| Title: | Integration of regulatory elements and multi-trait genomic and phenomic data to improve the accuracy of risk prediction of human complex diseases/traits |
| Lead Institution: | Xi'an Jiaotong University |
| Principal investigator: | Professor Tie-Lin Yang |
Application 46387
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About
Complex diseases in humans, such as cancer, heart disease, and depression, are caused by a variety of genetic factors, surrounding environment, lifestyle, among other influences. The ability to accurately assess the risk of developing such diseases before onset is becoming increasingly important in clinical practice. However, we are still unable to accurately predict the risk for most complex diseases. One of the ways that we can potentially improve our understanding of genetic risk for complex diseases is by understanding how different diseases are genetically similar to each other. For example, type II diabetes (TIID) and increased body mass index (BMI) are two traits that are genetically similar, and it has been shown that higher BMI leads to an increase in risk for TIID. Therefore, considering genetic information of related diseases/traits can be a very helpful tool when assessing risk for a particular disease/trait of interest. In this project, we aim to improve the risk prediction of complex diseases by taking into account the genetic information of related diseases/traits. We believe that this can provide the basis for prevention and individualized treatment.4 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 8767 | Integrative high-throughput enhancer surveying and functional verification divulges a YY2-condensed regulatory axis conferring risk for osteoporosis | Xiao-Feng Chen (+15) | 2024 | Cell Genomics |
| 9270 | LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files | Shan-Shan Dong (+5) | 2020 | Briefings in Bioinformatics |
| 10557 | Mendelian randomization analyses support causal relationships between brain imaging-derived phenotypes and risk of psychiatric disorders | Jing Guo (+10) | 2022 | Nature Neuroscience |
| 11897 | Revealing brain cell-stratified causality through dissecting causal variants according to their cell-type-specific effects on gene expression | Ruo-Han Hao (+7) | 2024 | Nature Communications |
Enabling scientific discoveries that improve human health