Single Nucleotide Polymorphism Chip Research Articles

Analysis of genetic diversity and structure of endangered Dengchuan cattle population using a single-nucleotide polymorphism chip

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.

Development and verification of a 10K liquid chip for Hainan black goat based on genotyping by pinpoint sequencing of liquid captured targets

BackgroundChina has thousands years of goat breeding and abundant goat genetic resources. Additionally, the Hainan black goat is one of the high-quality local goat breeds in China. In order to conserve the germplasm resources of the Hainan black goat, facilitate its genetic improvement and further protect the genetic diversity of goats, it is urgent to develop a single nucleotide polymorphism (SNP) chip for Hainan black goat.ResultsIn this study, we aimed to design a 10K liquid chip for Hainan black goat based on genotyping by pinpoint sequencing of liquid captured targets (cGPS). A total of 45,588 candidate SNP sites were obtained, 10,677 of which representative SNP sites were selected to design probes, which finally covered 9,993 intervals and formed a 10K cGPS liquid chip for Hainan black goat. To verify the 10K cGPS liquid chip, some southern Chinese goat breeds and a sheep breed with similar phenotype to the Hainan black goat were selected. A total of 104 samples were used to verify the clustering ability of the 10K cGPS liquid chip for Hainan black goat. The results showed that the detection rate of sites was 97.34% -99.93%. 84.5% of SNP sites were polymorphic. The heterozygosity rate was 3.08%-36.80%. The depth of more than 99.4% sites was above 10X. The repetition rate was 99.66%-99.82%. The average consistency between cGPS liquid chip results and resequencing results was 85.58%. In addition, the phylogenetic tree clustering analysis verified that the SNP sites on the chip had better clustering ability.ConclusionThese results indicate that we have successfully realized the development and verification of the 10K cGPS liquid chip for Hainan black goat, which provides a useful tool for the genome analysis of Hainan black goat. Moreover, the 10K cGPS liquid chip is conducive to the research and protection of Hainan black goat germplasm resources and lays a solid foundation for its subsequent breeding work.

Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models

BackgroundMetabolic disturbances adversely impact productive and reproductive performance of dairy cattle due to changes in endocrine status and immune function, which increase the risk of disease. This may occur in the post-partum phase, but also throughout lactation, with sub-clinical symptoms. Recently, increased attention has been directed towards improved health and resilience in dairy cattle, and genomic selection (GS) could be a helpful tool for selecting animals that are more resilient to metabolic disturbances throughout lactation. Hence, we evaluated the genomic prediction of serum biomarkers levels for metabolic distress in 1353 Holsteins genotyped with the 100K single nucleotide polymorphism (SNP) chip assay. The GS was evaluated using parametric models best linear unbiased prediction (GBLUP), Bayesian B (BayesB), elastic net (ENET), and nonparametric models, gradient boosting machine (GBM) and stacking ensemble (Stack), which combines ENET and GBM approaches.ResultsThe results show that the Stack approach outperformed other methods with a relative difference (RD), calculated as an increment in prediction accuracy, of approximately 18.0% compared to GBLUP, 12.6% compared to BayesB, 8.7% compared to ENET, and 4.4% compared to GBM. The highest RD in prediction accuracy between other models with respect to GBLUP was observed for haptoglobin (hapto) from 17.7% for BayesB to 41.2% for Stack; for Zn from 9.8% (BayesB) to 29.3% (Stack); for ceruloplasmin (CuCp) from 9.3% (BayesB) to 27.9% (Stack); for ferric reducing antioxidant power (FRAP) from 8.0% (BayesB) to 40.0% (Stack); and for total protein (PROTt) from 5.7% (BayesB) to 22.9% (Stack). Using a subset of top SNPs (1.5k) selected from the GBM approach improved the accuracy for GBLUP from 1.8 to 76.5%. However, for the other models reductions in prediction accuracy of 4.8% for ENET (average of 10 traits), 5.9% for GBM (average of 21 traits), and 6.6% for Stack (average of 16 traits) were observed.ConclusionsOur results indicate that the Stack approach was more accurate in predicting metabolic disturbances than GBLUP, BayesB, ENET, and GBM and seemed to be competitive for predicting complex phenotypes with various degrees of mode of inheritance, i.e. additive and non-additive effects. Selecting markers based on GBM improved accuracy of GBLUP.

