OBJECTIVES: Family data are a valuable data source in bioinformatic research. This is because family members often share common genetic and environmental exposures. Collecting this family data is traditionally very labor intensive but advances in electronic health record (EHR) data mining has proven useful when identifying pedigrees linked to longitudinal health histories. These are called e-pedigrees. Unfortunately, e-pedigrees tend to miss the oldest patients who inherently have the longest and richest health histories. A good source of family data from older generations includes obituaries, as they have a formulaic nature making them a good candidate for natural language processing (NLP) that can extract relationships to the decedent. While there have been several studies on obtaining such data from obituaries, we demonstrate for the first time approaches that tie that information to an EHR. METHODS: Natural language processing extraction resulted in 8 166 534 family members being abstracted from 567 279 obituaries published in the state of Wisconsin. After matching decedent and family members to patients in the EHR, we identified 200 033 unique patients that were put in 53 640 pedigrees. RESULTS: The largest pedigree consisted of 21 individuals. Heritability of adult height was quantified (H2=0.51±0.04, P<1.00e-07) demonstrating these data's use in genetic research. The heritability data, coupled with overlapping data in a biobank, suggested 80%-90% of familial relationships were accurately defined. CONCLUSION: The totality of these findings demonstrate obituaries with the oldest people in society can be highly informative for bioinformatic research. AVAILABILITY AND IMPLEMENTATION: Code is available on GitHub at https://github.com/jgmayer672/ObituaryNLP.

Electronic Health Records (EHRs) and genomic data are fruitful resources to find associations between genetic variants and human diseases. Medical researchers have used Phenome-Wide Association Study (PheWAS) to find diseases relevant to genomic variants, and Genome-Wide Association Study (GWAS) to discover genomic variants related to specific diseases. In the era of Big data, the quantity of disease and genomic data have significantly increased, resulting in long processing and research time for Big data analysis. We designed and implemented a prototype of Medical Big Data Analysis System that finds the links between genetic variants and human disease with PheWAS and GWAS dataset of a population-based biobank in Marshfield Clinic Institute. The system consists of front-end Web-based graphic user interface using Shiny application, and back-end Big data analysis servers using Apache Spark and Mongo database. We evaluated the associations with selected variants and diseases and present the performance and CPU/memory overhead of the system while varying the number of executors in Spark cluster, which provides readers with multiple factors for deploying medical Big data analysis system.

MOTIVATION: The use and functionality of Electronic Health Records (EHR) have increased rapidly in the past few decades. EHRs are becoming an important depository of patient health information and can capture family data. Pedigree analysis is a longstanding and powerful approach that can gain insight into the underlying genetic and environmental factors in human health, but traditional approaches to identifying and recruiting families are low-throughput and labor-intensive. Therefore, high-throughput methods to automatically construct family pedigrees are needed. RESULTS: We developed a stand-alone application: Electronic Pedigrees, or E-Pedigrees, which combines two validated family prediction algorithms into a single software package for high throughput pedigrees construction. The convenient platform considers patients' basic demographic information and/or emergency contact data to infer high-accuracy parent-child relationship. Importantly, E-Pedigrees allows users to layer in additional pedigree data when available and provides options for applying different logical rules to improve accuracy of inferred family relationships. This software is fast and easy to use, is compatible with different EHR data sources, and its output is a standard PED file appropriate for multiple downstream analyses. AVAILABILITY AND IMPLEMENTATION: The Python 3.3+ version E-Pedigrees application is freely available on: https://github.com/xiayuan-huang/E-pedigrees.

Motivation: Pedigree analysis is a longstanding and powerful approach to gain insight into the underlying genetic factors in human health, but identifying, recruiting and genotyping families can be difficult, time consuming and costly. Development of high throughput methods to identify families and foster downstream analyses are necessary. Results: This paper describes simple methods that allowed us to identify 173 368 family pedigrees with high probability using basic demographic data available in most electronic health records (EHRs). We further developed and validate a novel statistical method that uses EHR data to identify families more likely to have a major genetic component to their diseases risk. Lastly, we showed that incorporating EHR-linked family data into genetic association testing may provide added power for genetic mapping without additional recruitment or genotyping. The totality of these results suggests that EHR-linked families can enable classical genetic analyses in a high-throughput manner. Availability and implementation: Pseudocode is provided as supplementary information. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Genome-wide association studies (GWASs) are effective for describing genetic complexities of common diseases. Phenome-wide association studies (PheWASs) offer an alternative and complementary approach to GWAS using data embedded in the electronic health record (EHR) to define the phenome. International Classification of Disease version 9 (ICD9) codes are used frequently to define the phenome, but using ICD9 codes alone misses other clinically relevant information from the EHR that can be used for PheWAS analyses and discovery. RESULTS: As an alternative to ICD9 coding, a text-based phenome was defined by 23 384 clinically relevant terms extracted from Marshfield Clinic's EHR. Five single nucleotide polymorphisms (SNPs) with known phenotypic associations were genotyped in 4235 individuals and associated across the text-based phenome. All five SNPs genotyped were associated with expected terms (P<0.02), most at or near the top of their respective PheWAS ranking. Raw association results indicate that text data performed equivalently to ICD9 coding and demonstrate the utility of information beyond ICD9 coding for application in PheWAS.