1. Large-scale phenome-wide scan in twins using electronic health records June 29 th 2015 Scott Hebbring Marshfield Clinic Research Foundation University of Wisconsin Madison

2. Association studies GWAS: Thousands of variants associated with a few hundred phenotypes a. Relatively easy to recruit unrelated individuals b. Multiple testing challenges a. Weak effects b. Difficult to interpret biology c. Clinical utility? d. Disease limited PheWAS: Dramatically increases the number of diseases that can be studied a. Can start with biologically/clinically relevant variants b. May be limited to the same challenges of GWAS Family studies Linkage,Segregation Analysis, Heritability… a. Thousands of mutations in thousands of genes causing human diseases. b. Often easier to interpret biology c. large effect sizes d. Clinically relevant e. Difficult to recruit families f. One disease at a time Human Genetics

3. Classical Twins Studies 1. Gold standard for heritability studies Unique family/genetic relationships (monozygotic twins) Strong shared environmental exposures starting in utero 2. Rare (~20/1,000 births) 3. Difficult to recruit Largest twin registries include the Swedish and Danish twin registries (~200,000 twins) Others: UK Adult, Australian, Sri Lankan, and Chinese National Minnesota, Univ-Wash, MI-State, Mid-Atlantic twin registries. Sample ascertainment bias 4. Phenotypic data is often acquired by surveys and questionnaires and limited to only a few measurables. 5. Updating data is costly and labor intensive.

4. 2.6 Million patients Twin population -same last name -same date of birth -same billing account -same home address -key word “twin” Marshfield Clinic Twin Cohort (~16,000 patients)

5. Genet Epidemiol. 2014 Dec;38(8):692-8.

6. A.MCTC is one of the first cross sectional twin population ~80% accuracy B.Methods are easily translatable ~12,000 twins have been ID in Mayo’s EHR. C.Little to no zygosity data D.All patients are uniquely linked to Marshfield Clinic’s EHR. Phenotypic data is collected in real time Not disease limited Amendable to phenome- wide strategies? Genet Epidemiol. 2014 Dec;38(8):692-8.

7. Hypothesis: EHR-linked twin cohorts can be used for phenome-wide studies to identify diseases with genetic etiologies. Methods Population: MCTC and Mayo twin cohort (28,888 twins) Phenotypes were defined by collapsing ICD9 coding e.g., ICD9 100.01  100.0*  100.* For every phenotype/ICD9 codes, a p-value was estimated to determine if the disease co-occurred in twins more frequently that by chance. For every phenotype/ICD9 code, a relative risk was estimated which estimated the risk of disease if the other twin is affected relative to the population risk in the twin cohorts.

8. 9,906 and 5,987 unique phenotypes/ICD9 codes in MCTC and Mayo-TC, respectively 5,598 shared phenotypes/ICD9 codes Diseases in MCTC were more common than in Mayo-CT

9. Hypothesis: EHR-linked twin cohorts can be used for phenome-wide studies to identify diseases with genetic etiologies. Methods Population: MCTC and Mayo twin cohort (28,888 twins) Phenotypes were defined by collapsing ICD9 coding e.g., ICD9 100.01  100.0*  100.* For every phenotype/ICD9 codes, a p-value was estimated to determine if the disease co-occurred in twins more frequently that by chance. For every phenotype/ICD9 code, a relative risk was estimated which estimated the risk of disease if the other twin is affected relative to the population risk in the twin cohorts.

10. Phenome-wide Scan A.1,222 phenotypes/ICD9 codes were statistically enriched for concordance in MCTC (p<8.9E-6) 929 (76%) were replicated in Mayo-TC (p<0.05) B.928 phenotypes/ICD9 codes were statistically enriched for concordance in Mayo-TC 739 (80%) were replicated in MCTC C.1,406 phenotypes were statistically enriched for concordance by combined meta-analysis

