Graph construction method impacts variation representation and analyses in a bovine super-pangenome

Long read and preliminary pangenome analyses reveal breed-specific structural variations and novel sequences in Holstein and Jersey cattle

INTRODUCTION

Most SV studies in livestock rely on short-read sequencing, posing challenges in accurately characterizing large genomic variants due to their limited read length.

OBJECTIVES

Our goal is to reveal structural variation and novel sequences specific to Holstein and Jersey cattle breeds using long-read and pan-genome analyses.

METHODS

We sequenced 20 Holsteins and 8 Jersey cattle using PacBio HiFi to 20×, and integrated five read-based and one assembly-based SV caller to determine SVs.

RESULTS

We assembled the 28 genomes averaging 3.25 Gb with a contig N50 of 69.36 Mb and using the ARS-UCD1.2 reference, we acquired Holstein/Jersey SV catalogs with 74,068/54,689 events spanning 202/135 Mb (7.43 %/4.97 % of the genome). SVs were enriched in less conserved, non-coding, and non-regulatory regions. Comparing Holsteins with differing feed efficiency (FE), SVs unique to high FE were linked to energy metabolism and olfactory receptors, while those specific to low FE were associated with material transport. We constructed Holstein/Jersey pangenome graphs with 148,598/105,875 nodes and 208,891/147,990 edges, representing 47,028/37,137 biallelic and multi-allelic events, and 63.75/42.34 Mb of novel sequence. We observed SV count saturation with 20 Holsteins, while adding Jerseys significantly increased the SV count, highlighting breed-specific SV events.

CONCLUSION

Our long-read data and SV catalogs are valuable resources, revealing that the cattle genome is more complex than previously thought.

Taurine pangenome uncovers a segmental duplication upstream of KIT associated with depigmentation in white-headed cattle

Cattle have been selectively bred for coat color, spotting, and depigmentation patterns. The assumed autosomal dominant inherited genetic variants underlying the characteristic white head of Fleckvieh, Simmental, and Hereford cattle have not been identified yet, although the contribution of structural variation upstream of the KIT gene has been proposed. Here, we construct a graph pangenome from 24 haplotype assemblies representing seven taurine cattle breeds to identify and characterize the white-head-associated locus for the first time based on long-read sequencing data and pangenome analyses. We introduce a pangenome-wide association mapping approach that examines assembly path similarities within the graph to reveal an association between two most likely serial alleles of a complex structural variant (SV) 66 kb upstream of KIT and facial depigmentation. The complex SV contains a variable number of tandemly duplicated 14.3 kb repeats, consisting of LTRs, LINEs, and other repetitive elements, leading to misleading alignments of short and long reads when using a linear reference. We align 250 short-read sequencing samples spanning 15 cattle breeds to the pangenome graph, further validating that the alleles of the SV segregate with head depigmentation. We estimate an increased count of repeats in Hereford relative to Simmental and other white-headed cattle breeds from the graph alignment coverage, suggesting a large under-assembly in the current Hereford-based cattle reference genome, which had fewer copies. Our work shows that exploiting assembly path similarities within graph pangenomes can reveal trait-associated complex SVs.

Constructing a draft Indian cattle pangenome using short-read sequencing

Indian desi cattle, known for their adaptability and phenotypic diversity, represent a valuable genetic resource. However, a single reference genome often fails to capture the full extent of their genetic variation. To address this, we construct a pangenome for desi cattle by identifying and characterizing non-reference novel sequences (NRNS). We sequence 68 genomes from seven breeds, generating 48.35 billion short reads. Using the PanGenome Analysis (PanGA) pipeline, we identify 13,065 NRNS (~41 Mbp), with substantial variation across the population. Most NRNS were unique to desi cattle, with minimal overlap (4.1%) with the Chinese indicine pangenome. Approximately 40% of NRNS exhibited ancestral origins within the Bos genus and were enriched in genic regions, suggesting functional roles. These sequences are linked to quantitative trait loci for traits such as milk production. The pangenome approach enhances read mapping accuracy, reduces spurious single nucleotide polymorphism calls, and uncovers novel genetic variants, offering a deeper understanding of desi cattle genomics.

