Biology subjects

Schnable, J.

Publications and source records attributed to Schnable, J..

3 recordsLinked to original sources

bioRxiv2017

Functional Modeling of Plant Growth Dynamics

Recent advances in automated plant phenotyping have enabled the collection time series measurements from the same plants of a wide range of traits over different developmental time scales. The availability of time series phenotypic datasets has increased interest in statistical approaches for comparing patterns of change between different plant genotypes and different treatment conditions. Two widely used methods of modeling growth over time are point-wise analysis of variance (ANOVA) and parametric sigmoidal curve fitting. Point-wise ANOVA yields discontinuous growth curves, which do not reflect the true dynamics of growth patterns in plants. In contrast, fitting a parametric model to a time series of observations does capture the trend of growth, however these models require assumptions regarding the true pattern of plant growth. Depending on the species, treatment regime, and subset of the plant lifecycle sampled this assumptions will not always hold true. Here we introduce a different approach - functional ANOVA - which yields continuous growth curves without requiring assumptions regarding patterns of plant growth. We compare and validate this approach using data from an experiment measuring growth of two maize (Zea mays ssp. mays) genotypes under two water availability treatments over a 21-day period. Functional ANOVA enables a nonparametric estimation of the dynamics of changes in plant traits over time without assumptions regarding curve shape. In addition to estimating smooth curves of trait values over time, functional ANOVA also estimates the the derivatives of these curves - e.g. growth rates - simultaneously. Using two different subsampling strategies, we demonstrate that this functional ANOVA method enables the comparison of growth curves between plants phenotyped on non-overlapping days with little reduction in estimation accuracy. This means functional ANOVA based approaches can allow larger numbers of samples and biological replicates to be scored in a single experiment given fixed amounts of phenotyping infrastructure and personnel.

plant biology↗

bioRxiv2017

Largely unlinked gene sets targeted by selection for domestication syndrome phenotypes in maize and sorghum

The domestication of diverse grain crops from wild grasses resulted from artificial selection for a suite of overlapping traits producing changes referred to in aggregate as \"domestication syndrome\". Parallel phenotypic change can be accomplished by either selection on orthologous genes, or selection on non-orthologous genes with parallel phenotypic effects. To determine how often artificial selection for domestication traits in the grasses targeted orthologous genes, we employed resequencing data from wild and domesticated accessions of Zea (maize) and Sorghum (sorghum). Many \"classic\" domestication genes identified through QTL mapping in populations resulting from wild/domesticated crosses indeed show signatures of parallel selection in both maize and sorghum. However, the overall number of genes showing signatures of parallel selection in both species is not significantly different from that expected by chance. This suggests that, while a small number of genes will extremely large phenotypic effects have been targeted repeatedly by artificial selection during domestication, the optimization portion of domestication targeted small and largely non-overlapping subsets of all possible genes which could produce equivalent phenotypic alterations.

plant biology↗

bioRxiv2017

STAG-CNS: An Order-Aware Conserved Non-coding Sequences Discovery Tool For Arbitrary Numbers of Species

One method for identifying noncoding regulatory regions of a genome is to quantify rates of divergence between related species, as functional sequence will generally diverge more slowly. Most approaches to identifying these conserved noncoding sequences (CNS) based on alignment have had relatively large minimum sequence lengths ([>=]15 base pair) compared to the average length of known transcription factor binding sites. To circumvent this constraint, STAG-CNS integrates data from the promoters of conserved orthologous genes in three or more species simultaneously. Using data from up to six grass species made it possible to identify conserved sequences as short at 9 base pairs with FDP [<=] 0.05. These CNS exhibit greater overlap with open chromatin regions identified using DNase I hypersensitivity, and are enriched in the promoters of genes involved in transcriptional regulation. STAG-CNS was further employed to characterize loss of conserved noncoding sequences associated with retained duplicate genes from the ancient maize polyploidy. Genes with fewer retained CNS show lower overall expression, although this bias is more apparent in samples of complex organ systems containing many cell types, suggesting CNS loss may correspond to a reduced number of expression contexts rather than lower expression levels across the entire ancestral expression domain.

bioinformatics↗