FSCI 4600 Final

What is a DNA mixture and common sources

DNA mixture contain 2 or more individuals contribute DNA to the same sample, which makes the interpretation more complex than single source profile.

Commonly found on sexual assault evidence; touched objects; weapons; mixed biological stains

Real case work rarely gives you perfect DNA

different types of DNA mixture

Multiple Depositors: Two or more individuals physically deposit biological material at the same location

Low-level contributor: One contributor is present at a very low quantity, difficult to detect

Secondary Transfer: DNA is indirectly deposited require an intermediate surface

Not all contributors are equally visible

Factor that affect mixture detection

Numbers of contributors: More contributors = more peaks per locus

Contributor ratio: Unequal amounts = harder to see minor contributors

Allele Sharing: mask the true number of peaks

Total DNA Quantity: low input DNA causes amplifies stochastic effects

STR marker in Mixture Detection

STRs have many possible alleles

More variation = better ability to detect mixture

D18S51 - high diversity locus that makes the most informative markers for mixture interpretation (about 50 possible alleles)

TPOX about 15 possible alleles

Major VS. Minor Contributions in DNA Mixtures

Major Contributor- individuals who contributed more DNA

Minor contributor - individuals who contributed less DNA

Determine by observing peak heights (RFU) in the electropherogram.

Major- Taller peak and more consistent across loci

Minor-Shorter peaks, missing alleles

Mixture ratios

Estimate the mixture ratio - the relative amount of DNA each person contributed

Large differences in peak height = Larger major : Minor Ratio

5:1 Noticeably smaller peaks present- causes minor still detectable interpretation

10-20:1 some minor peaks missing - causes allele dropout

Detection depends on Peak height (RFU thresholds)

If you do not see the minor contributor, it does not mean they are not there.

How do we recognize a DNA Mixture

1. More than TWO peaks at a Locus. A single person can have at most 2 alleles per locus. 3 or more peaks indicate multiple contributors are present

2. Peak height imbalance (heterozygote imbalance). A single source profile deliver a similar in height profile. If one peak is much lower ( about 70% of its pair) may indicate a minor contributor.

3. Unusually Large Stutter Peaks. Stutter peaks are typically (10-15% or the parent peak). Too large could be a true allele from another contributor.

Heteroygote Balance in single source sample

Small peak / Large Peak > 70% indicates a single source profile

if the ratio is below 70%, indicates mixture; degradation; Inhibition

Stutter peak VS. Mixture peak

Normal stutter peak: small; <10-15% of the main peak; Always appears in a predicable position (n-1)

Mixture peak: Looks like stutter but larger

Not all small peaks are stutter artifacts.

Stutter can hide or mimic minor contributors, making mixture interpretation more difficult

Two person mixture pattern

4 peaks- no shared alleles, clearly a mixture

3 peaks- one peak taller than others, one shared allele

2 peaks - both shared alleles, looks like a single source

1 peak - Same homozygous genotype, indistinguishable from single source

Always consider peak height differences and ratios

Steps approach to mixture interpretation

1. Is it a mixture? more than 2 peaks at a locus; big peak height ratio; Unexpected allele patterns

2. Label the peaks: above analytical threshold

3. How Many contributors. Max alleles / 2 = minimum contributors

4. Who is Major and Minor, Tall peaks and shorter peaks

5. What genotype combination make sense

6. Compare to known profiles

Estimating Number of Contributor

Look across all loci, find the locus with the most alleles

Minimum contributors = Mix # of

Calculating Mixture Ratio

Major contributor (B+D)

Minor contributor (A+C)

Ratio = (B+D) / (A+C) = 2.3 : 1

Round to approximately 2:1 - The major contributor contributes roughly twice the DNA of the minor contributor

Challenges in Mixture interpretation

Allele Sharing

Three or More contributors - increases genotype combination possibilities

Low Template DNA

Stochastic Effect

Degradation and Inhibition

Probabilistic Genotyping

It uses continuous statistical models

Peak Heights

Allele Dropout

Stutter

Number of Contributors

STRmix Software

It is important because it uses real forensic labs, accepted in courts and helps analyze mixtures that are too complex to interpret manually

It tests millions of possible genotype combinations and uses the entire electropherogram. Accounts for peak height, dropout and stutter

STRmix preform interpretations faster, more consistently and with statistics

Single Nucleotide Polymorphisms (SNPs)

A single base change in DNA occurs at a specific location

Small change, but very important

Why dose SNP matter

There are so many of them

Extreme Abundance: every 1000 bp

Genome-Wide Coverage - located across all chromosomes

Give a big picture of DNA

Technology Enables: Next-generation sequencing (NGS) panels

SNP vs. STR

STR: Repeat length, Often 10+ per locus, high distinguish power, length (100-400+ bp), stutter artifacts: present complicates interpretation. Databases: CODIS, NDNAD

SNP: Single nucleotide substitution. only 2 bi-allelic, Lower discrimination power on one locus, Very small (<100bp), no stutter artifacts, clear signal, No standard forensic databases.

