Lecture 3: Interpretation of Genetic Variation

*****What is a major limitation of current global genomic databases?

• hint: has to do with race/ethnic groups

Genomic databases are European centric, balancing input is being worked on.

Humans share 99.9% of their DNA (99.9% identical)

• How does Geography affect our genomes and relatedness

• what factors cause genetic variation among human populations

1. Geography (relatedness amongst groups mimics geographic location)

2. Local adaptations (to factors like altitude, diet, and microbial pathogens)

3. Admixture with extinct hominid groups (like Neanderthals and Denisovans)

   1. Can influence disease risk eg. depression, severe COVID

***** What are the 3 IMPORTANT CAVEATS mentioned in this lecture?

IMPORTANT CAVEAT #1

• The amount of variation between individual is so great, it dwarfs the amount of interracial variation.

  • Populations are a continuum. No support from genetics for saying that the members of any racial group are genetically better or not

• There is an essential genetic equality of all human groups.

IMPORTANT CAVEAT #2

• All humans carry deleterious variants, estimated 10-12 variants each.

  • No individual can claim to be genetically superior to another.

• Variability in genes and their products can improve adaptability to environmental change, pathogen exposure

IMPORTANT CAVEAT #3

• Genetic changes are typically considered “good” or “bad”.

  • Ascertainment bias.

• Keep in mind temporal changes in the “value” of a mutation or allele. (value of a mutation (good vs. bad) is dependent on context)

  • The mutations which have helped us survive other times in human history may be “deleterious” now.

    • Eg. NIDDM, Type II diabetes, Hemochromatosis, Susceptibility to severe COVID

****If there is essential genetic equality of all human groups why do we ask for an individual’s ethnic background?

We use the variation present in human populations and its

frequency to help us evaluate whether a variant is clinically

significant. We ask for an individual’s ethnic background on our

requisitions for that purpose.

*****Genetic variants or alleles in the clinic can be:

• what are the 7 possible results

Does not change the protein encoded or the change has no effect on protein function or gene expression. Eg. F9 gene polymorphism.

1. Neutral

2. Benign

3. Likely benign

Protein function or gene expression is changed and an individual’s health is compromised. Eg. BRCA1 and cancer.

4. Pathogenic

5. Likely pathogenic

Unclear what effect change has on protein function or gene expression

6. Variant of unknown clinical significance (VUS, VOUS)

Protein function or gene expression is changed and has a beneficial or protective effect on an individual’s health. Eg. CCR5 and HIV susceptibility.

7. Positive/Protective

Is there perfection in anyones genome?

NO such thing as perfection

• Each of us carries variants in all classes of pathogenicity

*****How do we interpret the variation we see?

• What are 6 things we need?

How do we orient ourselves so when we talk about genetic variation, so we all are looking at the same positions in the genome……

• aka how To consistently identify and communicate genetic variations across research and clinical settings

Standardized genomic coordinates and nomenclature.

1. Look at chr + physical landmarks when looking through microscope eg. cytogenetic chromosome bands

Nomenclature

1. Genome build + exact nucleotide positions on the chr that we identify with molecular based techniques (eg. chr21:21,379,642–30,108,064del (hg18, NCBI build 36))

   1. Commonly used to describe large CNVs

2. Accession # for genomes sequences for Chr 21 in NCBI genbank eg.(NC_000021.5:g.34,450,000T>A) = a T to A substitution at g.34,450,000

   1. Commonly ysed to describe SNVs or SNPs

**Chromosome Structure Value: Why is chromosome architecture still important during high-resolution molecular testing?

Even with molecular techniques = high res (where we can focus on individual bp) knowledge of chr and their structure can still be key to interpreting how a variant occurred.

• Architecture of genome, condensed + folded structure of genome and the orientation within the nucleus are relevant ad being investigated for their impact in health and disease

*****What type of nomenclature format is typically used to describe large copy number variants (CNVs) vs. small SNPs + SNVs?

GENOME BUILDS = CNV

• Chromosome range notation based on a specific genome build

Accession Number = SNV + SNP

• 
  

Limitation of Nucleotide Coordinates

• Question: What is the limitation of using absolute nucleotide positions (like Genome builds and Accession #) alone?

This has value for some variants, but lacks info on what is happening at the level of the gene

• They do not automatically provide information about what is happening at the gene or coding level.

using only genome builds and accession numbers provides the exact physical coordinates of a mutation, but fails to explain its functional impact on a specific gene.

