Genomic Selection Using Extreme Phenotypes and Pre-Selection of SNPs in Large Yellow Croaker (Larimichthys crocea)

Genome-wide association study identified genes associated with ammonia nitrogen tolerance in Litopenaeus vannamei

Ammonia nitrogen tolerance is an economically important trait of the farmed penaeid shrimp Litopenaeus vannamei. To identify the genes associated with ammonia nitrogen tolerance, we performed an extreme phenotype genome-wide association study method (XP-GWAS) on a population of 200 individuals. The single nucleotide polymorphism (SNP) genotyping array method was used to construct the libraries and 36,048 SNPs were genotyped. Using the MLM, FarmCPU and Blink models, six different SNPs, located on SEQ3, SEQ4, SEQ5, SEQ7 and SEQ8, were determined to be significantly associated with ammonia nitrogen tolerance. By integrating the results of the GWAS and the biological functions of the genes, seven candidate genes (PDI, OZF, UPF2, VPS16, TMEM19, MYCBP2, and HOX7) were found to be associated with ammonia nitrogen tolerance in L. vannamei. These genes are involved in cell transcription, cell division, metabolism, and immunity, providing the basis for further study of the genetic mechanisms of ammonia nitrogen tolerance in L. vannamei. Further candidate gene association analysis in the offspring population revealed that the SNPs in the genes zinc finger protein OZF-like (OZF) and homeobox protein Hox-B7-like (HOX7) were significantly associated with ammonia nitrogen tolerance trait of L. vannamei. Our results provide fundamental genetic information that will be useful for further investigation of the molecular mechanisms of ammonia nitrogen tolerance. These associated SNPs may also be promising candidates for improving ammonia nitrogen tolerance in L. vannamei.

Subsampling and DNA pooling can increase gains through genomic selection in switchgrass

Genomic selection (GS) can accelerate breeding cycles in perennial crops such as the bioenergy grass switchgrass (Panicum virgatum L.). The sequencing costs of GS can be reduced by pooling DNA samples in the training population (TP), only sequencing TP phenotypic outliers, or pooling candidate population (CP) samples. These strategies were simulated for two traits (spring vigor and anthesis date) in three breeding populations. Sequencing only the outlier 50% of the TP phenotype distribution resulted in a penalty of <5% of the predictive ability, measured using cross-validation. Predictive ability also decreased when sequencing progressively fewer TP DNA pools, but TPs constructed from only two phenotypically contrasting DNA samples retained a mean of >80% predictive ability relative to individual TP sequencing. Novel group testing methods allowed greater than one CP individual to be screened per sequenced DNA sample but resulted in a predictive ability penalty. To determine the impact of reduced sequencing, genetic gain was calculated for seven GS scenarios with variable sequencing budgets. Reduced TP sequencing and most CP pooling methods were superior to individual sequence-based GS when sequencing resources were restricted (2,000 DNA samples per 5-yr cycle). Only one scenario was superior to individual sequencing when sequencing budgets were large (8,000 DNA samples per 5-yr cycle). This study highlights multiple routes for reduced sequencing costs in GS.

Genome‑wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing

Background

Growth traits are of great importance for poultry breeding and production and have been the topic of extensive investigation, with many quantitative trait loci (QTL) detected. However, due to their complex genetic background, few causative genes have been confirmed and the underlying molecular mechanisms remain unclear, thus limiting our understanding of QTL and their potential use for the genetic improvement of poultry. Therefore, deciphering the genetic architecture is a promising avenue for optimising genomic prediction strategies and exploiting genomic information for commercial breeding. The objectives of this study were to: (1) conduct a genome-wide association study to identify key genetic factors and explore the polygenicity of chicken growth traits; (2) investigate the efficiency of genomic prediction in broilers; and (3) evaluate genomic predictions that harness genomic features.

