University of Oulu

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

A proximal LAVA method for genome-wide association and prediction of traits with mixed inheritance patterns

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Author: Waldmann, Patrik1
Organizations: 1Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022013111308
Language: English
Published: Springer Nature, 2021
Publish Date: 2022-01-31
Description:

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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Series: BMC bioinformatics
ISSN: 1471-2105
ISSN-E: 1471-2105
ISSN-L: 1471-2105
Volume: 22
Issue: 1
Article number: 523
DOI: 10.1186/s12859-021-04436-6
OADOI: https://oadoi.org/10.1186/s12859-021-04436-6
Type of Publication: A1 Journal article – refereed
Field of Science: 1184 Genetics, developmental biology, physiology
113 Computer and information sciences
112 Statistics and probability
Subjects:
Funding: This work was supported by the Academy of Finland Profi 5 funding for mathematics and AI: data insight for high-dimensional dynamics [Grant 326291].
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