Thanathip Suwanasopee

Variance Components and Genetic Parameters for Milk Production and Lactation Pattern in an Ethiopian Multibreed Dairy Cattle Population

The objective of this study was to estimate variance components and genetic parameters for lactation milk yield (LY), lactation length (LL), average milk yield per day (YD), initial milk yield (IY), peak milk yield (PY), days to peak (DP) and parameters (ln(a) and c) of the modified incomplete gamma function (MIG) in an Ethiopian multibreed dairy cattle population. The dataset was composed of 5,507 lactation records collected from 1,639 cows in three locations (Bako, Debre Zeit and Holetta) in Ethiopia from 1977 to 2010. Parameters for MIG were obtained from regression analysis of monthly test-day milk data on days in milk. The cows were purebred (Bos indicus) Boran (B) and Horro (H) and their crosses with different fractions of Friesian (F), Jersey (J) and Simmental (S). There were 23 breed groups (B, H, and their crossbreds with F, J, and S) in the population. Fixed and mixed models were used to analyse the data. The fixed model considered herd-year-season, parity and breed group as fixed effects, and residual as random. The single and two-traits mixed animal repeatability models, considered the fixed effects of herd-year-season and parity subclasses, breed as a function of cow H, F, J, and S breed fractions and general heterosis as a function of heterozygosity, and the random additive animal, permanent environment, and residual effects. For the analysis of LY, LL was added as a fixed covariate to all models. Variance components and genetic parameters were estimated using average information restricted maximum likelihood procedures. The results indicated that all traits were affected (p<0.001) by the considered fixed effects. High grade B×F cows (3/16B 13/16F) had the highest least squares means (LSM) for LY (2,490±178.9 kg), IY (10.5±0.8 kg), PY (12.7±0.9 kg), YD (7.6±0.55 kg) and LL (361.4±31.2 d), while B cows had the lowest LSM values for these traits. The LSM of LY, IY, YD, and PY tended to increase from the first to the fifth parity. Single-trait analyses yielded low heritability (0.03±0.03 and 0.08±0.02) and repeatability (0.14±0.01 to 0.24±0.02) estimates for LL, DP and parameter c. Medium heritability (0.21±0.03 to 0.33±0.04) and repeatability (0.27±0.02 to 0.53±0.01) estimates were obtained for LY, IY, PY, YD and ln(a). Genetic correlations between LY, IY, PY, YD, ln(a), and LL ranged from 0.59 to 0.99. Spearman’s rank correlations between sire estimated breeding values for LY, LL, IY, PY, YD, ln(a) and c were positive (0.67 to 0.99, p<0.001). These results suggested that selection for IY, PY, YD, or LY would genetically improve lactation milk yield in this Ethiopian dairy cattle population.

Somatic Cells Count and Its Genetic Association with Milk Yield in Dairy Cattle Raised under Thai Tropical Environmental Conditions

Somatic cells count (SCC), milk yield (MY) and pedigree information of 2,791 first lactation cows that calved between 1990 and 2010 on 259 Thai farms were used to estimate genetic parameters and trends for SCC and its genetic association with MY. The SCC were log-transformed (lnSCC) to make them normally distributed. An average information-restricted maximum likelihood procedure was used to estimate variance components. A bivariate animal model that considered herd-yr-season, calving age, and regression additive genetic group as fixed effects, and animal and residual as random effects was used for genetic evaluation. Heritability estimates were 0.12 (SE = 0.19) for lnSCC, and 0.31 (SE = 0.06) for MY. The genetic correlation estimate between lnSCC and MY was 0.26 (SE = 0.59). Mean yearly estimated breeding values during the last 20 years increased for SCC (49.02 cells/ml/yr, SE = 26.81 cells/ml/yr; p = 0.08), but not for MY (0.37 kg/yr, SE = 0.87 kg/yr; p = 0.68). Sire average breeding values for SCC and MY were higher than those of cows and dams (p<0.01). Heritability estimates for lnSCC and MY and their low but positive genetic correlation suggested that selection for low SCC may be feasible in this population as it is in other populations of dairy cows. Thus, selection for high MY and low SCC should be encouraged in Thai dairy improvement programs to increase profitability by improving both cow health and milk yield.