Results

We identified five significant QTL, including one on chromosome 4 with major effects and four on chromosomes 1, 2, 17, and 27 with minor effects, accounting for 14.5 to 34.1% and 0.2 to 2.6% of the genomic additive genetic variance, respectively, and 23.3 to 46.7% and 0.6 to 4.5% of the observed predictive accuracy of breeding values, respectively. Further analysis showed that the QTL with minor effects collectively had a considerable influence, reflecting the polygenicity of the genetic background. The accuracy of genomic best linear unbiased predictions (BLUP) was improved by 22.0 to 70.3% compared to that of the conventional pedigree-based BLUP model. The genomic feature BLUP model further improved the observed prediction accuracy by 13.8 to 15.2% compared to the genomic BLUP model.

Conclusions

A major QTL and four minor QTL were identified for growth traits; the remaining variance was due to QTL effects that were too small to be detected. The genomic BLUP and genomic feature BLUP models yielded considerably higher prediction accuracy compared to the pedigree-based BLUP model. This study revealed the polygenicity of growth traits in yellow-plumage chickens and demonstrated that the predictive ability can be greatly improved by using genomic information and related features.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00672-9.

Toward Genome-Based Selection in Asian Seabass: What Can We Learn From Other Food Fishes and Farm Animals?

Due to the steadily increasing need for seafood and the plateauing output of fisheries, more fish need to be produced by aquaculture production. In parallel with the improvement of farming methods, elite food fish lines with superior traits for production must be generated by selection programs that utilize cutting-edge tools of genomics. The purpose of this review is to provide a historical overview and status report of a selection program performed on a catadromous predator, the Asian seabass (Lates calcarifer, Bloch 1790) that can change its sex during its lifetime. We describe the practices of wet lab, farm and lab in detail by focusing onto the foundations and achievements of the program. In addition to the approaches used for selection, our review also provides an inventory of genetic/genomic platforms and technologies developed to (i) provide current and future support for the selection process; and (ii) improve our understanding of the biology of the species. Approaches used for the improvement of terrestrial farm animals are used as examples and references, as those processes are far ahead of the ones used in aquaculture and thus they might help those working on fish to select the best possible options and avoid potential pitfalls.

Prediction of genomic breeding values based on pre-selected SNPs using ssGBLUP, WssGBLUP and BayesB for Edwardsiellosis resistance in Japanese flounder

Background

Edwardsiella tarda causes acute symptoms with ascites in Japanese flounder (Paralichthys olivaceus) and is a major problem for China’s aquaculture sector. Genomic selection (GS) has been widely adopted in breeding industries because it shortens generation intervals and results in the selection of individuals that have great breeding potential with high accuracy. Based on an artificial challenge test and re-sequenced data of 1099 flounders, the aims of this study were to estimate the genetic parameters of resistance to E. tarda in Japanese flounder and to evaluate the accuracy of single-step GBLUP (ssGBLUP), weighted ssGBLUP (WssGBLUP), and BayesB for improving resistance to E. tarda by using three subsets of pre-selected single nucleotide polymorphisms (SNPs). In addition, SNPs that are associated with this trait were identified using a single-SNP genome-wide association study (GWAS) and WssGBLUP.

Results

We estimated a heritability of 0.13 ± 0.02 for resistance to E. tarda in Japanese flounder. One million SNPs at fixed intervals were selected from 4,978,724 SNPs that passed quality controls. GWAS identified significant SNPs on chromosomes 14 and 24. WssGBLUP revealed that the putative quantitative trait loci on chromosomes 1 and 14 contained SNPs that explained more than 1% of the genetic variance. Three 50 k-SNP subsets were pre-selected based on different criteria. Compared with pedigree-based prediction (ABLUP), the three genomic methods evaluated resulted in at least 7.7% greater accuracy of predictions. The accuracy of these genomic prediction methods was almost unchanged when pre-selected trait-related SNPs were used for prediction.

Conclusions

Resistance to E. tarda in Japanese flounder has a low heritability. GWAS and WssGBLUP revealed that the genetic architecture of this trait is polygenic. Genomic prediction of breeding values performed better than ABLUP. It is feasible to implement genomic selection to increase resistance to E. tarda in Japanese flounder with 50 k SNPs. Based on the criteria used here, pre-selection of SNPs was not beneficial and other criteria for pre-selection should be considered.