Genetic advancements and future directions in ruminant livestock breeding: from reference genomes to multiomics innovations

Ruminant livestock provide a rich source of products, such as meat, milk, and wool, and play a critical role in global food security and nutrition. Over the past few decades, genomic studies of ruminant livestock have provided valuable insights into their domestication and the genetic basis of economically important traits, facilitating the breeding of elite varieties. In this review, we summarize the main advancements for domestic ruminants in reference genome assemblies, population genomics, and the identification of functional genes or variants for phenotypic traits. These traits include meat and carcass quality, reproduction, milk production, feed efficiency, wool and cashmere yield, horn development, tail type, coat color, environmental adaptation, and disease resistance. Functional genomic research is entering a new era with the advancements of graphical pangenomics and telomere-to-telomere (T2T) gap-free genome assembly. These advancements promise to improve our understanding of domestication and the molecular mechanisms underlying economically important traits in ruminant livestock. Finally, we provide new perspectives and future directions for genomic research on ruminant genomes. We suggest how ever-increasing multiomics datasets will facilitate future studies and molecular breeding in livestock, including the potential to uncover novel genetic mechanisms underlying phenotypic traits, to enable more accurate genomic prediction models, and to accelerate genetic improvement programs.

Comparative population pangenomes reveal unexpected complexity and fitness effects of structural variants

Structural variants (SVs) are widespread in vertebrate genomes, yet their evolutionary dynamics remain poorly understood. Using 45 long-read de novo genome assemblies and pangenome tools, we analyze SVs within three closely related species of North American jays (Aphelocoma, scrub-jays) displaying a 60-fold range in effective population size. We find rapid evolution of genome architecture, including ~100 Mb variation in genome size driven by dynamic satellite landscapes with unexpectedly long (> 10 kb) repeat units and widespread variation in gene content, influencing gene expression. SVs exhibit slightly deleterious dynamics modulated by variant length and population size, with strong evidence of adaptive fixation only in large populations. Our results demonstrate how population size shapes the distribution of SVs and the importance of pangenomes to characterizing genomic diversity.

Developing pangenomes for large and complex plant genomes and their representation formats

BACKGROUND

The development of pangenomes has revolutionized genomic studies by capturing the complete genetic diversity within a species. Pangenome assembly integrates data from multiple individuals to construct a comprehensive genomic landscape, revealing both core and accessory genomic elements. This approach enables the identification of novel genes, structural variations, and gene presence-absence variations, providing insights into species evolution, adaptation, and trait variation. Representing pangenomes requires innovative visualization formats that effectively convey the complex genomic structures and variations.

AIM

This review delves into contemporary methodologies and recent advancements in constructing pangenomes, particularly in plant genomes. It examines the structure of pangenome representation, including format comparison, conversion, visualization techniques, and their implications for enhancing crop improvement strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Earlier comparative studies have illuminated novel gene sequences, copy number variations, and presence-absence variations across diverse crop species. The concept of a pan-genome, which captures multiple genetic variations from a broad spectrum of genotypes, offers a holistic perspective of a species' genetic makeup. However, constructing a pan-genome for plants with larger genomes poses challenges, including managing vast genome sequence data and comprehending the genetic variations within the germplasm. To address these challenges, researchers have explored cost-effective alternatives to encapsulate species diversity in a single assembly known as a pangenome. This involves reducing the volume of genome sequences while focusing on genetic variations. With the growing prominence of the pan-genome concept in plant genomics, several software tools have emerged to facilitate pangenome construction. This review sheds light on developing and utilizing software tools tailored for constructing pan-genomes in plants. It also discusses representation formats suitable for downstream analyses, offering valuable insights into the genetic landscape and evolutionary dynamics of plant species. In summary, this review underscores the significance of pan-genome construction and representation formats in resolving the genetic architecture of plants, particularly those with complex genomes. It provides a comprehensive overview of recent advancements, aiding in exploring and understanding plant genetic diversity.

Pangenome graphs and their applications in biodiversity genomics

Complete datasets of genetic variants are key to biodiversity genomic studies. Long-read sequencing technologies allow the routine assembly of highly contiguous, haplotype-resolved reference genomes. However, even when complete, reference genomes from a single individual may bias downstream analyses and fail to adequately represent genetic diversity within a population or species. Pangenome graphs assembled from aligned collections of high-quality genomes can overcome representation bias by integrating sequence information from multiple genomes from the same population, species or genus into a single reference. Here, we review the available tools and data structures to build, visualize and manipulate pangenome graphs while providing practical examples and discussing their applications in biodiversity and conservation genomics across the tree of life.

