Sea lice are parasitic crustaceans that infest Atlantic salmon (Salmo salar). They attach to the skin of salmons and feed on tissue, mucus and blood, causing abrasion-like lesions, open injuries and stress that in turns lead to reduced growth rates, secondary infection due to opportunistic pathogens and increased mortality. Sea lice represent one of the most important threats to salmon aquaculture and welfare, causing millions of losses worldwide. Several species of lice can affect Atlantic salmon, with Lepeophtheirus salmonis being predominant in the northern hemisphere and Caligus rogercresseyi in the southern hemisphere. Better understanding of genetic resistance to both parasites is a prerequisite to include in selective breeding strategies to improve lice resistance. Here we study resistance to both L. salmonis and C. rogercresseyi in Atlantic salmon, working with the same salmon families in the two hemispheres to assess whether resistance to both parasites has the same genetic background.
Material and methods
During three consecutive years, 2017 to 2019, a total of 4 375, 3 730 and 5 346 Atlantic salmon fish were produced, respectively, belonging to 160 to 200 full-sib families from Benchmark Genetics breeding programme. For each year-class (YC) , the offspring were separated into two groups at the eyed eggs stage and sent to two different locations for rearing and disease challenge. Half of the offspring were challenged in Iceland with L. salmonis , while the other half were challenged in Chile using C. rogercresseyi . A s imilar challenge protocol was used across locations and year-classes. Briefly, fish were raised in separate family tanks until tagging , and then mixed. For the challenge, fish were separated into 2 to 4 tanks with a recirculating system, and 30 (in Iceland) or 40 (in Chile) copepodite of lice per fish were deposited in each tank. After 7 to 15 days, the number of lice (at sessile stage) attached to each fish were visually assessed and reported as sea lice count (SLC). The body weight (BW) of each fish was recorded before and after the challenge.
The fish were genotyped using 57K (Chile, YC2017) or 65K SNP (all other year-classes) array s (with 33K shared SNPs). Standard quality controls on SNPs and individuals were performed using PLINK  in each dataset separately , and then all were merged into a single dataset. Genotype imputation using FImpute3  was performed to obtain a final dataset with 61,065 SNPs .
Genetic parameters (variance, heritability and genetic correlation) were estimated using blupf90  . Genome wide association studies (G WAS) were performed with a mixed- linear-model implemented in GCTA  to detect QTL associated with resistance. The animal model used for variance component estimation and GWAS included tank, sex and counter (when available) as fixed effect and body weight as covariate .
Heritability of Atlantic salmon r esistance to each lice species was consistent across year-classes a nd estimated to be low for L. salmonis and moderate for C. rogercresseyi (Table 1) . A low to null genetic correlation was observed in YC2017 and YC2019 between resistance to the two lice species . In YC2018, a high positive genetic correlation was estimated for resistance to L. salmonis and resistance to C. rogercresseyi.
Body weight was highly heritable in both locations, with a higher heritability in Iceland than in Chile for YC17 and YC18 . High positive genetic correlations were estimated across locations for body weight measured within a year class.
The GWAS performed showed that r esistance to both sea lice species is highly polygenic. Only one QTL above the 5% genome wide significance threshold was identified, located on chromosome Ssa15 for resistance to C. rogercresseyi in YC2017.
Discussion and conclusion
Resistance to L. salmonis and resistance to C. rogercresseyi in Atlantic salmon are highly polygenic. It is unclear whether resistance to the two species share common genetic mechanisms. The absence of genetic correlation for resistance to the two sea lice species in YC2017 and YC2019 and the high positive correlation observed in YC20 18 might be because of differences in the challenge methods and the parasite counting procedure . Indeed, the size of the fish at challenge and the number of days of challenged and the number of people involved in the counting of the lice varied according to the location and the year, reflecting normal practices in the farms. A meta-analysis combining all three year-class will be performed to better understand the genetic architecture of sea lice resistance using a powerful dataset of over 10,000 fish.
 Chang CC, et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 2015;4:7 . https://doi.org/10.1186/s13742-015-0047-8.
 Sargolzaei M, et al. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 2014;15. https://doi.org/10.1186/1471-2164-15-478.
 Misztal I, et al. BLUPF90 and related programs (BGF90). Proc. 7th World Congr . Genet. Appl. Livest . Prod., vol. Comm. No.28, Montpellier, France.: 2002, p. 743–4.
 Yang J, et al. GCTA: A tool for genome-wide complex trait analysis. Am J Hum Genet 2011;88:76 –82. https://doi.org/10.1016/j.ajhg.2010.11.011.