The development of large-scale de-identified biomedical databases in the age of genomics—principles and challenges

Dankar, Fida K.; Ptitsyn, Andrey; Dankar, Samar K.

doi:10.1186/s40246-018-0147-5

Cited by 33 publications

(27 citation statements)

References 55 publications

(46 reference statements)

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“…DNA sequencing aggravates this problem as the genome is unique to every individual and can be used to predict future ailments for individuals and their blood relatives (such as Alzheimer's or schizophrenia). Such information has the potential to deny jobs and to isolate subjects socially [2]. In the face of these growing concerns, privacy experts are trying to explore alternative approaches to privacy protection.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory

Dankar¹,

Madathil²,

Dankar³

et al. 2019

JMIR Med Inform

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Background Biomedical research often requires large cohorts and necessitates the sharing of biomedical data with researchers around the world, which raises many privacy, ethical, and legal concerns. In the face of these concerns, privacy experts are trying to explore approaches to analyzing the distributed data while protecting its privacy. Many of these approaches are based on secure multiparty computations (SMCs). SMC is an attractive approach allowing multiple parties to collectively carry out calculations on their datasets without having to reveal their own raw data; however, it incurs heavy computation time and requires extensive communication between the involved parties. Objective This study aimed to develop usable and efficient SMC applications that meet the needs of the potential end-users and to raise general awareness about SMC as a tool that supports data sharing. Methods We have introduced distributed statistical computing (DSC) into the design of secure multiparty protocols, which allows us to conduct computations on each of the parties’ sites independently and then combine these computations to form 1 estimator for the collective dataset, thus limiting communication to the final step and reducing complexity. The effectiveness of our privacy-preserving model is demonstrated through a linear regression application. Results Our secure linear regression algorithm was tested for accuracy and performance using real and synthetic datasets. The results showed no loss of accuracy (over nonsecure regression) and very good performance (20 min for 100 million records). Conclusions We used DSC to securely calculate a linear regression model over multiple datasets. Our experiments showed very good performance (in terms of the number of records it can handle). We plan to extend our method to other estimators such as logistic regression.

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Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory

Dankar¹,

Madathil²,

Dankar³

et al. 2019

JMIR Med Inform

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“…En cuanto a experiencias internacionales en relación a bases de datos nacionales de información genética encontramos que un registro nacional biométrico basado en ADN ya es utilizado en Kuwait 5 . Así mismo se está implementando una base de datos poblacional con información genética en el Emirato Árabe de Dubái, el cual será utilizado principalmente para la prevención y tratamiento de enfermedades 20 . En China existe una base de datos nacional de ADN que contiene datos padres de niños perdidos, niños víctima de trata, extraviados o sin hogar.…”

Section: Discussionunclassified

Identificación genética de recién nacidos en Perú: un estudio piloto

et al. 2019

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Objetivo: Determinar la factibilidad de la identificación genética a un grupo de recién nacidos provenientes de un hospital público de Lima-Perú. Material y Método: Estudio descriptivo de corte transversal, realizado por Registro de Identificación y Estado Civil de Perú, en recién nacidos vivos y sus respectivas madres, provenientes del Hospital Carlos Lanfranco La Hoz (Puente Piedra-Lima) durante el mes de enero del 2015. Las muestras fueron colectadas en tarjetas FTA (Fast Technology for Analysis of nucleic acids) que permitieron un análisis directo por PCR (Polymerase Chain Reaction) y electroforesis capilar de 21 marcadores genéticos de tipo STR (Short Tandem Repeats), incluyendo el marcador amelogenina para la determinación del sexo. Resultados: Se incluyeron un total de 44 madres y 45 recién nacidos (existió un parto gemelar). La probabilidad de maternidad fue mayor al 99.9% en todos los casos. No se encontraron dificultades en la toma de muestra, ni en el transporte del material. El material biológico obtenido fue suficiente para la obtención de ADN para realizar la identificación del recién nacido. Conclusiones: El procedimiento de identificación genética fue factible de realizar en este hospital. Se identificaron etapas del proceso que podrían mejorarse para la posible aplicación de este procedimiento a una mayor escala en el Perú.

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“…While these platforms have demonstrated their utility for epidemiological research, implementation of available personalized omics information into primary care still faces multiple important challenges. The most significant obstacles include the sensitivity, privacy and highly distinguishable nature of the data, as well as issues pertaining to insufficient acceptance or lack of knowledge on the part of clinicians (Dankar, Ptitsyn, & Dankar, 2018; Hess, Fonseca, Scott, & Fagerness, 2015; Lauschke & Ingelman-Sundberg, 2016c).…”

Section: Pharmacogenomics and Next-generation Sequencingmentioning

confidence: 99%

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity

Lauschke

Zhou

Ingelman‐Sundberg

2019

Pharmacology & Therapeutics

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Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.

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The development of large-scale de-identified biomedical databases in the age of genomics—principles and challenges

Cited by 33 publications

References 55 publications

Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory

Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory

Identificación genética de recién nacidos en Perú: un estudio piloto

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity

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