Gretl-variation GRaph Evaluation TooLkit

MOTIVATION

As genome graphs are powerful data structures for representing the genetic diversity within populations, they can help identify genomic variations that traditional linear references miss, but their complexity and size makes the analysis of genome graphs challenging. We sought to develop a genome graph analysis tool that helps these analyses to become more accessible by addressing the limitations of existing tools. Specifically, we improve scalability and user-friendliness, and we provide many new statistics tailored to variation graphs for graph evaluation, including sample-specific features.

RESULTS

We developed an efficient, comprehensive, and integrated tool, gretl, to analyze genome graphs and gain insights into their structure and composition by providing a wide range of statistics. gretl can be utilized to evaluate different graphs, compare the output of graph construction pipelines with different parameters, as well as perform an in-depth analysis of individual graphs, including sample-specific analysis. With the assistance of gretl, novel patterns of genetic variation and potential regions of interest can be identified, for later, more detailed inspection. We demonstrate that gretl outperforms other tools in terms of speed, particularly for larger genome graphs.

AVAILABILITY AND IMPLEMENTATION

Commented Rust source code and documentation is available under MIT license at https://github.com/MoinSebi/gretl together with Python scripts and step-by-step usage examples. The package is available at Bioconda for easy installation.

Genome assembly of wisent (Bison bonasus) uncovers a deletion that likely inactivates the THRSP gene

The wisent (Bison bonasus) is Europe's largest land mammal. We produced a HiFi read-based wisent assembly with a contig N50 value of 91 Mb containing 99.7% of the highly conserved single copy mammalian genes which improves contiguity a thousand-fold over an existing assembly. Extended runs of homozygosity in the wisent genome compromised the separation of the HiFi reads into parental-specific read sets, which resulted in inferior haplotype assemblies. A bovine super-pangenome built with assemblies from wisent, bison, gaur, yak, taurine and indicine cattle identified a 1580 bp deletion removing the protein-coding sequence of THRSP encoding thyroid hormone-responsive protein from the wisent and bison genomes. Analysis of 725 sequenced samples across the Bovinae subfamily showed that the deletion is fixed in both Bison species but absent in Bos and Bubalus. The THRSP transcript is abundant in adipose, fat, liver, muscle, and mammary gland tissue of Bos and Bubalus, but absent in bison. This indicates that the deletion likely inactivates THRSP in bison. We show that super-pangenomes can reveal potentially trait-associated variation across phylogenies, but also demonstrate that haplotype assemblies from species that went through population bottlenecks warrant scrutiny, as they may have accumulated long runs of homozygosity that complicate phasing.

A versatile pipeline to identify convergently lost ancestral conserved fragments associated with convergent evolution of vocal learning

Molecular convergence in convergently evolved lineages provides valuable insights into the shared genetic basis of converged phenotypes. However, most methods are limited to coding regions, overlooking the potential contribution of regulatory regions. We focused on the independently evolved vocal learning ability in multiple avian lineages, and developed a whole-genome-alignment-free approach to identify genome-wide Convergently Lost Ancestral Conserved fragments (CLACs) in these lineages, encompassing noncoding regions. We discovered 2711 CLACs that are overrepresented in noncoding regions. Proximal genes of these CLACs exhibit significant enrichment in neurological pathways, including glutamate receptor signaling pathway and axon guidance pathway. Moreover, their expression is highly enriched in brain tissues associated with speech formation. Notably, several have known functions in speech and language learning, including ROBO family, SLIT2, GRIN1, and GRIN2B. Additionally, we found significantly enriched motifs in noncoding CLACs, which match binding motifs of transcriptional factors involved in neurogenesis and gene expression regulation in brain. Furthermore, we discovered 19 candidate genes that harbor CLACs in both human and multiple avian vocal learning lineages, suggesting their potential contribution to the independent evolution of vocal learning in both birds and humans.

PangeBlocks: customized construction of pangenome graphs via maximal blocks

BACKGROUND

The construction of a pangenome graph is a fundamental task in pangenomics. A natural theoretical question is how to formalize the computational problem of building an optimal pangenome graph, making explicit the underlying optimization criterion and the set of feasible solutions. Current approaches build a pangenome graph with some heuristics, without assuming some explicit optimization criteria. Thus it is unclear how a specific optimization criterion affects the graph topology and downstream analysis, like read mapping and variant calling.

