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Yi Jiang

Publications and source records attributed to Yi Jiang.

2 recordsLinked to original sources

Agonal factors distort gene-expression patterns in human postmortem brains

Agonal factors, the conditions that occur just prior to death, can impact the molecular quality of postmortem brains, influencing gene expression results. Nevertheless, study designs using postmortem brain tissue rarely, if ever, account for these factors, and previous studies had not documented nor adjusted for agonal factors. Our study used gene expression data of 262 samples from ROSMAP with the following terminal states recorded for each donor: surgery, fever, infection, unconsciousness, difficulty breathing, and mechanical ventilation. Performed differential gene expression and weighted gene co-expression network analyses (WGCNA), fever and infection were the primary contributors to brain gene expression changes. Fever and infection also contributed to brain cell-type specific gene expression and cell proportion changes. Furthermore, the gene expression patterns implicated in fever and infection were unique to other agonal factors. We also found that previous studies of gene expression in postmortem brains were confounded by variables of hypoxia or oxygen level pathways. Therefore, correction for agonal factors through probabilistic estimation of expression residuals (PEER) or surrogate variable analysis (SVA) is recommended to control for unknown agonal factors. Our analyses revealed fever and infection contributing to gene expression changes in postmortem brains and emphasized the necessity of study designs that document and account for agonal factors.

bioinformatics↗

Contribution of de novo non-coding mutations to autism and identification of risk genes from whole-genome sequencing of affected families

Analysis of de novo mutations (DNMs) from sequencing data of nuclear families has identified risk genes for many complex diseases, including multiple neurodevelopmental and psychiatric disorders. Most of these efforts have focused on mutations in protein-coding sequences. Evidence from genome-wide association studies (GWAS) strongly suggests that variants important to human diseases often lie in non-coding regions. Extending DNM-based approaches to non-coding sequences is, however, challenging because the functional significance of non-coding mutations is difficult to predict. We propose a new statistical framework for analyzing DNMs from whole-genome sequencing (WGS) data. This method, TADA-Annotations (TADA-A), is a major advance of the TADA method we developed earlier for DNM analysis in coding regions. TADA-A is able to incorporate many functional annotations such as conservation and enhancer marks, learn from data which annotations are informative of pathogenic mutations and combine both coding and non-coding mutations at the gene level to detect risk genes. It also supports meta-analysis of multiple DNM studies, while adjusting for study-specific technical effects. We applied TADA-A to WGS data of [~]300 autism family trios across five studies, and discovered several new autism risk genes. The software is freely available for all research uses.

Genetics↗