Mitigating loss of genetic diversity is a major global biodiversity challenge^1–4. To meet recent international commitments to maintain genetic diversity within species^5,6, we need to understand relationships between threats, conservation management and genetic diversity change. Here we conduct a global analysis of genetic diversity change via meta-analysis of all available temporal measures of genetic diversity from more than three decades of research. We show that within-population genetic diversity is being lost over timescales likely to have been impacted by human activities, and that some conservation actions may mitigate this loss. Our dataset includes 628 species (animals, plants, fungi and chromists) across all terrestrial and most marine realms on Earth. Threats impacted two-thirds of the populations that we analysed, and less than half of the populations analysed received conservation management. Genetic diversity loss occurs globally and is a realistic prediction for many species, especially birds and mammals, in the face of threats such as land use change, disease, abiotic natural phenomena and harvesting or harassment. Conservation strategies designed to improve environmental conditions, increase population growth rates and introduce new individuals (for example, restoring connectivity or performing translocations) may maintain or even increase genetic diversity. Our findings underscore the urgent need for active, genetically informed conservation interventions to halt genetic diversity loss.

Identification of genes or even nucleotides that are responsible for quantitative and adaptive trait variation is a difficult task due to the complex interdependence between a large number of genetic and environmental factors. The polymorphism of the mitogenome is one of the factors that can contribute to quantitative trait variation. However, the effects of the mitogenome have not been comprehensively studied, since large numbers of mitogenome sequences and recorded phenotypes are required to reach the adequate power of analysis. Current research in our group focuses on acquiring the necessary mitochondria sequence information and analysing its influence on the phenotype of a quantitative trait. To facilitate these tasks we have produced software for processing pedigrees that is optimised for maternal lineage analysis. We present MaGelLAn 1.0 (maternal genealogy lineage analyser), a suite of four Python scripts (modules) that is designed to facilitate the analysis of the impact of mitogenome polymorphism on quantitative trait variation by combining molecular and pedigree information. MaGelLAn 1.0 is primarily used to: (1) optimise the sampling strategy for molecular analyses; (2) identify and correct pedigree inconsistencies; and (3) identify maternal lineages and assign the corresponding mitogenome sequences to all individuals in the pedigree, this information being used as input to any of the standard software for quantitative genetic (association) analysis. In addition, MaGelLAn 1.0 allows computing the mitogenome (maternal) effective population sizes and probability of mitogenome (maternal) identity that are useful for conservation management of small populations. MaGelLAn is the first tool for pedigree analysis that focuses on quantitative genetic analyses of mitogenome data. It is conceived with the purpose to significantly reduce the effort in handling and preparing large pedigrees for processing the information linked to maternal lines. The software source code, along with the manual and the example files can be downloaded at http://lissp.irb.hr/software/magellan-1-0/ and https://github.com/sristov/magellan .

Substantial advances in livestock traits have been achieved primarily through selection strategies targeting variation in the nuclear genome, with little attention given to mitogenome variation. We analyzed the influence of the mitogenome on milk production traits of Holstein cattle in Croatia based on strategically generated next-generation sequencing data for 109 cows pedigree-linked to 7,115 milk production records (milk, fat, and protein yield) from 3,006 cows (first 5 lactations). Because little is known about the biology of the relationship between mitogenome variation and production traits, our quantitative genetic modeling was complex. Thus, the proportion of total variance explained by mitogenome inheritance was estimated using 5 different models: (1) a cytoplasmic model with maternal lineages (CYTO), (2) a haplotypic model with mitogenome sequences (HAPLO), (3) an amino acid model with unique amino acid sequences (AMINO), (4) an evolutionary model based on a phylogenetic analysis using Bayesian Evolutionary Analysis Sampling Trees phylogenetic analysis (EVOL), and (5) a mitogenome SNP model (SNPmt). The polygenic autosomal and X chromosome additive genetic effects based on pedigree were modeled, together with the effects of herd-year-season interaction, permanent environment, location, and age at first calving. The estimated proportions of phenotypic variance explained by mitogenome in 4 different models (CYTO, HAPLO, AMINO, and SNPmt) were found to be substantial given the size of mitogenome, ranging from 5% to 7% for all 3 milk traits. At the same time, a negligible proportion of the phenotypic variance was explained by mitogenome with the EVOL model. Similarly, in all models, no proportion of phenotypic variance was explained by the X chromosome. Although our results should be confirmed in other dairy cattle populations, including a large number of sequenced mitogenomes and nuclear genomes, the potential of utilizing mitogenome information in animal breeding is promising, especially as the acquisition of complete genome sequences becomes cost-effective.

While mitogenome mutations leading to pathological manifestations are rare, more than 200 such mutations have been described in humans. In contrast, pathogenic mitogenome mutations are rare in domestic animals and have not been described at all in cattle. In the small local Slovenian cattle breed Cika, we identified (next-generation sequencing) two cows with the T10432C mitogenome mutation in the ND4L gene, which corresponds to the human T10663C mutation known to cause Leber’s hereditary optic neuropathy (LHON). Pedigree analysis revealed that the cows in which the mutation was identified belong to two different maternal lineages with 217 individual cows born between 1997 and 2020. The identified mutation and its maternal inheritance were confirmed by Sanger sequencing across multiple generations, whereas no single analysis revealed evidence of heteroplasmy. A closer clinical examination of one cow with the T10432C mutation revealed exophthalmos, whereas histopathological examination revealed retinal ablations, subretinal oedema, and haemorrhage. The results of these analyses confirm the presence of mitochondrial mutation T10432C with homoplasmic maternal inheritance as well as clinical and histopathological signs similar to LHON in humans. Live animals with the mutation could be used as a suitable animal model that can improve our understanding of the pathogenesis of LHON and other mitochondriopathies.

This paper introduces a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are usually many, but not all possible, distinct haplotypes and few observations per haplotype. Further, haplotype frequencies tend to vary substantially - few haplotypes have high frequency and many haplotypes have low frequency. Such data structure challenge estimation of haplotype effects. However, haplotypes often differ only due to few mutations and leveraging these similarities can improve the estimation of haplotype effects. There is extensive literature on this topic. Here we build on these observations and develop an autoregressive model of order one that hierarchically models haplotype effects by leveraging phylogenetic relationships between the haplotypes described with a directed acyclic graph. The phylogenetic relationships can be either in a form of a tree or a network and we therefore refer to the model as the haplotype network model. The haplotype network model can be included as a component in a phenotype model to estimate associations between haplotypes and phenotypes. The key contribution of this work is that by leveraging the haplotype network structure we obtain a sparse model and by using hierarchical autoregression the flow of information between similar haplotypes is estimated from the data. We show with a simulation study that the hierarchical model can improve estimates of haplotype effects compared to an independent haplotype model, especially when there are few observations for a specific haplotype. We also compared it to a mutation model and observed comparable performance, though the haplotype model has the potential to capture background specific effects. We demonstrate the model with a case study of modeling the effect of mitochondrial haplotypes on milk yield in cattle.

The proceedings of the 12th World Congress on Genetics Applied to Livestock Production provide you with 816 papers representing the leading research in livestock genetics around the globe. This boo...