Single Reference Genome Research Articles

Assembly, comparative analysis, and utilization of a single haplotype reference genome for soybean.

Cultivar Williams 82 has served as the reference genome for the soybean research community since 2008, but is known to have areas of genomic heterogeneity among different sub-lines. This work provides an updated assembly (version Wm82.a6) derived from a specific sub-line known as Wm82-ISU-01 (seeds available under USDA accession PI 704477). The genome was assembled using Pacific BioSciences HiFi reads and integrated into chromosomes using HiC. The 20 soybean chromosomes assembled into a genome of 1.01Gb, consisting of 36 contigs. The genome annotation identified 48 387 gene models, named in accordance with previous assembly versions Wm82.a2 and Wm82.a4. Comparisons of Wm82.a6 with other near-gapless assemblies of Williams 82 reveal large regions of genomic heterogeneity, including regions of differential introgression from the cultivar Kingwa within approximately 30 Mb and 25 Mb segments on chromosomes 03 and 07, respectively. Additionally, our analysis revealed a previously unknown large (>20 Mb) heterogeneous region in the pericentromeric region of chromosome 12, where Wm82.a6 matches the 'Williams' haplotype while the other two near-gapless assemblies do not match the haplotype of either parent of Williams 82. In addition to the Wm82.a6 assembly, we also assembled the genome of 'Fiskeby III,' a rich resource for abiotic stress resistance genes. A genome comparison of Wm82.a6 with Fiskeby III revealed the nucleotide and structural polymorphisms between the two genomes within a QTL region for iron deficiency chlorosis resistance. The Wm82.a6 and Fiskeby III genomes described here will enhance comparative and functional genomics capacities and applications in the soybean community.

Addressing Technical Pitfalls in Pursuit of Molecular Factors That Mediate Immunoglobulin Gene Regulation.

The expressed Ab repertoire is a critical determinant of immune-related phenotypes. Ab-encoding transcripts are distinct from other expressed genes because they are transcribed from somatically rearranged gene segments. Human Abs are composed of two identical H and L chain polypeptides derived from genes in IGH locus and one of two L chain loci. The combinatorial diversity that results from Ab gene rearrangement and the pairing of different H and L chains contributes to the immense diversity of the baseline Ab repertoire. During rearrangement, Ab gene selection is mediated by factors that influence chromatin architecture, promoter/enhancer activity, and V(D)J recombination. Interindividual variation in the composition of the Ab repertoire associates with germline variation in IGH, implicating polymorphism in Ab gene regulation. Determining how IGH variants directly mediate gene regulation will require integration of these variants with other functional genomic datasets. In this study, we argue that standard approaches using short reads have limited utility for characterizing regulatory regions in IGH at haplotype resolution. Using simulated and chromatin immunoprecipitation sequencing reads, we define features of IGH that limit use of short reads and a single reference genome, namely 1) the highly duplicated nature of the DNA sequence in IGH and 2) structural polymorphisms that are frequent in the population. We demonstrate that personalized diploid references enhance performance of short-read data for characterizing mappable portions of the locus, while also showing that long-read profiling tools will ultimately be needed to fully resolve functional impacts of IGH germline variation on expressed Ab repertoires.

GWarrange: a pre- and post- genome-wide association studies pipeline for detecting phenotype-associated genome rearrangement events.

The use of k-mers to capture genetic variation in bacterial genome-wide association studies (bGWAS) has demonstrated its effectiveness in overcoming the plasticity of bacterial genomes by providing a comprehensive array of genetic variants in a genome set that is not confined to a single reference genome. However, little attempt has been made to interpret k-mers in the context of genome rearrangements, partly due to challenges in the exhaustive and high-throughput identification of genome structure and individual rearrangement events. Here, we present GWarrange, a pre- and post-bGWAS processing methodology that leverages the unique properties of k-mers to facilitate bGWAS for genome rearrangements. Repeat sequences are common instigators of genome rearrangements through intragenomic homologous recombination, and they are commonly found at rearrangement boundaries. Using whole-genome sequences, repeat sequences are replaced by short placeholder sequences, allowing the regions flanking repeats to be incorporated into relatively short k-mers. Then, locations of flanking regions in significant k-mers are mapped back to complete genome sequences to visualise genome rearrangements. Four case studies based on two bacterial species (Bordetella pertussis and Enterococcus faecium) and a simulated genome set are presented to demonstrate the ability to identify phenotype-associated rearrangements. GWarrange is available at https://github.com/DorothyTamYiLing/GWarrange.

