Genetic Parameters Of Traits Research Articles

Estimation of genetic parameters of pig reproductive traits.

In this study, we aimed to estimate the genetic parameters of the reproductive traits in three popular commercial pig breeds: Duroc, Landrace, and Yorkshire. Additionally, we evaluated the factors that influence these traits. We collected data from a large number of litters, including 1,887 Duroc, 21,787 Landrace, and 74,796 Yorkshire litters. Using the ASReml-R software to analyze 11 traits, which included: total number of pigs born (TNB); number of piglets born alive (NBA); number of piglets born healthy (NBH); number of piglets born weak (NBW); number of new stillborn piglets (NS); number of old stillborn piglets (OS); number of piglets born with malformation (NBM); number of mummified piglets (NM); total litter birthweight (LBW); litter average weight (LAW); duration of gestational period (GP). We investigated the effects of 4 fixed factors on the genetic parameters of these traits. Among the 11 reproductive-related traits, the gestational period belonged to the medium heritability traits (0.251-0.430), while remaining traits showed low heritability, ranging from 0.005 to 0.159. TNB, NBA, NBH, LBW had positive genetic correlation (0.737 ~ 0.981) and phenotype correlation (0.711 ~ 0.951). There was a negative genetic correlation between NBW and LAW (-0.452 ~ -0.978) and phenotypic correlation (-0.380 ~ -0.873). LBW was considered one of the most reasonable reproductive traits that could be used for breeding improvement. Repeatability of the three varieties was within the range of 0.000-0.097. In addition, the fixed effect selected in this study had a significant effect on Landrace and Yorkshire (p < 0.05). We found a positive correlation between LBW and TNB, NBA, and NBH, suggesting the potential for multi-trait association breeding. Factors such as farm, farrowing year, breeding season, and parity should be taken into consideration in practical production, as they may impact the reproductive performance of breeding pigs.

Genetic progress evaluation of growth traits in Moghani sheep

For evaluation of growth traits' genetic progress, pedigree information of 39408 Moghani sheep, between 1986 to 2016, at the breeding station of Moghani sheep was used. Studied traits included birth weight, weaning weight, six months weight, nine months weight, and one yearling weight. Variance components and genetic parameters of traits were estimated by restricted likelihood and six animal models using Wombat software. Direct heritability of birth weight, weaning weight, six months weight, nine months weight, and one yearling weight was (0.07, 0.09, 0.15, 0.13, and 0.20 respectively). Breeding values for the calculation of genetic trends for each trait were estimated by using the univariate best animal model. Phenotypic, genetic, and environmental trends of traits were estimated via regression of phenotypic average, breeding value average, and difference of breeding value from phenotypic value divided by birth year. The calculated genetic trends of birth weight, weaning weight, six months weight, nine months weight, and yearling weight were 0.008, 0.012, 0.054, 0.74, and 0.145 kg/year respectively, except for birth weight, the others were significant (p&lt;0.01). The genetic gain of traits including birth weight, weaning weight, six months weight, nine months weight, and one yearling weight was estimated (0.213, 1.071, 1.171, 1.164, and 1.324 kg respectively). At evaluated years, many fluctuations were observed in the genetic trend of traits. In other words, it seems at the studied years, there wasn't a comprehensive plan for genetic breeding and improvement of body weight traits in Moghani sheep.

Using a linear-threshold model to investigate the genetic relationship between survival and productive traits in Japanese quail

The main goal of breeding programmes is to maximise the genetic improvement of the economic traits of farm animals. Beside the economic traits, the birds’ survival has directly influenced on the economic gain. Therefore, this study aimed to investigate the genetic relationship between survival and productive traits in Japanese quail. A total of 1854 records were collected during four generations from 2017 to 2019 in Khorasan Razavi Agricultural Research and Training Centre (KRARTC). Data were used to estimate the genetic correlations among quail survival (S) with egg weight (EW) and body weight gain (BWG) traits. Linear-threshold model was used to estimate (co)variance components, and genetic parameters of traits via the Gibbs sampling method. The heritability of EW was 0.04, and the heritability estimates for survival at different ages ranged from 0.138 to 0.399 in joint analysis with EW. The highest negative genetic correlation between EW and S0–14 (i.e. survival up to 14 d) was obtained as −0.704. The highest and lowest heritability of weight gain traits for BWG0–7 and BWG7–14 were 0.583 and 0.116, respectively. The analyses of BWG traits with survival at different ages revealed that the genetic correlations ranged from 0.015 (between BWG0–7 and S0–14) to 0.638 (between BWG0–7 and S0–7). This study showed that the genetic selection for survival at different ages could be effective. On the other hand, selection for BWG0–7 could also indirectly improve the survival in quails because of the positive and high genetic correlation between BWG0–7 with S0–7. Highlights Genetic improvement of production traits in quail may lead to reduced survival in quail. Studies regarding on the survival of quail were yet not carried out. Heritability of the survival at different ages showed that genetic selection for this trait could improve the survival. Survival at different ages had a negative genetic correlation with egg weight (EW), and body weight gain (BWG) traits after 21 d of old.

