Dissecting genetic trends to understand breeding practices in livestock: a maternal pig line example

Background Understanding whether genomic selection has been effective in livestock and when the results of genomic selection became visible are essential questions which we have addressed in this paper. Three criteria were used to identify practices of breeding programs over time: (1) the point of divergence of estimated genetic trends based on pedigree-based best linear unbiased prediction (BLUP) versus single-step genomic BLUP (ssGBLUP), (2) the point of divergence of realized Mendelian sampling (RMS) trends based on BLUP and ssGBLUP, and (3) the partition of genetic trends into that contributed by genotyped and non-genotyped individuals and by males and females. Methods We used data on 282,035 animals from a commercial maternal line of pigs, of which 32,856 were genotyped for 36,612 single nucleotide polymorphisms (SNPs) after quality control. Phenotypic data included 228,427, 101,225, and 11,444 records for birth weight, average daily gain in the nursery, and feed intake, respectively. Breeding values were predicted in a multiple-trait framework using BLUP and ssGBLUP. Results The points of divergence of the genetic and RMS trends estimated by BLUP and ssGBLUP indicated that genomic selection effectively started in 2019. Partitioning the overall genetic trends into that for genotyped and non-genotyped individuals revealed that the contribution of genotyped animals to the overall genetic trend increased rapidly from ~ 74% in 2016 to 90% in 2019. The contribution of the female pathway to the genetic trend also increased since genomic selection was implemented in this pig population, which reflects the changes in the genotyping strategy in recent years. Conclusions Our results show that an assessment of breeding program practices can be done based on the point of divergence of genetic and RMS trends between BLUP and ssGBLUP and based on the partitioning of the genetic trend into contributions from different selection pathways. However, it should be noted that genetic trends can diverge before the onset of genomic selection if superior animals are genotyped retroactively. For the pig population example, the results showed that genomic selection was effective in this population.

35 Detecting Effective Starting Point of Genomic Selection by Divergent Trends from BLUP and ssGBLUP

Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree BLUP became biased because this method does not consider selection using genomic information. Hence, the point of divergence of genetic trends obtained with BLUP and Single-step genomic BLUP (ssGBLUP) may indicate the start date of genomic selection. This study aimed to find the start date of genomic selection for a set of economically important traits in pigs and Angus cattle by comparing genetic and Realized Mendelian sampling (RMS) trends obtained using BLUP and ssGBLUP. In pigs, the genetic trends from ssGBLUP and BLUP diverged for all traits with different intensity. In beef cattle, the trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with little divergence for birth weight. In pigs, the RMS trends from ssGBLUP and BLUP diverged strongly for one trait and weakly for another trait. In beef cattle, the RMS trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with little trend for birth weight. Divergence of the genetic trends from ssGBLUP and BLUP indicate onset of the genomic selection. Presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agree with industry practices across the two species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.

The Environmental Impact of Changes in Cow Productivity and Its Component Traits in South Africa's Landrace Beef Breeds

Any reduction in the carbon footprint of beef production will contribute to future sustainability. This study investigates the environmental impact (carbon footprint) of the changes that occurred in cow productivity in the cowherds of four South African landrace breeds over a period of 25–30 years. Cow productivity, defined as kilogram calf weaned per large stock unit mated, increased by 18.3, 10.0, 14.2, and 10.4% in the Afrikaner, Bonsmara, Drakensberger, and Nguni, respectively. This resulted in a decrease in the carbon footprint, as defined by the enteric methane emissions factor, of between 6.6 and 12.0%. Changes in the cow productivity component traits, viz. weaning weight, cow weight, and fertility (as measured by inter-calving period) was also investigated. In all breeds, except the Nguni, the change in the environmental trends were less than that of the direct genetic or phenotypic trends. The genetic trends for direct weaning weight in the Afrikaner, Bonsmara, and Drakensberger were +6.7, +11.7, and +6.2 kg, respectively. In the case of the Bonsmara and Drakensberger breeds, the genetic trends were larger than the phenotypic trends, which may indicate that the environment cannot support the higher “genetic” weaning weights. The genetic trends for mature cow weight in Bonsmara and Drakensberger were +15.9 and +15.1 kg, respectively, whereas in the Afrikaner and Nguni it was not significantly different from zero (R2 ≤ 0.24). The trends in the phenotypic mature cow weights were −8.3, +17.5, +8.5, and −17.2 kg for in the Afrikaner, Bonsmara, Drakensberger, and Nguni, respectively. Although the observed inter-calving period of all breeds declined, there was no genetic change in inter-calving period for any of the breeds. It is proposed that selection indices are developed for cow productivity, which can be extended to a carbon footprint selection index. It is recommended that similar studies be done on all the major beef breeds in South Africa, especially those that rely on regular importation of genetic material.

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines

BackgroundEstimation of genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI’s rice drought breeding program was used to estimate the genetic trends and assess the success of the breeding program. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes.ResultsA two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20-0.94 under the drought trials, and 0.43-0.83 under non-stress trials. Under non-stress conditions the genetic gain of 0.44% (21.20 kg/ha/year) for genotypes and 0.17 % (7.90 kg/ha/year) for checks was observed. The genetic trend under the drought conditions exhibited a positive trend with the genetic gain of 0.11% (1.98kg/ha/year) for genotypes and 0.55% (9.52kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.39% (12.13 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of >0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha).ConclusionsA positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI’s drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains.

