Additive genetic, non-additive genetic and permanent environmental effects for female reproductive performance in seasonal calving dairy females

Kelleher, Margaret; Buckley, F.; Evans, R.D.; Berry, D.P.

doi:10.1515/ijafr-2016-0002

Cited by 7 publications

(9 citation statements)

References 49 publications

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“…formance (Kelleher et al, 2016). Results from the present study reveal that such simple mathematics are not always valid, and not only does the association between EBV and phenotype differ by environment, but also the association between the heterosis coefficient and phenotype differs by environment.…”

Section: Discussionmentioning

confidence: 62%

How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit

Dunne

McParland

Kelleher³

et al. 2019

Journal of Dairy Science

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Sustainable dairy cow performance relies on coevolution in the development of breeding and management strategies. Tailoring breeding programs to herd performance metrics facilitates improved responses to breeding decisions. Although herd-level raw metrics on performance are useful, implicitly included within such statistics is the mean herd genetic merit. The objective of the present study was to quantify the expected response from selection decisions on additive and nonadditive merit by herd performance metrics independent of herd mean genetic merit. Performance traits considered in the present study were age at first calving, milk yield, calving to first service, number of services, calving interval, and survival. Herd-level best linear unbiased estimates (BLUE) for each performance trait were available on a maximum of 1,059 herds, stratified as best, average, and worst for each performance trait separately. The analyses performed included (1) the estimation of (co)variance for each trait in the 3 BLUE environments and (2) the regression of cow-level phenotypic performance on either the respective estimated breeding value (EBV) or the heterosis coefficient of the cow. A fundamental assumption of genetic evaluations is that 1 unit change in EBV equates to a 1 unit change in the respective phenotype; results from the present study, however, suggest that the realization of the change in phenotypic performance is largely dependent on the herd BLUE for that trait. Herds achieving more yield, on average, than expected from their mean genetic merit, had a 20% greater response to changes in EBV as well as 43% greater genetic standard deviation relative to herds within the worst BLUE for milk yield. Conversely, phenotypic performance in fertility traits (with the exception of calving to first service) tended to have a greater response to selection as well as a greater additive genetic standard deviation within the respective worst herd BLUE environments; this is suggested to be due to animals performing under more challenging environments leading to larger achievable gains. The attempts to exploit nonadditive genetic effects such as heterosis are often the basis of promoting cross-breeding, yet the results from the present study suggest that improvements in phenotypic performance is largely dependent on the environment. The largest gains due to heterotic effects tended to be within the most stressful (i.e., worst) BLUE environment for all traits, thus suggesting the heterosis effects can be beneficial in mitigating against poorer environments.

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Section: Discussionmentioning

confidence: 62%

How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit

Dunne

McParland

Kelleher³

et al. 2019

Journal of Dairy Science

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“…Several studies have reported the benefits of heterosis to include reduced dystocia (Heins et al, 2006a;Maltecca et al, 2006), fewer stillbirths (Heins et al, 2006a;Maltecca et al, 2006), greater longevity (Heins et al, 2006b;Kelleher et al, 2016), and improved fertility (Vance et al, 2013;Dezetter et al, 2015). In the present study, although the association between the level of heterosis and the odds of a young animal dying was only statistically significant (P < 0.001; Supplementary Table 3) for calves less than 3 d of age in beef herds, and animals aged between 731 and 1,095 d in dairy herds, no consistent trend across age groups was observed (results not presented).…”

Section: Heterosis and Recombinationmentioning

confidence: 99%

Risk factors associated with animal mortality in pasture-based, seasonal-calving dairy and beef herds1

