The epithelial C15ORF48/miR-147-NDUFA4 axis is an essential regulator of gut inflammation, energy metabolism, and the microbiome.

Chronic inflammation is epidemiologically linked to the pathogenesis of gastrointestinal diseases, including inflammatory bowel disease (IBD) and colorectal cancer (CRC). However, our understanding of the molecular mechanisms controlling gut inflammation remains insufficient, hindering the development of targeted therapies for IBD and CRC. In this study, we uncovered C15ORF48/miR-147 as a negative regulator of gut inflammation, operating through the modulation of epithelial cell metabolism. C15ORF48/miR-147 encodes two molecular products, C15ORF48 protein and miR-147-3p microRNA, which are predominantly expressed in the intestinal epithelium. C15ORF48/miR-147 ablation leads to gut dysbiosis and exacerbates chemically induced colitis in mice. C15ORF48 and miR-147-3p work together to suppress colonocyte metabolism and inflammation by silencing NDUFA4, a subunit of mitochondrial complex IV (CIV). Interestingly, the C15ORF48 protein, a structural paralog of NDUFA4, contains a unique C-terminal α-helical domain crucial for displacing NDUFA4 from CIV and its subsequent degradation. NDUFA4 silencing hinders NF-κB signaling activation and consequently attenuates inflammatory responses. Collectively, our findings have established the C15ORF48/miR-147-NDUFA4 molecular axis as an indispensable regulator of gut homeostasis, bridging mitochondrial metabolism and inflammation.

Arthropods are kin: Operationalizing Indigenous data sovereignty to respectfully utilize genomic data from Indigenous lands.

Indigenous peoples have cultivated biodiverse agroecosystems since time immemorial. The rise of metagenomics and high-throughput sequencing technologies in biodiversity studies has rapidly expanded the scale of data collection from these lands. A respectful approach to the data life cycle grounded in the sovereignty of indigenous communities is imperative to not perpetuate harm. In this paper, we operationalize an indigenous data sovereignty (IDS) framework to outline realistic considerations for genomic data that span data collection, governance, and communication. As a case study for this framework, we use arthropod genomic data collected from diversified and simplified farm sites close to and far from natural habitats within a historic Kanaka 'Oiwi (Indigenous Hawaiian) agroecosystem. Diversified sites had the highest Operational Taxonomic Unit (OTU) richness for native and introduced arthropods. There may be a significant spillover effect between forest and farm sites, as farm sites near a natural habitat had higher OTU richness than those farther away. We also provide evidence that management factors such as the number of Polynesian crops cultivated may drive arthropod community composition. Through this case study, we emphasize the context-dependent opportunities and challenges for operationalizing IDS by utilizing participatory research methods, expanding novel data management tools through the Local Contexts Hub, and developing and nurturing community partnerships-all while highlighting the potential of agroecosystems for arthropod conservation. Overall, the workflow and the example presented here can help researchers take tangible steps to achieve IDS, which often seems elusive with the expanding use of genomic data.

Reference genome of the long-jawed orb-weaver, Tetragnatha versicolor (Araneae: Tetragnathidae).

Climate-driven changes in hydrological regimes are of global importance and are particularly significant in riparian ecosystems. Riparian ecosystems in California provide refuge to many native and vulnerable species within a xeric landscape. California Tetragnatha spiders play a key role in riparian ecosystems, serving as a link between terrestrial and aquatic elements. Their tight reliance on water paired with the widespread distributions of many species make them ideal candidates to better understand the relative role of waterways versus geographic distance in shaping the population structure of riparian species. To assist in better understanding population structure, we constructed a reference genome assembly for Tetragnatha versicolor using long-read sequencing, scaffolded with proximity ligation Omni-C data. The near-chromosome-level assembly is comprised of 174 scaffolds spanning 1.06 Gb pairs, with a scaffold N50 of 64.1 Mb pairs and BUSCO completeness of 97.6%. This reference genome will facilitate future study of T. versicolor population structure associated with the rapidly changing environment of California.

