Xiangdong Ding

Genome wide association studies for milk production traits in Chinese Holstein population.

Genome-wide association studies (GWAS) based on high throughput SNP genotyping technologies open a broad avenue for exploring genes associated with milk production traits in dairy cattle. Motivated by pinpointing novel quantitative trait nucleotide (QTN) across Bos Taurus genome, the present study is to perform GWAS to identify genes affecting milk production traits using current state-of-the-art SNP genotyping technology, i.e., the Illumina BovineSNP50 BeadChip. In the analyses, the five most commonly evaluated milk production traits are involved, including milk yield (MY), milk fat yield (FY), milk protein yield (PY), milk fat percentage (FP) and milk protein percentage (PP). Estimated breeding values (EBVs) of 2,093 daughters from 14 paternal half-sib families are considered as phenotypes within the framework of a daughter design. Association tests between each trait and the 54K SNPs are achieved via two different analysis approaches, a paternal transmission disequilibrium test (TDT)-based approach (L1-TDT) and a mixed model based regression analysis (MMRA). In total, 105 SNPs were detected to be significantly associated genome-wise with one or multiple milk production traits. Of the 105 SNPs, 38 were commonly detected by both methods, while four and 63 were solely detected by L1-TDT and MMRA, respectively. The majority (86 out of 105) of the significant SNPs is located within the reported QTL regions and some are within or close to the reported candidate genes. In particular, two SNPs, ARS-BFGL-NGS-4939 and BFGL-NGS-118998, are located close to the DGAT1 gene (160bp apart) and within the GHR gene, respectively. Our findings herein not only provide confirmatory evidences for previously findings, but also explore a suite of novel SNPs associated with milk production traits, and thus form a solid basis for eventually unraveling the causal mutations for milk production traits in dairy cattle.

Best Linear Unbiased Prediction of Genomic Breeding Values Using a Trait-Specific Marker-Derived Relationship Matrix

Background With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework of mixed model equations, by using a matrix of relationships among individuals that is derived from the markers. Here we extend that approach by deriving a marker-based relationship matrix specifically for the trait of interest. Methodology/Principal Findings In the framework of mixed model equations, a new best linear unbiased prediction (BLUP) method including a trait-specific relationship matrix (TA) was presented and termed TABLUP. The TA matrix was constructed on the basis of marker genotypes and their weights in relation to the trait of interest. A simulation study with 1,000 individuals as the training population and five successive generations as candidate population was carried out to validate the proposed method. The proposed TABLUP method outperformed the ridge regression BLUP (RRBLUP) and BLUP with realized relationship matrix (GBLUP). It performed slightly worse than BayesB with an accuracy of 0.79 in the standard scenario. Conclusions/Significance The proposed TABLUP method is an improvement of the RRBLUP and GBLUP method. It might be equivalent to the BayesB method but it has additional benefits like the calculation of accuracies for individual breeding values. The results also showed that the TA-matrix performs better in predicting ability than the classical numerator relationship matrix and the realized relationship matrix which are derived solely from pedigree or markers without regard to the trait. This is because the TA-matrix not only accounts for the Mendelian sampling term, but also puts the greater emphasis on those markers that explain more of the genetic variance in the trait.

A genome-wide detection of copy number variations using SNP genotyping arrays in swine

BackgroundCopy Number Variations (CNVs) have been shown important in both normal phenotypic variability and disease susceptibility, and are increasingly accepted as another important source of genetic variation complementary to single nucleotide polymorphism (SNP). Comprehensive identification and cataloging of pig CNVs would be of benefit to the functional analyses of genome variation.ResultsIn this study, we performed a genome-wide CNV detection based on the Porcine SNP60 genotyping data of 474 pigs from three pure breed populations (Yorkshire, Landrace and Songliao Black) and one Duroc × Erhualian crossbred population. A total of 382 CNV regions (CNVRs) across genome were identified, which cover 95.76Mb of the pig genome and correspond to 4.23% of the autosomal genome sequence. The length of these CNVRs ranged from 5.03 to 2,702.7kb with an average of 250.7kb, and the frequencies of them varied from 0.42 to 20.87%. These CNVRs contains 1468 annotated genes, which possess a great variety of molecular functions, making them a promising resource for exploring the genetic basis of phenotypic variation within and among breeds. To confirmation of these findings, 18 CNVRs representing different predicted status and frequencies were chosen for validation via quantitative real time PCR (qPCR). Accordingly, 12 (66.67%) of them was successfully confirmed.ConclusionsOur results demonstrated that currently available Porcine SNP60 BeadChip can be used to capture CNVs efficiently. Our study firstly provides a comprehensive map of copy number variation in the pig genome, which would be of help for understanding the pig genome and provide preliminary foundation for investigating the association between various phenotypes and CNVs.

Genome-wide detection of copy number variations using high-density SNP genotyping platforms in Holsteins

BackgroundCopy number variations (CNVs) are widespread in the human or animal genome and are a significant source of genetic variation, which has been demonstrated to play an important role in phenotypic diversity. Advances in technology have allowed for identification of a large number of CNVs in cattle. Comprehensive explore novel CNVs in the bovine genome would provide valuable information for functional analyses of genome structural variation and facilitating follow-up association studies between complex traits and genetic variants.ResultsIn this study, we performed a genome-wide CNV detection based on high-density SNP genotyping data of 96 Chinese Holstein cattle. A total of 367 CNV regions (CNVRs) across the genome were identified, which cover 42.74Mb of the cattle genome and correspond to 1.61% of the genome sequence. The length of the CNVRs on autosomes range from 10.76 to 2,806.42 Kb with an average of 96.23 Kb. 218 out of these CNVRs contain 610 annotated genes, which possess a wide spectrum of molecular functions. To confirm these findings, quantitative PCR (qPCR) was performed for 17 CNVRs and 13(76.5%) of them were successfully validated.ConclusionsOur study demonstrates the high density SNP array can significantly improve the accuracy and sensitivity of CNV calling. Integration of different platforms can enhance the detection of genomic structure variants. Our results provide a significant replenishment for the high resolution map of copy number variation in the bovine genome and valuable information for investigation of genomic structural variation underlying traits of interest in cattle.

Genome-Wide Identification of Copy Number Variations in Chinese Holstein

Recent studies of mammalian genomes have uncovered the vast extent of copy number variations (CNVs) that contribute to phenotypic diversity. Compared to SNP, a CNV can cover a wider chromosome region, which may potentially incur substantial sequence changes and induce more significant effects on phenotypes. CNV has been becoming an alternative promising genetic marker in the field of genetic analyses. Here we firstly report an account of CNV regions in the cattle genome in Chinese Holstein population. The Illumina Bovine SNP50K Beadchips were used for screening 2047 Holstein individuals. Three different programes (PennCNV, cnvPartition and GADA) were implemented to detect potential CNVs. After a strict CNV calling pipeline, a total of 99 CNV regions were identified in cattle genome. These CNV regions cover 23.24 Mb in total with an average size of 151.69 Kb. 52 out of these CNV regions have frequencies of above 1%. 51 out of these CNV regions completely or partially overlap with 138 cattle genes, which are significantly enriched for specific biological functions, such as signaling pathway, sensory perception response and cellular processes. The results provide valuable information for constructing a more comprehensive CNV map in the cattle genome and offer an important resource for investigation of genome structure and genomic variation underlying traits of interest in cattle.

Accuracy of genomic prediction using low-density marker panels.

Genomic selection has been widely implemented in national and international genetic evaluation in the dairy cattle industry, because of its potential advantages over traditional selection methods and the availability of commercial high-density (HD) single nucleotide polymorphism (SNP) panels. However, this method may not be cost-effective for cow selection and for other livestock species, because the cost of HD SNP panels is still relatively high. One possible solution that can enable other species to benefit from this promising method is genomic selection with low-density (LD) SNP panels. In this simulation study, LD SNP panels designed with different strategies and different SNP densities were compared. The effects of number of quantitative trait loci, heritability, and effective population size were evaluated in the framework of genomic selection with LD SNP panels. Methodologies of Bayesian variable selection; BLUP with a trait-specific, marker-derived relationship matrix; and BLUP with a realized relationship matrix were employed to predict genomic estimated breeding values with both HD and LD SNP panels. Up to 95% of accuracy obtained by using an HD panel can be obtained by using only a small proportion of markers. The LD panel with markers selected on the basis of their effects always performs better than the LD panel with evenly spaced markers. Both the genetic architecture of the trait and the effective population size have a significant effect on the performance of the LD panels. We concluded that, to implement genomic selection with LD panels, a training population of sufficient size and genotyped with an HD panel is necessary. The trade-off between the LD panels with evenly spaced markers and selected markers must be considered, which depends on the number of target traits in a breeding program and the genetic architecture of these traits. Genomic selection with LD panels could be feasible and cost-effective, though before implementation, a further detailed genetic and economic analysis is recommended.

Consistency of linkage disequilibrium between Chinese and Nordic Holsteins and genomic prediction for Chinese Holsteins using a joint reference population

