Disease Heritability Inferred from Familial Relationships Reported in Medical Records

Polubriaginof, Fernanda; Vanguri, R.; Quinnies, Kayla M.; Belbin, Gillian M.; Yahi, Alexandre; Salmasian, Hojjat; Lorberbaum, Tal; Nwankwo, Victor; Li, Li; Shervey, Mark; Glowe, Patricia; Ionita‐Laza, Iuliana; Simmerling, Mary; Hripcsak, George; Bakken, Suzanne; Goldstein, David B.; Kiryluk, Krzysztof; Kenny, Eimear E.; Dudley, Joel T.; Vawdrey, David K.; Tatonetti, Nicholas P.

doi:10.1016/j.cell.2018.04.032

Cited by 82 publications

(76 citation statements)

References 33 publications

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“…The first set of simulations evaluated the blending admixture of two simulated populations with equal liability to a disease. The disease heritability was set at 50%, the mid-range heritability of polygenic diseases [65,66]. The disease SNP sets were built using the common low-effect genetic architecture, and the population genetics simulation progressed through generations.…”

Section: Admixture Of Populations With Matching Mean Prss: To What Exmentioning

confidence: 99%

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Future Preventive Gene Therapy of Polygenic Diseases from a Population Genetics Perspective

Oliynyk

2019

Preprint

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With the accumulation of scientific knowledge of the genetic causes of common diseases and continuous advancement of gene-editing technologies, gene therapies to prevent polygenic diseases may soon become possible. This study endeavored to assess population genetics consequences of such therapies. Computer simulations were used to evaluate the heterogeneity in causal alleles for polygenic diseases that could exist among geographically distinct populations. The results show that although heterogeneity would not be easily detectable by epidemiological studies following population admixture, even significant heterogeneity would not impede the outcomes of preventive gene therapies. Preventive gene therapies designed to correct causal alleles to a naturally-occurring neutral state of nucleotides would lower the prevalence of polygenic early-to middle-age-onset diseases in proportion to the decreased population relative risk attributable to the edited alleles. The outcome would manifest differently for late-onset diseases, for which the therapies would result in a delayed disease onset and decreased lifetime risk, however the lifetime risk would increase again with prolonging population life expectancy, which is a likely consequence of such therapies. If gene therapies that prevent heritable diseases were to be applied on a large scale, the decreasing frequency of risk alleles in populations would reduce the disease risk or delay the age of onset, even with a fraction of the population receiving such therapies. With ongoing population admixture, all groups would benefit over generations.

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Section: Admixture Of Populations With Matching Mean Prss: To What Exmentioning

confidence: 99%

“…The heritability of EMODs usually ranges from 30% to 80%, as documented by Wang et al [65] and Polubriaginof et al [66]. A heritability level of 50% was chosen for most simulations and analyses to represent a typical EMOD, and the common low-effect-size genetic architecture SNP set was assembled accordingly, as noted in section 4.3.…”

Section: Conceptual Summarymentioning

confidence: 99%

Future Preventive Gene Therapy of Polygenic Diseases from a Population Genetics Perspective

Oliynyk

2019

Preprint

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“…This information has not been used for genetic studies until recently. In previous work, we successfully reconstructed familial relationship networks of three medical centers using emergency contact information in EHR (Polubriaginof et al, 2018). This study involved in total 1.9 million subjects, which was much larger than any former single family-based study.…”

Section: Introductionmentioning

confidence: 99%

“…Relationship inference from the electronic health record (RIFTEHR) algorithm is used for extracting familial relationships from EHR (Polubriaginof et al, 2018). It maps emergency contact person to patients in the hospital database, and infers relationship networks and families.…”

