Removal of alleles by genome editing (RAGE) against deleterious load

Leveraging Multiomics Insights and Exploiting Wild Relatives' Potential for Drought and Heat Tolerance in Maize

Climate change, particularly drought and heat stress, may slash agricultural productivity by 25.7% by 2080, with maize being the hardest hit. Therefore, unraveling the molecular nature of plant responses to these stressors is vital for the development of climate-smart maize. This manuscript's primary objective was to examine how maize plants respond to these stresses, both individually and in combination. Additionally, the paper delved into harnessing the potential of maize wild relatives as a valuable genetic resource and leveraging AI-based technologies to boost maize resilience. The role of multiomics approaches particularly genomics and transcriptomics in dissecting the genetic basis of stress tolerance was also highlighted. The way forward was proposed to utilize a bunch of information obtained through omics technologies by an interdisciplinary state-of-the-art forward-looking big-data, cyberagriculture system, and AI-based approach to orchestrate the development of climate resilient maize genotypes.

How admixed captive breeding populations could be rescued using local ancestry information

This paper asks the question: can genomic information be used to recover a species that is already on the pathway to extinction due to genetic swamping from a related and more numerous population? We show that a breeding strategy in a captive breeding program can use whole genome sequencing to identify and remove segments of DNA introgressed through hybridisation. The proposed policy uses a generalized measure of kinship or heterozygosity accounting for local ancestry, that is, whether a specific genetic location was inherited from the target of conservation. We then show that optimizing these measures would minimize undesired ancestry while also controlling kinship and/or heterozygosity, in a simulated breeding population. The process is applied to real data representing the hybridized Scottish wildcat breeding population, with the result that it should be possible to breed out domestic cat ancestry. The ability to reverse introgression is a powerful tool brought about through the combination of sequencing with computational advances in ancestry estimation. Since it works best when applied early in the process, important decisions need to be made about which genetically distinct populations should benefit from it and which should be left to reform into a single population.

Evolutionary dynamics and adaptive benefits of deleterious mutations in crop gene pools

Mutations with deleterious consequences in nature may be conditionally deleterious in crop plants. That is, while some genetic variants may reduce fitness under wild conditions and be subject to purifying selection, they can be under positive selection in domesticates. Such deleterious alleles can be plant breeding targets, particularly for complex traits. The difficulty of distinguishing favorable from unfavorable variants reduces the power of selection, while favorable trait variation and heterosis may be attributable to deleterious alleles. Here, we review the roles of deleterious mutations in crop breeding and discuss how they can be used as a new avenue for crop improvement with emerging genomic tools, including HapMaps and pangenome analysis, aiding the identification, removal, or exploitation of deleterious mutations.

Genomics in animal breeding from the perspectives of matrices and molecules

BACKGROUND

This paper describes genomics from two perspectives that are in use in animal breeding and genetics: a statistical perspective concentrating on models for estimating breeding values, and a sequence perspective concentrating on the function of DNA molecules.

MAIN BODY

This paper reviews the development of genomics in animal breeding and speculates on its future from these two perspectives. From the statistical perspective, genomic data are large sets of markers of ancestry; animal breeding makes use of them while remaining agnostic about their function. From the sequence perspective, genomic data are a source of causative variants; what animal breeding needs is to identify and make use of them.

CONCLUSION

The statistical perspective, in the form of genomic selection, is the more applicable in contemporary breeding. Animal genomics researchers using from the sequence perspective are still working towards this the isolation of causative variants, equipped with new technologies but continuing a decades-long line of research.

