Psychometric validation of the Cystic Fibrosis Impact Questionnaire (CF-IQ): A patient-reported outcome assessing impacts of cystic fibrosis.

The Cystic Fibrosis (CF) Impact Questionnaire (CF-IQ) was qualitatively developed to assess the impact of CF in the context of treatment advancements and increased longevity. This study reports the CF-IQ validation. In this noninterventional validation study, people with CF completed the 40-item CF-IQ and validating patient-reported outcome measures (PROMs) via electronic diaries at enrollment (baseline) and at the 4-week follow-up.

Massively parallel reporter assay investigates shared genetic variants of eight psychiatric disorders.

A meta-genome-wide association study across eight psychiatric disorders has highlighted the genetic architecture of pleiotropy in major psychiatric disorders. However, mechanisms underlying pleiotropic effects of the associated variants remain to be explored. We conducted a massively parallel reporter assay to decode the regulatory logic of variants with pleiotropic and disorder-specific effects.

An Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional Regulation.

Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression. Previously, The ENCODE consortium mapped biochemical signals across many cell types and tissues and integrated these data to develop a Registry of 0.9 million human and 300 thousand mouse candidate cis-Regulatory Elements (cCREs) annotated with potential functions.

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations.

Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing human behavior. New technologies such as wearable sensors show promise in surmounting these limitations in that they measure heterogeneous behavior in a quantitative and unbiased fashion.

Network-based drug repurposing for psychiatric disorders using single-cell genomics.

Neuropsychiatric disorders lack effective treatments due to a limited understanding of underlying cellular and molecular mechanisms. To address this, we integrated population-scale single-cell genomics data and analyzed cell-type-level gene regulatory networks across schizophrenia, bipolar disorder, and autism (23 cell classes/subclasses). Our analysis revealed potential druggable transcription factors co-regulating known risk genes that converge into cell-type-specific co-regulated modules.