M. Haile-Mariam

Identification of genomic regions associated with inbreeding depression in Holstein and Jersey dairy cattle

BackgroundInbreeding reduces the fitness of individuals by increasing the frequency of homozygous deleterious recessive alleles. Some insight into the genetic architecture of fitness, and other complex traits, can be gained by using single nucleotide polymorphism (SNP) data to identify regions of the genome which lead to reduction in performance when identical by descent (IBD). Here, we compared the effect of genome-wide and location-specific homozygosity on fertility and milk production traits in dairy cattle.MethodsGenotype data from more than 43 000 SNPs were available for 8853 Holstein and 4138 Jersey dairy cows that were part of a much larger dataset that had pedigree records (338 696 Holstein and 64 049 Jersey animals). Measures of inbreeding were based on: (1) pedigree data; (2) genotypes to determine the realised proportion of the genome that is IBD; (3) the proportion of the total genome that is homozygous and (4) runs of homozygosity (ROH) which are stretches of the genome that are homozygous.ResultsA 1% increase in inbreeding based either on pedigree or genomic data was associated with a decrease in milk, fat and protein yields of around 0.4 to 0.6% of the phenotypic mean, and an increase in calving interval (i.e. a deterioration in fertility) of 0.02 to 0.05% of the phenotypic mean. A genome-wide association study using ROH of more than 50 SNPs revealed genomic regions that resulted in depression of up to 12.5 d and 260 L for calving interval and milk yield, respectively, when completely homozygous.ConclusionsGenomic measures can be used instead of pedigree-based inbreeding to estimate inbreeding depression. Both the diagonal elements of the genomic relationship matrix and the proportion of homozygous SNPs can be used to measure inbreeding. Longer ROH (>3 Mb) were found to be associated with a reduction in milk yield and captured recent inbreeding independently and in addition to overall homozygosity. Inbreeding depression can be reduced by minimizing overall inbreeding but maybe also by avoiding the production of offspring that are homozygous for deleterious alleles at specific genomic regions that are associated with inbreeding depression.

Comparison of heritabilities of dairy traits in Australian Holstein-Friesian cattle from genomic and pedigree data and implications for genomic evaluations

The reliability of genomic evaluations depends on the proportion of genetic variation explained by the DNA markers. In this study, we have estimated the proportion of variance in daughter trait deviations (DTDs) of dairy bulls explained by 45 993 genome wide single-nucleotide polymorphism (SNP) markers for 29 traits in Australian Holstein-Friesian dairy cattle. We compare these proportions to the proportion of variance in DTDs explained by the additive relationship matrix derived from the pedigree, as well as the sum of variance explained by both pedigree and marker information when these were fitted simultaneously. The proportion of genetic variance in DTDs relative to the total genetic variance (the total genetic variance explained by the genomic relationships and pedigree relationships when both were fitted simultaneously) varied from 32% for fertility to approximately 80% for milk yield traits. When fitting genomic and pedigree relationships simultaneously, the variance unexplained (i.e. the residual variance) in DTDs of the total variance for most traits was reduced compared to fitting either individually, suggesting that there is not complete overlap between the effects. The proportion of genetic variance accounted by the genomic relationships can be used to modify the blending equations used to calculate genomic estimated breeding value (GEBV) from direct genomic breeding value (DGV) and parent average. Our results, from a validation population of young dairy bulls with DTD, suggest that this modification can improve the reliability of GEBV by up to 5%.

Genetic parameters of fertility traits and their correlation with production, type, workability, liveweight, survival index, and cell count