Analysis of historical selection in winter wheat

KEY MESSAGE

Modeling of the distribution of allele frequency over year of variety release identifies major loci involved in historical breeding of winter wheat. Winter wheat is a major crop with a rich selection history in the modern era of crop breeding. Genetic gains across economically important traits like yield have been well characterized and are the major force driving its production. Winter wheat is also an excellent model for analyzing historical genetic selection. As a proof of concept, we analyze two major collections of winter wheat varieties that were bred in Western Europe from 1916 to 2010, namely the Triticeae Genome (TG) and WAGTAIL panels, which include 333 and 403 varieties, respectively. We develop and apply a selection mapping approach, Regression of Alleles on Years (RALLY), in these panels, as well as in simulated populations. RALLY maps loci under sustained historical selection by using a simple logistic model to regress allele counts on years of variety release. To control for drift-induced allele frequency change, we develop a hybrid approach of genomic control and delta control. Within the TG panel, we identify 22 significant RALLY quantitative selection loci (QSLs) and estimate the local heritabilities for 12 traits across these QSLs. By correlating predicted marker effects with RALLY regression estimates, we show that alleles whose frequencies have increased over time are heavily biased toward conferring positive yield effect, but negative effects in flowering time, lodging, plant height and grain protein content. Altogether, our results (1) demonstrate the use of RALLY to identify selected genomic regions while controlling for drift, and (2) reveal key patterns in the historical selection in winter wheat and guide its future breeding.

Rare and population-specific functional variation across pig lines

BACKGROUND

It is expected that functional, mainly missense and loss-of-function (LOF), and regulatory variants are responsible for most phenotypic differences between breeds and genetic lines of livestock species that have undergone diverse selection histories. However, there is still limited knowledge about the existing missense and LOF variation in commercial livestock populations, in particular regarding population-specific variation and how it can affect applications such as across-breed genomic prediction.

METHODS

We re-sequenced the whole genome of 7848 individuals from nine commercial pig lines (average sequencing coverage: 4.1×) and imputed whole-genome genotypes for 440,610 pedigree-related individuals. The called variants were categorized according to predicted functional annotation (from LOF to intergenic) and prevalence level (number of lines in which the variant segregated; from private to widespread). Variants in each category were examined in terms of their distribution along the genome, alternative allele frequency, per-site Wright's fixation index (F_ST), individual load, and association to production traits.

RESULTS

Of the 46 million called variants, 28% were private (called in only one line) and 21% were widespread (called in all nine lines). Genomic regions with a low recombination rate were enriched with private variants. Low-prevalence variants (called in one or a few lines only) were enriched for lower allele frequencies, lower F_ST, and putatively functional and regulatory roles (including LOF and deleterious missense variants). On average, individuals carried fewer private deleterious missense alleles than expected compared to alleles with other predicted consequences. Only a small subset of the low-prevalence variants had intermediate allele frequencies and explained small fractions of phenotypic variance (up to 3.2%) of production traits. The significant low-prevalence variants had higher per-site F_ST than the non-significant ones. These associated low-prevalence variants were tagged by other more widespread variants in high linkage disequilibrium, including intergenic variants.

CONCLUSIONS

Most low-prevalence variants have low minor allele frequencies and only a small subset of low-prevalence variants contributed detectable fractions of phenotypic variance of production traits. Accounting for low-prevalence variants is therefore unlikely to noticeably benefit across-breed analyses, such as the prediction of genomic breeding values in a population using reference populations of a different genetic background.

The 3366 chickpea genomes for research and breeding

Genome sequences provide an unprecedented resource to rapidly develop modern crops. A recent paper by Varshney et al. provides genome variation maps of 3366 chickpea accessions. Here, we highlight how this breakthrough research can fundamentally change breeding practices of chickpea and potentially other crops.

Supporting in situ conservation of the genetic diversity of crop wild relatives using genomic technologies

The last decade has witnessed huge technological advances in genomics, particularly in DNA sequencing. Here, we review the actual and potential application of genomics in supporting in situ conservation of crop wild relatives (CWRs). In addition to helping in prioritization of protection of CWR taxa and in situ conservation sites, genome analysis is allowing the identification of novel alleles that need to be prioritized for conservation. Genomics is enabling the identification of potential sources of important adaptive traits that can guide the establishment or enrichment of in situ genetic reserves. Genomic tools also have the potential for developing a robust framework for monitoring and reporting genome‐based indicators of genetic diversity changes associated with factors such as land use or climate change. These tools have been demonstrated to have an important role in managing the conservation of populations, supporting sustainable access and utilization of CWR diversity, enhancing accelerated domestication of new crops and forensic genomics thus preventing misappropriation of genetic resources. Despite this great potential, many policy makers and conservation managers have failed to recognize and appreciate the need to accelerate the application of genomics to support the conservation and management of biodiversity in CWRs to underpin global food security. Funding and inadequate genomic expertise among conservation practitioners also remain major hindrances to the widespread application of genomics in conservation.

