International Journal of Marine Science, 2025, Vol.15, No.4, 186-198 http://www.aquapublisher.com/index.php/ijms 194 6 Advantages and Challenges of Multigenomic Data in Population Research 6.1 Application cases of high-throughput sequencing in mackerel research The rise of high-throughput sequencing (HTS) technology has injected strong impetus into research on marine fish populations, including mackerels. In the study of mackerel, HTS mainly plays a role in two types: one is genome assembly and comparison, and the other is population genome diversity analysis. The former represents the construction of the reference genome of mackerel. Gao et al. (2024) combined PacBio and Hi-C technologies to assemble the chromosomal genome of broadband mackerel (Indo-Tai Mackerel). The genome is about 798 million bp, divided into 24 chromosomes, which are highly collinear with other fishes of the Macadae family (such as tuna). Gene annotation predicts 25 886 protein-encoded genes (Gao et al., 2024). This is the first high-quality whole genome of the genus Mackerel, which is of great significance to subsequent research. Researchers can use it to compare genes across species to find important genes that regulate growth, adapt to temperature, etc. With more mackerel species genome sequencing, mutual comparisons will reveal the genetic basis of the evolutionary history and adaptive differentiation of the genus. The second type of HTS application is population diversity analysis, that is, obtaining massive SNPs through simplified genome sequencing (such as RAD-seq, GBS, DArT-seq, etc.) to calculate population genetic parameters and detect structures. 6.2 Problems of data integration, standardization and interspecies comparison Although multigenomic data present unprecedented opportunities, there are also some challenges. One of them is the integration and standardization of data obtained from different researches. Since each study may use different sequencing platforms, different types of markers (such as ddRAD vs GBS vs whole genome resequencing), and different analyses of Pipeline, it is not easy to directly compare the results. Taking mackerel as an example, if one study uses RAD-seq to find some significant differentiated SNPs in Region A, and another study uses a genome-wide method to find some markers in Region B, we hope to compare the two to see if they have any commonalities. However, due to the different sequencing fragments, the marker coordinates are difficult to correspond directly, and the data need to be located on the reference genome for comparison (Huang et al., 2024). This requires high-quality reference sequences and annotation of each SNP. Fortunately, there is a broadband mackerel reference genome now, and studies should be encouraged to anchor data to the genome in the future, so that a public coordinate framework can be established to facilitate cross-research integration. Another standardization problem is the comparison of the traditional group plot results. The sample size and number of sites of different studies vary, and the calculated diversity indicators (such as H_e, π values) and differentiation indicators (such as F_ST) are comparatively limited. Certain standardized methods should be used, such as recalculating the metrics using the same downsampled set of sites, or introducing reference populations for calibration. In addition to data integration, comparability among different species is also a challenge. Although the genus Mackerel species have a relatively close relationship, they may have significant differences in genetic variation levels and population history. For example, Japanese mackerels may have experienced more serious historical bottlenecks leading to low diversity, while narrowband mackerels may remain large and effective. If compared indiscriminately, wrong conclusions will be drawn. Researchers should avoid directly comparing the F_ST equivalent values of different species, but should pay more attention to the patterns and reasons within each species. At the same time, parallel research designs are also needed among species. 6.3 The influence of genetic marker selection and research design on conclusions In population genetic research, the selected type of genetic marker and research design framework often directly affect the final conclusion, and research such as mackerel is no exception. Different genetic markers have their own biases. For example, mitochondrial DNA is only one-quarter of the effective population size of nuclear DNA due to maternal haploid inheritance, so it is more sensitive to bottlenecks and drift changes, and its ability to detect population differentiation is also different from that of nuclear markers (Canino et al., 2010). This can explain why mtDNA did not find differentiation of the Yellow Sea and East China Sea Japanese mackerel, while nuclear markers such as AFLP were detected. Because if the differentiation time is short, the maternal gene may not have fixed differences, and slight frequency changes in nuclear genes can be measured.
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