Chromosomal Localization of Candidate Genes for Fiber Growth and Color in Alpaca (Vicugna pacos)

Identification of genomic regions associated with differences in fleece type in Huacaya and Suri alpacas (Vicugna pacos)

The difference in fleece type is the distinguishing trait between the two types of alpacas (Vicugna pacos), Huacaya and Suri. The Suri fleece type has been found to be inherited dominantly over the Huacaya type, resulting in offspring with the Suri phenotype. The aim of our study was to map genomic regions associated with the two different fleece types. In this study, 91 alpacas (54 Huacayas and 37 Suris) from Germany and Switzerland were genotyped using the 76k alpaca SNP array. Only 59k chromosome-localised markers map to the alpaca reference assembly VicPac3.1, and after quality control 49 866 SNPs, were retained for population structure assessment and to conduct a genome-wide association study. Both principal component and neighbour-joining tree analysis showed that the two fleece-type cohorts overlapped rather than forming two distinct clusters. Genome-wide significantly associated markers were observed in the scaffold region of chromosome 16 (NW_021964192.1), which contains a cluster of keratin genes. A haplotype predominantly found in Suri alpacas has been identified which supports dominant inheritance. Variant filtering of nine whole-genome sequenced alpacas from both fleece types in the critical interval of 0.4 Mb did not reveal perfect segregation of either fleece type for specific variants. To our knowledge, this is the first study to use the recently developed species-specific SNP array to identify genomic regions associated with differences in fleece type in alpacas. There are still some limitations, such as the preliminary status of the reference assembly and the incomplete annotation of the alpaca genome.

Genome-wide analyses based on a novel donkey 40K liquid chip reveal the gene responsible for coat color diversity in Chinese Dezhou donkey

Dezhou donkey is one of the representative local breeds in China, which is mainly divided into two strains: Sanfen and Wutou. There are obvious differences in coat color between the two strains. The former shows light points around the eyes, around the muzzle and under the belly, while the latter is completely solid black. In this study, genome-wide association analysis was performed for the differences in coat color traits between the Sanfen (n = 97) and Wutou (n = 108) strains using a novel donkey 40K liquid chip developed based on GenoBaits technology, to identify genomic regions and causal genes that could explain this variation. We also used FST and The cross-population composite likelihood ratio test (XPCLR) analyses to explore selected regions related to coat color differences. We identified one significant region on chromosome 15, with the most significant SNP located within the agouti signaling protein (ASIP) gene. At the same time, both FST and XPCLR methods detected the same selected region on chromosome 15, and ASIP was the gene with the strongest signal. ASIP and melanocortin 1 receptor (MC1R) control the ratio of eumelanin to pheomelanin through their protein activity. They are deeply involved in the process of melanosome organation and melanogenesis, thus affecting mammals' coat color variation. We used a range of genome-wide approach to identify the genetic basis of coat color variation in Dezhou donkeys. The results provide a supplement to the color variation study in Chinese donkeys at the genome-wide level, and preliminarily verified the reliability of the Molbreeding Donkey No. 1 40K liquid chip.

Association analysis for SNPs of BAAT and COL1A1 genes with cashmere production performance and other production traits in Liaoning cashmere goats

This study aimed to investigate the relationship between the polymorphism of bile acid-CoA: amino acid N-acyltransferase (BAAT) and collagen type I alpha 1 chain (COL1A1) genes and the production performance of Liaoning Cashmere goat (LCG). The potential single nucleotide polymorphisms (SNPs) of LCG were detected by sequence comparison of BAAT and COL1A1 genes and PCR-Seq polymorphism, and the effect of SNPs on production performance was analyzed by SPSS software. The results showed that three SNPs loci were detected in BAAT gene: G7900A, T7967C, C7998T, and one SNP locus T6716C was detected in COL1AL gene. At G7900A locus, the dominant genotype for cashmere performance was GG, and the dominant genotype for body measurement traits and milk production traits was AG. At T7967C locus, the dominant genotype for cashmere performance was TT, and the dominant genotype for body measurement traits and milk production traits was CC. At C7998T locus, TT was the dominant genotype for cashmere performance, body measurement traits, and milk production traits. At the T6716C locus, TT was the dominant genotype for cashmere performance, body measurement traits, and milk production traits. H1H1: AACC is the dominant haplotype combination. Therefore, this study will provide a reliable reference for future research on cashmere production performance, body measurement traits, and milk production traits of LCG.

