Genome-wide detection of positive and balancing signatures of selection shared by four domesticated rainbow trout populations (Oncorhynchus mykiss).

BACKGROUND: Evolutionary processes leave footprints along the genome over time. Highly homozygous regions may correspond to positive selection of favorable alleles, while maintenance of heterozygous regions may be due to balancing selection phenomena. We analyzed data from 176 fish from four disconnected domestic rainbow trout populations that were genotyped using a high-density Axiom Trout genotyping 665K single nucleotide polymorphism array, including 20 from the US and 156 from three French lines. Using methods based on runs of homozygosity and extended haplotype homozygosity, we detected signatures of selection in these four populations. RESULTS: Nine genomic regions that included 253 genes were identified as being under positive selection in all four populations Most were located on chromosome 2 but also on chromosomes 12, 15, 16, and 20. In addition, four heterozygous regions that contain 29 genes that are putatively under balancing selection were also shared by the four populations. These were located on chromosomes 10, 13, and 19. Regardless of the homozygous or heterozygous nature of the regions, in each region, we detected several genes that are highly conserved among vertebrates due to their critical roles in cellular and nuclear organization, embryonic development, or immunity. We identified new candidate genes involved in rainbow trout fitness, as well as 17 genes that were previously identified to be under positive selection, 10 of which in other fishes (auts2, atp1b3, zp4, znf135, igf-1α, brd2, col9a2, mrap2, pbx1, and emilin-3). CONCLUSIONS: Using material from disconnected populations of different origins allowed us to draw a genome-wide map of signatures of positive selection that are shared between these rainbow trout populations, and to identify several regions that are putatively under balancing selection. These results provide a valuable resource for future investigations of the dynamics of genetic diversity and genome evolution during domestication.

Uncertainty in the mating strategy of honeybees causes bias and unreliability in the estimates of genetic parameters.

BACKGROUND: Breeding queens may be mated with drones that are produced by a single drone-producing queen (DPQ), or a group of sister-DPQs, but often only the dam of the DPQ(s) is reported in the pedigree. Furthermore, datasets may include colony phenotypes from DPQs that were open-mated at different locations, and thus to a heterogeneous drone population. METHODS: Simulation was used to investigate the impact of the mating strategy and its modelling on the estimates of genetic parameters and genetic trends when the DPQs are treated in different ways in the statistical evaluation model. We quantified the bias and standard error of the estimates when breeding queens were mated to one DPQ or a group of DPQs, assuming that this information was known or not. We also investigated four alternative strategies to accommodate the phenotypes of open-mated DPQs in the genetic evaluation: excluding their phenotypes, adding a dummy pseudo-sire in the pedigree, or adding a non-genetic (fixed or random) effect to the statistical evaluation model to account for the origin of the mates. RESULTS: The most precise estimates of genetic parameters and genetic trends were obtained when breeding queens were mated with drones of single DPQs that are correctly assigned in the pedigree. However, when they were mated with drones from one or a group of DPQs, and this information was not known, erroneous assumptions led to considerable bias in these estimates. Furthermore, genetic variances were considerably overestimated when phenotypes of colonies from open-mated DPQs were adjusted for their mates by adding a dummy pseudo-sire in the pedigree for each subpopulation of open-mating drones. On the contrary, correcting for the heterogeneous drone population by adding a non-genetic effect in the evaluation model produced unbiased estimates. CONCLUSIONS: Knowing only the dam of the DPQ(s) used in each mating may lead to erroneous assumptions on how DPQs were used and severely bias the estimates of genetic parameters and trends. Thus, we recommend keeping track of DPQs in the pedigree, and not only of the dams of DPQ(s). Records from DPQ colonies with queens open-mated to a heterogeneous drone population can be integrated by adding non-genetic effects to the statistical evaluation model.

Genetic architecture of acute hyperthermia resistance in juvenile rainbow trout (Oncorhynchus mykiss) and genetic correlations with production traits.

BACKGROUND: Selective breeding is a promising solution to reduce the vulnerability of fish farms to heat waves, which are predicted to increase in intensity and frequency. However, limited information about the genetic architecture of acute hyperthermia resistance in fish is available. Two batches of sibs from a rainbow trout commercial line were produced: the first (N = 1382) was phenotyped for acute hyperthermia resistance at nine months of age and the second (N = 1506) was phenotyped for main production traits (growth, body length, muscle fat content and carcass yield) at 20 months of age. Fish were genotyped on a 57 K single nucleotide polymorphism (SNP) array and their genotypes were imputed to high-density based on the parent's genotypes from a 665 K SNP array. RESULTS: The heritability estimate of resistance to acute hyperthermia was 0.29 ± 0.05, confirming the potential of selective breeding for this trait. Since genetic correlations of acute hyperthermia resistance with the main production traits near harvest age were all close to zero, selecting for acute hyperthermia resistance should not impact the main production traits, and vice-versa. A genome-wide association study revealed that resistance to acute hyperthermia is a highly polygenic trait, with six quantitative trait loci (QTL) detected, but explaining less than 5% of the genetic variance. Two of these QTL, including the most significant one, may explain differences in acute hyperthermia resistance across INRAE isogenic lines of rainbow trout. Differences in mean acute hyperthermia resistance phenotypes between homozygotes at the most significant SNP was 69% of the phenotypic standard deviation, showing promising potential for marker-assisted selection. We identified 89 candidate genes within the QTL regions, among which the most convincing functional candidates are dnajc7, hsp70b, nkiras2, cdk12, phb, fkbp10, ddx5, cygb1, enpp7, pdhx and acly. CONCLUSIONS: This study provides valuable insight into the genetic architecture of acute hyperthermia resistance in juvenile rainbow trout. We show that the selection potential for this trait is substantial and selection for this trait should not be too detrimental to improvement of other traits of interest. Identified functional candidate genes provide new knowledge on the physiological mechanisms involved in acute hyperthermia resistance, such as protein chaperoning, oxidative stress response, homeostasis maintenance and cell survival.

Whole-genome sequencing identifies interferon-induced protein IFI6/IFI27-like as a strong candidate gene for VNN resistance in European sea bass.

BACKGROUND: Viral nervous necrosis (VNN) is a major disease that affects European sea bass, and understanding the biological mechanisms that underlie VNN resistance is important for the welfare of farmed fish and sustainability of production systems. The aim of this study was to identify genomic regions and genes that are associated with VNN resistance in sea bass. RESULTS: We generated a dataset of 838,451 single nucleotide polymorphisms (SNPs) identified from whole-genome sequencing (WGS) in the parental generation of two commercial populations (A: 2371 individuals and B: 3428 individuals) of European sea bass with phenotypic records for binary survival in a VNN challenge. For each population, three cohorts were submitted to a red-spotted grouper nervous necrosis virus (RGNNV) challenge by immersion and genotyped on a 57K SNP chip. After imputation of WGS SNPs from their parents, quantitative trait loci (QTL) were mapped using a Bayesian sparse linear mixed model (BSLMM). We found several QTL regions that were specific to one of the populations on different linkage groups (LG), and one 127-kb QTL region on LG12 that was shared by both populations and included the genes ZDHHC14, which encodes a palmitoyltransferase, and IFI6/IFI27-like, which encodes an interferon-alpha induced protein. The most significant SNP in this QTL region was only 1.9 kb downstream of the coding sequence of the IFI6/IFI27-like gene. An unrelated population of four large families was used to validate the effect of the QTL. Survival rates of susceptible genotypes were 40.6% and 45.4% in populations A and B, respectively, while that of the resistant genotype was 66.2% in population B and 78% in population A. CONCLUSIONS: We have identified a genomic region that carries a major QTL for resistance to VNN and includes the ZDHHC14 and IFI6/IFI27-like genes. The potential involvement of the interferon pathway, a well-known anti-viral defense mechanism in several organisms (chicken, human, or fish), in survival to VNN infection is of particular interest. Our results can lead to major improvements for sea bass breeding programs through marker-assisted genomic selection to obtain more resistant fish.

Genetic architecture and genomic selection of fatty acid composition predicted by Raman spectroscopy in rainbow trout.

