Single- and Multi-Trait Genome-Wide Association Analyses Identify the Genetic Loci and Candidate Genes for Growth...

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.