China\'s local chicken breeds are rich in resources, and have formed different germplasm characteristics in the process of long-term selection and evolution. Scientific assessment of population genetic diversity and identification of inter-breed genetic structure are of great value to the protection and innovative utilization of local chicken breed resource. In order to evaluate the application effectiveness of 23K SNP chip \"Youxin-1\" in the analysis of genetic diversity and genetic structure of local chickens, we used RADseq to identify genomic genetic variation of 21 local chicken breeds and developed 23K chip \"Youxin-1\". The genetic statistics of each variety were calculated based on two sets of SNP data, and correlation, fitting and phylogenetic analysis were carried out to evaluate the application effectiveness of the chip. The results showed that the observed heterozygosity (Ho), polymorphism information content (PIC), inbred coefficient (FROH) and genetic differentiation coefficient (Fst) calculated based on the two SNP data sets were basically consistent in the 21 local chicken breeds. The genetic diversity of Langya chicken (LA), Piao chicken (PJ) and Wenchang chicken (WC) was relatively rich. The genetic diversity of Bian chickens (BJ), Langshan chickens (LS), Gushi chickens (GS), Dongxiang blue-eggshell chickens (DX) and Beijing fatty chickens (BY) was relatively poor, and the correlation coefficients of Ho, PIC, FROH and average Fst in the two groups were 0.794, 0.901, 0.926 and 0.984, respectively, all reaching extremely significant levels (P<0.01) with a high degree of fit (P<0.001) and R2 were 0.644, 0.827, 0.916 and 0.927. For the two sets of SNP data, the evolutionary tree constructed by neighbor-joining (NJ) method and maximum likelihood (ML) method was reasonable, and the 21 local chicken breeds were generally divided into six categories, which was consistent with the formation history and geographical distribution of the varieties. The 23K chip also realized reasonable clustering of the five new varieties without individual deviation. There are some differences in the estimation of genetic statistics using SNP with different densities, and data standardization is needed. 23K chip has good efficacy in the analysis of genetic diversity and structure of local chickens.
我国地方鸡品种资源丰富，且在长期的选择进化过程中形成了各异的种质特性。科学评估群体遗传多样性，明确品种间遗传结构，对地方鸡品种资源的保护与创新利用具有重要价值。为了评估23K SNP芯片“酉芯一号”在地方鸡遗传多样性和遗传结构分析中的应用效力，本文利用RADseq鉴定21个地方鸡种基因组遗传变异，并研发23K芯片“酉芯一号”。基于两组SNP数据集计算各品种的遗传统计量，开展相关性、拟合及系统发育分析，以评估芯片的应用效力。结果表明，基于两组SNP数据集计算的观察杂合度(Ho)、多态信息含量(PIC)、近交系数(FROH)和遗传分化系数(Fst)，在21个地方鸡种中趋势基本一致。琅琊鸡(LA)、瓢鸡(PJ)、文昌鸡(WC)的遗传多样性较为丰富；边鸡(BJ)、狼山鸡(LS)、固始鸡(GS)、东乡绿壳蛋鸡(DX)和北京油鸡(BY)的遗传多样性相对匮乏，两组Ho、PIC、FROH和平均Fst的相关系数分别为0.794、0.901、0.926和0.984，均达到极显著水平(P<0.01)，且拟合度较高(P<0.001)，R2分别为0.644、0.827、0.916和0.927。针对两组SNP数据集，采用邻接法(NJ)和极大似然法(ML)构建的进化树较为合理，将21个地方鸡种总体上分为6大类，与品种的形成历史和地理分布相吻合；23K芯片对5个新增品种亦实现有效聚类，没有出现个体偏离。利用不同密度的SNP估算遗传统计量会存在一定差异，需要进行数据和分析方法的标准化，23K芯片在地方鸡遗传多样性和结构分析中具有较好的效力。.

Camels, known as the \"Ship of the Desert,\" play a vital role in the ecosystems and economies of arid and semi-arid regions. They provide meat, milk, transportation, and other essential services, and their resilience to harsh environments makes them invaluable. Despite their similarities, camel breeds exhibit notable differences in size, color, and structure, with over 40 million camels worldwide. This number is projected to increase, underscoring their growing significance. Economically, camels are crucial for food production, tourism, and trade, with camel racing being particularly significant in Arab countries. Their unique physiological traits, such as low disease susceptibility and efficient water conservation, further enhance their value. Camel products, especially meat and milk, offer substantial nutritional and therapeutic benefits, contributing to their high demand. Genetic diversity studies have advanced our understanding of camels\' adaptation to extreme environments. Functional genomics and whole-genome sequencing have identified genes responsible for these adaptations, aiding breeding programs and conservation efforts. High-throughput sequencing has revealed genetic markers linked to traits like milk production and disease resistance. The development of SNP chips has revolutionized genetic studies by providing a cost-effective alternative to whole-genome sequencing. These tools facilitate large-scale genotyping, essential for conserving genetic diversity and improving breeding strategies. To prevent the depletion of camel genetic diversity, it is crucial to streamline in situ and ex situ conservation efforts to maintain their ecological and economic value. A comprehensive approach to camel conservation and genetic preservation, involving advanced genomic technologies, reproductive biotechniques, and sustainable management practices, will ensure their continued contribution to human societies.

