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PurposeAninvitedgroupofinternationalresearchersmetinLondonon7th/8th of April 2016. This is the first meeting of a multi-specialtygroupoffacialgeneticexpertsaroundtheworld.TheFacial Genetics meeting was organized by Stephen Richmond(Cardiff University) and Peter Claes (KU Leuven) as a directresult of the bilateral agreement between the Universitiespromoting shared research objectives not only to bring twoUniversities closer together but linking other like-mindedresearchers worldwide. The meeting was held in London,halfwaybetweenCardiffandLeuven.Eveningget-togetherTheformatofthemeetingwastoarrangeaget-togetherontheThursday evening to “break the ice” over a meal. You canalways tell a successful eveningwhen nobodywants to leave,eventhoughtheyhavebeenseatedfor3hours!19guestsenjoyaneveningintherestaurantPresentationsFriday began with a series of presentations from Peter Claes(Belgium) andMark Shriver (Penn State, USA) showing novelstatistically robust numeric methods to explore distinctivefacial features and gene associations. Mark did a stoicpresentation over the noise of road re-surfacing outside thewindow!ThiswasfollowedbySethWeinberg(Pittsburgh,USA)andMaryMarazita(Pittsburgh,USA)whoalsopresentedsoontobepublished dataonnewreplicatedgenesassociatedwithfacial features. Peter Hammond (Oxford) described theopposite effects on facial morphology resulting from genedosage sensitivity. Lavinia Paternoster (Bristol) re-traced theGWASprocessof the1stdiscoveryandreplicationof thePAX3gene associated with the nasion – mid-intercanthal distanceand methods for managing findings that are just below the10-8 threshold. Christoffer Nellåker (Oxford) describedmachine-learning techniques to extract phenotypic featuresfrom routine photographs. Marcus de Jong (Leiden, NL)described his techniques and enhancements to reduce errorsby 0.5mm in automated landmarking. Stephen Richmond(Cardiff) completed themorning againwith unpublished datarelated to heritability in a study of father and offspring andfemale twins as well as highlighting automated systems tocapture the shape of lips and noses. Caryl Wilson-Nagrani(Cardiff) present unpublished data on gene associations withlip traits and the increase in prevalenceof certain lip traits inparentsofcleftchildrencomparedtocontrols.

Thursday7thandFriday8thAprilFirstmeeting

OpenworkshopAfter lunch an inclusive and interactive meeting (Europeanstyle-all sitting in a large rectangle facing each other) wasmoderatedbyPeterClaestogainopinionsanddirectionsonthefollowingtopics.A) Dataacquisition

i) systems–accuracy/validityii) processingandregistrationofimagesiii) managingerrors

B) Phenotypingtechniquesi) categoricalsubjective/automatedii) facialanthropometricsiii) densesurfacemeshiv) preservingasymmetries

C) Genotypingtechniquesi) samplesii) arraysiii) imputation

D) Studytypes/databasesi) populationii) twins(heritability)iii) trios(heritability)

E) Combiningdatasetsi) admixturesii) ageiii) ancestryiv) ethnicity

F) Managementofdata i)Statisticalmanagement/processing

ClosingremarksThere was a very open and honest atmosphere throughoutthedaywithresearcherswillingtocollaborateandsharedataand software. The participants expressed how much theyenjoyed the meeting and there was a strong proposal fromthe group to organize a Facial Geneticsmeeting annually inLondon with two new coordinators each year. Those whocouldn’t make it this year will fix their diaries for the nextmeetinginMay2017.

Attendees - Front row: Yulia Hicks, Lisette Jong, Carine Carels, SethWeinberg, Hawraa Abbas, Alexei Zhurov, Raija Lahdesmaki CarylWilson-Nagrani, Middle row: Lisette Jong, Hilde Peteers, LaviniaPaternoster, Stephen Richmond, David Marshall, Mary Marazita,Markus De Jong, Back row: Pertti Pirttiniemi, Jason Hodgson, MarkShriver,PeterHammond,ChristofferNellåker,PeterClaes.

Wearelookingforwardtoanexcitingfutureofsharedadvancementsinthefieldoffacialgenetics.

Cardiff University© KU Leuven©Any material taken from this document must be fully referenced.

Contents Page

Overview 1

In Focus 2-4

Peter Claes 5

Mark Shriver 6

Seth Weinberg/ Mary Marazita 7 Peter Hammond 8

Lavinia Paternoster 9

Christoffer Nellåker 10

Markus de Jong 11

Stephen Richmond/ Caryl Wilson-Nagrani 12 Acknowledgements 13

2

Facial Genetics Workshop – In Focus

A)FacialDataacquisition

Various acquisition systems forcapturing3D faceswere identified.TheKonica/Minolta colour laser systems(Vivid 900/910) are now obsolete. Noone present had routine experience inusing the Dimensional Imaging system.Other systems such as the 3dMD andVectra H1 were discussed in moredetail. The Vectra H1 is a verytransportable system and can bepurchased for around £/€8k. Theflexibility of the system will enablemore facial images to be takencomparedtoastaticsystem.Therewasageneralconsensus that theVectraH1camera resolution was as good as the3dMD system. The acquisition of veryyoung individuals however, can proveto be challenging with the Vectra H1.TheVectraH1and3dMDsystemswerebeing compared in Pittsburg usingstandardized mannequin heads. TheVectraH1andKonicaMinolta910werealsobeingcomparedinCardiff.