The Bi-Allelic Nature of SNPs

SNPs are Bi-Allelic: Most SNPs have only 2 alleles

3 possible genotypes AA/AG/GG

What does this mean? Only 2 alleles - less variation per SNP

A single SNP is less informative. Analyze many SNPs together

Why use SNPs in Forensics

Degraded DNA: fragments <100bp, works for DNA that is old, damaged or broken

High Multiplexing Capacity: hundreds of SNPs in one test

SNPs are especially useful when DNA is damaged or limited

Advantages of SNPs

Small DNA Fragments: works with < 100bp DNA; Ideal for degraded samples

Slean Results: artifact-free signals; Easier to interpret than STRs

Automation-Friendly: Can analyze many samples quickly, highly reproducible

More than Identification: Can predict eye color, hair color and ancestry.

Disadvantages of SNPs

Low power per marker

Need many SNPs, 50-100+ markers; more cost

Mixtures are Harder- Limited variation and difficult to separate contributors

Limited Databases

How many SNPs are needed

25-45 minimum estimate for matching the discrimination power for standard CODIS

50-100 typical range used in forensic SNP panels

~1000 between SNPs

SNP Typing methods

Commonly used: Direct sequencing, reads the actual DNA sequence. Use NGS

Primer Extension (SNaPshot) targets specifc SNPs

Less commonly used: Microarrays and pyrosequencing

SNaPshot: Mini-sequencing

A method to identify SNP alleles and looks at one base in DNA

DNA region is amplified (PCR), primer binds next to the SNP, One base is added (ddNTP) and that base is fluorescently labeled.

Each base=different color

SNaPshot reads only one base. Color = SNP allele

SNaPshot protocol

PCR - Clean up extra primers and ddNTP - Extension add one base - electrophoresis (read color)

Reading SNP results

Each peak = a base (ATCG), each color = a different base

One peak = homozygous; Two Peaks = Heterozygous

SNP applications in Foresncis

Degraded DNA Analysis

Ancestry informative Markers (AIMs): SNPs vary between populations

Forensic Phenotyping: predict physical traits

SNPs in Degraded Samples

Historical and Archaeological Remains: bones, teeth and mummified tissues

Mass Disaster ID: Plane crashes, fires and natural disasters

SNPs and Ancestry Inference

Some SNP different between populations

Called Ancestry Informative Markers

Compare DNA to population databases

Limitations of using SNP and AIMs

The result represents statistical probabilities

The human population exists on a continuum

An admixed individual presents challenges for classification algorithms

Result for investigative leads only

Ethical and legal frameworks

Phenotypic Prediction from SNPs

Forensic DNA phenotyping (FDP) uses validated SNP panels to predict externally visible characteristics

Eye color- IrisPlex system uses 6 SNPs to predict eye color above 90% accuracy

HIrisPlex system hair color determination

Skin tone- HIrisPlex-S system adds skin color prediction

Other Biallelic Markers

Indels- Insertion/ Deletion Polymorphisms: Small DNA changes 1-10bp, slightly larger than SNPs

Alu element Plymorphisms - Large DNA insertion ~300bp. Used for ancestry/population studies

Next Generation Sequencing (NGS)

It reads the whole book

NGS-AKA Massively Parallel Sequencing (MPS) is a transformative technology that reads the actual DNA sequence directly, rather than measuring the size of DNA fragments as traditional capillary electrophoresis does.

Actual Sequence

Massively Parallel- looks at thousands of millions of DNA pieces

Sequence vs. Size, the exact sequence.

STRs vs. NGS

STR measures fragment size, detects ~ 20 loci, Make one type, Struggle with degraded / Low DNA, provide limited information

NGS- Actual DNA sequence, Thousands of loci, Multiple marker types, Works well with challenging samples

Much more detailed informaiton

NGS Overview

1. Fragment DNA

2. Add adapters- barcodes

3. Amplify DNA make thousands of copies

4. Sequence DNA, each base gives a detectable signal

5. Analyze Data

What can NGS analyze

STRs; SNPs; mtDNA; Whole Genome

Why NGS is Powerful in Forensics

1. Challenging Samples

2. Multi-Marker Efficiency

3. More detailed Results: Higher discriminatin power and helps with mistures

Use of NGS

Complex mixtures; Missing Person; Ancestry Inference; Phenotyping

Limitations of NGS

High Cost

Complex Bioinformatics

Validation Requirements

Uneven Adoption

NGS is one of the most powerful tools ever introduced to forensic genetics. Greater data complexity means greater potential for misinterpretation.