Genetic architecture of white striping in turkeys (Meleagris gallopavo).

White striping (WS) is a myopathy of growing concern to the turkey industry. It is rising in prevalence and has negative consequences for consumer acceptance and the functional properties of turkey meat. The objective of this studywas to conduct a genome-wide association study (GWAS) and functional analysis on WS severity. Phenotypic data consisted of white striping scored on turkey breast fillets (N = 8422) by trained observers on a 0-3 scale (none to severe). Of the phenotyped birds, 4667 genotypic records were available using a proprietary 65K single nucleotide polymorphism (SNP) chip. The SNP effects were estimated using a linear mixed model with a 30-SNP sliding window approach used to express the percentage genetic variance explained. Positional candidate genes were those located within 50kb of the top 1% of SNP windows explaining the most genetic variance. Of the 95 positional candidate genes, seven were further classified as functional candidate genes because of their association with both a significant gene ontology and molecular function term. The results of the GWAS emphasize the polygenic nature of the trait with no specific genomic region contributing a large portion to the overall genetic variance. Significant pathways relating to growth, muscle development, collagen formation, circulatory system development, cell response to stimulus, and cytokine production were identified. These results help to support published biological associations between WS and hypoxia and oxidative stress and provide information that may be useful for future-omics studies in understanding the biological associations with WS development in turkeys.

Large-scale genome-wide SNP analysis reveals the rugged (and ragged) landscape of global ancestry, phylogeny, and demographic history in chicken breeds.

The worldwide chicken gene pool encompasses a remarkable, but shrinking, number of divergently selected breeds of diverse origin. This study was a large-scale genome-wide analysis of the landscape of the complex molecular architecture, genetic variability, and detailed structure among 49 populations. These populations represent a significant sample of the world's chicken breeds from Europe (Russia, Czech Republic, France, Spain, UK, etc.), Asia (China), North America (USA), and Oceania (Australia). Based on the results of breed genotyping using the Illumina 60K single nucleotide polymorphism (SNP) chip, a bioinformatic analysis was carried out. This included the calculation of heterozygosity/homozygosity statistics, inbreeding coefficients, and effective population size. It also included assessment of linkage disequilibrium and construction of phylogenetic trees. Using multidimensional scaling, principal component analysis, and ADMIXTURE-assisted global ancestry analysis, we explored the genetic structure of populations and subpopulations in each breed. An overall 49-population phylogeny analysis was also performed, and a refined evolutionary model of chicken breed formation was proposed, which included egg, meat, dual-purpose types, and ambiguous breeds. Such a large-scale survey of genetic resources in poultry farming using modern genomic methods is of great interest both from the viewpoint of a general understanding of the genetics of the domestic chicken and for the further development of genomic technologies and approaches in poultry breeding. In general, whole genome SNP genotyping of promising chicken breeds from the worldwide gene pool will promote the further development of modern genomic science as applied to poultry.

Evaluation of non-additive genetic effects on carcass and meat quality traits in Korean Hanwoo cattle using genomic models