Genome-Wide Association Study of Fiber Diameter in Alpacas

The aim of this study was the identification of candidate genomic regions associated with fiber diameter in alpacas. DNA samples were collected from 1011 female Huacaya alpacas from two geographical Andean regions in Peru (Pasco and Puno), and three alpaca farms within each region. The samples were genotyped using an Affymetrix Custom Alpaca genotyping array containing 76,508 SNPs. After the quality controls, 960 samples and 51,742 SNPs were retained. Three association study methodologies were performed. The GWAS based on a linear model allowed us to identify 11 and 35 SNPs (-log10(p-values) > 4) using information on all alpacas and alpacas with extreme values of fiber diameter, respectively. The haplotype and marker analysis method allowed us to identify nine haplotypes with standardized haplotype heritability higher than six standard deviations. The selection signatures based on cross-population extended haplotype homozygosity (XP-EHH) allowed us to identify 180 SNPs with XP-EHH values greater than |3|. Four candidate regions with adjacent SNPs identified via two association methods of analysis are located on VPA6, VPA9, VPA29 and one chromosomally unassigned scaffold. This study represents the first analysis of alpaca whole genome association with fiber diameter, using a recently assembled alpaca SNP microarray.

Selection signatures for fiber production in commercial species: A review

Natural fibers derived from diverse animal species have gained increased attention in recent years due to their favorable environmental effects, long-term sustainability benefits, and remarkable physical and mechanical properties that make them valuable raw materials used for textile and non-textile production. Domestication and selective breeding for the economically significant fiber traits play an imperative role in shaping the genomes and, thus, positively impact the overall productivity of the various fiber-producing species. These selection pressures leave unique footprints on the genome due to alteration in the allelic frequencies at specific loci, characterizing selective sweeps. Recent advances in genomics have enabled the discovery of selection signatures across the genome using a variety of methods. The increased demand for 'green products' manufactured from natural fibers necessitates a detailed investigation of the genomes of the various fiber-producing plant and animal species to identify the candidate genes associated with important fiber attributes such as fiber diameter/fineness, color, length, and strength, among others. The objective of this review is to present a comprehensive overview of the concept of selection signature and selective sweeps, discuss the main methods used for its detection, and address the selection signature studies conducted so far in the diverse fiber-producing animal species.

Roan coat color in livestock

Since domestication, a wide variety of phenotypes including coat color variation has developed in livestock. This variation is mostly based on selective breeding. During the beginning of selective breeding, potential negative consequences did not become immediately evident due to low frequencies of homozygous animals and have been occasionally neglected. However, numerous studies of coat color genetics have been carried out over more than a century and, meanwhile, pleiotropic effects for several coat color genes, including disorders of even lethal impact, were described. Similar coat color phenotypes can often be found across species, caused either by conserved genes or by different genes. Even in the same species, more than one gene could cause the same or similar coat color phenotype. The roan coat color in livestock species is characterized by a mixture of white and colored hair in cattle, pig, sheep, goat, alpaca, and horse. So far, the genetic background of this phenotype is not fully understood, but KIT and its ligand KITLG (MGF) are major candidate genes in livestock species. For some of these species, pleiotropic effects such as subfertility in homozygous roan cattle or homozygous embryonic lethality in certain horse breeds have been described. This review aims to point out the similarities and differences of the roan phenotype across the following livestock species: cattle, pig, sheep, goat, alpaca, and horse; and provides the current state of knowledge on genetic background and pleiotropic effects.

Selection of Cashmere Fineness Functional Genes by Translatomics

Cashmere fineness is an important index to evaluate cashmere quality. Liaoning Cashmere Goat (LCG) has a large cashmere production and long cashmere fiber, but its fineness is not ideal. Therefore, it is important to find genes involved in cashmere fineness that can be used in future endeavors aiming to improve this phenotype. With the continuous advancement of research, the regulation of cashmere fineness has made new developments through high-throughput sequencing and genome-wide association analysis. It has been found that translatomics can identify genes associated with phenotypic traits. Through translatomic analysis, the skin tissue of LCG sample groups differing in cashmere fineness was sequenced by Ribo-seq. With these data, we identified 529 differentially expressed genes between the sample groups among the 27197 expressed genes. From these, 343 genes were upregulated in the fine LCG group in relation to the coarse LCG group, and 186 were downregulated in the same relationship. Through GO enrichment analysis and KEGG enrichment analysis of differential genes, the biological functions and pathways of differential genes can be found. In the GO enrichment analysis, 491 genes were significantly enriched, and the functional region was mainly in the extracellular region. In the KEGG enrichment analysis, the enrichment of the human papillomavirus infection pathway was seen the most. We found that the COL6A5 gene may affect cashmere fineness.

