Biology subjects

Carver, B.

Publications and source records attributed to Carver, B..

3 recordsLinked to original sources

bioRxiv2021

Development of the Wheat Practical Haplotype Graph Database as a Resource for Genotyping Data Storage and Genotype Imputation

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 93% imputation accuracy with 0.01x sequence coverage, which was only slightly lower than the accuracy obtained using the 0.5x sequence coverage (96.9%). Compared to Beagle, on average, PHG imputation was ~4% (p-value = 0.00027) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. The reduced accuracy of imputation with GBS data (90.4%) is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequlibrium and proportion of identity-by-descent regions among accessions in our reference panel. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

genomics↗

bioRxiv2020

ERG orchestrates chromatin interactions to drive prostate cell fate reprogramming

While cancer is commonly perceived as a disease of dedifferentiation, the hallmark of early stage prostate cancer is paradoxically the loss of more plastic basal cells and the abnormal proliferation of more differentiated secretory luminal cells. However, the mechanism of prostate cancer pro-luminal differentiation is largely unknown. Through integrating analysis of the transcription factors (TFs) from 806 human prostate cancers, we have identified that ERG highly correlated with prostate cancer luminal subtyping. ERG overexpression in luminal epithelial cells inhibits its normal plasticity to transdifferentiate into basal lineage and ERG supersedes PTEN-loss which favors basal differentiation. ERG knock-out disrupted prostate cell luminal differentiation, whereas AR knock-out had no such effects. Trp63 is a known master regulator of prostate basal lineage. Through analysis of 3D chromatin architecture, we found that ERG binds and inhibits the enhancer activity and chromatin looping of a Trp63 distal enhancer, thereby silencing its gene expression. Specific deletion of the distal ERG binding site resulted in the loss of ERG-mediated inhibition of basal differentiation. Thus, ERG orchestrates chromatin interactions and regulates prostate cell lineage toward pro-luminal program, as its fundamental role on lineage differentiation in prostate cancer initiation.

cancer biology↗

bioRxiv2019

Effectiveness of Genomic Selection by Response to Selection for Winter Wheat Variety Improvement

The genomic revolution opened up the possibility for predicting un-tested phenotypes in schemes commonly referred as genomic selection (GS). Considering the practicality of applying GS in the line development stage of a hard red winter (HRW) wheat variety development program (VDP), effectiveness of GS was evaluated by prediction accuracy, as well as by the response to selection across field seasons that demonstrated challenges for crop improvement under significant climate variability. Important breeding targets for HRW wheat improvement in the southern Great Plains of USA, including Grain Yield, Kernel Weight, Wheat Protein content, and Sodium Dodecyl Sulfate (SDS) Sedimentation Volume as a rapid test for predicting bread-making quality, were used to estimate GSs effectiveness across harvest years from 2014 (drought) to 2016 (normal). In general, nonparametric algorithms RKHS and RF produced higher accuracies in both same-year/environment cross validations and cross-year/environment predictions, for the purpose of line selection in this bi-parental doubled haploid (DH) population. Further, the stability of GS performance was greatest for SDS Sedimentation Volume but least for Wheat Protein content. To ensure long-term genetic gain, our study on selection response suggested that across this sample of environmental variability, and though there are cases where phenotypic selection (PS) might be still preferential, training conducted under drought stress or in suboptimal conditions could still provide an encouraging prediction outcome, when selection decisions were made in normal conditions. However, it is not advisable to use training information collected from a normal field season to predict trait performance under drought conditions. Further, the superiority of response to selection was most evident if the training population can be optimized. Core IdeasO_LIPrediction performance for winter wheat grain yield and end-use quality traits. C_LIO_LIPrediction accuracy evaluated by cross validations significantly overestimated. C_LIO_LINon-parametric algorithms outperform, when considering cross-year predictions. C_LIO_LIStrategically designing training population improves response to selection. C_LIO_LIResponse to selection varied across growing seasons/environments. C_LI

plant biology↗