Abstract
In the process of growth and development in life, gene expressions that control quantitative traits will turn on or off with time. Studies of longitudinal traits are of great significance in revealing the genetic mechanism of biological development. With the development of ultra-high-density sequencing technology, the associated analysis has tremendous challenges to statistical methods. In this paper, a longitudinal functional data association test (LFDAT) method is proposed based on the function-on-function regression model. LFDAT can simultaneously treat phenotypic traits and marker information as continuum variables and analyze the association of longitudinal quantitative traits and gene regions. Simulation studies showed that: 1) LFDAT performs well for both linkage equilibrium simulation and linkage disequilibrium simulation, 2) LFDAT has better performance for gene regions (include common variants, low-frequency variants, rare variants and mixture), and 3) LFDAT can accurately identify gene switching in the growth and development stage. The longitudinal data of the Oryza sativa projected shoot area is analyzed by LFDAT. It showed that there is the advantage of quick calculations. Further, an association analysis was conducted between longitudinal traits and gene regions by integrating the micro effects of multiple related variants and using the information of the entire gene region. LFDAT provides a feasible method for studying the formation and expression of longitudinal traits.
Highlights
With sequencing technology development, genome-wide association studies (GWASs) have identified thousands of genetic variants successfully (Robinson et al, 2014)
These methods were mainly divided into three types: 1) the burden test method based on the idea of merging (Madsen and Browning, 2009; Han and Pan, 2010; Morris and Zeggini, 2010; Price et al, 2010; Lin and Tang, 2011), 2) the variance composition method based on mixture effects (Liu et al, 2007; Kwee et al, 2008; Liu et al, 2008; Wu et al, 2010; Wu et al, 2011; Schifano et al, 2012; Chen et al, 2013), and 3) the method based on a functional data analysis (Luo et al, 2012; Svishcheva et al, 2015; Svishcheva et al, 2016a; Svishcheva et al, 2016b; Li et al, 2020)
Note: Common denotes gene regions only with common variants, Rare denotes gene regions only with rare variants, Low denotes gene regions only with low-frequency variants, Mixture one denotes gene regions with 20% of common variants and 80% of rare variants, and Mixture two denotes gene regions with 80% of common variants and 20% of rare variants
Summary
Genome-wide association studies (GWASs) have identified thousands of genetic variants successfully (Robinson et al, 2014). This research plays an important role in identifying the genetic associations of complex traits and diseases. GWASs that assess quantitative traits at a single time cannot better reveal the genetic mechanism of biological development. Longitudinal traits have always been a major scientific issue in biology. As early as 1962, Kheiralla and Whittingtom, 1962 found that genetic effects behave differently in different periods. In the eighth decade of the last century, Lewis, 1978 revealed the molecular mechanism of morphological development in Drosophila, which laid a foundation for developing trait developmental
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