Panacus: fast and exact pangenome growth and core size estimation.

Using a single linear reference genome poses a limitation to exploring the full genomic diversity of a species. The release of a draft human pangenome underscores the increasing relevance of pangenomics to overcome these limitations. Pangenomes are commonly represented as graphs, which can represent billions of base pairs of sequence. Presently, there is a lack of scalable software able to perform key tasks on pangenomes, such as quantifying universally shared sequence across genomes (the core genome ) and measuring the extent of genomic variability as a function of sample size ( pangenome growth ). We introduce Panacus (pangenome-abacus), a tool designed to rapidly perform these tasks and visualize the results in interactive plots. Panacus can process GFA files, the accepted standard for pangenome graphs, and is able to analyze a human pangenome graph with 110 million nodes in less than one hour. Panacus is implemented in Rust and is published as Open Source software under the MIT license. The source code and documentation are available at https://github.com/marschall-lab/panacus . Panacus can be installed via Bioconda at https://bioconda.github.io/recipes/panacus/README.html . Luca Parmigiani ( luca.parmigiani@uni-bielefeld.de ), Daniel Doerr ( daniel.doerr@hhu.de ).

Multiple hybridization events and repeated evolution of homoeologue expression bias in parthenogenetic, polyploid New Zealand stick insects.

During hybrid speciation, homoeologues combine in a single genome. Homoeologue expression bias (HEB) occurs when one homoeologue has higher gene expression than another. HEB has been well characterized in plants but rarely investigated in animals, especially invertebrates. Consequently, we have little idea as to the role that HEB plays in allopolyploid invertebrate genomes. If HEB is constrained by features of the parental genomes, then we predict repeated evolution of similar HEB patterns among hybrid genomes formed from the same parental lineages. To address this, we reconstructed the history of hybridization between the New Zealand stick insect genera Acanthoxyla and Clitarchus using a high-quality genome assembly from Clitarchus hookeri to call variants and phase alleles. These analyses revealed the formation of three independent diploid and triploid hybrid lineages between these genera. RNA sequencing revealed a similar magnitude and direction of HEB among these hybrid lineages, and we observed that many enriched functions and pathways were also shared among lineages, consistent with repeated evolution due to parental genome constraints. In most hybrid lineages, a slight majority of the genes involved in mitochondrial function showed HEB towards the maternal homoeologues, consistent with only weak effects of mitonuclear incompatibility. We also observed a proteasome functional enrichment in most lineages and hypothesize this may result from the need to maintain proteostasis in hybrid genomes. Reference bias was a pervasive problem, and we caution against relying on HEB estimates from a single parental reference genome.

Pangenome characterization and analysis of the NAC gene family reveals genes for Sclerotinia sclerotiorum resistance in sunflower (Helianthus annuus).

Sunflower (Helianthus annuus) is one of the most important economic crops in oilseed production worldwide. The different cultivars exhibit variability in their resistance genes. The NAC transcription factor (TF) family plays diverse roles in plant development and stress responses. With the completion of the H. annuus genome sequence, the entire complement of genes coding for NACs has been identified. However, the reference genome of a single individual cannot cover all the genetic information of the species. Considering only a single reference genome to study gene families will miss many meaningful genes. Apangenome-wide surveyand characterization of the NAC genes in sunflower species were conducted. In total, 139 HaNAC genes are identified, of which 114 are core and 25 are variable. Phylogenetic analysis of sunflower NAC proteins categorizes these proteins into 16 subgroups. 138 HaNACs are randomly distributed on 17 chromosomes. SNP-based haplotype analysis shows haplotype diversity of the HaNAC genes in wild accessions is richer than in landraces and modern cultivars. Ten HaNAC genes in the basal stalk rot (BSR) resistance quantitative trait loci (QTL) are found. A total of 26 HaNAC genes are differentially expressed in response to Sclerotinia head rot (SHR). A total of 137 HaNAC genes are annotated in Gene Ontology (GO) and are classified into 24 functional groups. GO functional enrichment analysis reveals that HaNAC genes are involved in various functions of the biological process. We identified NAC genes in H. annuus (HaNAC) on a pangenome-wide scale and analyzed S. sclerotiorum resistance-related NACs. This study provided a theoretical basis for further genomic improvement targeting resistance-related NAC genes in sunflowers.

Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples

We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole-slide image (WSI), optimized for patient-derived xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genome input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.

DNA methylation analysis to differentiate reference, breed, and parent-of-origin effects in the bovine pangenome era.

Most DNA methylation studies have used a single reference genome with little attention paid to the bias introduced due to the reference chosen. Reference genome artifacts and genetic variation, including single nucleotide polymorphisms (SNPs) and structural variants (SVs), can lead to differences in methylation sites (CpGs) between individuals of the same species. We analyzed whole-genome bisulfite sequencing data from the fetal liver of Angus (Bos taurus taurus), Brahman (Bos taurus indicus), and reciprocally crossed samples. Using reference genomes for each breed from the Bovine Pangenome Consortium, we investigated the influence of reference genome choice on the breed and parent-of-origin effects in methylome analyses. Our findings revealed that ∼75% of CpG sites were shared between Angus and Brahman, ∼5% were breed specific, and ∼20% were unresolved. We demonstrated up to ∼2% quantification bias in global methylation when an incorrect reference genome was used. Furthermore, we found that SNPs impacted CpGs 13 times more than other autosomal sites (P < $5 \times {10}^{ - 324}$) and SVs contained 1.18 times (P < $5 \times {10}^{ - 324}$) more CpGs than non-SVs. We found a poor overlap between differentially methylated regions (DMRs) and differentially expressed genes (DEGs) and suggest that DMRs may be impacting enhancers that target these DEGs. DMRs overlapped with imprinted genes, of which 1, DGAT1, which is important for fat metabolism and weight gain, was found in the breed-specific and sire-of-origin comparisons. This work demonstrates the need to consider reference genome effects to explore genetic and epigenetic differences accurately and identify DMRs involved in controlling certain genes.

Phased genomics reveals hidden somatic mutations and provides insight into fruit development in sweet orange.

Although revisiting the discoveries and implications of genetic variations using phased genomics is critical, such efforts are still lacking. Somatic mutations represent a crucial source of genetic diversity for breeding and are especially remarkable in heterozygous perennial and asexual crops. In this study, we focused on a diploid sweet orange (Citrus sinensis) and constructed a haplotype-resolved genome using high fidelity (HiFi) reads, which revealed 10.6% new sequences. Based on the phased genome, we elucidate significant genetic admixtures and haplotype differences. We developed a somatic detection strategy that reveals hidden somatic mutations overlooked in a single reference genome. We generated a phased somatic variation map by combining high-depth whole-genome sequencing (WGS) data from 87 sweet orange somatic varieties. Notably, we found twice as many somatic mutations relative to a single reference genome. Using these hidden somatic mutations, we separated sweet oranges into seven major clades and provide insight into unprecedented genetic mosaicism and strong positive selection. Furthermore, these phased genomics data indicate that genomic heterozygous variations contribute to allele-specific expression during fruit development. By integrating allelic expression differences and somatic mutations, we identified a somatic mutation that induces increases in fruit size. Applications of phased genomics will lead to powerful approaches for discovering genetic variations and uncovering their effects in highly heterozygous plants. Our data provide insight into the hidden somatic mutation landscape in the sweet orange genome, which will facilitate citrus breeding.

Abstract B062: Prevalence of pathogenic BRCA1/2 variants among Latinas in the All of Us Cohort

