Reduction In Generation Interval Research Articles

Genomic Analysis of Inbreeding and Coancestry in Nordic Jersey and Holstein Dairy Cattle Populations

In recent years, Genomic Selection (GS) has accelerated genetic gain in dairy cattle breeds worldwide. Despite the evident genetic progress, several dairy populations have also encountered challenges such as heightened inbreeding rates and reduced effective population sizes. The challenge has been to find a balance between achieving substantial genetic gain while managing genetic diversity within the population, thereby mitigating the negative effects of inbreeding depression. This study aims to elucidate the impact of GS on pedigree and genomic rates of inbreeding (ΔF) and coancestry (ΔC) in Nordic Jersey (NJ) and Holstein (NH) cattle populations. Furthermore, key genetic metrics including the generation interval (L), effective population size (Ne), and future effective population size (FNe) were assessed between 2 time periods, before and after GS, and across distinct animal cohorts in both breeds: females, bulls, and approved semen-producing bulls (AI-sires). Analysis of ΔF and ΔC revealed distinct trends across the studied periods and animal groups. Notably, there was a consistent increase in yearly ΔF for most animal groups in both breeds. An exception was observed in NH AI-sires, which demonstrated a slight decrease in yearly ΔF. Moreover, NJ displayed minimal changes in yearly ΔC between the periods, whereas NH exhibited elevated ΔC values across all animal groups. Particularly striking was the substantial increase in yearly ΔC within the NH female population, surging from 0.02% to 0.39% between the periods. Implementation of GS resulted in a reduction of the generation interval across all animal cohorts in both NJ and NH breeds. However, the extent of reduction was more pronounced in males compared with females. This reduction in generation interval influenced generational changes in ΔF and ΔC. Bulls and AI-sires of both breeds exhibited reduced generational ΔF between periods, in contrast to females that demonstrated an opposing pattern. Between the periods, NJ maintained a relatively stable Ne, 29.4 before and 30.3 after GS, while NH experienced a notable decline from 54.3 to 42.8. Female groups in both breeds displayed a negative Ne trend, while males demonstrated either neutral or positive Ne developments. Regarding FNe, NJ exhibited positive FNe development with an increase from 40.7 to 57.2. The opposite was observed in NH, where FNe decreased from 198.8 to 42.7. In summary, it was evident that the genomic methods could detect differences between the populations and changes in ΔF and ΔC more efficiently than pedigree methods. GS implementation yielded positive outcomes within the NJ population regarding the rate of coancestry but the opposite was observed with NH. Moreover, analysis of ΔC data hints at the potential to decrease future ΔF through informed mating strategies. Conversely, NH faces more pressing concerns, even though ΔF remains comparatively modest in contrast to what has been observed in other Holstein populations. These findings underscore the necessity of genomic control of inbreeding and coancestry with strategic changes in the Nordic breeding schemes for dairy to ensure long-term sustainability in the forthcoming years.

Appraisal and simulation of expected genetic gain for production and reproduction traits in Sahiwal cattle

The present study was undertaken with an objective to assess the expected genetic gain for production and reproduction traits, viz. 305-day milk yield (305DMY), average daily milk yield (ADMY), and calving to first insemination interval (CFI) in Sahiwal cattle. Data spread over a period of 29 years pertaining to production and reproduction traits of Sahiwal cattle maintained at an organized herd of ICAR-National Dairy Research Institute, Karnal, were utilized. Expected genetic gain per generation was assessed based on first and pooled 6 lactation records using 2 different methods, i.e. method I (ΔG = h2S) and II (ΔG = iσph2). Method II, which considered selection intensity and phenotypic standard deviation of the traits led to better estimation in Sahiwal cattle. Further, different parameters involved in methods I and II were simulated to evaluate the expected genetic gain in first lactation traits, viz. 305DMY, ADMY, and CFI. In method I, generation interval was decreased as well as increased for estimating expected genetic gain. Using method I, expected genetic gain increased by about 33, 33, and 43% for 305DMY, ADMY, and CFI with the reduction of generation interval (GI) from 5.31 to 4 years, whereas the expected genetic gain decreased by about 11, 17 and 14% for the above-mentioned traits, respectively, with the increase of GI from 5.31 to 6 years. In method II, with the increase of selection intensity and reduction of GI, a significant increase in ΔG/year for 305DMY, ADMY, and CFI was observed. Present study indicated that generation interval and proportion of Sahiwal animals selected should be less than 5 years and 75%, respectively, for achieving more than 50% expected ΔG/year for 305DMY, ADMY, and CFI in an organized herd.

