Development and Evaluation of a High-Throughput Single-Nucleotide Polymorphism Array for Large Yellow Croaker (Larimichthys crocea)

Immune regulation differences in Large yellow croaker with varied resistance to Cryptocaryon irritans infection

Large yellow croaker (Larimichthys crocea) is one of the main marine aquaculture species in China, but has faced considerable losses due to Cryptocaryon irritans infection. In this study, we successfully established a C. irritans-susceptible population of large yellow croaker by genomic selection technology. We then compared the immune genetic mechanisms of this susceptible population with those of a large yellow croaker population from eastern Fujian in response to C. irritans infection. GWAS identified 44 significant SNPs across 11 QTL regions on different chromosomes associated with C. irritans infection, with most located on chromosomes 1 and 24. Notably, the QTL region on chromosome 1 overlapped with the resistance QTL region mapped in the C. irritans-resistant population previously established by our team, underscoring its crucial role in conferring resistance to C. irritans infection. RNA-Seq analysis revealed significant differences in immune responses between the two groups, with the susceptible group specifically activating the Jak/Stat signaling pathway and upregulating interleukin-related genes, including il11a, il-5r and il-20r. A combined analysis of the GWAS and RNA-Seq data revealed that cspg4 was located in the overlapping QTL region on chromosome 1 associated with resistance. Upon infection, the expression of cspg4 was significantly higher in the susceptible group compared to the control group. As a downstream factor of interleukins, cspg4 may regulate interleukin expression by activating the Jak/Stat pathway, thereby influencing the body's normal immune defense functions. These findings provide new insights into the mechanisms of host-parasite immune responses and highlight potential therapeutic targets.

Genome-Wide Association Study Reveals Growth-Related SNPs and Candidate Genes in Largemouth Bass (Micropterus salmoides) Adapted to Hypertonic Environments

Sustainable development of the largemouth bass industry is hindered by limited freshwater resources. Consequently, the expansion of farming space by brackish and saline water aquaculture has become imperative. Largemouth bass is an economically important freshwater fish species. However, there is presently a lack of germplasm resources with the capacity to adapt to hypertonic environments and maintain rapid growth. A genome-wide association study is a technique used for the detection of genetic variants associated with specific phenotypic traits. In this study, we firstly applied this technique to explore the potential single-nucleotide polymorphism (SNP) locus and candidate genes associated with rapid growth and adaptation to the hypertonic environment of largemouth bass. A total of 10 potential growth-related SNPs were obtained on chromosome 16, and SNP16:4120214 was a significant SNP peak. Based on these SNPs, 23 candidate genes were annotated in the genome, including Nkcc1, Mapkap1, Hmgcs1, Slc27a6, and Shroom3. Shroom3 expression was significantly higher in individuals enriched for the most growth-advantageous genotypes. Shroom3 upregulation is beneficial for fish growth in hyperosmotic environments. This study provides insight into the genetic basis of rapid growth in hypertonic environments and foundational information for the future breeding of salt-tolerant largemouth bass.

Epi-SSA: A novel epistasis detection method based on a multi-objective sparrow search algorithm