Abstract Latinas in the United States are disproportionately affected by breast cancer, but little is known about genetic risk factors for breast cancer in this population. Genetic variants in BReast CAncer genes 1 and 2 (BRCA1/2) are known to be associated with heritable cancer risk, yet the prevalence of BRCA1/2 pathogenic variants in the Latina population is not well characterized. This study aims to examine the prevalence of pathogenic BRCA1/2 variants among Latinas in the NIH All of Us research program, which includes a large number of individuals representative of the diversity of the United States population. Using data from the All of Us research program, we will analyze the prevalence of these variants among Latinas, in addition to all other racial and ethnic groups. To date, the All of Us cohort contains 245,400 genomes, and of those 41,940 (17%) are from self-identified Hispanic/Latino/or Spanish participants. We will compare the prevalence of pathogenic, or likely pathogenic, variants across self-reported race/ethnicity and continental genetic ancestry categories. Our study will leverage the diversity of All of Us and enable us to compare our data with those from more Eurocentric databases. With these data, we can potentially 1) identify novel variants that could predispose to increased cancer risk; and 2) clarify the previous designations of variants characterized to have undetermined/unknown significance due to the limitations of using a single Eurocentric reference genome. Identifying the actual prevalence of pathogenic BRCA1/2 variants among Latinas is important for improving breast cancer prevention, detection, and treatment in this population. The findings of this study have the potential to inform genetic counseling and testing recommendations for Latina patients at high risk of developing breast cancer, as well as others. This will also enable healthcare providers to better understand the unique genetic factors contributing to breast cancer risk among this population. Ultimately, this knowledge can improve the care of Latina patients and contribute to the broader effort to advance health equity and reduce disparities in breast cancer outcomes among Latinas. Citation Format: Cathy Samayoa, Erick Olivares, Catherine Gavile, Adriana Visbal. Prevalence of pathogenic BRCA1/2 variants among Latinas in the All of Us Cohort [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B062.

Comparing methods for constructing and representing human pangenome graphs

BackgroundAs a single reference genome cannot possibly represent all the variation present across human individuals, pangenome graphs have been introduced to incorporate population diversity within a wide range of genomic analyses. Several data structures have been proposed for representing collections of genomes as pangenomes, in particular graphs.ResultsIn this work, we collect all publicly available high-quality human haplotypes and construct the largest human pangenome graphs to date, incorporating 52 individuals in addition to two synthetic references (CHM13 and GRCh38). We build variation graphs and de Bruijn graphs of this collection using five of the state-of-the-art tools: Bifrost, mdbg, Minigraph, Minigraph-Cactus and pggb. We examine differences in the way each of these tools represents variations between input sequences, both in terms of overall graph structure and representation of specific genetic loci.ConclusionThis work sheds light on key differences between pangenome graph representations, informing end-users on how to select the most appropriate graph type for their application.

A pangenome graph reference of 30 chicken genomes allows genotyping of large and complex structural variants

BackgroundThe red junglefowl, the wild outgroup of domestic chickens, has historically served as a reference for genomic studies of domestic chickens. These studies have provided insight into the etiology of traits of commercial importance. However, the use of a single reference genome does not capture diversity present among modern breeds, many of which have accumulated molecular changes due to drift and selection. While reference-based resequencing is well-suited to cataloging simple variants such as single-nucleotide changes and short insertions and deletions, it is mostly inadequate to discover more complex structural variation in the genome.MethodsWe present a pangenome for the domestic chicken consisting of thirty assemblies of chickens from different breeds and research lines.ResultsWe demonstrate how this pangenome can be used to catalog structural variants present in modern breeds and untangle complex nested variation. We show that alignment of short reads from 100 diverse wild and domestic chickens to this pangenome reduces reference bias by 38%, which affects downstream genotyping results. This approach also allows for the accurate genotyping of a large and complex pair of structural variants at the K feathering locus using short reads, which would not be possible using a linear reference.ConclusionsWe expect that this new paradigm of genomic reference will allow better pinpointing of exact mutations responsible for specific phenotypes, which will in turn be necessary for breeding chickens that meet new sustainability criteria and are resilient to quickly evolving pathogen threats.

52 The Power of Pangenome Assemblies in Ruminants

Abstract High quality reference genomes are the bases of all genomic research and they have facilitated a wealth of discoveries and research tools for the improvement of livestock species. These works have enabled major advances in the fields of animal science and genetics and impacted livestock production and associated industries for the benefit of society. However, because a single reference genome fails to represent all of the genetic material present across populations, it inherently limits the ability to comprehensively evaluate all genomic variants within a species. A complete understanding of how small and large genomic variants, from single nucleotide polymorphisms (SNP) to larger structural variations (SV) including insertions/deletions (InDels) and copy number variation (CNVs), have been selected for over time for specific environments and phenotypes, remains elusive. To address this, it is necessary to generate a catalog of genomic variants in populations subjected to natural or artificial selection pressures and evaluate their relationship among phenotypic classes. The pangenome approach is a means to comprehensively identify and catalog genomic variation and representation that will make more suitable reference for mapping population sequence data for variant detection and frequency evaluation. The goal of the most pangenome projects is to provide genomic resources to represent the immense genetic diversity of this species more completely. This will increase our understanding of genetic variation and its role in dictating different phenomes, empowering scientists to predict the physiological outcomes more effectively from genomic variation towards the improvement of breeding strategies. These pangenomes can then be used to discover the underlying mechanism of biological traits and inform future breeding strategies and support sustainable production of this global source of food and fiber.

