters of occlud-ing teeth may also result from the same develop-mental process. Here we test these two hypotheses(hypothesis 1: mesial and distal cusp pair orienta-tions results from significant pleiotropy; hypoth-esis 2: maxillary and mandibular inter-arch cusppair orientations result from significant pleiotro-py) using modern quantitative genetic analyses ofdental variation from a captive pedigreed breedingcolony of baboons.

At first, our chosen approach to the dissection ofthe genetic architecture for dental developmentalvariation may seem to be in direct contrast tomore established approaches of molecular devel-opmental biology and genetics; however, theyactually are complementary. While the latter

L.J. HLUSKO ET AL.270

investigate directly the effects and/or expression ofknown candidate loci on, respectively, target traitsor in target tissues in developing organisms, weutilize quantitative genetic approaches to statisti-cally detect and measure the effects of initiallyunidentified genes on phenotypic variation in fullydifferentiated traits in adults. Once detected,available extensions to these statistical geneticapproaches allow us to localize the genetic effectsto specific chromosomal regions.These approaches are derived entirely from the

classical genetics description of the phenotypicvariance in a trait (s2

P) as an additive function ofthe genetic (s2

G) and environmental (s2E) var-

iances, such that, in its simplest forms2

P ¼ s2G þ s2

E. Classical quantitative genetics the-ory describes the phenotypic covariance amongrelatives for a trait similarly, and its methodsallow us to take advantage of the fact that thedegree of genetic and phenotypic similarity be-tween any relative pair is proportional to theirkinship (Falconer, ’89; Lynch and Walsh, ’98) todetect and quantify the relative contributions ofgenes and environmental factors – includingrandom, unmeasured factors – to this covariance(Almasy and Blangero, ’98).Additionally, multivariate extensions to this

approach allow us to detect shared or correlatedgenetic and non-genetic effects between differentphenotypes, such as different dental crown dimen-sions or morphologies (Almasy and Blangero, ’98).Genetic correlations are indicative of pleiotropy –i.e., the effect of a gene or suite of genes onvariation in more than one phenotype. Because wehypothesize that patterns of pleiotropy betweendental trait pairs in adults may represent theresults of coordinated developmental processes,we used multivariate variance decompositionmethods to analyze data on such trait pairs in apedigreed population of non-human primates.We find that a significant proportion of the

phenotypic variance in molar loph orientationdoes result from the additive effects of genes.The results of bivariate quantitative geneticanalyses suggest that antimeric, morphologicallyhomologous characters across the dental arcadeand between teeth along the mandibular tooth rowresult from complete pleiotropy (additive geneticcorrelation of one), whereas variation in maxillarymorphological homologous cusp pairs is influencedby incomplete pleiotropy. In contrast, traits on thesame tooth crown are partially (maxillary) orcompletely (mandibular) genetically independent.These results necessitate that the proposed hy-

potheses of patterning mechanisms be modified aswe progress in our understanding of the geneticand developmental mechanisms that contribute toprimate dental variation and evolution.

MATERIALS AND METHODS

The baboon population

The world’s largest captive, pedigreed breedingcolony of baboons (43,000) is housed at theSouthwest National Primate Research Center(SNPRC) at the Southwest Foundation for Bio-medical Research in San Antonio, Texas. Thecolony is maintained in pedigrees (with all mat-ings controlled) and genetic marker maps havebeen constructed using data obtain from B1,000of the animals, making this colony unique andimportant for the quantitative genetic analysis ofnormal phenotypic variation in primates (Rogerset al., ’99).

Data for this study were collected from highresolution plaster dental casts of 630 pedigreedbaboons, Papio hamadryas. The sample has afemale to male sex ratio approximating 2:1 withindividuals ranging in age from 4.6 to 30 years.The protocol for collecting the dental casts isoutlined in detail elsewhere (Hlusko et al., 2002).The Institutional Animal Care and Use Commit-tee, in accordance with the established guidelines(National Research Council, ’96), approved allprocedures related to the treatment of the baboonsduring the conduct of this study.

The computer package PEDSYS (Dyke, ’96) wasused for all pedigree data management andpreparation. The animals from which data werecollected are distributed across eleven extendedpedigrees. The mean number of animals with dataper pedigree was 44, and these individualstypically occupied the lower two or three genera-tions of each pedigree. Genetic management of thecolony was started over 20 years ago and allows fordata collection from non-inbred animals. All non-founder animals in this study resulted frommatings that were random with respect to dental,skeletal, and developmental phenotype.

Data collection protocol

Digital photographs were taken of high-resolu-tion plaster replicas of each molar using a protocoldescribed elsewhere (Hlusko et al., 2002). All dataused in this analysis were collected from thesephotographs. Measurements were not collectedfrom broken or unusually worn molars. Because of


differential wear and breakage, not all molarsfrom all individuals could be used in the study.Consequently, from our overall sample of 630individuals, the sample sizes for each phenotyperanged between 168–455.Cusp position was assessed for all maxillary and

mandibular molars via the orientation of themesial and distal lophs relative to an approximatemesiodistal axis of the molar. We established thisrelationship via angle measurements (Fig. 2). Areference line was drawn as a tangent connectingthe lingual-most points on the occlusal view of thetooth crown for mandibular molars and the twobuccal/labial-most points on the crown edge formaxillary molars. Using Optimas

rwe measured

the mesial angle of the mesial and distal lophsrelative to this reference line. Measurements weretaken three times and averaged.When developing the measurement protocol our

goal was to document cusp pair orientation.Ideally this measurement would be independentof crown size, however orientations not influencedby size proved to be more problematic to replicate.The most replicable of the methods tried was toorient the cusp pairs off of the most vertical side ofthe molar crown, as this was the least affected bywear (the lingual side of the mandibular molarand the buccal/labial side of the maxillary molar).Therefore, our angle measurements by necessityinclude a bias due to molar width. The ramifica-tions of this bias will be discussed later.