RESULTS

In this paper, by leveraging the notion of maximal block in a Multiple Sequence Alignment (MSA), we reframe the pangenome graph construction problem as an exact cover problem on blocks called Minimum Weighted Block Cover (MWBC). Then we propose an Integer Linear Programming (ILP) formulation for the MWBC problem that allows us to study the most natural objective functions for building a graph. We provide an implementation of the ILP approach for solving the MWBC and we evaluate it on SARS-CoV-2 complete genomes, showing how different objective functions lead to pangenome graphs that have different properties, hinting that the specific downstream task can drive the graph construction phase.

CONCLUSION

We show that a customized construction of a pangenome graph based on selecting objective functions has a direct impact on the resulting graphs. In particular, our formalization of the MWBC problem, based on finding an optimal subset of blocks covering an MSA, paves the way to novel practical approaches to graph representations of an MSA where the user can guide the construction.

Building pangenome graphs

Pangenome graphs can represent all variation between multiple reference genomes, but current approaches to build them exclude complex sequences or are based upon a single reference. In response, we developed the PanGenome Graph Builder, a pipeline for constructing pangenome graphs without bias or exclusion. The PanGenome Graph Builder uses all-to-all alignments to build a variation graph in which we can identify variation, measure conservation, detect recombination events and infer phylogenetic relationships.

Graph-Based Pan-Genome Reveals the Pattern of Deleterious Mutations during the Domestication of Saccharomyces cerevisiae

The "cost of domestication" hypothesis suggests that the domestication of wild species increases the number, frequency, and/or proportion of deleterious genetic variants, potentially reducing their fitness in the wild. While extensively studied in domesticated species, this phenomenon remains understudied in fungi. Here, we used Saccharomyces cerevisiae, the world's oldest domesticated fungus, as a model to investigate the genomic characteristics of deleterious variants arising from fungal domestication. Employing a graph-based pan-genome approach, we identified 1,297,761 single nucleotide polymorphisms (SNPs), 278,147 insertion/deletion events (indels; <30 bp), and 19,967 non-redundant structural variants (SVs; ≥30 bp) across 687 S. cerevisiae isolates. Comparing these variants with synonymous SNPs (sSNPs) as neutral controls, we found that the majority of the derived nonsynonymous SNPs (nSNPs), indels, and SVs were deleterious. Heterozygosity was positively correlated with the impact of deleterious SNPs, suggesting a role of genetic diversity in mitigating their effects. The domesticated isolates exhibited a higher additive burden of deleterious SNPs (dSNPs) than the wild isolates, but a lower burden of indels and SVs. Moreover, the domesticated S. cerevisiae showed reduced rates of adaptive evolution relative to the wild S. cerevisiae. In summary, deleterious variants tend to be heterozygous, which may mitigate their harmful effects, but they also constrain breeding potential. Addressing deleterious alleles and minimizing the genetic load are crucial considerations for future S. cerevisiae breeding efforts.

Structural variation in humans and our primate kin in the era of telomere-to-telomere genomes and pangenomics

Structural variants (SVs) account for the majority of base pair differences both within and between primate species. However, our understanding of inter- and intra-species SV has been historically hampered by the quality of draft primate genomes and the absence of genome resources for key taxa. Recently, advances in long-read sequencing and genome assembly have begun to radically reshape our understanding of SVs. Two landmark achievements include the publication of a human telomere-to-telomere (T2T) genome as well as the development of the first human pangenome reference. In this review, we first look back to the major works laying the foundation for these projects. We then examine the ways in which T2T genome assemblies and pangenomes are transforming our understanding of and approach to primate SV. Finally, we discuss what the future of primate SV research may look like in the era of T2T genomes and pangenomics.

Beef Cattle Genome Project: Advances in Genome Sequencing, Assembly, and Functional Genes Discovery

Beef is a major global source of protein, playing an essential role in the human diet. The worldwide production and consumption of beef continue to rise, reflecting a significant trend. However, despite the critical importance of beef cattle resources in agriculture, the diversity of cattle breeds faces severe challenges, with many breeds at risk of extinction. The initiation of the Beef Cattle Genome Project is crucial. By constructing a high-precision functional annotation map of their genome, it becomes possible to analyze the genetic mechanisms underlying important traits in beef cattle, laying a solid foundation for breeding more efficient and productive cattle breeds. This review details advances in genome sequencing and assembly technologies, iterative upgrades of the beef cattle reference genome, and its application in pan-genome research. Additionally, it summarizes relevant studies on the discovery of functional genes associated with key traits in beef cattle, such as growth, meat quality, reproduction, polled traits, disease resistance, and environmental adaptability. Finally, the review explores the potential of telomere-to-telomere (T2T) genome assembly, structural variations (SVs), and multi-omics techniques in future beef cattle genetic breeding. These advancements collectively offer promising avenues for enhancing beef cattle breeding and improving genetic traits.