The traditional genetic evaluation methods generally consider additive genetic effects only and often ignore non-additive (dominance and epistasis) effects that may have contributions in genetic variation of complex traits of livestock species. The available dense single nucleotide polymorphisms (SNPs) panels offer to investigate the potential benefits of including non-additive genetic effects in the genomic evaluation’s models. Data from 16,971 genotyped (Illumina Bovine 50K SNP chip) Korean Hanwoo cattle were used to estimate genetic variance components and prediction accuracy of genomic breeding values (GEBV) for four carcass and meat quality traits: carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MS). Five different genetic models were evaluated through including additive, dominance and epistatic interactions (additive by additive, A×A; additive by dominance, A×D and dominance by dominance, D×D) successively in the models. The estimates of additive genetic variances and narrow sense heritabilities (ha2) were found similar across the evaluated models and traits except when additive interaction (A×A) was included. The dominance variance estimates relative to phenotypic variance ranged from 1.7 to 3.4% for CWT and MS traits, whereas, they were close to zero for EMA and BFT traits. The magnitude of A×A epistatic heritability (haa2) ranged between 14.8 and 27.7% in all traits. However, heritability estimates for A×D and D×D epistatic interactions (had2 and hdd2) were quite low compared to haa2 and were contributed only 0.0 to 9.7% of the total phenotypic variation. In general, broad sense heritability (hG2) estimates were almost twice (ranged between 0.54 and 0.68) the ha2 for all of the investigated traits. The inclusion of dominance effects did not improve the prediction accuracy of GEBV but improved 2.0 to 3.0% when epistatic effects included in the model. More importantly, rank correlation revealed that partitioning of variance components considering dominance and epistatic effects in the model would enable to re-rank of top animals with better prediction of GEBV. The present result suggest that dominance and epistatic effects could be included in the genomic evaluation model for better estimates of variance components and more accurate prediction of GEBV for carcass and meat quality traits in Korean Hanwoo cattle.

A genotyping array for the globally invasive vector mosquito, Aedes albopictus

BackgroundAlthough 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.MethodsWe 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.ResultsProbes 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.ConclusionsThe Aealbo chip is highly accurate, is concordant with genotypes from WGS with high sequence coverage, and may be more accurate than low-coverage WGS.Graphical

Genome-wide association study revealed the reason for the decrease in grain iron concentration during wheat breeding process in China

Iron (Fe) deficiency affects over two billion people worldwide. Developing high-yielding wheat (Triticum aestivum L.) cultivars with high grain Fe concentration (GFeC) is a key step toward alleviating malnutrition in humans. In this study, we used a diversity panel of 388 wheat accessions to examine the GFeC in four field environments. We found that the mean GFeC of the four environments decreased with the release year. And GFeC showed poor correlation with grain yield or the three yield factors in the four environments. Importantly, wheat cultivars in the major wheat-producing region of China showed the lowest GFeC. The GFeC phenotype was used for a genome-wide association study (GWAS) using a 660 K single-nucleotide polymorphism (SNP) chip, and 17 quantitative trait loci (QTLs) were identified on chromosomes 3B, 4 A, 5 A, 6B, 6D, 7 A, 7B, and 7D. However, no universal QTLs were detected among the 17 identified QTLs and 2280 yield-related QTLs. Through allelic analysis, two homologous genes, TraesCS7A01G197400 and TraesCS7D01G198900, were identified as potential candidate genes for GFeC, while the favourable alleles for GFeC were lost during the selection breeding process in China. The insights gained from this study showed that the yield dilution effect was not applied to the decrease in GFeC, which should be mainly caused by the loss of favourable alleles during the wheat breeding process in China. Therefore, wheat breeders should pay more attention to other traits to improve GFeC rather than the yield dilution effect.

Expected values for the accuracy of predicted breeding values accounting for genetic differences between reference and target populations