Genetic analyses of hunting traits in leashed and non-leashed test systems in Norwegian Elkhound

ABSTRACTNorwegian Elkhound Grey is used for both leashed and non-leashed tracking. This study provides influence of fixed effects and estimates of genetic parameters of traits related to both types of tracks. In total, 5393 records from 1991 animals tested in the years 1995–2014 on lashed performance (5 traits), and 5405 records from 2070 animals tested in the years 2005–2014 on non-leashed performance (10 traits) were included in the dataset. The pedigree file contained 8623 animals. Heritabilities were generally low for the non-leashed traits and a bit higher for the leashed traits. Genetic correlations within trait group were very highly positive for the leashed dog traits, but variable among some traits in the non-leashed group. Genetic correlations between performance traits in the two groups were generally negative and unfavourable. Genetic trends were generally positive for the time periods.

Genetic parameters of traits at the juvenile stage of different assai palm tree progenies

Abstract: The objective of this work was to estimate genetic parameters of traits at the juvenile stage of different assai palm (Euterpe oleracea) tree progenies, as well as to select among and within the most promising for fruit production. A total of 34 half-sib and 16 full-sib progenies were evaluated in a completely randomized design with eight replicates and one plant per plot. Nine traits were measured in five harvests, and the data were analyzed using the mixed model approach. The genetic variance was significant for most of the traits. Progeny-mean heritabilities showed moderate magnitudes, ranging from 51% for number of dead leaves to 59% for leaf sheath length. In general, the genetic correlations were positive and had magnitudes varying from moderate to very high. The genetic gains were more expressive for the traits plant height, leaf sheath length, and girth circumference. The full-sib progenies P33, P37, and P42 are promising for fruit production.

Joint analysis of longitudinal feed intake and single recorded production traits in pigs using a novel Horizontal model1

A novel Horizontal model is presented for multitrait analysis of longitudinal traits through random regression analysis combined with single recorded traits. Weekly ADFI on test for Danish Duroc, Landrace, and Yorkshire boars were available from the national test station and were collected from 30 to 100 kg BW. Single recorded production traits of ADG from birth to 30 kg BW (ADG30), ADG from 30 to 100 kg BW (ADG100), and lean meat percentage (LMP) were available from breeding herds or the national test station. The Horizontal model combined random regression analysis of feed intake (FI) with single recorded traits of ADG100, LMP, and ADG30. In the Horizontal model, the FI data were horizontally structured with FI on each week as a "trait." The additive genetic and litter effects were modeled to be common across different FI records by reducing the rank of the covariance matrices using second- and first-order Legendre polynomials of age on test, respectively. The fixed effect and random residual variance were estimated for each weekly FI trait. Residual feed intake (RFI) was derived from the conditional distribution of FI given the breeding values of ADG100 and LMP. The heritability of FI varied by week on test in Duroc (0.12 to 0.19), Landrace (0.13 to 0.22), and Yorkshire (0.21 to 0.23). The heritability of RFI was lowest and highest in wk 6 (0.03) and 10 (0.10), respectively, in Duroc and wk 7 (0.04 and 0.02) and 1 (0.09 and 0.20), respectively, in Landrace and Yorkshire. The proportion of FI genetic variance explained by RFI ranged from 20 to 75% in Duroc, from 19 to 75% in Landrace, and from 11 to 91% in Yorkshire. Average daily gain from 30 to 100 kg BW and ADG30 heritabilities were moderate in Duroc (0.24 and 0.22, respectively), Landrace (0.34 and 0.25, respectively), and Yorkshire (0.34 and 0.22, respectively). Lean meat percentage heritability was moderate in Duroc (0.37) and large in Landrace (0.62) and Yorkshire (0.60). The genetic correlation of FI with ADG100 increased by week on test followed by a 32% decrease from wk 7 in Duroc and a 7% decrease in dam line breeds. Defining RFI as genetically independent of production traits leads to consistent and easy interpretable breeding values. The genetic parameters of traits in the feed efficiency complex and their dynamics over the test period showed breed differences that could be related to the fatness and growth potential of the breeds. The Horizontal model can be used to simultaneously analyze repeated and single recorded traits through proper modeling of the environmental variances and covariances.

