Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems.

Background: Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administrative purposes raise questions about the consistency and reproducibility of EHR-based multimorbidity research.

Methods: Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph.

Solid Organ Transplant Recipients Exhibit More TET2-Mutant Clonal Hematopoiesis of Indeterminate Potential Not Driven by Increased Transplantation Risk.

Purpose: Solid organ transplant recipients comprise a unique population of immunosuppressed patients with increased risk of malignancy, including hematologic neoplasms. Clonal hematopoiesis of indeterminate potential (CHIP) represents a known risk factor for hematologic malignancy and this study describes the prevalence and patterns of CHIP mutations across several types of solid organ transplants.

Experimental Design: We use two national biobank cohorts comprised of >650,000 participants with linked genomic and longitudinal phenotypic data to describe the features of CHIP across 2,610 individuals who received kidney, liver, heart, or lung allografts.

Multiomic profiling of human clonal hematopoiesis reveals genotype and cell-specific inflammatory pathway activation.

Clonal hematopoiesis (CH) is an age-associated phenomenon that increases the risk of hematologic malignancy and cardiovascular disease. CH is thought to enhance disease risk through inflammation in the peripheral blood.1 Here, we profile peripheral blood gene expression in 66 968 single cells from a cohort of 17 patients with CH and 7 controls.

A practical approach to curate clonal hematopoiesis of indeterminate potential in human genetic data sets.

Clonal hematopoiesis of indeterminate potential (CHIP) is a common form of age-related somatic mosaicism that is associated with significant morbidity and mortality. CHIP mutations can be identified in peripheral blood samples that are sequenced using approaches that cover the whole genome, the whole exome, or targeted genetic regions; however, differentiating true CHIP mutations from sequencing artifacts and germ line variants is a considerable bioinformatic challenge. We present a stepwise method that combines filtering based on sequencing metrics, variant annotation, and population-based associations to increase the accuracy of CHIP calls.