How can machine learning algorithms accurately predict polygenic risk scores for complex traits?
Best insight from top research papers
Machine learning algorithms can accurately predict polygenic risk scores for complex traits by addressing challenges such as high dimensionality, different effect sizes of genomic regions, and gene-environment interactions. Approaches like the Spatial Autoregressive Model with Adaptive Lasso (SARAL), Multikernel Linear Mixed Model with Adaptive Lasso (KLMM-AL), and methods utilizing probabilistic graphical models (PGMs) offer solutions. These methods account for noise signals, rare variants, heterogeneous effect sizes, and genetic/environmental influences. Additionally, incorporating distance covariance and correlation in feature screening procedures enhances variable selection for genetic risk prediction. By considering the high dimensions of genes, hierarchical structures of interactions, and subject correlations in family-based studies, these algorithms provide robust risk predictions for complex diseases.
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| Papers (5) | Insight |
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4 Citations | The paper proposes a penalized method using a linear mixed effect model to detect gene-environment interactions, offering superior performance in simulation studies and robust correlation estimates in family-based populations. |
36 Citations | Distance covariance-based variable selection method can enhance accuracy in predicting polygenic risk scores by building models on training data, tuning parameters on a separate set, and predicting on a testing set. |
2 Citations | Machine learning algorithms can predict polygenic risk scores by leveraging observational Gaussian family data to decompose PGMs into genetic and environmental factors, enhancing accuracy in complex trait prediction. |
9 Citations | The multikernel linear mixed model with adaptive lasso (KLMM-AL) efficiently predicts complex phenotypes by considering heterogeneous genomic region effects and selecting predictive regions adaptively. |
4 Citations | Spatial autoregressive model with adaptive lasso (SARAL) reduces noise in high-dimensional sequencing data, enhancing accuracy in predicting polygenic risk scores for complex traits. |
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