Lecture 15: Individual Identity and Forensics October 17, 2011

Announcements  Problem 2 in lab 7 is now for extra credit: 3 points  Open computer lab hours and office hours posted on lab website  Extra computers available in 5109 LSB and in my office (5200 LSB)  Exam 2 on November 2

Last Time  More on the Structure program  Population assignment  Individual Identity

Today  Genetic identity and forensic identification  Effects of population structure on probability of identity  Paternity analysis

Likelihood Approaches  Prior probability often difficult to calculate in same framework as DNA evidence  Ignore prior probabilities and calculate likelihood ratio  Allows explicit calculation of relative probability of observing an event, relative to competing hypothesis

Individual Identity: Likelihood  Assume you find skin cells and blood under fingernails of a murder victim  A hitman for the Sicilian mob is seen exiting the apartment  You gatherin DNA evidence from the skin cells and suspect  What is H 1 and what is H 2 ?

Likelihood ratio for forensic DNA evidence  Matching a suspect to a piece of physical DNA evidence requires comparing likelihoods of two alternative hypotheses:  H 1 is that the sample comes from the suspect  H 2 is that the sample is from a person unrelated to the suspect  E is the DNA evidence  P(E|H1) =1 if the DNA profile of the suspect matches the DNA profile of the evidence  P(E|H2) is probability of observing the suspect genotype in an unrelated person in the population (i.e., the match probability)

Genetic typing in forensics  Highly polymorphic loci provide unique ‘fingerprint’ for each individual  Tie suspects to blood stains, semen, skin cells, hair  Revolutionized criminal justice in last decade  Also used in disasters and forensic anthropology  Principles of population genetics must be applied in calculating and interpreting probability of identity

Markers in Genetic Typing  Many sets of markers commonly used  Sets of highly polymorphic microsatellites (also called VNTR (Variable Number of Tandem Repeats), STR (Short Tandem Repeat) or SSR (Simple Sequence Repeat)) :  Profiler Plus, SGM Plus, Cofiler, Identifiler, PowerPlex16  Most are amplified in a single multiplex reaction and analyzed in a single capillary  Very high “exclusion power” (ability to differentiate individuals)

Match Probability  Probability of observing a genotype at locus k by chance in population is a function of allele frequencies: for m loci Homozygote Heterozygote  Assumes unlinked (independent loci) and Hardy- Weinberg equilibrium

Probability of Identity  Probability 2 randomly selected individuals have same profile at locus k: HomozygotesHeterozygotes for m loci  Exclusion Probability (E): E=1-P

What if the slimy mob defense attorney argues that the most likely perpetrator is the mob hitman’s brother, who has conveniently “disappeared”? Does the general match probability apply to near relatives?

Probability of identity for full sibs Heterozygotes 2 alleles IBD 1 allele IBD 0 alleles IBD General Probability of Identity for Full Sibs: Homozygotes 2 alleles IBD 0 alleles IBD

Probability of identity for full sibs For a locus with 5 alleles, each at a frequency of 0.2: P ID = P IDsib = Probability of identity unrelated individuals

What is minimum probability of identity for full sibs?

Application to bear and wolf populations  Collect anonymous hair samples in wild populations  Populations have high relatedness within restricted areas: family structure Woods et al Loci

Which allele frequency to use?  Human populations show some level of substructuring  F ST generally < 0.03  Challenge is to choose proper ethnic group and account for gene flow from other groups Illinois Caucasian Georgia Caucasian U.S. Black

Substructure in human populations  G ST is quite high among the 5 major groups of human populations for CODIS microsatellites  Relatively low within groups, but not 0!

NRC (1996) recommendations  Use population that provides highest probability of observing the genotype (unless other information is known)  Correct homozygous genotypes for substructure within selected population (e.g., Native Americans, hispanics, African Americans, caucasians, Asian Americans)  No correction for heterozygotes HomozygotesHeterozygotes

Why is it ‘conservative’ (from the standpoint of proving a match) to ignore substructure for heterozygotes?

Example: World Trade Center Victims  Match victims using DNA collected from toothbrushes, hair brushes, or relatives  Exact matches not guaranteed  Allele dropout  Use likelihood to match samples to victims

Limitations of F ST  F ST is a long, integrated look into the evolutionary/ecological history of a population: may not represent status quo  Assumptions of the model frequently violated:  Island model unrealistic  Selection is often an important factor  Mutation may not be negligible  Sampling error!

Alternatives to F ST  Direct measurements of movement: mark- recapture  Genetic structure of paternal and maternal gametes only  Chloroplast and mitochondrial DNA  Pollen gametes  Parentage analysis: direct determination of the parents of particular offspring through DNA fingerprinting

Parentage Analysis  Directly estimate real- time gene flow  Different approaches depending on goals and configuration of system  Need increasingly polymorphic markers depending on approach:  unique allele < paternity < maternity < parentage Lowe, Harris, and Ashton 2004

Paternity Exclusion Analysis  Determine multilocus genotypes of all mothers, offspring, and potential fathers  Determine paternal gamete by “subtracting” maternal genotype from that of each offspring.  Infer paternity by comparing the multilocus genotype of all gametes to those of all potential males in the population  Assign paternity if all potential males, except one, can be excluded on the basis of genetic incompatibility with the observed pollen gamete genotype  Unsampled males must be considered