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Allopolyploidy is a prevalent process in plants, having important physiological, ecological and evolutionary consequences. Transcriptomic responses to genomic merger and doubling have been demonstrated in many allopolyploid systems, encompassing a diversity of phenomena including homoeolog expression bias, genome dominance, expression-level dominance and revamping of co-expression networks. Notwithstanding the foregoing, there remains a need to develop a conceptual framework that will stimulate a deeper understanding of these diverse phenomena and their mechanistic interrelationships. Here we introduce considerations relevant to this framework with a focus on cis–trans interactions among duplicated genes and alleles in hybrids and allopolyploids. By extending classic allele-specific expression analysis to the allopolyploid level, we distinguish the distinct effects of progenitor regulatory interactions from the novel intergenomic interactions that arise from genome merger and allopolyploidization. This perspective informs experiments designed to reveal the molecular genetic basis of gene regulatory control, and will facilitate the disentangling of genetic from epigenetic and higher-order effects that impact gene expression. Finally, we suggest that the extended cis–trans model may help conceptually unify several presently disparate hallmarks of allopolyploid evolution, including genome-wide expression dominance and biased fractionation, and lead to a new level of understanding of phenotypic novelty accompanying polyploidy.

Summary Allele‐specific expression (ASE) can lead to phenotypic diversity and evolution. However, the mechanisms regulating ASE are not well understood, particularly in woody perennial plants. In this study, we investigated ASE genes in the apple cultivar ‘Royal Gala’ (RG). A high quality chromosome‐level genome was assembled using a homozygous tetra‐haploid RG plant, derived from anther cultures. Using RNA‐sequencing (RNA‐seq) data from RG flower and fruit tissues, we identified 2091 ASE genes. Compared with the haploid genome of ‘Golden Delicious’ (GD), a parent of RG, we distinguished the genomic sequences between the two alleles of 817 ASE genes, and further identified allele‐specific presence of a transposable element (TE) in the upstream region of 354 ASE genes. These included MYB110a that encodes a transcription factor regulating anthocyanin biosynthesis. Interestingly, another ASE gene, MYB10 also showed an allele‐specific TE insertion and was identified using genome data of other apple cultivars. The presence of the TE insertion in both MYB genes was positively associated with ASE and anthocyanin accumulation in apple petals through analysis of 231 apple accessions, and thus underpins apple flower colour evolution. Our study demonstrated the importance of TEs in regulating ASE on a genome‐wide scale and presents a novel method for rapid identification of ASE genes and their regulatory elements in plants.

Summary Genome phasing is a recently developed assembly method that separates heterozygous eukaryotic genomic regions and builds haplotype‐resolved assemblies. Because differences between haplotypes are ignored in most published de novo genomes, assemblies are available as consensus genomes consisting of haplotype mixtures, thus increasing the need for genome phasing. Here, we review the operating principles and characteristics of several freely available and widely used phasing tools (TrioCanu, FALCON‐Phase, and ALLHiC). An examination of downstream analyses using haplotype‐resolved genome assemblies in plants indicated significant differences among haplotypes regarding chromosomal rearrangements, sequence insertions, and expression of specific alleles that contribute to the acquisition of the biological characteristics of plant species. Finally, we suggest directions to solve addressing limitations of current genome‐phasing methods. This review provides insights into the current progress, limitations, and future directions of de novo genome phasing, which will enable researchers to easily access and utilize genome‐phasing in studies involving highly heterozygous complex plant genomes.

