Genomics advances for boosting aquaculture breeding programs in Spain

Integration of host-pathogen functional genomics data into the chromosome-level genome assembly of turbot (Scophthalmus maximus)

“…The positions of 49 ROHi (Aramburu et al, 2020) identified in the previous turbot chromosome-level assembly (ASM318616v1; Maroso et al, 2018) were transferred to the last version (ASM1334776v1; Martínez et al, 2021). These ROHi are suggestive signatures of domestication or selection generated in breeding programs during more than 30 years of turbot farming (Martínez, 2016). Similarly, the highest associated QTL-markers or related to adaptive variation in wild or farm populations were relocated in the updated turbot genome assembly.…”

“…Recently, Aramburu et al (2020) took advantage of the amount of genomic information existing in turbot to check for selective sweeps around previously reported signatures of selection and QTL for productive traits in wild and farmed populations (Martínez, 2016;do Prado et al, 2018ado Prado et al, , 2018b through characterization of Runs of Homozygosity islands (ROHi; Metzger et al, 2015) and heterozygosity across its genome. The availability of a highly contiguous chromosome-level genome assembly based on long-read sequencing (97% of the assembly integrated in the n = 22 turbot chromosomes; Martínez et al, 2021) opens the opportunity, as suggested by these authors, for integrating all previous genomic information related to productive traits and adaptive variation, and further updating functional annotation.…”

Multiomics uncovers the epigenomic and transcriptomic response to viral and bacterial stimulation in turbot

“…Functional annotation of the turbot transcriptome has been performed against high-quality reference genomes (Figueras et al, 2016; Maroso et al, 2018; Xu et al, 2020; Martinez et al, 2021), including for immune-organs stimulated with viruses (Diaz-Rosales et al, 2012), bacteria (Millán et al, 2011; Libran-Pérez et al, 2022) and parasites (Pardo et al, 2012; Robledo et al, 2014; Ronza et al, 2016; Valle et al, 2020). Candidate genes for disease resistance have been further explored by mapping differentially expressed genes (DEGs) within QTL regions (Martínez, 2016; Saura et al, 2019; Aramburu et al, 2023). However, limited attention has been given to non-coding regulatory elements, beyond a recent analysis of chromatin accessibility focussed on early development (Guerrero-Peña et al, 2023).…”