BackgroundGenetic merit, or breeding values as referred to in livestock and crop breeding programs, is one of the keys to the successful selection of animals in commercial farming systems. The developments in statistical methods during the twentieth century and single nucleotide polymorphism (SNP) chip technologies in the twenty-first century have revolutionized agricultural production, by allowing highly accurate predictions of breeding values for selection candidates at a very early age. Nonetheless, for many breeding populations, realized accuracies of predicted breeding values (PBV) remain below the theoretical maximum, even when the reference population is sufficiently large, and SNPs included in the model are in sufficient linkage disequilibrium (LD) with the quantitative trait locus (QTL). This is particularly noticeable over generations, as we observe the so-called erosion of the effects of SNPs due to recombinations, accompanied by the erosion of the accuracy of prediction. While accurately quantifying the erosion at the individual SNP level is a difficult and unresolved task, quantifying the erosion of the accuracy of prediction is a more tractable problem. In this paper, we describe a method that uses the relationship between reference and target populations to calculate expected values for the accuracies of predicted breeding values for non-phenotyped individuals accounting for erosion. The accuracy of the expected values was evaluated through simulations, and a further evaluation was performed on real data.ResultsUsing simulations, we empirically confirmed that our expected values for the accuracy of PBV accounting for erosion were able to correctly determine the prediction accuracy of breeding values for non-phenotyped individuals. When comparing the expected to the realized accuracies of PBV with real data, only one out of the four traits evaluated presented accuracies that were significantly higher than the expected, approaching h2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{{\ ext{h}}}^{2}}$$\\end{document}.ConclusionsWe defined an index of genetic correlation between reference and target populations, which summarizes the expected overall erosion due to differences in allele frequencies and LD patterns between populations. We used this correlation along with a trait’s heritability to derive expected values for the accuracy (R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}$$\\end{document}) of PBV accounting for the erosion, and demonstrated that our derived ER|erosion\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{E}}\\left[{\ ext{R}}|{\ ext{erosion}}\\right]$$\\end{document} is a reliable metric.

Genome-wide association study for the level of prolificacy in Cameroon’s native goat

ABSTRACT Income from goats highly depends on prolificacy, which is difficult to improve by traditional breeding methods. The study aimed to identify SNP markers for prolificacy, using a case–control genome-wide association study (GWAS) on 111 genotyped Cameroon native goat (CNG) does, based on the 50 K single nucleotide polymorphism (SNP) chip panel. None of the top SNPs reached the significant p-value of 5 × 10−8. The highest p-value was 0.0009. Despite the number of cases being about a quarter of the number of controls, the highest allele frequency of some of the top 20 variants in the cases was indicative of their potential role in the trait. These top variants included the following 15: rs268285661, rs268235169, rs268236449, rs268235135, rs268240394 in Sphingosine-1-phosphate phosphatase 2 (SGPP2) gene, rs268283635 in Solute carrier family 24 member 2 (SLC24A2) gene, rs268251678 in Androgen-induced gene 1 (AIG1) gene, rs268267018, rs268239617, rs268281364, rs268273029, rs268286941, rs268236144, rs268233233 in CEP126 gene and rs268278159, respectively. Our findings indicate that GWAS enable the identification of some loci within genes, with known biological functions and pathways in human being and mice animal model but far-ranging to what was previously hypothesized and tested in goat.

Identification of eQTLs using different sets of single nucleotide polymorphisms associated with carcass and body composition traits in pigs

BackgroundMapping expression quantitative trait loci (eQTLs) in skeletal muscle tissue in pigs is crucial for understanding the relationship between genetic variation and phenotypic expression of carcass traits in meat animals. Therefore, the primary objective of this study was to evaluate the impact of different sets of single nucleotide polymorphisms (SNP), including scenarios removing SNPs pruned for linkage disequilibrium (LD) and SNPs derived from SNP chip arrays and RNA-seq data from liver, brain, and skeletal muscle tissues, on the identification of eQTLs in the Longissimus lumborum tissue, associated with carcass and body composition traits in Large White pigs. The SNPs identified from muscle mRNA were combined with SNPs identified in the brain and liver tissue transcriptomes, as well as SNPs from the GGP Porcine 50 K SNP chip array. Cis- and trans-eQTLs were identified based on the skeletal muscle gene expression level, followed by functional genomic analyses and statistical associations with carcass and body composition traits in Large White pigs.ResultsThe number of cis- and trans-eQTLs identified across different sets of SNPs (scenarios) ranged from 261 to 2,539 and from 29 to 13,721, respectively. Furthermore, 6,180 genes were modulated by eQTLs in at least one of the scenarios evaluated. The eQTLs identified were not significantly associated with carcass and body composition traits but were significantly enriched for many traits in the “Meat and Carcass” type QTL. The scenarios with the highest number of cis- (n = 304) and trans- (n = 5,993) modulated genes were the unpruned and LD-pruned SNP set scenarios identified from the muscle transcriptome. These genes include 84 transcription factor coding genes.ConclusionsAfter LD pruning, the set of SNPs identified based on the transcriptome of the skeletal muscle tissue of pigs resulted in the highest number of genes modulated by eQTLs. Most eQTLs are of the trans type and are associated with genes influencing complex traits in pigs, such as transcription factors and enhancers. Furthermore, the incorporation of SNPs from other genomic regions to the set of SNPs identified in the porcine skeletal muscle transcriptome contributed to the identification of eQTLs that had not been identified based on the porcine skeletal muscle transcriptome alone.

The Effect of Sow Maternal Behavior on the Growth of Piglets and a Genome-Wide Association Study.

Sows' maternal behavior is important for improving piglet survival and growth; thus, breeding for good mothering sows is necessary for pig production. However, there is little research on the genetic mechanism of maternal behavior. In this study, a comparative analysis of piglets' growth traits between good and bad maternal behavior groups and a genome-wide association study (GWAS) was performed to elucidate the impact of sows' maternal behavior on piglet growth and identify candidate genes and markers of sow's maternal behaviors. Comparing the growth traits of piglets between good and bad sows' maternal behavior groups, the results showed that the growth traits of piglets from sows with good maternal behavior were better than those from sows with bad maternal behavior and especially for the multiparous sows group, this comparative difference was significant. For the intensive study of the genetic mechanisms of sows' maternal behavior, a total of 452 sows were genotyped using the Illumina Porcine 50K SNP Chip, and 4 traits, including biting piglets (BP), crushing piglets (CP), trampling piglets (TP) and screaming test (ST), were examined. Using a GWAS, 20 single nucleotide polymorphisms (SNPs) were found to be associated with these traits. Within 1 Mb upstream and downstream of the significant SNPs screened, 138 genes were obtained. After pathway enrichment and gene annotation, HIP1, FZD9 and HTR7 were identified as important candidate genes affecting sows' maternal behaviors. These findings preliminarily elucidate the genetic basis of sows' maternal behavior traits and provide candidate genes and markers for molecular breeding in pigs.

Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs.

Preselected variants associated with the trait of interest from genome-wide association studies (GWASs) are available to improve genomic prediction in pigs. The objectives of this study were to use preselected variants from a large GWAS meta-analysis to assess the impact of single-nucleotide polymorphism (SNP) preselection strategies on genome prediction of growth and carcass traits in pigs. We genotyped 1018 Large White pigs using medium (50k) SNP arrays and then imputed SNPs to sequence level by utilizing a reference panel of 1602 whole-genome sequencing samples. We tested the effects of different proportions of selected top SNPs across different SNP preselection strategies on genomic prediction. Finally, we compared the prediction accuracies by employing genomic best linear unbiased prediction (GBLUP), genomic feature BLUP and three weighted GBLUP models. SNP preselection strategies showed an average improvement in accuracy ranging from 0.3 to 2% in comparison to the SNP chip data. The accuracy of genomic prediction exhibited a pattern of initial increase followed by decrease, or continuous decrease across various SNP preselection strategies, as the proportion of selected top SNPs increased. The highest level of prediction accuracy was observed when utilizing 1 or 5% of top SNPs. Compared with the GBLUP model, the utilization of estimated marker effects from a GWAS meta-analysis as SNP weights in the BLUP|GA model improved the accuracy of genomic prediction in different SNP preselection strategies. The new SNP preselection strategies gained from this study bring opportunities for genomic prediction in limited-size populations in pigs.

Weighted single-step GWAS identified candidate genes associated with carcass traits in a Chinese yellow-feathered chicken population

