| Title: | Deep learning prediction of complex traits |
| Lead Institution: | Ecole Polytechnique Federale de Lausanne (EPFL) |
| Principal investigator: | Professor Jacques Fellay |
Application 27081
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About
The genetic architecture of complex human traits is still largely unclear, with known genetic associations only explaining a fraction of the estimated heritability. There has been no report yet describing the use of artificial intelligence for phenotypic trait prediction. We here aim at developing deep learning algorithms that can predict complex human traits from genome-wide genotyping data, starting with highly heritable but genetically poorly explained phenotypes like height and body mass index. Developing new strategies and tools that will allow the community to extract more information from large-scale genotyping data is a necessary step on the road to more personalized healthcare. We will attempt to train models that can predict complex phenotypes (e.g. height and BMI) from [1] lists of associated polymorphisms previously identified through GWAS approaches, and [2] all common human genetic variation, without a priori selection of variants. We will use different variants of Deep Neural Networks to find and quantify correlations between the SNPs and the phenotypes. Full Cohort1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 7149 | Improving polygenic prediction with genetically inferred ancestry | Olivier Naret (+5) | 2022 | Human Genetics and Genomics Advances |
Enabling scientific discoveries that improve human health