BackgroundIn China, the reference population of genotyped Holstein cattle is relatively small with to date, 80 bulls and 2091 cows genotyped with the Illumina 54 K chip. Including genotyped Holstein cattle from other countries in the reference population could improve the accuracy of genomic prediction of the Chinese Holstein population. This study investigated the consistency of linkage disequilibrium between adjacent markers between the Chinese and Nordic Holstein populations, and compared the reliability of genomic predictions based on the Chinese reference population only or the combined Chinese and Nordic reference populations.MethodsGenomic estimated breeding values of Chinese Holstein cattle were predicted using a single-trait GBLUP model based on the Chinese reference dataset, and using a two-trait GBLUP model based on a joint reference dataset that included both the Chinese and Nordic Holstein data.ResultsThe extent of linkage disequilibrium was similar in the Chinese and Nordic Holstein populations and the consistency of linkage disequilibrium between the two populations was very high, with a correlation of 0.97. Genomic prediction using the joint versus the Chinese reference dataset increased reliabilities of genomic predictions of Chinese Holstein bulls in the test data from 0.22, 0.15 and 0.11 to 0.51, 0.47 and 0.36 for milk yield, fat yield and protein yield, respectively. Using five-fold cross-validation, reliabilities of genomic predictions of Chinese cows increased from 0.15, 0.12 and 0.15 to 0.26, 0.17 and 0.20 for milk yield, fat yield and protein yield, respectively.ConclusionsThe linkage disequilibrium between the two populations was very consistent and using the combined Nordic and Chinese reference dataset substantially increased reliabilities of genomic predictions for Chinese Holstein cattle.

Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows

Genomic selection using dense markers covering the whole genome is a tool for the genetic improvement of livestock and is revolutionizing the breeding system in dairy cattle. Progeny-tested bulls have been used to form reference populations in almost all countries where genomic selection has been implemented. In this study, the accuracy of genomic prediction when cows are used to form the reference population was investigated. The reference population consisted of 3,087 cows. All individuals were genotyped with Illumina BovineSNP50. After genotype imputation and editing, 48,676 single nucleotide polymorphisms were available for analysis. Two methods, genomic BLUP (GBLUP) and BayesB, were used to render genomic estimated breeding values (GEBV) for 5 milk production traits. Accuracies of GEBV were assessed in 3 ways: r(GEBV,EBV) (the correlation between GEBV and conventional EBV) in 67 progeny-tested bulls, rGEBV,EBV from a 5-fold cross validation in the 3,087 cow reference population, and the theoretical accuracy (for GBLUP) calculated in the same way as for conventional BLUP. The results showed that using GBLUP, the r(GEBV,EBV) and theoretical accuracy of genomic prediction in Chinese Holstein ranged from 0.59 to 0.76 and 0.70 to 0.80, respectively, which was 0.13 to 0.30 and 0.23 to 0.33 higher than the accuracies of conventional pedigree index, respectively. The results indicate that, as an alternative, genomic selection using cows in the reference population is feasible.

Genome‐wide association studies for hematological traits in swine

Improving immune capacity may increase the profitability of animal production if it enables animals to better cope with infections. Hematological traits play pivotal roles in animal immune capacity and disease resistance. Thus far, few studies have been conducted using a high-density swine SNP chip panel to unravel the genetic mechanism of the immune capability in domestic animals. In this study, using mixed model-based single-locus regression analyses, we carried out genome-wide association studies, using the Porcine SNP60 BeadChip, for immune responses in piglets for 18 hematological traits (seven leukocyte traits, seven erythrocyte traits, and four platelet traits) after being immunized with classical swine fever vaccine. After adjusting for multiple testing based on permutations, 10, 24, and 77 chromosome-wise significant SNPs were identified for the leukocyte traits, erythrocyte traits, and platelet traits respectively, of which 10 reached genome-wise significance level. Among the 53 SNPs for mean platelet volume, 29 are located in a linkage disequilibrium block between 32.77 and 40.59 Mb on SSC6. Four genes of interest are located within the block, providing genetic evidence that this genomic segment may be considered a candidate region relevant to the platelet traits. Other candidate genes of interest for red blood cell, hemoglobin, and red blood cell volume distribution width also have been found near the significant SNPs. Our genome-wide association study provides a list of significant SNPs and candidate genes that offer valuable information for future dissection of molecular mechanisms regulating hematological traits.

A genome-wide association study identifies two novel promising candidate genes affecting Escherichia coli F4ab/F4ac susceptibility in swine.

Enterotoxigenic Escherichia coli (ETEC) expressing F4 fimbria is the major pathogenic bacteria causing diarrhoea in neonatal and post-weaning piglets. Previous studies have revealed that the susceptibility to ETEC F4ab/F4ac is an autosomal Mendelian dominant trait and the loci controlling the F4ab/F4ac receptor are located on SSC13q41, between markers SW207 and S0283. To pinpoint these loci and further validate previous findings, we performed a genome-wide association study (GWAS) using a two generation family-based population, consisting of 301 piglets with phenotypes of susceptibility to ETEC F4ab/F4ac by the vitro adhesion test. The DNA of all piglets and their parents was genotyped using the Illumina PorcineSNP60 BeadChip, and 50,972 and 50,483 SNPs were available for F4ab and F4ac susceptibility, respectively, in the association analysis after quality control. In summary, 28 and 18 significant SNPs (p<0.05) were detected associated with F4ab and F4ac susceptibility respectively at genome-wide significance level. From these significant findings, two novel candidate genes, HEG1 and ITGB5, were firstly identified as the most promising genes underlying F4ab/F4ac susceptibility in swine according to their functions and positions. Our findings herein provide a novel evidence for unravelling genetic mechanism of diarrhoea risk in piglets.