Itwasreiteratedthatstandardizationoffacial posture with lips together in aneutral pose is key to acquiring areproducibleimage.Astandardizedwayto quantify spurious expressions iscurrentlymissing.Agoodquantificationof spurious expressions allows for thecorrection of image data, prior tosubsequentanalysis.

B)Phenotypingtechniques

Various facial phenotyping approaches exist,going from conventional morphometric analysisusingdistances,angles…(facialanthropometrics)to geometric morphometrics using completelandmark configurations typically followed by aprincipal componentanalysis (PCA).Theamountof facial landmarks used are either sparse orspatially-dense, manually or automaticallyindicated. Additionally, complex facial traits canbe identified subjectively and/or automatically,resultinginlabeled/categoricaldata.

Eachphenotypeapproachhas itsadvantagesanddisadvantages. Facial anthropometrics forexample creates the ability tomeasure the samefacial traits across different datasets and can berelatedtohistoricalpopulationcohorts.However,subsequent analysis is typically limited to beunivariate and combined interpretations of theresultsarechallenging.PCAofcompletelandmarkconfigurationsprovidesmultivariateanalyses,butthe extraction of principal components isdependentontheunderlyingdataset.Categoricalphenotypingoffacesallowstheabilitytocapturecomplex shape variations, but requires thedetermination of possible categories andextensive training to generate labels for all datasamples.

The morning presentations showed clear homogenous and overlapping research goals and interests amongst all participants. In contrast, the research equipment, datasets and approaches were very heterogeneous, ensuing different research results and outcomes. In an attempt to make future work reproducible and to stimulate collaborations, different aspects of a typical facial genetics research pipelines were discussed. During the afternoon session participants were able to relate their experiences, opinions and express willingness to share datasets as well as software.

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Facial Genetics Workshop – In Focus (Continued)

Reference:Yang J, Zaitlen NA, Goddard ME, Visscher PM, Price AL.Advantages and pitfalls in the application of mixed-modelassociationmethods.NatGenet.2014Feb;46(2):100-6.

All phenotyping approaches can benefit fromautomated procedures, to enhance repeatabilityon large datasets. Some automated landmarkingandlabelingtechniquesarebecomingavailable.

Insomefacialevaluationsfacialasymmetriesarepreservedandinothersaveragedfortheleftandright sides of the face. The two techniquesrequirefurtherevaluation.

Currently phenotypic traits are typically definedbytheinvestigatorspriortosubsequentanalysis.Another focus would be to learn interestingphenotypic traits by a genotype driven dataanalysis (heritability study e.g.). Such an inversefocus can be further empowered by the use ofmachine learning. However,warningshave beenraised that machine learning is not to beoverestimated,asitrequiresalargeofamountoflearning data and a good knowledge of theunderlying algorithms to avoid over-fitting andfindingspuriousphenotypictraitsofinterest.

C) Genotypingtechniques

Likephenotyping,genotypingtechniquesarealsodifferent, using different sampling material(saliva, blood…), customized genotyping arraysversuspersonalgenomicscompanies…

Apromisingwayforwardistheabilityto imputemissing genotypes following theHapmapand/or1000 Genome imputation protocols. Availablesoftware includes Minimac3 an IMPUTE2.Genotyping and imputation usually follows astandardapproachasusedinothercomplextraitconsortia(e.g.GIANT).

A different importance is given to possible SNPassociations when the SNP was actuallygenotypedorimputed,withastrongerbelieveintheformer.

Current state-of-the-art on GWAS in a variety oftraits, typically reports on both genotyped andimputedSNPanalysis.Hardlyanydifferencesareobserved, confirming that imputed SNP data isvaluabletooltouse.

Asuggestionwasmadetothinkaboutphenotypeimputation to similarly obtain comparablephenotypingacrossdatasets.

D)Studytypes/databases

All study types are valid and contribute to theknowledgeoffacialform.

Two publically available data repositories exist.The FaceBase data contain Caucasian andTanzaniansamples(approxiamtely2500ofeach).TheALSPACsampleconsistsof474715yearoldchildren and995 fatherswith 465 sons and 530daughters.

Otherdatabases areaccessible throughbilateralresearch agreements. For example, Twin databasesareavailablethroughVisigen.

Heritability can be evaluated using GCTA, anapproach that gained a lot of interest and thatuses genomic informationof (mixed)relatedandunrelated individuals. The study design alwaysdependsonthequestiontobeasked.Therangeofstudy designs provides the opportunities to askdifferent questions about heritability(twin/families/populations),familialsegregationofraretraits(trios)aswellasgeneticassociationfor population level variance in the phenotype(populations). The relativephenotype/ genotypecontributions of parents to offspring is currentlytheleaststudiedinnormalfacialvariation.

