ArticlePatterns (New York, N.Y.)2020
Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning.
Article in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Genome-wide association studies in horticultural crops: decoding genetic diversity for precision breeding.Horticulture research · 2026Article
- CALANGO: A phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species.Patterns (New York, N.Y.) · 2023Article
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Authors and funding
13 authors.
Funding
Abstract
The genetic effect explains the causality from genetic mutations to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a deep association kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects where existing approaches fail. When applied to four real-world GWAS datasets including cancers and schizophrenia, our DAK discovered potential casual pathways, including the association between dilated cardiomyopathy pathway and schizophrenia.
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Registered trials
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