Supergene regions maintain alleles of multiple genes in tight linkage through suppressed recombination. Despite their importance in determining complex phenotypes, our empirical understanding of early supergene evolution is limited. Here we focus on the young ‘social’ supergene of fire ants, a powerful system for disentangling the effects of evolutionary antagonism and suppressed recombination. We hypothesize that gene degeneration and social antagonism shaped the evolution of the fire ant supergene, resulting in distinct patterns of gene expression. We test these ideas by identifying allelic differences between supergene variants, characterizing allelic expression across populations, castes and body parts, and contrasting allelic expression biases with differences in expression between social forms. We find strong signatures of gene degeneration and gene-specific dosage compensation. On this background, a small portion of the genes has the signature of adaptive responses to evolutionary antagonism between social forms. Red fire ants ( Solenopsis invicta ) are native to South America, but the species has spread to North America, Australia and New Zealand where it can be an invasive pest. A reason for this species’ invasiveness types of colonies : one with a single egg-laying queen and another with several queens. However, it is not possible to simply add more queens to a colony with one queen. Instead, the number of queens in a colony is controlled genetically, by a chromosome known as the ‘social chromosome’. Like many other animals, red fire ants are diploid: their cells have two copies of each chromosome, which can carry two different versions of each gene. The social chromosome is no different, and it comes in two variants, SB and Sb. Each ant can therefore have either two SB chromosomes, leading to a colony with a single queen; or one SB chromosome and one Sb chromosome, leading to a colony with multiple queens. Ants with two copies of the Sb variant die when they are young, so the Sb version is inherited in a similar way to how the Y chromosome is passed on in humans. However, the social chromosome in red fire ants appeared less than one million years ago, making it much younger than the human Y chromosome, which is 180 million years old. This makes the social chromosome a good candidate for examining the early evolution of special chromosome variants that are only inherited. How differences between the SB and the Sb chromosomes are evolving is an open question, however. Perhaps each version of the social chromosome has been optimised through natural selection to one colony type. Another suggestion is that the Sb chromosome has degenerated over time because its genes cannot be ‘reshuffled’ as they would be on normal chromosomes. Martinez-Ruiz et al. compared genetic variants on the SB and Sb chromosomes, along with their expression in different types of ant colonies. The analysis showed that the Sb variant is in fact breaking down because of the lack of gene shuffling. This loss is compensated by intact copies of the same genes found on the SB variant, which explains why ants with the Sb variant can only survive if they also carry the SB version. Only a handful of genes on the social chromosomes appear to have been optimised by natural selection. Therefore Martinez-Ruiz et al. concluded the differences between the two chromosomes that lead to different colony types are collateral effects of Sb’s inability to reshuffle its genes. This work reveals how a special chromosome similar to the Y chromosome in humans evolved. It also shows how multiple complex evolutionary forces can shape a species’ genetic makeup and social forms.

Recent advances in the selective breeding of broilers and layers have made poultry production one of the fastest‐growing industries. In this study, a transcriptome variant calling approach from RNA‐seq data was used to determine population diversity between broilers and layers. In total, 200 individuals were analyzed from three different chicken populations (Lohmann Brown (LB), n = 90), Lohmann Selected Leghorn (LSL, n = 89), and Broiler (BR, n = 21). The raw RNA‐sequencing reads were pre‐processed, quality control checked, mapped to the reference genome, and made compatible with Genome Analysis ToolKit for variant detection. Subsequently, pairwise fixation index (FST) analysis was performed between broilers and layers. Numerous candidate genes were identified, that were associated with growth, development, metabolism, immunity, and other economically significant traits. Finally, allele‐specific expression (ASE) analysis was performed in the gut mucosa of LB and LSL strains at 10, 16, 24, 30, and 60 weeks of age. At different ages, the two‐layer strains showed significantly different allele‐specific expressions in the gut mucosa, and changes in allelic imbalance were observed across the entire lifespan. Most ASE genes are involved in energy metabolism, including sirtuin signaling pathways, oxidative phosphorylation, and mitochondrial dysfunction. A high number of ASE genes were found during the peak of laying, which were particularly enriched in cholesterol biosynthesis. These findings indicate that genetic architecture as well as biological processes driving particular demands relate to metabolic and nutritional requirements during the laying period shape allelic heterogeneity. These processes are considerably affected by breeding and management, whereby elucidating allele‐specific gene regulation is an essential step towards deciphering the genotype to phenotype map or functional diversity between the chicken populations. Additionally, we observed that several genes showing significant allelic imbalance also colocalized with the top 1% of genes identified by the FST approach, suggesting a fixation of genes in cis‐regulatory elements.

CONTENTS: Summary 923 I. Introduction 924 II. Early studies of heterosis 924 III. Heterosis in diverse species 926 IV. Gene expression studies 926 V. Protein metabolism 927 VI. Allele-specific gene expression 928 VII. Quality control 929 VIII. A synthesis model 930 IX. Putting the model to work 932 Acknowledgements 933 References 933 SUMMARY: Hybrids between genetically diverse varieties display enhanced growth, and increased total biomass, stress resistance and grain yield. Gene expression and metabolic studies in maize, rice and other species suggest that protein metabolism plays a role in the growth differences between hybrids and inbreds. Single trait heterosis can be explained by the existing theories of dominance, overdominance and epistasis. General multigenic heterosis is observed in a wide variety of different species and is likely to share a common underlying biological mechanism. This review presents a model to explain differences in growth and yield caused by general multigenic heterosis. The model describes multigenic heterosis in terms of energy-use efficiency and faster cell cycle progression where hybrids have more efficient growth than inbreds because of differences in protein metabolism. The proposed model is consistent with the observed variation of gene expression in different pairs of inbred lines and hybrid offspring as well as growth differences in polyploids and aneuploids. It also suggests an approach to enhance yield gains in both hybrid and inbred crops via the creation of an appropriate computational analysis pipeline coupled to an efficient molecular breeding program.