Genetic load: genomic estimates and applications in non-model animals

Genetic variation, which is generated by mutation, recombination and gene flow, can reduce the mean fitness of a population, both now and in the future. This 'genetic load' has been estimated in a wide range of animal taxa using various approaches. Advances in genome sequencing and computational techniques now enable us to estimate the genetic load in populations and individuals without direct fitness estimates. Here, we review the classic and contemporary literature of genetic load. We describe approaches to quantify the genetic load in whole-genome sequence data based on evolutionary conservation and annotations. We show that splitting the load into its two components - the realized load (or expressed load) and the masked load (or inbreeding load) - can improve our understanding of the population genetics of deleterious mutations.

Profiling of open chromatin in developing pig (Sus scrofa) muscle to identify regulatory regions

There is very little information about how the genome is regulated in domestic pigs (Sus scrofa). This lack of knowledge hinders efforts to define and predict the effects of genetic variants in pig breeding programs. To address this knowledge gap, we need to identify regulatory sequences in the pig genome starting with regions of open chromatin. We used the "Improved Protocol for the Assay for Transposase-Accessible Chromatin (Omni-ATAC-Seq)" to identify putative regulatory regions in flash-frozen semitendinosus muscle from 24 male piglets. We collected samples from the smallest-, average-, and largest-sized male piglets from each litter through five developmental time points. Of the 4661 ATAC-Seq peaks identified that represent regions of open chromatin, >50% were within 1 kb of known transcription start sites. Differential read count analysis revealed 377 ATAC-Seq defined genomic regions where chromatin accessibility differed significantly across developmental time points. We found regions of open chromatin associated with downregulation of genes involved in muscle development that were present in small-sized fetal piglets but absent in large-sized fetal piglets at day 90 of gestation. The dataset that we have generated provides a resource for studies of genome regulation in pigs and contributes valuable functional annotation information to filter genetic variants for use in genomic selection in pig breeding programs.

Genomic Selection in Aquaculture Species

To date, genomic prediction has been conducted in about 20 aquaculture species, with a preference for intra-family genomic selection (GS). For every trait under GS, the increase in accuracy obtained by genomic estimated breeding values instead of classical pedigree-based estimation of breeding values is very important in aquaculture species ranging from 15% to 89% for growth traits, and from 0% to 567% for disease resistance. Although the implementation of GS in aquaculture is of little additional investment in breeding programs already implementing sib testing on pedigree, the deployment of GS remains sparse, but could be boosted by adaptation of cost-effective imputation from low-density panels. Moreover, GS could help to anticipate the effect of climate change by improving sustainability-related traits such as production yield (e.g., carcass or fillet yields), feed efficiency or disease resistance, and by improving resistance to environmental variation (tolerance to temperature or salinity variation). This chapter synthesized the literature in applications of GS in finfish, crustaceans and molluscs aquaculture in the present and future breeding programs.

Problems of mini-pig breeding

This article provides an overview of some problems of the breeding and reproduction of laboratory minipigs. The most obvious of these are the lack of centralized accounting of breeding groups, uniform selection standards for reproduction and evaluation of breeding animals, as well as minimizing the accumulation of fitness-reducing mutations and maintaining genetic diversity. According to the latest estimates, there are at least 30 breeding groups of mini-pigs systematically used as laboratory animals in the world. Among them, there are both breed formations represented by several colonies, and breeding groups consisting of a single herd. It was shown that the main selection strategy is selection for the live weight of adults of 50–80 kg and the adaptation of animals to a specific type of biomedical experiments. For its implementation in the breeding of foreign mini-pigs, selection by live weight is practiced at 140- and 154-day-old age. It was indicated that different herds of mini-pigs have their own breeding methods to counteract inbred depression and maintain genetic diversity. Examples are the maximization of coat color phenotypes, the cyclical system of matching parent pairs, and the structuring of herds into subpopulations. In addition, in the breeding of foreign mini-pigs, molecular genetic methods are used to monitor heterozygosity. Every effort is made to keep the number of inbred crosses in the breeding of laboratory mini-pigs to a minimum, which is not always possible due to their small number. It is estimated that to avoid close inbreeding, the number of breeding groups should be at least 28 individuals, including boars of at least 4 genealogical lines and at least 4 families of sows. The accumulation of genetic cargo in herds of mini-pigs takes place, but the harmful effect is rather the result of erroneous decisions of breeders. Despite the fact that when breeding a number of mini-pigs, the goal was to complete the herds with exclusively white animals, in most breeding groups there is a polymorphism in the phenotype of the coat color

