Search

Summary: Sequence elimination is one of the main mechanisms that increases the divergence among homoeologous chromosomes after allopolyploidization to enhance the stability of recently established lineages, but it can cause a loss of some economically important genes. Synthetic hexaploid wheat (SHW) is an important source of genetic variation to the natural hexaploid wheat (NHW) genepool that has low genetic diversity. Here, we investigated the change between SHW and NHW genomes by utilizing a large germplasm set of primary synthetics and synthetic derivatives. Reproducible segment elimination (RSE) was declared if a large chromosomal chunk (>5 cM) produced no aligned reads in more than five SHWs. RSE in five genomic regions was the major source of variation between SHW and NHW. One RSE eliminated almost the complete short arm of chromosome 1B, which contains major genes for flour quality, disease resistance and different enzymes. The occurrence of RSE was highly dependent on the choice of diploid and tetraploid parental lines, their ancestral subpopulation and admixture, e.g. SHWs derived from Triticum dicoccon or from one of two Aegilops tauschii subpopulations were almost free of RSE, while highly admixed parents had higher RSE rates. The rate of RSE in synthetic derivatives was almost double that in primary synthetics. Genome‐wide association analysis detected four loci with minor effects on the occurrence of RSE, indicating that both parental lines and genetic factors were affecting the occurrence of RSE. Therefore, pre‐pre‐breeding strategies should be applied before introducing SHW into pre‐breeding programs to ensure genomic stability and avoid undesirable gene loss. Significance Statement: This study documented the occurrence of multiple reproducible sequence eliminations in synthetic hexaploid wheat compared with natural hexaploid wheat; a phenomenon affected by the choice of parental lines, their admixture and genetic factors. These sequence eliminations caused significant gene loss and could affect the fertility of the progeny of natural/synthetic hexaploid hybrids, therefor it is important to avoid these in practical breeding programs. [ABSTRACT FROM AUTHOR]

Australia has one of the oldest modern wheat breeding programs worldwide although the crop was first introduced to the country in 1788. Breeders selected wheat with high adaptation to different Australian climates, while ensuring satisfactory yield and quality. This artificial selection left distinct genomic signatures that can be used to retrospectively understand breeding targets, and to detect economically important alleles. To study the effect of artificial selection on modern cultivars and cultivars released in different Australian states, we genotyped 482 Australian cultivars representing the history of wheat breeding in Australia since 1840. Computer simulation showed that 86 genomic regions were significantly affected by artificial selection. Characterization of 18 major genes known to affect wheat adaptation, yield, and quality revealed that many were affected by artificial selection and contained within regions under selection. Similarly, many reported QTL and genes for yield, quality, and adaptation were also contained in regions affected by artificial selection. These included TaCwi‐A1, TaGw2‐6A, Sus‐2B, TaSus1‐7A, TaSAP1‐7A, Glu‐A1, Glu‐B1, Glu‐B3, PinA, PinB, Ppo‐D1, Psy‐A1, Psy‐A2, Rht‐A1, Rht‐B1, Ppd‐D1, Vrn‐A1, Vrn‐B1, and Cre8. Interestingly, 17 regions affected by artificial selection were in moderate‐to‐high linkage disequilibrium with each other with an average r2 value of 0.35 indicating strong simultaneous selection on specific alleles. These regions included Glu‐B1, TaGw2‐6A, Cre8, Ppd‐D1, Rht‐B1, Vrn‐B1, TaSus1‐7A, TaSAP1‐7A, and Psy‐A1 plus multiple QTL affecting wheat yield and yield components. These results highlighted the effects of the long‐term artificial selection on Australian wheat germplasm and identified putative regions underlying important traits in wheat. [ABSTRACT FROM AUTHOR]

Abstract: We have developed the first comprehensive simulator for polyploid genomes (PolySim) and demonstrated its value by performing large‐scale simulations to examine the effect of different population parameters on the evolution of polyploids. PolySim is unlimited in terms of ploidy, population size or number of simulated loci. Our process considered the evolution of polyploids from diploid ancestors, polysomic inheritance, inbreeding, recombination rate change in polyploids and gene flow from lower to higher ploidies. We compared the number of segregating single nucleotide polymorphisms, minor allele frequency, heterozygosity, R2 and average kinship relatedness between different simulated scenarios, and to real data from polyploid species. As expected, allotetraploid populations showed no difference from their ancestral diploids when population size remained constant and there was no gene flow or multivalent (MV) pairing between subgenomes. Autotetraploid populations showed significant differences from their ancestors for most parameters and diverged from their ancestral populations faster than allotetraploids. Autotetraploids can have significantly higher heterozygosity, relatedness and extended linkage disequilibrium compared with allotetraploids. Interestingly, autotetraploids were more sensitive to increasing selfing rate and decreasing population size. MV formation can homogenize allotetraploid subgenomes, but this homogenization requires a higher MV rate than previously proposed. Our results can be considered as the first building block to understand polyploid population evolutionary dynamics. PolySim can be used to simulate a wide variety of polyploid organisms that mimic empirical populations, which, in combination with quantitative genetics tools, can be used to investigate the power of genomewide association, genomic selection or breeding programme designs in these species. [ABSTRACT FROM AUTHOR]

