Domestication and human selection have formed diverse goat breeds with characteristic phenotypes. This process correlated with the fixation of causative genetic variants controlling breed-specific traits within regions of reduced genetic diversity, so called selection signatures or selective sweeps. Using whole genome sequencing of DNA pools (pool-seq) from 20 genetically diverse modern goat breeds and bezoars, we identified 2,239 putative selection signatures. In two Pakistani goat breeds, Pak Angora and Barbari, we found selection signatures in a region harboring KIT, a gene involved in melanoblast development, migration, and survival. The search for candidate causative variants responsible for these selective sweeps revealed two different copy number variants (CNVs) downstream of KIT that were exclusively present in white Pak Angora and white-spotted Barbari goats. Several Swiss goat breeds selected for specific coat colors showed selection signatures at the ASIP locus encoding the agouti signaling protein. Analysis of these selective sweeps revealed four different CNVs associated with the white or tan (A^Wt), Swiss markings (A^sm), badgerface (A^b), and the newly proposed peacock (A^pc) allele. RNA-seq analyses on skin samples from goats with the different CNV alleles suggest that the identified structural variants lead to an altered expression of ASIP between eumelanistic and pheomelanistic body areas. Our study yields novel insights into the genetic control of pigmentation by identifying six functionally relevant CNVs. It illustrates how structural changes of the genome have contributed to phenotypic evolution in domestic goats. Domestic animals have been selected for hundreds or sometimes even thousands of years for traits that were appreciated by their human owners. This process correlated with the fixation of causative genetic variants controlling breed-specific traits within regions of reduced genetic diversity, so called selection signatures or selective sweeps. We conducted a comprehensive screen for selection signatures in 20 phenotypically and genetically diverse modern goat breeds and identified a total of 2,239 putative selection signatures in our dataset. Follow-up experiments on selection signatures harboring known candidate genes for coat color revealed six different copy number variants (CNVs). Two of these CNVs were located in the 3’-flanking region of KIT and associated with a completely white coat color phenotype in Pak Angora goats and a white-spotted coat color phenotype in Barbari goats, respectively. The other four CNVs were located at the ASIP locus. They were associated with four different types of coat color patterning in seven Swiss goat breeds. Their functional effect is mediated by region-specific quantitative changes in ASIP mRNA expression. Our study illustrates how structural changes of the genome have contributed to phenotypic evolution in domestic goats.

The development of high throughput SNP genotyping technologies has improved the genetic dissection of simple and complex traits in many species including cats. The properties of feline 62,897 SNPs Illumina Infinium iSelect DNA array are described using a dataset of over 2,000 feline samples, the most extensive to date, representing 41 cat breeds, a random bred population, and four wild felid species. Accuracy and efficiency of the array’s genotypes and its utility in performing population-based analyses were evaluated. Average marker distance across the array was 37,741 Kb, and across the dataset, only 1% (625) of the markers exhibited poor genotyping and only 0.35% (221) showed Mendelian errors. Marker polymorphism varied across cat breeds and the average minor allele frequency (MAF) of all markers across domestic cats was 0.21. Population structure analysis confirmed a Western to Eastern structural continuum of cat breeds. Genome-wide linkage disequilibrium ranged from 50–1,500 Kb for domestic cats and 750 Kb for European wildcats (Felis silvestris silvestris). Array use in trait association mapping was investigated under different modes of inheritance, selection and population sizes. The efficient array design and cat genotype dataset continues to advance the understanding of cat breeds and will support monogenic health studies across feline breeds and populations.

Goat domestication and human selection for valued traits have formed diverse breeds with characteristic phenotypes. This process led to the fixation of causative genetic variants controlling breed-specific traits within regions of reduced genetic diversity—so-called “selection signatures”. We previously reported an analysis of selection signatures based on pooled whole-genome sequencing data of 20 goat breeds and bezoar goats. In the present study, we reanalyzed the data and focused on a subset of eight Pakistani goat breeds (Angora, Barbari, Beetal, Dera Din Panah, Kamori, Nachi, Pahari, Teddy). We identified 749 selection signatures based on reduced heterozygosity in these breeds. A search for signatures that are shared across large-sized goat breeds revealed that five medium-to-large-sized Pakistani goat breeds had a common selection signature on chromosome 6 in a region harboring the LCORL gene, which has been shown to modulate height or body size in several mammalian species. Fine-mapping of the region confirmed that all five goat breeds with the selection signature were nearly fixed for the same haplotype in a ~191 kb region spanning positions 37,747,447–37,938,449. From the pool sequencing data, we identified a frame-shifting single base insertion into an isoform-specific exon of LCORL as a potential candidate causal variant mediating the size-increasing effect. If this preliminary result can be confirmed in independent replication studies, genotyping of this variant might be used to improve breeding programs and the selection for stature in goats.

Evolution remains an incessant process in viruses, allowing them to elude the host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Emerging and re-emerging diseases are among the significant public health concerns globally. The revival of dengue is mainly due to the potential for naturally arising mutations to induce genotypic alterations in serotypes. These transformations could lead to future outbreaks, underscoring the significance of studying DENV evolution in endemic regions. Predicting the emerging Dengue Virus (DENV) genome is crucial as the virus disrupts host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging DENV serotypes. While Recurrent Neural Networks (RNN) were initially designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing the contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generated sequences were analyzed along with available DENV serotype sequences to find conserved motifs in the genome through MEME Suite (version 5.5.5). The generative model showed an accuracy of 93 %, and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score, all to be around 99.00 %, demonstrating the classification model's reliability. Our model classified the generated sequences as DENV-4, exhibiting 65.99 % similarity to DENV-4 and around 63–65 % similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum of 90 % similarity underscored their uniqueness.

Background Natural and artificial selection tend to cause variability that contributes to shape the genome of livestock in a way that differentiates them among the animal kingdom. The particular aim here is to identify positive selection signatures with whole genome pooled-sequence data of Pakistani Teddy goat. Methods and results Paired-end alignment of 635,357,043 reads of Teddy goat with (ARS1) reference genome assembly was carried out. Pooled-Heterozygosity (Hp) and Tajima’s D (TD) are applied for validation and getting better hits of selection signals, while pairwise F_ST statistics is conducted on Teddy vs. Bezoar (wild goat ancestor) for genomic differentiation, moreover annotation of regions under positive selection was also performed. Hp score with − ZHp > 5 detected six windows having highest hits on Chr. 29, 9, 25, 15 and 14 that harbor HRASLS5, LACE1 and AXIN1 genes which are candidate for embryonic development, lactation and body height. Secondly, − ZTD value of > 3.3 showed 4 windows with very strong hits on Chr.5 & 9 which harbor STIM1 and ADM genes related to body mass and weight. Lastly, − ZF_ST < − 5 generated four strong signals on Chr.5 & 12 harbor LOC102183233 gene. Other significant selection signatures encompass genes associated with wool production, prolificacy and coat colors traits in this breed. Conclusions In brief, this study identified the genes under selection in Pakistani Teddy goat that will be helpful to refining the marker-assisted breeding policies and converging required production traits within and across other goat breeds and to explore full genetic potential of this valued species of livestock.