11. Phenome-wide Scan

14. MCTCMayo-TCCombined ICD9DiseaseAffectedP-valueRRAffectedP-valueRRP-value 382.9Unspecific otitis media 4,3185.0E-2031.81,1304.4E-2527.12.3E-451 382.0Suppurative and unspecified otitis media 4,5143.4E-2021.71,2754.8E-2315.61.6E-429 465.9Acute upper respiratory infections of unspecified site 5,2721.5E-1381.41,2238.2E-2586.51.1E-392 465Acute upper respiratory infections of multiple or unspecified sites 5,2971.2E-1371.41,2502.0E-2536.22.1E-387 462Acute pharyngitis 5,2024.9E-1231.39501.2E-2248.04.8E-344 520.6Disturbances in tooth eruption 1,3508.3E-1223.62301.1E-9027.74.4E-209 783.4Lack of expected normal physiological development in childhood 7266.5E-1348.24161.6E-739.04.9E-204 520Disorders of tooth development and eruption 1,5567.3E-1173.03115.0E-8716.21.7E-200 786.2Cough 4,2454.1E-801.27201.8E-1226.63.4E-199 466.1Acute bronchiolitis 5753.9E-14612.42121.0E-4515.91.7E-188 315Specific delays in development 8911.8E-1346.35012.9E-575.82.2E-188 367Disorders of refraction and accommodation 3,6458.1E-1161.57181.7E-744.56.1E-187 780.6Fever and other physiologic disturbances of temperature regulation 2,8751.6E-901.66641.6E-815.31.0E-168 315.3Developmental speech or language disorder 4515.9E-11314.02842.7E-5412.06.1E-164 367.1Myopia 2,1449.5E-1012.12156.0E-6120.52.1E-158 Top non V-codes and perinatal codes

15. Hypothesis: EHR-linked twin cohorts can be used for phenome-wide studies to identify diseases with genetic etiologies. Methods Population: MCTC and Mayo twin cohort (28,888 twins) Phenotypes were defined by collapsing ICD9 coding e.g., ICD9 100.01  100.0*  100.* For every phenotype/ICD9 codes, a p-value was estimated to determine if the disease co-occurred in twins more frequently that by chance. For every phenotype/ICD9 code, a relative risk was estimated which estimated the risk of disease if the other twin is affected relative to the population risk in the twin cohorts.

16. Relative Risks RR=relative risk ADF=average disease frequency

17. 1,455 phenotypes/ICD9 codes had at least one concordant pair in both cohorts 498 and 139 phenotypes had RRs >10 and >100 in both cohorts, respectively

18. MCTCMayo-TCCombine d ICD9DiseaseAffectedConcordantP-valueRRAffectedConcordantP-valueRRP-value 282.6Sickle-cell disease312.7E-042,747211.6E-046,0968.0E-07 282Hereditary spherocytosis312.7E-042,747313.7E-042,0321.7E-06 356.1Peroneal muscular atrophy 312.7E-042,747313.7E-042,0321.7E-06 282.49Other thalassemia312.7E-042,747936.1E-096774.7E-11 334.3Other cerebellar ataxia312.7E-042,747611.5E-034066.3E-06 426.82Long QT syndrome414.9E-041,3741881.4E-175423.3E-19 Genetic diseases with large estimated RRs

19. Same-Sex Opposite-Sex

20. Same-Sex Opposite-Sex

21. Potential limitations 1. Limited by the inherent challenges of ICD9 coding. 2. Parental/Familial biases 3. Lack of zygosity still limits this approach NLP or blood types may help enrich for specific twin types. Conclusions 1. Most diseases are not random events in the twins. a. 1,406/5,598 (25%) of phenotypes are statistically enriched in pairs of twins b. ~1% of phenotypes have RRs < 1.0 2. Genetics plays an important component to the diseases process for thousands of diseases. 3. Family data may be efficiently captured in in EHR and may be used to predict, prevent, and treat human disease for the advancement of “precision medicine.”

22. Precision Medicine Future of genomic research Populations Genome

23. Precision Medicine Future of genomic research Populations Genome Phenome

24. Families Precision Medicine Future of genomic research Populations Genome Phenome

25. Acknowledgements Marshfield Clinic: Murray Brilliant Peggy Peissig Steven Schrodi Zhan (Harold) Ye John Mayer many more… Mayo Clinic: Jyotishman Pathak Yijing Cheng Funding: NHGRI1U01HG006389 NLMK22LM011938 NCATS9U54TR000021 NCRR1UL1RR025011 Marshfield Clinic Research Foundation Marshfield Clinic donors