BACKGROUND: In response to major challenges regarding the supply and sustainability of marine ingredients in aquafeeds, the aquaculture industry has made a large-scale shift toward plant-based substitutions for fish oil and fish meal. But, this also led to lower levels of healthful n-3 long-chain polyunsaturated fatty acids (PUFAs)-especially eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids-in flesh. One potential solution is to select fish with better abilities to retain or synthesise PUFAs, to increase the efficiency of aquaculture and promote the production of healthier fish products. To this end, we aimed i) to estimate the genetic variability in fatty acid (FA) composition in visceral fat quantified by Raman spectroscopy, with respect to both individual FAs and groups under a feeding regime with limited n-3 PUFAs; ii) to study the genetic and phenotypic correlations between FAs and processing yields- and fat-related traits; iii) to detect QTLs associated with FA composition and identify candidate genes; and iv) to assess the efficiency of genomic selection compared to pedigree-based BLUP selection. RESULTS: Proportions of the various FAs in fish were indirectly estimated using Raman scattering spectroscopy. Fish were genotyped using the 57 K SNP Axiom™ Trout Genotyping Array. Following quality control, the final analysis contained 29,652 SNPs from 1382 fish. Heritability estimates for traits ranged from 0.03 ± 0.03 (n-3 PUFAs) to 0.24 ± 0.05 (n-6 PUFAs), confirming the potential for genomic selection. n-3 PUFAs are positively correlated to a decrease in fat deposition in the fillet and in the viscera but negatively correlated to body weight. This highlights the potential interest to combine selection on FA and against fat deposition to improve nutritional merit of aquaculture products. Several QTLs were identified for FA composition, containing multiple candidate genes with indirect links to FA metabolism. In particular, one region on Omy1 was associated with n-6 PUFAs, monounsaturated FAs, linoleic acid, and EPA, while a region on Omy7 had effects on n-6 PUFAs, EPA, and linoleic acid. When we compared the effectiveness of breeding programmes based on genomic selection (using a reference population of 1000 individuals related to selection candidates) or on pedigree-based selection, we found that the former yielded increases in selection accuracy of 12 to 120% depending on the FA trait. CONCLUSION: This study reveals the polygenic genetic architecture for FA composition in rainbow trout and confirms that genomic selection has potential to improve EPA and DHA proportions in aquaculture species.

A simulation study of a honeybee breeding scheme accounting for polyandry, direct and maternal effects on colony performance.

BACKGROUND: Efficient breeding programs are difficult to implement in honeybees due to their biological specificities (polyandry and haplo-diploidy) and complexity of the traits of interest, with performances being measured at the colony scale and resulting from the joint effects of tens of thousands of workers (called direct effects) and of the queen (called maternal effects). We implemented a Monte Carlo simulation program of a breeding plan designed specifically for Apis mellifera's populations to assess the impact of polyandry versus monoandry on colony performance, inbreeding level and genetic gain depending on the individual selection strategy considered, i.e. complete mass selection or within-family (maternal lines) selection. We simulated several scenarios with different parameter setups by varying initial genetic variances and correlations between direct and maternal effects, the selection strategy and the polyandry level. Selection was performed on colony phenotypes. RESULTS: All scenarios showed strong increases in direct breeding values of queens after 20 years of selection. Monoandry led to significantly higher direct than maternal genetic gains, especially when a negative correlation between direct and maternal effects was simulated. However, the relative increase in these genetic gains depended also on their initial genetic variability and on the selection strategy. When polyandry was simulated, the results were very similar with either 8 or 16 drones mated to each queen. Across scenarios, polyandrous mating resulted in equivalent or higher gains in performance than monoandrous mating, but with considerably lower inbreeding rates. Mass selection conferred a ~ 20% increase in performance compared to within-family selection, but was also accompanied by a strong increase in inbreeding levels (25 to 50% higher). CONCLUSIONS: Our study is the first to compare the long-term effects of polyandrous versus monoandrous mating in honeybee breeding. The latter is an emergent strategy to improve specific traits, such as resistance to varroa, which can be difficult or expensive to phenotype. However, if used during several generations in a closed population, monoandrous mating increases the inbreeding level of queens much more than polyandrous mating, which is a strong limitation of this strategy.

Genome-wide estimates of genetic diversity, inbreeding and effective size of experimental and commercial rainbow trout lines undergoing selective breeding.