Correlated Response on Growth Traits and Their Variabilities to Selection for Ovulation Rate in Rabbits Using Genetic Trends and a Cryopreserved Control Population

The aim of this work was to estimate correlated responses in growth traits and their variabilities in an experiment of selection for ovulation rate during 10 generations in rabbits. Individual weight at 28 days old (IW28, kg) and at 63 days old (IW63, kg) was analyzed, as well as individual growth rate (IGR = IW63 − IW28, kg). The variability of each growth trait was calculated as the absolute value of the difference between the individual value and the mean value of their litter. Data were analyzed using Bayesian methodology. The estimated heritabilities of IW28, IW63 and IGR were low, whereas negligible heritabilities were obtained for growth variability traits. The common litter effect was high for all growth traits, around 30% of the phenotypic variance, whereas low maternal effect for all growth traits was obtained. Low genetic correlations between ovulation rate and growth traits were found, and also between ovulation rate and the variability of growth traits. Therefore, genetic trends methods did not show correlated responses in growth traits. A similar result was also obtained using a cryopreserved control population.

Detecting effective starting point of genomic selection by divergent trends from BLUP and ssGBLUP in pigs, beef cattle, and broilers

Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree BLUP became biased because this method does not consider selection using genomic information. Hence, the effective starting point of genomic selection can be detected in two possible ways including the divergence of genetic trends and Realized Mendelian sampling (RMS) trends obtained with BLUP and Single-step genomic BLUP (ssGBLUP). This study aimed to find the start date of genomic selection for a set of economically important traits in three livestock species by comparing trends obtained using BLUP and ssGBLUP. Three datasets were used for this purpose: a pig dataset with 117k genotypes and 1.3M animals in pedigree, an Angus cattle dataset consisted of ~842k genotypes and 11.5M animals in pedigree, and a purebred broiler chicken dataset included ~154k genotypes and 1.3M birds in pedigree were used. The genetic trends for pigs diverged for the genotyped animals born in 2014 for average daily gain and backfat. In beef cattle, the trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with little divergence for birth weight. In broiler chickens, the genetic trends estimated by ssGBLUP and BLUP diverged at breeding cycle 6 for two out of three production traits. The RMS trends for the genotyped pigs diverged for animals born in 2014, more for average daily gain than for backfat. In beef cattle, the RMS trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with a trivial trend for birth weight. In broiler chickens, the RMS trends from ssGBLUP and BLUP diverged strongly for two production traits at breeding cycle 6, with a slight divergence for another trait. Divergence of the genetic trends from ssGBLUP and BLUP indicates onset of the genomic selection. The presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agree with industry practices across the three species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.

Genetic parameters and genetic trends of conformation and management traits in Dairy Gir cattle

ABSTRACT The objective of this study was to estimate genetic parameters and genetic trends of different conformation and management traits regularly measured within the context of the National Dairy Gir Breeding Program (PNMGL). The estimation of genetic and residual variances for each trait was performed using average information restricted maximum likelihood (AI-REML) procedure in AIREMLF90 program software. The population was divided into three subpopulations constituted by measured females (with phenotype records), all females, and males. Linear regressions were applied for each trait, considering two periods of birth (1st period: 1938-1996; 2nd period: 1997-2012). The estimated heritability of conformation and management traits varied from 0.01 to 0.53, denoting a perspective of genetic improvement through selection and corrective matings for purebred Dairy Gir populations. The average genetic changes in conformation and management traits were, in general, variable and inexpressive, showing that the selection of Dairy Gir may have had been directed essentially to increase milk yield. The analysis of the two periods of birth indicated that some linear traits present progress (although inexpressive) in the 2nd period (more recent period).

Genetic trends in Fusarium head blight resistance due to 20 years of winter wheat breeding and cooperative testing in the Northern US.

Fusarium head blight (FHB) is a devastating disease of wheat and barley. In the US, a significant long-term investment in breeding FHB resistant cultivars began after the 1990s. However, to this date, no study has been performed to understand and monitor the rate of genetic progress in FHB resistance as a result of this investment. Using 20 years of data (1998 to 2018) from the Northern Uniform (NU) and Preliminarily Northern Uniform (PNU) winter wheat scab nurseries which consisted of 1068 genotypes originating from 9 different institutions, we studied the genetic trends in FHB resistance within the northern soft red winter wheat growing region using mixed model analyses. For the FHB resistance traits incidence, severity, Fusarium damaged kernels (FDK), and deoxynivalenol content, the rate of genetic gain in disease resistance was estimated to be 0.30 ± 0.1, 0.60 ± 0.09, 0.37 ± 0.11 points per year, and 0.11 ± 0.05 ppm per year, respectively. Among the five FHB resistance QTL assayed for test entries from 2012 to 2018, the frequencies of favorable alleles from Fhb 2DL Wuhan1 W14, Fhb Ernie 3Bc, and Fhb 5A Ning7840 was close to zero across the years. The frequency of the favorable at Fhb1 and Fhb 5A Ernie ranged from 0.08 to 0.33 and 0.06 to 0.20 respectively across years, and there was no trend in changes in allele frequencies over years. Overall, this study showed that substantial genetic progress has been made towards improving resistance to FHB. It is apparent that the current investment in public wheat breeding for FHB resistance is achieving results and will continue to play a vital role in reducing FHB levels in growers’ fields.