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Facial Genetics Workshop – In Focus (Continued)

E)CombiningDatasets:

Theaimistoincreasethenumberofsamplesandpower in association and heritabilityinvestigations.

One approach is to perform a meta-analysis ontheresultsofeachdatasetseparately.Whenusingfacial anthropometrics this is certainly possible,under the constraint that the same landmarkingarraywasused,whichisgenerallynotthecase.Incontrast, when using PCA based facial traits ameta-analysis becomes less straightforward toexecute.

Another approach is to actually pull the rawimages of different datasets together and toensure for a consistent phenotyping. It may beappropriate to circulate a facial calibrationdataset to ensure reliability and validity ofmanual and automated landmarking techniques.Effects of different scanning equipment andoperators are to be investigated as previouslynoted.

Itbecomesmorechallengingwhendatasets frompopulations with different backgrounds and agerangesarecombined.Specialattentionwasgivenduring the discussion about dealing with veryyoung individuals inasample.Facial growth anddevelopmentisacomplexandnon-linearprocess,making correction and/or condition of facialvariationonagequestionableandopentofurtherimprovement. Linear correction methods areinherentlyflawedandawayforwardmightbetheuse of kernel-based correction methods. Forexample, using the concept of “facial signatures”in the work of Peter Hammond. The key is thateach facialsample ismatchedagainstanaverageface from a group of faces sharing the sameconfounding values, such as age, background,sex…

F) Managementofdata

Data management is not to be underestimated,andpublicallyavailabledatarepositories,suchasFaceBase, can provide the (existing) necessarysupport.

Restrictions on making data publically availablearemostlydue to signed informed consents thatare too restrictive. Older datasets have thisproblem in which the participants did notconsentedfortheirdatatobepublicallyavailable.Future sampling efforts, preferably employ theinformed consents as used in FaceBase.RespectiveinvestigatorsinvolvedintheFaceBaseproject,caneasilybecontactedtogiveadviceonfutureethicsapplicationsandsamplingprotocols.

The creation of publically available softwarerepositorieswas greatly supported and couldbeaninterestingextensiontoFaceBase.

The weather and road works did not detract from a very successful meeting. Feedback from the meeting:

“Ienjoyeditverymuchandmyimpressionwasthateveryoneelsedidtoo!Lookforwardtomeetingupwith

youallagain”.

Themeetingwas brought to a close followed bydrinksinthebarandorjourneyhome!

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Hierarchical Modular Facial Phenotyping Peter Claes and Dzemila Sero

Genome Wide Association Studies (GWAS) focus on “p< 5 x 10-8”, which is simply the minimum cut-off point to claim a SNP discovery. Pairing relevant genetic SNPs with relevant phenotypic traits can be challenging. The current trend in GWAS, is to increase thenumber of SNPs (up to 10M) improving thechance of including relevant SNPs. Investigatingsuch a multitude of SNPs is in contrast to therelatively fewfacial featurese.g.noseandmouthwidthorprincipalcomponentscoresoflandmarkconfigurations. The phenotypic complement togenomics is phenomics which aims to obtainhigh-throughput and high-dimensionalphenotyping (Houle et al., 2010). Advances infacialdataacquisitiontechniqueswithincreasingresolutions can provide detailed surface facialmorphology to the level needed for phenomics.Facial phenotyping needs to be complete andcompact. Completeness - captures all relevantphenotypic traits/variations. Compactness –minimum dataset needed to reduce thecomputational burdenwhen analyzing each SNPinaGWASandtoavoidcompromisingthestudy-widesignificance(p<10-8).However,atfirstsightcompleteness and compactness appears to becontradictory. For example, spatially-denselandmarking is complete but lacks compactness,having to deal with thousands of variables.Principal Component Analysis (PCA) providescompactness but may not be comprehensive aslocal facial variations are often diluted or evenmissed.

We are currently investigating the use ofmodularity in facial morphology in combinationwith multivariate statistics to obtain a completeand compact phenomic facial phenotyping forGWAS.Thehumanfaceismadeupofmanypartsthat function as a whole. Individuals displaydiverse and heterogeneous facial units due totheir specific role. The face is organized intomultiplefacets,eachtoperformaspecifictaskinaccordance to its embryological origin, anatomyandfunction.

Inthepast20yearsofresearchinmolecularandbiologysystems,theintegrationintosubunitshasbeen categorized under the heading ofmodularity, which implies the division of astructure into several parts that display highintegration within the variables (modules), butfew and weak interactions across the modules.Following the constructs of modularity ingeometric morphometrics we were able tohierarchically subdivide the face into a series ofglobaltolocalmodulesasshowninFigure1,for6hierarchicallevelsresultingin63modules.Allthelandmarks in each of these modules separatelyundergo a Generalized Procrustes Alignment(GPA) followed by a PCA reduction of thedimensionality.