• Genome Builds (e.g., GRCh37, GRCh38): These act like a map of the entire human genome.

• Accession Numbers (e.g., NC_000001.11): These identify a specific chromosome or sequence within that map.

*****When evaluating the clinical significance of a genetic variant, why is it essential to establish a single consensus reference transcript and how is this transcript typically selected?

Alternative splicing of mRNA occurs in most human genes

• more than 1 version of RNA transcript = more than 1 product can be produced from the same gene

  • These may be expressed at diff times, diff conditions, tissues and time in development

This need to be considered when evaluating a variants significance

• a variant's impact depends heavily on which transcript and protein isoform are affected

  • eg. a variant might fall in an exon or splice site that is skipped or in a tissue-specific transcripts, meaning the variant could be neutral in one isoform but pathogenic in another

Reference transcript = LONGEST mRNA

*****How do we describe what we see in a genetic variant?

• How to number a gene location (hint: relative to what)

Numbering a gene location is always done relative to the REFERENCE transcript (NM#)

[Reference Sequence] : [Level & Location] [What Changed] -> [Protein Consequence].

• uses Coding sequence (EXONS) of a gene as a reference (c.#)

• Mentions what consequences there are on the protein (p.#) or mRNA (r.#)

****Explain how Introns and Exon positions are denoted in gene location nomenclature with HGVS

Since it uses the coding sequence as a reference, Nucleotides in EXONS (the parts that code for protein) receive a number all the way until the stop codon.

• Coding DNA numbering (c.) only counts the exons.

When a mutation happens inside an INTRON, HGVS uses a + or - to show how many steps into the intron the mutation is located:

• + = 3’ of a splice site (aka. after the stop codon)

• - = 5’ upstream of a transcription start

The “A” of the Start codon -→ always = 1

Example: c.77+1 = Go to exon position 77. Move 1 letter forward (+1) into the intron

****Common codes used in description of variants: Nomenclature rules

• What do the HGVS prefixes c., g., r., and p. stand for

The Level (Prefix): Tells you what kind of molecule is being looked at:

c. = coding sequence on DNA

g. = genomic sequence

r. = RNA/transcript sequence

p. = Protein sequence

****Common codes used in description of variants: Nomenclature rules

• What does * and > mean?

• * indicates the presence of a stop codon

• > = nucleotide substitution

***Please denote what this nomenclature means

*****Assessing Genetic variants for clinical significance: What is the DEFAULT classification of a genetic variance?

• Pathogenic (P)

• Likely Pathogenic (LP)

• Variant of Uncertain Significance (VUS)

• Likely Benign (LB)

• Benign (B)

UNCERTAIN SIGNIFICANCE = default until you have evidence other wise

• NO ANSWER IS BETTER THAN WRONG ANSWER

*****What type of Evidence Evaluated in the SCORING process for clinical significance?

1. Population Data

2. Variant Type

3. Clinical Observations

4. Functional Studies

5. Population Studies

6. In silico/ Computational Data

How has ACMG variant classification guidelines EVOLVED from 2008 → 2014? for GERMLINE variants

• Germ line = inherited genetic changes in sperm or egg cells that affect every cell in the body

• Somatic mutations = acquired genetic changes in non-reproductive cells that happen after conception and cannot be passed to children

Evolved from a rigid, outcome-based classification system → standardized, evidence-weighted framework

*****How does the different evidence looked affect if a variant is considered more benign or pathogenic

• eg. how does de novo status influence if a variant is pathogenic vs. benign

for: Population data, Computational, Functional, Family (Segregation), De novo, allelic data (variant data)

1. Population data:

   1. Many people in the population have the variant = strong indicator of Benign

   2. Variant is absent in public genome data bases = moderate indicator of pathogenic

   3. Variant is prevalent in affected populations is > than contol = strong pathogenic indicator

2. Computational and Predictive Data: Uses computer algorithms (e.g., SIFT, PolyPhen) to predict how a DNA change alters protein structure or function.

