Functional mapping of dynamic traits with robust t-distribution

Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies

Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of the findings are in question if valid inferential procedures are not available to quantify the uncertainty of the findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models which can yield uncertainty quantification measures, including confidence (or Bayesian credible) intervals, p values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the "unpenalized loss function + penalty term" formulation for regularization methods and the "likelihood function × shrinkage prior" framework for regularized Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. The numerical results show that robust Bayesian analysis incorporating exact sparsity has yielded not only superior estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared with alternative methods, especially in the presence of heavy-tailed model errors and outliers.

Computational dissection of genetic variation modulating the response of multiple photosynthetic phenotypes to the light environment

BACKGROUND

The expression of biological traits is modulated by genetics as well as the environment, and the level of influence exerted by the latter may vary across characteristics. Photosynthetic traits in plants are complex quantitative traits that are regulated by both endogenous genetic factors and external environmental factors such as light intensity and CO2 concentration. The specific processes impacted occur dynamically and continuously as the growth of plants changes. Although studies have been conducted to explore the genetic regulatory mechanisms of individual photosynthetic traits or to evaluate the effects of certain environmental variables on photosynthetic traits, the systematic impact of environmental variables on the dynamic process of integrated plant growth and development has not been fully elucidated.

RESULTS

In this paper, we proposed a research framework to investigate the genetic mechanism of high-dimensional complex photosynthetic traits in response to the light environment at the genome level. We established a set of high-dimensional equations incorporating environmental regulators to integrate functional mapping and dynamic screening of gene‒environment complex systems to elucidate the process and pattern of intrinsic genetic regulatory mechanisms of three types of photosynthetic phenotypes of Populus simonii that varied with light intensity. Furthermore, a network structure was established to elucidate the crosstalk among significant QTLs that regulate photosynthetic phenotypic systems. Additionally, the detection of key QTLs governing the response of multiple phenotypes to the light environment, coupled with the intrinsic differences in genotype expression, provides valuable insights into the regulatory mechanisms that drive the transition of photosynthetic activity and photoprotection in the face of varying light intensity gradients.

CONCLUSIONS

This paper offers a comprehensive approach to unraveling the genetic architecture of multidimensional variations in photosynthetic phenotypes, considering the combined impact of integrated environmental factors from multiple perspectives.

The Genetic Architecture of Juvenile Growth Traits in the Conifer Torreya grandis as Revealed by Joint Linkage and Linkage Disequilibrium Mapping

Despite its high economical and ornamental values, Torreya grandis, a dioecious non-timber coniferous species, has long been an underrepresented species. However, the advent and application of advanced genotyping technologies have stimulated its genetic research, making it possible to gain new insight into the genetic architecture of complex traits that may not be detected for model species. We apply an open-pollination (OP) mapping strategy to conduct a QTL mapping experiment of T. grandis, in which nearly 100 unrelated trees randomly chosen from the species' natural distribution and their half-sib progeny are simultaneously genotyped. This strategy allows us to simultaneously estimate the recombination fractions and linkage disequilibrium (LD) coefficients between each pair of markers. We reconstruct a high-density linkage map of 4,203 SNPs covering a total distance of 8,393.95 cM and plot pairwise normalized LD values against genetic distances to build up a linkage-LD map. We identify 13 QTLs for stem basal diameter growth and 4 QTLs for stem height growth in juvenile seedlings. From the linkage-LD map, we infer the evolutionary history of T. grandis and each of its QTLs. The slow decay of QTL-related LDs indicates that these QTLs and their harboring genomic regions are evolutionarily relatively young, suggesting that they can better utilized by clonal propagation rather than through seed propagation. Genetic results from the OP sampling strategy could provide useful guidance for genetic studies of other dioecious species.

Functional Mapping of Quantitative Trait Loci (QTLs) Associated With Plant Performance in a Wheat MAGIC Mapping Population

In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Trait data from single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding.