Before fertility traits were incorporated into selection, dairy cattle breeding primarily focused on production traits, which resulted in an unfavorable decline in the reproductive performance of ...dairy cattle. This reduced fertility is constantly challenging the dairy industry on the efficiency and sustainability of dairy production. Recent development of genomic selection on fertility traits has stabilized and even reversed the decreasing trend, showing the effectiveness of genomic selection. Meanwhile, genome-wide association studies (GWAS) have been performed to identify quantitative trait loci (QTL) and candidate genes associated with dairy fertility, providing a better understanding of the genetic architecture of fertility traits. In this review, we provide an overview of the genetics of fertility traits, summarize the findings from existing GWAS of female fertility in dairy cattle, and update the recent research progress in US dairy cattle. Because of the polygenic nature of fertility traits, many GWAS of dairy fertility tended to be underpowered. Only 1 major QTL, on BTA18, was identified across multiple studies. This QTL was associated with a range of fertility traits from conception to calving, but the candidate gene or mutation is still missing. Collectively, with the promising success from genomic selection but low power of GWAS on dairy fertility traits, this review calls for continuous data collection of fertility traits to enable more powerful studies of dairy fertility in the future.
With the development of molecular marker technology in the 1980s, the fate of plant breeding has changed. Different types of molecular markers have been developed and advancement in sequencing ...technologies has geared crop improvement. To explore the knowledge about molecular markers, several reviews have been published in the last three decades; however, all these reviews were meant for researchers with advanced knowledge of molecular genetics. This review is intended to be a synopsis of recent developments in molecular markers and their applications in plant breeding and is devoted to early researchers with a little or no knowledge of molecular markers. The progress made in molecular plant breeding, genetics, genomic selection and genome editing has contributed to a more comprehensive understanding of molecular markers and provided deeper insights into the diversity available for crops and greatly complemented breeding stratagems. Genotyping-by-sequencing and association mapping based on next-generation sequencing technologies have facilitated the identification of novel genetic markers for complex and unstructured populations. Altogether, the history, the types of markers, their application in plant sciences and breeding, and some recent advancements in genomic selection and genome editing are discussed.
Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, ...late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding.
LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and ...hybrid maize lines. LightGBM exhibits superior performance in terms of prediction precision, model stability, and computing efficiency through a series of benchmark tests. We also assess the factors that are essential to ensure the best performance of genomic selection prediction by taking complex scenarios in crop hybrid breeding into account. LightGBM has been implemented as a toolbox, CropGBM, encompassing multiple novel functions and analytical modules to facilitate genomically designed breeding in crops.
Selection in the New Zealand radiata pine (Pinus radiata D. Don) breeding program relies on wide-scale testing to adequately sample environmental variation. The program uses genomic selection for the ...early selection of parents for the next breeding cycle, but genomic selection may not perform as expected in the presence of crossover-type genotype-by-environment interaction (GxE) if such environments are poorly represented in the training population. This study uses empirical data to assess the magnitude of GxE to guide the selection and deployment strategy for radiata pine in New Zealand. Data was collected from eight well-connected and replicated cloned full-sib progeny trials across major radiata pine growing regions in New Zealand. We applied a second-order factor analytic model (FA2) with additive and non-additive variance components to characterise GxE. Three model types were used: uncorrected pedigree, marker-corrected pedigree and marker-based relatedness. This study found that the average additive genetic correlations among sites were 0.76 for DBH and 0.94 for DEN when estimated with marker-based relatedness. Models that use marker-based relatedness, without considering non-additive effects, provide a marginally superior fit compared to models that use pedigree or incorporate non-additive effects. Our study suggests that while GxE is present, its magnitude does not warrant regionalising (subdividing) radiata pine breeding zones for the North Island of New Zealand.
•This study assesses the magnitude of genotype-by-environment interaction in New Zealand’s radiata pine breeding program.•Three model types were used: uncorrected pedigree, marker-corrected pedigree, and marker-based relatedness.•Average additive genetic correlations in genomic models were 0.76 for DBH and 0.94 for DEN.•Models using marker-based relatedness provided a marginally superior fit.•The study found insufficient support to regionalise the radiata pine breeding program.
Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of ...production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics.
Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In ...contrast to classical approaches that emphasize the need for resource-intensive phenotyping at all stages of artificial selection, genomic selection dramatically reduces the need for phenotyping. Genomic selection relies on advances in machine learning and the availability of genotyping data to predict agronomically relevant phenotypic traits. Here we provide a systematic review of machine learning approaches applied for genomic selection of single and multiple traits in major crops in the past decade. We emphasize the need to gather data on intermediate phenotypes, e.g. metabolite, protein, and gene expression levels, along with developments of modeling techniques that can lead to further improvements of genomic selection. In addition, we provide a critical view of factors that affect genomic selection, with attention to transferability of models between different environments. Finally, we highlight the future aspects of integrating high-throughput molecular phenotypic data from omics technologies with biological networks for crop improvement.
The cost of genotyping remains a persistent barrier to the adoption of genomic selection for many breeding programs. This is particularly prominent in the aquaculture industry because the high number ...and diversity of cultured species impedes the reduction of genotyping costs through a shared genetic panel (e.g., a high-density SNP array). One solution is to reserve high-density genotyping for key individuals and apply a low-density SNP panel along with pedigree-informed imputation to the remaining individuals. We examined the possibility of further lowering the cost of this strategy by targeting microhaplotypes instead of SNPs in the low-density panel, which could allow smaller panels to be used. We simulated Pacific oyster, eastern oyster, and Atlantic salmon breeding programs for three generations and compared imputation and GEBV accuracy using low-density panels targeting SNPs or microhaplotypes. Panels targeting microhaplotypes yielded higher imputation and GEBV accuracy than that of equally sized panels targeting SNPs. In the Pacific and eastern oyster simulations, close to the maximum imputation and GEBV accuracy was reached when the low-density panel contained 150–250 microhaplotypes or 350–450 SNPs. In the Atlantic salmon simulations, this level of accuracy was reached with low-density panels of 350–450 microhaplotypes or 650–750 SNPs. Using low-density panels targeting microhaplotypes instead of SNPs can reduce the cost of genotyping and thereby make genomic selection feasible for a wider range of programs.
•Low-density genotyping and imputation can reduce the cost of genomic selection.•Low-density microhaplotype panels outperform SNP panels for imputation.•Use of low-density microhaplotype panels can further reduce genotyping costs.
Vibrio parahaemolyticus (V. parahaemolyticus) carrying the toxic plasmid of pirA and pirB has been identified as the causative agent of acute hepatopancreatic necrosis disease (AHPND), which has ...caused serious economic loss to the aquaculture industry of penaeid shrimp Litopenaeus vannamei (L. vannamei) in recent years. To effectively control the outbreak of this disease, breeding of Vibrio resistant broodstocks of L. vannamei was regarded as an important approach. Due to the advantages in selection accuracy and efficiency, genomic selection (GS) was expected to be a feasible alternative to accelerate the genetic improvement of disease resistance traits. In the present study, the heritability of shrimp resistance to V. parahaemolyticus was estimated and the feasibility of GS was evaluated in L. vannamei based on the real and simulation data. The heritability of shrimp resistance against V. parahaemolyticus was around 0.15–0.24, which indicated that the genetic improvement can be achieved by selective breeding. Subsequent analysis for GS based on real data showed that the genomic best linear unbiased prediction (GBLUP) can result in more accurate prediction than the traditional pedigree-based best linear unbiased prediction (PBLUP), with a 6.8% increase in the prediction accuracy for the survival time, and a 3.5% increase for binary survival. Similarly, for the simulated data, a relative increase (3.0% and 5.0%) in the prediction accuracy was obtained for survival time and binary survival when comparing the PBLUP to GBLUP. Overall results suggest that GS could be an alternative approach to improve the genetic gains in L. vannamei for the resistance to V. parahaemolyticus.
•Near-to-moderate heritability has been detected for the resistance of Litopenaeus vannamei against Vibrio parahaemolyticus•Genomic selection can be a powerful tool to improve the resistance of L. vannamei against V. parahaemolyticus.•The GBLUP can be a promising model for the application of genomic selection in L. vannamei.
PDF
