# Single- and Multi-Trait Genome-Wide Association Analyses Identify the Genetic Loci and Candidate Genes for Growth Traits in Plecoglossus altivelis

## TL;DR

This study identifies genetic markers and candidate genes for growth in Plecoglossus altivelis, a valuable fish species, to support more effective breeding programs.

## Contribution

The study integrates single- and multi-trait GWAS methods to identify stable genetic loci and candidate genes for growth traits in Plecoglossus altivelis.

## Key findings

- Twenty-nine SNPs significantly associated with seven traits were identified through combined GWAS analyses.
- Five candidate genes (LOC131530706, LOC134022516, abat, slc25a12, dnah10) were found to be stable across both single- and multi-trait GWAS methods.
- The study provides molecular markers and genetic resources for improving growth traits in Plecoglossus altivelis.

## Abstract

This study aimed to identify the key genes controlling growth in the economically important fish, Plecoglossus altivelis, to enable faster genetic improvement through breeding. Ayu is an anadromous teleost fish—a group characterized by bony skeletons, which includes the vast majority of farmed fish species. It is prized for its delicate texture and unique melon-like aroma. We analyzed the DNA of 426 Plecoglossus altivelis to find variations called Single-Nucleotide Polymorphisms (SNPs), which are single-letter changes in the genetic code that serve as markers for traits. Using a Genome-Wide Association Study (GWAS) approach, we scanned these SNPs to find those linked to growth. For robust results, we used two complementary software tools: GCTA (version 1.94.1), which effectively accounts for family relatedness among individuals, and GEMMA (version 0.98), which excels at analyzing multiple traits simultaneously to find genes with broad effects. Our integrated analysis successfully identified several significant SNPs and candidate genes (e.g., abat, slc25a12) associated with growth. In conclusion, this study successfully achieved its objective by mapping the genetic architecture of growth in Plecoglossus altivelis, delivering crucial molecular markers and candidate genes for future marker-assisted and genomic selection breeding programs.

With the rapid development of genomic big data and genome-wide association study technologies, massive genomic data are available for the genetic dissection, development and utilization of important economic traits. Various GWAS algorithms have become increasingly efficient, enabling high-performance processing of these massive datasets. This has made it possible to conduct genetic dissection of economic traits based on big data and advanced statistical methods, which will provide accurate target loci for future trait improvement and genetic manipulation, greatly accelerating the process of genetic breeding. In this study, genotyping of 426 fish was performed using the T7 sequencing platform and 555,242 SNPs distributed across all the chromosomes were screened by data cleaning. We compared the performance of two GWAS methods, GCTA and GEMMA, in both single-trait and multi-trait frameworks. Twenty-nine SNPs significantly associated with seven traits were identified through single and multi-trait combined GWAS. Single-trait GWAS analysis using GCTA identified 1047 and 1452 significant loci for six growth traits and one sex trait (phenotypic sex, male or female) respectively, ultimately revealing 10 candidate genes, including slc48a1a, filip1L, nedd9, Crebbpa, LOC134024622, zbtb18, LOC117378376, LOC131530706, syde2, and col24a1. Similarly, 671 and 642 significant SNPs were detected with GEMMA for single-trait GWAS associated with six growth traits and the sex trait, respectively. In total, 16 candidate genes were mapped for these seven traits. Multi-trait GWAS was also performed using GEMMA for the six growth traits (sex was included as a covariate). The traits were grouped into five combinations based on their genetic correlations. A total of 37 SNPs were identified, corresponding to 10 candidate genes: LOC131530706, LOC134022516, abat, maml3, cica, LOC124013321, slc25a12, dnah10, syt9a, and LOC136932979. Notably, five overlapping candidate genes (LOC131530706, LOC134022516, abat, slc25a12 and dnah10) were also identified in both single- and multi-trait GWAS methods of GEMMA, highlighting their genetic stability and significance. The two GWAS methods, GCTA and GEMMA, identified two genes that were the same. The results of this study provide molecular markers and genetic resources for the improvement of growth traits in Plecoglossus altivelis.