Genetic parameter estimates for methane emission during lactation from breath and potential inaccuracies in reliabilities assuming a repeatability versus random regression model.

Methane (CH4) emissions will be added to many national ruminant breeding programs in the coming years. Little is known about the covariance structure of CH4 traits over a lactation, which is important for optimizing recording strategies and to establish optimal genetic evaluation models. Our aim was to study CH4 over a lactation using random regression (RR) models, and to compare the accuracy to a fixed regression repeatability model under different phenotyping strategies. Data were available from repeated measurements of CH4 concentrations (ppm), recorded in the feed bins of milking robots, on 52 commercial dairy farms in the Netherlands. In total, 36,370 averaged weekly records were available from 4,664 cows. Genetic parameters were estimated using a fixed regression model, and a RR model with 1st to 5th order Legendre polynomials for the additive genetic and within lactation permanent environmental effect. The mean heritability was 0.17 ± 0.04, and the mean within lactation repeatability was 0.56 ± 0.03. The genetic correlations between days in milk were high and ranged from 0.34 ± 0.36 to 1.00 ± < 0.01. Permanent environmental correlations showed large deviations and ranged from -0.73 ± 0.08 to 1.00 ± < 0.01. With a large number of full lactation daughter CH4 records per bull, the reliability was not sensitive to using the fixed versus RR model. However, when shorter periods were recorded at the start and end of the lactation, the fixed regression model resulted in a loss of reliability up to 28% for bulls. Assuming the fixed model when the true (co)variance structure is reflected by the RR model, more than twice as long recording from the start of lactation was required to achieve maximum reliability for a bull. Thus, a too simplistic model could result in implementing too little recording, and lower genetic gains than predicted from the reliability.

Heritability and genetic correlations between enteric methane production and concentration recorded by GreenFeed and sniffers on dairy cows.

To reduce methane (CH4) emissions of dairy cows by animal breeding, CH4 measurements have to be recorded on thousands of individual cows. Currently, several techniques are used to phenotype cows for CH4, differing in costs and applicability. However, there is uncertainty about the agreement between techniques. To judge the similarity and repeatability between measurements of different recording techniques, the repeatability, heritability, and genetic correlation are useful metrics. Therefore, our objective was to estimate (1) the repeatability and heritability for CH4 and carbon dioxide production recorded by GreenFeed (GF) and for CH4 and carbon dioxide concentration measured by cost-effective but less accurate sniffers, and (2) the genetic correlation between CH4 recorded with these 2 different on farm and high throughput techniques. Data were available from repeated measurements of CH4 production (grams/day) by GF units and of CH4 concentration (ppm) by sniffers, recorded on commercial dairy farms in the Netherlands. The final data comprised 24,284 GF daily means from 822 cows, 170,826 sniffer daily means from 1,800 cows, and 1,786 daily means from 75 cows by both GF and sniffer (in the same period). Additionally, CH4 records were averaged per week. For daily and weekly mean GF CH4 the heritabilities were 0.19 ± 0.02 and 0.33 ± 0.04, and for daily and weekly mean sniffer CH4 the heritabilities were similar and were 0.18 ± 0.01 and 0.32 ± 0.02, respectively. Phenotypic correlations between GF CH4 production and sniffer CH4 concentration were moderate (0.39 ± 0.03 for daily means and 0.37 ± 0.05 for weekly means). However, genetic correlations were high; 0.71 ± 0.13 for daily means and 0.76 ± 0.15 for weekly means. The high genetic correlation indicates that selection on low CH4 concentrations (ppm) recorded by the cost-effective sniffer method, will result in reduced CH4 production (grams/day) as recorded with GF.

Selective breeding as a mitigation tool for methane emissions from dairy cattle.

The global livestock sector, particularly ruminants, contributes substantially to the total anthropogenic greenhouse gases. Management and dietary solutions to reduce enteric methane (CH4) emissions are extensively researched. Animal breeding that exploits natural variation in CH4 emissions is an additional mitigation solution that is cost-effective, permanent, and cumulative. We quantified the effect of including CH4 production in the Dutch breeding goal using selection index theory. The current Dutch national index contains 15 traits, related to milk yield, longevity, health, fertility, conformation and feed efficiency. From the literature, we obtained a heritability of 0.21 for enteric CH4 production, and genetic correlations of 0.4 with milk lactose, protein, fat and DM intake. Correlations between enteric CH4 production and other traits in the breeding goal were set to zero. When including CH4 production in the current breeding goal with a zero economic value, CH4 production increases each year by 1.5 g/d as a correlated response. When extrapolating this, the average daily CH4 production of 392 g/d in 2018 will increase to 442 g/d in 2050 (+13%). However, expressing the CH4 production as CH4 intensity in the same period shows a reduction of 13%. By putting economic weight on CH4 production in the breeding goal, selective breeding can reduce the CH4 intensity even by 24% in 2050. This shows that breeding is a valuable contribution to the whole set of mitigation strategies that could be applied in order to achieve the goals for 2050 set by the EU. If the decision is made to implement animal breeding strategies to reduce enteric CH4 production, and to achieve the expected breeding impact, there needs to be a sufficient reliability of prediction. The only way to achieve that is to have enough animals phenotyped and genotyped. The power calculations offer insights into the difficulties that will be faced in trying to record enough data. Recording CH4 data on 100 farms (with on average 150 cows each) for at least 2 years is required to achieve the desired reliability of 0.40 for the genomic prediction.

Efficient and accurate computation of base generation allele frequencies.

Allele frequencies are used for several aspects of genomic prediction, with the assumption that these are equal to the allele frequency in the base generation of the pedigree. The current standard method, however, calculates allele frequencies from the current genotyped population. We compared the current standard method with BLUP and general least squares (GLS) methods explicitly targeting the base population to determine whether there is a more accurate and still efficient method of calculating allele frequencies that better represents the base generation. A data set based on a typical dairy population was simulated for 325,266 animals; the last 100,078 animals in generations 9 to 12 of the population were genotyped, with 1,670 SNP markers. For the BLUP method, several SNP genotypes were analyzed with a multitrait model by assuming a heritability of 0.99 and no genetic correlation among them. This method was limited by the time required for each BLUP to converge (approximately 6 min per BLUP run of 15 SNP). The GLS method had 2 implementations. The first implementation, using imputation on the fly and multiplication of sparse matrices, was very efficient and required just 49 s and 1.3 GB of random access memory. The second implementation, using a dense full A22-1 matrix, was very inefficient and required more than 1 d of wall clock time and more than 118.2 GB of random access memory. When no selection was considered in the simulations, all methods predicted equally well. When selection was introduced, higher correlations between the estimated allele frequency and known base generation allele frequency were observed for BLUP (0.96 ± 0.01) and GLS (0.97 ± 0.01) compared with the current standard method (0.87 ± 0.01). The GLS method decreased in accuracy when introducing incomplete pedigree, with 25% of sires in the first 5 generations randomly replaced as unknown to erroneously identify founder animals (0.93 ± 0.01) and a further decrease for 8 generations (0.91 ± 0.01). There was no change in accuracy when introducing 5% genotyping errors (0.97 ± 0.01), 5% missing genotypes (0.97 ± 0.01), or both 5% genotyping errors and missing genotypes (0.97 ± 0.01). The GLS method provided the most accurate estimates of base generation allele frequency and was only slightly slower compared with the current method. The efficient implementation of the GLS method, therefore, is very well suited for practical application and is recommended for implementation.