Lesson 4 Slide

Applied Molecular Cellular Biology

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🧬 Genetics of Endometriosis — Grant Montgomery

Goal: Understand how genetics influence a complex, painful disease affecting millions of women.

🌺 What is Endometriosis?

Definition: Growths that look like uterine lining (endometrium) appearing outside the uterus — on the peritoneum, ovaries, or bowel.
Cell mix: Lesions contain diverse cells, forming a unique microenvironment.
Common triggers: Early puberty, heavy bleeding.
Symptoms: Vary a lot — pain, GI disorders, headaches, depression, fatigue.
Problem: Overlapping conditions confuse diagnosis → delays of 6–10 years on average!

📊 Endometriosis in Numbers

Affects ~1 in 9 women in Australia.
Major cause of pain + infertility.
Economic impact: ~$7–9 billion/year (≈ DKK 28–36 billion).
No known cause or cure (more common than breast cancer).
Genetic link: First-degree relatives → 2–3× higher risk.
Genetic contribution: ≈ 50% of total risk.

🧠 Understanding Genetic Risk

🔹 Monogenic diseases

Simple inheritance, single gene defects: Cystic fibrosis, Sickle cell, Huntington’s, Hemophilia, Duchenne MD.

🔹 Complex diseases (like endometriosis)

Many genes each with small effects.
Heritability: 30–70%.
Require Genome-Wide Association Studies (GWAS) instead of single-gene mapping.

🧬 Twin Study (Treloar et al., 1999)

First to estimate heritability: ~51% genetic risk.
Foundation for mapping genes linked to endometriosis.
Early “candidate gene” studies failed → need bigger, unbiased genome scans.

🌐 GWAS Revolution

Wellcome Trust Case Control Consortium (2007) showed how to map genetic risk in common diseases using thousands of cases.

🧩 Discovery of Genetic Risk Factors

Rahmioglu et al., 2023 (Nature Genetics):

42 genomic regions linked to endometriosis.
49 independent signals, 31 new discoveries.
Replication in multiple studies = strong confidence.
Purpose: Understand disease biology, not just find markers.

Next steps:

Fine mapping → pinpoint causal variants.
Functional studies → identify target genes & pathways.

🧭 How Do Genetic Variants Increase Risk?

Mortlock et al., 2020

Most risk SNPs = non-coding regions → regulate genes indirectly.
Some in coding regions alter proteins directly.
Regulation can happen in the endometrium or other tissues (like blood).

🧬 Epigenetics & Gene Regulation

NIH Roadmap & ENCODE projects: Mapped 111+ epigenomes → help locate where regulatory SNPs act. Epigenetic data = vital to interpret non-coding variants.

📈 Gene Expression and Menstrual Cycle

Key insight: ~46% of genes change expression through the menstrual cycle! → So, researchers must control for cycle stage when comparing patients.

After correcting for stage + multiple testing: → No significant gene expression differences between cases and controls.

This means genetic regulation, not expression level alone, likely drives risk.

🧪 Linking GWAS and Expression (eQTL)

Zhu et al., 2016; Umans et al., 2021 Integrating GWAS with expression QTL (eQTL) data can pinpoint causal genes. Used Summary data–based Mendelian Randomization (SMR) to test causality.

🧩 Case Study 1 — SRP14-AS1 (Chromosome 15)

Rahmioglu et al., 2023

SNPs here affect SRP14 expression in endometrium & blood.
Also modify DNA methylation and chromatin interactions → possibly affect BMF, which influences sex hormone binding and hormone bioavailability. 🧠 Takeaway: Hormone regulation = key mechanism.

🧩 Case Study 2 — FGD6/VEZT (Chromosome 12)

Mortlock et al., 2021

Another strong risk locus linked to cell adhesion and epithelial structure.
Functional experiments support its causal role.

🐢 Why Progress Is Slow

Most GWAS “hits” don’t match known eQTLs:

Non-coding SNPs = under strong evolutionary constraint, often in enhancers far from target genes.
eQTL SNPs = usually near genes, less constrained. ➡️ Mismatch explains why linking SNPs to genes is hard.

🔍 Key Causal Candidates (Rahmioglu et al., 2023)

42 loci, 49 signals. High-confidence variants include:

SYNE1, ESR1, LNC-LBCS, HOXA10, HOXC10, LINC00629
Regulation of SRP14, HOXB9, TRA2A in endometrium.
Stronger effects seen in severe disease cases.
Shared genetics with pain, GI disorders, migraine, depression, ovarian cancer.

🧩 Clinical impact: Understanding these overlaps can improve diagnosis and treatment.

🔄 Pathways Emerging

Common pathways involve:

Reproductive system development
Cell proliferation
Epithelial differentiation 🕸️ Key genes interact in overlapping networks → amplify small effects into big outcomes.