Linear measurements of mesiodistal length andbuccolingual width of each loph were collectedfrom the same digital images, also using Optimas.

Analytical methods

Statistical genetic analyses were conducted bymeans of a maximum likelihood based variancedecomposition approach implemented in the com-puter package SOLAR (Almasy and Blangero, ’98).Accordingly, the phenotypic covariance for eachtrait within a pedigree in this study is modeled asO ¼ 2Fs2

G þ s2E, where F is a matrix of kinship

coefficients for all relative pairs in a pedigree, s2G

is the additive genetic variance, I is an identitymatrix (composed of ones along the diagonal andzeros for all off diagonal elements), and s2

E is theenvironmental variance. Because the componentsof the phenotypic variance also are additive, suchthat s2

P ¼ s2G þ s2

E, we estimated heritability, orthe proportion of the phenotypic variance attribu-table to additive genetic effects, as h2 ¼ s2

G=s2P.

Phenotypic variance attributable to non-geneticfactors is estimated as e2¼1�h2. Additionally, weestimated the mean effects of sex, age, mesiodistallength, and mesial and distal buccolingual width ofthe crown on the loph angles recorded for eachmolar studied.

Using extensions to univariate genetic analysisthat encompass the multivariate state (Hopperand Mathews, ’82; Lange and Boehnke, ’83;

Fig. 2. Occlusal views of a maxillary left first molar and mandibular left second molar showing the protocol for collectingangle of loph orientation. The white line represents the mesiodistal reference line. The black lines were drawn between thebuccal/labial and lingual most points of the lophs. The angle of the black line relative to the reference line was used as thequantification of loph orientation. See text for further explanation.


Boehnke et al., ’87), we followed an approachdescribed in detail elsewhere (Mahaney et al., ’95)to model the multivariate phenotype of an in-dividual as a linear function of the measurementson the individual’s traits, the means of these traitsin the population, the covariates, and theirregression coefficients, plus the additive geneticvalues and random environmental deviations.From this model, we obtained the phenotypicvariance-covariance matrix from which we parti-tioned the additive genetic and random environ-mental variance-covariance matrices, given therelationships (kinship coefficients) observed in thepedigree. From these two variance-covariancematrices, we estimated the additive genetic corre-lation, rG, and the environmental correlation, rE,between trait pairs. Respectively, these correla-tions are estimates of the additive effects of sharedgenes (i.e., pleiotropy) and shared environmental(i.e., unmeasured and nongenetic) factors on thevariance in a trait.The genetic and environmental components of

the phenotypic correlation matrix are additive,like those of the corresponding variance-covar-iance matrix, so we could use the maximumlikelihood estimates of the additive genetic andenvironmental correlations to obtain the totalphenotypic correlation between two traits, rP, as

rP ¼ffiffiffiffiffih21

q ffiffiffiffiffih22

qrG þ

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffið1� h2

1Þq ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ð1� h22Þ

qrE:

We conducted a series of bivariate quantitativegenetic analyses of all loph angle-related traitpairs using multivariate extensions to the basicvariance decomposition methods implemented inSOLAR (Almasy and Blangero, ’98). These ana-lyses were subsumed into two categories: analysesof trait pairs on two antimeric teeth within thesame dental arch and analyses of serial trait pairson the same side of the same arch. We used thisapproach to obtain simultaneous maximum like-lihood estimates of the phenotypic means (m),phenotypic standard deviations (s), heritabilities(h2), and the mean effects of covariates on alltraits, as well as the genetic and environmentalcorrelations between them.Significance of the maximum likelihood esti-

mates for heritability and other parameters wasassessed by means of likelihood ratio tests. Twicethe difference of the maximum likelihoods of ageneral model (in which all parameters wereestimated) and a restricted model (in which thevalue of a parameter to be tested was heldconstant at some value, usually zero), were

compared. This difference is distributed asympto-tically approximately as either a 1

2:12 mixture of w2

and a point mass at zero, for tests of parameterslike h2 for which a value of zero in a restrictedmodel is at a boundary of the parameter space, oras a w2 variate for tests of covariates for which zerois not a boundary value (Hopper and Mathews,’82). In both cases degrees of freedom is equal tothe difference in the number of estimated para-meters in the two models (Boehnke et al., ’87).However, in tests of parameters like h2, whosevalues may be fixed at a boundary of theirparameter space in the null model, the appropriatesignificance level is obtained by halving theP-value (Boehnke et al., ’87).

For bivariate models in which genetic correla-tions are found to be significantly greater thanzero and indicative of pleiotropy, additionaltests are performed to compare the likelihood ofa model in which the value of the geneticcorrelation is fixed at 1.00 or zero to that of theunrestricted model in which the value of thegenetic correlation is estimated. A significantdifference between the likelihoods of the restrictedand polygenic models suggests incomplete pleio-tropy; i.e., not all of the additive genetic variancein the two traits is due to the effects of the samegene or genes.