Prognostic prediction and gene regulation network of EIF2S2 in hepatocellular carcinoma based on data mining

BACKGROUND

Hepatocellular carcinoma (HCC) is a malignant tumor with a high fatality rate, predicting poor prognosis and therapeutic effect. Screening potential prognostic genes in HCC could be a creative way to advance clinical treatment. Eukaryotic translation initiation factor 2 subunit beta (EIF2S2) has reportedly been linked to several tumors, including liver cancer, but the prognostic predictions remain unknown. Therefore, we aimed to clarify the prognostic role and interaction network of EIF2S2 in HCC using bioinformatics data.

METHODS

We screened EIF2S2 using the Oncomine, Ualcan, and TCGA databases. R software was used to analyze the mRNA level and clinicopathological characteristics of hepatocellular carcinoma. Evaluation of the correlations between EIF2S2 and patients' survival was made using the Kaplan-Meier curves and Cox proportional hazards regression model. Then, the influence of EIF2S2 gene mutations on the prognosis of patients was explored by cBioPortal. The protein-protein interaction network of 50 similar genes related to EIF2S2 was implemented by GEPIA2 and Metascape. The LinkedOmics database allowed us to carry out Gene Set Enrichment Analysis. Finally, we constructed the EIF2S2 kinase, miRNA, and transcription factor target networks using GeneMANIA.

RESULTS

EIF2S2 mRNA was overexpressed in HCC and was closely associated with clinicopathological features, including gender, age, race, tumor grade, and stage. There was no correlation between EIF2S2 genetic mutations and prognostic survival. Combining Cox proportional hazards regression model analyses, high-expressed EIF2S2 predicted poor prognosis in HCC patients. Additionally, we screened the top three EIF2S2-related genes (PFDN4, HM13, and SNRPD1), the 50 similar genes, and then constructed a 50-similar-gene protein-protein interaction network identified by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using Metascape. EIF2S2 target networks in HCC were identified in kinase, miRNA, and transcription factor networks, including the mitogen-activated protein kinase 1 (MAPK1), miRNAs (Mir-144), and transcription factors (GGAANCGGAANY_UNKNOWN) using GeneMANIA.

CONCLUSIONS

EIF2S2 plays a crucial role in the gene-regulating network of HCC and may be a potential prognostic marker or therapeutic target for HCC patients.

ssGBLUP Method Improves the Accuracy of Breeding Value Prediction in Huacaya Alpaca

Simple Summary

Alpaca breeding takes place in the most entrenched areas of the Andes, where the conditions to implement genetic improvement programs are very difficult. Likewise, taking phenotypic records is limited in its ability to predict genetic merit accurately. For this reason, genomic information is shown as an alternative that helps to predict the genetic values of fiber traits more precisely. This study showed how genomic information increased precision by 2.623% for the fiber diameter, 6.442% for the standard deviation of the fiber diameter, and 1.471% for the percentage of medullation compared to traditional methods for predicting genetic merit, suggesting that adding genomic data in prediction models could be beneficial for alpaca breeding programs in the future.

Abstract

Improving textile characteristics is the main objective of alpaca breeding. A recently developed SNP chip for alpacas could potentially be used to implement genomic selection and accelerate genetic progress. Therefore, this study aimed to compare the increase in prediction accuracy of three important fiber traits: fiber diameter (FD), standard deviation of fiber diameter (SD), and percentage of medullation (PM) in Huacaya alpacas. The data contains a total pedigree of 12,431 animals, 24,169 records for FD and SD, and 8386 records for PM and 60,624 SNP markers for each of the 431 genotyped animals of the Pacomarca Genetic Center. Prediction accuracy of breeding values was compared between a classical BLUP and a single-step Genomic BLUP (ssGBLUP). Deregressed phenotypes were predicted. The accuracies of the genetic and genomic values were calculated using the correlation between the predicted breeding values and the deregressed values of 100 randomly selected animals from the genotyped ones. Fifty replicates were carried out. Accuracies with ssGBLUP improved by 2.623%, 6.442%, and 1.471% on average for FD, SD, and PM, respectively, compared to the BLUP method. The increase in accuracy was relevant, suggesting that adding genomic data could benefit alpaca breeding programs.

Analysis of m6A methylation in skin tissues of different sex Liaoning cashmere goats

N⁶-methyladenosine (m⁶A) is the most frequent internal modification of mRNA and lncRNA in eukaryotes. We used two high-throughput sequencing method, m⁶A-seq and RNA-seq to identify pivotal m⁶A-modified genes in cashmere fineness and fiber growth. 8062 m⁶A peaks were detected by m⁶A-seq, including 2157 upregulated and 6445 downregulated. Furthermore, by comparing m⁶A-modified genes of the male Liaoning Cashmere Goat (M-LCG) and female Liaoning Cashmere Goat (F-LCG) skin tissues, we get 862 differentially expressed m⁶A-modified genes. To identify differently expressed m⁶A genes associated with cashmere fineness, 11 genes were selected for validation using real time fluorescent quantitative PCR in M-LCG and F-LCG. This study provides an acadamic basis on the molecular regulation mechanism of m⁶A modification in cashmere growth process.

