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Large-scale phenome-wide scan in twins using electronic health records
June 29th 2015
Scott HebbringMarshfield 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 individualsb. Multiple testing challengesa. Weak effectsb. Difficult to interpret biologyc. Clinical utility?d. Disease limited
PheWAS: Dramatically increases the number of diseases that can be studieda. Can start with biologically/clinically relevant variantsb. 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 biologyc. large effect sizesd. Clinically relevante. Difficult to recruit familiesf. One disease at a time
Human Genetics
Classical Twins Studies
1. Gold standard for heritability studiesUnique family/genetic relationships (monozygotic twins)Strong shared environmental exposures starting in utero
2. Rare (~20/1,000 births)
3. Difficult to recruitLargest 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.
l
l
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Madison Milwaukee
Marshfield
Marshfield Clinic Personalized Medicine Research Project
Personalized Medicine Research Project Study Area (19 Zip Codes in Central WI)
Marshfield Clinic Primary Service Area
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. 2014 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. 2014 Dec;38(8):692-8.
Hypothesis: EHR-linked twin cohorts can be used for phenome-wide studies to identify diseases with genetic
etiologies.
MethodsPopulation: 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.
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.
MethodsPopulation: 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.
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
Phenome-wide Scan
Phenome-wide Scan
MCTC Mayo-TC Combined
ICD9 Disease Affected P-value RR Affected P-value RR P-value382.9 Unspecific otitis media 4,318 5.0E-203 1.8 1,130 4.4E-252 7.1 2.3E-451382.0 Suppurative and unspecified otitis media 4,514 3.4E-202 1.7 1,275 4.8E-231 5.6 1.6E-429465.9 Acute upper respiratory infections of
unspecified site 5,272 1.5E-138 1.4 1,223 8.2E-258 6.5 1.1E-392
465 Acute upper respiratory infections of multiple or unspecified sites 5,297 1.2E-137 1.4 1,250 2.0E-253 6.2 2.1E-387
462 Acute pharyngitis 5,202 4.9E-123 1.3 950 1.2E-224 8.0 4.8E-344520.6 Disturbances in tooth eruption 1,350 8.3E-122 3.6 230 1.1E-90 27.7 4.4E-209783.4 Lack of expected normal physiological
development in childhood 726 6.5E-134 8.2 416 1.6E-73 9.0 4.9E-204
520 Disorders of tooth development and eruption 1,556 7.3E-117 3.0 311 5.0E-87 16.2 1.7E-200786.2 Cough 4,245 4.1E-80 1.2 720 1.8E-122 6.6 3.4E-199466.1 Acute bronchiolitis 575 3.9E-146 12.4 212 1.0E-45 15.9 1.7E-188315 Specific delays in development 891 1.8E-134 6.3 501 2.9E-57 5.8 2.2E-188367 Disorders of refraction and accommodation 3,645 8.1E-116 1.5 718 1.7E-74 4.5 6.1E-187780.6 Fever and other physiologic disturbances of
temperature regulation 2,875 1.6E-90 1.6 664 1.6E-81 5.3 1.0E-168
315.3 Developmental speech or language disorder 451 5.9E-113 14.0 284 2.7E-54 12.0 6.1E-164367.1 Myopia 2,144 9.5E-101 2.1 215 6.0E-61 20.5 2.1E-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.
MethodsPopulation: 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.
Relative Risks
RR=relative riskADF=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
MCTC Mayo-TC Combined
ICD9 Disease Affected Concordant P-value RR Affected Concordant P-value RR P-value
282.6 Sickle-cell disease 3 1 2.7E-04 2,747 2 1 1.6E-04 6,096 8.0E-07
282 Hereditary spherocytosis 3 1 2.7E-04 2,747 3 1 3.7E-04 2,032 1.7E-06
356.1 Peroneal muscular atrophy
3 1 2.7E-04 2,747 3 1 3.7E-04 2,032 1.7E-06
282.49 Other thalassemia 3 1 2.7E-04 2,747 9 3 6.1E-09 677 4.7E-11
334.3 Other cerebellar ataxia 3 1 2.7E-04 2,747 6 1 1.5E-03 406 6.3E-06
426.82 Long QT syndrome 4 1 4.9E-04 1,374 18 8 1.4E-17 542 3.3E-19
Genetic diseases with large estimated RRs
Same-Sex
Opposite-Sex
Same-Sex
Opposite-Sex
Potential limitations1. Limited by the inherent challenges of ICD9 coding.
2. Parental/Familial biases
3. Lack of zygosity still limits this approachNLP or blood types may help enrich for specific twin types.
Conclusions1. Most diseases are not random events in the twins.
a. 1,406/5,598 (25%) of phenotypes are statistically enriched in pairs of twinsb. ~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.”
PrecisionMedicine
Future of genomic research
Populations Genome
PrecisionMedicine
Future of genomic research
Populations Genome
Phenome
Families
PrecisionMedicine
Future of genomic research
Populations Genome
Phenome
Acknowledgements
Marshfield Clinic:Murray BrilliantPeggy PeissigSteven SchrodiZhan (Harold) YeJohn Mayermany more…
Mayo Clinic:Jyotishman PathakYijing Cheng
Funding:NHGRI 1U01HG006389NLM K22LM011938NCATS 9U54TR000021NCRR 1UL1RR025011
Marshfield Clinic Research FoundationMarshfield Clinic donors
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