Managing genetic diversity in breeding programs of small populations: the case of French local chicken breeds.

BACKGROUND: On-going climate change will drastically modify agriculture in the future, with a need for more sustainable systems, in particular regarding animal production. In this context, genetic diversity is a key factor for adaptation to new conditions: local breeds likely harbor unique adaptive features and represent a key component of diversity to reach resilience. However, local breeds often suffer from small population sizes, which puts these valuable resources at risk of extinction. In chickens, population management programs were initiated a few decades ago in France, relying on a particular niche market that aims at promoting and protecting local breeds. We conducted a unique comprehensive study of 22 French local breeds, along with four commercial lines, to evaluate their genetic conservation status and the efficiency of the population management programs. RESULTS: Using a 57K single nucleotide polymorphism (SNP) chip, we demonstrated that both the between- and within-breed genetic diversity levels are high in the French local chicken populations. Diversity is mainly structured according to the breeds' selection and history. Nevertheless, we observed a prominent sub-structuring of breeds according to farmers' practices in terms of exchange, leading to more or less isolated flocks. By analysing demographic parameters and molecular information, we showed that consistent management programs are efficient in conserving genetic diversity, since breeds that integrated such programs earlier had older inbreeding. CONCLUSIONS: Management programs of French local chicken breeds have maintained their genetic diversity at a good level. We recommend that future programs sample as many individuals as possible, with emphasis on both males and females from the start, and focus on a quick and strong increase of population size while conserving as many families as possible. We also stress the usefulness of molecular tools to monitor small populations for which pedigrees are not always available. Finally, the breed appears to be an appropriate operational unit for the conservation of genetic diversity, even for local breeds, for which varieties, if present, could also be taken into account.

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.

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.].

APIS: An auto-adaptive parentage inference software that tolerates missing parents.

In the context of parentage assignment using genomic markers, key issues are genotyping errors and an absence of parent genotypes because of sampling, traceability or genotyping problems. Most likelihood-based parentage assignment software programs require a priori estimates of genotyping errors and the proportion of missing parents to set up meaningful assignment decision rules. We present here the R package APIS, which can assign offspring to their parents without any prior information other than the offspring and parental genotypes, and a user-defined, acceptable error rate among assigned offspring. Assignment decision rules use the distributions of average Mendelian transmission probabilities, which enable estimates of the proportion of offspring with missing parental genotypes. APIS has been compared to other software (CERVUS, VITASSIGN), on a real European seabass (Dicentrarchus labrax) single nucleotide polymorphism data set. The type I error rate (false positives) was lower with APIS than with other software, especially when parental genotypes were missing, but the true positive rate was also lower, except when the theoretical exclusion power reached 0.99999. In general, APIS provided assignments that satisfied the user-set acceptable error rate of 1% or 5%, even when tested on simulated data with high genotyping error rates (1% or 3%) and up to 50% missing sires. Because it uses the observed distribution of Mendelian transmission probabilities, APIS is best suited to assigning parentage when numerous offspring (>200) are genotyped. We have demonstrated that APIS is an easy-to-use and reliable software for parentage assignment, even when up to 50% of sires are missing.