Summary: The application of genomics in crops has the ability to significantly improve genetic gain for agriculture. Many marker‐dense tools have been developed, but few have seen broad adoption in plant genomics due to issues of significant variations of genome size, levels of ploidy, single nucleotide polymorphism (SNP) frequency and reproductive habit. When combined with limited breeding activities, small research communities and scant sequence resources, the suitability of popular systems is often suboptimal and routinely fails to effectively balance cost‐effectiveness and sample throughput. Genotyping‐by‐sequencing (GBS) encompasses a range of protocols including resequencing of the transcriptome. This study describes a skim GBS‐transcriptomics (GBS‐t) approach developed to be broadly applicable, cost‐effective and high‐throughput while still assaying a significant number of SNP loci. A range of crop species with differing levels of ploidy and degree of inbreeding/outbreeding were chosen, including perennial ryegrass, a diploid outbreeding forage grass; phalaris, a putative segmental allotetraploid outbreeding forage grass; lentil, a diploid inbreeding grain legume; and canola, an allotetraploid partially outbreeding oilseed. GBS‐t was validated as a simple and largely automated, cost‐effective method which generates sufficient SNPs (from 89 738 to 231 977) with acceptable levels of missing data and even genome coverage from c. 3 million sequence reads per sample. GBS‐t is therefore a broadly applicable system suitable for many crops, offering advantages over other systems. The correct choice of subsequent sequence analysis software is important, and the bioinformatics process should be iterative and tailored to the specific challenges posed by ploidy variation and extent of heterozygosity. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) has been recognized as offering numerous potential benefits for ryegrass (Lolium perenne L.) breeding. While the theoretical benefits of GS in ryegrass breeding are clear, the best way to apply GS in practical breeding programs remains to be determined. The present study aimed to investigate genomic breeding options that best balance genetic gain, breeding costs, and the level of inbreeding using stochastic simulation. Nine GS scenarios were tested, including different numbers of selection candidates (10,000, 5000, and 2000 F1 seedlings) and three reference population sizes for GS composed of plots representing a sward-based trial (500, 200, and 100 plots). Low to moderate prediction accuracy was achieved for productivity traits across cycles (i.e., 0.1–0.45 for yield [h2 = 0.3]). Scenarios with larger reference populations (i.e., 500 plots) achieved higher prediction accuracy but, when considering field trial costs, were more expensive per unit of genetic gain. All nine GS scenarios delivered significantly higher genetic gain (up to fourfold) than the conventional breeding scenario over a 20-yr period. Scenarios with moderate selection intensity on F1 seedlings with fewer plots tested in field gave the most genetic gain per dollar invested (i.e., 2000 or 5000 F1 seedlings and 100 plots). However, all GS scenarios reduced genetic diversity in the breeding population more than phenotypic selection, highlighting the need to mitigate inbreeding when applying GS in perennial ryegrass. [ABSTRACT FROM AUTHOR]

In livestock, genotype x environment interaction (G x E) has been widely investigated, with genotype defined at the level of subspecies, breeds, individual animals within a breed (for example performance of offspring of elite sires across environments), and genotypes at single-nucleotide polymorphisms (SNPs). Environments can be described by category (e.g., tropical vs. temperate, high vs. low farm input levels, countries) and by continuous variables such as temperature. To predict breeding values of genotypes in environments described by categories, multitrait models with each category a different trait are used. The models are now being used to predict genomic estimated breeding values (GEBV) for different environments such as the value of a bull's genetics for his daughter's milk production in different countries. The multitrait genomic model has also been used to enable reference populations to be merged across environments and across countries, leading to more accurate GEBV. When the environment can be described by a continuous variable, random regression models have been used to predict response of genotypes to the environment. For example, these models have been used to determine if there are SNP genotypes associated with less sensitivity of milk production to increasing temperature. In both livestock and plant breeding, methods that use genomic information can better cope with a reduced degree of replication of individuals across environments, as it is actually the alleles that must be replicated across environments. More accurate estimates of G x E with the genomic approach may therefore be achievable than was possible in the past. [ABSTRACT FROM AUTHOR]

Critically endangered breeds and populations are often crossed with more common breeds or subspecies. This results in genetic admixture that can be undesirable when it challenges the genetic integrity of wild and domestic populations, causing a loss in special characteristics or unique genetic material and ultimately extinction. Here, we present two genomic selection strategies, using genome-wide DNA markers, to recover the genomic content of the original endangered population from admixtures. Each strategy relies on the estimation of the proportion of nonintrogressed genome in individuals based on a different method: either genomic prediction or identification of breed-specific haplotypes. Then, breeding programs that remove introgressed genomic information can be designed. To test these strategies, we used empirical 50K SNP array data from two pure sheep breeds, Merino (used as target breed), Poll Dorset and an existing admixed population of both breeds. Sheep populations with varying degrees of introgression and admixture were simulated starting from these real genotypes. Both strategies were capable of identifying segment origin, and both removed up to the 100% of the Poll Dorset segments. While the selection process led to substantial inbreeding, we controlled it by imposing a minimum number of individuals contributing to the next generation. [ABSTRACT FROM AUTHOR]

Genome wide association studies (GWAS) have largely succeeded family-based linkage studies in livestock and human populations as the preferred method to map loci for complex or quantitative traits. However, the type of results produced by the two analyses contrast sharply due to differences in linkage disequilibrium (LD) imposed by the design of studies. In this paper, we demonstrate that association and linkage studies are in agreement provided that (i) the effects from both studies are estimated appropriately as random effects, (ii) all markers are fitted simultaneously and (iii) appropriate adjustments are made for the differences in LD between the study designs. We demonstrate with real data that linkage results can be predicted by the sum of association effects. Our association study captured most of the linkage information because we could predict the linkage results with moderate accuracy. We suggest that the ability of common single nucleotide polymorphism (SNP) to capture the genetic variance in a population will depend on the effective population size of the study organism. The results provide further evidence for many loci of small effect underlying complex traits. The analysis suggests a more informed method for GWAS is to fit statistical models where all SNPs are analysed simultaneously and as random effects. [ABSTRACT FROM AUTHOR]