Ecological network structure in response to community assembly processes over evolutionary time.

The dynamic structure of ecological communities results from interactions among taxa that change with shifts in species composition in space and time. However, our ability to study the interplay of ecological and evolutionary processes on community assembly remains relatively unexplored due to the difficulty of measuring community structure over long temporal scales. Here, we made use of a geological chronosequence across the Hawaiian Islands, representing 50 years to 4.15 million years of ecosystem development, to sample 11 communities of arthropods and their associated plant taxa using semiquantitative DNA metabarcoding. We then examined how ecological communities changed with community age by calculating quantitative network statistics for bipartite networks of arthropod-plant associations. The average number of interactions per species (linkage density), ratio of plant to arthropod species (vulnerability) and uniformity of energy flow (interaction evenness) increased significantly in concert with community age. The index of specialization H 2 ' has a curvilinear relationship with community age. Our analyses suggest that younger communities are characterized by fewer but stronger interactions, while biotic associations become more even and diverse as communities mature. These shifts in structure became especially prominent on East Maui (~0.5 million years old) and older volcanos, after enough time had elapsed for adaptation and specialization to act on populations in situ. Such natural progression of specialization during community assembly is probably impeded by the rapid infiltration of non-native species, with special risk to younger or more recently disturbed communities that are composed of fewer specialized relationships.

High density linkage maps, genetic architecture, and genomic prediction of growth and wood properties in Pinus radiata.

BACKGROUND: The growing availability of genomic resources in radiata pine paves the way for significant advances in fundamental and applied genomic research. We constructed robust high-density linkage maps based on exome-capture genotyping in two F1 populations, and used these populations to perform quantitative trait locus (QTL) scans, genomic prediction and quantitative analyses of genetic architecture for key traits targeted by tree improvement programmes. RESULTS: Our mapping approach used probabilistic error correction of the marker data, followed by an iterative approach based on stringent parameters. This approach proved highly effective in producing high-density maps with robust marker orders and realistic map lengths (1285-4674 markers per map, with sizes ranging from c. 1643-2292 cM, and mean marker intervals of 0.7-2.1 cM). Colinearity was high between parental linkage maps, although there was evidence for a large chromosomal rearrangement (affecting ~ 90 cM) in one of the parental maps. In total, 28 QTL were detected for growth (stem diameter) and wood properties (wood density and fibre properties measured by Silviscan) in the QTL discovery population, with 1-3 QTL of small to moderate effect size detected per trait in each parental map. Four of these QTL were validated in a second, unrelated F1 population. Results from genomic prediction and analyses of genetic architecture were consistent with those from QTL scans, with wood properties generally having moderate to high genomic heritabilities and predictive abilities, as well as somewhat less complex genetic architectures, compared to growth traits. CONCLUSIONS: Despite the economic importance of radiata pine as a plantation forest tree, robust high-density linkage maps constructed from reproducible, sequence-anchored markers have not been published to date. The maps produced in this study will be a valuable resource for several applications, including the selection of marker panels for genomic prediction and anchoring a recently completed de novo whole genome assembly. We also provide the first map-based evidence for a large genomic rearrangement in radiata pine. Finally, results from our QTL scans, genomic prediction, and genetic architecture analyses are informative about the genomic basis of variation in important phenotypic traits.

Genomic selection for resistance to mammalian bark stripping and associated chemical compounds in radiata pine.

