Integration of GWAS and omics QTL reveals molecular mechanisms underlying neurodegenerative diseases
Poster No:
119
Submission Type:
Abstract Submission
Authors:
Haojing Duan1, Yundi Hu2, Xiaolei Lin2, JianFeng Feng1
Institutions:
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2School of Data Science, Fudan University, Shanghai, China
First Author:
Haojing Duan
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Co-Author(s):
JianFeng Feng
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Introduction:
Global trend of population aging is leading to an increasing prevalence of neurodegenerative diseases (Hou et al., 2019). Recent large-scale Genome-Wide Association Studies (GWASs) and multi-type studies of molecular quantitative trait (molQTL) have enhanced our understanding of trait-associated genetic variations (de Klein et al., 2023; Kibinge et al., 2020). However, most studies integrated only one or a few types of QTLs into analyses and overlooked shared mechanisms underlying neurodegenerative diseases.
Methods:
In this study, we curated the most recent GWAS summary statistics of European ancestry for five neurodegenerative diseases (Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia, Lewy body dementia and Parkinson's disease), as well as QTL summary data from blood samples of predominantly European for six molecular phenotypes (gene expression, DNA methylation, chromatin accessibility, histone modification, RNA splicing, protein). Associations between molecular phenotypes and neurodegenerative diseases were identified by jointly analyzing six types of molQTL data with GWAS summary data using OPERA (Omics PlEiotRopic Association) approach (Wu et al., 2023), which is a Bayesian generalization of the summary-data-based Mendelian randomization and heterogeneity independent instruments approach. Next, we performed a genome-wide correlation across five neurodegenerative diseases using multivariable LD-Score regression (LDSC). We calculated a common factor GWAS for these five diseases using genomic structural equation modeling (Genomic SEM) (Grotzinger et al., 2019), which represented a shared genetic basis among neurodegenerative diseases and was subsequently utilized in the identification of pleiotropy associations. Within pleiotropy associations from the common factor GWAS, we performed gene set enrichment analyses for pleiotropy (or causal) molecular phenotypes using clusterProfiler (Wu et al., 2021) and SynGO (Koopmans et al., 2019), and examined brain region expression and cell type expression of shared causal variants based on Allen Human Brain Atlas dataset (Hawrylycz et al., 2012).
Results:
We used OPERA approach to identify pleiotropic associations of different combinations of molecular phenotypes with neurodegenerative diseases. More than one-third of the GWAS signals were shared with at least one molecular phenotype, with DNA methylation and gene expression explaining the most GWAS signals in six types of QTL (Figure 1a). As an example of joint pleiotropic associations, we observed that SLC24A4 and RIN3 genes were associated with Alzheimer's disease jointly with three other types of molecular phenotypes including DNA methylation, histone modification and RNA splicing (Figure 1b). The LDSC demonstrated that neurodegenerative diseases exhibit concordant genetic correlations (Figure 2a), which supports our use of GenomicSEM to derive a common factor that profiles the genetic architecture of five diseases. There were 26 independent significant GWAS signals identified in the common factor GWAS (Figure 2b), of which 40.7% of the GWAS signals could be explained by performing OPERA, including NSF and SNCA genes that are involved in the regulation of multiple diseases. The enrichment analysis indicated that 16p11.2 proximal deletion syndrome, SNARE binding, neurotransmitter release, and membrane fusion are implicated in the shared mechanisms of neurodegenerative diseases (Figure 2c). Furthermore, the shared causal variants were found to present similar expression patterns in some specific brain regions, such as substantia nigra and hippocampus, and were highly expressed in microglia and oligodendrocyte precursor cell (Figure 2d and 2e).
Conclusions:
Our research identified the molecular phenotypes associated with each neurodegenerative disease, and elucidated the genetic structure and shared molecular mechanism between them, providing insights for the development of precision medicine and therapies.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Genetics:
Genetic Association Studies
Genetic Modeling and Analysis Methods 2
Transcriptomics
Keywords:
Degenerative Disease
Phenotype-Genotype
Statistical Methods
Other - multi-omics; genome-wide association studies; pleiotropy analysis; pathway
1|2Indicates the priority used for review
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