Individuals sampled in pedigrees are not allindependent of one another and statistical teststhat do not account for patterns of kinship withinthe sample may yield misleading significanceestimates. The effect of kinship on independencewithin a pedigree may be appreciated by compar-ing the original N to an estimate of the effectivesample size as nes ¼ ss2=2 varðssÞ, where ss2 is theestimated residual phenotypic variance of thequantitative trait from our maximized, unrest-ricted quantitative genetic model (as described inBlangero et al., ’92).

RESULTS

Quantitative genetic analyses of 18 of the 24angle phenotypes yield significant heritabilityestimates (Table 1a, b), demonstrating that asignificant proportion of the population variancein loph orientation results from additive geneticeffects. Total h2 estimates center around 0.29,meaning that, on average, 29% of the totalphenotypic variance in these traits is attributableto the additive effects of genes. The mean propor-tion of the total phenotypic variance in these traitsattributable to covariates is 0.24. On average, the


proportion of the variance remaining to beexplained – i.e., the residual ‘‘environmental’’variance due to the effects of unmeasured envir-

onmental factors, measurement error, dominanceaffects, and environmental variance, is approxi-mately 0.47. Our models do not show significant

TABLE IA. Quantitative genetic analyses of individual maxillary loph angle phenotypes1: summary statistics, maximum likelihoodparameter estimates, and covariate e¡ects summaries

RM1ma RMl da RM2 ma RM2 da RM2 ma RM3 da LM1ma LM1da LM2 ma LM2 da LM3 ma LM3 da

Mean 96.11 92.73 97.06 92.64 100.19 93.96 96.10 93.18 97.86 93.97 100.35 94.01Variance 8.51 9.55 11.27 8.75 17.86 18.29 6.28 6.65 11.72 10.69 160.29 17.43Low value 86.28 77.81 85.33 81.30 81.59 81.76 87.64 77.89 82.54 76.65 90.58 82.38High value 105.82 100.61 107.83 101.03 112.78 106.31 102.66 102.02 107.37 103.58 112.89 105.21n 308 308 436 436 197 168 344 330 434 434 241 252nes 265.8 264.7 310.4 266.5 175.8 296.4 302.7 307.9p-value o0.001 o0.01 o0.001 o0.001 ns ns o0.001 o0.001 o0.001 o0.001 ns nsTotal h2 0.327 0.244 0.216 0.223 0.318 0.308 0.273 0.300Total c2 0.244 0.248 0.394 0.335 0.260 0.234 0.344 0.257Total e2 0.429 0.508 0.390 0.442 0.422 0.458 0.383 0.443Residual h2 0.432 0.324 0.356 0.336 0.429 0.402 0.416 0.4047SE 70.174 70.141 70.099 70.109 70.137 70.142 70.109 70.123b length k kb mes width mmm mmm mmm mmm mmm mmm mmm mmmb dist width kkk kkk kkk kkk kkk kkk kkk kkkb age kk kb sex k k kkk kkk kkb age2 k k k k kb agensex k k k k

1Arrows indicate direction of covariate e¡ect (m¼positive, k¼less than 1 or negative). Number of arrows indicates the signi¢cance level of thecovariate listed in the left-hand column in the polygenic model:mmm p-valueo0.001;mm p-valueo0.01;m p-valueo0.1.Total c2¼amount of phenotypicvariance attributable to covariates.Totalh2¼(Residualh2)(1�Total c2).Total e2¼[1�(Total c2+Totalh2)]. n¼original sample size;nes¼e¡ective samplesize, see text for explanation.

TABLE IB. Quantitative genetic analyses of individual mandibular loph angle phenotypes1: summary statistics, maximum likelihoodparameter estimates, and covariate e¡ects summaries

RM1ma RM1da RM2 ma RM2 da RM3 ma RM3 da LM1ma LM1da LM2 ma LM2 da LM3 ma LM3 da

Mean 83.82 86.22 83.38 84.54 80.30 820.44 83.00 85.16 82.81 83.86 81.87 84.10Variance 6.83 6.21 5.33 7.03 7.74 10.06 7.67 6.86 6.51 7.56 6.88 8.126Low value 76.23 79.28 76.80 75.33 70.42 70.67 75.19 75.88 73.60 77.13 74.03 73.77High value 90.64 92.09 89.82 92.33 90.15 101.00 93.06 91.76 89.99 91.89 89.43 92.31N 227 227 337 344 436 436 259 274 332 333 424 424nes 197.4 172.0 310.4 301.5 243.8 244.4 300.8 303.8 181.5 255.2p-value o0.001 o0.001 o0.01 o0.001 ns o0.001 ns o0.01 o0.01 o0.001 o0.01 o0.001Total h2 0.343 0.591 0.166 0.282 0.236 0.207 0.202 0.299 0.243 0.402Total c2 0.177 0.132 0.336 0.306 0.273 0.144 0.194 0.178 0.140 0.107Total e2 0.480 0.277 0.498 0.412 0.491 0.649 0.604 0.523 0.617 0.491Residual h2 0.417 0.681 0.25 0.406 0.325 0.242 0.250 0.364 0.283 0.4507SE 70.183 70.179 70.144 70.133 70.117 70.137 70.133 70.122 70.124 70.111b lengthb mes width kkk kkk kkk kkk kkk kkk kkk kkk kkk kkb dist width mmm mmm mmm mmm mmm mmm mmm mmm mmm mmb age k kb sex kk kb age2

b agensex

1Arrows indicate direction of covariate e¡ect (m¼positive, k¼less than 1 or negative). Number of arrows indicates the signi¢cance level of thecovariate listed in the left-hand column in the polygenic model:mmm p-valueo0.001;mm p-valueo0.01;m p-valueo0.1.Total c2¼amount of phenotypicvariance attributable to covariates.Total h2¼(Residual h2) (1�Total c2).Total e2¼[1�(Total c2+Total h2)]. n¼original sample size; nes¼e¡ective sam-ple size, see text for explanation.


