The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock

Livestock radiated out from domestication centres to most regions of the world, gradually adapting to diverse environments, from very hot to sub-zero temperatures and from wet and humid conditions to deserts. The climate is changing; generally global temperature is increasing, although there are also more extreme cold periods, storms, and higher solar radiation. These changes impact livestock welfare and productivity. This review describes advances in the methodology for studying livestock genomes and the impact of the environment on animal production, giving examples of discoveries made. Sequencing livestock genomes has facilitated genome-wide association studies to localize genes controlling many traits, and population genetics has identified genomic regions under selection or introgressed from one breed into another to improve production or facilitate adaptation. Landscape genomics, which combines global positioning and genomics, has identified genomic features that enable animals to adapt to local environments. Combining the advances in genomics and methods for predicting changes in climate is generating an explosion of data which calls for innovations in the way big data sets are treated. Artificial intelligence and machine learning are now being used to study the interactions between the genome and the environment to identify historic effects on the genome and to model future scenarios.

Population Genetic Diversity and Structure of an Endangered Salicaceae Species in Northeast China: Chosenia arbutifolia (Pall.) A. Skv.

Chosenia arbutifolia (Pall.) A. Skv. is a unique and endangered species belonging to the Salicaceae family. It has great potential for ornamental and industrial use. However, human interference has led to a decrease in and fragmentation of its natural populations in the past two decades. To effectively evaluate, utilize, and conserve available resources, the genetic diversity and population structure of C. arbutifolia were analyzed in this study. A total of 142 individuals from ten provenances were sampled and sequenced. Moderate diversity was detected among these, with a mean expected heterozygosity and Shannon’s Wiener index of 0.3505 and 0.5258, respectively. The inbreeding coefficient was negative, indicating a significant excess of heterozygotes. The fixation index varied from 0.0068 to 0.3063, showing a varied genetic differentiation between populations. Analysis of molecular variance demonstrated that differentiation accounted for 82.23% of the total variation among individuals, while the remaining 17.77% variation was between populations. Furthermore, the results of population structure analysis indicated that the 142 individuals originated from three primitive groups. To provide genetic information and help design conservation and management strategies, landscape genomics analysis was performed by investigating loci associated with environmental variables. Eighteen SNP markers were associated with altitude and annual average temperature, of which five were ascribed with specific functions. In conclusion, the current study furthers the understanding of C. arbutifolia genetic architecture and provides insights for germplasm protection.

Deciphering Local Adaptation Of Native Indian Cattle (Bos Indicus) Breeds Using Landscape Genomics And In-Silico Prediction Of Deleterious SNP Effects On Protein Structure And Function

Present study aimed to explore genomic basis of adaptation of Indian native cattle and to predict the impact of key SNPs on amino acid changes that affect protein function. Four native cattle breeds belonging to contrasting landscape and climatic conditions were genotyped using Illumina 777 K BovineHD BeadChip: Siri & Ladakhi from cold hilly areas, and Kankrej and Hallikar from hot arid and semi-arid regions, respectively. The R.SamBada package in R was used to perform the genotype-environment association analysis. A total of 1,12,780 significant (q < 0.05), models with 30,350 unique SNPs were obtained. Significantly associated SNPs had impact on 4,435 genes and 141 pathways. Only ten SNP variants had a SIFT score of < 0.05 (deleterious), and only two of them, each lying in the genes CRYBA1 and USP18, were predicted to be deleterious with high confidence.. RaptorX predicted the tertiary structures of proteins encoded by wild and mutant variants of these genes. The quality of the models was determined using Ramachandran plots and RaptorX parameters, indicating that they are accurate. RaptorX and I-Mutant 2.0 softwares revealed significant differences among wild and mutant proteins. Identified adaptive alleles might be responsible for the local adaptation of these cattle breeds.

The climatic and genetic heritage of Italian goat breeds with genomic SNP data

Local adaptation of animals to the environment can abruptly become a burden when faced with rapid climatic changes such as those foreseen for the Italian peninsula over the next 70 years. Our study investigates the genetic structure of the Italian goat populations and links it with the environment and how genetics might evolve over the next 50 years. We used one of the largest national datasets including > 1000 goats from 33 populations across the Italian peninsula collected by the Italian Goat Consortium and genotyped with over 50 k markers. Our results showed that Italian goats can be discriminated in three groups reflective of the Italian geography and its geo-political situation preceding the country unification around two centuries ago. We leveraged the remarkable genetic and geographical diversity of the Italian goat populations and performed landscape genomics analysis to disentangle the relationship between genotype and environment, finding 64 SNPs intercepting genomic regions linked to growth, circadian rhythm, fertility, and inflammatory response. Lastly, we calculated the hypothetical future genotypic frequencies of the most relevant SNPs identified through landscape genomics to evaluate their long-term effect on the genetic structure of the Italian goat populations. Our results provide an insight into the past and the future of the Italian local goat populations, helping the institutions in defining new conservation strategy plans that could preserve their diversity and their link to local realities challenged by climate change.

The genomic basis of domestic colonisation and dispersal in Chagas disease vectors

The biology of vector adaptation to the human habitat remains poorly understood for many arthropod-borne diseases but underpins effective and sustainable disease control. We adopted a landscape genomics approach to investigate gene flow, signatures of local adaptation, and drivers of population structure among multiple linked wild and domestic population pairs in Rhodnius ecuadoriensis, an important vector of Chagas Disease. Evidence of high triatomine gene flow (FST) between wild and domestic ecotopes at sites throughout the study area indicate insecticide-based control will be hindered by constant re-infestation of houses. Genome scans revealed genetic loci with strong signal of local adaptation to the domestic setting, which we mapped to annotated regions in the Rhodnius prolixus genome. Our landscape genomic mixed effects models showed Rhodnius ecuadoriensis population structure and connectivity is driven by landscape elevation at a regional scale. Our ecologically- and spatially-explicit vector dispersal model enables targeted vector control and recommends spatially discrete, periodic interventions to local authorities as more efficacious than current, haphazard approaches. In tandem, evidence for parallel genomic adaptation to colonisation of the domestic environment at multiple sites sheds new light on the evolutionary basis of adaptation to the human host in arthropod vectors.

Optimising sampling design and sequencing strategy for the genomic analysis of quantitative traits in natural populations

Mapping the genes underlying ecologically-relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterisation of a species’ genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling and which sequencing strategy to implement. Here we determine through simulation, the sampling scheme that maximises the power to map the genetic basis of a complex trait in an outbreeding species across an idealised landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that QTL detection power and prediction accuracy are higher when more populations over the landscape are sampled and this is more cost-effectively done with pool sequencing than with individual sequencing. Additionally, we recommend sampling populations from areas of high genetic diversity. As progress in sequencing enables the integration of trait-based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.