Genome-wide association studies typically considers epistatic interactions as a crucial factor in exploring complex diseases. However, the current methods primarily concentrate on the detection of two-order epistatic interactions, with flaws in accuracy. In this work, we introduce a novel method called Epi-SSA, which can be better utilized to detect high-order epistatic interactions. Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions. To evaluate its performance, we conducted a comprehensive comparison between Epi-SSA and seven other methods using five simulation datasets: DME 100, DNME 100, DME 1000, DNME 1000 and DNME3 100. The DME 100 dataset encompasses eight second-order epistasis disease models with marginal effects, each comprising 100 simulated data instances, featuring 100 SNPs per instance, alongside 800 case and 800 control samples. The DNME 100 encompasses eight second-order epistasis disease models without marginal effects and retains other properties consistent with DME 100. Experiments on the DME 100 and DNME 100 datasets were designed to evaluate the algorithms' capacity to detect epistasis across varying disease models. The DME 1000 and DNME 1000 datasets extend the complexity with 1000 SNPs per simulated data instance, while retaining other properties consistent with DME 100 and DNME 100. These experiments aimed to gauge the algorithms' adaptability in detecting epistasis as the number of SNPs in the data increases. The DNME3 100 dataset introduces a higher level of complexity with six third-order epistasis disease models, otherwise paralleling the structure of DNME 100, serving to test the algorithms' proficiency in identifying higher-order epistasis. The highest average F-measures achieved by the seven other existing methods on the five datasets are 0.86, 0.86, 0.41, 0.56, and 0.79 respectively, while the average F-measures of Epi-SSA on the five datasets are 0.92, 0.97, 0.79, 0.86, and 0.97 respectively. The experimental results demonstrate that the Epi-SSA algorithm outperforms other methods in a variety of epistasis detection tasks. As the number of SNPs in the data set increases and the order of epistasis rises, the advantages of the Epi-SSA algorithm become increasingly pronounced. In addition, we applied Epi-SSA to the analysis of the WTCCC dataset, uncovering numerous genes and gene pairs that might play a significant role in the pathogenesis of seven complex diseases. It is worthy of note that some of these genes have been relatedly reported in the Comparative Toxicogenomics Database (CTD). Epi-SSA is a potent tool for detecting epistatic interactions, which aids us in further comprehending the pathogenesis of common and complex diseases. The source code of Epi-SSA can be obtained at https://osf.io/6sqwj/.

Advances in biotechnology and breeding innovations in China's marine aquaculture

Biotechnology is the key driving force behind the sustainable development of aquaculture, as biological innovation would significantly improve the capabilities of aquatic breeding and achieve independent and controllable seeding sources to ensure food safety. In this article, we have analyzed the current status and existing problems of marine aquaculture in China. Based on these data, we have summarized the recent (especially the last 10 years) biotechnological innovation and breeding progress of marine aquaculture in China, including whole genome sequencing, sex-related marker screening, genomic selection, and genome editing, as well as progress of improved marine fish varieties in China. Finally, the perspectives in this field have been discussed, and three future countermeasures have been proposed.

Decoding the fish genome opens a new era in important trait research and molecular breeding in China

Aquaculture represents the fastest-growing global food production sector, as it has become an essential component of the global food supply. China has the world's largest aquaculture industry in terms of production volume. However, the sustainable development of fish culture is hindered by several concerns, including germplasm degradation and disease outbreaks. The practice of genomic breeding, which relies heavily on genome information and genotypephenotype relationships, has significant potential for increasing the efficiency of aquaculture production. In 2014, the completion of the genome sequencing and annotation of the Chinese tongue sole signified the beginning of the fish genomics era in China. Since then, domestic researchers have made dramatic progress in functional genomic studies. To date, the genomes of more than 60 species of fish in China have been assembled and annotated. Based on these reference genomes, evolutionary, comparative, and functional genomic studies have revolutionized our understanding of a wide range of biologically and economically important traits of fishes, including growth and development, sex determination, disease resistance, metamorphosis, and pigmentation. Furthermore, genomic tools and breeding techniques such as SNP arrays, genomic selection, and genome editing have greatly accelerated genetic improvement through the incorporation of functional genomic information into breeding activities. This review aims to summarize the current status, advances, and perspectives of the genome resources, genomic study of important traits, and genomic breeding techniques of fish in China. The review will provide aquaculture researchers, fish breeders, and farmers with updated information concerning fish genomic research and breeding technology. The summary will help to promote the genetic improvement of production traits and thus will support the sustainable development of fish aquaculture.

Integrative analysis of GWAS and transcriptome reveals p53 signaling pathway mediates resistance to visceral white-nodules disease in large yellow croaker

Visceral white-nodules disease (VWND), caused by Pseudomonas plecoglossicida, is one of the primary causes of morbidity and mortality in large yellow croaker aquaculture. Host disease resistance is a heritable trait that involves complex regulatory processes. However, the regulatory mechanism of bacterial resistance in large yellow croaker is still unclear. This study attempted to systematically evaluate the major genetic loci and transcriptional regulatory mechanisms associated with the resistance to VWND in large yellow croaker by crossover method studies. A large population of large yellow croaker was challenged with P. plecoglossicida, with survival time recorded and samples were taken for genotyping. Meanwhile, spleen samples that were used for RNA-seq to compare their transcriptomic profiles before and after infection were taken from resistant populations (RS) and susceptible control populations (CS) bred using the genomic selection (GS) technique. Genome-wide association analyses using 46 K imputed SNP genotypes highlighted that resistance is a polygenic trait. The integrative analysis results show the co-localization of the cd82a gene between disease resistance-related genetic loci and comparative transcriptional analysis. And functional enrichment analysis showed differential enrichment of the p53 signaling pathway in RS and CS groups, suggesting that there may be cd82a-mediated p53 signaling pathway activation for VWND resistance. This large-scale study provides further evidence for the heritability and transcriptional regulatory mechanisms of host inheritance of VWND resistance.

Development of a High-Density 665 K SNP Array for Rainbow Trout Genome-Wide Genotyping

Single nucleotide polymorphism (SNP) arrays, also named « SNP chips », enable very large numbers of individuals to be genotyped at a targeted set of thousands of genome-wide identified markers. We used preexisting variant datasets from USDA, a French commercial line and 30X-coverage whole genome sequencing of INRAE isogenic lines to develop an Affymetrix 665 K SNP array (HD chip) for rainbow trout. In total, we identified 32,372,492 SNPs that were polymorphic in the USDA or INRAE databases. A subset of identified SNPs were selected for inclusion on the chip, prioritizing SNPs whose flanking sequence uniquely aligned to the Swanson reference genome, with homogenous repartition over the genome and the highest Minimum Allele Frequency in both USDA and French databases. Of the 664,531 SNPs which passed the Affymetrix quality filters and were manufactured on the HD chip, 65.3% and 60.9% passed filtering metrics and were polymorphic in two other distinct French commercial populations in which, respectively, 288 and 175 sampled fish were genotyped. Only 576,118 SNPs mapped uniquely on both Swanson and Arlee reference genomes, and 12,071 SNPs did not map at all on the Arlee reference genome. Among those 576,118 SNPs, 38,948 SNPs were kept from the commercially available medium-density 57 K SNP chip. We demonstrate the utility of the HD chip by describing the high rates of linkage disequilibrium at 2–10 kb in the rainbow trout genome in comparison to the linkage disequilibrium observed at 50–100 kb which are usual distances between markers of the medium-density chip.

Development of a high-throughput SNP array for sea cucumber (Apostichopus japonicus) and its application in genomic selection with MCP regularized deep neural networks

High-throughput single nucleotide polymorphism (SNP) genotyping assays are powerful tools for genetic studies and genomic breeding applications for many species. Though large numbers of SNPs have been identified in sea cucumber (Apostichopus japonicus), but, as yet, no high-throughput genotyping platform is available for this species. In this study, we designed and developed a high-throughput 24 K SNP genotyping array named HaishenSNP24K for A. japonicus, based on the multi-objective-local optimization (MOLO) algorithm and HD-Marker genotyping method. The SNP array exhibited a relatively high genotyping call rate (> 96%), genotyping accuracy (>95%) and exhibited highly polymorphic in sea cucumber populations. In addition, we also assessed its application in genomic selection (GS). Deep neural networks (DNN) that can capture the complicated interactions of genes have been proposed as a promising tool in GS for SNP-based genomic prediction of complex traits in animal breeding. To overcome the problem of over-fitting when using the HaishenSNP24K array as high-dimensional DNN input, we developed minmax concave penalty (MCP) regularization for sparse deep neural networks (DNN-MCP) that finds an optimal sparse structure of a DNN by minimizing the square error subject to the non-convex penalty MCP on the parameters (weights and biases). Compared to two linear models, namely RR-GBLUP and Bayes B, and the nonlinear model DNN, DNN-MCP has greatly improved the genomic prediction ability for three quantitative traits (e.g., wet weight, dry weight and survival time) in the sea cucumber population. To the best of our knowledge, this is the first work to develop a high-throughput SNP array for A. japonicus and a new model DNN-MCP for genomic prediction of complex traits in GS. The present results provide evidence that supports the HaishenSNP24K array with DNN-MCP will be valuable for genetic studies and molecular breeding in A. japonicus.