Identifying SNP associations and predicting disease risk from Genome-wide association studies using LassoNet
Sajwani, H. M.; Feng, S. F.
Show abstract
In this paper, we show that under certain conditions LassoNet [1] should outperform threshold of significance (P-value) methods for identifying multi-SNP disease associations and predicting disease risk using data from Genome Wide Association Studies. To demonstrate this, we built a genotype-phenotype simulation to comprehensively benchmark each methods performance in variant selection and in predicting disease risk. Our results suggest that LassoNet, with its ease of implementation, should be added to the biomedical informaticians toolkit. We release code to replicate our results.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Joint Modeling of Effect Sizes for Two Correlated Traits: Characterizing Trait Properties to Enhance Polygenic Risk Prediction 96%
- Improving polygenic prediction from summary data by learning patterns of effect sharing across multiple phenotypes. 96%
- Identifying Causal Variants by Fine Mapping Across Multiple Studies 95%
Similar papers in this journal
- Sparse Multitask group Lasso for Genome-Wide Association Studies 96%
- Association Tests Using Copy Number Profile Curves (CONCUR) Enhances Power in Rare Copy Number Variant Analysis 94%
- Biological networks and GWAS: comparing and combining network methods to understand the genetics of familial breast cancer susceptibility in the GENESIS study 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.