differences in these relative proportions betweenthe maxillary and mandibular loph orientations/angles.Likelihood ratio tests of the models for the

individual angle phenotypes identify several sig-nificant covariates. In our maximized models,mesial and distal loph buccolingual widthscontribute significantly to loph angle variation.For the mandibular molars, mesial buccolingualwidth has a negative mean effect, and distalbuccolingual width has a positive mean effect onloph angle. The maxillary molars show theopposite relationship: mesial buccolingual widthhas a positive effect, whereas distal buccolingualwidth has a negative effect. In comparison, themean effects of other covariates (mesiodistallength, age, sex, age2, and agensex) are usually

not significant or appear to contribute little to lophangle. Due to the fact that dental crown develop-ment ceases following emergence of a tooth intothe oral cavity, age in these analyses acts as aproxy for wear. Significant age-related effectsare detected in some of our analyses, but notconsistently.

The results of our bivariate quantitative geneticanalyses are presented in Table 2. Antimericanalyses estimate the genetic and environmentalcorrelations between loph angles on opposite sidesof the same dental arch, for example the RM1

versus LM1 mesial angle. Of the eight bivariateanalyses in this category, seven of the geneticcorrelations are either estimated to equal one orare not significantly different from one. Theeighth is estimated to be 0.79. The non-genetic

TABLE 2. Bivariate statistical genetic analyses: Maximum-likelihood estimates (MLE) of genetic and environmental correlations1

Correlations Signi¢cance, P(Hypothesis)

Phenotype pairs N rG rE rG¼0 jrGj ¼ 1 rE¼0 jrEj ¼ 1�

RM1da^LM1da 215 0.793 0.112 0.0070 0.1300 0.7200 0.0300RM2ma ^ LM2ma 296 1.000 0.306 0.0200 ^ 0.0500 o0.0001RM2da^LM2da 296 0.985 0.283 0.0008 0.9200 0.0800 o0.0001

Antimeres RM3da^LM3da 380 0.757 0.055 0.0076 0.2980 0.6636 o0.0001RM1ma ^ LM1ma 288 1.000 �0.689 o0.0001 ^ 0.0850 0.6800RM2ma^LM2ma 454 0.900 0.171 o0.0001 0.3337 0.1554 0.0002RM1da^LM1da 288 1.000 0.172 0.0004 ^ 0.2900 0.0004RM2da^LM2da 454 0.952 �0.066 o0.0001 0.6064 0.6806 o0.0001

RM1ma ^ RM2ma 270 1.000 0.0117 0.0100 ^ 0.9400 o0.0001RM1da^RM2da 208 1.000 �0.228 0.0010 ^ 0.3600 0.1000RM2da^RM3da 375 0.639 0.111 0.0305 0.03867 0.4596 o0.0001

Serial^between teeth LM1da^LM2da 270 0.774 0.254 0.0100 0.5700 0.1200 0.0003LM2ma^LM3ma 384 1.000 0.194 0.0229 ^ 0.1470 0.0004LM2da^LM3da 384 0.849 0.009 0.0027 0.4367 0.9489 o0.0001RM1ma^RM2ma 377 0.409 0.199 0.0890 0.0020 0.1726 o0.0001RM1da^RM2da 377 0.610 0.103 0.0121 0.0085 0.5044 o0.0001LM1ma^LM2ma 402 0.279 0.092 0.2209 o0.0001 0.5711 o0.0001LM1da^LM2da 402 0.453 0.130 0.0868 0.0022 0.4315 o0.0001

RM1ma^RM1da 220 �0.083 0.479 0.7828 0.0036 0.0655 0.1166RM2ma^RM2da 337 0.407 0.460 0.2400 0.0200 0.0050 o0.0001LM2ma ^ LM2da 325 0.225 0.480 0.5139 0.0100 0.0020 o0.0001

Serial ^ same tooth LM3ma ^ LM3da 424 0.427 0.438 0.1112 0.0037 0.0026 o0.0001RM1ma^RM1da 305 0.351 0.349 0.3000 0.0010 0.1200 0.0010RM2ma^RM2da 455 0.353 0.656 0.0690 o0.0001 o0.0001 o0.0001LM1ma^LM1da 324 0.067 0.451 0.8400 0.0010 0.0200 0.0005LM2ma^LM2da 434 0.745 0.536 0.0001 o0.0001 0.0005 o0.0001

1MLE: maximum likelihood estimate.P(Hypothesis): probability of the hypothesis (indicated in columns below) being true given the available pedigreed data.ma¼mesial loph angle.da¼distal loph angle.mw¼mesial loph buccolingual width.dw¼distal loph buccolingual width.