Carcass traits in broiler chickens are complex traits that are influenced by multiple genes. To gain deeper insights into the genetic mechanisms underlying carcass traits, here we conducted a weighted single-step genome-wide association study (wssGWAS) in a population of Chinese yellow-feathered chicken. The objective was to identify genomic regions and candidate genes associated with carcass weight (CW), eviscerated weight with giblets (EWG), eviscerated weight (EW), breast muscle weight (BMW), drumstick weight (DW), abdominal fat weight (AFW), abdominal fat percentage (AFP), gizzard weight (GW), and intestine length (IL). A total of 1,338 broiler chickens with phenotypic and pedigree information were included in this study. Of these, 435 chickens were genotyped using a 600K single nucleotide polymorphism chip for association analysis. The results indicate that the most significant regions for 9 traits explained 2.38% to 5.09% of the phenotypic variation, from which the region of 194.53 to 194.63Mb on chromosome 1 with the gene RELT and FAM168A identified on it was significantly associated with CW, EWG, EW, BMW, and DW. Meanwhile, the 5 traits have a strong genetic correlation, indicating that the region and the genes can be used for further research. In addition, some candidate genes associated with skeletal muscle development, fat deposition regulation, intestinal repair, and protection were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that the genes are involved in processes such as vascular development (CD34, FGF7, FGFR3, ITGB1BP1, SEMA5A, LOXL2), bone formation (FGFR3, MATN1, MEF2D, DHRS3, SKI, STC1, HOXB1, HOXB3, TIPARP), and anatomical size regulation (ADD2, AKT1, CFTR, EDN3, FLII, HCLS1, ITGB1BP1, SEMA5A, SHC1, ULK1, DSTN, GSK3B, BORCS8, GRIP2). In conclusion, the integration of phenotype, genotype, and pedigree information without creating pseudo-phenotype will facilitate the genetic improvement of carcass traits in chickens, providing valuable insights into the genetic architecture and potential candidate genes underlying carcass traits, enriching our understanding and contributing to the breeding of high-quality broiler chickens.

Integrative analysis of genome‐wide association study and transcriptomics to identify potential candidate genes influencing drip loss in Beijing Black pigs

AbstractUnderstanding the genetic factors related to meat drip loss is of great importance for animal breeding and production. In this study, we employed a combination of genome‐wide association study (GWAS) mapping and RNA sequencing (RNA‐seq) data to effectively identify potentially functional single nucleotide polymorphisms (SNPs) as well as candidate genes associated with drip loss (DL) in Beijing Black pigs. Initially, we conducted a single‐ and multi‐trait GWAS on drip loss traits in 441 Beijing Black pigs at 24 (DL24) and 48 (DL48) hours postmortem using the Illumina pig 50K SNP chip. Five SNPs with annotations for four genes (FGGY, LHFPL6, OSBPL1A, and NMNAT3) were consistently identified in single or multiple trait GWAS results, indicating their potential pleiotropic effects on drip loss. Next, a comprehensive comparative transcriptomic analysis was performed on samples of Beijing Black pigs exhibiting extremely high and low drip loss, resulting in the identification of 21 differentially expressed genes (DGEs) as potential candidates. Additionally, protein–protein interaction (PPI) network analysis revealed reciprocal regulatory relationships between FOXO1, OSBPL1A, DOCK1 (identified from GWAS) and the candidate DGEs obtained from RNA‐seq data. Therefore, we propose that these genes may impact drip loss traits through gene interactions. In conclusion, our integrative analysis screened candidate genes that may affect the drip loss traits in Beijing Black pigs, which provides crucial insights into the molecular mechanisms of drip loss and serves as a theoretical reference for improving meat quality in Beijing Black pigs.

Genetic diversity and population structure of modern wheat (Triticum aestivum L.) cultivars in Henan Province of China based on SNP markers