   1. Majority of the data suggest variation will have no impact on gene product = Supports Benign

   2. Much of the data suggest deleterious effect of gene product = support Pathogenic

   3. Novel change that causes as change in different pathogenic mutation that is already known = Moderate pathogenic

   4. Variant causes SAME change as an established pathogenic variant = Strong pathogenic

   5. Variant causes LOF (loss of function) where it is a known mechanism of disease = Very strong pathogenic

3. Functional Data: real-world, laboratory-bench experiments

   1. Experiments show variant does not disrupt cellular or protein function = strong Benign

   2. Studies directly demonstrate a severe deleterious effect on protein function = Strong pathogenic

4. Family Data (segregation): Showing whether a specific variant is inherited together with a disease

   1. Non-segregation. Affected family members do not carry the variant, or unaffected members carry it when they shouldn't = Benign

   2. Increase in segregation (aka. disease phenotype is prevalent along side variant = inherited) = Pathogenic

5. De Novo Data

   1. De novo = Indicates pathogenic

6. Allelic Data: Looks at the relationship between the variant and other variants in the same gene.

   1. The variant is observed in trans (on the opposite chromosome copy) with a known dominant pathogenic variant, or in cis (on the same copy) with another pathogenic variant, meaning it doesn't cause disease on its own. = Benign

   2. For recessive conditions, detecting this variant in trans with a known pathogenic variant on the other copy points toward pathogenicity = Pathogenic

Summarize this slide

1. Pop data: High incidence in data = benign. Low/ absent = Pathogenic

2. Variant Type: Mild or silent changes = lean benign. Severe protein-disrupting changes (nonsense, frameshift, splice site dinucleotides) lean pathogenic.

3. Clinical Observations: Patient and family case history (e.g., co-occurrence patterns, family segregation, or number of affected families). High number of family cases = pathogenic

4. Experimental Studies: reputable experimental results are only weak/support of pathogenicity or benign (do not strongly influence categorization)

5. Computational Data: In silico predictive tools act as indirect evidence, evaluating whether a change is likely completely neutral or highly deleterious.

****Interpretation of variants form NGS (nxt gen seq). What is the work flow? What tools + data bases are used?

Tools:

• Databases: ClinGen, ClinVar, NCBI dbSNP, and gnomAD (Genome Aggregation Database).

• In Silico (Computational) Tools: SIFT, PolyPhen, Mutation Taster, and REVEL.

• Clinical/Family Analysis: Familial segregation, pedigree analysis, and cis/trans variant configuration.

Why must clinicians be cautious when relying on databases and in silico tools?

Because data in databases and predictions generated by computational tools can be wrong or inaccurate.

****What is the suggest classification of this assessment and WHY?

UNCERTAIN SIGNIFICANCE

• The default unless enough evidence to classify as BENIGN, LIKELY BENIGN, LIKELY PATHOGENIC OR PATHOGENIC

All the categories that are in the yellow area (needed to be confident evidence) = 0 = no evidence

****True or false: the suggested classification of a variant can be OVERRIDED?

TRUE

******If the suggested classification can be overriden → what type of variant can be manually overridden to "likely benign"?

Synonymous variant (silent mutation) with in silico data that suggests no splice change can be called as likely benign

*****What evidence of Pathogenicity puts a varints in the following categories

1. PVS

2. PS

3. PM

4. PP

1. BA

2. BS

3. BP

1. PVS (Pathogenic Very Strong): null variant - nonsense, frameshift etc.- where LOF is a known mechanism

2. PS (Pathogenic Strong): confirmed de novo mutations, well-established functional studies, very high prevalence in affected individuals

3. PM (Pathogenic Moderate): located in a mutational hot spot, absent/ low in general population controls

4. PP (Pathogenic Supporting): co-segregation in family members, in silico computational evidence, reputable sources recently reports variant as pathogenic

1. BA (Benign Stand-Alone): allele frequency high in general populations, which automatically rules out rare disease causation

2. BS (Benign Strong): allele frequency is greater than expected for the disease, studies show no effect, lack of segregation in affected family members

3. BP (Benign Supporting): synonymous variants with no predicted splice alterations, nucleotide not highly conserved, reputable source recently report as benign

*****Above what % allele freq. in population data can pathogenicity of a variant be automatically ruled out? aka. labelled as BA (Benign Stand-Alone)

5% +

******is BS1: Above what % allele freq. is considered GREATER THAN EXPECTED for disease incidence and why?

0.1% and above

• b/c that is the baseline probability

*****What are somethings we need to Consider when Assessing CNV’s in genes?

• Truncating

• In frame

• Alternative splicing

• Key functional domain

eg. In this BRCA2 case it was classified as PATHOGENIC despite being in frame because it deleted a key functional DNA-binding domain.

What is Familial Hypercholesteremia?

• Autosomal vs. sex chromosome disorder

• Incidence? 1/out of how many

• What gene?

• Treatment?

• Heterozygotes vs. Homozygotes disease affect

• How does early identification help?

