Dataset Information

Size matters: how population size influences genotype-phenotype association studies in anonymized data.

ABSTRACT: OBJECTIVE:Electronic medical records (EMRs) data is increasingly incorporated into genome-phenome association studies. Investigators hope to share data, but there are concerns it may be "re-identified" through the exploitation of various features, such as combinations of standardized clinical codes. Formal anonymization algorithms (e.g., k-anonymization) can prevent such violations, but prior studies suggest that the size of the population available for anonymization may influence the utility of the resulting data. We systematically investigate this issue using a large-scale biorepository and EMR system through which we evaluate the ability of researchers to learn from anonymized data for genome-phenome association studies under various conditions. METHODS:We use a k-anonymization strategy to simulate a data protection process (on data sets containing clinical codes) for resources of similar size to those found at nine academic medical institutions within the United States. Following the protection process, we replicate an existing genome-phenome association study and compare the discoveries using the protected data and the original data through the correlation (r(2)) of the p-values of association significance. RESULTS:Our investigation shows that anonymizing an entire dataset with respect to the population from which it is derived yields significantly more utility than small study-specific datasets anonymized unto themselves. When evaluated using the correlation of genome-phenome association strengths on anonymized data versus original data, all nine simulated sites, results from largest-scale anonymizations (population ?100,000) retained better utility to those on smaller sizes (population ?6000-75,000). We observed a general trend of increasing r(2) for larger data set sizes: r(2)=0.9481 for small-sized datasets, r(2)=0.9493 for moderately-sized datasets, r(2)=0.9934 for large-sized datasets. CONCLUSIONS:This research implies that regardless of the overall size of an institution's data, there may be significant benefits to anonymization of the entire EMR, even if the institution is planning on releasing only data about a specific cohort of patients.

SUBMITTER: Heatherly R

PROVIDER: S-EPMC4260994 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Size matters: how population size influences genotype-phenotype association studies in anonymized data.

Heatherly Raymond R Denny Joshua C JC Haines Jonathan L JL Roden Dan M DM Malin Bradley A BA

Journal of biomedical informatics 20140716

<h4>Objective</h4>Electronic medical records (EMRs) data is increasingly incorporated into genome-phenome association studies. Investigators hope to share data, but there are concerns it may be "re-identified" through the exploitation of various features, such as combinations of standardized clinical codes. Formal anonymization algorithms (e.g., k-anonymization) can prevent such violations, but prior studies suggest that the size of the population available for anonymization may influence the ut ...[more]

PMID: 25038554

Similar Datasets

Project description:With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map has important consequences for gene identification and may shed light on the evolvability of organisms.

Project description:The French Canadian population of Quebec is a unique, well-known founder population with religious, linguistic, and geographic isolation. The genetics of retinitis pigmentosa (RP) in Quebec is not well studied thus far. The purpose of our study was to establish the genetic architecture of autosomal dominant RP (adRP) and to characterize the phenotypes associated with new adRP mutations in Quebec.Sanger sequencing of the commonly mutated currently known adRP genes was performed in a clinically well-characterized cohort of 60 adRP French Canadian families. Phenotypes were analyzed by projected visual acuity (best corrected), Goldmann visual fields, optical coherence tomography (OCT), fundus autofluorescence (FAF), and ERG. The potential effect of the novel mutations was assessed using in silico bioinformatic tools. The pathogenicity of all variants was then confirmed by segregation analysis within the families, when available.We identified the causal mutation/gene in 24 of our adRP families, as 24 (40%) of 60 patients had adRP mutations in six known adRP genes. Eleven (46%) of these mutations were in RHO, four mutations (17%) were found in SNRNP200, three mutations (12.5%) in PRPH2/RDS, three mutations (12.5%) in TOPORS, two mutations (8%) in PRPF31, and one mutation (4%) in IMPDH1. Four mutations were novel. We identified new mutations in RHO (p.S270I), PRPF31 (p.R288W), IMPDH1 (p.Q318H), and TOPORS (p.H889R); the rest were previously reported. We present the genotype-phenotype characteristics of the four novel missense mutations.This is the first large screening of adRP genes in the founder population of Quebec. Our prevalence of known adRP genes is 40% in the French Canadian population, which is lower than in other adRP populations around the world, illustrating the uniqueness of the French Canadian population. Our findings are crucial in expanding the current understanding of the genotypic-phenotypic spectrum of RP and documenting the genetic architecture of our founder population.

Dataset Information

Size matters: how population size influences genotype-phenotype association studies in anonymized data.

Publications

Size matters: how population size influences genotype-phenotype association studies in anonymized data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets