Genome-wide association study of milk production traits in a crossbred dairy sheep population using three statistical models

Milk production is one of the most important characteristics of dairy sheep, and the identification of genes affecting milk production traits is critical to understanding the genetics and improve milk production in future generations. Three statistical techniques, namely GWAS, ridge-regression BLUP and BayesC π , were used to identify SNPs in significant association with three milk production traits (milk yield, fat yield and protein yield) in a crossbred dairy sheep population. The results suggested that chromosomes 1, 3, 4, 5, 7 and 11 were likely to harbor genes important to milk production because these chromosomes had the greatest top-100-SNP variance contributions on the three milk production traits. The GWAS analysis identified between 74 and 288 genome-wide significant SNP (P < 0.05) whereas the BayesCπ model revealed between six and 63 SNPs, each with >95% posterior probability of inclusion as having a non-zero association effect on at least one of the three milk production traits. Positional candidate genes for milk production in sheep were searched, based on the sheep genomic assembly OAR version 3.1, such as those which map position coincided with or was located within 0.1 Mbp of a genome-wide suggestive or significant SNP. These identified SNPs and candidate genes supported some previous findings and also added new information about genetic markers for genetic improvement of lactation in dairy sheep, but keeping in mind that the majority of these positional candidate genes are not necessarily true causative loci for these traits and future validations are thus necessary.

Estimation of genomic breed composition of individual animals in composite beef cattle

Three statistical models (an admixture model, linear regression, and ridge-regression BLUP) and two strategies for selecting SNP panels (uniformly spaced vs. maximum Euclidean distance of SNP allele frequencies between ancestral breeds) were compared for estimating genomic-estimated breed composition (GBC) in Brangus and Santa Gertrudis cattle, respectively. Animals were genotyped with a GeneSeek Genomic Profiler bovine low-density version 4 SNP chip. The estimated GBC was consistent among the uniformly spaced SNP panels, and values were similar between the three models. However, estimated GBC varied considerably between the three methods when using fewer than 10 000 SNPs that maximized the Euclidean distance of allele frequencies between the ancestral breeds. The admixture model performed most consistently across various SNP panel sizes. For the other two models, stabilized estimates were obtained with an SNP panel size of 20 000 SNPs or more. Based on the uniformly spaced 20K SNP panel, the estimated GBC was 69.8-70.5% Angus and 29.5-30.2% Brahman for Brangus, and 63.9-65.3% Shorthorn and 34.7-36.1% Brahman in Santa Gertrudis. The estimated GBC of ancestries for Santa Gertrudis roughly agreed with the pedigree-expected values. However, the estimated GBC in Brangus showed a considerably larger Angus composition than the pedigree-expected value (62.5%). The elevated Angus composition in the Brangus could be due to the mixture of some 1/2 Ultrablack animals (Brangus × Angus). Another reason could be the consequences of selection in Brangus cattle for phenotypes where the Angus breed has advantages.

A unified local objective function for optimally selecting SNPs on arrays for agricultural genomics applications

Over the years, ad-hoc procedures were used for designing SNP arrays, but the procedures and strategies varied considerably case by case. Recently, a multiple-objective, local optimization (MOLO) algorithm was proposed to select SNPs for SNP arrays, which maximizes the adjusted SNP information (E score) under multiple constraints, e.g. on MAF, uniformness of SNP locations (U score), the inclusion of obligatory SNPs and the number and size of gaps. In the MOLO, each chromosome is split into equally spaced segments and local optima are selected as the SNPs having the highest adjusted E score within each segment, conditional on the presence of obligatory SNPs. The computation of the adjusted E score, however, is empirical, and it does not scale well between the uniformness of SNP locations and SNP informativeness. In addition, the MOLO objective function does not accommodate the selection of uniformly distributed SNPs. In the present study, we proposed a unified local function for optimally selecting SNPs, as an amendment to the MOLO algorithm. This new local function takes scalable weights between the uniformness and informativeness of SNPs, which allows the selection of SNPs under varied scenarios. The results showed that the weighting between the U and the E scores led to a higher imputation concordance rate than the U score or E score alone. The results from the evaluation of six commercial bovine SNP chips further confirmed this conclusion.

Development and evaluation of a low-density single-nucleotide polymorphism chip specific to Bos indicus cattle

Context Genomic selection has been of increasing interest in the genetic improvement of Zebu cattle, particularly for quantitative traits that are difficult or expensive to measure, such as carcass traits and meat tenderness. The success of genomic selection depends on several factors, and at its core is the availability of single-nucleotide polymorphism (SNP) chips that are appropriately designed for Bos indicus cattle. However, the currently available commercial bovine SNP chips are mostly designed for Bos taurus cattle. There are two commercial Bos indicus SNP chips; namely, GeneSeek genomic profiler high-density Bos indicus (GGP-HDi) SNP chip and a low-density (LD) Bos indicus SNP chip (Z chip), but these two Bos indicus SNP chips were built with mixed contents of SNPs for Bos indicus and Bos taurus cattle, due to limited availability of genotype data from Bos indicus cattle. Aims To develop a new GGP indicus 35000 SNP chip specifically for Bos indicus cattle, which has a low cost, but high accuracy of imputation to Illumina BovineHD chips. Methods The design of the chip consisted of 34000 optimally selected SNPs, plus 1000 SNPs pre-reserved for those on the Y chromosome, ‘causative’ mutations for a variety of economically relevant traits, genetic health conditions and International Society for Animal Genetics globally recognised parentage markers for those breeds of cattle. Key results The present results showed that this new indicus LD SNP chip had considerably increased minor allele frequencies in indicus breeds than the previous Z-chip. It demonstrated with high imputation accuracy to HD SNP genotypes in five indicus breeds, and with considerable predictability on 14 growth and reproduction traits in Nellore cattle. Conclusions This new indicus LD chip represented a successful effort to leverage existing knowledge and genotype resources towards the public release of a cost-effective LD SNP chip specifically for Bos indicus cattle, which is expected to replace the previous GGP indicus LD chip and to supplement the existing GGP-HDi 80000 SNP chip. Implications A new SNP chip specifically designed for Bos indicus, with high power of imputation to Illumina BovineHD technology and with excellent coverage of the whole genome, is now available on the market for Bos indicus cattle, and Bos indicus and Bos taurus crosses.

Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples

SNP arrays are widely used in genetic research and agricultural genomics applications, and the quality of SNP genotyping data is of paramount importance. In the present study, SNP genotyping concordance and discordance were evaluated for commercial bovine SNP arrays based on two types of quality assurance (QA) samples provided by Neogen GeneSeek. The genotyping discordance rates (GDRs) between chips were on average between 0.06% and 0.37% based on the QA type I data and between 0.05% and 0.15% based on the QA type II data. The average genotyping error rate (GER) pertaining to single SNP chips, based on the QA type II data, varied between 0.02% and 0.08% per SNP and between 0.01% and 0.06% per sample. These results indicate that genotyping concordance rate was high (i.e. from 99.63% to 99.99%). Nevertheless, mitochondrial and Y chromosome SNPs had considerably elevated GDRs and GERs compared to the SNPs on the 29 autosomes and X chromosome. The majority of genotyping errors resulted from single allotyping errors, which also included the opposite instances for allele 'dropout' (i.e. from AB to AA or BB). Simultaneous allotyping errors on both alleles (e.g. mistaking AA for BB or vice versa) were relatively rare. Finally, a list of SNPs with a GER greater than 1% is provided. Interpretation of association effects of these SNPs, for example in genome-wide association studies, needs to be taken with caution. The genotyping concordance information needs to be considered in the optimal design of future bovine SNP arrays.

Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low-density SNP genotypes

Reliable genomic prediction of breeding values for quantitative traits requires the availability of sufficient number of animals with genotypes and phenotypes in the training set. As of 31 October 2016, there were 3,797 Brangus animals with genotypes and phenotypes. These Brangus animals were genotyped using different commercial SNP chips. Of them, the largest group consisted of 1,535 animals genotyped by the GGP-LDV4 SNP chip. The remaining 2,262 genotypes were imputed to the SNP content of the GGP-LDV4 chip, so that the number of animals available for training the genomic prediction models was more than doubled. The present study showed that the pooling of animals with both original or imputed 40K SNP genotypes substantially increased genomic prediction accuracies on the ten traits. By supplementing imputed genotypes, the relative gains in genomic prediction accuracies on estimated breeding values (EBV) were from 12.60% to 31.27%, and the relative gain in genomic prediction accuracies on de-regressed EBV was slightly small (i.e. 0.87%-18.75%). The present study also compared the performance of five genomic prediction models and two cross-validation methods. The five genomic models predicted EBV and de-regressed EBV of the ten traits similarly well. Of the two cross-validation methods, leave-one-out cross-validation maximized the number of animals at the stage of training for genomic prediction. Genomic prediction accuracy (GPA) on the ten quantitative traits was validated in 1,106 newly genotyped Brangus animals based on the SNP effects estimated in the previous set of 3,797 Brangus animals, and they were slightly lower than GPA in the original data. The present study was the first to leverage currently available genotype and phenotype resources in order to harness genomic prediction in Brangus beef cattle.

Epidemiological study of parasite infection in a cow-calf beef herd in Quebec

The patterns of gastrointestinal and pulmonary nematode infections in a previously untreated Aberdeen Angus cow-calf herd were observed between May 1988 and December 1990. The cow-calf herd and replacement heifers were on separate pastures. The relatively high mean faecal egg counts of cows and heifers at the time of turnout were mainly owing to the maturation of hypobiotic worms. The strongyle egg counts of calves began to rise soon after turnout onto pasture and reached peak levels at the end of the grazing season. The number of infective larvae on pasture was highest during September/October. Ostertagia, Cooperia and Nematodirus were the most prevalent genera found at necropsy and on pasture. Larvae of these nematodes were able to overwinter on pasture and Ostertagia larvae, additionally, were able to overwinter in the host as arrested early fourth stage larvae. The high egg output of cows at the time of turnout may serve as a source of infection for their calves and be responsible for the late-season rise in pasture larval counts.

Predictable and unpredictable shock stimulates gastric contractility and causes mucosal injury in rats

The effects of tailshock on gastric contractility and lesions were investigated in rats exposed to 100 1-mA tailshocks while confined inside plastic tubes. A light preceded each shock in one group and was randomly presented with respect to shock in the other. Following the session, animals were given 3 hr of rest before being sacrificed. Contractility of the corpus of the stomach was measured by means of chronically implanted extraluminal force transducers. Contractility was measured in 10-min blocks and analyzed by computer. Lesions were quantified by inspection; quantitative histology was performed on corpus and antrum sections. Signaled (n = 13) and unsignaled (n = 17) shock stimulated high-amplitude gastric contractions in fasted rats, which continued for 2 hr after the shock session. Cumulative contractile activity (1.5-hr shock plus 2-hr rest) in shocked animals was twice that in restrained and unrestrained control animals (n = 19, p less than .05), and contractile activity had a 30%-40% greater average amplitude than after a meal. Compared with unrestrained controls, shocked rats had visibly more mucosal injury (2.2 +/- 0.5 mm2 vs. 0.1 +/- 0 mm2). Larger cumulative contractile activity was associated with a larger area of erosions (r = .36, p less than .05). Frequency and duration of contractions did not distinguish between shocked and unshocked groups. We conclude that in rats, signaled and unsignaled tailshock stimulates persistent, high-amplitude gastric contractions and is associated with injury of the mucosa of the stomach.

Predictable and unpredictable shock stimulates gastric contractility and causes mucosal injury in rats

The effects of tailshock on gastric contractility and lesions were investigated in rats exposed to 100 1-mA tailshocks while confined inside plastic tubes. A light preceded each shock in one group and was randomly presented with respect to shock in the other. Following the session, animals were given 3 hr of rest before being sacrificed. Contractility of the corpus of the stomach was measured by means of chronically implanted extraluminal force transducers. Contractility was measured in 10-min blocks and analyzed by computer. Lesions were quantified by inspection; quantitative histology was performed on corpus and antrum sections. Signaled (n = 13) and unsignaled (n = 17) shock stimulated high-amplitude gastric contractions in fasted rats, which continued for 2 hr after the shock session. Cumulative contractile activity (1.5-hr shock plus 2-hr rest) in shocked animals was twice that in restrained and unrestrained control animals (n = 19, p less than .05), and contractile activity had a 30%-40% greater average amplitude than after a meal. Compared with unrestrained controls, shocked rats had visibly more mucosal injury (2.2 +/- 0.5 mm2 vs. 0.1 +/- 0 mm2). Larger cumulative contractile activity was associated with a larger area of erosions (r = .36, p less than .05). Frequency and duration of contractions did not distinguish between shocked and unshocked groups. We conclude that in rats, signaled and unsignaled tailshock stimulates persistent, high-amplitude gastric contractions and is associated with injury of the mucosa of the stomach.