This study aimed to evaluate the genetic diversity and structure within the Dengchuan cattle population and effectively protect and utilize their germplasm resources. Herein, the single-nucleotide polymorphisms (SNPs) of 100 Dengchuan cattle (46 bulls and 54 cows) were determined using the GGP Bovine 100K SNP Beadchip. The results showed that among the Dengchuan cattle, a total of 101,220 SNPs were detected, and there were 83,534 SNPs that passed quality control, of which 85.7% were polymorphic. The average genetic distance based on identity-by-state (IBS) within the conservation population of Dengchuan cattle was 0.26 ± 0.02. A total of 3,999 genome-length runs of homozygosity (ROHs) were detected in the Dengchuan cattle, with ROH lengths primarily concentrated in the range of 1-5 Mb, accounting for 87.02% of the total. The average inbreeding coefficient based on ROHs was 4.6%, within the conservation population of Dengchuan cattle, whereas it was 4.9% for bulls, and the Wright inbreeding coefficient (FIS) value was 2.4%, demonstrating a low level of inbreeding within the Dengchuan cattle population. Based on neighbor-joining tree analysis, the Dengchuan cattle could be divided into 16 families. In summary, the conservation population of Dengchuan cattle displays relatively abundant diversity and a moderate genetic relationship. Inbreeding was observed among a few individuals, but the overall inbreeding level of the population remained low. It is important to maintain this low level of inbreeding when introducing purebred bloodlines to expand the core group. This approach will ensure the long-term conservation of Dengchuan cattle germplasm resources and prevent loss of genetic diversity.

The mosquito Aedes aegypti is the primary vector of many human arboviruses such as dengue, yellow fever, chikungunya, and Zika, which affect millions of people worldwide. Population genetic studies on this mosquito have been important in understanding its invasion pathways and success as a vector of human disease. The Axiom aegypti1 SNP chip was developed from a sample of geographically diverse A. aegypti populations to facilitate genomic studies on this species. We evaluate the utility of the Axiom aegypti1 SNP chip for population genetics and compare it with a low-depth shotgun sequencing approach using mosquitoes from the native (Africa) and invasive ranges (outside Africa). These analyses indicate that results from the SNP chip are highly reproducible and have a higher sensitivity to capture alternative alleles than a low-coverage whole-genome sequencing approach. Although the SNP chip suffers from ascertainment bias, results from population structure, ancestry, demographic, and phylogenetic analyses using the SNP chip were congruent with those derived from low-coverage whole-genome sequencing, and consistent with previous reports on Africa and outside Africa populations using microsatellites. More importantly, we identified a subset of SNPs that can be reliably used to generate merged databases, opening the door to combined analyses. We conclude that the Axiom aegypti1 SNP chip is a convenient, more accurate, low-cost alternative to low-depth whole-genome sequencing for population genetic studies of A. aegypti that do not rely on full allelic frequency spectra. Whole-genome sequencing and SNP chip data can be easily merged, extending the usefulness of both approaches.