Ring

McCarthy

Kelleher

et al. 2018

Journal of Animal Science

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Animal mortality is indicative of animal health and welfare standards, which are of growing concern to the agricultural industry. The objective of the present study was to ascertain risk factors associated with mortality at multiple life stages in pasture-based, seasonal-calving dairy and beef herds. Males and females were stratified into seven life stages based on age (0 to 2 d, 3 to 7 d, 8 to 30 d, 31 to 182 d, 183 to 365 d, 366 to 730 d, and 731 to 1,095 d) whereas females with ≥1 calving event were further stratified into five life stages based on cow parity number (1, 2, 3, 4, and 5). Mortality was defined as whether an animal died during each life stage; only animals that either survived the entire duration or died during a life stage were considered. The data, following edits, consisted of 4,404,122 records from 1,358,712 animals. Multivariable logistic regression was used to estimate the logit of the probability of mortality in each life stage separately. The odds of a young animal (i.e., aged ≤ 1,095 d) dying was generally greater if veterinary assistance was required at their birth relative to no assistance (odds ratio [OR]: 3.10 to 31.85), if the animal was a twin relative to a singleton (OR: 1.46 to 2.31) or if the animal was male relative to female (OR: 1.14 to 6.15). Moreover, the odds of a cow (i.e., females with ≥1 calving event) dying were greater when she required veterinary assistance at calving (OR: 2.69 to 7.55) compared with a cow that did not require any assistance, if she produced twin relative to singleton progeny (OR: 1.59 to 2.03) or male relative to female progeny (OR: 1.09 to 1.20). Additionally, the odds of a first or second parity cow dying when she herself had received veterinary assistance at birth were only 0.63 to 0.66 times that of a cow that was provided no assistance at birth. For both young animals and cows, the odds of dying generally increased with herd size, whereas animals residing in expanding herds had lower odds of dying. Results from the present study indicate that the risk factors associated with mortality in pasture-based, seasonal-calving herds are similar to those reported in literature in confinement, nonseasonal-calving herds. Moreover, the present study identifies that these risk factors are similar in both dairy and beef herds, yet the magnitude of the association often differs and also changes with life stage.

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“…Indicators including age at first calving, days from calving to first breeding, days open, the length of the calving interval, pregnancy rates, and the number of services per conception can be used to evaluate the reproductive performance in dairy cows [10][11][12]. Although studies have shown the existence of genetic variance for reproductive performance, the additive genetic variance reported accounted for only a small fraction of the total variance [13][14][15]. There are strong motives for including reproductive performance in genetic selection programs of dairy cows, but the very low heritability estimates reported for most of the considered reproductive performance measures makes selection for reproductive performance not effective enough [9,16].…”

Section: Introductionmentioning

confidence: 99%

A Genome-Wide Association Study for Calving Interval in Holstein Dairy Cows Using Weighted Single-Step Genomic BLUP Approach

Atashi

Salavati

Koster

et al. 2020

Animals

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Simple Summary: Reproductive performance is an important factor, which determines productive life and drives culling decisions in dairy herds. There are strong motives for including reproductive performance in genetic selection programs of dairy cows; however, low heritability estimates reported for reproductive performance measures limit the genetic selection efficiency. More effective genetic selection could be achieved using genomic information. The aim of this study was to identify genomic region(s) associated with the length of the calving interval in Holstein cows. The accuracies of genomic estimated breeding values (GEBVs) with single-step genomic BLUP (ssGBLUP) and the pedigree-based BLUP were compared as well. The results showed that the accuracies of GEBVs using the single-step genomic BLUP were much higher than those estimated using the pedigree-based BLUP. We identified three genomic regions (BTA3, BTA6, and BTA7) associated with the length of the calving interval in Holstein dairy cows. Abstract:The aim of the present study was to identify genomic region(s) associated with the length of the calving interval in primiparous (n = 6866) and multiparous (n = 5071) Holstein cows. The single nucleotide polymorphism (SNP) solutions were estimated using a weighted single-step genomic best linear unbiased prediction (WssGBLUP) approach and imputed high-density panel (777 k) genotypes. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. The results showed that the accuracies of GEBVs with WssGBLUP improved by +5.4 to +5.7, (primiparous cows) and +9.4 to +9.7 (multiparous cows) percent points over accuracies from the pedigree-based BLUP. The most accurate genomic evaluation was provided at the second iteration of WssGBLUP, which was used to identify associated genomic regions using a windows-based GWAS procedure. The proportion of additive genetic variance explained by windows of 50 consecutive SNPs (with an average of 165 Kb) was calculated and the region(s) that accounted for equal to or more than 0.20% of the total additive genetic variance were used to search for candidate genes. Three windows of 50 consecutive SNPs (BTA3, BTA6, and BTA7) were identified to be associated with the length of the calving interval in primi-and multiparous cows, while the window with the highest percentage of explained genetic variance was located on BTA3 position 49.42 to 49.52 Mb. There were five genes including ARHGAP29, SEC24D, METTL14, SLC36A2, and SLC36A3 inside the windows associated with the length of the calving interval. The biological

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Additive genetic, non-additive genetic and permanent environmental effects for female reproductive performance in seasonal calving dairy females

Cited by 7 publications

References 49 publications

How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit

How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit

Risk factors associated with animal mortality in pasture-based, seasonal-calving dairy and beef herds1

A Genome-Wide Association Study for Calving Interval in Holstein Dairy Cows Using Weighted Single-Step Genomic BLUP Approach

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