Biology subjects

Tezuka, A.

Publications and source records attributed to Tezuka, A..

3 recordsLinked to original sources

bioRxiv2018

Prediction of environmental response in field-grown rice using expression-dynamics-QTL

How genetic variations affect gene expression dynamics of field-grown plants remains unclear. Using statistical analysis of large-scale time-series RNA-sequencing of field-grown rice from chromosome segment substitution lines (CSSLs), we identified 1675 expression dynamics quantitative trait loci (edQTLs) leading to polymorphisms in expression dynamics under field conditions. Based on the edQTL and environmental information, we successfully predicted gene expression under environments different from training environments, and in rice cultivars with more complex genotypes than the CSSLs. Overall, edQTL identification helped understanding the genetic architecture of expression dynamics under field conditions, which is difficult to assess with laboratory experiments1.The prediction of expression based on edQTL and environmental information will contribute to crop breeding by increasing the accuracy of trait prediction under diverse conditions.

bioinformatics↗

bioRxiv2018

Genetic basis of transgressive segregation in rice heading phenotypes

Transgressive segregation produces hybrid progeny phenotypes that exceed parental phenotypes. Unlike heterosis, extreme phenotypes caused by transgressive segregation are heritably stable. We examined transgressive phenotypes of flowering time in rice. Our previous study examined days to flowering (heading; DTH) in six F2 populations for which the parents had distal DTH, and found very few transgressive phenotypes. Here, we demonstrate that transgressive segregation in F2 populations occurred between parents with proximal DTH. DTH phenotypes of the A58 x Kitaake F2 progenies frequently exceeded those of both parents. Both A58 and Kitaake are japonica rice cultivars adapted to Hokkaido, Japan, which is a high-latitude region, and have short DTH. Among the four known loci required for short DTH, three loci had common alleles in A58 and Kitaake, and only the one locus had different alleles. This result indicates that there is a similar genetic basis for DTH between the two varieties. We identified five new quantitative trait loci (QTLs) associated with transgressive DTH phenotypes by genome-wide single nucleotide polymorphism (SNP) analysis. Each of these QTLs showed different degrees of additive effects on DTH, and two QTLs had epistatic effect on each other. These results demonstrated that genome-wide SNP analysis facilitated detection of genetic loci associated with the extreme phenotypes and revealed that the transgressive phenotypes were produced by exchanging complementary alleles of a few minor QTLs in the similar parental genotypes.

genetics↗

bioRxiv2017

Gauss-power mixing distributions comprehensively describe stochastic variations in RNA-seq data

MotivationGene expression levels exhibit stochastic variations among genetically identical organisms under the same environmental conditions. In many recent transcriptome analyses based on RNA sequencing (RNA-seq), variations in gene expression levels among replicates were assumed to follow a negative binomial distribution although the physiological basis of this assumption remain unclear.\n\nResultsIn this study, RNA-seq data were obtained from Arabidopsis thaliana under eight conditions (21-27 replicates), and the characteristics of gene-dependent distribution profiles of gene expression levels were analyzed. For A. thaliana and Saccharomyces cerevisiae, the distribution profiles could be described by a Gauss-power mixing distribution derived from a simple model of a stochastic transcriptional network containing a feedback loop. The distribution profiles of gene expression levels were roughly classified as Gaussian, power law-like containing a long tail, and mixed. The fitting function predicted that gene expression levels with long-tailed distributions would be strongly influenced by feedback regulation. Thus, the features of gene expression levels are correlated with their functions, with the levels of essential genes tending to follow a Gaussian distribution and those of genes encoding nucleic acid-binding proteins and transcription factors exhibiting long-tailed distributions.\n\nAvailabilityFastq files of RNA-seq experiments were deposited into the DNA Data Bank of Japan Sequence Read Archive as accession no. DRA005887. Quantified expression data are available in supplementary information.\n\nContactawa@hiroshima-u.ac.jp\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