A unified model for functional and statistical epistasis and its application in quantitative trait loci analysis

Abstract

Interaction between genes, or epistasis, is found to be common and it is a key, concept for understanding adaptation and evolution of natural populations, response to selection in breeding programs, and determination of complex disease. Current]),, two independent classes of models are used to study epistasis. Statistical models focus on maintaining desired statistical properties for detection and estimation of genetic effects and for the decomposition of genetic variance using average effects of allele Substitutions in populations as parameters. Functional models focus on the evolutionary consequences of the attributes of the genotype-phenotype map using natural effects of allele substitutions as parameters. Here we provide a new, general and unified model framework: the natural and orthogonal interactions (NOIA) model. NOIA implements tools for transforming genetic effects measured in One Population to the ones of other populations (e.g., between two experimental designs for QTL) and parameters of statistical and functional epistasis into each other (thus enabling us to obtain functional estimates of QTL), as demonstrated numerically. We develop graphical interpretations of functional and statistical models as regressions of the genotypic values on the gene content, which illustrates the difference between the models-the constraint on the slope of the functional regression-and when the models are equivalent. Furthermore, we use our theoretical foundations to conceptually clarify functional and statistical epistasis, discuss the advantages of NOIA over previous theory, and stress the importance of linking functional and statistical models.

Published in

Genetics
2007, volume: 176, number: 2, pages: 1151-1167
Publisher: GENETICS

SLU Authors

• Alvarez-Castro, Jose

  • Uppsala University

• Carlborg, Örjan

  • Uppsala University

UKÄ Subject classification

Animal and Dairy Science
Genetics and Genomics
Veterinary Science

Publication identifier

• DOI: https://doi.org/10.1534/genetics.106.067348

Permanent link to this page (URI)

https://res.slu.se/id/publ/57907