Genomic diversity and population structure of the Leonberger dog breed.

BACKGROUND: Leonberger is a giant dog breed formed in the 1850s in Germany. Its post-World War II popularity has resulted in a current global population of ~ 30,000 dogs. The breed has predispositions to neurodegenerative disorders and cancer, which is likely due in large part to limited genetic diversity. However, to date there is no scientific literature on the overall demography and genomic architecture of this breed. RESULTS: We assessed extensive pedigree records, SNP array genotype data, and whole-genome sequences (WGS) on 142,072, 1203 and 39 Leonberger dogs, respectively. Pedigree analyses identified 22 founder animals and revealed an apparent popular sire effect. The average pedigree-based inbreeding coefficient of 0.29 and average kinship of 0.31 show a dramatic loss of genetic diversity. The observed average life span decreased over time from 9.4 years in 1989 to 7.7 years in 2004. A global health survey confirmed a high prevalence of cancer and neurological disorders. Analysis of SNP-based runs of homozygosity (ROH) identified 125,653 ROH with an average length of 5.88 Mb, and confirmed an average inbreeding coefficient of 0.28. Genome-wide filtering of the WGS data revealed 28 non-protein-changing variants that were present in all Leonberger individuals and a list of 22 potentially pathogenic variants for neurological disorders of which 50% occurred only in Leonbergers and 50% occurred rarely in other breeds. Furthermore, one of the two mtDNA haplogroups detected was present in one dog only. CONCLUSIONS: The increasing size of the Leonberger population has been accompanied by a considerable loss of genetic diversity after the bottleneck that occurred in the 1940s due to the intensive use of popular sires resulting in high levels of inbreeding. This might explain the high prevalence of certain disorders; however, genomic data provide no evidence for fixed coding variants that explain these predispositions. The list of candidate causative variants for polyneuropathy needs to be further evaluated. Preserving the current genetic diversity is possible by increasing the number of individuals for breeding while restricting the number of litters per sire/dam. In addition, outcrossing would help optimize long-term genetic diversity and contribute to the sustainability and health of the population.

Correction to: Genomic diversity and population structure of the Leonberger dog breed.

An amendment to this paper has been published and can be accessed via the original article.

Effects of pedigree errors on the efficiency of conservation decisions.

Conservation schemes often aim at increasing genetic diversity by minimizing kinship, and the best method to achieve this goal, when pedigree data is available, is to apply optimal contributions. Optimal contributions calculate contributions per animal so that the weighted average mean kinship among candidate parents is minimized. This approach assumes that pedigree data is correct and complete. However, in practice, pedigrees often contain errors: parents are recorded incorrectly or even missing. We used simulations to investigate the effect of these two types of errors on minimizing kinship. Our findings show that a low percentage of wrong parent information reduces the effect of optimal contributions. When the percentage of wrong parent information is above 15%, the population structure and type of errors, should be taken into account before applying optimal contributions. Optimal contributions based on pedigrees with missing parent information hampers conservation of genetic diversity; however, missing parent information can be corrected. It is crucial to know which animals are founders. We strongly recommend that pedigree registration include whether missing parents are either true founders or non-founders.

History and structure of the closed pedigreed population of Icelandic Sheepdogs.

BACKGROUND: Dog breeds lose genetic diversity because of high selection pressure. Breeding policies aim to minimize kinship and therefore maintain genetic diversity. However, policies like mean kinship and optimal contributions might be impractical. Cluster analysis of kinship can elucidate the population structure, since this method divides the population in clusters of related individuals. Kinship-based analyses have been carried out on the entire Icelandic Sheepdog population, a sheep-herding breed. RESULTS: Analyses showed that despite increasing population size and deliberately transferring dogs, considerable genetic diversity has been lost. When cluster analysis was based on kinships calculated seven generation backwards, as performed in previous studies, results differ markedly from those based on calculations going back to the founder-population, and thus invalidate recommendations based on previous research. When calculated back to the founder-population, kinship-based clustering reveals the distribution of genetic diversity, similarly to strategies using mean kinship. CONCLUSION: Although the base population consisted of 36 Icelandic Sheepdog founders, the current diversity is equivalent to that of only 2.2 equally contributing founders with no loss of founder alleles in descendants. The maximum attainable diversity is 4.7, unlikely achievable in a non-supervised breeding population like the Icelandic Sheepdog. Cluster analysis of kinship coefficients can provide a supporting tool to assess the distribution of available genetic diversity for captive population management.

Estimating relatedness between individuals in general populations with a focus on their use in conservation programs.

Relatedness estimators are widely used in genetic studies, but effects of population structure on performance of estimators, criteria to evaluate estimators, and benefits of using such estimators in conservation programs have to date received little attention. In this article we present new estimators, based on the relationship between coancestry and molecular similarity between individuals, and compare them with existing estimators using Monte Carlo simulation of populations, either panmictic or structured. Estimators were evaluated using statistical criteria and a diversity criterion that minimized relatedness. Results show that ranking of estimators depends on the population structure. An existing estimator based on two-gene and four-gene coefficients of identity performs best in panmictic populations, whereas a new estimator based on coancestry performs best in structured populations. The number of marker alleles and loci did not affect ranking of estimators. Statistical criteria were insufficient to evaluate estimators for their use in conservation programs. The regression coefficient of pedigree relatedness on estimated relatedness (beta2) was substantially lower than unity for all estimators, causing overestimation of the diversity conserved. A simple correction to achieve beta2 = 1 improves both existing and new estimators. Using relatedness estimates with correction considerably increased diversity in structured populations, but did not do so or even decreased diversity in panmictic populations.