Development of a 76k Alpaca (Vicugna pacos) Single Nucleotide Polymorphisms (SNPs) Microarray

Small farm producers’ sustenance depends on their alpaca herds and the production of fiber. Genetic improvement of fiber characteristics would increase their economic benefits and quality of life. The incorporation of molecular marker technology could overcome current limitations for the implementation of genetic improvement programs. Hence, the aim of this project was the generation of an alpaca single nucleotide polymorphism (SNP) microarray. A sample of 150 Huacaya alpacas from four farms, two each in Puno and Cerro de Pasco were used for SNP discovery by genotyping by sequencing (GBS). Reduced representation libraries, two per animal, were produced after DNA digestion with ApeK1 and double digestion with Pst1-Msp1. Ten alpaca genomes, sequenced at depths between 12× to 30×, and the VicPac3.1 reference genome were used for read alignments. Bioinformatics analysis discovered 76,508 SNPs included in the microarray. Candidate genes SNPs (302) for fiber quality and color are also included. The microarray SNPs cover 90.5% of the genome length with a density of about 39 ± 2.51 SNPs/Mb of DNA at an average interval of 26.45 ± 18.57 kbp. The performance was evaluated by genotyping 30 family trios and comparing them to their pedigrees, as well as comparing microarray to GBS genotypes. Concordance values of 0.93 and 0.94 for ApeK1 and Pst1-Msp1 generated SNPs were observed. Similarly, 290 fiber quality and color candidate gene SNPs were validated. Availability of this microarray will facilitate genome-wide association studies, marker-assisted selection and, in time, genomic selection.

Cytogenetic Mapping of 35 New Markers in the Alpaca (Vicugna pacos)

Alpaca is a camelid species of broad economic, biological and biomedical interest, and an essential part of the cultural and historical heritage of Peru. Recently, efforts have been made to improve knowledge of the alpaca genome, and its genetics and cytogenetics, to develop molecular tools for selection and breeding. Here, we report cytogenetic mapping of 35 new markers to 19 alpaca autosomes and the X chromosome. Twenty-eight markers represent alpaca SNPs, of which 17 are located inside or near protein-coding genes, two are in ncRNA genes and nine are intergenic. The remaining seven markers correspond to candidate genes for fiber characteristics (BMP4, COL1A2, GLI1, SFRP4), coat color (TYR) and development (CHD7, PAX7). The results take the tally of cytogenetically mapped markers in alpaca to 281, covering all 36 autosomes and the sex chromosomes. The new map assignments overall agree with human–camelid conserved synteny data, except for mapping BMP4 to VPA3, suggesting a hitherto unknown homology with HSA14. The findings validate, refine and correct the current alpaca assembly VicPac3.1 by anchoring unassigned sequence scaffolds, and ordering and orienting assigned scaffolds. The study contributes to the improvement in the alpaca reference genome and advances camelid molecular cytogenetics.

Genome-wide analyses reveal the regions involved in the phenotypic diversity in Sicilian pigs

Nero Siciliano (Sicilian Black, SB) is a local pig breed generally of uniform black color. In addition to this officially recognized breed, there are animals showing morphological characteristics resembling the SB but with gray hair (Sicilian Grey, SG). The SG, compared with the SB, also shows a more compact structure with greater transverse diameters, higher average daily gains and lower thickness of the back fat. In this study, using the Illumina PorcineSNP60 BeadChip, we run genome-wide analyses to identify regions that may explain the phenotypic differences between SB (n = 21) and SG (n = 27) individuals. Combining the results of the two case-control approaches (GWAS and F_ST ), we identified two significant regions, one on SSC5 (95 401 083 bp) and one on SSC15 (55 051 435 bp), which contains several candidate genes related to growth traits in pig. The results of the Bayesian population differentiation approach identified a marker near the MGAT4C, a gene associated with average daily gain in pigs. Finally, scanning the genome for runs of homozygosity islands, we found that the two groups have different runs of homozygosity islands, with several candidate genes involved in coat color (in SG) or related to different pig performance traits (in SB). In summary, the two analyzed groups differed for several phenotypic traits, and genes involved in these traits (growth, meat traits and coat color) were detected. This study provided another contribution to the identification of genomic regions involved in phenotypic variability in local pig populations.