Genomic prediction and risk stratification for common diseases

1. Genomic prediction and risk stratification for common diseases John Whitfield

2. To test, or not to test, that is the question Any surgeon knows how to operate A good surgeon knows when to operate A very good surgeon knows when not to operate (source unknown)

3. Current situation • Existing predictors, often based on biochemical or physiological measurements, can assess risk of some common diseases (e.g. CVD, T2D) • Intervention, in high-risk people, can reduce disease risk • Question: • Can we improve prediction (and prevention) by adding genetic information • for diseases where predictors already exist? • for other common diseases?

4. Innovation Concept – Data – Trial – Implementation – Evaluation Phased approach (compare drug trials) Failure at an early stage terminates the process

5. Prediction and Stratification Risk of disease Prediction Diagnosis Monitoring Prevention Treatment Probability of response

6. Common Diseases Causes of Death Australia, 2010

7. Common and uncommon diseases Cause: Common Uncommon Disease: Common Uncommon

8. Common and uncommon diseases Cause: Common Uncommon Disease: Common Uncommon

9. Understanding genetic prediction Diseases Monogenic Polygenic Mutations Germline Somatic Genotyping Targeted Genome-wide

10. ‘Genetic Architecture’ of Disease Manolio et al, Nature 2009;461:747-53. Adapted from McCarthy & Hirschhorn, Human Molecular Genetics. 2009;17:R156–R165

11. Genetic Causes of Variation in Risk Causes of variation within the majority of the population - polygenic Causes of extreme variation - Mendelian

12. Example: LDL-C Polygenic Mendelian

13. Similar mixtures of polygenic and Mendelian genetic risk occur for most common diseases: • Cardiovascular and metabolic diseases • Cancers (bowel, breast, prostate ….. ) • Neurodegenerative disease (Alzheimer, Parkinson … ) • Psychiatric disorders (schizophrenia, autism … )

14. Genetic Causes of Variation in Risk Causes of variation within the majority of the population - polygenic Causes of extreme variation - Mendelian

15. Polygenic Disease Genome-wide Association Study Measure phenotype(s) on thousands of people Genotype hundreds of thousands of SNPs on all of them Analyse association between genotype at each SNP and each phenotype Publish results Find other people with similar data Meta-analyse combined data Publish results Repeat steps 5 - 7

16. GWAS, June 2013 All phenotypes http://www.genome.gov/gwastudies/

17. GWAS, June 2013 CVD (N = 153) http://www.genome.gov/gwastudies/

18. How can this information be translated into useful risk predictions?

19. Genetic Risk Score Identify risk-increasing alleles and estimate per-allele relative risks Compute risk-allele count, with or without weighting for risk estimates

20. Genetic Risk Score For CHD, 25 loci Schunkert et al., 2011;43:333-8 Very few people in the highest and lowest risk score categories.

21. Genetic Risk Score For CHD, 25 loci Schunkert et al., 2011;43:333-8 Very few people in the highest and lowest risk score categories. Next highest and lowest categories show ORs ≈ 1.8 and 0.5, compared to median

22. Genetic Risk Score – ROC analysis GRS from 9 SNPs not known to be associated with traditional CHD risk factors. Baseline AUC estimates include age, sex, smoking, diabetes, systolic blood pressure, antihypertensive medication use, total cholesterol, and HDL-C. GRS AUC estimates include these traditional risk factors.

23. Genetic Risk Score – Recurrence 498 patients with diagnosis of MI or angina, mean age 57. Annual follow-up, mean 7 years. Genotyping for 48 SNPs, unweighted risk-allele score. 5 out of 48 SNPs significantly associated with major adverse events during follow-up. Traditional risk factors were not predictive. Genetic risk score showed HR = 3.0 between highest and lowest tertiles.

24. Polygenic risk in other common diseases • Type 2 diabetes • Similar to CHD: multiple significant loci identified but GRS does not add value AUC comparison Bao et al., American Journal of Epidemiology 2013;178:1197-1207. Meta-analysis of 23 studies comparing conventional (sex, age, BMI, family history) and genetic risk assessments. GRS AUCs were 0.55 to 0.68.

25. Polygenic risk in other common diseases • Type 2 diabetes DOI: 10.1371/journal.pmed.1001647 Ten-year incidence, by quartiles of genetic risk: BMI < 25 0.25%, 0.44%, 0.53%, 0.89% BMI 25 to < 30 1.29%, 2.03%, 2.50%, 3.33% BMI ≥ 30 4.22%, 5.78%, 5.83%, 7.99%

26. Polygenic risk in other common diseases • Prostate cancer • Large dataset, promising but preliminary prediction • Study on 25,000 cases and 24,000 controls (Eeles et al, Nature Genetics 2013;45:385-391) • 72 loci with significant results, possibly accounting for about 30% of familial risk • Men in top 1% of GRS have 4.7 x population risk • Men in top 10% of GRS have 2.7 x population risk • But: • Risk estimated from discovery dataset - ? Over-estimated

27. Polygenic risk in other common diseases • Age-related macular degeneration • Several loci associated with substantially increased risk • Prospective study of prevalent and incident AMD in 1446 people Seddon et al., Invest Ophthalmol Vis Sci 2009;50:2044-2053 • But: • Risk estimated from discovery dataset - ? Over-estimated

28. Genetic Risk Score • Results so far show little practical improvement from adding Genetic Risk Scores. What could change this? • Larger studies and more typed SNPs  more loci identified. • Additional loci are likely to have smaller effects than those already known • Genotyping of uncommon SNPs  novel loci with larger effects • Only a few people will be affected by these rarer SNPs • Better selection of people for testing • Concentrate on people with positive family history? • Cascade testing for relatives of top 5-10% of GRS results?

30. Genetic Causes of Variation in Risk Causes of variation within the majority of the population - polygenic Causes of extreme variation - Mendelian

31. Polygenic risk in carriers of uncommon mutations • Breast cancer – BRCA1, BRCA2 BRCA1 carriers BRCA2 carriers ≈ 20% ≈ 65% BRCA2 carriers

32. Convergence • Common variants: • Chip-based SNP genotyping • Genome-wide • Multi-SNP genetic risk score • Rare variants: • Sanger sequencing • Targeted • Detection of pathogenic mutations • Both common and rare variants: • ‘Next-generation’ sequencing • Calculation of multi-SNP genetic risk score • AND • Detection of pathogenic mutations

33. “Nobody in the twenty-first century should have a pregnancy without being screened for these microdeletions,” says Matthew Rabinowitz, Natera’s chief executive. Companies are trying to stand out by expanding the number of conditions their tests check for ….. some companies have designs to sequence the entire fetal genome. Nature, 6th March 2014

34. Some (out of many) issues: • Depth of coverage • Accuracy • Interpretation • Cost • What to do with unrequested results

35. Conclusions • Multiple genetic loci have been identified for most common diseases • Genetic risk scores based on these loci or SNPs correlate with risk in independent cohorts, but have not improved prediction compared to pre-existing methods for coronary heart disease or type 2 diabetes • A large number of people need to be tested in order to identify the small proportion at substantially elevated risk • Best results are likely from a combination of family history, conventional risk factors, and genetic risk scores.

37. Weighted sum of 54 height-increasing alleles, R2 = 6% Average of parents’ heights, R2 = 40%