Publications by authors named "Qiaolan Deng"
Article Synopsis
- Genome-wide association studies (GWAS) have found many genetic variants linked to traits and diseases, but understanding how these variants function remains difficult, especially when variants affect multiple traits.
- The proposed graph-GPA 2.0 (GGPA 2.0) framework merges GWAS data for various phenotypes and includes functional annotations, leading to improved detection of disease-associated variants and better estimation of disease relationships.
- In analyses of autoimmune and psychiatric disorders, GGPA 2.0 identified significant epigenetic marks related to specific immune cells and brain regions, while also revealing shared genetic connections between conditions like bipolar disorder and schizophrenia.
Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits.
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