Genetic diversity of Olive flounder (Paralichthys olivaceus) and the impact of selective breeding on Korean populations

This study aimed to identify the population structure and genetic diversity of olive flounder (Paralichthys olivaceus) in Korea and to examine the potential for genetic improvement in aquaculture populations. PCA showed NIFS and FarmA as closely related clusters, while FarmB exhibited moderate differentiation with greater variability. Fst analysis indicated high similarity between NIFS and farmed populations (0.021-0.043) but significant differentiation from wild populations (0.274-0.295). Admixture analysis highlighted a shared ancestral component (over 70%) among NIFS and farmed populations, contrasting with the unique genetic makeup of wild populations. The phylogenetic tree confirmed these patterns, with NIFS and FarmA forming close branches, FarmB showing intermediate placement, and wild populations clustering separately. Additionally, genomic estimated breeding values for body weight showed no significant differences between FarmA and FarmB, while prediction accuracy was higher for FarmA (47%) compared to FarmB (45%), indicating a closer genetic relationship between NIFS and FarmA. These findings emphasize the critical role of selective breeding and gene flow in shaping the genetic structure of farmed populations, offering valuable insights for improving growth traits and maintaining genetic diversity in aquaculture.

Genotyping-by-sequencing and weighted gene co-expression network analysis of genes responsive against Potato virus Y in commercial potato cultivars

Potato is considered a key component of the global food system and plays a vital role in strengthening world food security. A major constraint to potato production worldwide is the Potato Virus Y (PVY), belonging to the genus Potyvirus in the family of Potyviridae. Selective breeding of potato with resistance to PVY pathogens remains the best method to limit the impact of viral infections. Understanding the genetic diversity and population structure of potato germplasm is important for breeders to improve new cultivars for the sustainable use of genetic materials in potato breeding to PVY pathogens. While, genetic diversity improvement in modern potato breeding is facing increasingly narrow genetic basis and the decline of the genetic diversity. In this research, we performed genotyping-by-sequencing (GBS)-based diversity analysis on 10 commercial potato cultivars and weighted gene co-expression network analysis (WGCNA) to identify candidate genes related to PVY-resistance. WGCNA is a system biology technique that uses the WGCNA R software package to describe the correlation patterns between genes in multiple samples. In terms of consumption, these cultivars are a high rate among Iranian people. Using population structure analysis, the 10 cultivars were clustered into three groups based on the 118343 single nucleotide polymorphisms (SNPs) generated by GBS. Read depth ranged between 5 and 18. The average data size and Q30 of the reads were 145.98 Mb and 93.63%, respectively. Based on the WGCNA and gene expression analysis, the StDUF538, StGTF3C5, and StTMEM161A genes were associated with PVY resistance in the potato genome. Further, these three hub genes were significantly involved in defense mechanism where the StTMEM161A was involved in the regulation of alkalization apoplast, the StDUF538 was activated in the chloroplast degradation program, and the StGTF3C5 regulated the proteins increase related to defense in the PVY infected cells. In addition, in the genetic improvement programs, these hub genes can be used as genetic markers for screening commercial cultivars for PVY resistance. Our survey demonstrated that the combination of GBS-based genetic diversity germplasm analysis and WGCNA can assist breeders to select cultivars resistant to PVY as well as help design proper crossing schemes in potato breeding.

Integration of Morphometrics and Machine Learning Enables Accurate Distinction between Wild and Farmed Common Carp

Morphology and feature selection are key approaches to address several issues in fisheries science and stock management, such as the hypothesis of admixture of Caspian common carp (Cyprinus carpio) and farmed carp stocks in Iran. The present study was performed to investigate the population classification of common carp in the southern Caspian basin using data mining algorithms to find the most important characteristic(s) differing between Iranian and farmed common carp. A total of 74 individuals were collected from three locations within the southern Caspian basin and from one farm between November 2015 and April 2016. A dataset of 26 traditional morphometric (TMM) attributes and a dataset of 14 geometric landmark points were constructed and then subjected to various machine learning methods. In general, the machine learning methods had a higher prediction rate with TMM datasets. The highest decision tree accuracy of 77% was obtained by rule and decision tree parallel algorithms, and “head height on eye area” was selected as the best marker to distinguish between wild and farmed common carp. Various machine learning algorithms were evaluated, and we found that the linear discriminant was the best method, with 81.1% accuracy. The results obtained from this novel approach indicate that Darwin’s domestication syndrome is observed in common carp. Moreover, they pave the way for automated detection of farmed fish, which will be most beneficial to detect escapees and improve restocking programs.