The integration of genomic data into genetic evaluations can facilitate the rapid selection of superior genotypes and accelerate the breeding cycle in trees. In this study, 390 trees from 74 control-pollinated families were genotyped using a 36K Axiom SNP array. A total of 15,624 high-quality SNPs were used to develop genomic prediction models for mammalian bark stripping, tree height, and selected primary and secondary chemical compounds in the bark. Genetic parameters from different genomic prediction methods-single-trait best linear unbiased prediction based on a marker-based relationship matrix (genomic best linear unbiased prediction), multitrait single-step genomic best linear unbiased prediction, which integrated the marker-based and pedigree-based relationship matrices (single-step genomic best linear unbiased prediction) and the single-trait generalized ridge regression-were compared to equivalent single- or multitrait pedigree-based approaches (ABLUP). The influence of the statistical distribution of data on the genetic parameters was assessed. Results indicated that the heritability estimates were increased nearly 2-fold with genomic models compared to the equivalent pedigree-based models. Predictive accuracy of the single-step genomic best linear unbiased prediction was higher than the ABLUP for most traits. Allowing for heterogeneity in marker effects through the use of generalized ridge regression did not markedly improve predictive ability over genomic best linear unbiased prediction, arguing that most of the chemical traits are modulated by many genes with small effects. Overall, the traits with low pedigree-based heritability benefited more from genomic models compared to the traits with high pedigree-based heritability. There was no evidence that data skewness or the presence of outliers affected the genomic or pedigree-based genetic estimates.

Chasing genetic correlation breakers to stimulate population resilience to climate change.

Global climate change introduces new combinations of environmental conditions, which is expected to increase stress on plants. This could affect many traits in multiple ways that are as yet unknown but will likely require the modification of existing genetic relationships among functional traits potentially involved in local adaptation. Theoretical evolutionary studies have determined that it is an advantage to have an excess of recombination events under heterogeneous environmental conditions. Our study, conducted on a population of radiata pine (Pinus radiata D. Don), was able to identify individuals that show high genetic recombination at genomic regions, which potentially include pleiotropic or collocating QTLs responsible for the studied traits, reaching a prediction accuracy of 0.80 in random cross-validation and 0.72 when whole family was removed from the training population and predicted. To identify these highly recombined individuals, a training population was constructed from correlation breakers, created through tandem selection of parents in the previous generation and their consequent mating. Although the correlation breakers showed lower observed heterogeneity possibly due to direct selection in both studied traits, the genomic regions with statistically significant differences in the linkage disequilibrium pattern showed higher level of heretozygosity, which has the effect of decomposing unfavourable genetic correlation. We propose undertaking selection of correlation breakers under current environmental conditions and using genomic predictions to increase the frequency of these 'recombined' individuals in future plantations, ensuring the resilience of planted forests to changing climates. The increased frequency of such individuals will decrease the strength of the population-level genetic correlations among traits, increasing the opportunity for new trait combinations to be developed in the future.

microRNA-142 guards against autoimmunity by controlling Treg cell homeostasis and function.

Regulatory T (Treg) cells are critical in preventing aberrant immune responses. Posttranscriptional control of gene expression by microRNA (miRNA) has recently emerged as an essential genetic element for Treg cell function. Here, we report that mice with Treg cell-specific ablation of miR-142 (hereafter Foxp3CremiR-142fl/fl mice) developed a fatal systemic autoimmune disorder due to a breakdown in peripheral T-cell tolerance. Foxp3CremiR-142fl/fl mice displayed a significant decrease in the abundance and suppressive capacity of Treg cells. Expression profiling of miR-142-deficient Treg cells revealed an up-regulation of multiple genes in the interferon gamma (IFNγ) signaling network. We identified several of these IFNγ-associated genes as direct miR-142-3p targets and observed excessive IFNγ production and signaling in miR-142-deficient Treg cells. Ifng ablation rescued the Treg cell homeostatic defect and alleviated development of autoimmunity in Foxp3CremiR-142fl/fl mice. Thus, our findings implicate miR-142 as an indispensable regulator of Treg cell homeostasis that exerts its function by attenuating IFNγ responses.

Towards eradicating the nuisance of numts and noise in molecular biodiversity assessment.

