A proximal LAVA method for genome-wide association and prediction of traits with mixed inheritance patterns
Waldmann, Patrik (2021-10-26)
Waldmann, P. A proximal LAVA method for genome-wide association and prediction of traits with mixed inheritance patterns. BMC Bioinformatics 22, 523 (2021). https://doi.org/10.1186/s12859-021-04436-6
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https://urn.fi/URN:NBN:fi-fe2022013111308
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Abstract
The genetic basis of phenotypic traits is highly variable and usually divided into mono-, oligo- and polygenic inheritance classes. Relatively few traits are known to be monogenic or oligogeneic. The majority of traits are considered to have a polygenic background. To what extent there are mixtures between these classes is unknown. The rapid advancement of genomic techniques makes it possible to directly map large amounts of genomic markers (GWAS) and predict unknown phenotypes (GWP). Most of the multi-marker methods for GWAS and GWP falls into one of two regularization frameworks. The first framework is based on ℓ₁-norm regularization (e.g. the LASSO) and is suitable for mono- and oligogenic traits, whereas the second framework regularize with the ℓ₂-norm (e.g. ridge regression; RR) and thereby is favourable for polygenic traits. A general framework for mixed inheritance is lacking.
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