RNA‐seq may replace gene expression microarrays in the near future. Using RNA‐seq, the expression of a gene can be estimated using the total number of sequence reads mapped to that gene, known as the total read count (TReC). Traditional expression quantitative trait locus (eQTL) mapping methods, such as linear regression, can be applied to TReC measurements after they are properly normalized. In this article, we show that eQTL mapping, by directly modeling TReC using discrete distributions, has higher statistical power than the two‐step approach: data normalization followed by linear regression. In addition, RNA‐seq provides information on allele‐specific expression (ASE) that is not available from microarrays. By combining the information from TReC and ASE, we can computationally distinguish cis‐ and trans‐eQTL and further improve the power of cis‐eQTL mapping. Both simulation and real data studies confirm the improved power of our new methods. We also discuss the design issues of RNA‐seq experiments. Specifically, we show that by combining TReC and ASE measurements, it is possible to minimize cost and retain the statistical power of cis‐eQTL mapping by reducing sample size while increasing the number of sequence reads per sample. In addition to RNA‐seq data, our method can also be employed to study the genetic basis of other types of sequencing data, such as chromatin immunoprecipitation followed by DNA sequencing data. In this article, we focus on eQTL mapping of a single gene using the association‐based method. However, our method establishes a statistical framework for future developments of eQTL mapping methods using RNA‐seq data (e.g., linkage‐based eQTL mapping), and the joint study of multiple genetic markers and/or multiple genes.

RNA sequencing allows one to study allelic imbalance of gene expression, which may be due to genetic factors or genomic imprinting (i.e., higher expression of maternal or paternal allele). It is desirable to model both genetic and parent-of-origin effects simultaneously to avoid confounding and to improve the power to detect either effect. In studies of genetically tractable model organisms, separation of genetic and parent-of-origin effects can be achieved by studying reciprocal cross of two inbred strains. In contrast, this task is much more challenging in outbred populations such as humans. To address this challenge, we propose a new framework to combine experimental strategies and novel statistical methods. Specifically, we propose to study genetic and imprinting effects in family trios with RNA-seq data from the children and genotype data from both parents and children, and quantify genetic effects by cis-eQTLs. Towards this end, we have extended our method that studies the eQTLs of RNA-seq data (Sun, Biometrics 2012, 68(1): 1-11) to model both cis-eQTL and parent-of-origin effects, and evaluated its performance using extensive simulations. Since sample size may be limited in family trios, we have developed a data analysis pipeline that borrows information from external data of unrelated individuals for cis-eQTL mapping. We have also collected RNA-seq data from the children of 30 family trios, applied our method to analyze this dataset, and identified some previously reported imprinted genes as well as some new candidates of imprinted genes.

With the advent of new sequencing technologies, the evolution of gene expression is becoming a subject of intensive genomic research, with sparking debates upon the role played by these kinds of changes in adaptive evolution and speciation. In this article, we model expression evolution in species differing by their reproductive systems. We consider different rates of sexual versus asexual reproduction and the different type of parthenogenesis (apomixis and the various modes of automixis). We show that competition for expression leads to two selective processes on cis-regulatory regions that act independently to organism-level adaptation. Coevolution within regulatory networks allows these processes to occur without strongly modifying expression levels. First, cis-regulatory regions such as enhancers evolve in a runaway fashion because they automatically become associated to chromosomes purged from deleterious mutations (“Enhancer Runaway process”). Second, in clonal or nearly clonal species, homologous cis-regulatory regions tend to diverge, which leads to haploidization of expression, when they are sufficiently isolated from one another (“Enhancer Divergence process”). We show how these two processes cooccur and vary depending on the level of outcrossing, gene conversion, mitotic recombination, or recombination in automictic species. This study offers thus a baseline to understand patterns of expression evolution across the diversity of eukaryotic species.

Background Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. Results To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAF RNA ) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS , where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available ( https://github.com/HorvathLab/NGS ) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source. Conclusions SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.