correlations are low or not significantly differentfrom zero. The analyses of R&LM2 mesial anglesand R&LM2 distal angles yield negative rEcorrelations. Further analyses are needed todetermine if these negative estimates are statisti-cally anomalous.The serial analyses were performed on two

types of comparisons. The first is betweenmorphologically homologous traits on teeth alongthe same tooth row, such as the RM1 and RM2

mesial angles. Ten comparisons were possiblefrom these data sets. All six mandibular pairsare estimated to have high genetic correlations,five of which are not significantly differentfrom one. None of the non-genetic correlationswere found to be significantly different fromzero.All four of the maxillary, serial, between-teeth

comparisons return lower rG estimates. All aresignificantly different from one; three are alsosignificantly different from zero. The rE estimatesare all low and not significantly different fromzero.The second component to the serial analyses is

the relationship between the mesial and distalloph angles on the same molar crown. Weperformed these analyses on the eight molars forwhich both loph angles were found to havesignificant heritability estimates. For the mandib-ular molars, the genetic correlations betweenmesial and distal loph angle variance on the samemolar crown are not significantly different fromzero. For both maxilla and mandible, all but one ofthe non-genetic correlations are estimated to bebetween zero and one. In contrast, the maxillarymolar same tooth rG are all significantly differentfrom one, and half are also significant from zero.Inter-arch bivariate analyses were also per-

formed (results not shown). However, pairedsample sizes are small and analytical results areinconclusive.

DISCUSSION

Heritability estimates

We find that a significant proportion of lophangle variance in this population is due to theadditive effects of genes. Understanding additivegenetic contributions to phenotypic variance iscritical to evolutionary studies, as well as toanimal and plant breeders, because these effectsreflect how strongly the trait will respond toselection, both natural and artificial (Falconer,’89; Lynch and Walsh, ’98; Hartl and Jones, 2001).

Studies of variation between synchronic anddiachronic primate populations show that cuspproportions, number, and position do vary insignificant ways (Frisch, ’63; Swindler et al., ’67;Swindler and Orlosky ’74;). The results presentedhere demonstrate that the inherent assumption ofthese authors, that changes in such phenotypescould reflect either an adaptive response toselection or result from processes such as drift, istenable (i.e., their assumptions imply an under-lying additive genetic component).

Genetic and covariate effects

Though it is important to establish this additivegenetic foundation for morphological studies, thedetection and characterization of a significantgenetic component for loph angle variation is ofconsiderably more interest than the actual herit-ability point estimates themselves. Quantitativegenetic analytical approaches, such as the oneemployed here, enable us to estimate the relativecontributions of various covariates and non-genetic factors to the population variance in lophangle (Almasy and Blangero, ’98). This may allowus some additional insights into genetic andenvironmental sources of variation in develop-mental mechanisms underlying dental phenotypicvariation, more than was possible in manyprevious odontological heritability analyses (e.g.,Alvesalo and Tigerstedt, ’74; Sirianni and Swind-ler, ’75; Townsend and Brown, ’78; Potter et al.,’83). Additionally, it may facilitate the generationof hypotheses concerning the developmental me-chanisms themselves.

Our models indicate that a large portion,approximately 50%, of the variance is attributableto non-genetic or ‘‘environmental’’ effects. Thiscomponent of the variance includes factors such asunidentified covariate effects, dominance, mea-surement error, etc.

Not all of the potential covariates screened inthese analyses were found to be significant. Thesignificant covariates include sex and age tominimal extents, and predominantly molar crownwidth measurements. These account for approxi-mately 20% of the phenotypic variance.

Sex of the individual is found to be significant inseven of the 18 polygenic models. Age and variantsof age by sex interactions are also significant inseven of the models, though the significance levelof these contributions is minimal for six of these. Dueto tooth development and mineralization, growthis halted at eruption and molar morphology


is only altered by wear and breakage from thatpoint on. As noted previously, we did not includebroken or unusually worn molars in these ana-lyses. Therefore, age acts as a proxy for normalocclusal wear in these models. Future analysesusing established wear stages as a covariate willenable us to confirm this relationship. For nowhowever, we interpret the minimal significance ofthe mean effect of age to indicate that wear doesnot contribute substantively to the variance inthese measures in this population of animals. Weconclude similarly for sex. This latter observationaccords well with studies of morphological varia-tion at the population level; where cusp number,positioning, and proportion are not sexuallydimorphic (Frisch, ’63; Swindler ’67; Swindlerand Orlosky, ’74; Uchida, et al., ’98).The most significant and consistent covariate

effect across these 11 analyses involves the widthmeasurements. This probably results from theinterdependence between loph orientation andloph width inherent in our measurement protocol.Specific molar cusp positioning has not beenextensively studied in human and primate odon-tology. However, the few relevant studies suggestthat the covariance identified in our analyses maybe suspect. Peretz and Smith (’93) and Peretz et al.(’97, ’98) performed a series of analyses on therelationship between size and shape in deciduousfourth premolars (deciduous second molars) ver-sus permanent first molars. They find strongcorrelations between various size measurements,and strong correlations between various shape-oriented variables, but not between these twocategories (Peretz and Smith, ’93; Peretz et al.,’98). This suggests ‘‘that the two processes developin an independent pattern and rate’’ (Peretz et al.,’98:533). Cusps do appear to shift positionwhen early and late stages of crown mineraliza-tion are compared (Peretz et al., ’97, ’98). Thecombination of these results suggests that cusppositioning may be genetically independent ofcrown size, though as cusps develop the processof mineralization does ultimately influence cusppositioning (Polly, ’98). Likewise, the oppositeinfluence of loph width on loph angle in maxillaryversus mandibular molars may be an artifact ofour measurement protocol, as we estimated lophposition relative to the most vertical side of themolar crown (lingual for mandibular molars andbuccal/labial for maxillary molars), the orientationof which is influenced by loph width.We tested an alternate protocol that corrects for