****Assessing missense vs. synonymous variation: How to begin assessing a variant aka. what are the first couple things you would do?

See what is known about the variant and location

• compare the patient to data sets

In this data: green = common variant + red = pathogenic

*****The results for our patient with high LDL

• HOW TO CLASSIFY? + WHAT ARE THE NEXT STEPS?

  • benign, likely benign

  • Uncertain

  • Pathogenic, likely pathogenic

LIKELY PATHOGENIC

Nxt steps = TREATMENT + potential Assessment of family members

*****The results for our patient If they had another variant of the a.a.

• HOW TO CLASSIFY? + WHAT ARE THE NEXT STEPS?

  • benign, likely benign

  • Uncertain

  • Pathogenic, likely pathogenic

What is the diagnostic odyssey?

A set of steps for suspected mitochondrial DNA (mtDNA) disorders

• structured, multi-tiered approach that progresses starting from non-invasive clinical INVESTIGATIONS to invasive tissue biopsies and specialized genetic sequencing.

*****How are Mito variants classified

• What are 2 things taken into consideration and WHY?

1. are they part of a Haplotype?

   1. A haplotype (or sub-haplogroup) represents a specific cluster of mutations shared by individuals who belong to the same maternal lineage. Thus if a haplotype is present it means that many people have this variant and it is likely benign (ancestral marker)

      • disease-causing mutations are frequently heteroplasmic (a mix of healthy and mutated mitochondrial DNA within the same cell),

2. is the variant Disease associated

What is the key take away?

human mitochondrial DNA (mtDNA) is distinct in different ethnic groups

• evolves along clear, non-recombining maternal lineages (haplogroups) that map directly to global geographic ancestries, tracing back to a common African origin

What is the main message?

Mut of mito genes ==> multisystem diseases

Mutations within the mitochondrial genome lead to complex, overlapping multi-system diseases because mitochondria provide essential cellular energy (ATP) to tissues all across the body.

• Mitochondrial mutations affect many organs and systems

******Mitochondria INHERITENCE

• why is there variable penetrance and expressivity?

MATERNAL inheritance

• only mom passes it on

• If dad has variant non of children are affected because he does not pass on

Penetrance and expressivity is dependent on the amount of affected mitochondria in the patient

*****What are some considerations for CLINICAL REPORTS

• what is the frame work to make a good clinical report

• What do the clinicians want to know?

• How do we make it concise and meaningful?

Start of with:

• Clinical Lab Interpretation: start with clinically relevant interpretation so physician can immediately grasp what the results mean for their patient.

• Clinical Report Generation: what to include and not include depends on the clinical request

  • Accurate test methodologies and limitations

  • explicitly categorizing pathogenic variants vs. Variants of Uncertain Significance (VUS)

  • Report incidental findings

  • genetic counseling consideration

  • Recommendations for subsequent testing.

• Educational Support for End Users: Provide consultation on reports and implications of variants identified and need for additional testing (help clinician interpret the report if needed)

*****What type of results can you expect:

• How are the following variants categorized/ referred to?

• what affects if the disease is observed

*****What type of results can you expect:

• In this patient what can these results mean?

*****What type of results can you expect:

• what are the possible results When a Patient is said to carry a VUS

WHY? what is an example of where this went wrong/ wrong person was tested?

A boy had sudden cardiac death. Since the boy died he could not be used to test for the cause thus they tested the boys brother.

• They detected a LQT variant in the brother → Doctors assumed this genetic change was the deadly killer of the boy. Because of this guess, they surgically put a heart-shocking device (a defibrillator) inside the healthy brother.

• The mistake spread: They tested the rest of the family. Over 20 relatives had the exact same genetic spelling change. Doctors told all of them that they had a deadly heart condition.

• The truth came out later: Scientists realized that the genetic change was completely harmless. It was just a normal family trait, not a disease.

Testing the wrong family member first can cause a cascade of false diagnoses and unnecessary, invasive medical procedures across an entire family.

• Review of ECGs in those family members not consistent with long QT

• Review of variant showed it had not been definitively linked to long QT

• DNA Test repeated on blood sample from affected collected at autopsy

• Pathogenic variant found in another gene linked to sudden cardiac death

THE DOCTORS SHOULD HAVE TESTED THE DEAD BOY INSTEAD OF THE BROTHER TO DETERMINE THE CAUSE OF DEATH

****Work flow for molecular autopsy

• aka called postmortem genetic testing