Breeding custom-designed crops for improved drought adaptation

The current pace of crop improvement is inadequate to feed the burgeoning human population by 2050. Higher, more stable, and sustainable crop production is required against a backdrop of drought stress, which causes significant losses in crop yields. Tailoring crops for drought adaptation may hold the key to address these challenges and provide resilient production systems for future harvests. Understanding the genetic and molecular landscape of the functionality of alleles associated with adaptive traits will make designer crop breeding the prospective approach for crop improvement. Here, we highlight the potential of genomics technologies combined with crop physiology for high-throughput identification of the genetic architecture of key drought-adaptive traits and explore innovative genomic breeding strategies for designing future crops.

AlphaSimR: an R package for breeding program simulations

This paper introduces AlphaSimR, an R package for stochastic simulations of plant and animal breeding programs. AlphaSimR is a highly flexible software package able to simulate a wide range of plant and animal breeding programs for diploid and autopolyploid species. AlphaSimR is ideal for testing the overall strategy and detailed design of breeding programs. AlphaSimR utilizes a scripting approach to building simulations that is particularly well suited for modeling highly complex breeding programs, such as commercial breeding programs. The primary benefit of this scripting approach is that it frees users from preset breeding program designs and allows them to model nearly any breeding program design. This paper lists the main features of AlphaSimR and provides a brief example simulation to show how to use the software.

Designing Future Crops: Genomics-Assisted Breeding Comes of Age

Over the past decade, genomics-assisted breeding (GAB) has been instrumental in harnessing the potential of modern genome resources and characterizing and exploiting allelic variation for germplasm enhancement and cultivar development. Sustaining GAB in the future (GAB 2.0) will rely upon a suite of new approaches that fast-track targeted manipulation of allelic variation for creating novel diversity and facilitate their rapid and efficient incorporation in crop improvement programs. Genomic breeding strategies that optimize crop genomes with accumulation of beneficial alleles and purging of deleterious alleles will be indispensable for designing future crops. In coming decades, GAB 2.0 is expected to play a crucial role in breeding more climate-smart crop cultivars with higher nutritional value in a cost-effective and timely manner.

Conserved features of non-primate bilaminar disc embryos and the germline

Post-implantation embryo development commences with a bilaminar disc in most mammals, including humans. Whereas access to early human embryos is limited and subject to greater ethical scrutiny, studies on non-primate embryos developing as bilaminar discs offer exceptional opportunities for advances in gastrulation, the germline, and the basis for evolutionary divergence applicable to human development. Here, we discuss the advantages of investigations in the pig embryo as an exemplar of development of a bilaminar disc embryo with relevance to early human development. Besides, the pig has the potential for the creation of humanized organs for xenotransplantation. Precise genetic engineering approaches, imaging, and single-cell analysis are cost effective and efficient, enabling research into some outstanding questions on human development and for developing authentic models of early human development with stem cells.

Understanding Omics Driven Plant Improvement and de novo Crop Domestication: Some Examples

In the current era, one of biggest challenges is to shorten the breeding cycle for rapid generation of a new crop variety having high yield capacity, disease resistance, high nutrient content, etc. Advances in the "-omics" technology have revolutionized the discovery of genes and bio-molecules with remarkable precision, resulting in significant development of plant-focused metabolic databases and resources. Metabolomics has been widely used in several model plants and crop species to examine metabolic drift and changes in metabolic composition during various developmental stages and in response to stimuli. Over the last few decades, these efforts have resulted in a significantly improved understanding of the metabolic pathways of plants through identification of several unknown intermediates. This has assisted in developing several new metabolically engineered important crops with desirable agronomic traits, and has facilitated the de novo domestication of new crops for sustainable agriculture and food security. In this review, we discuss how "omics" technologies, particularly metabolomics, has enhanced our understanding of important traits and allowed speedy domestication of novel crop plants.