this bias by using the mesiodistal axis of the tooth

as the line from which the angles are estimated.In analyses applying this protocol to data for twomandibular molars, width accounts for less than1% of the overall phenotypic variance. However,maximum likelihood estimates of heritabilitiesand correlations (from the bivariate analyses)closely approximated those obtained using thefirst protocol described earlier in this paper (datanot shown). Therefore, while the detected meaneffects of tooth width on cusp position measuresmay be artifacts of our reported measurementprotocol, their inclusion in our models does notsubstantively affect the results and conclusionspresented in this paper.

Pleiotropy

In theory, there are two possible causes ofgenetic correlation (see e.g., Bulmer, ’74; Lynchand Walsh ’98): pleiotropy and gametic phasedisequilibrium, including linkage disequilibrium;however, we feel that the latter is unlikely toexplain the genetic correlations estimated in thisstudy. Given the age and non-inbred nature of thepedigreed baboon population from which weobtained our data, we posit that gametic phasedisequilibrium – i.e., non-random associationbetween genotypes at different, unlinked loci – isnot particularly extensive. Additionally, the rela-tionship between linkage disequilibrium (LD) andgenetic distance (on the same chromosome) inthese baboons seems similar to that observed fornon-inbred human populations (e.g., Jorde et al.,’94; Abecasis et al., 2001): i.e., LD is only moderatebetween 50 kb and 500 kb and inconsistentlymanifested at distances beyond these two bound-aries. Further, we envision molar cusp patterns tobe complex phenotypes whose variation is due tothe effects of multiple genes at multiple loci,multiple developmental environmental factors,and multiple interactions between the two. There-fore, we believe that is it is unlikely that evidencefor complete pleiotropy – i.e., a situation whereinall the additive genetic variance in two traitsappears due to the effects of the same gene(s) –would actually result from multiple cases of nearlytotal gametic phase disequilibrium at multiple,different loci throughout the genome.

Antimeric pleiotropy

Odontological studies in both humans andnonhuman primates have long been based on theassumption that right and left antimeres aregenetic and developmental equivalents, typically


relying on only one side of the dentition torepresent the whole (Scott and Turner, ’97).Bilateral fluctuating dental asymmetry is gener-ally assumed to result from disturbances to, ornoise in, the developmental process (e.g., Perzi-gian, ’77; Keiser, ’92), as may be caused by illness,pre- and/or postnatal trauma, or other stressessuch as malnourishment. Fluctuating asymmetryis not typically thought to result from geneticpatterning. Consequently, we anticipated andfound that bivariate analyses of traits on anti-meres performed in this study yield geneticcorrelations of one. The non-genetic, or environ-mental correlations are greater than zero but stilllow. This suggests that the nongenetic effectsinfluencing loph angles on opposite sides of thesame dental arch in this healthy baboon popula-tion overlap to some degree, but are largelyindependent.

Serial genetic correlations

Our results also indicate that in this population100% of the genetic effects determining loph anglevariation in the mandibular molars are sharedbetween serial morphological homologues, suchthat the genetic determinants of variation inmandibular first molar mesial angle are identicalto those of the second molar mesial angle. Serialmorphological homologues on the maxillary mo-lars also have a high genetic correlation, althoughthis is estimated to be significantly different fromone. This high degree of pleiotropy accords withpredictions from morphological studies (VanValen, ’94) that find high levels of phenotypiccorrelation.In contrast, the non-genetic correlation between

serial morphological homologues is low or zero.This again suggests that non-genetic perturba-tions to the patterning process are essentiallyindependent, and possibly, if not probably, theresult of the offset between molars in mineraliza-tion and eruption times (Phillips-Conroy andJolly, ’88; Kahumbu and Eley, ’91).Comparisons between traits on the same crown

return surprisingly different results from theserial comparisons between molar crowns. Allmesial and distal loph genetic correlations arefound to be significantly different from one. In themandibular molars, this correlation is not signifi-cantly different from zero, whereas in the max-illary molars it is. Therefore, for mandibularmolars, the genetic effects determining variationin the mesial loph appear to be independent

of those that determine variation in the distalloph. Maxillary molar mesial and distal lophorientation appears to results from incompletepleiotropy.

The mandibular estimates in particular standin contrast to the hypothesis that pleiotropywould be high within crowns. Developmentalstudies suggest considerable pleiotropy betweenmesial and distal loph orientation, and theremay well be such pleiotropy at more fundamentalstages of development. However, in this baboonpopulation the genetic effects that determinepopulation level variation in loph angles onthe same molar are independent to varyingdegrees.

Although the genetic correlation for loph anglevariation on the same molar crown is low or zero,there is a significant nongenetic correlation.Therefore, the nongenetic influences are sharedto a certain degree. This probably results from theclose timing and spatial positioning of these traitsduring tooth organogenesis. The developmentalpicture that can be drawn from these resultssuggests that non-genetic effects rapidly becomeindependent of each other as the phenotypesbecome more distant, in terms of both ontogenetictime and space.