Modeling lactation curve for genetic evaluation of Murrah buffaloes

In this study, four lactation curve models viz. Gamma-type function (GF), Mixed log function (MLF), Exponential function (EF) and Polynomial regression function (PRF) were fitted to predict the weekly test-day milk yields pertaining to 961 Murrah buffaloes progeny of 101 sires spread over a period of 36 years (1977-2012) maintained at ICAR-NDRI, Karnal. The PRF was the best fit with highest R2 (99.3%) and lowest RMSE (0.3%) which was used subsequently to estimate the first lactation 305-day or less milk yield (FL305DMY) from weekly test-day milk yields. The sires were evaluated using four linear models viz. Least Squares(LSQ), Simple Regressed Least Squares (SRLS), Sire (BLUP-SM) and animal (BLUP-AM) models from actual as well as PRF predicted FL305DMYs. BLUP-AM was the most efficient, among the sire evaluation models, in predicting the breeding values. Sire evaluation on the basis of predicted data (using PRF) was similar to that on the basis of actual data indicating that the best fitted lactation curve function can be used as an alternative for early animal evaluation and reduction of generation interval.

Increased genetic gains in multi-trait sheep indices using female reproductive technologies combined with optimal contribution selection and genomic breeding values

Female reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro fertilisation and embryo transfer (JIVET) can produce multiple offspring per mating in sheep and cattle. In breeding programs this allows for higher female selection intensity and, in the case of JIVET, a reduction in generation interval, resulting in higher rates of genetic gain. Low selection accuracy of young females entering JIVET has often dissuaded producers from using this technology. However, genomic selection (GS) could increase selection accuracy of candidates at a younger age to help increase rates of genetic gain. This increase might vary for different traits in multiple trait breeding programs depending on genetic parameters and the practicality of recording, particularly for hard to measure traits. This study used both stochastic (animals) and deterministic (GS) simulation to evaluate the effect of reproductive technologies on the genetic gain for various traits in sheep breeding programs, both with and without GS. Optimal contribution selection was used to manage inbreeding and to optimally assign reproductive technologies to individual selection candidates. Two Australian sheep industry indexes were used – a terminal sire index that focussed on growth and carcass traits (the ‘Lamb 2020’ index) and a Merino index that focuses on wool traits, bodyweight, and reproduction (MP+). We observed that breeding programs using artificial insemination or natural mating (AI/N) + MOET, compared with AI/N alone, yielded an extra 39% and 27% genetic gain for terminal and Merino indexes without GS, respectively. However, the addition of JIVET to AI/N + MOET without GS only yielded an extra 1% genetic gain for terminal index and no extra gain in the Merino index. When GS was used in breeding programs, we observed AI/N + MOET + JIVET outperformed AI/N + MOET by 21% and 33% for terminal and Merino indexes, respectively. The implementation of GS increased genetic gain where reproductive technologies were used by 9–34% in Lamb 2020 and 37–98% in MP+. Individual trait response to selection varied in each breeding program. The combination of GS and reproductive technologies allowed for greater genetic gain in both indexes especially for hard to measure traits, but had limited effect on the traits that already had a large amount of early age records.

Genetic parameters of 305 days and monthly test-day milk yields in Murrah buffaloes

The present investigation was carried out using first lactation 9,071 monthly test-day milk yield records of 965 Murrah buffaloes that calved from 1977 to 2012 sired by 98 bulls maintained at ICAR-National Dairy Research Institute, Karnal. Mixed model analysis was carried out by least-squares maximum likelihood programme to study the effects of genetic and non-genetic factors on first lactation 305-day milk yield and monthly test-day (TD) milk yield records of Murrah buffaloes. The highest monthly test-day milk yield was observed in TD3 (7.64 kg) and lowest in TD10 (4.46 kg). The average first lactation 305 days or less milk yield (FL305DMY) was 1806.45±16.99 kg. The effect of season of calving, period of calving and age at first calving was highly significant on FL305DMY and on most of the monthly test-day milk yields. The heritability estimate for FL305DMY was 0.18±0.08 and for monthly test-day milk yields it ranged from 0.09 (TD6) to 0.18 (TD3). The genetic and phenotypic correlations among monthly test-day milk yields and with 305 days milk yield were highly significant. The present investigation revealed that the non-genetic factors such as season of calving in general and year of calving in particular, might be considered when performing an evaluation of Murrah buffaloes based on monthly test-day milk yield records. High genetic and phenotypic correlation among monthly test-day milk yields and with first lactation 305 days milk yield suggested that these test-day yields could be used as the selection criteria, leading to a reduction in generation interval.

Breeding Objectives Indicate Value of Genomics for Beef Cattle

Genomics may have much to offer in the implementation of breeding objectives. Advantages of genomic prediction include increased accuracy of expected progeny differences (EPDs) for traits that have been components of routine genetic evaluations. Perhaps more importantly, genomic prediction makes it possible to include traits that are too costly or too difficult to measure, and traits that are measured too late in life or are sex-limited such that candidates for selection cannot have EBV with high accuracy at the time when selection decisions are made. Genomically enhanced EPDs may also allow for a marked reduction in generation interval, thus accelerating the annual rate of genetic improvement. Here, the value of genomic prediction, on a trait-by-trait basis, is extended to explore the contribution of genomic prediction to selection for a multitrait breeding objective indicative of economic merit. A simple two-trait objective indicative of feed efficiency is illustrated first, followed by objectives for terminal and maternal strains of Angus.

Acceleration of genetic gain in cattle by reduction of generation interval.

Genomic selection (GS) approaches, in combination with reproductive technologies, are revolutionizing the design and implementation of breeding programs in livestock species, particularly in cattle. GS leverages genomic readouts to provide estimates of breeding value early in the life of animals. However, the capacity of these approaches for improving genetic gain in breeding programs is limited by generation interval, the average age of an animal when replacement progeny are born. Here, we present a cost-effective approach that combines GS with reproductive technologies to reduce generation interval by rapidly producing high genetic merit calves.

A review of how dairy farmers can use and profit from genomic technologies

New genomic technologies can help farmers to (1) achieve higher annual rates of genetic gain through using genomically tested bulls in their herds, (2) select for ‘difficult’ to measure traits, such as feed conversion efficiency, methane emissions and energy balance, (3) select the best heifers to become herd replacements, (4) sell pedigree heifers at a premium, (5) use mating plans to optimise rates of genetic gain while controlling inbreeding, (6) achieve certainty in parentage of individual cows and (7) avoid genetic defects that could arise from mating cows to bulls that are known carriers of genetic diseases that are the result of a single lethal mutation. The first use does not require genotyping females and could approximately double the net income per cow that arises due to genetic improvement, mainly through a reduction in generation interval. On the basis of current rates of genetic gain, the net profit from using genotyped bulls could be worth AU$20/cow per year and is permanent and cumulative. One of the most powerful uses of genomic selection is to select for economically important, yet difficult- or expensive-to-measure traits, such as residual feed intake or energy balance. Provided the accuracy of genomic breeding values is high enough (i.e. correlation between the true and estimated breeding values), these traits lend themselves well to genomic selection. For selecting replacement heifers, if genotyping costs are AU$50/cow, the net profit of genotyping 40 heifers to select the top 20 as replacements (per 100 cows) would be worth approximately AU$41 per cow. However, using parent average estimated breeding-value information is free and can already be used to select replacement heifers. So, genotyping costs would need to be very low to be more profitable than selecting on parent average estimated breeding value. However, extra value from genotyping can also be captured by using other strategies. For example, mating plans that use genomic relationships rather than pedigree relationships to capture inbreeding are superior in terms of reducing progeny inbreeding at a desired level of genetic gain, although pedigree does an adequate job. So, again, the benefits of genotyping are small (<AU$10). Ascertainment of pedigree is an additional use of genotyping and is potentially worth ~AU$30 per cow. Avoidance of genetic diseases and selling of pedigree heifers have a value that should be estimated case-by-case. Because genotyping costs continue to fall, it may become increasingly popular to capture the extra value from genotyping females.

Selection for postweaning growth in inbred Hereford cattle: the Fort Keogh, Montana line 1 example.

Demographic characteristics and genetic trends in birth weight and pre- and postweaning ADG were examined in a population of Hereford cattle (Line 1). Line 1 was founded largely from two paternal half-sib sires and has been selected for postweaning growth. There were pedigree records on 951 members of the base population that predated 1935, when data collection began. Numbers of records analyzed using mixed-model methodology were 4,716 birth weight, 4,427 preweaning ADG, and 3,579 postweaning ADG. Birth weight and preweaning ADG were considered to have direct and maternal genetic components. Inbreeding accumulated rapidly from 1935 to 1960 and more slowly (.22%/yr) thereafter. Any reduction in additive genetic variance due to inbreeding and selection may have been offset by a concurrent reduction in generation interval that was observed as time progressed. Expected selection differential for 365-d weight, averaged over sexes, was 31.2 kg per generation. For birth weight, annual genetic trends in direct and maternal effects were 42 +/- 3 g and 15 +/- 3 g, respectively. Annual direct and maternal genetic trends for preweaning ADG were .70 +/- .06 g/d and .63 +/- .06 g/d, respectively. Direct response in postweaning ADG was linear and equal to 5.3 +/- .6 g.d-1.yr-1. As a result, estimated breeding values of birth weight, 200-d weight, and 365-d weight increased by 3.2 kg, 14.5 kg, and 62.4 kg, respectively, from 1935 to 1989. Selection within Line 1 was effective in increasing genetic potential for growth over 13 generations. No selection plateau was observed in any of the traits examined.