Genomic Prediction of Columnaris Disease Resistance in Catfish

Catfish is an important aquaculture species in the USA. Columnaris disease is distributed worldwide, affecting a wide variety of fish species including catfish . It leads to huge economic losses each year to the US catfish industry. Channel catfish in general is highly resistant to the disease, while blue catfish is highly susceptible. Genomic selection is an effective and accurate way to predict the breeding values and thus was expected to improve the prediction veracity of columnaris disease resistance in catfish effectively. In this study, two different methods, elastic net genomic best linear unbiased prediction (ENGBLUP) and genomic best linear unbiased prediction (GBLUP), were used to predict the columnaris disease resistance evaluated by binary survival status. Cross-validation showed that the prediction accuracy of ENGBLUP and GBLUP was 0.7347 and 0.4868, respectively, showing that ENGBLUP had a high prediction accuracy. It was shown that fitting QTL and polygenic effect with different distribution will improve genomic prediction accuracy for binary traits. In this study, an accurate and effective genomic selection method was proposed to predict the columnaris resistance in catfish, and its application should be beneficial to catfish breeding.

Pooled genotyping strategies for the rapid construction of genomic reference populations1

The growing concern with the environment is making important for livestock producers to focus on selection for efficiency-related traits, which is a challenge for commercial cattle herds due to the lack of pedigree information. To explore a cost-effective opportunity for genomic evaluations of commercial herds, this study compared the accuracy of bulls' genomic estimated breeding values (GEBV) using different pooled genotype strategies. We used ten replicates of previously simulated genomic and phenotypic data for one low (t1) and one moderate (t2) heritability trait of 200 sires and 2,200 progeny. Sire's GEBV were calculated using a univariate mixed model, with a hybrid genomic relationship matrix (h-GRM) relating sires to: 1) 1,100 pools of 2 animals; 2) 440 pools of 5 animals; 3) 220 pools of 10 animals; 4) 110 pools of 20 animals; 5) 88 pools of 25 animals; 6) 44 pools of 50 animals; and 7) 22 pools of 100 animals. Pooling criteria were: at random, grouped sorting by t1, grouped sorting by t2, and grouped sorting by a combination of t1 and t2. The same criteria were used to select 110, 220, 440, and 1,100 individual genotypes for GEBV calculation to compare GEBV accuracy using the same number of individual genotypes and pools. Although the best accuracy was achieved for a given trait when pools were grouped based on that same trait (t1: 0.50-0.56, t2: 0.66-0.77), pooling by one trait impacted negatively on the accuracy of GEBV for the other trait (t1: 0.25-0.46, t2: 0.29-0.71). Therefore, the combined measure may be a feasible alternative to use the same pools to calculate GEBVs for both traits (t1: 0.45-0.57, t2: 0.62-0.76). Pools of 10 individuals were identified as representing a good compromise between loss of accuracy (~10%-15%) and cost savings (~90%) from genotype assays. In addition, we demonstrated that in more than 90% of the simulations, pools present higher sires' GEBV accuracy than individual genotypes when the number of genotype assays is limited (i.e., 110 or 220) and animals are assigned to pools based on phenotype. Pools assigned at random presented the poorest results (t1: 0.07-0.45, t2: 0.14-0.70). In conclusion, pooling by phenotype is the best approach to implementing genomic evaluation using commercial herd data, particularly when pools of 10 individuals are evaluated. While combining phenotypes seems a promising strategy to allow more flexibility to the estimates made using pools, more studies are necessary in this regard.