Section: Introductionmentioning

confidence: 99%

Familial relationships in electronic health records (EHR) v2

Liu

Tatonetti

2019

Preprint

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12Heritability is an important statistic for evaluating genetic contribution to phenotypes. 13 Estimating heritability, however, requires a laborious recruitment of a large number of 14 relatives. Electronic health records (EHR) contain massive relative information in 15 emergency contact forms. Recently, we presented RIFTEHR, an algorithm for 16 extracting relationships from EHR. Here, we present an updated version and 17 reconstructed 4.2 million familial relationships from the latest New York-18 Presbyterian/Columbia University Irving Medical Center (CUIMC) EHR system. The 19number of updated relationships is 30 percent more than the last version. We present a 20 new implementation of RIFTEHR, which runs in linear time, thus largely improves the 21 speed of the algorithm. We also present a data encryption method, to protect patient 22 2018). This study involved in total 1.9 million subjects, which was much larger than 41 any former single family-based study. This holds the promise that EHR can be utilized 42 for large scale relative recruitment for genetic studies. 43 44 Relationship inference from the electronic health record (RIFTEHR) algorithm is used 45 for extracting familial relationships from EHR (Polubriaginof et al., 2018). It maps 46 emergency contact person to patients in the hospital database, and infers relationship 47 73 74Mapping emergency contact to patients 75 The original Python implementation was described before (Polubriaginof et al., 2018). 76The new implementation preprocesses data in Python, such as lower case converting 77 for names, and finds exact matches for first name, last name, phone number, zip code, 78 or their combinations using MySQL. The output of the new implementation is the same 79 with the original one, including patient identifier, relationship, emergency contact 80 identifier and the unique match combinations. 81 82 Execution time of mapping 83 0.5, 1, 2 or 4 percent of 1.6 million emergency contact and 5.8 million patient 84 demographics data from CUIMC EHR system were randomly selected as input datasets 85 of the algorithm. Each dataset was fed to both implementations. The processor 86 execution time of Python scripts was calculated using the time package. And the 87 processor execution time of each SQL query was returned automatically in MySQL. 88 Time for all scripts and queries were added up as the total execution time. 89 90 Data encryption for patient privacy 91The algorithm takes in the identifiable data, and output the according encrypted patient 92 identifier, first name, last name, phone number and zip code data. A patient identifier 93 and its encryption mapping table is also generated by the algorithm, for analysis which 94 needs linkage to phenotypes in EHR. This encryption should be done before using 95 RIFTEHR. 97 Results:98 Updated familial relationships 99 In the following context, we refer to results from (Polubriaginof et al., 2018) as the 100 original or version 1, and refer to results from this study as the updated or version 2. 102We retr...

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“…Genealogical records can reflect social and cultural structures, and record the flow of genetic material throughout history. In recent years, very large pedigree records have come into existence, owing to collaborative digitization of large genealogical records [1,2] and to digitization of large cohorts collected by healthcare providers, spanning up to millions of individuals [3][4][5][6][7]. Such population-scale pedigrees allow investigating the sociological and epidemiological history of human populations on a scale that is orders of magnitude larger than existing studies.…”

Section: Introductionmentioning

confidence: 99%

Estimating variance components in population scale family trees

Shor

Geiger

Erlich

et al. 2018

Preprint

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The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals. Such pedigrees provide the opportunity to answer genetic and epidemiological questions in scales much larger than previously possible. Linear mixed models (LMMs) are often used for analysis of pedigree data. However, LMMs cannot naturally scale to large pedigrees spanning millions of individuals, owing to their steep computational and storage requirements. Here we propose a novel modeling framework called Sparse Cholesky factorIzation LMM (SciLMM), that alleviates these difficulties by exploiting the sparsity patterns found in large pedigree data. The proposed framework can construct a matrix of genetic relationships between trillions of pairs of individuals in several hours, and can fit the corresponding LMM in several days. We demonstrate the capabilities of SciLMM via simulation studies and by estimating the heritability of longevity in a very large pedigree spanning millions of individuals and over five centuries of human history. The SciLMM framework enables the analysis of extremely large pedigrees that was not previously possible. SciLMM

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Disease Heritability Inferred from Familial Relationships Reported in Medical Records

Cited by 82 publications

References 33 publications

Future Preventive Gene Therapy of Polygenic Diseases from a Population Genetics Perspective

Future Preventive Gene Therapy of Polygenic Diseases from a Population Genetics Perspective

Familial relationships in electronic health records (EHR) v2

Estimating variance components in population scale family trees

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