BackgroundHenan is the province with the greatest wheat production in China. Although more than 100 cultivars are used for production, many cultivars are still insufficient in quality, disease resistance, adaptability and yield potential. To overcome these limitations, it is necessary to constantly breed new cultivars to maintain the continuous and stable growth of wheat yield and quality. To improve breeding efficiency, it is important to evaluate the genetic diversity and population genetic structure of its cultivars. However, there are no such reports from Henan Province. Therefore, in this study, single nucleotide polymorphism (SNP) markers were used to study the population genetic structure and genetic diversity of 243 wheat cultivars included in a comparative test of wheat varieties in Henan Province, aiming to provide a reference for the utilization of backbone parents and the selection of hybrid combinations in the genetic improvement of wheat cultivars.ResultsIn this study, 243 wheat cultivars from Henan Province of China were genotyped by the Affymetrix Axiom Wheat660K SNP chip, and 21 characteristics were investigated. The cultivars were divided into ten subgroups; each subgroup had distinct characteristics and unique utilization value. Furthermore, based on principal component analysis, Zhoumai cultivars were the main hybrid parents, followed by Aikang 58, high-quality cultivars, and Shandong cultivars. Genetic diversity analysis showed that 61.3% of SNPs had a high degree of genetic differentiation, whereas 33.4% showed a moderate degree. The nucleotide diversity of subgenome B was relatively high, with an average π value of 3.91E-5; the nucleotide diversity of subgenome D was the lowest, with an average π value of 2.44E-5.ConclusionThe parents used in wheat cross-breeding in Henan Province are similar, with a relatively homogeneous genetic background and low genetic diversity. These results will not only contribute to the objective evaluation and utilization of the tested cultivars but also provide insights into the current conditions and existing challenges of wheat cultivar breeding in Henan Province, thereby facilitating the scientific formulation of breeding objectives and strategies to improve breeding efficiency.

Genomic prediction based on selective linkage disequilibrium pruning of low-coverage whole-genome sequence variants in a pure Duroc population

BackgroundAlthough the accumulation of whole-genome sequencing (WGS) data has accelerated the identification of mutations underlying complex traits, its impact on the accuracy of genomic predictions is limited. Reliable genotyping data and pre-selected beneficial loci can be used to improve prediction accuracy. Previously, we reported a low-coverage sequencing genotyping method that yielded 11.3 million highly accurate single-nucleotide polymorphisms (SNPs) in pigs. Here, we introduce a method termed selective linkage disequilibrium pruning (SLDP), which refines the set of SNPs that show a large gain during prediction of complex traits using whole-genome SNP data.ResultsWe used the SLDP method to identify and select markers among millions of SNPs based on genome-wide association study (GWAS) prior information. We evaluated the performance of SLDP with respect to three real traits and six simulated traits with varying genetic architectures using two representative models (genomic best linear unbiased prediction and BayesR) on samples from 3579 Duroc boars. SLDP was determined by testing 180 combinations of two core parameters (GWAS P-value thresholds and linkage disequilibrium r2). The parameters for each trait were optimized in the training population by five fold cross-validation and then tested in the validation population. Similar to previous GWAS prior-based methods, the performance of SLDP was mainly affected by the genetic architecture of the traits analyzed. Specifically, SLDP performed better for traits controlled by major quantitative trait loci (QTL) or a small number of quantitative trait nucleotides (QTN). Compared with two commercial SNP chips, genotyping-by-sequencing data, and an unselected whole-genome SNP panel, the SLDP strategy led to significant improvements in prediction accuracy, which ranged from 0.84 to 3.22% for real traits controlled by major or moderate QTL and from 1.23 to 11.47% for simulated traits controlled by a small number of QTN.ConclusionsThe SLDP marker selection method can be incorporated into mainstream prediction models to yield accuracy improvements for traits with a relatively simple genetic architecture, however, it has no significant advantage for traits not controlled by major QTL. The main factors that affect its performance are the genetic architecture of traits and the reliability of GWAS prior information. Our findings can facilitate the application of WGS-based genomic selection.

Fine mapping and sequence analysis reveal a promising candidate gene encoding a novel NB-ARC domain derived from wild rice (Oryza officinalis) that confers bacterial blight resistance.