Evaluation of Genomic Selection for Seven Economic Traits in Yellow Drum (Nibea albiflora)

Yellow drum (Nibea albiflora) is an important maricultural fish in China, and genetic improvement is necessary for this species. This research evaluated the application of genomic selection methods to predict the genetic values of seven economic traits for yellow drum. Using genome-wide single-nucleotide polymorphisms (SNPs), we estimated the genetic parameters for seven traits, including body length (BL), swimming bladder index (SBI), swimming bladder weight (SBW), body thickness (BT), body height (BH), body length/body height ratio (LHR), and gonad weight index (GWI). The heritability estimates ranged from 0.309 to 0.843. We evaluated the prediction performance of various statistical methods, and no one method provided the highest predictive ability for all traits. We then evaluated and compared the use of genome-wide association study (GWAS)-informative SNPs and random SNPs for prediction and found that GWAS-informative SNPs obviously increased. It only needed 5 and 100 informative SNPs for LHR and BT to achieve almost the same predictive abilities as using genome-wide SNPs, and for BL, SBI, SBW, BH, and GWI, about 1000 to 3000 informative SNPs were needed to achieve whole-genome level predictive abilities. It can be concluded from the test results that breeders can use fewer SNPs to save the breeding costs of genomic selection for some traits.

First Genome-wide Association Analysis for Growth Traits in the Largest Coral Reef-Dwelling Bony Fishes, the Giant Grouper (Epinephelus lanceolatus)

The giant grouper, Epinephelus lanceolatus, is the largest coral reef-dwelling bony fish species. However, despite extremely fast growth performance and the considerable economic importance in this species, its genetic regulation of growth remains unknown. Here, we performed the first genome-wide association study (GWAS) for five growth traits in 289 giant groupers using 42,323 single nucleotide polymorphisms (SNPs) obtained by genotyping-by-sequencing (GBS). We identified a total of 36 growth-related SNPs, of which 11 SNPs reached a genome-wide significance level. The phenotypic variance explained by these SNPs varied from 7.09% for body height to 18.42% for body length. Moreover, 22 quantitative trait loci (QTLs) for growth traits, including nine significant QTLs and 13 suggestive QTLs, were found on multiple chromosomes. Interestingly, the QTL (LG17: 6934451) was shared between body weight and body height, while two significant QTLs (LG7: 22596399 and LG15: 11877836) for body length were consistent with the associated regions of total length at the genome-wide suggestive level. Eight potential candidate genes close to the associated SNPs were selected for expression analysis, of which four genes (phosphatidylinositol transfer protein cytoplasmic 1, protein tyrosine phosphatase receptor type E, alpha/beta hydrolase domain-containing protein 17C, and vascular endothelial growth factor A-A) were differentially expressed and involved in metabolism, development, response stress, etc. This study improves our understanding of the complex genetic architecture of growth in the giant grouper. The results contribute to the selective breeding of grouper species and the conservation of coral reef fishes.

Predicting Growth Traits with Genomic Selection Methods in Zhikong Scallop (Chlamys farreri)

Selective breeding is a common and effective approach for genetic improvement of aquaculture stocks with parental selection as the key factor. Genomic selection (GS) has been proposed as a promising tool to facilitate selective breeding. Here, we evaluated the predictability of four GS methods in Zhikong scallop (Chlamys farreri) through real dataset analyses of four economical traits (e.g., shell length, shell height, shell width, and whole weight). Our analysis revealed that different GS models exhibited variable performance in prediction accuracy depending on genetic and statistical factors, but non-parametric method, including reproducing kernel Hilbert spaces regression (RKHS) and sparse neural networks (SNN), generally outperformed parametric linear method, such as genomic best linear unbiased prediction (GBLUP) and BayesB. Furthermore, we demonstrated that the predictability relied mainly on the heritability regardless of GS methods. The size of training population and marker density also had considerable effects on the predictive performance. In practice, increasing the training population size could better improve the genomic prediction than raising the marker density. This study is the first to apply non-linear model and neural networks for GS in scallop and should be valuable to help develop strategies for aquaculture breeding programs.

