A multi-tissue atlas of regulatory variants in cattle

Characterization of genetic regulatory variants acting on livestock gene expression is essential for interpreting the molecular mechanisms underlying traits of economic value and for increasing the rate of genetic gain through artificial selection. Here we build a Cattle Genotype-Tissue Expression atlas (CattleGTEx) as part of the pilot phase of the Farm animal GTEx (FarmGTEx) project for the research community based on 7,180 publicly available RNA-sequencing (RNA-seq) samples. We describe the transcriptomic landscape of more than 100 tissues/cell types and report hundreds of thousands of genetic associations with gene expression and alternative splicing for 23 distinct tissues. We evaluate the tissue-sharing patterns of these genetic regulatory effects, and functionally annotate them using multiomics data. Finally, we link gene expression in different tissues to 43 economically important traits using both transcriptome-wide association and colocalization analyses to decipher the molecular regulatory mechanisms underpinning such agronomic traits in cattle.

Genetic and epigenetic architecture of paternal origin contribute to gestation length in cattle

The length of gestation can affect offspring health and performance. Both maternal and fetal effects contribute to gestation length; however, paternal contributions to gestation length remain elusive. Using genome-wide association study (GWAS) in 27,214 Holstein bulls with millions of gestation records, here we identify nine paternal genomic loci associated with cattle gestation length. We demonstrate that these GWAS signals are enriched in pathways relevant to embryonic development, and in differentially methylated regions between sperm samples with long and short gestation length. We reveal that gestation length shares genetic and epigenetic architecture in sperm with calving ability, body depth, and conception rate. While several candidate genes are detected in our fine-mapping analysis, we provide evidence indicating ZNF613 as a promising candidate for cattle gestation length. Collectively, our findings support that the paternal genome and epigenome can impact gestation length potentially through regulation of the embryonic development.

Lingzhao Fang et al. studied the paternal genetic variants that affect gestational length in cattle. They found that paternal genes from pathways involved in embryonic development were associated with gestation length, and that these were often found in differentially methylated regions of the genome.

Use of the Illumina Bovine3K BeadChip in dairy genomic evaluation

Genomic evaluations using genotypes from the Illumina Bovine3K BeadChip (3K) became available in September 2010 and were made official in December 2010. The majority of 3K-genotyped animals have been Holstein females. Approximately 5% of male 3K genotypes and between 3.7 and 13.9%, depending on registry status, of female genotypes had sire conflicts. The chemistry used for the 3K is different from that of the Illumina BovineSNP50 BeadChip (50K) and causes greater variability in the accuracy of the genotypes. Approximately 2% of genotypes were rejected due to this inaccuracy. A single nucleotide polymorphism (SNP) was determined to be not usable for genomic evaluation based on percentage missing, percentage of parent-progeny conflicts, and Hardy-Weinberg equilibrium discrepancies. Those edits left 2,683 of the 2,900 3K SNP for use in genomic evaluations. The mean minor allele frequencies (MAF) for Holstein, Jersey, and Brown Swiss were 0.32, 0.28, and 0.29, respectively. Eighty-one SNP had both a large number of missing genotypes and a large number of parent-progeny conflicts, suggesting a correlation between call rate and accuracy. To calculate a genomic predicted transmitting ability (GPTA) the genotype of an animal tested on a 3K is imputed to the 45,187 SNP included in the current genomic evaluation based on the 50K. The accuracy of imputation increases as the number of genotyped parents increases from none to 1 to both. The average percentage of imputed genotypes that matched the corresponding actual 50K genotypes was 96.3%. The correlation of a GPTA calculated from a 3K genotype that had been imputed to 50K and GPTA from its actual 50K genotype averaged 0.959 across traits for Holsteins and was slightly higher for Jerseys at 0.963. The average difference in GPTA from the 50K- and 3K-based genotypes across trait was close to 0. The evaluation system has been modified to accommodate the characteristics of the 3K. The low cost of the 3K has greatly increased genotyping of females. Prior to the availability of the 3K (August 2010), female genotyping accounted for 38.7% of the genotyped animals. In the past year, the portion of total genotypes from females across all chip types rose to 59.0%.

Technical note: adjustment of traditional cow evaluations to improve accuracy of genomic predictions

Genomic evaluations are calculated using deregressed predicted transmitting abilities (PTA) from traditional evaluations to estimate effects of single nucleotide polymorphisms. The direct genomic value (sum of an animal's marker effects) should be consistent with traditional PTA, which is the case for bulls. However, traditional PTA of yield traits (milk, fat, and protein) for genotyped cows are higher than their direct genomic values. To ensure that characteristics of cow PTA for yield traits were more similar to those for bull PTA, mean and variance of cow Mendelian sampling (PTA minus parent average) were adjusted to be similar to those of bulls. The same adjustments were used for all genotyped cows in a breed. To determine gains in reliabilities, predictions were made for bulls with August 2010 evaluations that did not have traditional evaluations in August 2006. By adjusting cow PTA and parent averages of genotyped animals, Holstein and Jersey regressions of August 2010 deregressed PTA on genomic evaluations based on August 2006 data became closer to 1 for the adjusted predictor population compared with the unadjusted predictor population. Evaluation bias was decreased for Holsteins when the predictor population was adjusted. Mean gain in reliability over parent average increased 3.5 percentage points across yield traits for Holsteins and 0.9 percentage points for Jerseys when the predictor population was adjusted. The accuracy of genomic evaluations for Holsteins and Jerseys was increased through better use of information from cows.

The genomic evaluation system in the United States: past, present, future

Implementation of genomic evaluation has caused profound changes in dairy cattle breeding. All young bulls bought by major artificial insemination organizations now are selected based on such evaluation. Evaluation reliability can reach approximately 75% for yield traits, which is adequate for marketing semen of 2-yr-old bulls. Shortened generation interval from using genomic evaluations is the most important factor in increasing the rate of genetic improvement. Genomic evaluations are based on 42,503 single nucleotide polymorphisms (SNP) genotyped with technology that became available in 2007. The first unofficial USDA genomic evaluations were released in 2008 and became official for Holsteins, Jerseys, and Brown Swiss in 2009. Evaluation accuracy has increased steadily from including additional bulls with genotypes and traditional evaluations (predictor animals). Some of that increase occurs automatically as young genotyped bulls receive a progeny test evaluation at 5 yr of age. Cow contribution to evaluation accuracy is increased by decreasing mean and variance of their evaluations so that they are similar to bull evaluations. Integration of US and Canadian genotype databases was critical to achieving acceptable initial accuracy and continues to benefit both countries. Genotype exchange with other countries added predictor bulls for Brown Swiss. In 2010, a low-density chip with 2,900 SNP and a high-density chip with 777,962 SNP were released. The low-density chip has increased greatly the number of animals genotyped and is expected to replace microsatellites in parentage verification. The high-density chip can increase evaluation accuracy by better tracking of loci responsible for genetic differences. To integrate information from chips of various densities, a method to impute missing genotypes was developed based on splitting each genotype into its maternal and paternal haplotypes and tracing their inheritance through the pedigree. The same method is used to impute genotypes of nongenotyped dams based on genotyped progeny and mates. Reliability of resulting evaluations is discounted to reflect errors inherent in the process. Further increases in evaluation accuracy are expected because of added predictor animals and more SNP. The large population of existing genotypes can be used to evaluate new traits; however, phenotypic observations must be obtained for enough animals to allow estimation of SNP effects with sufficient accuracy for application to the general population.

Modeling extended lactations of Holsteins

Modeling extended lactations for the US Holsteins is useful because a majority (>55%) of the cows in the present population produce lactations longer than 305 d. In this study, 9 empirical and mechanistic models were compared for their suitability for modeling 305-d and 999-d lactations of US Holsteins. A pooled data set of 4,266,597 test-day yields from 427,657 (305-d complete) lactation records from the AIPL-USDA database was used for model fitting. The empirical models included Wood (WD), Wilmink (WIL), Rook (RK), monophasic (MONO), diphasic (DIPH), and lactation persistency (LPM) functions; Dijkstra (DJ), Pollott (POL), and new-multiphasic (MULT) models comprised the mechanistic counterparts. Each model was separately tested on 305-d (>280 days in milk) and 999-d (>800 days in milk) lactations for cows in first parity and those in third and greater parities. All models were found to produce a significant fit for all 4 scenarios (2 parity groups and 2 lactation lengths). However, the resulting parameter estimates for the 4 scenarios were different. All models except MONO, DIPH, and LPM yielded residuals with absolute values smaller than 2 kg for the entire period of the 305-d lactations. For the extended lactations, the prediction errors were larger. However, the RK, DJ, POL, and MULT models were able to predict daily yield within a +/- 3 kg range for the entire 999-d period. The POL and MULT models (having 6 and 12 parameters, respectively) produced the lowest mean square error and Bayesian information criteria values, although the differences from the other models were small. Conversely, POL and MULT were often associated with poor convergence and highly correlated, unreliable, or biologically atypical parameter estimates. Considering the computational problems of large mechanistic models and the relative predictive ability of the other models, smaller models such as RK, DJ, and WD were recommended as sufficient for modeling extended lactations unless mechanistic details on the extended curves are needed. The recommended models were also satisfactory in describing fat and protein yields of 305-d and 999-d lactations of all parities.