The result is a complete description of a globaland local perspective of facial variation that iscompactly described in detail for each of thesemodules. SNP associations are tested using amultivariate canonical correlation analysesagainst all of the PCs simultaneously in each ofthemodules,resultingin63dependenttests.Using 1490 European subjects in a discoverypanel, 25 SNPs, out of 100000 genome-wideselected SNPs in low LD, achieved genome-widesignificance.AnexampleSNP,rs2765529 locatedinTBX15,isshown inFigure2.TheSNPshowsahighly localized effect in the forehead, which isconsistentwiththefacialcharacteristicoffrontalbossingintheCousinSyndrome.

Peter Claes & Dzemila Sero KU Leuven, ESAT/PSI Medical Imaging Research Center, UZ Gasthuisberg Herestraat 49 - box 7003 3000 Leuven Belgium

Figure2-log10p-valueswithrs2765529,left:compactvisualization(verticalaxis=levelindex,horizontalaxis=moduleindex),Right:visualizationonthefaceusingthemodulesateachlevelseparately.

Figure1Hierarchicalmodularizationoffacialmorphology.

ReferenceHouleD,GovindarajuDR,OmholtS.Phenomics:thenextchallenge.NatRevGenet.2010Dec;11(12):855-66.

Conclusion Facial morphology comprises of many traits which implies high-dimensional and multivariate quantitative characteristics. Future facial phenotyping techniques are necessary to deal with these issues.

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Modeling Genes and Other Factors Affecting Variation in the Human Face Mark Shriver

Over the last 5 years there has been little concerted effort focused on applying modern human genetic methods to explore the biological basis of facial features. Difficulties in addressing the geneticunderpinnings of human genetic variationinclude;

1) the complexityof thehumanface–arguably,the face is a composite of hundreds ofconnecteddevelopmentalmodules

2) the small effect sizes expected for manyallelesaffectingtypical-rangevariation,

3) the lack of a sufficient human geneticanalytical framework for mapping highlymultivariatetraits.

Although there are indeed well studied facialmetrics (inter-landmark distances, angles, andparameters from data reduction methods), it isimportant to be as agnostic as possible withrespect to the definition of what sort of facial“traits” might be affected by variables like SNPgenotypes. The approach, exemplified bybootstrapped response-based imputationmodeling (BRIM; Claes et al., 2014) defines thetraitofinterestsimultaneouswiththeassociationtest. Instead of asking, does this gene affect aspecific facial trait,weaskdoany facial featuresvarywithvariationinthisgene?Thisapproachtophenotypic definition is fundamental and thereare similarities between this approach and thework of Kun Tang and colleagues and PeterHammondandcolleagues.Overcoming the issueof small average effects ofalleles requires not only efficient statisticalmethods, but large sample sizes. For over 15years we have employed an innovativeparticipantrecruitmentapproachwhichprovidesgenetic testresults toparticipants.Over thepastthree years the “direct-to-consumer” based testwe have provided to the participants is also theprimary data source for GWAS and other genemapping approaches. We have also beensampling across the range of human populationand our current collection numbers about 8,500with a plan to collect an additional 5,000 newparticipantsoverthenextfouryears.Regardless,theeffortweorotherindividualgroupsinvestincollecting participants, there will always be agreater benefit by working together. The FacialGenetics Workshop provides the opportunity ofpoolingdatasourcesandexpertise.Thereisarealneed for, and happy to help coordinate andrecruitforanopenpublicdataset.

Ourmost recent facialmodels that include over3,500 participants from many different humanpopulations help illustrate approaches todiscovering both the genetic and non-geneticeffectsonfacialvariation,modelingtheimportantvariables,andvalidating the results.Weare abletomodelquiteeffectivelytheeffectsofanumberfactors affecting typical-range variation in thehumanface.These includeage,bodysize(heightandweight),ancestry,andsex(Figures1and2).Although, strictly speaking, wemay not need tounderstandtheeffectsofthesevariablestodetectthegenesaffectingvariationinthe face,veryfeware interested in the narrow question of whichare these genes. Not only do growth anddevelopment have fundamental effects on thehuman face, but these processes along with thefactors affecting genetic and biologicalbackground are too easily dismissed as simplysexandancestry.Finally, an important source of data on humanfacesishumansthemselvesasobserverswhocanbeaskedtomatch,rate,andjudgefacialidentitiesand characteristics. The usefulness of humanobservers in our studies of human facialfemininity/masculinity and facial ancestryhighlighting both the accuracy of the observerassessmentsaswellas the inherentbiasessomeofwhicharecompellinglyconsistentwithsimple,butunexpected,evolutionaryexpectations.

Mark D. Shriver Professor of Anthropology and Genetics Penn State University

ReferenceClaes P, Liberton DK, Daniels K, Rosana KM, Quillen EE,PearsonLN,McEvoyB,BauchetM,ZaidiAA,YaoW,TangH,BarshGS,AbsherDM,PutsDA,RochaJ,BelezaS,PereiraRW, Baynam G, Suetens P, Vandermeulen D, Wagner JK,BosterJS,ShriverMD.Modeling3DfacialshapefromDNA.PLoSGenet.2014Mar20;10(3).