First and second parity data on calving interval (CI, days), survival to next lactation (Surv, scored 1 or 0), calving to first service interval (CFS, days), 25-day first service non-return rate (FNRR, scored 1 or 0), and insemination or submission rate (InsemR, scored 1 or 0) of Holstein-Friesian cattle were analysed using a sire model to estimate genetic parameters. The estimated genetic parameters were used to obtain predicted transmitting ability (PTA) of sires for fertility traits and Surv, including 6-week pregnancy rate (6-w PR). PTA for 6-w PR was calculated based on an estimated heritability of 0.07 and genetic and environmental correlations with the other fertility traits and Surv. In addition, approximate genetic correlations of fertility traits and Surv with milk yield, type traits, workability (likability, milking speed, temperament), survival index (a measure of survival calculated from estimated breeding values on survival, likability, and type traits), bodyweight, and cell count were estimated. Heritability (h 2 ) of fertility traits was 2-4% in the first parity and 1-2% in the second parity. Genetic correlations between fertility traits were generally higher in magnitude than environmental correlations, particularly in the first parity. The difference in PTA between the best and worst sires was high (21 days in CI and 21% in 6-w PR), showing the scope for selection. Approximate genetic correlations between fertility and most traits that are currently evaluated were low to moderate. Milk, protein and fat yield, body size, overall type, mammary system, udder texture, muzzle width, angularity, body depth, chest width, foot angle, and rear attachment width were unfavourably correlated (0.1-0.5) with most fertility traits. Fat and protein % were favourably correlated with both CI and 6-w PR (~0.2). Pin set was moderately favourably correlated (0.28) with 6-w PR. Surv was favourably (positively) correlated with temperament, likability, and survival index (~0.5). The wide variation in particular in CI and 6-w PR between bulls and the generally unfavourable approximate genetic correlations of fertility traits with most traits for which selection is currently practiced suggest that genetic evaluation for fertility should be introduced. AR Fe rt t t ry M. H am et al

Genomic selection for tolerance to heat stress in Australian dairy cattle

Temperature and humidity levels above a certain threshold decrease milk production in dairy cattle, and genetic variation is associated with the amount of lost production. To enable selection for improved heat tolerance, the aim of this study was to develop genomic estimated breeding values (GEBV) for heat tolerance in dairy cattle. Heat tolerance was defined as the rate of decline in production under heat stress. We combined herd test-day recording data from 366,835 Holstein and 76,852 Jersey cows with daily temperature and humidity measurements from weather stations closest to the tested herds for test days between 2003 and 2013. We used daily mean values of temperature-humidity index averaged for the day of test and the 4 previous days as the measure of heat stress. Tolerance to heat stress was estimated for each cow using a random regression model with a common threshold of temperature-humidity index=60 for all cows. The slope solutions for cows from this model were used to define the daughter trait deviations of their sires. Genomic best linear unbiased prediction was used to calculate GEBV for heat tolerance for milk, fat, and protein yield. Two reference populations were used, the first consisted of genotyped sires only (2,300 Holstein and 575 Jersey sires), and the other included genotyped sires and cows (2,189 Holstein and 1,188 Jersey cows). The remainder of the genotyped sires were used as a validation set. All animals had genotypes for 632,003 single nucleotide polymorphisms. When using only genotyped sires in the reference set and only the first parity data, the accuracy of GEBV for heat tolerance in relation to changes in milk, fat, and protein yield were 0.48, 0.50, and 0.49 in the Holstein validation sires and 0.44, 0.61, and 0.53 in the Jersey validation sires, respectively. Some slight improvement in the accuracy of prediction was achieved when cows were included in the reference population for Holsteins. No clear improvements in the accuracy of genomic prediction were observed when data from the second and third parities were included. Correlations of GEBV for heat tolerance with Australian Breeding Values for other traits suggested heat tolerance had a favorable genetic correlation with fertility (0.29-0.39 in Holsteins and 0.15-0.27 in Jerseys), but unfavorable correlations for some production traits. Options to improve heat tolerance with genomic selection in Australian dairy cattle are discussed.

The effect of changing cow production and fitness traits on net income and greenhouse gas emissions from Australian dairy systems

The aim of this study was to compare the effect of changing a range of biological traits on farm net income and greenhouse gas emissions (expressed in carbon dioxide equivalents, CO2-eq.) in the Australian dairy cow population. An average cow was modeled, using breed-average information for Holsteins and Jerseys from the Australian Dairy Herd Improvement Scheme. A Markov chain approach was used to describe the steady-state herd structure, as well as estimate the CO2-eq. emissions per cow and per kilogram of milk solids. The effects of a single unit change in herd milk volume, fat and protein yields, live weight, survival, dry matter intake, somatic cell count, and calving interval were assessed. With the traits studied, the only single-unit change that would bring about a desirable increase in both net income and reduced emissions intensity per cow and per kilogram of milk solids in Australian dairy herds would be an increase in survival and reductions in milk volume, live weight, DMI, SCC, and calving interval. The models developed can be used to assess lifetime dairy system abatement options by breeding, feeding, and management. Selective breeding and appropriate management can both improve health, fertility, and feed utilization of Australian dairy systems and reduce its environmental impact.