The objective of this study was to assess the effect of using or not using the genotypes of the parents of a cow for imputing SNPs on the estimation of genomic inbreeding coefficients of cows. Imputation (i.e., genotyped plus imputed) genotypes from 68,127 Italian Holstein dairy cows registered in the Italian National Association of Holstein, Brown, and Jersey Breeders were analyzed. Cows were genotyped with the high-density (HD) Illumina Infinium BovineHD BeadChip and GeneSeek Genomic Profiler HD-150K, and the medium-density (MD) GeneSeek Genomic Profiler 3, GeneSeek Genomic Profiler 4, GeneSeek MD, and the Labogena MD. To assess differences among estimators, genomic inbreeding coefficients were estimated with 4 PLINK v1.9 estimators (F, Fhat1,Fhat2, andFhat3), 2 genomic relationship matrix- (grm) based estimators (Fgrm and Fgrm2, with the latter including also pedigree information), and one estimator of runs of homozygosity (ROH; FROH). Assuming that the correct genomic inbreeding coefficients should be those estimated from genotyped SNPs, a comparison of the genomic inbreeding coefficients estimated either with the genotyped SNPs or the SNPs after imputation was made. Information on the presence or absence of genotypic information from sire, dam, and maternal grandsire during the imputation was investigated. Genomic inbreeding coefficients estimated with genotyped SNPs or SNPs after imputation were consistent for F, Fhat3, Fgrm2, and FROH, when at least one of the parents was genotyped. Biased (mainly higher) genomic inbreeding coefficients of imputation SNPs were observed in cows that were genotyped with MD SNP panels whose SNPs were poorly represented in the selected imputation SNP dataset and also did not have their parents genotyped, when compared with what would be expected based on actual genotype data. For cows genotyped with MD the estimators Fhat1, Fhat2, and Fgrm provided higher genomic inbreeding coefficients of imputation SNPs even with both parents and the maternal grandsire genotyped. Overall, FROH was the most robust estimator, followed by F and Fhat3. Our findings suggest that SNPs selection, parental genotyping and estimator should be considered for designing imputation strategies in dairy cattle for estimating genomic inbreeding with imputation SNPs. For computing genomic inbreeding coefficients, it is recommendable to have at least one parent genotyped and use an ROH-based estimator.

BACKGROUND: Although whole-genome sequencing (WGS) is the preferred genotyping method for most genomic analyses, limitations are often experienced when studying genomes characterized by a high percentage of repetitive elements, high linkage, and recombination deserts. The Asian tiger mosquito (Aedes albopictus), for example, has a genome comprising up to 72% repetitive elements, and therefore we set out to develop a single-nucleotide polymorphism (SNP) chip to be more cost-effective. Aedes albopictus is an invasive species originating from Southeast Asia that has recently spread around the world and is a vector for many human diseases. Developing an accessible genotyping platform is essential in advancing biological control methods and understanding the population dynamics of this pest species, with significant implications for public health.
METHODS: We designed a SNP chip for Ae. albopictus (Aealbo chip) based on approximately 2.7 million SNPs identified using WGS data from 819 worldwide samples. We validated the chip using laboratory single-pair crosses, comparing technical replicates, and comparing genotypes of samples genotyped by WGS and the SNP chip. We then used the chip for a population genomic analysis of 237 samples from 28 sites in the native range to evaluate its usefulness in describing patterns of genomic variation and tracing the origins of invasions.
RESULTS: Probes on the Aealbo chip targeted 175,396 SNPs in coding and non-coding regions across all three chromosomes, with a density of 102 SNPs per 1 Mb window, and at least one SNP in each of the 17,461 protein-coding genes. Overall, 70% of the probes captured the genetic variation. Segregation analysis found that 98% of the SNPs followed expectations of single-copy Mendelian genes. Comparisons with WGS indicated that sites with genotype disagreements were mostly heterozygotes at loci with WGS read depth < 20, while there was near complete agreement with WGS read depths > 20, indicating that the chip more accurately detects heterozygotes than low-coverage WGS. Sample sizes did not affect the accuracy of the SNP chip genotype calls. Ancestry analyses identified four to five genetic clusters in the native range with various levels of admixture.
CONCLUSIONS: The Aealbo chip is highly accurate, is concordant with genotypes from WGS with high sequence coverage, and may be more accurate than low-coverage WGS.

This study aimed at evaluating the quality of imputation accuracy (IA) by marker (IAm) and by individual (IAi) in US crossbred dairy cattle. Holstein × Jersey crossbreds were used to evaluate IA from a low- (7K) to a medium-density (50K) SNP chip. Crossbred animals, as well as their sires (53), dams (77), and maternal grandsires (63), were all genotyped with a 78K SNP chip. Seven different scenarios of reference populations were tested, in which some scenarios used different family relationships and others added random unrelated purebred and crossbred individuals to those different family relationship scenarios. The same scenarios were tested on Holstein and Jersey purebred animals to compare these outcomes against those attained in crossbred animals. The genotype imputation was performed with findhap (version 4) software (VanRaden, 2015). There were no significant differences in IA results depending on whether the sire of imputed individuals was Holstein and the dam was Jersey, or vice versa. The IA increased significantly with the addition of related individuals in the reference population, from 86.70 ± 0.06% when only sires or dams were included in the reference population to 90.09 ± 0.06% when sire (S), dam (D), and maternal grandsire genomic data were combined in the reference population. In all scenarios including related individuals in the reference population, IAm and IAi were significantly superior in purebred Jersey and Holstein animals than in crossbreds, ranging from 90.75 ± 0.06 to 94.02 ± 0.06%, and from 90.88 ± 0.11 to 94.04 ± 0.10%, respectively. Additionally, a scenario called SPB+DLD(where PB indicates purebread and LD indicates low density), similar to the genomic evaluations performed on US crossbred dairy, was tested. In this scenario, the information from the 5 evaluated breeds (Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey) genotyped with a 50K SNP chip and genomic information from the dams genotyped with a 7K SNP chip were combined in the reference population, and the IAm and IAi were 80.87 ± 0.06% and 80.85 ± 0.08%, respectively. Adding randomly nonrelated genotyped individuals in the reference population reduced IA for both purebred and crossbred cows, except for scenario SPB+DLD, where adding crossbreds to the reference population increased IA values. Our findings demonstrate that IA for US Holstein × Jersey crossbred ranged from 85 to 90%, and emphasize the significance of designing and defining the reference population for improved IA.