Figure2Populationvariation

Figure2FacialeffectsofBMI

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Using FaceBase resources to investigate the genetics of normal human facial variation Seth Weinberg and Mary Marazita

The first of these studies was agenome-wide association analysisof20faciallineardistancesderivedfrom 3D facial surface images of3118 healthy participants. Ourmajor findings included genome-widesignificantassociations (p<5x 10-8) for cranial base width at14q21.1 and 20q12, intercanthalwidth at 1p13.3 and Xq13.2, nasalwidthat20p11.22,nasalala lengthat 14q11.2, and upper facial depthat 11q22.1. Of note, several genesintheassociatedregionsareknownto play roles in craniofacialdevelopment or in syndromesaffecting the face: MAFB, PAX9,ALX3,HDAC8,andPAX1.In our next study, we tested forgenomic associations with facialshape phenotypes (principalcomponents), derived from ageometric morphometric analysisof 3D facial landmarks. Weobservedagenome-widesignificantassociation with principalcomponent10,whichexplainedjustover3%oftheshapevariationandprimarily involved upper lip,philtrumandnoselandmarks.

The top signal involved a SNPwithin 500b downstream of thegene RPGRIP1L, which whenmutated results in Meckelsyndrome(type5). This syndromeand the mouse knockout sharesignificant mid-facial phenotypesincludingcleftlip.Finally, we presented some initialresults of a candidate SNP analysisof cranial vault shape involvinggenes implicated in cranio-synostosis. We identified severalsuggestivehits,themostinterestinginvolving SNPs in the IHH gene,which in our data was associatedwith cranial vault length. This isnotable because CNVs in IHH areassociated with a syndromic formof craniosynostosis involving thesagittal suture and resulting inexcessivelengtheningofthehead.

Recent genetic studies of normal human facial traits using data available through the National Institute of Dental and Craniofacial Research (NIDCR) FaceBase Consortium were presented.

FaceBase:Comprehensivedataandresourcesforcraniofacialresearchers

Overall, our results provide evidence thatcommon variants in regions harboring genes ofknowncraniofacialfunctioncontributetonormalvariationincraniofacialfeatures. Ourworkmayprovide insights into the pathways andmechanisms controlling normal and abnormalcraniofacialmorphogenesis.

Seth Weinberg and Mary Marazita Center for Craniofacial and Dental Genetics Department of Oral Biology School of Dental Medicine University of Pittsburgh 100 Technology Drive, Suite 500 Pittsburgh, PA 15219

ReferenceHochheiser,AronowBJ,ArtingerK,BeatyTH,BrinkleyJF,ChaiY,ClouthierD,CunninghamML,DixonM,DonahueLR,FraserSE,HallgrimssonB,IwataJ,KleinO,MarazitaML,MurrayJC,MurrayS,deVillenaFP,PostlethwaitJ,PotterS,ShapiroL,SpritzR,ViselA,WeinbergSM,TrainorPA.TheFaceBaseConsortium:acomprehensiveprogramtofacilitatecraniofacialresearch.DevBiol.2011Jul15;355(2):175-82.

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A) InversionofBeckwith-WiedemanntoSilver-

Russell

B)Inversionofdup4p16.3toWolf-Hirschhorn

Face Shape And Gene Dosage Sensitivity Peter Hammond

Peter Hammond, Nuffield Department of Obstetrics and Gynaecology, Oxford University, UK.

Other genomic regions with similaropposingfaceshape-copynumbervariation(CNV)relationshipshavebeenidentifiedbutremain unpublished due to the lack ofreliableorpublishedcasesintheliterature.For example, C), to the right, facialinversions of individuals with Prader-Willisyndrome due to maternal uniparentaldisomy (UPD) of 15q11q13 haveremarkable similarity topublished casesof15q11q13paternalduplication.Normative inversion inverts every featureof a face relative to the matched normwhereasagenomicCNV isoftena localizeddeletion or duplication. Therefore, morerealistic comparisons are faces associatedwithreciprocalcopynumbervariation.

ClinicalgeneticistswhohavepatientswithreciprocalCNVsofknowndosagesensitivegenesshouldcontacttheauthoratpeter-hammond@obs-gyn.ox.ac.uktoinvestigateopposingeffectsonfaceshape.

Opposing variation in growth, headsize, cognition and behaviour isknown to result from deletions andreciprocal duplications of somegenomicregions.Normative inversionof face shape, opposing differencefrom a matched norm, was recentlyproposed as a basis for investigatingthe effects of gene dosage oncraniofacial development. Usingdensesurfacemodellingtechniquestomatch any face to a facial norm ofcontrols of matched age, sex andethnicity, the individual’s face shapedifferences from the matched normare reversed to produce a normativeinversion.

