Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework.

Background: Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms.

Methods: Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages.

Findings: The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder.

Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics.

Introduction: Frailty Index (FI) is a common measure of frailty, which has been advocated as a routine clinical test by many guidelines. The genetic and phenotypic relationships of FI with cardiovascular indicators (CIs) and behavioral characteristics (BCs) are unclear, which has hampered ability to monitor FI using easily collected data.

Objectives: This study is designed to investigate the genetic and phenotypic associations of frailty with CIs and BCs, and further to construct a model to predict FI.

Genetic evidence for T-wave area from 12-lead electrocardiograms to monitor cardiovascular diseases in patients taking diabetes medications.

Aims Many studies indicated use of diabetes medications can influence the electrocardiogram (ECG), which remains the simplest and fastest tool for assessing cardiac functions. However, few studies have explored the role of genetic factors in determining the relationship between the use of diabetes medications and ECG trace characteristics (ETC). Methods Genome-wide association studies (GWAS) were performed for 168 ETCs extracted from the 12-lead ECGs of 42,340 Europeans in the UK Biobank.

Genome-wide association and Mendelian randomization analysis provide insights into the shared genetic architecture between high-dimensional electrocardiographic features and ischemic heart disease.

Observational studies have revealed that ischemic heart disease (IHD) has a unique manifestation on electrocardiographic (ECG). However, the genetic relationships between IHD and ECG remain unclear. We took 12-lead ECG as phenotypes to conduct genome-wide association studies (GWAS) for 41,960 samples from UK-Biobank (UKB).

Inferring the genetic relationship between brain imaging-derived phenotypes and risk of complex diseases by Mendelian randomization and genome-wide colocalization.

Observational studies consistently disclose brain imaging-derived phenotypes (IDPs) as critical markers for early diagnosis of both brain disorders and cardiovascular diseases. However, it remains unclear about the shared genetic landscape between brain IDPs and the risk of brain disorders and cardiovascular diseases, restricting the applications of potential diagnostic techniques through brain IDPs. Here, we reported genetic correlations and putative causal relationships between 921 brain IDPs, 20 brain disorders and six cardiovascular diseases by leveraging their large-scale genome-wide association study (GWAS) summary statistics.

Distinct sequence features underlie microdeletions and gross deletions in the human genome.

Microdeletions and gross deletions are important causes (~20%) of human inherited disease and their genomic locations are strongly influenced by the local DNA sequence environment. This notwithstanding, no study has systematically examined their underlying generative mechanisms. Here, we obtained 42,098 pathogenic microdeletions and gross deletions from the Human Gene Mutation Database (HGMD) that together form a continuum of germline deletions ranging in size from 1 to 28,394,429 bp.

Prioritization of schizophrenia risk genes from GWAS results by integrating multi-omics data.

Schizophrenia (SCZ) is a polygenic disease with a heritability approaching 80%. Over 100 SCZ-related loci have so far been identified by genome-wide association studies (GWAS). However, the risk genes associated with these loci often remain unknown.

Identifying Common Genes, Cell Types and Brain Regions Between Diseases of the Nervous System.

Diseases of the nervous system are widely considered to be caused by genetic mutations, and they have been shown to share pathogenic genes. Discovering the shared mechanisms of these diseases is useful for designing common treatments. In this study, by reviewing 518 articles published after 2007 on 20 diseases of the nervous system, we compiled data on 1607 mutations occurring in 365 genes, totals that are 1.