Heritability Of Meat Quality Traits Research Articles

Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs

BackgroundPork quality can directly affect customer purchase tendency and meat quality traits have become valuable in modern pork production. However, genetic improvement has been slow due to high phenotyping costs. In this study, whole genome sequence (WGS) data was used to evaluate the prediction accuracy of genomic best linear unbiased prediction (GBLUP) for meat quality in large-scale crossbred commercial pigs.ResultsWe produced WGS data (18,695,907 SNPs and 2,106,902 INDELs exceed quality control) from 1,469 sequenced Duroc × (Landrace × Yorkshire) pigs and developed a reference panel for meat quality including meat color score, marbling score, L* (lightness), a* (redness), and b* (yellowness) of genomic prediction. The prediction accuracy was defined as the Pearson correlation coefficient between adjusted phenotypes and genomic estimated breeding values in the validation population. Using different marker density panels derived from WGS data, accuracy differed substantially among meat quality traits, varied from 0.08 to 0.47. Results showed that MultiBLUP outperform GBLUP and yielded accuracy increases ranging from 17.39% to 75%. We optimized the marker density and found medium- and high-density marker panels are beneficial for the estimation of heritability for meat quality. Moreover, we conducted genotype imputation from 50K chip to WGS level in the same population and found average concordance rate to exceed 95% and r2 = 0.81.ConclusionsOverall, estimation of heritability for meat quality traits can benefit from the use of WGS data. This study showed the superiority of using WGS data to genetically improve pork quality in genomic prediction.

41 Estimating the heritability of meat quality traits in turkeys

Abstract Genetic selection for improved meat quality traits has been successfully implemented in many livestock species. The objective of this study was to estimate the heritability of several meat quality traits to assess their selection potential in turkeys. Pedigree toms (n = 1,033) were processed at a commercial facility and live weight, breast meat yield (as a percentage of live weight), ultimate pH, color (CIELAB values), drip loss, cooking loss, and shear force were recorded on M. pectoralis superficialis (fillet). White striping was also rated on a 1–4 scale. Heritabilities were estimated using univariate animal models in ASReml version 4.1. Hatch week and age at slaughter were included as fixed effects in the mode,l and 32 generations of pedigree records were used. Breast meat yield (h2 = 0.62; SE = 0.090) showed the highest heritability and was higher compared to previous estimates, probably due to the smaller sample size. Live weight (h2 = 0.31; SE = 0.078), ultimate pH (h2 = 0.36; SE = 0.087), lightness (h2 = 0.28; SE = 0.086), redness (h2 = 0.22; SE = 0.075), and white striping score (h2 = 0.27; SE = 0.085) all had moderate heritabilities. The estimate for ultimate pH was similar to previous studies in broilers but was high compared to previous studies in turkeys. Estimates for color were similar to those found in previous studies involving turkeys. Drip loss and cooking loss had similar heritability estimates of 0.13 (SE = 0.071) and 0.10 (SE = 0.064), respectively, which were akin to previous estimates in pork. Shear force (h2 = 0.02; SE = 0.056) was found to have a very low heritability. In conclusion, the heritability estimates provided in this study show great potential for the inclusion of meat quality traits in selection programs of turkeys. This study is part of a larger project working towards the implementation of genomic information in the selection of turkeys for improved meat quality.

Microbiability of meat quality and carcass composition traits in swine.