Bateson (1894) recognized similarities in pat-terns of serially homologous structures andlikened these to Chladni figures, frequency inter-ference in wave patterns. Butler (’39, ’56) speci-fically addressed such patterns in the dentition,proposing that classes of teeth derive from one‘type.’ Variation of tooth shape within each classmay result from identical tooth primordia reactingto morphogens. In this scenario, known as thefield theory, ultimate tooth shape is determined byextrinsic factors.

The clone theory, proposed by Osborn (’78),contrasts with the field theory in that each toothin a class is produced by the replication of theoriginal type or polar tooth (M1 for the molarfield), and morphology is predetermined by in-trinsic factors. The concept of dental fields hasbeen explored primarily through studies of mor-phological variation and correlation (e.g., Dahl-berg, ’45; Van Valen, ’61; Lombardi, ’75;Henderson and Greene, ’75), though informationfrom human genetic disorders has also beenexamined (Line, 2001). However, none of thesestudies is particularly conclusive and scenarioscan be drawn from most of this researchto support both theories. Neither of these twomodels has yet to be reconciled with either the


odontogenetic or reaction-diffusion dental pattern-ing mechanisms described previously.Our estimates of the genetic correlation be-

tween mesial and distal loph variation are relevantto the debate between the field and clone theories.If tooth primordial cusp positioning were deter-mined via extrinsic morphogens, then consider-able pleiotropy would be expected not onlybetween teeth, but between structures on thesame tooth crown. Here we find that mesial lophvariation between molars along the tooth row isdetermined by complete pleiotropy, as is distalloph variation. However mesial and distal lophvariation on the same crown appears to begenetically independent in mandibular molarsand partially independent in maxillary molars.We interpret these data to mean that minormolar morphological variation may not beextrinsically influenced by genes once toothprimordia are established. Rather, our resultssuggest that minor variation in cusp positionis determined intrinsically. It remains to bedetermined if tooth buds themselves are initatedvia an odontogenetic code or a reaction-diffusionprocess.The concept of morphological integration was

first introduced by Olson and Miller (’58), wasrevived by Cheverud and colleagues (Cheverud,’82; Cheverud et al., ’83), and has receivedconsiderable attention and refining since (Mag-wene, 2001; Stock, 2001; VonDassow and Munro,’99). This is the concept that phenotypic traits willbe tightly correlated when they share a commondevelopmental pathway and/or ultimate function.As such, individual morphological traits can beconceptualized as parts of sets. Several approacheshave been employed to identify these sets, fromphenotypic statistical correlations (Olson andMiller, ’58), quantitative genetics (Cheverud,’96), and neontological ontogeny (Shubin andWake, ’96).As Cheverud and colleagues have demonstrated

(Cheverud, ’96; Leamy et al., ’99; Workman et al.,2002), quantitative genetic analyses provide ameans through which to identify morphologicalintegration and ultimately correlate morphologi-cally integrated sets with QTL effects. Quantita-tive genetic analyses are preferable to tests ofphenotypic correlation (when possible) becausegenetic and nongenetic correlations can be esti-mated separately. Our investigation of dentalvariation in this pedigreed baboon colony isyielding information about morphological integra-tion in the primate dentition, providing further

evidence of hierarchical modularity in the denti-tion (Stock, 2001).

The evidence for pleiotropic effects in thebaboon dentition presented here may be indicativeof modularity different from what previously hasbeen proposed. Based on gene expression data,Jernvall et al. (2000) propose that the patterningmechanism for the mesial loph in mandibularmolars is duplicated to create the more distal cusppairs or lophs on the same tooth crown. Thisimplies that variation in the mesial cusp pair willbe repeated/copied in the subsequent pairs, andaccords with the concept that morphologicalmetamerism results from the duplication of apatterning mechanism (Weiss, ’90). Jernvall et al.(2000) propose two fundamental mechanisms tomolar crown patterning. The first is the repetitionof the mesial pair of cusps and the second iscontrol over the length of the molar crown thatultimately determines the number of repeatedcusp pairs.

Based on our quantitative genetic analysis ofbaboon dental variation, we suggest that there ismodularity that integrates features from differentmolars independently of other parts of the molarcrown. We infer that mesial molar lophs compriseone module and distal lophs another (Fig. 3). Thisis a modification of Jernvall et al.’s (2000)hypothesis. Building on Van Valen’s (’70) conceptof prepatterns, we suggest that the molar pre-pattern may have more than one cusp pair, theoriginal pair and a duplicated set. The original andrepeated segments may share the same funda-mental genetic determinants, such as are identi-fied in the gene expression studies of mouse andvole molars (Keranen et al., ’98; Jernvall et al.,2000). However, the duplicated segments are then

Fig. 3. Diagram showing possible new level of modularityin molar development, indicated with dotted lines. See ‘‘serialgenetic correlations’’ section in discussion.


free to vary independently following the concept ofmetameric variation (Bateson 1894; Weiss ’90).If this prepattern provides the foundation for all

molars, then the genetic mechanisms determiningvariation in the mesial lophs will all be shared andthe genetic mechanisms causing variation in thedistal lophs will also all be shared. However, thetwo lophs may have developed independent genet-ic determinants for minor morphological varia-tion. This independence may have been expectedgiven the developmental delay in mesial and distalloph formation. There are examples of normaladult phenotypic variation being determined veryearly in development making this hypothesisreasonable (Richardson, ’99). We cannot speculateat this time whether such a mechanism would bemorphostatic or morphodynamic (Salazar-Cuidadet al., 2003).