Thetalkdemonstratedthat for fivegenomicregions, 4p16.3, 7q11.23, 11p15, 16p13.3and 17p11.2, such inversion for individualswith a duplication or (epi)-mutationproduces facial forms remarkably similar tothoseassociatedwithadeletionoropposite(epi)-mutation of the same region, and viceversa. For example, in A), a face of anindividual with Beckwith-Wiedemannsyndrome (BWS) is inverted to INV-BWSresultinginafaceshapethatisveryclosetothe face (immediately below) of anindividual with Silver-Russell syndrome(SRS). Similarly, in B), the face of anindividual with a duplication of 4p16.3 isinverted toproduce facial featuresvery likethe individual below with Wolf-Hirschhornsyndrome(WHS),associatedwithadeletionofthatregion.

ReferenceHammondP,McKeeS,SuttieM,AllansonJ,CobbenJM,MaasSM,QuarrellO,SmithAC,LewisS,TassabehjiM,SisodiyaS,MattinaT,HennekamR.Oppositeeffectsonfacialmorphologyduetogenedosagesensitivity.HumGenet.2014Sep;133(9):1117-25.

C)InversionsofindividualswithPrader-Willisyndrome

9

A GWAS has previously beenconducted on the ALSPAC data,using 2185 individuals in thediscovery analysis and 1622 inthereplication.Weidentifiedfourvariantsinthediscovery that met genome-widesignificance (p<5x10-8). One ofthese replicated (p=4x10-7), thiswasrs7559271inthePAX3gene.The prominence of the nasalbridge (mid-endocanthion tonasion)isinfluencedbythePAX3gene(Figure2).This study had a fairly smallsample size and so had limitedpower to detect associations.GWAS of facial traits also suffersfrom a huge multiple testingproblem,furtherlimitingpower.Following the model of GWASstudiesinothercomplextraits,itis also important that meta-analysis is conducted on allavailabledatatoincreasesamplesize.GWAS studies in other complex

Genetic Aetiology Of Facial Traits Lavinia Paternoster

Lavinia Paternoster, MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK

Various statistical methods can be used to explore genetic aetiology of complex traits; Genome-wide association studies (GWAS), SNP heritability estimates, genetic correlations and Mendelian randomization. These have all been successful for many other complex traits and we expect facial genetics to follow the same path. Genome-wide association studies(GWAS), SNP heritability estimates,genetic correlations and Mendelianrandomizationhaveallbeensuccessfulformanyothercomplextraitsanditisexpectedthatfacialgeneticswillfollowthesamepath.IntheALSPACstudywehave 3D facial images from 4747offspring at age 15 (and 995 fathers).All these facial shells have beenmanually landmarked for 21 easilyidentifiable facial features (Figure 1).An additional 7 mid-points werecreated notably themid-endocanthionpoint. Imputed genetic data is alsoavailablefor8327offspring.

Figure1:21faciallandmarksidentifiedandafurther7mid-pointsgenerated.

To have success in future facial GWAS studies, we must be able to reduce the number of tests carried out. This can be achieved in different ways:

• use dimension reduction techniques, such as principle components analysis (PCA), however this was not fruitful in the previous GWAS and other dimension reduction methods may need to be explored.

• select only those phenotypes for which there is evidence of high heritability. This information could come from family studies or using whole genome approaches using unrelated individuals.

ReferencePaternosterL,ZhurovAI,TomaAM,KempJP,StPourcainB,TimpsonNJ,McMahonG,McArdleW,RingSM, SmithGD,RichmondS,EvansDM.Genome-wideassociationstudyof three-dimensional facialmorphology identifiesavariant inPAX3associatedwithnasionposition.AmJHumGenet.2012Mar9;90(3):478-85.

Figure2:TheeffectofthePAX3geneontheprominenceofthenasalbridge

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Computer Vision For Phenotyping In Rare Diseases Christoffer Nellåker, Quentin Ferry, Mohsan Alvi, Mitchell Dawson, Julia Steinberg, Caleb Webber, Chris Ponting, David R FitzPatrick, Andrew Zisserman

Christoffer Nellåker Department of Physiology, Anatomy and Genetics, Oxford University, UK.

ReferenceFerryQ,SteinbergJ,WebberC,FitzPatrickDR,PontingCP, Zisserman A, Nellåker C. Diagnostically relevantfacial gestalt information from ordinary photos. Elife.2014Jun24;3:e02020.

Facial genetics research is currentlypursuing3Dimagingmodalities,howeverwe work with using normal 2Dphotographs. Thevast volumesofdigitalphotography imaging being collected inglobally allow us to apply the latestcomputervisionandmachine learningatthe scale of big data. Ordinaryphotographs are highly variable andtypically spurious (non-phenotyperelevant)signaldominatetheappearanceofanimage.

Using computer vision and machine learning approaches to analyse facial photographs allows us to perform computational phenotyping to narrow the search space for rare disease diagnoses.

We developed a computational pipelineto automatically analyse images (Figure1).Thepipeline findsa face inan image,annotates key facial feature points andextract feature vectors. Next we embedfaces in a multidimensional space ofpossible faces and apply machinelearning.Thelearningreshapesthespaceto cause spurious signal dimensions tocompress and the disease phenotypevectors to expand. In the resultingClinical Face Phenotype Space (CFPS)patients cluster together, allowing anarrowing of the search space for adisease diagnosis. Crucially the spacegeneralises to unseen, untrained diseasediagnoses(Figure2).