Impact of Maturity Rate of Daughters on Genetic Ranking of Holstein Bulls

If genetic evaluations are calculated with a single-trait repeatability model, evaluation changes may be attributed in part to bulls that have daughters that deviate considerably from the typical response to aging. Differences in maturity rate of bull daughters were examined to determine whether they influence change in bull evaluations. Standardized milk records for Holsteins that first calved between 1960 and 1998 were used to calculate 12 tailored predicted transmitting abilities (PTA) for each bull. Predicted transmitting abilities were tailored from combinations of 4 annual cut-off dates and 3 parities. Date screening selected cows first calving before January of 1996, 1997, 1998, or 1999. Parity screening selected milk records from the first 1, 2, or 3 parities. Therefore, 4 evaluations (PTA1) included only first-parity records available for daughters and contemporaries prior to the respective years designated. Four more evaluations (PTA(1,2)) included the records from the first 2 parities for cows first calving prior to those same year cutoffs; likewise, the last 4 evaluations (PTA(1,2,3)) included records from the first 3 parities. Stability of bull evaluations (standard deviations of differences as well as correlations between bull evaluations) across time was compared. Bulls born after 1984 with > or =500 daughters were of interest because of the high precision of evaluations and recent activity. Tailored PTA of those bulls had more uniformity across years in mean records per daughter than did official USDA PTA. Standard deviation of differences in PTA1, PTA(1,2), and PTA(1,2,3) for milk between evaluation years 1996 and 1997 were 28, 28, and 27 kg compared with 63 kg for official evaluations; similarly, between 1996 and 1999, SD were 36, 32, and 32 kg compared with 80 kg. Results suggested that a modification to the current evaluation model to account for maturity rate should reduce fluctuations in individual bull PTA across time and may improve accuracy of evaluations.

Genetic evaluation of calving ease for Brown Swiss and Jersey bulls from purebred and crossbred calvings

The objective of this study was to examine the feasibility of implementing routine national calving ease (CE) genetic evaluations of Brown Swiss (BS) and Jersey (JE) sires that include records of crossbred calvings. Records were available for 11,793 BS calvings, 3431 BS-sired crosses, 65,293 JE calvings, and 7090 JE-sired crosses. Evaluations were performed for each breed using only purebred calvings and using both purebred and crossbred calvings. In the latter evaluations, the sire-maternal grandsire model used for the routine evaluation of Holstein (HO) CE was modified to include a fixed breed composition effect to account for differences between purebred and crossbred calvings. Jersey cows had very little calving difficulty (0.5 to 0.7%) and JE bulls had a very small range of evaluations, suggesting that a routine JE evaluation would be of little value. Results from the BS evaluations suggest a routine evaluation would provide BS breeders with a useful tool for genetic improvement. Further examination of data showed that many BS calvings were in mixed herds with HO calvings. As a result, a joint evaluation for BS and HO bulls was developed. The BS data showed that there is similar genetic variability as found in the HO population, which suggests implementation of a routine evaluation including BS CE would be of value. It appears BS bulls may produce daughters with superior maternal calving ability compared with HO. Validation of the joint evaluation was performed by comparing results with the routine HO evaluation. Holstein solutions from the joint evaluation were comparable to results from the routine HO-only evaluation. Correlations among solutions and evaluations showed HO evaluations were not adversely affected by BS data and BS sires were reranked as compared with the BS-only evaluation.