Genetic analyses of fertility and predictor traits in Holstein herds with low and high mean calving intervals and in Jersey herds

Genetic parameters were estimated with the aim of identifying useful predictor traits for the genetic evaluation of fertility. For this study, data included calving interval (CI), days from calving to first service (CFS), pregnancy diagnosis, lactation length (LL), daily milk yield close to 90 d of lactation (milk yield), and survival to second lactation on Australian Holstein and Jersey cows. The effect of level of fertility, measured here as CI, on correlations among traits was investigated by dividing the Holstein herds into those that managed short CI (proxy for seasonal-calving herds) and long CI (proxy for herds that practice extended lactations). In all cases, genetic correlations of CI with CFS, pregnancy, and LL were high (>0.7). Genetic correlations between fertility and predictor traits were generally similar in the 2 Holstein herd groups and in Jerseys. However, some differences in both the direction and strength of correlations were observed. In Jerseys, the genetic correlation between CI and survival was positive, but in Holstein herds, this correlation was negative. Particularly in low mean CI herds, the correlation suggests that cows with a genetic potential for longer CI were more likely to be culled. The genetic correlation of CI with survival was intermediate in high mean CI Holstein herds. Furthermore, Jersey cows with a high genetic potential for milk yield had a higher chance of surviving than those with low genetic potential. In contrast, the genetic correlation between milk yield and survival in low mean CI Holstein herds was near zero. The high genetic correlation between CI and LL suggests that LL could be used as proxy for CI in cows that do not calve again. Although the phenotypic variance for CI in high mean CI herds was nearly twice that in Jerseys and low mean CI herds, we found no bull reranking for CI due to having daughters in low or high mean CI herds. However, the ranges in estimated breeding values (EBV) were narrower in low mean CI herds than in high mean CI herds. The genetic trend in cows and bulls showed that CI EBV were increasing by 0.3 to 0.8 d/yr in both Holstein and Jersey. Phenotypically, CI was increasing by 2 d/yr in high mean CI Holstein herds and by 1 d/yr in Jersey and low mean CI Holstein herds. However, in recent years, both phenotypic and genetic trends have stabilized. In summary, if the main trait for genetic evaluation of fertility is CI, predictor traits such as milk yield, survival, LL, and other fertility traits can be used in joint analyses to increase reliability of bull EBV. If the genetic evaluation is to be carried out simultaneously for Holstein and Jersey using the same variance-covariance matrix, survival should not be used as a predictor because its correlation with CI is different in Jersey than in Holstein. On the other hand, LL could be used instead of CI for cows that do not calve again in both breeds and herd groups.

Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations)

Test-day milk yield and somatic cell count data over extended lactation (lactation to 540-600 days) were analysed considering part lactations as different traits and fitting random regression (RR) models. Data on Australian Jersey and Holstein Friesian (HF) were used to demonstrate the shape of the lactation curve and data on HF were used for genetic study. Test-day data from about 100 000 cows that calved between 1998 and 2005 were used for this study. In all analyses, a sire model was used.When part lactations were considered as different traits, protein yield early in the lactation (e.g. first 2 months) had a genetic correlation of about 0.8 with protein yield produced after 300 days of lactation. Genetic correlations between lactation stages that are adjacent to each other were high (0.9 or more) within parity. Across parities, genetic correlations were high for both protein and milk yield if they are within the same stage of lactation. Phenotypic correlations were lower than genetic correlations. Heritability of milk-yield traits estimated from the RR model varied from 0.15 at the beginning of the lactation to as high as 0.37 by the 4th month of lactation. All genetic correlations between different days in milk were positive, with the highest correlations between adjacent days in milk and decreasing correlations with increasing time-span. The pattern of genetic correlations between milk yield in the second 300 days (301 to 600 days of lactation) do not markedly differ from the pattern in the first 300 days of lactation. The lowest estimated genetic correlation was 0.15 between milk yield on days 45 and 525 of lactation. The result from this study shows that progeny of bulls with high estimated breeding values for yield traits and those that produce at a relatively high level in the first few months are the most likely candidates for use in herds favouring extended lactations.