Imputation Accuracy from Low to Moderate Density Single Nucleotide Polymorphism Chips in a Thai Multibreed Dairy Cattle Population

The objective of this study was to investigate the accuracy of imputation from low density (LDC) to moderate density SNP chips (MDC) in a Thai Holstein-Other multibreed dairy cattle population. Dairy cattle with complete pedigree information (n = 1,244) from 145 dairy farms were genotyped with GeneSeek GGP20K (n = 570), GGP26K (n = 540) and GGP80K (n = 134) chips. After checking for single nucleotide polymorphism (SNP) quality, 17,779 SNP markers in common between the GGP20K, GGP26K, and GGP80K were used to represent MDC. Animals were divided into two groups, a reference group (n = 912) and a test group (n = 332). The SNP markers chosen for the test group were those located in positions corresponding to GeneSeek GGP9K (n = 7,652). The LDC to MDC genotype imputation was carried out using three different software packages, namely Beagle 3.3 (population-based algorithm), FImpute 2.2 (combined family- and population-based algorithms) and Findhap 4 (combined family- and population-based algorithms). Imputation accuracies within and across chromosomes were calculated as ratios of correctly imputed SNP markers to overall imputed SNP markers. Imputation accuracy for the three software packages ranged from 76.79% to 93.94%. FImpute had higher imputation accuracy (93.94%) than Findhap (84.64%) and Beagle (76.79%). Imputation accuracies were similar and consistent across chromosomes for FImpute, but not for Findhap and Beagle. Most chromosomes that showed either high (73%) or low (80%) imputation accuracies were the same chromosomes that had above and below average linkage disequilibrium (LD; defined here as the correlation between pairs of adjacent SNP within chromosomes less than or equal to 1 Mb apart). Results indicated that FImpute was more suitable than Findhap and Beagle for genotype imputation in this Thai multibreed population. Perhaps additional increments in imputation accuracy could be achieved by increasing the completeness of pedigree information.

Effect of daily fluctuations in ambient temperature on reproductive failure traits of Landrace and Yorkshire sows under Thai tropical environmental conditions.

The aim of this study was to determine the effects of daily ranges and maximum ambient temperatures, and other risk factors on reproductive failure of Landrace (L) and Yorkshire (Y) sows under an open-house system in Thailand. Daily ambient temperatures were added to information on 35,579 litters from 5929 L sows and 1057 Y sows from three commercial herds. The average daily temperature ranges (ADT) and the average daily maximum temperatures (PEAK) in three gestation periods from the 35th day of gestation to parturition were classified. The considered reproductive failure traits were the occurrences of mummified fetuses (MM), stillborn piglets (STB), and piglet death losses (PDL) and an indicator trait for number of piglets born alive below the population mean (LBA). A multiple logistic regression model included farrowing herd-year-season (HYS), breed group of sow (BG), parity group (PAR), number of total piglets born (NTB), ADT1, ADT2, ADT3, PEAK1, PEAK2, and PEAK3 as fixed effects, while random effects were animal, repeated observations, and residual. Yorkshire sows had a higher occurrence of LBA than L sows (P = 0.01). The second to fifth parities sows had lower reproductive failures than other parities. The NTB regression coefficients of log-odds were positive (P < 0.01) for all traits. Narrower ranges of ADT3 increased the occurrence of MM, STB, and PDL (P < 0.01), while higher PEAK3 increased the occurrence of MM, STB, PDL, and LBA (P < 0.001). To reduce the risk of reproductive failures, particularly late in gestation, producers would need to closely monitor their temperature management strategies.

Pathway enrichment and protein interaction network analysis for milk yield, fat yield and age at first calving in a Thai multibreed dairy population