As an important genotyping platform, SNP chips are essential for implementing genomic selection. In this article, we introduced the development of a liquid SNP chip panel for dairy goats. This panel contains 54,188 SNPs based on genotyping by targeted sequencing (GBTS) technology. The source of SNPs in the panel were from the whole-genome resequencing of 110 dairy goats from three European and two Chinese indigenous dairy goat breeds. The performance of this liquid SNP chip panel was evaluated by genotyping 200 additional goats. Fifteen of them were randomly selected for whole-genome resequencing. The average capture ratio of the panel design loci was 98.41%, and the genotype concordance with resequencing reached 98.02%. We further used this chip panel to conduct genome-wide association studies (GWAS) to detect genetic loci that affect coat color in dairy goats. A single significant association signal for hair color was found on chromosome 8 at 31.52-35.02 Mb. The TYRP1 gene, which is associated with coat color in goats, was identified to be located at this genomic region (chromosome 8: 31,500,048-31,519,064). The emergence of high-precision and low-cost liquid microarrays will improve the analysis of genomics and breeding efficiency of dairy goats.

Chinese Red Steppe Cattle (CRS), a composite cattle breed, is well known for its milk production, high slaughter rate, carcass traits, and meat quality. Nowadays, it is widely bred in Jilin and Hebei Province and the Inner Mongolia Autonomous region. However, the population structure and the genetic basis of prominent characteristics of CRS are still unknown. In this study, we systematically describe their population structure, genetic diversity, and selection signature based on genotyping data from 61 CRS individuals with GGP Bovine 100 K chip. The results showed that CRS cattle had low inbreeding levels and had formed a unique genetic structure feature. Using two complementary methods (including comprehensive haplotype score and complex likelihood ratio), we identified 1291 and 1285 potentially selected genes, respectively. There were 141 genes annotated in common 106 overlapping genomic regions covered 5.62 Mb, including PLAG1, PRKG2, DGAT1, PARP10, TONSL, ADCK5, and BMP3, most of which were enriched in pathways related to muscle growth and differentiation, milk production, and lipid metabolism. This study will contribute to understanding the genetic mechanism behind artificial selection and give an extensive reference for subsequent breeding.

Blue mussels from the genus Mytilus are an abundant component of the benthic community, found in the high latitude habitats. These foundation species are relevant to the aquaculture industry, with over 2 million tonnes produced globally each year. Mussels withstand a wide range of environmental conditions and species from the Mytilus edulis complex readily hybridize in regions where their distributions overlap. Significant effort has been made to investigate the consequences of environmental stress on mussel physiology, reproductive isolation, and local adaptation. Yet our understanding on the genomic mechanisms underlying such processes remains limited. In this study, we developed a multi species medium-density 60 K SNP-array including four species of the Mytilus genus. SNPs included in the platform were called from 138 mussels from 23 globally distributed mussel populations, sequenced using a whole-genome low coverage approach. The array contains polymorphic SNPs which capture the genetic diversity present in mussel populations thriving across a gradient of environmental conditions (~59 K SNPs) and a set of published and validated SNPs informative for species identification and for diagnosis of transmissible cancer (610 SNPs). The array will allow the consistent genotyping of individuals, facilitating the investigation of ecological and evolutionary processes in these taxa. The applications of this array extend to shellfish aquaculture, contributing to the optimization of this industry via genomic selection of blue mussels, parentage assignment, inbreeding assessment and traceability. Further applications such as genome wide association studies (GWAS) for key production traits and those related to environmental resilience are especially relevant to safeguard aquaculture production under climate change.