DNA metabarcoding is a popular methodology for biodiversity assessment and increasingly used for community level analysis of intraspecific genetic diversity. The evolutionary history of hundreds of specimens can be captured in a single collection vial. However, the method is not without pitfalls, which may inflate or misrepresent recovered diversity metrics. Nuclear pseudogene copies of mitochondrial DNA (numts) have been particularly difficult to control because they can evolve rapidly and appear deceptively similar to true mitochondrial sequences. While the problem of numts has long been recognized for traditional sequencing approaches, the issues they create are particularly evident in metabarcoding in which the identity of individual specimens is generally not known. In this issue of Molecular Ecology Resources, Andújar et al. (2021) provide an easy to implement bioinformatic approach to reduce erroneous sequences due to numts and residual noise in metabarcoding data sets. The metaMATE software designates input sequences as authentic (mtDNA haplotypes) or nonauthentic (numts and erroneous sequences) by comparison to reference data and by analysing nucleotide substitution patterns. Filtering is applied over a range of abundance thresholds and the choice to proceed with a more rigid or less strict sequence removal strategy is at the researchers' discretion. This is a valuable addition to a growing number of complementary tools for improving the reliability of modern biodiversity monitoring.

Marker Selection in Multivariate Genomic Prediction Improves Accuracy of Low Heritability Traits.

Multivariate analysis using mixed models allows for the exploration of genetic correlations between traits. Additionally, the transition to a genomic based approach is simplified by substituting classic pedigrees with a marker-based relationship matrix. It also enables the investigation of correlated responses to selection, trait integration and modularity in different kinds of populations. This study investigated a strategy for the construction of a marker-based relationship matrix that prioritized markers using Partial Least Squares. The efficiency of this strategy was found to depend on the correlation structure between investigated traits. In terms of accuracy, we found no benefit of this strategy compared with the all-marker-based multivariate model for the primary trait of diameter at breast height (DBH) in a radiata pine (Pinus radiata) population, possibly due to the presence of strong and well-estimated correlation with other highly heritable traits. Conversely, we did see benefit in a shining gum (Eucalyptus nitens) population, where the primary trait had low or only moderate genetic correlation with other low/moderately heritable traits. Marker selection in multivariate analysis can therefore be an efficient strategy to improve prediction accuracy for low heritability traits due to improved precision in poorly estimated low/moderate genetic correlations. Additionally, our study identified the genetic diversity as a factor contributing to the efficiency of marker selection in multivariate approaches due to higher precision of genetic correlation estimates.

Indication of Quantitative Multiple Disease Resistance to Foliar Pathogens in Pinus radiata D.Don in New Zealand.

Increasing resistance against foliar diseases is an important goal in the Pinus radiata D.Don breeding program in New Zealand, and screening for resistance has been in place for some time, since the late 1960s. The current study presents results of four progeny trials within the breeding program to investigate whether multiple disease resistance could be detected against three different needle diseases in P. radiata: Dothistroma needle blight (DNB) caused by Dothistroma septosporum, Cyclaneusma needle cast (CNC) caused by Cyclaneusma minus, and red needle cast (RNC) caused by Phytophthora pluvialis. Four progeny trials in the North Island of New Zealand were available to estimate heritabilities and between-trait genetic correlations. Two of the trials were assessed for DNB, involving 63 full-sib families. A third trial was assessed for CNC, involving 172 half-sib families, and a fourth trial was assessed for RNC, involving 170 half-sib families. Disease resistances had moderate estimates of heritability (0.28-0.48) in all trials. We investigated the potential for multiple disease resistance to the three foliar diseases by estimating genetic correlations between disease resistances using a spatial linear mixed model. The correlation between DNB and CNC resistance was favorable and strong (0.81), indicating that genotypes that are highly resistant to DNB also have a high resistance to CNC. These results suggest that selection based on resistance to DNB could allow for simultaneous indirect selection for resistance to CNC, usually only expressed at a later age. This would allow selections to be made earlier due to the earlier expression of DNB than CNC and reduce the number of expensive disease assessments being undertaken. Conversely, genetic correlation estimates for RNC with DNB and CNC were close to zero, and very imprecise. As such, later-age assessments for this disease would still be required.

Effect of trait's expression level on single-step genomic evaluation of resistance to Dothistroma needle blight.