Genome engineering for crop improvement and future agriculture

Feeding the ever-growing population is a major challenge, especially in light of rapidly changing climate conditions. Genome editing is set to revolutionize plant breeding and could help secure the global food supply. Here, I review the development and application of genome editing tools in plants while highlighting newly developed techniques. I describe new plant breeding strategies based on genome editing and discuss their impact on crop production, with an emphasis on recent advancements in genome editing-based plant improvements that could not be achieved by conventional breeding. I also discuss challenges facing genome editing that must be overcome before realizing the full potential of this technology toward future crops and food production.

5Gs for crop genetic improvement

Here we propose a 5G breeding approach for bringing much-needed disruptive changes to crop improvement. These 5Gs are Genome assembly, Germplasm characterization, Gene function identification, Genomic breeding (GB), and Gene editing (GE). In our view, it is important to have genome assemblies available for each crop and a deep collection of germplasm characterized at sequencing and agronomic levels for identification of marker-trait associations and superior haplotypes. Systems biology and sequencing-based mapping approaches can be used to identify genes involved in pathways leading to the expression of a trait, thereby providing diagnostic markers for target traits. These genes, markers, haplotypes, and genome-wide sequencing data may be utilized in GB and GE methodologies in combination with a rapid cycle breeding strategy.

Inbreeding depression for kit survival at birth in a rabbit population under long-term selection

Background

Accumulation of detrimental mutations in small populations leads to inbreeding depression of fitness traits and a higher frequency of genetic defects, thus increasing risk of extinction. Our objective was to quantify the magnitude of inbreeding depression for survival at birth, in a closed rabbit population under long-term selection.

Methods

We used an information theory-based approach and multi-model inference to estimate inbreeding depression and its purging with respect to the trait ‘kit survival at birth’ over a 25-year period in a closed population of Pannon White rabbits, by analysing 22,718 kindling records. Generalised linear mixed models based on the logit link function were applied, which take polygenic random effects into account.

Results

Our results indicated that inbreeding depression occurred during the period 1992–1997, based on significant estimates of the z-standardised classical inbreeding coefficient z.F_L (CI_95% − 0.12 to − 0.03) and of the new inbreeding coefficient of the litter z.F_NEWL (CI_95% − 0.13 to − 0.04) as well as a 59.2% reduction in contributing founders. Inbreeding depression disappeared during the periods 1997–2007 and 2007–2017. For the period 1992–1997, the best model resulted in a significantly negative standardised estimate of the new inbreeding coefficient of the litter and a significantly positive standardised estimate of Kalinowski’s ancestral inbreeding coefficient of the litter (CI_95% 0.01 to 0.17), which indicated purging of detrimental load. Kindling season and parity had effects on survival at birth that differed across the three periods of 1992–1997, 1997–2007 and 2007–2017.

Conclusions

Our results support the existence of inbreeding depression and its purging with respect to kit survival at birth in this Pannon White rabbit population. However, we were unable to exclude possible confounding from the effects of parity and potentially other environmental factors during the study period, thus our results need to be extended and confirmed in other populations.

Novel approach to incorporate information about recessive lethal genes increases the accuracy of genomic prediction for mortality traits

The genetic underpinnings of calf mortality can be partly polygenic and partly due to deleterious effects of recessive lethal alleles. Prediction of the genetic merits of selection candidates should thus take into account both genetic components contributing to calf mortality. However, simultaneously modeling polygenic risk and recessive lethal allele effects in genomic prediction is challenging due to effects that behave differently. In this study, we present a novel approach where mortality risk probabilities from polygenic and lethal allele components are predicted separately to compute the total risk probability of an individual for its future offspring as a basis for selection. We present methods for transforming genomic estimated breeding values of polygenic effect into risk probabilities using normal density and cumulative distribution functions and show computations of risk probability from recessive lethal alleles given sire genotypes and population recessive allele frequencies. Simulated data were used to test the novel approach as implemented in probit, logit, and linear models. In the simulation study, the accuracy of predicted risk probabilities was computed as the correlation between predicted mortality probabilities and observed calf mortality for validation sires. The results indicate that our novel approach can greatly increase the accuracy of selection for mortality traits compared with the accuracy of predictions obtained without distinguishing polygenic and lethal gene effects.