Incorporating heifer feed efficiency in the Australian selection index using genomic selection

The economic benefit of expanding the Australian Profit Ranking (APR) index to include residual feed intake (RFI) was evaluated using a multitrait selection index. This required the estimation of genetic parameters for RFI and genetic correlations using single nucleotide polymorphism data (genomic) correlations with other traits. Heritabilities of RFI, dry matter intake (DMI), and all the traits in the APR (milk, fat, and protein yields; somatic cell count; fertility; survival; milking speed; and temperament), and genomic correlations between these traits were estimated using a Bayesian framework, using data from 843 growing Holstein heifers with phenotypes for DMI and RFI, and bulls with records for the other traits. Heritability estimates of DMI and RFI were 0.44 and 0.33, respectively, and the genomic correlation between them was 0.03 and nonsignificant. The genomic correlations between the feed-efficiency traits and milk yield traits were also close to zero, ranging between -0.11 and 0.10. Positive genomic correlations were found for DMI with stature (0.16) and with overall type (0.14), suggesting that taller cows eat more as heifers. One issue was that the genomic correlation estimates for RFI with calving interval (ClvI) and with body condition score were both unfavorable (-0.13 and 0.71 respectively), suggesting an antagonism between feed efficiency and fertility. However, because of the relatively small numbers of animals in this study, a large 95% probability interval existed for the genomic correlation between RFI and ClvI (-0.66, 0.36). Given these parameters, and a genetic correlation between heifer and lactating cow RFI of 0.67, inclusion of RFI in the APR index would reduce RFI by 1.76 kg/cow per year. Including RFI in the APR would result in the national Australian Holstein herd consuming 1.73 × 10(6) kg less feed, which is worth 0.55 million Australian dollars (A

Characterizing homozygosity across United States, New Zealand and Australian Jersey cow and bull populations.

) per year and is 3% greater than is currently possible to achieve. Other traits contributing to profitability, such as milk production and fertility, will also improve through selection on this index; for example, ClvI would be reduced by 0.53 d/cow per year, which is 96% of the gain for this trait that is achieved without RFI in the APR.

Genetic markers for lactation persistency in primiparous Australian dairy cows.

BackgroundDairy cattle breeding objectives are in general similar across countries, but environment and management conditions may vary, giving rise to slightly different selection pressures applied to a given trait. This potentially leads to different selection pressures to loci across the genome that, if large enough, may give rise to differential regions with high levels of homozygosity. The objective of this study was to characterize differences and similarities in the location and frequency of homozygosity related measures of Jersey dairy cows and bulls from the United States (US), Australia (AU) and New Zealand (NZ).ResultsThe populations consisted of a subset of genotyped Jersey cows born in US (n = 1047) and AU (n = 886) and Jersey bulls progeny tested from the US (n = 736), AU (n = 306) and NZ (n = 768). Differences and similarities across populations were characterized using a principal component analysis (PCA) and a run of homozygosity (ROH) statistic (ROH45), which counts the frequency of a single nucleotide polymorphism (SNP) being in a ROH of at least 45 SNP. Regions that exhibited high frequencies of ROH45 and those that had significantly different ROH45 frequencies between populations were investigated for their association with milk yield traits. Within sex, the PCA revealed slight differentiation between the populations, with the greatest occurring between the US and NZ bulls. Regions with high levels of ROH45 for all populations were detected on BTA3 and BTA7 while several other regions differed in ROH45 frequency across populations, the largest number occurring for the US and NZ bull contrast. In addition, multiple regions with different ROH45 frequencies across populations were found to be associated with milk yield traits.ConclusionMultiple regions exhibited differential ROH45 across AU, NZ and US cow and bull populations, an interpretation is that locations of the genome are undergoing differential directional selection. Two regions on BTA3 and BTA7 had high ROH45 frequencies across all populations and will be investigated further to determine the gene(s) undergoing directional selection.