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).

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

Saved in:
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:
Language: English
Published: Springer Nature, 2021
Publish Date: 2022-01-31


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.

see all

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
Type of Publication: A1 Journal article – refereed
Field of Science: 1184 Genetics, developmental biology, physiology
113 Computer and information sciences
112 Statistics and probability
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].
Copyright information: © The Author(s), 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.