BACKGROUND: Selective breeding is a relatively recent practice in aquaculture species compared to terrestrial livestock. Nevertheless, the genetic variability of farmed salmonid lines, which have been selected for several generations, should be assessed. Indeed, a significant decrease in genetic variability due to high selection intensity could have occurred, potentially jeopardizing the long-term genetic progress as well as the adaptive capacities of populations facing change(s) in the environment. Thus, it is important to evaluate the impact of selection practices on genetic diversity to limit future inbreeding. The current study presents an analysis of genetic diversity within and between six French rainbow trout (Oncorhynchus mykiss) experimental or commercial lines based on a medium-density single nucleotide polymorphism (SNP) chip and various molecular genetic indicators: fixation index (FST), linkage disequilibrium (LD), effective population size (Ne) and inbreeding coefficient derived from runs of homozygosity (ROH). RESULTS: Our results showed a moderate level of genetic differentiation between selected lines (FST ranging from 0.08 to 0.15). LD declined rapidly over the first 100 kb, but then remained quite high at long distances, leading to low estimates of Ne in the last generation ranging from 24 to 68 depending on the line and methodology considered. These results were consistent with inbreeding estimates that varied from 10.0% in an unselected experimental line to 19.5% in a commercial line, and which are clearly higher than corresponding estimates in ruminants or pigs. In addition, strong variations in LD and inbreeding were observed along the genome that may be due to differences in local rates of recombination or due to key genes that tended to have fixed favorable alleles for domestication or production. CONCLUSIONS: This is the first report on ROH for any aquaculture species. Inbreeding appeared to be moderate to high in the six French rainbow trout lines, due to founder effects at the start of the breeding programs, but also likely to sweepstakes reproductive success in addition to selection for the selected lines. Efficient management of inbreeding is a major goal in breeding programs to ensure that populations can adapt to future breeding objectives and SNP information can be used to manage the rate at which inbreeding builds up in the fish genome.

Detection of quantitative trait loci for maternal traits using high-density genotypes of Blonde d'Aquitaine beef cattle.

BACKGROUND: The genetic determinism of the calving and suckling performance of beef cows is little known whereas these maternal traits are of major economic importance in beef cattle production systems. This paper aims to identify QTL regions and candidate genes that affect maternal performance traits in the Blonde d'Aquitaine breed. Three calving performance traits were studied: the maternal effect on calving score from field data, the calving score and pelvic opening recorded in station for primiparous cows. Three other traits related to suckling performance were also analysed: the maternal effect on weaning weight from field data, milk yield and the udder swelling score recorded in station for primiparous cows. A total of 2,505 animals were genotyped from various chip densities and imputed in high density chips for 706,791 SNP. The number of genotyped animals with phenotypes ranged from 1,151 to 2,284, depending on the trait considered. RESULTS: QTL detections were performed using a Bayes C approach. Evidence for a QTL was based on Bayes Factor values. Putative candidate genes were proposed for the QTL with major evidence for one of the six traits and for the QTL shared by at least two of the three traits underlying either calving or suckling performance. Nine candidate genes were proposed for calving performance among the nine highlighted QTL regions. The neuroregulin gene on chromosome 27 was notably identified as a very likely candidate gene for maternal calving performance. As for suckling abilities, seven candidate genes were identified among the 15 highlighted QTL. In particular, the Group-Specific Component gene on chromosome 6, which encodes vitamin D binding protein, is likely to have a major effect on maternal weaning weight in the Blonde d'Aquitaine breed. This gene had already been linked to milk production and clinical mastitis in dairy cattle. CONCLUSION: In the near future, these QTL findings and the preliminary proposals of candidate genes which act on the maternal performance of beef cows should help to identify putative causal mutations based on sequence data from different cattle breeds.

Insights into the genetic variation of maternal behavior and suckling performance of continental beef cows.