Effectiveness of national and regional sire evaluations in predicting future-daughter milk yield

National and regional bull evaluations were compared for ability to predict standardized milk yield of future daughters. Correlations between evaluations and first-, second-, and third-parity yields of future daughters were calculated within herd-year-month group. Mean correlations with predicted yield of future daughters across the United States were higher for national (0.109, 0.111, and 0.082 for first, second, and third parities, respectively) than for Northeast (0.098, 0.085, and 0.061) Holstein evaluations; corresponding correlations for future Northeast daughters were similar. Bull evaluations based on the first 5 parities of daughters that first calved through 1991 from either California, North Central, Northeast, or Southeast regions as well as from the entire United States were compared with standardized milk yields of daughters that calved later. Correlations with first-, second-, and third-parity yields of future daughters were higher (from 0.001 to 0.011) for national than for regional evaluations. National evaluations were better predictors of future-daughter yield, especially for California and the Southeast. Evaluations based on only first parity were slightly better than those based on the first 5 parities in predicting first-parity yield for 3 of 4 regions but were far less useful in predicting second-or third-parity yield regardless of region. Regional evaluations included fewer bulls because of limited numbers of daughters in each region. The top 100 bulls for genetic merit for milk yield based on regional rankings were inferior to the top 100 bulls based on national ranking by 25 to 173 kg. Reliance on regional rather than national evaluations would reduce current US genetic gains.

Mathematical representations of correlations among yield traits and somatic cell score on test day

Prediction of lactation yields and accuracies of yields for use in genetic evaluation can be improved by including information from test day correlations, especially for milk recording plans that vary in the numbers of milk weights recorded and component samples taken. Daily milk weights for 658 lactations of Canadian cows and monthly test records of milk, fat, and protein yields and somatic cell scores for 500,000 lactations of US cows were used to estimate phenotypic correlations between test days within herd-year. Correlations between daily yields for a designated interval between test days generally were highest for midlactation and were lowest for early and late lactation. Regression (two linear, two quadratic, and interaction effects) on mean DIM and interval between test days predicted correlations with a squared correlation of 0.94 for daily milk yields. Similar relationships were found for US monthly data. Variation in sampling was reduced, computer memory was minimized, and positive definiteness was guaranteed by fitting regressions on simply defined sources of correlation. An autoregressive matrix represented the within-trait correlations very well. The equations developed could be used to derive covariances and, subsequently, to estimate lactation yields and accuracies from combinations of individual daily milk, fat, and protein yields and somatic cell score.

Detection of putative loci affecting milk production and composition, health, and type traits in a United States Holstein population

Quantitative trait loci affecting milk yield and composition, health, and type traits were studied for seven large grandsire families of US Holstein using the granddaughter design. The families were genotyped at 20 microsatellite markers on 15 chromosomes, and the effects of the marker alleles were analyzed for 28 traits (21 type traits, 5 milk yield and composition traits, somatic cell score, and productive herd life). Markers BM415 on chromosome 6 and BM6425 on chromosome 14 were associated with effects on protein percentage in a single grandsire family. The latter marker had a lower probability of being associated with changes in milk yield and fat percentage in the same family. Increases in productive herd life were associated with an allele at marker BM719 on chromosome 16 in one grandsire family.

Use of linear type and production data to supplement early predicted transmitting abilities for productive life

Genetic evaluations for the length of productive life based on actual DHIA culling data have been available in the US since January 1994. Although substantial genetic variation in productive life exists, the reliability of selection is often low, particularly for recently progeny-tested bulls having daughters that have not yet had an opportunity to be culled. Correlated production and conformation traits, which have higher heritability than productive life and are available earlier in life, may be used to enhance evaluations of productive life for young bulls that have little or no direct culling information available. Genetic correlations between productive life and milk, fat, dairy form, and udder traits ranged from +0.22 to +0.46. The maximum reliability of the indirect prediction of productive life from 16 correlated type and production traits was 0.56, and the maximum reliability from a subset of 10 traits was 0.51. Indirect information about productive life that was derived from type and production traits was combined with actual culling information to increase the total amount of available information for many recently progeny-tested bulls. The procedures described herein for enhancing direct evaluations for the productive life of dairy sires with indirect information about production and type were implemented by the USDA Animal Improvement Programs Laboratory and the Holstein Association USA in July 1994.

Application of a canonical transformation to detection of quantitative trait loci with the aid of genetic markers in a multi-trait experiment

Effects of individual quantitative trait loci (QTLs) can be isolated with the aid of linked genetic markers. Most studies have analyzed each marker or pair of linked markers separately for each trait included in the analysis. Thus, the number of contrasts tested can be quite large. The experimentwise type-I error can be readily derived from the nominal type-I error if all contrasts are statistically independent, but different traits are generally correlated. A new set of uncorrelated traits can be derived by application of a canonical transformation. The total number of effective traits will generally be less than the original set. An example is presented for DNA microsatellite D21S4, which is used as a marker for milk production traits of Israeli dairy cattle. This locus had significant effects on milk and protein production but not on fat. It had a significant effect on only one of the canonical variables that was highly correlated with both milk and protein, and this variable explained 82% of the total variance. Thus, it can be concluded that a single QTL is affecting both traits. The effects on the original traits could be derived by a reverse transformation of the effects on the canonical variable.

