Table Of ContentMOLECULAR
PHOTOFITTING
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MOLECULAR
PHOTOFITTING
P R E D I C T I N G A N C E S T R Y A N D
P H E N O T Y P E U S I N G D N A
Tony N. Frudakis
With a Chapter 1 Introduction
by Mark D. Shriver
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T A B L E O F C O N T E N T S
Foreword xi
Preface xiii
Acknowledgments xv
Chapter1 ForensicDNAAnalysis:FromModestBeginningstoMolecularPhotofitting,Genics,
Genetics,Genomics,andthePertinentPopulationGeneticsPrinciples 1
PartI:MarkD.Shriver
Introduction:BriefHistoryofDNAinForensicSciences 1
TheStatisticsofForensicDNAAnalysis 6
TheNatureofHumanGeneticVariation 10
PopulationGeneticsandPopulationGenomics 11
ThePromiseofMolecularPhotofittingasaToolinForensicScience 16
PartII:
TheBasicPrinciples 19
LackofHumanDiversityRelativetoOtherSpecies 32
Chapter2 AncestryandAdmixture 35
WhatAreAncestryandAdmixture? 35
TheNeedforMolecularTestsforAncestry 37
AncestryInformativeMarkers 43
BiogeographicalAncestryAdmixtureasaToolforForensicsandPhysicalProfiling 54
Chapter3 BiogeographicalAncestryAdmixtureEstimation—TheoreticalConsiderations 57
EstimatingbyAnthropometricTraitValue 57
AdmixtureandGeneFlowEstimatedfromSingleLoci 59
AdmixtureinIndividualSamples 68
UsingtheHanisMethodonPopulationModelsk>2 71
ParameterUncertainty 75
BayesianMethodsforAccommodatingParameterUncertainty 84
SamplingError 88
AssumptionsaboutMarkerLinkageandIntensityofAdmixtureinParents 90
Pritchard’sStructureProgram 92
InDefenseofaSimpleAdmixtureModel 94
PracticalConsiderationsforBuildinganAncestryAdmixtureTest 95
vi TABLE OF CONTENTS
SelectingAIMsfromtheGenome—HowManyAreNeeded? 103
ComparingthePowerofSpecificLociforSpecificResolutions 110
GenomicCoverageofAIMs 114
MoreElaborateMethodsofSelectingMarkersforInformationContent 115
ShannonInformation 116
FischerianInformationContent 118
InformativenessforAssignment 119
TypeofPolymorphisms 123
InterpretationofAncestryEstimates 125
ObjectiveInterpretation 132
GeneticMappingandAdmixture 133
Appendix(AncestryFrequencyTable) 137
Chapter4 BiogeographicalAncestryAdmixtureEstimation—PracticalityandApplication 145
TheDistributionofHumanGeneticVariabilityandChoiceofPopulationModel 146
MarkerSelection 163
SampleCollection 164
PresentingIndividualBiogeographicalAncestry(BGAA)Results 179
ConceptualIssues 190
Chapter5 CharacterizingAdmixturePanels 203
ParentalSamplePlots 203
ModelChoicesandDimensionality 205
SizeofConfidenceContours 210
Repeatability 213
Sensitivity 220
AnalysisofResultsforGenealogists 222
AnalysisofResultsforNongenealogists 230
BlindChallengeofConcordancewithSelf-AssessedRace 232
ConfidenceIntervalWarping 235
SampledPedigrees 237
SimulatedPedigrees 240
ComparingDifferentAlgorithmswiththeSameAIMPanel 241
AnalysisUsingSubsetsofMarkers 243
ResolvingSampleMixtures 245
SampleQuantity 250
NonhumanDNA 251
PerformancewithAlteredParentalAlleleFrequencies 253
CorrelationwithAnthropometricPhenotypes 257
Simulations 260
CreatingSimulatedSamples 262
TABLE OF CONTENTS vii
SourceofErrorMeasuredwithSimulations 263
RelationshipbetweenErrorinPopulationsandwithinIndividuals 265
Precisionofthe71AIMPanelfromSimulations 268
TrendsinBiasfromthe71AIMPanel 270
95%ConfidenceThresholdfor71AIMPanel 273
Precisionofthe171AIMPanelfromSimulations 275
MLEThresholdsforAssumptionofBonaFideAffiliation 277
Comparisonof71and171AIMPanels 277
ObservedandExpectedBias 277
WhatDotheSimulationsTeachUsaboutInterpretingBGA
AdmixtureResults? 279
BiasSymmetry 280
ImpactofMLEAlgorithmDimensionality 282
SimulationsofAdmixedIndividuals 284
MLEPrecisionfromtheTrianglePlots 285
ConfidenceofNonzeroAffiliation 286
StandardDeviationfromConfidenceIntervals 286
TestingtheRelationbetweenConfidenceMeasuresinIndividuals
andPopulations 288
SpaceoutsidetheTrianglePlot 289
CombinedSourcesSuggestanAverageError 294
Chapter6 ApportionmentofAutosomalDiversityWithContinentalMarkers 297
TheNeedforPopulationDatabases—WordsMeanLessThanData 297
TrendsonanEthnicLevel:AutosomalVersusSexChromosome
Pattern 299
WhatDoContinentalAncestryAIMsSayaboutEthnicity? 303
TheSignificanceofFractionalAffiliationResultsonaPopulation
Level 305
ReconstructingHumanHistoriesfromAutosomalAdmixtureResults 310
SharedRecentAncestryVersusAdmixture:WhatDoesFractional
