Genome-wide association study of age at puberty and its (co)variances with fertility and stature in growing and lactating Holstein-Friesian dairy cattle.

Reproductive performance is a key determinant of cow longevity in a pasture-based, seasonal dairy system. Unfortunately, direct fertility phenotypes such as intercalving interval or pregnancy rate tend to have low heritabilities and occur relatively late in an animal's life. In contrast, age at puberty (AGEP) is a moderately heritable, early-in-life trait that may be estimated using an animal's age at first measured elevation in blood plasma progesterone (AGEP4) concentrations. Understanding the genetic architecture of AGEP4 in addition to genetic relationships between AGEP4 and fertility traits in lactating cows is important, as is its relationship with body size in the growing animal. Thus, the objectives of this research were 3-fold. First, to estimate the genetic and phenotypic (co)variances between AGEP4 and subsequent fertility during first and second lactations. Second, to quantify the associations between AGEP4 and height, length, and BW measured when animals were approximately 11 mo old (standard deviation = 0.5). Third, to identify genomic regions that are likely to be associated with variation in AGEP4. We measured AGEP4, height, length, and BW in approximately 5,000 Holstein-Friesian or Holstein-Friesian × Jersey crossbred yearling heifers across 54 pasture-based herds managed in seasonal calving farm systems. We also obtained calving rate (CR42, success or failure to calve within the first 42 d of the seasonal calving period), breeding rate (PB21, success or failure to be presented for breeding within the first 21 d of the seasonal breeding period) and pregnancy rate (PR42, success or failure to become pregnant within the first 42 d of the seasonal breeding period) phenotypes from their first and second lactations. The animals were genotyped using the Weatherby's Versa 50K SNP array (Illumina, San Diego, CA). The estimated heritabilities of AGEP4, height, length, and BW were 0.34 (90% credibility interval [CRI]: 0.30, 0.37), 0.28 (90% CRI: 0.25, 0.31), 0.21 (90% CRI: 0.18, 0.23), and 0.33 (90% CRI: 0.30, 0.36), respectively. In contrast, the heritabilities of CR42, PB21 and PR42 were all <0.05 in both first and second lactations. The genetic correlations between AGEP4 and these fertility traits were generally moderate, ranging from 0.11 to 0.60, whereas genetic correlations between AGEP4 and yearling body-conformation traits ranged from 0.02 to 0.28. Our GWAS highlighted a genomic window on chromosome 5 that was strongly associated with variation in AGEP4. We also identified 4 regions, located on chromosomes 14, 6, 1, and 11 (in order of decreasing importance), that exhibited suggestive associations with AGEP4. Our results show that AGEP4 is a reasonable predictor of estimated breeding values for fertility traits in lactating cows. Although the GWAS provided insights into genetic mechanisms underpinning AGEP4, further work is required to test genomic predictions of fertility that use this information.

Genome-wide association study of anogenital distance and its (co)variances with fertility in growing and lactating Holstein-Friesian dairy cattle.

Anogenital distance (AGD) is a moderately heritable trait that can be measured at a young age that may provide an opportunity to indirectly select for improved fertility in dairy cattle. In this study, we characterized AGD and its genetic and phenotypic relationships with a range of body stature and fertility traits. We measured AGD, shoulder height, body length, and body weight in a population of 5,010 Holstein-Friesian and Holstein-Friesian × Jersey crossbred heifers at approximately 11 mo of age (AGD1). These animals were born in 2018 across 54 seasonal calving, pasture-based dairy herds. A second measure of AGD was collected in a subset of herds (n = 17; 1,956 animals) when the animals averaged 29 mo of age (AGD2). Fertility measures included age at puberty (AGEP), then time of calving, breeding, and pregnancy during the first and second lactations. We constructed binary traits reflecting the animal's ability to calve during the first 42 d of their herd's seasonal calving period (CR42), be presented for breeding during the first 21 d of the seasonal breeding period (PB21) and become pregnant during the first 42 d of the seasonal breeding period (PR42). The posterior mean of sampled heritabilities for AGD1 was 0.23, with 90% of samples falling within a credibility interval (90% CRI) of 0.20 to 0.26, whereas the heritability of AGD2 was 0.29 (90% CRI 0.24 to 0.34). The relationship between AGD1 and AGD2 was highly positive, with a genetic correlation of 0.89 (90% CRI 0.82 to 0.94). Using a GWAS analysis of 2,460 genomic windows based on 50k genotype data, we detected a region on chromosome 20 that was highly associated with variation in AGD1, and a second region on chromosome 13 that was moderately associated with variation in AGD1. We did not detect any genomic regions associated with AGD2 which was measured in fewer animals. The genetic correlation between AGD1 and AGEP was 0.10 (90% CRI 0.00 to 0.19), whereas the genetic correlation between AGD2 and AGEP was 0.30 (90% CRI 0.15 to 0.44). The timing of calving, breeding, and pregnancy (CR42, PB21, and PR42) during first or second lactations exhibited moderate genetic relationships with AGD1 (0.19 to 0.52) and AGD2 (0.46 to 0.63). Genetic correlations between AGD and body stature traits were weak (≤0.16). We conclude that AGD is a moderately heritable trait, which may have value as an early-in-life genetic predictor for reproductive success during lactation.

Pregnancy status predicted using milk mid-infrared spectra from dairy cattle.

Accurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002-0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status.

Animal medical genetics: a historical perspective on more than 50 years of research into genetic disorders of animals at Massey University.

Over the last 50 years, there have been major advances in knowledge and technology regarding genetic diseases, and the subsequent ability to control them in a cost-effective manner. This review traces these advances through research into genetic diseases of animals at Massey University (Palmerston North, NZ), and briefly discusses the disorders investigated during that time, with additional detail for disorders of major importance such as bovine α-mannosidosis, ovine ceroid-lipofuscinosis, canine mucopolysaccharidosis IIIA and feline hyperchylomicronaemia. The overall research has made a significant contribution to veterinary medicine, has provided new biological knowledge and advanced our understanding of similar disorders in human patients, including testing various specific therapies prior to human clinical trials.

Sire selection and genetic improvement of dairy cattle assuming pure market competition.

Breeding companies and farmers rely on selection indices to identify sires they expect to improve production system profitability. Such indices combine estimates of genetic merit for individual traits with corresponding economic values that are fixed based on circumstances at a particular time. Perfect market competition has also been proposed as an economic basis to value alternative sires. The objective of this study was to propose an economic model and develop it for the evaluation of dairy sires. The pure competition model (PUC) was used to evaluate the relationship between a profitability index based on the PUC approach versus the traditional selection index approach for 330 dairy sires comprising Holstein-Friesians, Jerseys, and Ayrshires. The correlation between these 2 selection indices was only 0.56, indicating that the conventional selection index did not correlate well with an index based on the PUC model. In particular, the higher ranking bulls were overvalued using the conventional selection index. Our study concluded that the use of fixed economic values is problematic for the delivery of consistent rankings in selection indices. In contrast, sire rankings based on PUC are more reliable because the sires are evaluated on the basis of efficiency gains rather than production while accounting for market prices and marginal values of dairy outputs over time.

Increasing the accuracy of genomic prediction in pure-bred Limousin beef cattle by including cross-bred Limousin data and accounting for an F94L variant in MSTN.

Explicitly fitting effects for major genes or QTL that account for a large percentage of variation in a whole genomic prediction model may increase prediction accuracy. This study compared approaches to account for a major effect of an F94L variant in the MSTN gene within the genomic prediction using bovine whole-genomic SNP markers. Among the beef cattle breeds, Limousin have been known to have an F94L variant that is not present in Angus. The reference population in this study consisted of 3060 beef cattle including pure-bred Limousin (PL), cross-bred Limousin with Angus (LF) and pure-bred Angus, genotyped using a BovineSNP50 BeadChip and directly for the MSTN-F94L variant. We compared prediction accuracies in PL animals using the three datasets from only the PL population, admixed PL and LF (AL) or multibreed analysis using all of the PL, LF and Angus (MB) population according to four-fold cross-validation after K-means clustering. The MSTN-F94L variant was the most strongly associated with five traits (birth weight, calving ease direct, milk, weaning weight and yield grade) among the 13 measured traits in PL and AL populations. Fitting the MSTN-F94L variant as a random effect, the genomic prediction accuracies for birth weight increased by 2.7% in PL, by 2.2% in AL and by 3.2% in MB. Prediction accuracies for five traits increased in the MB analysis. Fitting MSTN-F94L as a fixed effect in PL, AL and MB analyses resulted in increased prediction accuracy in PL for eight traits. Prediction accuracies can be improved by including a causal variant in genomic evaluation compared with simply using whole-genome SNP markers. Fitting the causal variant as a fixed effect along with markers fitted as random effects resulted in greater prediction accuracies for most traits. Causal variants should be genotyped along with SNP markers.

Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle.

The use of Fourier-transform mid-infrared (FTIR) spectroscopy is of interest to the dairy industry worldwide for predicting milk composition and other novel traits that are difficult or expensive to measure directly. Although there are many valuable applications for FTIR spectra, noise from differences in spectral responses between instruments is problematic because it reduces prediction accuracy if ignored. The purpose of this study was to develop strategies to reduce the impact of noise and to compare methods for standardizing FTIR spectra in order to reduce between-instrument variability in multiple-instrument networks. Noise levels in bands of the infrared spectrum caused by the water content of milk were characterized, and a method for identifying and removing outliers was developed. Two standardization methods were assessed and compared: piecewise direct standardization (PDS), which related spectra on a primary instrument to spectra on 5 other (secondary) instruments using identical milk-based reference samples (n = 918) analyzed across the 6 instruments; and retroactive percentile standardization (RPS), whereby percentiles of observed spectra from routine milk test samples (n = 2,044,094) were used to map and exploit primary- and secondary-instrument relationships. Different applications of each method were studied to determine the optimal way to implement each method across time. Industry-standard predictions of milk components from 2,044,094 spectra records were regressed against predictions from spectra before and after standardization using PDS or RPS. The PDS approach resulted in an overall decrease in root mean square error between industry-standard predictions and predictions from spectra from 0.190 to 0.071 g/100 mL for fat, from 0.129 to 0.055 g/100 mL for protein, and from 0.143 to 0.088 g/100 mL for lactose. Reductions in prediction error for RPS were similar but less consistent than those for PDS across time, but similar reductions were achieved when PDS coefficients were updated monthly and separate primary instruments were assigned for the North and South Islands of New Zealand. We demonstrated that the PDS approach is the most consistent method to reduce prediction errors across time. We also showed that the RPS approach is sensitive to shifts in milk composition but can be used to reduce prediction errors, provided that secondary-instrument spectra are standardized to a primary instrument with samples of broadly equivalent milk composition. Appropriate implementation of either of these approaches will improve the quality of predictions based on FTIR spectra for various downstream applications.

A comparison of identity-by-descent and identity-by-state matrices that are used for genetic evaluation and estimation of variance components.

The genetic covariance matrix conditional on pedigree is proportional to the pedigree-based additive relationship matrix (PARM), which is twice the matrix of identity-by-descent (IBD) probabilities. In genomic prediction, IBD probabilities in the PARM, which are expected genetic similarities between relatives that are derived from the pedigree, are substituted by realized similarities that are derived from genotypes to obtain a genomic additive relationship matrix (GARM). Different definitions of similarity lead to different GARMs, and two commonly used GARMS are the matrix G, which is based on an allele substitution effect model, and the matrix T, which is based on an allele effect model. We show that although the two matrices T and G are not proportional, they give identical predictions of differences between breeding values. When genomic information is used for variance component estimation, the GARM Gx is computed from genotype covariates that have been standardized to have unit variance. That approach is equivalent to fitting a random regression model using the same standardized covariates. We show that under Hardy-Weinberg and linkage equilibria (LE) that the genetic variance is kσγ2, where σγ2 is the variance of a randomly sampled element from the vector of k substitution effects. However, if linkage disequilibrium (LD) has been generated through selection, covariances between genotypes at different loci will be negative, and therefore, the additive genetic variance will be lower than kσγ2. When the GARM Gx is assumed to be proportional to the genetic covariance matrix, the parameter being estimated is kσγ2. We have demonstrated by simulation that kσγ2 overestimates the additive genetic variance when LD is generated by selection. We argue that unlike the PARM, GARMs are not proportional to a genetic covariance matrix conditional on the observed causal genotypes. The objective here is to recognize the difference between these covariance matrices and its implications.

Epistatic interactions associated with fatty acid concentrations of beef from angus sired beef cattle.

BACKGROUND: Consumers are becoming increasingly conscientious about the nutritional value of their food. Consumption of some fatty acids has been associated with human health traits such as blood pressure and cardiovascular disease. Therefore, it is important to investigate genetic variation in content of fatty acids present in meat. Previously publications reported regions of the cattle genome that are additively associated with variation in fatty acid content. This study evaluated epistatic interactions, which could account for additional genetic variation in fatty acid content. RESULTS: Epistatic interactions for 44 fatty acid traits in a population of Angus beef cattle were evaluated with EpiSNPmpi. False discovery rate (FDR) was controlled at 5 % and was limited to well-represented genotypic combinations. Epistatic interactions were detected for 37 triacylglyceride (TAG), 36 phospholipid (PL) fatty acid traits, and three weight traits. A total of 6,181, 7,168, and 0 significant epistatic interactions (FDR < 0.05, 50-animals per genotype combination) were associated with Triacylglyceride fatty acids, Phospholipid fatty acids, and weight traits respectively and most were additive-by-additive interactions. A large number of interactions occurred in potential regions of regulatory control along the chromosomes where genes related to fatty acid metabolism reside. CONCLUSIONS: Many fatty acids were associated with epistatic interactions. Despite a large number of significant interactions, there are a limited number of genomic locations that harbored these interactions. While larger population sizes are needed to accurately validate and quantify these epistatic interactions, the current findings point towards additional genetic variance that can be accounted for within these fatty acid traits.

Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers.

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single-nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic-based [genomic best linear unbiased prediction (GBLUP)-REML and BayesC] and pedigree-based (PBLUP-REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP-REML across traits, from 0 to 0.03 with GBLUP-REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic-based methods were small (0.01-0.05), with GBLUP-REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP-REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population.

Validation and further characterization of a major quantitative trait locus associated with host response to experimental infection with porcine reproductive and respiratory syndrome virus.

Infectious diseases are costly to the swine industry; porcine reproductive and respiratory syndrome (PRRS) is the most devastating. In earlier work, a quantitative trait locus associated with resistance/susceptibility to PRRS virus was identified on Sus scrofa chromosome 4 using approximately 560 experimentally infected animals from a commercial cross. The favorable genotype was associated with decreased virus load and increased weight gain (WG). The objective here was to validate and further characterize the association of the chromosome 4 region with PRRS resistance using data from two unrelated commercial crossbred populations. The validation populations consisted of two trials each of approximately 200 pigs sourced from different breeding companies that were infected with PRRS virus and followed for 42 days post-infection. Across all five trials, heritability estimates were 0.39 and 0.34 for viral load (VL; area under the curve of log-transformed viremia from 0 to 21 days post-infection) and WG to 42 days post-infection respectively. Effect estimates of SNP WUR10000125 in the chromosome 4 region were in the same directions and of similar magnitudes in the two new trials as had been observed in the first three trials. Across all five trials, the 1-Mb region on chromosome 4 explained 15 percent of genetic variance for VL and 11 percent for WG. The effect of the favorable minor allele at SNP WUR10000125 was dominant. Ordered genotypes for SNP WUR10000125 showed that the effect was present irrespective of whether the favorable allele was paternally or maternally inherited. These results demonstrate that selection for host response to PRRS virus infection could reduce the economic impact of PRRS.

Use of robust multivariate linear mixed models for estimation of genetic parameters for carcass traits in beef cattle.

Assumptions of normality of residuals for carcass evaluation may make inferences vulnerable to the presence of outliers, but heavy-tail densities are viable alternatives to normal distributions and provide robustness against unusual or outlying observations when used to model the densities of residual effects. We compare estimates of genetic parameters by fitting multivariate Normal (MN) or heavy-tail distributions (multivariate Student's t and multivariate Slash, MSt and MS) for residuals in data of hot carcass weight (HCW), longissimus muscle area (REA) and 12th to 13th rib fat (FAT) traits in beef cattle using 2475 records from 2007 to 2008 from a large commercial operation in Nebraska. Model comparisons using deviance information criteria (DIC) favoured MSt over MS and MN models, respectively. The posterior means (and 95% posterior probability intervals, PPI) of v for the MSt and MS models were 5.89 ± 0.90 (4.35, 7.86) and 2.04 ± 0.18 (1.70, 2.41), respectively. Smaller values of posterior densities of v for MSt and MS models confirm that the assumption of normally distributed residuals is not adequate for the analysis of the data set. Posterior mean (PM) and posterior median (PD) estimates of direct genetic variances were variable with MSt having the highest mean value followed by MS and MN, respectively. Posterior inferences on genetic variance were, however, comparable among the models for FAT. Posterior inference on additive heritabilities for HCW, REA and FAT using MN, MSt and MS models indicated similar and moderate heritability comparable with the literature. Posterior means of genetic correlations for carcass traits were variable but positive except for between REA and FAT, which showed an antagonistic relationship. We have demonstrated that genetic evaluation and selection strategies will be sensitive to the assumed model for residuals.

Genome-wide association study for egg production and quality in layer chickens.

Discovery of genes with large effects on economically important traits has for many years been of interest to breeders. The development of SNP panels which cover the whole genome with high density and, more importantly, that can be genotyped on large numbers of individuals at relatively low cost, has opened new opportunities for genome-wide association studies (GWAS). The objective of this study was to find genomic regions associated with egg production and quality traits in layers using analysis methods developed for the purpose of whole genome prediction. Genotypes on over 4500 birds and phenotypes on over 13,000 hens from eight generations of a brown egg layer line were used. Birds were genotyped with a custom 42K Illumina SNP chip. Recorded traits included two egg production and 11 egg quality traits (puncture score, albumen height, yolk weight and shell colour) at early and late stages of production, as well as body weight and age at first egg. Egg weight was previously analysed by Wolc et al. (2012). The Bayesian whole genome prediction model--BayesB (Meuwissen et al. 2001) was used to locate 1 Mb regions that were most strongly associated with each trait. The posterior probability of a 1 Mb window contributing to genetic variation was used as the criterion for suggesting the presence of a quantitative trait locus (QTL) in that window. Depending upon the trait, from 1 to 7 significant (posterior probability >0.9) 1 Mb regions were found. The largest QTL, a region explaining 32% of genetic variance, was found on chr4 at 78 Mb for body weight but had pleiotropic effects on other traits. For the other traits, the largest effects were much smaller, explaining <7% of genetic variance, with regions on chromosomes 2, 12 and 17 explaining above 5% of genetic variance for albumen height, shell colour and egg production, respectively. In total, 45 of 1043 1 Mb windows were estimated to have a non-zero effect with posterior probability > 0.9 for one or more traits.

Genome-wide prediction of age at puberty and reproductive longevity in sows.

Traditional selection for sow reproductive longevity is ineffective due to low heritability and late expression of the trait. Incorporation of DNA markers into selection programs is potentially a more practical approach for improving sow lifetime productivity. Using a resource population of crossbred gilts, we explored pleiotropic sources of variation that influence age at puberty and reproductive longevity. Of the traits recorded before breeding, only age at puberty significantly affected the probability that females would produce a first parity litter. The genetic variance explained by 1-Mb windows of the sow genome, compared across traits, uncovered regions that influence both age at puberty and lifetime number of parities. Allelic variants of SNPs located on SSC5 (27-28 Mb), SSC8 (36-37 Mb) and SSC12 (1.2-2 Mb) exhibited additive effects and were associated with both early expression of puberty and a greater than average number of lifetime parities. Combined analysis of these SNPs showed that an increase in the number of favorable alleles had positive impact on reproductive longevity, increasing number of parities by up to 1.36. The region located on SSC5 harbors non-synonymous alleles in the arginine vasopressin receptor 1A (AVPR1A) gene, a G-protein-coupled receptor associated with social and reproductive behaviors in voles and humans and a candidate for the observed effects. This region is characterized by high levels of linkage disequilibrium in different lines and could be exploited in marker-assisted selection programs across populations to increase sow reproductive longevity.

Association of polymorphisms in solute carrier family 27, isoform A6 (SLC27A6) and fatty acid-binding protein-3 and fatty acid-binding protein-4 (FABP3 and FABP4) with fatty acid composition of bovine milk.

The main goal of this study was to develop tools for genetic selection of animals producing milk with a lower concentration of saturated fatty acids (SFA) and a higher concentration of unsaturated fatty acids (UFA). The reasons for changing milk fatty acid (FA) composition were to improve milk technological properties, such as for production of more spreadable butter, and milk nutritional value with respect to the potentially adverse effects of SFA on human health. We hypothesized that genetic polymorphisms in solute carrier family 27, isoform A6 (SLC27A6) fatty acid transport protein gene and fatty acid binding protein (FABP)-3 and FABP-4 (FABP3 and FABP4) would affect the selectivity of FA uptake into, and FA redistribution inside, mammary epithelial cells, resulting in altered FA composition of bovine milk. The objectives of our study were to discover genetic polymorphisms in SLC27A6, FABP3, and FABP4, and to test those polymorphisms for associations with milk FA composition. The results showed that after pairwise comparisons between SLC27A6 haplotypes for significantly associated traits, haplotype H3 was significantly associated with 1.37 weight percentage (wt%) lower SFA concentration, 0.091 lower SFA:UFA ratio, and 0.17 wt% lower lauric acid (12:0) concentration, but 1.37 wt% higher UFA and 1.24 wt% higher monounsaturated fatty acid (MUFA) concentrations compared with haplotype H1 during the first 3 mo of lactation. Pairwise comparisons between FABP4 haplotypes for significantly associated traits showed that haplotype H3 was significantly associated with 1.04 wt% lower SFA concentration, 0.079 lower SFA:UFA ratio, 0.15 wt% lower lauric acid (12:0), and 0.27 wt% lower myristic acid (14:0) concentrations, but 1.04 wt% higher UFA and 0.91 wt% higher MUFA concentrations compared with haplotype H1 during the first 3 mo of lactation. Percentages of genetic variance explained by H3 versus H1 haplotype substitutions for SLC27A6 and FABP4 ranged from 2.50 to 4.86% and from 4.91 to 7.22%, respectively. Tag single nucleotide polymorphisms were identified to distinguish haplotypes H3 of SLC27A6 and FABP4 from others encompassing each gene. We found no significant associations between FABP3 haplotypes and milk FA composition. In conclusion, polymorphisms in FABP4 and SLC27A6 can be used to select for cattle producing milk with lower concentrations of SFA and higher concentrations of UFA.

Sterol regulatory element binding transcription factor 1 (SREBF1) polymorphism and milk fatty acid composition.

Milk is known to contain high concentrations of saturated fatty acids-such as palmitic (16:0), myristic (14:0), and lauric (12:0) acids-that can raise plasma cholesterol in humans, making their presence in milk undesirable. The main objective of our candidate gene study was to develop genetic markers that can be used to improve the healthfulness of bovine milk. The sterol regulatory element binding transcription factor 1 (SREBF1) known to regulate the transcription of lipogenic genes together with SREBF chaperone and insulin induced gene 1 were the candidate genes. The results showed significant association of the overall SREBF1 haplotypes with milk production and variations in lauric (12:0) and myristic (14:0) acid concentrations in milk. Haplotype H1 of SREBF1 was the most desirable to improve milk healthfulness because it was significantly associated with lower lauric (12:0) and myristic (14:0) acid concentrations compared with haplotype H3 of SREBF1, and lower lauric acid (12:0) concentration compared with haplotype H2 of SREBF1. Haplotype H1 of SREBF1, however, was significantly associated with lower milk production compared with haplotype H3 of SREBF1. We did not detect any significant associations between genetic polymorphisms in insulin induced gene 1 (INSIG1) and SREBF chaperone and milk fatty acid composition. In conclusion, genetic polymorphisms in SREBF1 can be used to develop genetic tools for the selection of animals producing milk with healthier fatty acid composition.

Analysis of egg production in layer chickens using a random regression model with genomic relationships.

Random regression models allow for analysis of longitudinal data, which together with the use of genomic information are expected to increase accuracy of selection, when compared with analyzing average or total production with pedigree information. The objective of this study was to estimate variance components for egg production over time in a commercial brown egg layer population using genomic relationship information. A random regression reduced animal model with a marker-based relationship matrix was used to estimate genomic breeding values of 3,908 genotyped animals from 6 generations. The first 5 generations were used for training, and predictions were validated in generation 6. Daily egg production up to 46 wk in lay was accumulated into 85,462 biweekly (every 2 wk) records for training, of which 17,570 were recorded on genotyped hens and the remaining on their nongenotyped progeny. The effect of adding additional egg production data of 2,167 nongenotyped sibs of selection candidates [16,037 biweekly (every 2 wk) records] to the training data was also investigated. The model included a 5th order Legendre polynomial nested within hatch-week as fixed effects and random terms for coefficients of quadratic polynomials for genetic and permanent environmental components. Residual variance was assumed heterogeneous among 2-wk periods. Models using pedigree and genomic relationships were compared. Estimates of residual variance were very similar under both models, but the model with genomic relationships resulted in a larger estimate of genetic variance. Heritability estimates increased with age up to mid production and decreased afterward, resulting in an average heritability of 0.20 and 0.33 for pedigree and genomic models. Prediction of total egg number was more accurate with the genomic than with the pedigree-based random regression model (correlation in validation 0.26 vs. 0.16). The genomic model outperformed the pedigree model in most of the 2-wk periods. Thus, results of this study show that random regression reduced animal models can be used in breeding programs using genomic information and can result in substantial improvements in the accuracy of selection for trajectory traits.

Accuracy of genomic prediction using an evenly spaced, low-density single nucleotide polymorphism panel in broiler chickens.

One approach for cost-effective implementation of genomic selection is to genotype training individuals with a high-density (HD) panel and selection candidates with an evenly spaced, low-density (ELD) panel. The purpose of this study was to evaluate the extent to which the ELD approach reduces the accuracy of genomic estimated breeding values (GEBV) in a broiler line, in which 1,091 breeders from 3 generations were used for training and 160 progeny of the third generation for validation. All birds were genotyped with an Illumina Infinium platform HD panel that included 20,541 segregating markers. Two subsets of HD markers, with 377 (ELD-1) or 766 (ELD-2) markers, were selected as ELD panels. The ELD-1 panel was genotyped using KBiosciences KASPar SNP genotyping chemistry, whereas the ELD-2 panel was simulated by adding markers from the HD panel to the ELD-1 panel. The training data set was used for 2 traits: BW at 35 d on both sexes and hen house production (HHP) between wk 28 and 54. Methods Bayes-A, -B, -C and genomic best linear unbiased prediction were used to estimate HD-marker effects. Two scenarios were used: (1) the 160 progeny were ELD-genotyped, and (2) the 160 progeny and their dams (117 birds) were ELD-genotyped. The missing HD genotypes in ELD-genotyped birds were imputed by a Gibbs sampler, capitalizing on linkage within families. In scenario (1), the correlation of GEBV for BW (HHP) of the 160 progeny based on observed HD versus imputed genotypes was greater than 0.94 (0.98) with the ELD-1 panel and greater than 0.97 (0.99) with the ELD-2 panel. In scenario (2), the correlation of GEBV for BW (HHP) was greater than 0.92 (0.96) with the ELD-1 panel and greater than 0.95 (0.98) with the ELD-2 panel. Hence, in a pedigreed population, genomic selection can be implemented by genotyping selection candidates with about 400 ELD markers with less than 6% loss in accuracy. This leads to substantial savings in genotyping costs, with little sacrifice in accuracy.

A missense mutation in AGTPBP1 was identified in sheep with a lower motor neuron disease.

A type of lower motor neuron (LMN) disease inherited as autosomal recessive in Romney sheep was characterized with normal appearance at birth, but with progressive weakness and tetraparesis after the first week of life. Here, we carried out genome-wide homozygosity mapping using Illumina Ovine SNP50 BeadChips on lambs descended from one carrier ram, including 19 sheep diagnosed as affected and 11 of their parents that were therefore known carriers. A homozygous region of 136 consecutive single-nucleotide polymorphism (SNP) loci on chromosome 2 was common to all affected sheep and it was the basis for searching for the positional candidate genes. Other homozygous regions shared by all affected sheep spanned eight or fewer SNP loci. The 136-SNP region contained the sheep ATP/GTP-binding protein 1 (AGTPBP1) gene. Mutations in this gene have been shown to be related to Purkinje cell degeneration (pcd) phenotypes including ataxia in mice. One missense mutation c.2909G>C on exon 21 of AGTPBP1 was discovered, which induces an Arg to Pro substitution (p.Arg970Pro) at amino-acid 970, a conserved residue for the catalytic activity of AGTPBP1. Genotyping of this mutation showed 100% concordant rate with the recessive pattern of inheritance in affected, carrier, phenotypically normal and unrelated normal individuals. This is the first report showing a mutant AGTPBP1 is associated with a LMN disease in a large mammal animal model. Our finding raises the possibility of human patients with the same etiology caused by this gene or other genes in the same pathway of neuronal development.

In a shake of a lamb's tail: using genomics to unravel a cause of chondrodysplasia in Texel sheep.

Chondrodysplasia in Texel sheep is a recessively inherited disorder characterized by dwarfism and angular deformities of the forelimbs. A genome-wide association study using the Illumina OvineSNP50 BeadChip on 15 sheep diagnosed as affected and eight carriers descended from three affected rams was conducted to uncover the genetic cause. A homozygous region of 25 consecutive single nucleotide polymorphism (SNP) loci was identified in all affected sheep, covering a region of 1 Mbp on ovine chromosome 4. Seven positional candidate genes - including the solute carrier family 13 (sodium/sulphate symporters), member 1 (SLC13A1) - were identified and used to search for new SNPs for fine mapping of the causal locus. The SLC13A1 gene, encoding a sodium/sulphate transporter, was the primary candidate gene attributable to similar phenotypes observed in the Slc13a1 knockout mouse model. We discovered a 1-bp deletion of T (g.25513delT) at the 107 bp position of exon 3 in the SLC13A1 gene. Genotyping by direct sequencing and restriction fragment length polymorphism analysis for this mutation showed that all 15 affected sheep were g.25513delT/g.25513delT; the eight carriers were g.25513delT/T and 54 normal controls were T/T. The mutation g.25513delT shifts the open reading frame of SLC13A1 to introduce a stop codon and truncate C-terminal amino acids. It was concluded that the g.25513delT mutation in the SLC13A1 gene was responsible for the chondrodysplasia seen in these Texel sheep. This knowledge can be used to identify carriers with the defective g.[25513delT] allele to avoid at-risk matings to improve animal welfare and decrease economic losses.