The impact of gut microbiome composition was investigated at different stages of production (weaning, Mid‐test and Off‐test) on meat quality and carcass composition traits of 1,123 three‐way crossbred pigs. Data were analysed using linear mixed models which included the fixed effects of dam line, contemporary group and gender as well as the random effects of pen, animal and microbiome information at different stages. The contribution of the microbiome to all traits was prominent although it varied over time, increasing from weaning to Off‐test for most traits. Microbiability estimates of carcass composition traits were greater than that of meat quality traits. Among all of the traits analysed, belly weight (BEL) had a higher microbiability estimate (0.29 ± 0.04). Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but affected the estimates of carcass composition traits. Fat depth had a greater decrease (10%) in genomic heritability at Off‐test. High microbial correlations were found among different traits, particularly with traits related to fat deposition with a decrease in the genomic correlation up to 20% for loin weight and BEL. This suggested that genomic correlation was partially contributed by genetic similarity of microbiome composition. The results indicated that better understanding of microbial composition could aid the improvement of complex traits, particularly the carcass composition traits in swine by inclusion of microbiome information in the genetic evaluation process.

Genetic analysis of carcass and meat quality traits in Nelore cattle1.

The objective of this study was to estimate genetic parameters for carcass and meat quality traits, as well as their genetic correlations using pedigree and genomic information. A total of 3,716; 3,702; 3,439; 3,705; and 3,714 records of 12th-13th rib LM area (LMA), backfat thickness (BF), HCW, marbling score (MARB), and Warner-Bratzler peak shear force (WBSF), respectively, were used. Animals were genotyped with BovineHD BeadChip and GeneSeek Genomic Profiler Indicus HD - GGP75Ki panel. The (co)variance components were estimated by Bayesian inference using a multitrait ssGBLUP analysis. The animal model included fixed effects of contemporary group (defined by the combination of farm and year of birth, and management group at yearling) and age of animal at slaughtering as a covariate (linear). Direct additive genetic and residual effects were fitted as random. The posterior means and SD of heritabilities for LMA, BF, HCW, MARB, and WBSF were 0.28 (0.03), 0.21 (0.04), 0.21 (0.04), 0.12 (0.04), and 0.11 (0.03), respectively. The posterior means for genetic correlations between LMA and meat quality were positive and moderate with MARB (0.38 ± 0.12) and negative with WBSF (-0.47 ± 0.12). Low genetic correlations were estimated between BF and WBSF (-0.03 ± 0.16) and between HCW and MARB (-0.04 ± 0.14), indicating that these traits are not controlled by the same set or linked genes. Carcass traits (LMA, BF, and HCW) presented moderate heritability providing quick response to the selection purpose. The estimates of heritability for meat quality traits (MARB and WBSF) were low and indicate that the rate of genetic improvement for these traits would be slow. Genetic correlations indicated that selection for carcass traits would not be strongly antagonistic for improving meat quality.

Genetic Parameters of Reproductive and Meat Quality Traits in Korean Berkshire Pigs

Genetic parameters of Berkshire pigs for reproduction, carcass and meat quality traits were estimated using the records from a breeding farm in Korea. For reproduction traits, 2,457 records of the total number of piglets born (TNB) and the number of piglets born alive (NBA) from 781 sows and 53 sires were used. For two carcass traits which are carcass weight (CW) and backfat thickness (BF) and for 10 meat quality traits which are pH value after 45 minutes (pH45m), pH value after 24 hours (pH24h), lightness in meat color (LMC), redness in meat color (RMC), yellowness in meat color (YMC), moisture holding capacity (MHC), drip loss (DL), cooking loss (CL), fat content (FC), and shear force value (SH), 1,942 pig records were used to estimate genetic parameters. The genetic parameters for each trait were estimated using VCE program with animal model. Heritability estimates for reproduction traits TNB and NBA were 0.07 and 0.06, respectively, for carcass traits CW and BF were 0.37 and 0.57, respectively and for meat traits pH45m, pH24h, LMC, RMC, YMC, MHC, DL, CL, FC, and SH were 0.48, 0.15, 0.19, 0.36, 0.28, 0.21, 0.33, 0.45, 0.43, and 0.39, respectively. The estimate for genetic correlation coefficient between CW and BF was 0.27. The Genetic correlation between pH24h and meat color traits were in the range of −0.51 to −0.33 and between pH24h and DL and SH were −0.41 and −0.32, respectively. The estimates for genetic correlation coefficients between reproductive and meat quality traits were very low or zero. However, the estimates for genetic correlation coefficients between reproductive traits and drip and cooking loss were in the range of 0.12 to 0.17 and −0.14 to −0.12, respectively. As the estimated heritability of meat quality traits showed medium to high heritability, these traits may be applicable for the genetic improvement by continuous measurement. However, since some of the meat quality traits showed negative genetic correlations with carcass traits, an appropriate breeding scheme is required that carefully considers the complexity of genetic parameters and applicability of data.