BACKGROUND: In beef cattle, maternal care is critical for calf survival and growth. Our objective was to evaluate the major sources of additive genetic variation in maternal behavior and suckling performance in two genetically close beef breeds. METHODS: Maternal performance was assessed based on maternal behavior (MB), milk yield (MY) and udder swelling score (US) of 1236 Blonde d'Aquitaine cows and 1048 Limousin cows. MB was scored just after calving to describe the intensity of the dam's protective behavior towards her calf. Most of the cows were genotyped using the low-density chip EuroG10K BeadChip, and imputed to the high-density 770K panel within breed. Genetic parameters for each trait were estimated for each breed under a multi-trait best linear unbiased prediction animal model. Genomic analysis was performed for each breed using the high-density genotypes and a Bayesian variable selection method. RESULTS: Heritabilities were low for MB (0.11-0.13), intermediate for MY (0.33-0.45) and high for US (0.47-0.64). Genetic correlations between the traits ranged from 0.31 to 0.58 and 0.72 to 0.99 for the Blonde d'Aquitaine and Limousin breeds, respectively. Two quantitative trait loci (QTL) were detected for MB in Blonde d'Aquitaine with NPY1R and ADRA2A as candidate causative genes. Thirty to 56 QTL were detected for MY and US in both breeds and 12 candidate genes were identified as having a role in the genetic variation of suckling performance. Since very few pleiotropic QTL were detected, there was little biological explanation for the moderate (0.57) to very high (0.99) genetic correlations estimated between MY and US in the Blonde d'Aquitaine and Limousin cows, respectively. In Blonde d'Aquitaine, the correlation was largely due to the pleiotropic QTL detected in the region upstream of the CG gene, while in Limousin, this region was only identified for US, thus attesting the difference in genetic architecture between the breeds. CONCLUSIONS: Our findings question the assumption that two populations that have close genetic links share many QTL. Nevertheless, we identified four candidate genes that may explain a substantial amount of the genetic variation in suckling performance of these two breeds.

Construction of a large collection of small genome variations in French dairy and beef breeds using whole-genome sequences.

BACKGROUND: In recent years, several bovine genome sequencing projects were carried out with the aim of developing genomic tools to improve dairy and beef production efficiency and sustainability. RESULTS: In this study, we describe the first French cattle genome variation dataset obtained by sequencing 274 whole genomes representing several major dairy and beef breeds. This dataset contains over 28 million single nucleotide polymorphisms (SNPs) and small insertions and deletions. Comparisons between sequencing results and SNP array genotypes revealed a very high genotype concordance rate, which indicates the good quality of our data. CONCLUSIONS: To our knowledge, this is the first large-scale catalog of small genomic variations in French dairy and beef cattle. This resource will contribute to the study of gene functions and population structure and also help to improve traits through genotype-guided selection.

Economic aspects of implementing genomic evaluations in a pig sire line breeding scheme.

BACKGROUND: Replacing pedigree-based BLUP evaluations by genomic evaluations in pig breeding schemes can result in greater selection accuracy and genetic gains, especially for traits with limited phenotypes. However, this methodological change would generate additional costs. The objective of this study was to determine whether additional expenditures would be more profitably devoted to implementing genomic evaluations or to increasing phenotyping capacity while retaining traditional evaluations. METHODS: Stochastic simulation was used to simulate a population with 1050 breeding females and 50 boars that was selected for 10 years for a breeding goal with two uncorrelated traits with heritabilities of 0.4. The reference breeding scheme was based on phenotyping 13 770 candidates per year for trait 1 and 270 sibs of candidates per year for trait 2, with selection based on pedigree-based BLUP estimated breeding values. Increased expenditures were allocated to either increasing the phenotyping capacity for trait 2 while maintaining traditional evaluations, or to implementing genomic selection. The genomic scheme was based on two training populations: one for trait 2, consisting of phenotyped sibs of the candidates whose number increased from 1000 to 3430 over time, and one for trait 1, consisting of the selection candidates. Several genomic scenarios were tested, where the size of the training population for trait 1, and the number of genotyped candidates pre-selected based on their parental estimated breeding value, varied. RESULTS: Both approaches resulted in higher genetic trends for the population breeding goal and lower rates of inbreeding compared to the reference scheme. However, even a very marked increase in phenotyping capacity for trait 2 could not match improvements achieved with genomic selection when the number of genotyped candidates was large. Genotyping just a limited number of pre-selected candidates significantly reduced the extra costs, while preserving most of the benefits in terms of genetic trends and inbreeding. Implementing genomic evaluations was the most efficient approach when major expenditure was possible, whereas increasing phenotypes was preferable when limited resources were available. CONCLUSIONS: Economic decisions on implementing genomic evaluations in a pig nucleus population must take account of population characteristics, phenotyping and genotyping costs, and available funds.

High-density marker imputation accuracy in sixteen French cattle breeds.

