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

ABSTRACT: 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.

SUBMITTER: Jafari O

PROVIDER: S-EPMC9315565 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCommon carp (Cyprinus carpio) is one of the most important aquaculture species with an annual global production of 3.4 million metric tons. It is also an important ornamental species as well as an important model species for aquaculture research. To improve the economically important traits of this fish, a number of genomic resources and genetic tools have been developed, including several genetic maps and a bacterial artificial chromosome (BAC)-based physical map. However, integrated genetic and physical maps are not available to study quantitative trait loci (QTL) and assist with fine mapping, positional cloning and whole genome sequencing and assembly. The objective of this study was to integrate the currently available BAC-based physical and genetic maps.ResultsThe genetic map was updated with 592 novel markers, including 312 BAC-anchored microsatellites and 130 SNP markers, and contained 1,209 genetic markers on 50 linkage groups, spanning 3,565.9 cM in the common carp genome. An integrated genetic and physical map of the common carp genome was then constructed, which was composed of 463 physical map contigs and 88 single BACs. Combined lengths of the contigs and single BACs covered a physical length of 498.75 Mb, or around 30% of the common carp genome. Comparative analysis between common carp and zebrafish genomes was performed based on the integrated map, providing more insights into the common carp specific whole genome duplication and segmental rearrangements in the genome.ConclusionWe integrated a BAC-based physical map to a genetic linkage map of common carp by anchoring BAC-associated genetic markers. The density of the genetic linkage map was significantly increased. The integrated map provides a tool for both genetic and genomic studies of common carp, which will help us to understand the genomic architecture of common carp and facilitate fine mapping and positional cloning of economically important traits for genetic improvement and modification.

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

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