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The genomic imbalance caused by varying the dosage of individual chromosomes or chromosomal segments (aneuploidy) has more detrimental effects than altering the dosage of complete chromosome sets (ploidy). Previous analysis of maize (Zea mays) aneuploids revealed global modulation of gene expression both on the varied chromosome (cis) and the remainder of the genome (trans). However, little is known regarding the role of microRNAs (miRNAs) under genomic imbalance. Here, we report the impact of aneuploidy and polyploidy on the expression of miRNAs. In general,cismiRNAs in aneuploids present a predominant gene-dosage effect, whereastransmiRNAs trend toward the inverse level, although other types of responses including dosage compensation, increased effect, and decreased effect also occur. By contrast, polyploids show less differential miRNA expression than aneuploids. Significant correlations between expression levels of miRNAs and their targets are identified in aneuploids, indicating the regulatory role of miRNAs on gene expression triggered by genomic imbalance.

Alterations in DNA methylation have been linked to the development and progression of many diseases. The bisulfite sequencing technique presents methylation profiles at base resolution. Count data on methylated and unmethylated reads provide information on the methylation level at each CpG site. As more bisulfite sequencing data become available, these data are increasingly needed to infer methylation aberrations in diseases. Automated and powerful algorithms also need to be developed to accurately identify differentially methylated regions between treatment groups. This study adopts a Bayesian approach using the hidden Markov model to account for inherent dependence in read count data. Given the expense of sequencing experiments, few replicates are available for each treatment group. A Bayesian approach that borrows information across an entire chromosome improves the reliability of statistical inferences. The proposed hidden Markov model considers location dependence among genomic loci by incorporating correlation structures as a function of genomic distance. An iterative algorithm based on expectation-maximization is designed for parameter estimation. Methylation states are inferred by identifying the optimal sequence of latent states from observations. Real datasets and simulation studies that mimic the real datasets are used to illustrate the reliability and success of the proposed method.

The non‐essential supernumerary maize (Zea mays) B chromosome (B) has recently been shown to contain active genes and to be capable of impacting gene expression of the A chromosomes. However, the effect of the B chromosome on gene expression is still unclear. In addition, it is unknown whether the accumulation of the B chromosome has a cumulative effect on gene expression. To examine these questions, the global expression of genes, microRNAs (miRNAs), and transposable elements (TEs) of leaf tissue of maize W22 plants with 0–7 copies of the B chromosome was studied. All experimental genotypes with B chromosomes displayed a trend of upregulated gene expression for a subset of A‐located genes compared to the control. Over 3000 A‐located genes are significantly differentially expressed in all experimental genotypes with the B chromosome relative to the control. Modulations of these genes are largely determined by the presence rather than the copy number of the B chromosome. By contrast, the expression of most B‐located genes is positively correlated with B copy number, showing a proportional gene dosage effect. The B chromosome also causes increased expression of A‐located miRNAs. Differentially expressed miRNAs potentially regulate their targets in a cascade of effects. Furthermore, the varied copy number of the B chromosome leads to the differential expression of A‐located and B‐located TEs. The findings provide novel insights into the function and properties of the B chromosome.