BACKGROUND: Genotyping with the medium-density Bovine SNP50 BeadChip® (50K) is now standard in cattle. The high-density BovineHD BeadChip®, which contains 777,609 single nucleotide polymorphisms (SNPs), was developed in 2010. Increasing marker density increases the level of linkage disequilibrium between quantitative trait loci (QTL) and SNPs and the accuracy of QTL localization and genomic selection. However, re-genotyping all animals with the high-density chip is not economically feasible. An alternative strategy is to genotype part of the animals with the high-density chip and to impute high-density genotypes for animals already genotyped with the 50K chip. Thus, it is necessary to investigate the error rate when imputing from the 50K to the high-density chip. METHODS: Five thousand one hundred and fifty three animals from 16 breeds (89 to 788 per breed) were genotyped with the high-density chip. Imputation error rates from the 50K to the high-density chip were computed for each breed with a validation set that included the 20% youngest animals. Marker genotypes were masked for animals in the validation population in order to mimic 50K genotypes. Imputation was carried out using the Beagle 3.3.0 software. RESULTS: Mean allele imputation error rates ranged from 0.31% to 2.41% depending on the breed. In total, 1980 SNPs had high imputation error rates in several breeds, which is probably due to genome assembly errors, and we recommend to discard these in future studies. Differences in imputation accuracy between breeds were related to the high-density-genotyped sample size and to the genetic relationship between reference and validation populations, whereas differences in effective population size and level of linkage disequilibrium showed limited effects. Accordingly, imputation accuracy was higher in breeds with large populations and in dairy breeds than in beef breeds. More than 99% of the alleles were correctly imputed if more than 300 animals were genotyped at high-density. No improvement was observed when multi-breed imputation was performed. CONCLUSION: In all breeds, imputation accuracy was higher than 97%, which indicates that imputation to the high-density chip was accurate. Imputation accuracy depends mainly on the size of the reference population and the relationship between reference and target populations.

Genetic control of multiple births in low ovulating mammalian species.

In mammals, litter size is a highly variable trait. Some species such as humans or cattle are monotocous, with one or sometimes two newborns per birth, whereas others, the polytocous species such as mice or pigs, are highly prolific and often produce a dozen newborns at each farrowing. In monotocous species, however, two or three newborns per birth may sometime be unwanted. In more polytocous species such as sheep or pigs, litter size is studied in order to increase livestock prolificacy. By contrast, twinning rates in humans or cattle may increase birth difficulties and health problems in the newborns. In this context, the aim of our review was to provide a clearer understanding of the genetic and physiological factors that control multiple births in low-ovulating mammalian species, with particular focus on three species: sheep, cattle, and humans, where knowledge of the ovulation rate in one may enlighten findings in the others. This article therefore reviews the phenotypic and genetic variability observed with respect to ovulation and twinning rates. It then presents the QTL and major genes that have been identified in each species. Finally, we draw a picture of the diversity of the physiological mechanisms underlying multiple ovulation. Although several major genes have been discovered in sheep, QTL detection methods in humans or cattle have suggested that the determinism of litter size is complex and probably involves several genes in order to explain variations in the number of ovulations.

Regulation of anti-Müllerian hormone production in domestic animals.

In mammals, anti-Müllerian hormone (AMH) expression is detected in the granulosa cells of all growing follicles and is highest in healthy small antral follicles, which contribute most significantly to AMH endocrine levels. AMH is a reliable endocrine marker of this population of gonadotrophin-responsive follicles in ruminants and, over the longer term, plasma AMH concentrations are characteristic of individual animals. In the cow, plasma AMH concentrations follow specific dynamic profiles throughout the prepubertal period, the oestrous cycle and the change from gestation to the post partum period, with the alterations most likely reflecting numerical changes in the population of high AMH-producing follicles. In granulosa cells, bone morphogenetic proteins (BMP) enhance AMH gene expression and AMH synthesis, with these effects antagonised by FSH. BMP could both support follicular growth and contribute significantly to the induction and/or maintenance of AMH expression in small growing follicles. AMH expression decreases sharply in large follicles when they become oestrogenic, suggesting a role for FSH and/or oestradiol in these changes, but the underlying mechanisms remain hypothetical. A better understanding of the factors and mechanisms regulating AMH production is needed to propose new strategies for managing the reserve of primordial and small growing follicles, as well as for improving embryo production.

Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools.