Screening 52 single nucleotide polymorphisms for extreme value of glycolytic potential and drip loss in pigs

Summary Low heritability of meat quality traits and the lack of their systematic registration in breeding programs have encouraged the search for single nucleotide polymorphisms (SNPs) located within genes coding the proteins involved in muscle and fat metabolism. In this report, a panel of 52 SNPs was used to find which alleles and genotypes are more/less frequent in groups of pigs differentiated by extreme value of glycolytic potential (GP) and drip loss (DL). The analysis was carried out in 52 fatteners (chosen from 246 pigs), of which 28 were Landrace and 27 Landrace x Yorkshire. Two designs were performed: I, fatteners were divided into two groups showing extreme value of GP (<125 versus >145), II, fatteners were divided into two groups showing extreme value of DL (<6.0 versus >6.0). Allele frequency differences between the phenotypic groups of extreme GL or DL were not influenced by the breed. The frequency of 52 SNPs alleles for each of group was calculated and a chi-squared test was used to estimate the significance of differences in allele frequencies between alternative groups in each experimental design. Three SNPs (DECR1, PPARGC1, MC4R) and another two (CYP21, SFRS1) showed significant differences between groups of extreme GP and DL, respectively. To exemplify and validate potential associations of candidate SNPs for GP and DL, 293 fatteners representing three commercial breeds/crosses (95 Landrace, 66 Landrace x Yorkshire and 132 Landrace x Yorkshire x Duroc were genotyped for DECR1 and CYP21 by PCR-RFLP assays. DECR1 showed significant associations with GP in Landrace and Landrace x Yorkshire x Duroc fatteners. CYP21 showed significant associations with DL in all breeds/crosses. Interestingly, the CYP21 polymorphism revealed adverse associations trend in Landrace x Yorkshire x Duroc pigs in comparison to Landrace and Landrace x Yorkshire fatteners.

Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian pigs: 1. Description of traits and heritability estimates

Data from 1799 Large White and 1522 Landrace boars were used to obtain heritability and litter effect estimates for performance, carcass and meat quality traits applying a REML procedure fitting an animal model. Analysed traits included average daily gain from three to 18 weeks of age (ADG1) and average daily gain, feed intake and feed conversion ratio from 18 to 22 weeks of age. Carcass traits included two backfat measurements and one muscle depth measurement taken on the live animal using realtime ultrasound and on the carcass using Hennesy Chong equipment. Further carcass traits included weight of the whole back leg (BLW) and weight of the ham (LMW). Meat quality traits included pH recorded 45 min and 24 h after slaughter, colour of the m. longissimus dorsi and m. multifidus dorsi, drip loss percentage and intramuscular fat content. For ADG1 heritability and litter effect estimates were 0.10(±0.05) and 0.20(±0.04) in Large White and 0.48(±0.09) and 0.08(±0.04) in Landrace. Data structure might have limited simultaneous estimation of these two effects. Heritability estimates ranged from 0.13 to 0.27(±0.04 to 0.05) for further performance traits and from 0.45 to 0.62(±0.05 to 0.07) for backfat measurements. Heritability estimates were lower for Hennesy Chong measurements in comparison to realtime ultrasound measurements. Heritability estimates for BLW and LMW were 0.22(±0.05) and 0.38(±0.07). Estimates of heritabilities for meat quality traits ranged from 0.14 to 0.35(±0.04 to 0.06).