Functional Mapping of Multiple Dynamic Traits

TL;DR

This paper introduces a multivariate functional mapping model to identify QTLs influencing multiple developmental traits over time, using nonparametric regression and mixture models, demonstrated on soybean biomass data.

Contribution

Developed a novel multivariate functional mapping approach for multiple dynamic traits that accounts for unknown genotypes and pleiotropic effects.

Findings

Successfully identified QTLs affecting soybean biomass traits.

Demonstrated the model's effectiveness through simulation studies.

Provided insights into genetic control of complex dynamic traits.

Abstract

Many biological phenomena undergo developmental changes in time and space. Functional mapping, which is aimed at mapping genes that affect developmental patterns, is instrumental for studying the genetic architecture of biological changes. Often biological processes are mediated by a network of developmental and physiological components and, therefore, are better described by multiple phenotypes. In this article, we develop a multivariate model for functional mapping that can detect and characterize quantitative trait loci (QTLs) that simultaneously control multiple dynamic traits. Because the true genotypes of QTLs are unknown, the measurements for the multiple dynamic traits are modeled using a mixture distribution. The functional means of the multiple dynamic traits are estimated using the nonparametric regression method, which avoids any parametric assumption on the functional means. We propose the profile likelihood method to estimate the mixture model. A likelihood ratio test is exploited to test for the existence of pleiotropic effects on distinct but developmentally correlated traits. A simulation study is implemented to illustrate the finite sample performance of our proposed method. We also demonstrate our method by identifying QTLs that simultaneously control three dynamic traits of soybeans. The three dynamic traits are the time-course biomass of the leaf, the stem, and the root of the whole soybean. The genetic linkage map is constructed with 950 microsatellite markers. The new model can aid in our comprehension of the genetic control mechanisms of complex dynamic traits over time.