Imputation has become a standard practice in modern genetic research to increase genome coverage and improve accuracy of genomic selection and genome-wide association study as a large number of samples can be genotyped at lower density (and lower cost) and, imputed up to denser marker panels or to sequence level, using information from a limited reference population. Most genotype imputation algorithms use information from relatives and population linkage disequilibrium. A number of software for imputation have been developed originally for human genetics and, more recently, for animal and plant genetics considering pedigree information and very sparse SNP arrays or genotyping-by-sequencing data. In comparison to human populations, the population structures in farmed species and their limited effective sizes allow to accurately impute high-density genotypes or sequences from very low-density SNP panels and a limited set of reference individuals. Whatever the imputation method, the imputation accuracy, measured by the correct imputation rate or the correlation between true and imputed genotypes, increased with the increasing relatedness of the individual to be imputed with its denser genotyped ancestors and as its own genotype density increased. Increasing the imputation accuracy pushes up the genomic selection accuracy whatever the genomic evaluation method. Given the marker densities, the most important factors affecting imputation accuracy are clearly the size of the reference population and the relationship between individuals in the reference and target populations.

Temporal and region-specific variations in genome-wide inbreeding effects on female size and reproduction traits of rainbow trout.

Recent studies have shown that current levels of inbreeding, estimated by runs of homozygosity (ROH), are moderate to high in farmed rainbow trout lines. Based on ROH metrics, the aims of our study were to (i) quantify inbreeding effects on female size (postspawning body weight, fork length) and reproduction traits (spawning date, coelomic fluid weight, spawn weight, egg number, average egg weight) in rainbow trout, and (ii) identify both the genomic regions and inbreeding events affecting performance. We analysed the performance of 1346 females under linear animal models including random additive and dominance genetics effects, with fixed covariates accounting for inbreeding effects at different temporal and genomic scales. A significant effect of genome-wide inbreeding (F) was only observed for spawning date and egg weight, with performance variations of +12.3% and -3.8%, respectively, for 0.1 unit increase in F level. At different local genomic scales, we observed highly variable inbreeding effects on the seven traits under study, ranging from increasing to decreasing trait values. As widely reported in the literature, the main scenario observed during this study was a negative impact of recent inbreeding. However, other scenarios such as positive effects of recent inbreeding or negative impacts of old inbreeding were also observed. Although partial dominance appeared to be the main hypothesis explaining inbreeding depression for all the traits studied, the overdominance hypothesis might also play a significant role in inbreeding depression affecting fecundity (egg number and mass) traits in rainbow trout. These findings suggest that region-specific inbreeding can strongly impact performance without necessarily observing genome-wide inbreeding effects. They shed light on the genetic architecture of inbreeding depression and its evolution along the genome over time. The use of region-specific metrics may enable breeders to more accurately manage the trade-off between genetic merit and the undesirable side effects associated with inbreeding.

Development of a High-Density 665 K SNP Array for Rainbow Trout Genome-Wide Genotyping.

Single nucleotide polymorphism (SNP) arrays, also named « SNP chips ¼, enable very large numbers of individuals to be genotyped at a targeted set of thousands of genome-wide identified markers. We used preexisting variant datasets from USDA, a French commercial line and 30X-coverage whole genome sequencing of INRAE isogenic lines to develop an Affymetrix 665 K SNP array (HD chip) for rainbow trout. In total, we identified 32,372,492 SNPs that were polymorphic in the USDA or INRAE databases. A subset of identified SNPs were selected for inclusion on the chip, prioritizing SNPs whose flanking sequence uniquely aligned to the Swanson reference genome, with homogenous repartition over the genome and the highest Minimum Allele Frequency in both USDA and French databases. Of the 664,531 SNPs which passed the Affymetrix quality filters and were manufactured on the HD chip, 65.3% and 60.9% passed filtering metrics and were polymorphic in two other distinct French commercial populations in which, respectively, 288 and 175 sampled fish were genotyped. Only 576,118 SNPs mapped uniquely on both Swanson and Arlee reference genomes, and 12,071 SNPs did not map at all on the Arlee reference genome. Among those 576,118 SNPs, 38,948 SNPs were kept from the commercially available medium-density 57 K SNP chip. We demonstrate the utility of the HD chip by describing the high rates of linkage disequilibrium at 2-10 kb in the rainbow trout genome in comparison to the linkage disequilibrium observed at 50-100 kb which are usual distances between markers of the medium-density chip.

Optimization of Genomic Selection to Improve Disease Resistance in Two Marine Fishes, the European Sea Bass (Dicentrarchus labrax) and the Gilthead Sea Bream (Sparus aurata).