BACKGROUND: Many conifer breeding programs are paying increasing attention to breeding for resistance to needle disease due to the increasing importance of climate change. Phenotyping of traits related to resistance has many biological and temporal constraints that can often confound the ability to achieve reliable phenotypes and consequently, reliable genetic progress. The development of next generation sequencing platforms has also enabled implementation of genomic approaches in species lacking robust reference genomes. Genomic selection is, therefore, a promising strategy to overcome the constraints of needle disease phenotyping. RESULTS: We found high accuracy in the prediction of genomic breeding values in the disease-related traits that were well characterized, reaching 0.975 for genotyped individuals and 0.587 for non-genotyped individuals. This compared well with pedigree-based accuracies of up to 0.746. Surprisingly, poorly phenotyped disease traits also showed very high accuracy in terms of correlation of predicted genomic breeding values with pedigree-based counterparts. However, this was likely caused by the fact that both were clustered around the population mean, while deviations from the population mean caused by genetic effects did not appear to be well described. Caution should therefore be taken with the interpretation of results in poorly phenotyped traits. CONCLUSIONS: Implementation of genomic selection in this test population of Pinus radiata resulted in a relatively high prediction accuracy of needle loss due to Dothistroma septosporum compared with a pedigree-based approach. Using genomics to avoid biological/temporal constraints where phenotyping is reliable appears promising. Unsurprisingly, reliable phenotyping, resulting in good heritability estimates, is a fundamental requirement for the development of a reliable prediction model. Furthermore, our results are also specific to the single pathogen mating-type that is present in New Zealand, and may change with future incursion of other pathogen varieties. There is no doubt, however, that once a robust genomic prediction model is built, it will be invaluable to not only select for host tolerance, but for other economically important traits simultaneously. This tool will thus future-proof our forests by mitigating the risk of disease outbreaks induced by future changes in climate.

Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information.

BACKGROUND: Non-key traits (NKTs) in radiata pine (Pinus radiata D. Don) refer to traits other than growth, wood density and stiffness, but still of interest to breeders. Branch-cluster frequency, stem straightness, external resin bleeding and internal checking are examples of such traits and are targeted for improvement in radiata pine research programmes. Genomic selection can be conducted before the performance of selection candidates is available so that generation intervals can be reduced. Radiata pine is a species with a long generation interval, which if reduced could significantly increase genetic gain per unit of time. The aim of this study was to evaluate the accuracy and predictive ability of genomic selection and its efficiency over traditional forward selection in radiata pine for the following NKTs: branch-cluster frequency, stem straightness, internal checking, and external resin bleeding. RESULTS: Nine hundred and eighty-eight individuals were genotyped using exome capture genotyping by sequencing (GBS) and 67,168 single nucleotide polymorphisms (SNPs) used to develop genomic estimated breeding values (GEBVs) with genomic best linear unbiased prediction (GBLUP). The documented pedigree was corrected using a subset of 704 SNPs. The percentage of trio parentage confirmed was about 49% and about 50% of parents were re-assigned. The accuracy of GEBVs was 0.55-0.75 when using the documented pedigree and 0.61-0.80 when using the SNP-corrected pedigree. A higher percentage of additive genetic variance was explained and a higher predictive ability was observed when using the SNP-corrected pedigree than using the documented pedigree. With the documented pedigree, genomic selection was similar to traditional forward selection when assuming a generation interval of 17 years, but worse than traditional forward selection when assuming a generation interval of 14 years. After the pedigree was corrected, genomic selection led to 37-115% and 13-77% additional genetic gain over traditional forward selection when generation intervals of 17 years and 14 years were assumed, respectively. CONCLUSION: It was concluded that genomic selection with a pedigree corrected by SNP information was an efficient way of improving non-key traits in radiata pine breeding.

A high-density exome capture genotype-by-sequencing panel for forestry breeding in Pinus radiata.