Overcoming Limitations to Deep Learning in Domesticated Animals with TrioTrain

Variant calling across diverse species remains challenging as most bioinformatics tools default to assumptions based on human genomes. DeepVariant (DV) excels without joint genotyping while offering fewer implementation barriers. However, the growing appeal of a "universal" algorithm has magnified the unknown impacts when used with non-human genomes. Here, we use bovine genomes to assess the limits of human-genome-trained models in other species. We introduce the first multi-species DV model that achieves a lower Mendelian Inheritance Error (MIE) rate during single-sample genotyping. Our novel approach, TrioTrain, automates extending DV for species without Genome In A Bottle (GIAB) resources and uses region shuffling to mitigate barriers for SLURM-based clusters. To offset imperfect truth labels for animal genomes, we remove Mendelian discordant variants before training, where models are tuned to genotype the offspring correctly. With TrioTrain, we use cattle, yak, and bison trios to build 30 model iterations across five phases. We observe remarkable performance across phases when testing the GIAB human trios with a mean SNP F1 score >0.990. In HG002, our phase 4 bovine model identifies more variants at a lower MIE rate than DeepTrio. In bovine F1-hybrid genomes, our model substantially reduces inheritance errors with a mean MIE rate of 0.03 percent. Although constrained by imperfect labels, we find that multi-species, trio-based training produces a robust variant calling model. Our research demonstrates that exclusively training with human genomes restricts the application of deep-learning approaches for comparative genomics.

Pangenome-genotyped structural variation improves molecular phenotype mapping in cattle

Expression and splicing quantitative trait loci (e/sQTL) are large contributors to phenotypic variability. Achieving sufficient statistical power for e/sQTL mapping requires large cohorts with both genotypes and molecular phenotypes, and so, the genomic variation is often called from short-read alignments, which are unable to comprehensively resolve structural variation. Here we build a pangenome from 16 HiFi haplotype-resolved cattle assemblies to identify small and structural variation and genotype them with PanGenie in 307 short-read samples. We find high (>90%) concordance of PanGenie-genotyped and DeepVariant-called small variation and confidently genotype close to 21 million small and 43,000 structural variants in the larger population. We validate 85% of these structural variants (with MAF > 0.1) directly with a subset of 25 short-read samples that also have medium coverage HiFi reads. We then conduct e/sQTL mapping with this comprehensive variant set in a subset of 117 cattle that have testis transcriptome data, and find 92 structural variants as causal candidates for eQTL and 73 for sQTL. We find that roughly half of the top associated structural variants affecting expression or splicing are transposable elements, such as SV-eQTL for STN1 and MYH7 and SV-sQTL for CEP89 and ASAH2 Extensive linkage disequilibrium between small and structural variation results in only 28 additional eQTL and 17 sQTL discovered when including SVs, although many top associated SVs are compelling candidates.

Natural uORF variation in plants

Taking advantage of natural variation promotes our understanding of phenotypic diversity and trait evolution, ultimately accelerating plant breeding, in which the identification of causal variations is critical. To date, sequence variations in the coding region and transcription level polymorphisms caused by variations in the promoter have been prioritized. An upstream open reading frame (uORF) in the 5' untranslated region (5' UTR) regulates gene expression at the post-transcription or translation level. In recent years, studies have demonstrated that natural uORF variations shape phenotypic diversity. This opinion article highlights recent researches and speculates on future directions for natural uORF variation in plants.

Pig pangenome graph reveals functional features of non-reference sequences

BACKGROUND

The reliance on a solitary linear reference genome has imposed a significant constraint on our comprehensive understanding of genetic variation in animals. This constraint is particularly pronounced for non-reference sequences (NRSs), which have not been extensively studied.

