Cédric Coulonges on 26 September talked about two papers.
- Zeng P, Zhou X. 2017. Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models. Nat Commun 8 (1), 456 (full text)
What is the genetic architecture of the complex traits. Different models make different assumptions about the distribution of effect sizes. This paper describes a method that automatically chooses the best underlying distribution based on the genetic data; this makes for greater statistical power.
- Khera AV et al. 2018. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 50 (9), 1219–1224 (PMID: 30104762)
The second paper was an important read. The polygenic score has become a promising approach with applications ranging from bioengineering to precision medicine. Although its use in the clinic is yet a little far, a letter in Nature Genetics shows its potentiel in identifying high-risk patients.
This also highlighted the potential use of LDPred in our own analyses.
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