Development of genome-wide resources for application in genomic selection or genome-wide association studies, in the absence of full reference genomes, present a challenge to the forestry industry, where longer breeding cycles could benefit from the accelerated selection possible through marker-based breeding value predictions. In particular, large conifer megagenomes require a strategy to reduce complexity, whilst ensuring genome-wide coverage is achieved. Using a transcriptome-based reference template, we have successfully developed a high density exome capture genotype-by-sequencing panel for radiata pine (Pinus radiata D.Don), capable of capturing in excess of 80,000 single nucleotide polymorphism (SNP) markers with a minor allele frequency above 0.03 in the population tested. This represents approximately 29,000 gene models from a core set of 48,914 probes. A set of 704 SNP markers capable of pedigree reconstruction and differentiating individual genotypes were tested within two full-sib mapping populations. While as few as 70 markers could reconstruct parentage in almost all cases, the impact of missing genotypes was noticeable in several offspring. Therefore, 60 sets of 110 randomly selected SNP markers were compared for both parentage reconstruction and clone differentiation. The performance in parentage reconstruction showed little variation over 60 iterations. However, there was notable variation in discriminatory power between closely related individuals, indicating a higher density SNP marker panel may be required to elucidate hidden relationships in complex pedigrees.

Nanopore sequencing of long ribosomal DNA amplicons enables portable and simple biodiversity assessments with high phylogenetic resolution across broad taxonomic scale.

BACKGROUND: In light of the current biodiversity crisis, DNA barcoding is developing into an essential tool to quantify state shifts in global ecosystems. Current barcoding protocols often rely on short amplicon sequences, which yield accurate identification of biological entities in a community but provide limited phylogenetic resolution across broad taxonomic scales. However, the phylogenetic structure of communities is an essential component of biodiversity. Consequently, a barcoding approach is required that unites robust taxonomic assignment power and high phylogenetic utility. A possible solution is offered by sequencing long ribosomal DNA (rDNA) amplicons on the MinION platform (Oxford Nanopore Technologies). FINDINGS: Using a dataset of various animal and plant species, with a focus on arthropods, we assemble a pipeline for long rDNA barcode analysis and introduce a new software (MiniBar) to demultiplex dual indexed Nanopore reads. We find excellent phylogenetic and taxonomic resolution offered by long rDNA sequences across broad taxonomic scales. We highlight the simplicity of our approach by field barcoding with a miniaturized, mobile laboratory in a remote rainforest. We also test the utility of long rDNA amplicons for analysis of community diversity through metabarcoding and find that they recover highly skewed diversity estimates. CONCLUSIONS: Sequencing dual indexed, long rDNA amplicons on the MinION platform is a straightforward, cost-effective, portable, and universal approach for eukaryote DNA barcoding. Although bulk community analyses using long-amplicon approaches may introduce biases, the long rDNA amplicons approach signifies a powerful tool for enabling the accurate recovery of taxonomic and phylogenetic diversity across biological communities.

Correction to: Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories.

Published online: 6 July 2018This article was originally published under standard licence, but has now been made available under a [CC BY 4.0] license. The PDF and HTML versions of the paper have been modified accordingly.

Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories.

Genomic selection is expected to enhance the genetic improvement of forest tree species by providing more accurate estimates of breeding values through marker-based relationship matrices compared with pedigree-based methodologies. When adequately robust genomic prediction models are available, an additional increase in genetic gains can be made possible with the shortening of the breeding cycle through elimination of the progeny testing phase and early selection of parental candidates. The potential of genomic selection was investigated in an advanced Eucalyptus nitens breeding population focused on improvement for solid wood production. A high-density SNP chip (EUChip60K) was used to genotype 691 individuals in the breeding population, which represented two seed orchards with different selection histories. Phenotypic records for growth and form traits at age six, and for wood quality traits at age seven were available to build genomic prediction models using GBLUP, which were compared to the traditional pedigree-based alternative using BLUP. GBLUP demonstrated that breeding value accuracy would be improved and substantial increases in genetic gains towards solid wood production would be achieved. Cross-validation within and across two different seed orchards indicated that genomic predictions would likely benefit in terms of higher predictive accuracy from increasing the size of the training data sets through higher relatedness and better utilization of LD.

