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

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

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.

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)

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

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 Dec;38(8):692-8.

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., ICD  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.

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

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., ICD  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.

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

Phenome-wide Scan

MCTCMayo-TCCombined ICD9DiseaseAffectedP-valueRRAffectedP-valueRRP-value 382.9Unspecific otitis media 4,3185.0E ,1304.4E E Suppurative and unspecified otitis media 4,5143.4E ,2754.8E E Acute upper respiratory infections of unspecified site 5,2721.5E ,2238.2E E Acute upper respiratory infections of multiple or unspecified sites 5,2971.2E ,2502.0E E Acute pharyngitis 5,2024.9E E E Disturbances in tooth eruption 1,3508.3E E E Lack of expected normal physiological development in childhood E E E Disorders of tooth development and eruption 1,5567.3E E E Cough 4,2454.1E E E Acute bronchiolitis E E E Specific delays in development E E E Disorders of refraction and accommodation 3,6458.1E E E Fever and other physiologic disturbances of temperature regulation 2,8751.6E E E Developmental speech or language disorder E E E Myopia 2,1449.5E E E-158 Top non V-codes and perinatal codes

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., ICD  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.

Relative Risks RR=relative risk ADF=average disease frequency

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

MCTCMayo-TCCombine d ICD9DiseaseAffectedConcordantP-valueRRAffectedConcordantP-valueRRP-value 282.6Sickle-cell disease312.7E-042, E-046,0968.0E Hereditary spherocytosis312.7E-042, E-042,0321.7E Peroneal muscular atrophy 312.7E-042, E-042,0321.7E Other thalassemia312.7E-042, E E Other cerebellar ataxia312.7E-042, E E Long QT syndrome414.9E-041, E E-19 Genetic diseases with large estimated RRs

Same-Sex Opposite-Sex

Same-Sex Opposite-Sex

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 < 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.”

Precision Medicine Future of genomic research Populations Genome

Precision Medicine Future of genomic research Populations Genome Phenome

Families Precision Medicine Future of genomic research Populations Genome Phenome

Acknowledgements Marshfield Clinic: Murray Brilliant Peggy Peissig Steven Schrodi Zhan (Harold) Ye John Mayer many more… Mayo Clinic: Jyotishman Pathak Yijing Cheng Funding: NHGRI1U01HG NLMK22LM NCATS9U54TR NCRR1UL1RR Marshfield Clinic Research Foundation Marshfield Clinic donors