Transposons contribute to the functional diversification of the head, gut, and ovary transcriptomes across Drosophila natural strains.

Transcriptomes are dynamic, with cells, tissues, and body parts expressing particular sets of transcripts. Transposable elements (TEs) are a known source of transcriptome diversity; however, studies often focus on a particular type of chimeric transcript, analyze single body parts or cell types, or are based on incomplete TE annotations from a single reference genome. In this work, we have implemented a method based on de novo transcriptome assembly that minimizes the potential sources of errors while identifying a comprehensive set of gene-TE chimeras. We applied this method to the head, gut, and ovary dissected from five Drosophila melanogaster natural strains, with individual reference genomes available. We found that ∼19% of body part-specific transcripts are gene-TE chimeras. Overall, chimeric transcripts contribute a mean of 43% to the total gene expression, and they provide protein domains for DNA binding, catalytic activity, and DNA polymerase activity. Our comprehensive data set is a rich resource for follow-up analysis. Moreover, because TEs are present in virtually all species sequenced to date, their role in spatially restricted transcript expression is likely not exclusive to the species analyzed in this work.

Accurate and fast graph-based pangenome annotation and clustering with ggCaller.

Bacterial genomes differ in both gene content and sequence mutations, which underlie extensive phenotypic diversity, including variation in susceptibility to antimicrobials or vaccine-induced immunity. To identify and quantify important variants, all genes within a population must be predicted, functionally annotated, and clustered, representing the "pangenome." Despite the volume of genome data available, gene prediction and annotation are currently conducted in isolation on individual genomes, which is computationally inefficient and frequently inconsistent across genomes. Here, we introduce the open-source software graph-gene-caller (ggCaller). ggCaller combines gene prediction, functional annotation, and clustering into a single workflow using population-wide de Bruijn graphs, removing redundancy in gene annotation and resulting in more accurate gene predictions and orthologue clustering. We applied ggCaller to simulated and real-world bacterial data sets containing hundreds or thousands of genomes, comparing it to current state-of-the-art tools. ggCaller has considerable speed-ups with equivalent or greater accuracy, particularly with data sets containing complex sources of error, such as assembly contamination or fragmentation. ggCaller is also an important extension to bacterial genome-wide association studies, enabling querying of annotated graphs for functional analyses. We highlight this application by functionally annotating DNA sequences with significant associations to tetracycline and macrolide resistance in Streptococcus pneumoniae, identifying key resistance determinants that were missed when using only a single reference genome. ggCaller is a novel bacterial genome analysis tool with applications in bacterial evolution and epidemiology.

Pangenome graphs in infectious disease: a comprehensive genetic variation analysis of Neisseria meningitidis leveraging Oxford Nanopore long reads.

Whole genome sequencing has revolutionized infectious disease surveillance for tracking and monitoring the spread and evolution of pathogens. However, using a linear reference genome for genomic analyses may introduce biases, especially when studies are conducted on highly variable bacterial genomes of the same species. Pangenome graphs provide an efficient model for representing and analyzing multiple genomes and their variants as a graph structure that includes all types of variations. In this study, we present a practical bioinformatics pipeline that employs the PanGenome Graph Builder and the Variation Graph toolkit to build pangenomes from assembled genomes, align whole genome sequencing data and call variants against a graph reference. The pangenome graph enables the identification of structural variants, rearrangements, and small variants (e.g., single nucleotide polymorphisms and insertions/deletions) simultaneously. We demonstrate that using a pangenome graph, instead of a single linear reference genome, improves mapping rates and variant calling for both simulated and real datasets of the pathogen Neisseria meningitidis. Overall, pangenome graphs offer a promising approach for comparative genomics and comprehensive genetic variation analysis in infectious disease. Moreover, this innovative pipeline, leveraging pangenome graphs, can bridge variant analysis, genome assembly, population genetics, and evolutionary biology, expanding the reach of genomic understanding and applications.