Approaches to variant discovery for conifer transcriptome sequencing.

There is a wide diversity of bioinformatic tools available for the assembly of next generation sequence and subsequence variant calling to identify genetic markers at scale. Integration of genomics tools such as genomic selection, association studies, pedigree analysis and analysis of genetic diversity, into operational breeding is a goal for New Zealand's most widely planted exotic tree species, Pinus radiata. In the absence of full reference genomes for large megagenomes such as in conifers, RNA sequencing in a range of genotypes and tissue types, offers a rich source of genetic markers for downstream application. We compared nine different assembler and variant calling software combinations in a single transcriptomic library and found that Single Nucleotide Polymorphism (SNPs) discovery could vary by as much as an order of magnitude (8,061 SNPs up to 86,815 SNPs). The assembler with the best realignment of the packages trialled, Trinity, in combination with several variant callers was then applied to a much larger multi-genotype, multi-tissue transcriptome and identified 683,135 in silico SNPs across a predicted 449,951 exons when mapped to the Pinus taeda ver 1.01e reference.

Effect of Hidden Relatedness on Single-Step Genetic Evaluation in an Advanced Open-Pollinated Breeding Program.

Open-pollinated (OP) mating is frequently used in forest tree breeding due to the relative temporal and financial efficiency of the approach. The trade-off is the lower precision of the estimated genetic parameters. Pedigree/sib-ship reconstruction has been proven as a tool to correct and complete pedigree information and to improve the precision of genetic parameter estimates. Our study analyzed an advanced generation Eucalyptus population from an OP breeding program using single-step genetic evaluation. The relationship matrix inferred from sib-ship reconstruction was used to rescale the marker-based relationship matrix (G matrix). This was compared with a second scenario that used rescaling based on the documented pedigree. The proposed single-step model performed better with respect to both model fit and the theoretical accuracy of breeding values. We found that the prediction accuracy was superior when using the pedigree information only when compared with using a combination of the pedigree and genomic information. This pattern appeared to be mainly a result of accumulated unrecognized relatedness over several breeding cycles, resulting in breeding values being shrunk toward the population mean. Using biased, pedigree-based breeding values as the base with which to correlate predicted GEBVs, resulted in the underestimation of prediction accuracies. Using breeding values estimated on the basis of sib-ship reconstruction resulted in increased prediction accuracies of the genotyped individuals. Therefore, selection of the correct base for estimation of prediction accuracy is critical. The beneficial impact of sib-ship reconstruction using G matrix rescaling was profound, especially in traits with inbreeding depression, such as stem diameter.

Exploration of genetic architecture through sib-ship reconstruction in advanced breeding population of Eucalyptus nitens.

Accurate inference of relatedness between individuals in breeding population contributes to the precision of genetic parameter estimates, effectiveness of inbreeding management and the amount of genetic progress delivered from breeding programs. Pedigree reconstruction has been proven to be an efficient tool to correct pedigree errors and recover hidden relatedness in open pollinated progeny tests but the method can be limited by the lack of parental genotypes and the high proportion of alien pollen from outside the breeding population. Our study investigates the efficiency of sib-ship reconstruction in an advanced breeding population of Eucalyptus nitens with only partially tracked pedigree. The sib-ship reconstruction allowed the identification of selfs (4% of the sample) and the exploration of their potential effect on inbreeding depression in the traits studied. We detected signs of inbreeding depression in diameter at breast height and growth strain while no indications were observed in wood density, wood stiffness and tangential air-dry shrinkage. After the application of a corrected sib-ship relationship matrix, additive genetic variance and heritability were observed to increase where signs of inbreeding depression were initially detected. Conversely, the same genetic parameters for traits that appeared to be free of inbreeding depression decreased in size. It therefore appeared that greater genetic variance may be due, at least in part, to contributions from inbreeding in these studied populations rather than a removal of inbreeding as is traditionally thought.