The implication being that CFPS hasgeneralised to reflect aspects of theunderlying space of disease phenotypevariation. This is supported by asignificant correlation with functionalgenetic pathway analyses for geneticassociationsinCFPS.We are building a global consortium ofclinical genetic researchers to progressthisresearch.Aspartofthiswearekeento interact with the 3D imaging facialgenetics community to integrate 2D and3D analyses approaches. To this effect apilot project with Prof. Peter Hammondhas begun but we hope that the FacialGeneticsWorkshop communitywill be agood forum to build collaboratively onthispromisingresearchavenue.

Figure1:Computationalpipeline–fromphotographtodiagnosis

Figure2:Narrowingofthesearchspacetoidentifyraredisease.

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Figure1:Theellipsoidfittingandmapprojectionprocessofthehumanface.A)originalpointcloud,B)ellipsoidfitting,C)3Dmapprojectionbasedonanellipsoid

An Automatic 3D Facial Landmarking Algorithm Using 2D Gabor Wavelets

Markus A. de Jong

Markus A. de Jong Department of Oral & Maxillofacial Surgery, Special Dental Care, and Orthodontics, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands

ReferencedeJongM,BoehringerS,KayserM,WollsteinA,RuffC,Dunaway D, Hysi P, Spector T, Liu F, Niessen W,Koudstaal M, Wolvius E. An automatic 3D faciallandmarkingalgorithmusing2DGaborWavelets.IEEETransactionsOnImageProcessing,Vol.25,No.2,580-588,2016.

Landmark registration of the human face is an important pre-requisite to determine facial traits for heritability, genetic associations, and medical conditions with distinctive facial characteristics.

Introduction

Theapproach is toworkwith2D-projectionsof 3D-surface data and to employ 2Dalgorithms on that transformed data. In thisprocess the original detailed surface data isretainedthroughout.

Theprocess1. Mapprojection:

a region of interest is defined from thefrontal face.A two-stage ellipsoidprocessisusedtoregistertheface(Figure1).

2. Imagefeaturelayergeneration:• the texture of the 3D model is rendered

orthographically using the 3D editingsoftware Blender under full brightnessconditions, i.e. without artificial shadowsorspecularreflections.

• thereliefmap(heightmap)isconstructed.• 3 feature layersarederived:x-axis,y-axis

andtheLaplacianofGaussian(Figure2).

3.Trainingandlandmarking:Elastic Bunch Graph Matching method isemployed (EBGM). A maximum correlationtemplatesearchisperformedbetweenasetofexample images and the image to belandmarked.

The features used are Gabor-Wavelettransformations of the 21 facial landmarks(Figure 3). The search is performed perlandmarkwithpre-definedconstraints.Finally,theregistered2D landmarksaremappedbackinto3D,invertingtheprojection.EBMG was run on all feature layerssimultaneously, i.e. Gabor-Wavelet coefficientswere extracted fromall layersusing the sameGabor-Waveletsandresultingcoefficientswerecombined into a single vector. The EBGMalgorithm is trainedusing 30 imageswith the21manuallyplacedlandmarks.Finally, the 2D landmark pixel coordinatesweremappedbacktothenearestpointsinthereliefmapmodel and subsequently to the 3Dlandmark coordinates in the 3D cloud. This isachieved by using the inverse of themappingperformed or, more efficiently, using vertexindexing.

Conclusions The automatic landmarking of facial 3D surface data requires little investment in the training phase in a single iteration (30 training samples taking 1 minute for 21 landmarks). Errors of 1-2mm are achieved for most of the facial landmarks with facial images of differing quality, gender and ethnicity. The algorithm can be easily and quickly re-trained when a different set of landmarks is required.

Figure2:The5featurelayersgeneratedfromahumanfaceimagebasedonthemapprojection.A) photographic, B) heightmap, C) derivativewithrespecttoX-axis,(D)Y-axis,(E)heightmapLaplacianofGaussian

PhotographicA B

C D E

Figure3:21faciallandmarksA B C

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Facial Growth And Facial Genetics Stephen Richmond, Caryl Wilson-Nagrani, Alexei Zhurov, Jelena Djordjevic, Nizar Mhani,

Pertti Perrtiniemi

ThestudiesSix studies i) normal variation in a population of 15 year old children (2233 Males and 2514 females -ALSPAC),ii)Fathersandoffspring(995fathers;465sons-530daughters-ALSPAC),iii)UKtwins(263MZand341DZ-Visigen)iv)Welsh(n=98)andFinnish(n=56)facialcohorts,v)Liptraitsandcleftgenes(ALSPAC15yearolds),vi)Cleftandnon-clefttrios(n=597)werereviewed.

Conclusions • 2½ datasets (fathers/offspring and female twins)

confirm relative importance of similar facial features.