ContinentalAffiliationforanEthnicGroupMean? 311
ReturningBrieflytotheNamingProblem—Relevancefor
InterpretingtheApportionmentofAutosomalDiversity 313
ASamplingofEthnicitiesUsingthe171AIMPanel 316
InterpretationofAncestryProfilesforEthnicPopulations 322
EastAsianAdmixtureintheMiddleEastandSouthAsia 337
ResolutionwithinContinentsBasedontheFour-PopulationModel 351
InterpretationofContinentalBGAResultsinLightofWhatWe
HaveLearnedfromApplicationtoEthnicPopulations 352
viii TABLE OF CONTENTS
AppropriatenessofaFour-PopulationModel 355
DoAlleleFrequencyEstimationErrorsAccountfortheSecondaryAffiliations
inEthnicSubpopulations? 357
IndicationsofCrypticPopulationStructure 359
Chapter7 ApportionmentofAutosomalDiversitywithSubcontinentalMarkers 361
SubpopulationAIMsandEthnicStratification 361
WithintheEuropeanBGAGroup—ABriefHistoryofEuropeans 363
HowDoWeSubdivideEuropeansforForensicsUse? 366
DevelopmentofaWithin-EuropeanAIMPanel 367
TheEuro1.0AIMPanelforaFour-PopulationSubcontinentalModel 369
EstablishingtheOptimalParentalRepresentatives 370
BlindChallengewithEthnicallyAdmixedEuropean-AmericanSamples 378
PopulationIsolatesandTransplants 380
CorrelationswithAnthropometricTraits 384
TestError 387
HierarchicalNatureofEuro1.0—PriorInformationRequired 397
Euro1.0PedigreesasanAidtoInterpretingResults 403
Euro1.0—InterpretationofVariationwithinGroups 407
AnHistoricalPerspective 410
MoreDetailedSubpopulationStratifications—k¼7 412
WhatDotheGroupsNOR1,NOR2 ... Mean? 414
EvaluatingtheResultsfromthek¼7EuropeanModel 416
ComparisonwithPreviousStudiesBasedonGeneMarkers 417
ComparisonwithResultsfromOtherStudies 419
BlindChallengeofthek¼7ModelResultswithEthnicSamples 420
CorrelationwithAnthropometricTraits 423
Pedigrees 425
SubstantialVariationinAdmixturewithinEthnicGroups 425
AlternativeStylesforEstimatingEthnicAdmixture 427
Chapter8 IndirectMethodsforPhenotypeInference 429
EstimatesofGenomicAncestryAllowsforInferenceofCertainPhenotypes 429
PhenotypeVariationasaFunctionofHumanPopulationHistoryandIndividualAncestry 430
SourcesofPhenotypicVariation 432
EmpiricalObservationofAdmixture-BasedCorrelationEnablesGeneralization 438
EmpiricismasaToolfortheIndirectMethodofMolecularPhotofitting 440
ReverseFacialRecognitionUsingGenomicAncestryEstimates 450
EstimatingPhenotypefrom2DDigitalPhotographs 454
EstimatingPhenotypefrom3DDigitalPhotographs 456
ExamplesofDatabaseQueries—GlobalCharacteristicsfromDigitalPhotographs 458
TABLE OF CONTENTS ix
ExamplesofDatabaseQueries—EthnicDescriptorsandGeopoliticalAffiliations 461
VariationandParameterizationofDatabaseObservations 464
CanSocialConstructSuchasRaceBeInferredfromDNA? 468
IndirectApproachUsingFinerPopulationModels 472
IndirectInferenceofSkinPigmentation 477
SourcesoftheAncestry-SkinPigmentationCorrelation 485
CanWeInferMKnowingGenomicAncestry? 488
InferencesofCompositeCharacteristics 490
WhyNotUsetheDirectMethodInstead? 490
IndirectInferenceofIrisPigmentation 491
Chapter9 DirectMethodofPhenotypeInference 497
Pigmentation 500
HistoryofPigmentationResearch 503
TheGeneticsofHumanPigmentation—AComplexPuzzle 504
BiochemicalMethodsofQuantifyingPigment 507
IrisColor 513
IrisColorPhenotyping:TheNeedforaThoughtfulApproach 514
MakingIrisColorMeasurements 517
PopulationSurveysofIrisMelaninIndex(IMI)Values 523
RelationofIMItoSelf-DescribedIrisColor 524
HistoryofGeneticResearchonIrisColor 527
RecentHistoryofAssociationMappingResults 530
OCA2—ThePrimaryIrisColorGene 534
AnEmpiricalOCA2-BasedClassifierfortheInferenceofIrisColor 543
TheEmpiricalMethodofDirectPhenotypeInference 553
CaseReports 558
HairColor 562
SkinPigmentation 583
FinalConsiderationsfortheDirectInferenceofSkinPigmentation 596
Chapter10 TheFirstCaseStudiesofMolecularPhotofitting 599
CaseReports 599
LouisianaSerialKillerMultiagencyHomicideTaskForceInvestigation 599
OperationMinstead 603
TheBoulder,ColoradoChaseCase 607
OtherCases 607
Chapter11 ThePoliticsandEthicsofGeneticAncestryTesting 609
Resistance 610
Articles—InsightintoPublicReaction 613
Description:In the field of forensics, there is a critical need for genetic tests that can function in a predictive or inferential sense, before suspects have been identified, and/or for crimes for which DNA evidence exists but eye-witnesses do not. Molecular Photofitting fills this need by describing the proce