RESULTS

In this study, we constructed a pig pangenome graph using 21 pig assemblies and identified 23,831 NRSs with a total length of 105 Mb. Our findings revealed that NRSs were more prevalent in breeds exhibiting greater genetic divergence from the reference genome. Furthermore, we observed that NRSs were rarely found within coding sequences, while NRS insertions were enriched in immune-related Gene Ontology terms. Notably, our investigation also unveiled a close association between novel genes and the immune capacity of pigs. We observed substantial differences in terms of frequencies of NRSs between Eastern and Western pigs, and the heat-resistant pigs exhibited a substantial number of NRS insertions in an 11.6 Mb interval on chromosome X. Additionally, we discovered a 665 bp insertion in the fourth intron of the TNFRSF19 gene that may be associated with the ability of heat tolerance in Southern Chinese pigs.

CONCLUSIONS

Our findings demonstrate the potential of a graph genome approach to reveal important functional features of NRSs in pig populations.

Pangenome graphs improve the analysis of structural variants in rare genetic diseases

Rare DNA alterations that cause heritable diseases are only partially resolvable by clinical next-generation sequencing due to the difficulty of detecting structural variation (SV) in all genomic contexts. Long-read, high fidelity genome sequencing (HiFi-GS) detects SVs with increased sensitivity and enables assembling personal and graph genomes. We leverage standard reference genomes, public assemblies (n = 94) and a large collection of HiFi-GS data from a rare disease program (Genomic Answers for Kids, GA4K, n = 574 assemblies) to build a graph genome representing a unified SV callset in GA4K, identify common variation and prioritize SVs that are more likely to cause genetic disease (MAF < 0.01). Using graphs, we obtain a higher level of reproducibility than the standard reference approach. We observe over 200,000 SV alleles unique to GA4K, including nearly 1000 rare variants that impact coding sequence. With improved specificity for rare SVs, we isolate 30 candidate SVs in phenotypically prioritized genes, including known disease SVs. We isolate a novel diagnostic SV in KMT2E, demonstrating use of personal assemblies coupled with pangenome graphs for rare disease genomics. The community may interrogate our pangenome with additional assemblies to discover new SVs within the allele frequency spectrum relevant to genetic diseases.

Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle

Breeding bulls are well suited to investigate inherited variation in male fertility because they are genotyped and their reproductive success is monitored through semen analyses and thousands of artificial inseminations. However, functional data from relevant tissues are lacking in cattle, which prevents fine-mapping fertility-associated genomic regions. Here, we characterize gene expression and splicing variation in testis, epididymis, and vas deferens transcriptomes of 118 mature bulls and conduct association tests between 414,667 molecular phenotypes and 21,501,032 genome-wide variants to identify 41,156 regulatory loci. We show broad consensus in tissue-specific and tissue-enriched gene expression between the three bovine tissues and their human and murine counterparts. Expression- and splicing-mediating variants are more than three times as frequent in testis than epididymis and vas deferens, highlighting the transcriptional complexity of testis. Finally, we identify genes (WDR19, SPATA16, KCTD19, ZDHHC1) and molecular phenotypes that are associated with quantitative variation in male fertility through transcriptome-wide association and colocalization analyses.

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

BACKGROUND

The 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.

METHODS

We present a pangenome for the domestic chicken consisting of thirty assemblies of chickens from different breeds and research lines.

RESULTS

We 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.

CONCLUSIONS

We 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.

Chromosome-level genome assembly of Korean native cattle and pangenome graph of 14 Bos taurus assemblies

This study presents the first chromosome-level genome assembly of Hanwoo, an indigenous Korean breed of Bos taurus taurus. This is the first genome assembly of Asian taurus breed. Also, we constructed a pangenome graph of 14 B. taurus genome assemblies. The contig N50 was over 55 Mb, the scaffold N50 was over 89 Mb and a genome completeness of 95.8%, as estimated by BUSCO using the mammalian set, indicated a high-quality assembly. 48.7% of the genome comprised various repetitive elements, including DNAs, tandem repeats, long interspersed nuclear elements, and simple repeats. A total of 27,314 protein-coding genes were identified, including 25,302 proteins with inferred gene names and 2,012 unknown proteins. The pangenome graph of 14 B. taurus autosomes revealed 528.47 Mb non-reference regions in total and 61.87 Mb Hanwoo-specific regions. Our Hanwoo assembly and pangenome graph provide valuable resources for studying B. taurus populations.

Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals

Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.