Genetic analysis of reproductive traits and antibody response in a PRRS outbreak herd1

Porcine reproductive and respiratory syndrome (PRRS) is the most economically significant disease impacting pig production in North America, Europe, and Asia, causing reproductive losses such as increased rates of stillbirth and mummified piglets. The objective of this study was to explore the genetic basis of host response to the PRRS virus (PRRSV) in a commercial multiplier sow herd before and after a PRRS outbreak, using antibody response and reproductive traits. Reproductive data comprising number born alive (NBA), number alive at 24 h (NA24), number stillborn (NSB), number born mummified (NBM), proportion born dead (PBD), number born dead (NBD), number weaned (NW), and number of mortalities through weaning (MW) of 5,227 litters from 1,967 purebred Landrace sows were used along with a pedigree comprising 2,995 pigs. The PRRS outbreak date was estimated from rolling averages of farrowing traits and was used to split the data into a pre-PRRS phase and a PRRS phase. All 641 sows in the herd during the outbreak were blood sampled 46 d after the estimated outbreak date and were tested for anti-PRRSV IgG using ELISA (sample-to-positive [S/P] ratio). Genetic parameters of traits were estimated separately for the pre-PRRS and PRRS phase data sets. Sows were genotyped using the PorcineSNP60 BeadChip, and genome-wide association studies (GWAS) were performed using method Bayes B. Heritability estimates for reproductive traits ranged from 0.01 (NBM) to 0.12 (NSB) and from 0.01 (MW) to 0.12 (NBD) for the pre-PRRS and PRRS phases, respectively. S/P ratio had heritability (0.45) and strong genetic correlations with most traits, ranging from -0.72 (NBM) to 0.73 (NBA). In the pre-PRRS phase, regions associated with NSB and PBD explained 1.6% and 3% of the genetic variance, respectively. In the PRRS phase, regions associated with NBD, NSB, and S/P ratio explained 0.8%, 11%, and 50.6% of the genetic variance, respectively. For S/P ratio, 2 regions on SSC 7 (SSC7) separated by 100 Mb explained 40% of the genetic variation, including a region encompassing the major histocompatibility complex, which explained 25% of the genetic variance. These results indicate a significant genomic component associated with PRRSV antibody response and NSB in this data set. Also, the high heritability and genetic correlation estimates for S/P ratio during the PRRS phase suggest that S/P ratio could be used as an indicator of the impact of PRRS on reproductive traits.

Estimates of genetic parameters in peanut lines based on peg-linked traits

Genetic parameters of traits related to peanut pegs were assessed from F 2 lines carried out on two environments, in Pernambuco state - Brazil . Ninety lines and ancestors were evaluated in completely randomized block based on these traits: total number of pegs, pegs located at lower third of the plant, main axis height and number of pods per plant. Reproductive efficiency was also estimated based on number of pods per plant (NVP)/total number of pegs (NGT) and number of pods per plant (NVP)/pegs located at lower third of the plant (NGTI) rations. Variance components were estimated by Restricted Maximum Likelihood Method (REML) and the genotypic values were predicted by Best Linear Unbiased Prediction (BLUP). The heritability estimates were low and median for all traits. It was found that selection based on these traits has limited benefits on initial breeding. Genetic correlations were found between total number of pegs (NGT) x number of pods per plant (NVP) and pegs located at lower third of the plant (NGTI) x number of pods per plant (NVP). The traits total number of pegs (NGT) and pegs located at lower third of the plant (NGTI) could be used on further selection procedures focusing on pod productions.