Disease outbreaks are a major threat to the aquaculture industry, and can be controlled by selective breeding. With the development of high-throughput genotyping technologies, genomic selection may become accessible even in minor species. Training population size and marker density are among the main drivers of the prediction accuracy, which both have a high impact on the cost of genomic selection. In this study, we assessed the impact of training population size as well as marker density on the prediction accuracy of disease resistance traits in European sea bass (Dicentrarchus labrax) and gilthead sea bream (Sparus aurata). We performed a challenge to nervous necrosis virus (NNV) in two sea bass cohorts, a challenge to Vibrio harveyi in one sea bass cohort and a challenge to Photobacterium damselae subsp. piscicida in one sea bream cohort. Challenged individuals were genotyped on 57K-60K SNP chips. Markers were sampled to design virtual SNP chips of 1K, 3K, 6K, and 10K markers. Similarly, challenged individuals were randomly sampled to vary training population size from 50 to 800 individuals. The accuracy of genomic-based (GBLUP model) and pedigree-based estimated breeding values (EBV) (PBLUP model) was computed for each training population size using Monte-Carlo cross-validation. Genomic-based breeding values were also computed using the virtual chips to study the effect of marker density. For resistance to Viral Nervous Necrosis (VNN), as one major QTL was detected, the opportunity of marker-assisted selection was investigated by adding a QTL effect in both genomic and pedigree prediction models. As training population size increased, accuracy increased to reach values in range of 0.51-0.65 for full density chips. The accuracy could still increase with more individuals in the training population as the accuracy plateau was not reached. When using only the 6K density chip, accuracy reached at least 90% of that obtained with the full density chip. Adding the QTL effect increased the accuracy of the PBLUP model to values higher than the GBLUP model without the QTL effect. This work sets a framework for the practical implementation of genomic selection to improve the resistance to major diseases in European sea bass and gilthead sea bream.

Corrigendum: Optimization of Genomic Selection to Improve Disease Resistance in Two Marine Fishes, The European Sea Bass (Dicentrarchus labrax) and the Gilthead Sea Bream (Sparus aurata).

[This corrects the article DOI: 10.3389/fgene.2021.665920.].

Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss.

One of the top priorities of the aquaculture industry is the genetic improvement of economically important traits in fish, such as those related to processing and quality. However, the accuracy of genetic evaluations has been hindered by a lack of data on such traits from a sufficiently large population of animals. The objectives of this study were thus threefold: (i) to estimate genetic parameters of growth-, yield-, and quality-related traits in rainbow trout (Oncorhynchus mykiss) using three different phenotyping technologies [invasive and non-invasive: microwave-based, digital image analysis, and magnetic resonance imaging (MRI)], (ii) to detect quantitative trait loci (QTLs) associated with these traits, and (iii) to identify candidate genes present within these QTL regions. Our study collected data from 1,379 fish on growth, yield-related traits (body weight, condition coefficient, head yield, carcass yield, headless gutted carcass yield), and quality-related traits (total fat, percentage of fat in subcutaneous adipose tissue, percentage of fat in flesh, flesh colour); genotypic data were then obtained for all fish using the 57K SNP Axiom® Trout Genotyping array. Heritability estimates for most of the 14 traits examined were moderate to strong, varying from 0.12 to 0.67. Most traits were clearly polygenic, but our genome-wide association studies (GWASs) identified two genomic regions on chromosome 8 that explained up to 10% of the genetic variance (cumulative effects of two QTLs) for several traits (weight, condition coefficient, subcutaneous and total fat content, carcass and headless gutted carcass yields). For flesh colour traits, six QTLs explained 1-4% of the genetic variance. Within these regions, we identified several genes (htr1, gnpat, ephx1, bcmo1, and cyp2x) that have been implicated in adipogenesis or carotenoid metabolism, and thus represent good candidates for further functional validation. Finally, of the three techniques used for phenotyping, MRI demonstrated particular promise for measurements of fat content and distribution, while the digital image analysis-based approach was very useful in quantifying colour-related traits. This work provides new insights that may aid the development of commercial breeding programmes in rainbow trout, specifically with regard to the genetic improvement of yield and flesh-quality traits as well as the use of invasive and/or non-invasive technologies to predict such traits.