Genetic variation in lactation means of somatic cell scores for six breeds of dairy cattle

Lactation means of somatic cell scores from sample days were used to estimate the components of variation for additive genetic, permanent environmental, and herd-sire interaction effects. Data included records of 4314 Ayrshire, 7845 Brown Swiss, 18,115 Guernsey, 1,135,752 Holstein, 67,862 Jersey, and 787 Milking Shorthorn cows from across the US. Records were preadjusted for length of lactation. Fixed effects of herd-year, calving age, and calving month were included in animal models for estimation of variance components. Additive genetic estimates from REML relative to a phenotypic variance of 1.00 were .07 for Ayrshires, .07 for Brown Swiss, .11 for Guernseys, .09 for Holsteins, .09 for Jerseys, and .08 for Milking Shorthorns; permanent environmental estimates were .25, .26, .22, .21, .20, and .35; and herdsire interaction estimates were .04, .02, .00, .02, .02, and .01. Effects of calving age were similar for all regions of the US but differed for Jerseys and Holsteins. Effects of calving month were similar for all breeds. Cows calving during summer had the highest lactation means for somatic cell score from sample days. Impact of calving month was greatest in the Southeast.

Bayesian analysis of linkage between genetic markers and quantitative trait loci. I. Prior knowledge

Prior information on gene effects at individual quantitative trait loci (QTL) and on recombination rates between marker loci and QTL is derived. The prior distribution of QTL gene effects is assumed to be exponential with major effects less likely than minor ones. The prior probability of linkage between a marker and another single locus is a function of the number and length of chromosomes, and of the map function relating recombination rate to genetic distance among loci. The prior probability of linkage between a marker locus and a quantitative trait depends additionally on the number of detectable QTL, which may be determined from total additive genetic variance and minimum detectable QTL effect. The use of this prior information should improve linkage tests and estimates of QTL effects.

Bayesian analysis of linkage between genetic markers and quantitative trait loci. II. Combining prior knowledge with experimental evidence

A Bayesian method was developed for identifying genetic markers linked to quantitative trait loci (QTL) by analyzing data from daughter or granddaughter designs and single markers or marker pairs. Traditional methods may yield unrealistic results because linkage tests depend on number of markers and QTL gene effects associated with selected markers are overestimated. The Bayesian or posterior probability of linkage combines information from a daughter or granddaughter design with the prior probability of linkage between a marker locus and a QTL. If the posterior probability exceeds a certain quantity, linkage is declared. Upon linkage acceptance, Bayesian estimates of marker-QTL recombination rate and QTL gene effects and frequencies are obtained. The Bayesian estimates of QTL gene effects account for different amounts of information by shrinking information from data toward the mean or mode of a prior exponential distribution of gene effects. Computation of the Bayesian analysis is feasible. Exact results are given for biallelic QTL, and extensions to multiallelic QTL are suggested.

Prediction of transmitting abilities for Holstein type traits

Heritabilities and genetic and phenotypic correlations among 14 linear type traits were estimated from Holstein Association data by multiple trait REML. Data used for parameter estimation were records of 779,391 daughters of 871 sires included in the January 1988 sire evaluation. Each daughter was represented by her appraisal closest to 30 mo of age. Highest heritability was .37 for stature, and lowest was .10 for foot angle. Gains in reliability from using correlated traits in multiple trait prediction were large for some traits (up to 60% for foot angle for cows). Final score variance parameters were estimated from 953,596 records, which were 43% of records included in the national sire evaluation. Sire models that adjusted or did not adjust for merit of mates were compared. Heritability of final score was .27 with adjustment for merit of mates by subtraction of predicted transmitting ability of dam from daughter's record compared with .29 if mate was ignored. Evaluations for type for several popular older sires were reduced moderately by adjustment for merit of mates, but estimated genetic trend increased slightly. An improved genetic grouping procedure that considers group effects as inherited was adapted for use in sire models. Parameter estimates and models presented were implemented by the Holstein Association for computing July 1988 genetic evaluations for linear traits and final score.