Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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http://dx.doi.org/10.1016/j.tig.2024.04.008 | DOI Listing |
Biol Psychiatry
December 2024
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China. Electronic address:
Background: Psychiatric disorders pose an enormous economic and emotional burden on individuals, their families and society. Given that the current analysis of the pathogenesis of psychiatric disorders remains challenging and time-consuming, elucidating the modifiable risk factors becomes crucial for the diagnosis and management of psychiatric disorders. However, inferring the causal risk factors in these disorders from disparate data sources is challenging due to constraints in data collection and analytical capabilities.
View Article and Find Full Text PDFNat Methods
December 2024
School of Life Sciences, Westlake University, Hangzhou, China.
Alzheimers Dement
November 2024
Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Nucleic Acids Res
October 2024
Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China.
Understanding how genetic variants influence molecular phenotypes in different cellular contexts is crucial for elucidating the molecular and cellular mechanisms behind complex traits, which in turn has spurred significant advances in research into molecular quantitative trait locus (xQTL) at the cellular level. With the rapid proliferation of data, there is a critical need for a comprehensive and accessible platform to integrate this information. To meet this need, we developed xQTLatlas (http://www.
View Article and Find Full Text PDFbioRxiv
May 2024
Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA.
Transcriptome data is commonly used to understand genome function via quantitative trait loci (QTL) mapping and to identify the molecular mechanisms driving genome wide association study (GWAS) signals through colocalization analysis and transcriptome-wide association studies (TWAS). While RNA sequencing (RNA-seq) has the potential to reveal many modalities of transcriptional regulation, such as various splicing phenotypes, such studies are often limited to gene expression due to the complexity of extracting and analyzing multiple RNA phenotypes. Here, we present Pantry (Pan-transcriptomic phenotyping), a framework to efficiently generate diverse RNA phenotypes from RNA-seq data and perform downstream integrative analyses with genetic data.
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