Symposium review: Exploiting homozygosity in the era of genomics-Selection, inbreeding, and mating programs

The advent of genomic selection paved the way for an unprecedented acceleration in genetic progress. The increased ability to select superior individuals has been coupled with a drastic reduction in the generation interval for most dairy populations, representing both an opportunity and a challenge. Homozygosity is now rapidly accumulating in dairy populations. Currently, inbreeding depression is managed mostly by culling at the farm level and by controlling the overall accumulation of homozygosity at the population level. A better understanding of how homozygosity and recessive load are related will guarantee continued genetic improvement while curtailing the accumulation of harmful recessives and maintaining enough genetic variability to ensure the possibility of selection in the face of changing environmental conditions. In this review, we present a snapshot of the current dairy selection structure as it relates to response to selection and accumulation of homozygosity, briefly outline the main approaches currently used to manage inbreeding and overall variability, and present some approaches that can be used in the short term to control accumulation of harmful recessives while maintaining sustained selection pressure.

Accuracy of whole-genome sequence imputation using hybrid peeling in large pedigreed livestock populations

BACKGROUND

The coupling of appropriate sequencing strategies and imputation methods is critical for assembling large whole-genome sequence datasets from livestock populations for research and breeding. In this paper, we describe and validate the coupling of a sequencing strategy with the imputation method hybrid peeling in real animal breeding settings.

METHODS

We used data from four pig populations of different size (18,349 to 107,815 individuals) that were widely genotyped at densities between 15,000 and 75,000 markers genome-wide. Around 2% of the individuals in each population were sequenced (most of them at 1× or 2× and 37-92 individuals per population, totalling 284, at 15-30×). We imputed whole-genome sequence data with hybrid peeling. We evaluated the imputation accuracy by removing the sequence data of the 284 individuals with high coverage, using a leave-one-out design. We simulated data that mimicked the sequencing strategy used in the real populations to quantify the factors that affected the individual-wise and variant-wise imputation accuracies using regression trees.

RESULTS

Imputation accuracy was high for the majority of individuals in all four populations (median individual-wise dosage correlation: 0.97). Imputation accuracy was lower for individuals in the earliest generations of each population than for the rest, due to the lack of marker array data for themselves and their ancestors. The main factors that determined the individual-wise imputation accuracy were the genotyping status, the availability of marker array data for immediate ancestors, and the degree of connectedness to the rest of the population, but sequencing coverage of the relatives had no effect. The main factors that determined variant-wise imputation accuracy were the minor allele frequency and the number of individuals with sequencing coverage at each variant site. Results were validated with the empirical observations.

CONCLUSIONS

We demonstrate that the coupling of an appropriate sequencing strategy and hybrid peeling is a powerful strategy for generating whole-genome sequence data with high accuracy in large pedigreed populations where only a small fraction of individuals (2%) had been sequenced, mostly at low coverage. This is a critical step for the successful implementation of whole-genome sequence data for genomic prediction and fine-mapping of causal variants.

Evolutionary insights into plant breeding

Crop domestication is a fascinating area of study, as shown by a multitude of recent reviews. Coupled with the increasing availability of genomic and phenomic resources in numerous crop species, insights from evolutionary biology will enable a deeper understanding of the genetic architecture and short-term evolution of complex traits, which can be used to inform selection strategies. Future advances in crop improvement will rely on the integration of population genetics with plant breeding methodology, and the development of community resources to support research in a variety of crop life histories and reproductive strategies. We highlight recent advances related to the role of selective sweeps and demographic history in shaping genetic architecture, how these breakthroughs can inform selection strategies, and the application of precision gene editing to leverage these connections.