• Different patterns of heritability of facial landmarks in male and female offspring – particularly ch, chr & alae (F) and prn, pg (M)

• Male and female facial proportions will change between 12-18 years of age.

• Lips have distinctive features most present at birth can be identified easily on individuals after a period of training.

• These lip traits may be useful in understanding facial development, inheritance and CLP.

• Parental lip trait and gene interaction may increase the risk of CLP

• Automated systems will help in some areas of facial trait recognition.

Figure1Radiusofspheres=Cubicrootofthefrequencyoflandmarksof71,253possibleratiosfor28landmarks(bluemales–redfemales),includingmidpoints(yellowMandgreenF)h2=0.6.

ALSPAC15yearoldcohort1The first 7 principal components offacial variation highlight the mostimportant facial features in thispopulation.i) facialsizeandheightii) widthofthefaceiii) noseprominenceiv) lipandchinprotrusionv) eyedepthvi) verticalnoseheightvii)mouthwidthanddepth

TwinsUK(Visigen)LookingattheheritabilityofMZandDZ twins (h2=2(rMZ-rDZ)), theprincipal components were similarfor both scaled and unscaled 3Dfacial shells. These PC’s alsocompared favourably with the 15yearolds.Facesizeandheightwereimportant in the scaled and liplengthinbothscaledandunscaled.

FathersandoffspringThe narrow sense heritability(h2>0.60) was calculated for thefrequency of ratios that involve alandmark (represented by the cubicroot of the radius – Figure 1).Progressing through the levels of h2it was noticed that asymmetry wasless and that the heritability oflandmarks associated with the lipsand nose were much higher in thedaughters compared to sons. Thefrequency of ratios involving thenasion and mid-endocanthion (n-men) were high for sons anddaughters (h2>0.73) which wouldexplain the discovery of the Pax3gene associated with theselandmarks.2

Cleftandnon-clefttriosThe prevalence of lip traits amongparents of cleft and non-cleft trioshighlighted the predictive value ofthe lip features. 9 lip traits werestatisticallysignificant between caseandcontrols.

LiptraitsandgeneassociationsTheWilson-Richmondliptraitscale3was used to phenotype the 15 yearold ALSPAC children (Figure 2). Toillustrate this both Mick Jagger andher daughter Georgia May have agroove/notch in theupper lip andadouble lower lip border with verysimilarlipshape.14candidateSNP’swereassociatedwith18lipfeatures.High risk alleles were associatedwith V-shaped cupid’s bow, narrowphiltrum, upturned commissuresand deep groove. An automatedlip/nose shape recognition iscurrentlybeingtrialled

References1. Toma AM, Zhurov AI, Playle R, Marshall D, Rosin PL,

Richmond S. The assessment of facial variation in 4747 British school children. Eur J Orthod. 2012 Dec;34(6):655-64

2. Paternoster L, Zhurov AI, Toma AM, Kemp JP, St Pourcain B, Timpson NJ, McMahon G, McArdle W, Ring SM, Smith GD, Richmond S, Evans DM. Genome-wide association study of three-dimensional facial morphology identifies a variant in PAX3 associated with nasion position. Am J Hum Genet. 2012 Mar 9;90(3):478-85.

3. Wilson C, Playle R, Toma A, Zhurov A, Ness A, Richmond S. The prevalence of lip vermilion morphological traits in a 15-year-old population. Am J Med Genet A. 2013 Jan;161A(1):4-12.

Facial growth cohorts (WelshFinnish)AUKgrowthcohortverifiesthecloserelationship of hard and soft tissuelandmarks. The Welsh and Finnishcohorts highlight the difference infacial proportions in boys and girlsfrom 11 to 17 years of age whichmay explain dominance of h2 ofdaughters over sons in the fathersand offspring study. The lip shapechanges very little in males andfemales from 11 to 17 years of ageand growth will not influence thedominanceof the fathers features intheirdaughters.

Abbreviations:ALSPAC:AvonLongitudinalStudyofParentsandChildren.Visigen:consortiumdedicatedtouncoverthegeneticfoundationsofvisibletraits

Figure2Wilson-Richmondlipscalehighlightingnotchinupperlip(yellow)anddoublelowerborder(red).

Stephen Richmond Orthodontic Department, Cardiff Dental School

Sequential studies undertaken in Cardiff collaborating with the Universities of Bristol, Oulu and Pittsburgh, also with Visigen have highlighted interesting aspects of facial heritability and facial variation.

13

“Weallexpecttomeetupagainandhopefullythosewhocouldnottakepartthisyearwillbeabletomakethenextmeeting”.

Editors:StephenRichmondandPeterClaes

Copyright of Cardiff University and KU Leuven Any material taken from this document must be fully referenced.

Stephen Richmond Cardiff University Dental School College of Biomedical and Life Sciences Heath Park Cardiff CF14 4XY United Kingdom

richmonds@cardiff.ac.uk

Peter Claes Medical Imaging Research Center UZ Herestraat 49 - box 7003 3000 Leuven Belgium peter.claes@kuleuven.be