Bacterial blight disease of rice caused by Xanthomonas oryzae pv. oryzae (Xoo) is one of the most serious constraints in rice production. The most sustainable strategy to combat the disease is the deployment of host plant resistance. Earlier, we identified an introgression line, IR 75084-15-3-B-B, derived from Oryza officinalis possessing broad-spectrum resistance against Xoo. In order to understand the inheritance of resistance in the O. officinalis accession and identify genomic region(s) associated with resistance, a recombinant inbred line (RIL) mapping population was developed from the cross Samba Mahsuri (susceptible to bacterial blight) × IR 75084-15-3-B-B (resistant to bacterial blight). The F2 population derived from the cross segregated in a phenotypic ratio of 3: 1 (resistant susceptible) implying that resistance in IR 75084-15-3-B-B is controlled by a single dominant gene/quantitative trait locus (QTL). In the F7 generation, a set of 47 homozygous resistant lines and 47 homozygous susceptible lines was used to study the association between phenotypic data obtained through screening with Xoo and genotypic data obtained through analysis of 7K rice single-nucleotide polymorphism (SNP) chip. Through composite interval mapping, a major locus was detected in the midst of two flanking SNP markers, viz., Chr11.27817978 and Chr11.27994133, on chromosome 11L with a logarithm of the odds (LOD) score of 10.21 and 35.93% of phenotypic variation, and the locus has been named Xa48t. In silico search in the genomic region between the two markers flanking Xa48t identified 10 putatively expressed genes located in the region of interest. The quantitative expression and DNA sequence analysis of these genes from contrasting parents identified the Os11g0687900 encoding an NB-ARC domain-containing protein as the most promising gene associated with resistance. Interestingly, a 16-bp insertion was noticed in the untranslated region (UTR) of the gene in the resistant parent, IR 75084-15-3-B-B, which was absent in Samba Mahsuri. The association of Os11g0687900 with resistance phenotype was further established by sequence-based DNA marker analysis in the RIL population. A co-segregating PCR-based INDEL marker, Marker_Xa48, has been developed for use in the marker-assisted breeding of Xa48t.

Uncovering the candidate genes related to sheep body weight using multi-trait genome-wide association analysis.

In sheep, body weight is an economically important trait. This study sought to map genetic loci related to weaning weight and yearling weight. To this end, a single-trait and multi-trait genome-wide association study (GWAS) was performed using a high-density 600 K single nucleotide polymorphism (SNP) chip. The results showed that 43 and 56 SNPs were significantly associated with weaning weight and yearling weight, respectively. A region associated with both weaning and yearling traits (OARX: 6.74-7.04 Mb) was identified, suggesting that the same genes could play a role in regulating both these traits. This region was found to contain three genes (TBL1X, SHROOM2 and GPR143). The most significant SNP was Affx-281066395, located at 6.94 Mb (p = 1.70 × 10-17), corresponding to the SHROOM2 gene. We also identified 93 novel SNPs elated to sheep weight using multi-trait GWAS analysis. A new genomic region (OAR10: 76.04-77.23 Mb) with 22 significant SNPs were discovered. Combining transcriptomic data from multiple tissues and genomic data in sheep, we found the HINT1, ASB11 and GPR143 genes may involve in sheep body weight. So, multi-omic anlaysis is a valuable strategy identifying candidate genes related to body weight.

Design and validation of high-density SNP array of goats and population stratification of Indian goat breeds

Goats are the supporting pillars of rural economy contributing significantly to meat and milk production in India. It is a species targeted for fulfilling the interdependent goals of poverty reduction and creation of employment for supporting the rural income. The increased demand for goat products necessitates their genetic characterization and improvement to augment the production of native breeds. Bi-allelic, genome wide, densely placed single nucleotide polymorphism (SNP) markers are most suitable for this purpose. This paper describes the design and validation of an Affymetrix Axiom-based high-density (HD) SNP chip for goats. The array was designed using a panel of 225 samples from 15 diverse goat breeds of India. In total, more than 38 million high quality SNPs were subjected to stringent filtering and 626,975 SNPs were finally tiled on the array. The average coverage of SNPs in our chip is one SNP per four kilobase (kb), providing a denser coverage of the goat genome than previously available arrays. The HD chip (Axiom_Cahi) was validated by genotyping 443 samples from 26 indigenous goat breeds/populations. The results revealed 95.83% markers to be highly informative and polymorphic in Indian goats. Multivariate analysis indicated population structuring, as 15 breeds could be segregated using the designed array. Phylogenetic analysis suggested stratification of breeds by geographic proximity. This HD SNP chip for goats is a valuable resource for genomic selection, genome wide association as well as population genetic studies in goats.