Deep learning for plant genomics and crop improvement

Our era has witnessed tremendous advances in plant genomics, characterized by an explosion of high-throughput techniques to identify multi-dimensional genome-wide molecular phenotypes at low costs. More importantly, genomics is not merely acquiring molecular phenotypes, but also leveraging powerful data mining tools to predict and explain them. In recent years, deep learning has been found extremely effective in these tasks. This review highlights two prominent questions at the intersection of genomics and deep learning: 1) how can the flow of information from genomic DNA sequences to molecular phenotypes be modeled; 2) how can we identify functional variants in natural populations using deep learning models? Additionally, we discuss the possibility of unleashing the power of deep learning in synthetic biology to create novel genomic elements with desirable functions. Taken together, we propose a central role of deep learning in future plant genomics research and crop genetic improvement.

The Fate of Deleterious Variants in a Barley Genomic Prediction Population

Targeted identification and purging of deleterious genetic variants has been proposed as a novel approach to animal and plant breeding. This strategy is motivated, in part, by the observation that demographic events and strong selection associated with cultivated species pose a "cost of domestication." This includes an increase in the proportion of genetic variants that are likely to reduce fitness. Recent advances in DNA resequencing and sequence constraint-based approaches to predict the functional impact of a mutation permit the identification of putatively deleterious SNPs (dSNPs) on a genome-wide scale. Using exome capture resequencing of 21 barley lines, we identified 3855 dSNPs among 497,754 total SNPs. We generated whole-genome resequencing data of Hordeum murinum ssp. glaucum as a phylogenetic outgroup to polarize SNPs as ancestral vs. derived. We also observed a higher proportion of dSNPs per synonymous SNPs (sSNPs) in low-recombination regions of the genome. Using 5215 progeny from a genomic prediction experiment, we examined the fate of dSNPs over three breeding cycles. Adjusting for initial frequency, derived alleles at dSNPs reduced in frequency or were lost more often than other classes of SNPs. The highest-yielding lines in the experiment, as chosen by standard genomic prediction approaches, carried fewer homozygous dSNPs than randomly sampled lines from the same progeny cycle. In the final cycle of the experiment, progeny selected by genomic prediction had a mean of 5.6% fewer homozygous dSNPs relative to randomly chosen progeny from the same cycle.

Potential of Genome Editing to Improve Aquaculture Breeding and Production

Aquaculture is the fastest growing food production sector and is rapidly becoming the primary source of seafood for human diets. Selective breeding programs are enabling genetic improvement of production traits, such as disease resistance, but progress is limited by the heritability of the trait and generation interval of the species. New breeding technologies, such as genome editing using CRISPR/Cas9 have the potential to expedite sustainable genetic improvement in aquaculture. Genome editing can rapidly introduce favorable changes to the genome, such as fixing alleles at existing trait loci, creating de novo alleles, or introducing alleles from other strains or species. The high fecundity and external fertilization of most aquaculture species can facilitate genome editing for research and application at a scale that is not possible in farmed terrestrial animals.

Accelerating crop genetic gains with genomic selection

Genomic prediction based on additive genetic effects can accelerate genetic gain. There are opportunities for further improvement by including non-additive effects that access untapped sources of genetic diversity. Several studies have reported a worrying gap between the projected global future demand for plant-based products and the current annual rates of production increase, indicating that enhancing the rate of genetic gain might be critical for future food security. Therefore, new breeding technologies and strategies are required to significantly boost genetic improvement of future crop cultivars. Genomic selection (GS) has delivered considerable genetic gain in animal breeding and is becoming an essential component of many modern plant breeding programmes as well. In this paper, we review the lessons learned from implementing GS in livestock and the impact of GS on crop breeding, and discuss important features for the success of GS under different breeding scenarios. We highlight major challenges associated with GS including rapid genotyping, phenotyping, genotype-by-environment interaction and non-additivity and give examples for opportunities to overcome these issues. Finally, the potential of combining GS with other modern technologies in order to maximise the rate of crop genetic improvement is discussed, including the potential of increasing prediction accuracy by integration of crop growth models in GS frameworks.