Functional mapping for genetic control of programmed cell death

Abstract

"Naturally occurring" or "programmed" cell death (PCD) in which the cell uses specialized cellular machinery to kill itself is a ubiquitous phenomenon that occurs early in organ development. Such a cell suicide mechanism that enables metazoans to control cell number and eliminate cells threatening the organism's survival has been thought to be under genetic control. In this report, we develop a novel statistical model for mapping specific genes or quantitative trait loci (QTL) that are responsible for the PCD process based on polymorphic molecular markers. This model incorporates the biological mechanisms of PCD that undergoes two different developmental stages, exponential growth and polynomial death. We derived a parametric approach to model the exponential growth and a nonparametric approach based on the Legendre function to model the polynomial death. A series of stationary and nonstationary models has been used to approximate the structure of the covariance matrix among cell numbers at a multitude of different times. The statistical behavior of our model is investigated through simulation studies and validated by a real example in rice.quantitative trait loci; semiparametric model; mean-covariance structure model; EM-Simplex algorithm; order selection

Integrating Image-Based Phenomics and Association Analysis to Dissect the Genetic Architecture of Temporal Salinity Responses in Rice

“…Time series data sets derived from image-based phenotyping have been combined with functional data analysis to examine shoot and root growth in response to various environmental conditions (Walter et al, 2002;van der Weele et al, 2003;Chen et al, 2014;Poiré et al, 2014;Bac-Molenaar et al, 2015). Functional data analysis can be combined with conventional genetic analysis such as linkage mapping and GWAS to identify loci that may be regulating dynamic processes (Cui et al, 2006;Wu and Lin, 2006;He et al, 2010;Das et al, 2011;Bac-Molenaar et al, 2015).…”

A Semiparametric Approach for Composite Functional Mapping of Dynamic Quantitative Traits

“…Yang et al (2006) used the Legendre polynomial to model the time-dependent QTL effects. A similar idea was employed in Cui et al (2006) and Lin and Wu (2006), who incorporated the Legendre-based transformation to model some particular stage of growth or one aspect of a joint longitudinal and time-to-event analysis. Second, composite functional mapping can be extended to explore the effects of interaction between different QTL (Kao and Zeng 2002) and QTL and environments (Zhao et al 2004b,c) on variation in a dynamic trait by expanding Equation 4 to interaction terms with quantitative genetic theory (Lynch and Walsh 1998).…”

Genetic Dissection of Complex Traits: From Functional Mapping to Systems Mapping

“…Unlike the traditional static models that analyze phenotypic traits at individual time points, the central motivation of dynamic models lies in the study of the temporal pattern of genetic variation for a quantitative trait in a time course [13] and the identification of specific genes (i.e., quantitative trait loci or QTLs) that determine such a time-dependent change of the trait [14][15][16][17][18]. These models, called functional mapping [15], have been instrumental for detecting and mapping dynamic QTLs for individuals traits, such as stem growth and root growth in forest trees [19], plant height in rice [20], tiller number increase in rice [21], biomass growth in soybeans [22], body mass growth in mice [23,24], body height growth in humans [25] and drug response [26].…”