The Effect of Methodological Considerations on the Construction of Gene-Based Plant Pan-genomes.

Pan-genomics is an emerging approach for studying the genetic diversity within plant populations. In contrast to common resequencing studies that compare whole genome sequencing data with a single reference genome, the construction of a pan-genome (PG) involves the direct comparison of multiple genomes to one another, thereby enabling the detection of genomic sequences and genes not present in the reference, as well as the analysis of gene content diversity. Although multiple studies describing PGs of various plant species have been published in recent years, a better understanding regarding the effect of the computational procedures used for PG construction could guide researchers in making more informed methodological decisions. Here, we examine the effect of several key methodological factors on the obtained gene pool and on gene presence-absence detections by constructing and comparing multiple PGs of Arabidopsis thaliana and cultivated soybean, as well as conducting a meta-analysis on published PGs. These factors include the construction method, the sequencing depth, and the extent of input data used for gene annotation. We observe substantial differences between PGs constructed using three common procedures (de novo assembly and annotation, map-to-pan, and iterative assembly) and that results are dependent on the extent of the input data. Specifically, we report low agreement between the gene content inferred using different procedures and input data. Our results should increase the awareness of the community to the consequences of methodological decisions made during the process of PG construction and emphasize the need for further investigation of commonly applied methodologies.

HaploCart: Human mtDNA haplogroup classification using a pangenomic reference graph.

Current mitochondrial DNA (mtDNA) haplogroup classification tools map reads to a single reference genome and perform inference based on the detected mutations to this reference. This approach biases haplogroup assignments towards the reference and prohibits accurate calculations of the uncertainty in assignment. We present HaploCart, a probabilistic mtDNA haplogroup classifier which uses a pangenomic reference graph framework together with principles of Bayesian inference. We demonstrate that our approach significantly outperforms available tools by being more robust to lower coverage or incomplete consensus sequences and producing phylogenetically-aware confidence scores that are unbiased towards any haplogroup. HaploCart is available both as a command-line tool and through a user-friendly web interface. The C++ program accepts as input consensus FASTA, FASTQ, or GAM files, and outputs a text file with the haplogroup assignments of the samples along with the level of confidence in the assignments. Our work considerably reduces the amount of data required to obtain a confident mitochondrial haplogroup assignment.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Comparative gene retention analysis in barley, wild emmer, and bread wheat pangenome lines reveals factors affecting gene retention following gene duplication

BackgroundGene duplication is a prevalent phenomenon and a major driving force underlying genome evolution. The process leading to the fixation of gene duplicates following duplication is critical to understand how genome evolves but remains fragmentally understood. Most previous studies on gene retention are based on gene duplicate analyses in single reference genome. No population-based comparative gene retention analysis has been performed to date.ResultsTaking advantage of recently published genomic data in Triticeae, we dissected a divergent homogentisate phytyltransferase (HPT2) lineage caught in the middle stage of gene fixation following duplication. The presence/absence of HPT2 in barley (diploid), wild emmer (tetraploid), and bread wheat (hexaploid) pangenome lines appears to be associated with gene dosage constraint and environmental adaption. Based on these observations, we adopted a phylogeny-based orthology inference approach and performed comparative gene retention analyses across barley, wild emmer, and bread wheat. This led to the identification of 326 HPT2-pattern-like genes at whole genome scale, representing a pool of gene duplicates in the middle stage of gene fixation. Majority of these HPT2-pattern-like genes were identified as small-scale duplicates, such as dispersed, tandem, and proximal duplications. Natural selection analyses showed that HPT2-pattern-like genes have experienced relaxed selection pressure, which is generally accompanied with partial positive selection and transcriptional divergence. Functional enrichment analyses showed that HPT2-pattern-like genes are over-represented with molecular-binding and defense response functions, supporting the potential role of environmental adaption during gene retention. We also observed that gene duplicates from larger gene family are more likely to be lost, implying a gene dosage constraint effect. Further comparative gene retention analysis in barley and bread wheat pangenome lines revealed combined effects of species-specific selection and gene dosage constraint.ConclusionsComparative gene retention analyses at the population level support gene dosage constraint, environmental adaption, and species-specific selection as three factors that may affect gene retention following gene duplication. Our findings shed light on the evolutionary process leading to the retention of newly formed gene duplicates and will greatly improve our understanding on genome evolution via duplication.