👩‍🔬 The GRD Lab Team (UQ + Aalborg)

Prof. Grant Montgomery (leader)
Drs. Brett McKinnon, Sally Mortlock, Sugarniya Subramaniam
PhD students: Sushma Marla, Fei Yang, Isabelle McGrath, Li Ying Thong, Isaac Barffour, Sharat Alturi
Expertise across genetics, cell biology, data science, and clinical collaboration.

❤️ Summary Takeaways

Topic	Key Point
Disease	Chronic, painful, complex, underdiagnosed
Risk	~50% genetic
GWAS	42 loci discovered, many non-coding
Mechanism	Mostly regulatory (non-coding → gene expression, hormones)
Progress	Slow due to non-overlap between GWAS and eQTL
Overlap	Shares genetics with pain, mood, and inflammatory disorders
Goal	Identify causal genes and pathways → better treatment and understanding

Quiz

Score: 0/30 (0%)

Q0. What percentage of endometriosis risk is attributed to genetic factors according to twin studies?

25%

40%

51%

70%

Q1. Which of the following best describes endometriosis lesions?

They are bacterial infections of the uterus

They are cystic structures limited to ovaries

They are growths resembling endometrium found outside the uterus

They are benign tumors of muscle tissue

Q2. Which study design is primarily used to identify genetic risk factors in complex diseases like endometriosis?

Linkage analysis

Genome-wide association studies (GWAS)

Candidate gene approach

Twin concordance testing

Q3. How many genomic regions were identified as being associated with endometriosis in the 2023 Nature Genetics study by Rahmioglu et al.?

Q4. What is a major reason why linking GWAS variants to target genes is difficult?

Most variants are in coding regions

Most variants are in non-coding regulatory regions

Variants are rapidly evolving

Variants only appear in males

Q5. Which gene on chromosome 15 is affected by variants modifying expression and methylation in endometrium and blood?

VEZT

HOXA10

SRP14

GREB1

Q6. The FGD6/VEZT locus on chromosome 12 is primarily associated with what biological function?

Immune regulation

Cell adhesion and epithelial structure

Metabolic signaling

DNA repair

Q7. Approximately what fraction of genes show significant expression changes across the menstrual cycle?

10%

25%

46%

75%

Q8. What is the estimated prevalence of endometriosis among Australian women?

1 in 20

1 in 15

1 in 9

1 in 5

Q9. Which of the following is NOT typically associated with endometriosis?

Depression

Gastrointestinal disorders

Asthma

Fatigue

Q10. What was the first study to estimate the heritability of endometriosis?

Montgomery et al., 2008

Treloar et al., 1999

Rahmioglu et al., 2023

Fung et al., 2018

Q11. Which approach integrates GWAS and gene expression data to identify causal genes?

Linkage equilibrium mapping

Summary-based Mendelian Randomization (SMR)

Knockout mouse studies

Chromatin immunoprecipitation sequencing

Q12. Which hormone-related gene encodes a protein that regulates the bioavailability of estrogen and testosterone?

BMF

FGD6

WNT4

ESR1

Q13. What is one major economic impact of endometriosis in Australia?

$1-2 billion per year

$7-9 billion per year

$20 billion per year

$500 million per year

Q14. Which of these pathways is most implicated in endometriosis development?

Cell proliferation and epithelial differentiation

Neural synapse formation

Immune cell apoptosis

Fatty acid metabolism

Q15. Endometriosis lesions typically contain a simple, homogenous population of cells.

True

False

Q16. Endometriosis affects fewer women than breast cancer.

True

False

Q17. Women with a first-degree relative affected by endometriosis have double or triple the risk of developing it themselves.

True

False

Q18. Most GWAS hits for endometriosis are located in coding regions of the genome.

True

False

Q19. No differences in gene expression between endometriosis and control samples remain after correcting for menstrual cycle stage.

True

False

Q20. Endometriosis is considered a monogenic disease.

True

False

Q21. Genome-wide association studies have identified over 40 loci associated with endometriosis risk.

True

False

Q22. FGD6 and VEZT loci are associated with hormone metabolism.

True

False

Q23. Variants in the SRP14-AS1 region can affect multiple genes through chromatin interactions.

True

False

Q24. Most eQTLs overlap with GWAS signals for endometriosis.

True

False

Q25. GWAS ‘hits’ tend to be under stronger selective constraint than eQTL variants.

True

False

Q26. Endometriosis shares genetic correlations with migraine, depression, and ovarian cancer.

True

False

Q27. VEZT/FGD6 and GREB1 have been confirmed as causal loci in endometrium for endometriosis.

True

False

Q28. The GRD lab team includes both data analysts and cell biologists working together.

True

False

Q29. Endometriosis typically takes only 1–2 years from symptom onset to diagnosis.

True

False