1 / 22

Special Thanks!

Bayesian Risk Analysis Workshop Questions Shuji Ogino, M.D., Ph.D. AMP Training and Education Committee Brigham and Women’s Hospital Dana-Farber Cancer Institute Harvard Medical School I would appreciate any feedback shuji_ogino@dfci.harvard.edu. Special Thanks!.

jonathon
Télécharger la présentation

Special Thanks!

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Bayesian Risk AnalysisWorkshop QuestionsShuji Ogino, M.D., Ph.D.AMP Training and Education CommitteeBrigham and Women’s HospitalDana-Farber Cancer InstituteHarvard Medical SchoolI would appreciate any feedbackshuji_ogino@dfci.harvard.edu

  2. Special Thanks! • Rob Wilson, Pam Flodman, Pam Hawley, Bert Gold and Wayne Grody; collaborators of risk analysis projects • Jean Amos Wilson, Vicky Pratt and Ed Highsmith; helpful suggestions • AMP Program Committee, Training and Education Committee, and Genetics Subdivision • Early-birds, thank you!

  3. Q1. Cystic fibrosis Non-Hispanic Caucasian family Carrier screening negative for the ACMG 23 mutation panel CF Sensitivity = mutation detection rate = 88% Specificity = 100%

  4. Hints • Sensitivity = positive / all carriers (or patients); you want them to be positive • Specificity = negative / all non-carriers (or controls); you want them to be negative

  5. Hints • For alternative possibilities (e.g., carrier vs. non-carrier), always assume one of them is true (e.g., she is a carrier), then calculate the probability that the test result (negative) happens (= conditional probability) • Assuming = key to success in Bayes

  6. Q2. Cystic fibrosis testing Same mutation panel Negative for the same 23 mutation panel Carrier risk? Classic CF patient Tested for the ACMG 23 mutation panel Only one p.F508del detected p.F508del constitutes 72% of all disease alleles

  7. Hints • Be careful when test result on proband is available • Especially watch for undetectable mutation !

  8. Q3. Tough but common question. Cystic fibrosis testing Different mutation panels Negative for an expanded mutation panel that detects 93% of Non-Hispanic Caucasian disease alleles Classic CF patient Tested for the ACMG 23 mutation panel (that detects 88% of all disease alleles) Only one p.F508del detected Carrier risk?

  9. Hints • What is the probability that mutations undetectable by the 23-mutation panel can be detected by the expanded panel?

  10. Q4. Cystic fibrosis testing Only one detectable mutation (Almost imaginary scenario, but prelude to Q5) Non-Hispanic Caucasian family Only one p.F508del detected by prenatal testing with ACMG 23-panel (that detects 88% of disease alleles). p.F508del constitutes 72% of all disease alleles What is CF disease risk?

  11. Hints • Assume AFFECTED fetus, then calculate conditional probabilities • Assume CARRIER fetus, then calculate conditional probabilities • Assume NON-CARRIER fetus, then calculate conditional probabilities For further reading: Ogino et al. J Med Genet 2004;41:e70.

  12. Q5. Cystic fibrosis testing Only one detectable mutation Non-Hispanic Caucasian family Carrier screening shows p.F508del by the ACMG 23 mutation panel Fetal echogenic bowel (EB)+ Only one p.F508del by prenatal testing (ACMG 23 mutation panel that detects 88% of disease alleles). p.F508del = 72% of all mutant alleles What is CF disease risk? Cond. prob. of EB if affected = 0.11 if a carrier = 0.00089 if a non-carrier = 0.00035

  13. Hints • Start Bayesian analysis from the top • Assume carrier father, then calculate conditional probabilities • Assume non-carrier father, then calculate conditional probabilities • For further reading: Ogino et al. J Med Genet 2004;41:e70.

  14. From here: Advanced questions for other diseases Three reasons to do: 1. You can have fun in an airplane to the meeting 2. We can go over if time allows at the workshop (answers will be available) 3. I am always happy to discuss

  15. Q6. Autosomal Dominant Disease Affected Unaffected. Carrier risk? II-2 Age 65 Unaffected. Carrier risk? III-1 Penetrance at age 65 = 0.6 at age 40 = 0.3 Age 40

  16. Hints • One comprehensive Bayesian table gives all correct answers at once • Information of a child may modify risks of the parents and grandparents

  17. Q7. Autosomal Dominant Disease with Age-dependent Penetrance Affected Unaffected at age 50 Heterozygous risk? Disease risk by age 70? Penetrance by age 50 = 0.4 by age 70 = 0.75

  18. Hints • If he is a carrier, what is the risk to become symptomatic from age 50 to 70?

  19. Q8. Consanguinity: IV-1’s risk for rare AR disease I-1 I-2 II-1 II-2 II-4 II-3 III-2 III-3 III-1 III-4 IV-1 Carrier IV-2 AR disease V-1

  20. Hints • Watch for dependent possibilities • Start from a key person (connector between consanguineous couple and proband).

  21. Q9. Isolated Case of X-linked Recessive Disease Carrier Risk? I-2 II-3 II-4 II-1 II-2 Carrier Risk? Carrier Risk? Age 30, 36 asymptomatic Age 2 months DMD Assume  =  (same maternal and paternal de novo mutation rate)

  22. Hints • Start analysis from the top • One comprehensive Bayesian table can give all answers at once • Carrier risk = 4 (given  = ) for a woman with no relative affected with lethal X-liked recessive disease

More Related