Relationship between maxillary andmandibular molars

Morphological studies of synchronic and dia-chronic populations also suggest that the max-illary and mandibular dentitions probably resultfrom the pleiotropic effects of many of the samegenes; and this is logical given the strong selectionfor proper occlusion (Marshall and Butler, ’66;Van Valen, ’94). Olson and Miller (’58) studiedmorphological integration in the postcanine denti-tion of the South American monkey Aotus trivir-gatus and found differences in patterns ofcorrelation between linear size measures of upperand lower teeth, where length and width weremore strongly correlated in maxillary molarscompared to mandibular molars. Their resultssuggest the presence of pleiotropic effects and acomplex relationship between the upper and lowerjaws. A study of bat molar cusp development foundthat during all stages of odontogenesis maxillaryand mandibular molar cusps would properlyocclude if they were placed into articulation(Marshall and Butler, ’66).Though our knowledge of the early development

of the dentition is continually increasing (Thesleffand Sharpe, ’95; Stock et al., ’97; Weiss et al., ’98;Peters and Balling, ’99; Zhao et al., 2000a), we stilldo not know what genetic processes enable themaxillary and mandibular arches to arrive at suchsimilar morphologies but still retain the ability toevolve independently. Early in development, thefirst arch of the embryo gives rise to the right andleft mandibular arches that grow distally from thebody and join at midline to form the mandibular

symphysis. The maxillary arch forms from boththe first arch and the frontonasal mass. Themaxillary incisors derive from the frontonasaltissue whereas the maxillary canines, premolars,and molars derive from the first arch processes.Focusing solely on the non-incisal teeth, there areseveral feasible processes that determine toothrow patterning (Weiss et al., ’98). Gene expressionstudies to date have been unable to provide clearevidence as to which, if any, of these models ispotentially correct.

Our bivariate analyses of inter-arch loph orien-tation are underpowered and therefore provide noconclusive evidence at this time. However, theobserved differences and similarities between thetwo sets of intra-arch bivariate analyses demon-strate that the genetic architectures of the maxillaand mandible are similar but not identical. This isreminiscent of inter-arch analyses performed fortwo other morphological characters from thissame pedigreed baboon population, the interconu-lus and interconulid. These are ancillary cusps onthe side of molar crowns whose degree of expres-sion is influenced by incomplete pleiotropy (Hlus-ko and Mahaney, 2003).

Future research

Narrow-sense heritability estimates, the esti-mates of the proportion of the phenotypic variancedue to the additive effects of genes, have implica-tions for finding genes for traits like loph orienta-tion. Statistical power to detect and localizequantitative trait loci (QTLs) influencing varia-tion in loph orientation, or any other quantitativetrait, is largely a function of the QTL-specificheritabilityFi.e., the proportion of the variance inthe trait attributable to the effect of the QTLFforwhich the heritability estimate described in thisreport provides the upper bound. Demonstratingthat variation in loph angle is significantlyheritable is prerequisite to searching for the genesresponsible for that heritable component. A wholegenome linkage map is available for this popula-tion of baboons (Rogers et al., 2000) and themajority of animals for which we have loph anglemeasurements are genotyped at the marker locithat comprise this map. We are currently under-taking a QTL analysis of these loph orientationdata to test the hypothesis that one of the fourgenes identified in Jernvall et al.’s (2000) studyinfluence normal population-level variation in thisbaboon colony.


SUMMARY

This quantitative genetic analysis of baboondental variation provides three new insights intothe mechanisms that underlie normal variation inloph orientation. First we find that maxillary andmandibular molar loph orientation is determinedby similar proportions of additive genetic effectsand nongenetic effects.Second, we find that variation in mandibular

molar serial repeats of morphologically homolo-gous traits is also determined by identical geneticaffects. However, the same comparisons in themaxillary molars indicate incomplete pleiotropy.We also find that variation in the orientation ofdifferent lophs on the same crown is geneticallyindependent in mandibular molars but exhibitspartial pleiotropy in maxillary molars. This latterresult contrasts with the high pleiotropy weanticipated given previous gene expression stu-dies. We interpret this genetic independence topossibly result from a previously unidentified levelof modularity in the dentition. One module iscomprised of all mesial molar lophs in an arch andanother, of all the distal molar lophs. This mayresult from a duplication event in a putative molarprepattern.Third, our bivariate analyses demonstrate that

the intra-arch genetic relationships between lophsdiffer in the maxillary molars compared to themandibular molars. Therefore, somewhat differ-ent genetic architectures appear to pattern themaxillary and mandibular dental arcades.Given the conserved nature of developmental

pathways, it is possible that the mechanismsunderlying normal dental phenotypic variation inthis captive baboon breeding colony may alsounderlie variation in other primate populations,both past and present. Therefore, these resultsmay be applicable to the study of a wide range ofextant and extinct mammalian taxa.

ACKNOWLEDGEMENTS

We thank J. Rogers, K.D. Carey, K. Rice and theVeterinary Staff of the Southwest Foundation forBiomedical Research; J. Cheverud (WashingtonUniversity) for access to specimens; A. Walker andK. Weiss for project support and development; L.Holder for assistance with data collection. Thismaterial is based upon work supported by theNational Science Foundation under Award No.BCS-0130277 and the University of Illinois atUrbana-Champaign Research Board. NIH/NCRR

P51 RR013986 supports the Southwest NationalPrimate Research Center.

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