Genomic Selection Using BayesCπ and GBLUP for Resistance Against Edwardsiella tarda in Japanese Flounder (Paralichthys olivaceus)

The Japanese flounder is one of the most widely farmed economic flatfish species throughout eastern Asia including China, Korea, and Japan. Edwardsiella tarda is a major species of pathogenic bacteria that causes ascites disease and, consequently, a huge economy loss for Japanese flounder farming. After generation selection, traditional breeding methods can hardly improve the E. tarda resistance effectively. Genomic selection is an effective way to predict the breeding potential of parents and has rarely been used in aquatic breeding. In this study, we chose 931 individuals from 90 families, challenged by E. tarda from 2013 to 2015 as a reference population and 71 parents of these families as selection candidates. 1,934,475 markers were detected via genome sequencing and applied in this study. Two different methods, BayesCπ and GBLUP, were used for genomic prediction. In the reference population, two methods led to the same accuracy (0.946) and Pearson's correlation results between phenotype and genomic estimated breeding value (GEBV) of BayesCπ and GBLUP were 0.912 and 0.761, respectively. In selection candidates, GEBVs from two methods were highly similar (0.980). A comparison of GEBV with the survival rate of families that were structured by selection candidates showed correlations of 0.662 and 0.665, respectively. This study established a genomic selection method for the Japanese flounder and for the first time applied this to E. tarda resistance breeding.

The identification of growth, immune related genes and marker discovery through transcriptome in the yellow drum (Nibea albiflora)

Yellow drum (Nibea albiflora) is a commercially important marine fish, which is widely distributed in the coastal waters of China, Japan and Korea. Wild yellow drum resources have dramatically declined due to overfishing and ocean pollution. Genetic data can contribute to biodiversity conservation and protection. And molecular markers can play important roles in genetic breeding and aid in germplasm preservation in fish. In this study, 11 tissues (brain, heart, liver, kidney, muscle, head kidney, skin, fin, spleen, gonad and air bladder) were collected for pooled RNA sequencing. The unigenes were assembled using Trinity and EvidentialGene, and were then aligned to nr, nt, Swiss-Prot GO, KEGG, and KOG for annotation. Molecular markers (e.g. simple sequence repeat, SSR and single nucleotide polymorphism, SNP) were detected using MIcroSAtellite identification tool (MISA) and Genome Analysis Tool Kit (GATK). All clean reads were assembled into 109,209 transcripts, and 31,183 unigenes were generated after pruning and classifying, ranging from 201 to 19,857 bp in length (1230 bp in average), and 26,728 (85.7%) assembled unigenes had significant hits in public databases. Total of 27 and 103 unigenes were respectively identified as involved in growth- and immune-related pathways in the N. albiflora transcriptome. In addition, we identified a considerable quantity of molecular markers, including 11,484 SSRs and 56,186 SNPs. The growth- and immune-relevant genes and the molecular markers identified here provided a meaningful reference gene set and laid a foundation for future genetic selection and breeding for this species.

QTL Mapping for Resistance to Iridovirus in Asian Seabass Using Genotyping-by-Sequencing

Identifying quantitative trait loci (QTL) for viral disease resistance is of particular importance in selective breeding programs of fish species. Genetic markers linked to QTL can be useful in marker-assisted selection (MAS) for elites resistant to specific pathogens. Here, we conducted a genome scan for QTL associated with Singapore grouper iridovirus (SGIV) resistance in an Asian seabass (Lates calcarifer) family, using a high-density linkage map generated with genotyping-by-sequencing. One genome-wide significant and three suggestive QTL were detected at LG21, LG6, LG13, and LG15, respectively. The phenotypic variation explained (PVE) by the four QTL ranged from 7.5 to 15.6%. The position of the most significant QTL at LG21 was located between 31.88 and 36.81 cM. The SNP marker (SNP130416) nearest to the peak of this QTL was significantly associated with SGIV resistance in an unrelated multifamily population. One candidate gene, MECOM, close to the peak of this QTL region, was predicted. Evidence of alternative splicing was observed for MECOM and one specific category of splicing variants was differentially expressed at 5 days post-SGIV infection. The QTL detected in this study are valuable resources and can be used in the selective breeding programs of Asian seabass with regard to resistance to SGIV.