Objective This research aimed to determine biological pathways and protein-protein interaction (PPI) networks for 305-d milk yield (MY), 305-d fat yield (FY), and age at first calving (AFC) in the Thai multibreed dairy population. Methods Genotypic information contained 75,776 imputed and actual single nucleotide polymorphisms (SNP) from 2,661 animals. Single-step genomic best linear unbiased predictions were utilized to estimate SNP genetic variances for MY, FY, and AFC. Fixed effects included herd-year-season, breed regression and heterosis regression effects. Random effects were animal additive genetic and residual. Individual SNP explaining at least 0.001% of the genetic variance for each trait were used to identify nearby genes in the National Center for Biotechnology Information database. Pathway enrichment analysis was performed. The PPI of genes were identified and visualized of the PPI network. Results Identified genes were involved in 16 enriched pathways related to MY, FY, and AFC. Most genes had two or more connections with other genes in the PPI network. Genes associated with MY, FY, and AFC based on the biological pathways and PPI were primarily involved in cellular processes. The percent of the genetic variance explained by genes in enriched pathways (303) was 2.63% for MY, 2.59% for FY, and 2.49% for AFC. Genes in the PPI network (265) explained 2.28% of the genetic variance for MY, 2.26% for FY, and 2.12% for AFC. Conclusion These sets of SNP associated with genes in the set enriched pathways and the PPI network could be used as genomic selection targets in the Thai multibreed dairy population. This study should be continued both in this and other populations subject to a variety of environmental conditions because predicted SNP values will likely differ across populations subject to different environmental conditions and changes over time.

Genetic parameters, predictions, and rankings for semen production traits in a Thailand multi-breed dairy population using genomic-polygenic and polygenic models

The objectives were to compare estimates of variance components, genetic parameters, prediction accuracies, and rankings of bulls for semen volume (VOL), number of sperm (NS), and motility (MOT) using genomic-polygenic (GPRM) and polygenic repeatability models (PRM). The dataset comprised 13,535 VOL, 12,773 NS, and 12,660 MOT from 131 bulls collected from 2001 to 2017 in the Semen Production and Dairy Genetic Evaluation Center of the Dairy Farming Promotion Organization of Thailand. Genotypic data encompassed 76,519 actual and imputed SNP from 72 animals. The three-trait GPRM and PRM included the fixed effects of contemporary group, ejaculate order, age of bull, ambient temperature, and heterosis. Random effects were animal additive genetic, permanent environmental, and residual. Variance components and genetic parameters were estimated using AIREMLF90. GPRM heritabilities were slightly greater than PRM for MOT (0.27 compared with 0.24), and slightly less for VOL (0.11 compared with 0.12), and NS (0.17 compared with 0.19). Repeatabilities were slightly less for GPRM than PRM (0.44 compared with 0.45 for MOT, 0.26 compared with 0.28 for NS, and 0.20 compared with 0.21 for VOL). Additive genetic correlations were high between NS and MOT (GPRM: 0.76, PRM: 0.78), moderate between VOL and NS (GPRM: 0.43, PRM: 0.55), and near zero between VOL and MOT (GPRM: -0.13, PRM: 0.04). Rank correlations between GPRM and PRM estimated breeding values (EBV) were high for all traits. The similarity between GPRM and PRM results suggested that SNP data from the small number of genotyped animals had a minimal impact on genetic predictions in this population.

Characterization of biological pathways associated with semen traits in the Thai multibreed dairy population

The objective of this research was to characterize biological pathways associated with semen volume (VOL), number of sperm (NS), and sperm motility (MOT) of dairy bulls in the Thai multibreed dairy population. Phenotypes for VOL (n = 13,535), NS (n = 12,773), and MOT (n = 12,660) came from 131 bulls of the Dairy Farming Promotion Organization of Thailand. Genotypic data consisted of 76,519 imputed and actual single nucleotide polymorphisms (SNP) from 72 animals. The SNP variances for VOL, NS, and MOT were estimated using a three-trait genomic-polygenic repeatability model. Fixed effects were contemporary group, ejaculate order, age of bull, ambient temperature, and heterosis. Random effects were animal additive genetic, permanent environmental, and residual. Individual SNP explaining at least 0.001% of the total genetic variance for each trait were selected to identify associated genes in the NCBI database (UMD Bos taurus 3.1 assembly) using the R package Map2NCBI. A set of 1,999 NCBI genes associated with all three semen traits was utilized for the pathway analysis conducted with the ClueGO plugin of Cytoscape using information from the Kyoto Encyclopedia of Genes and Genomes database. The pathway analysis revealed seven significant biological pathways involving 127 genes that explained 1.04% of the genetic variance for VOL, NS, and MOT. These genes were known to affect cell structure, motility, migration, proliferation, differentiation, survival, apoptosis, signal transduction, oxytocin release, calcium channel, neural development, and immune system functions related to sperm morphology and physiology during spermatogenesis.

Genetic relationships between length of productive life and lifetime production efficiency in a commercial swine herd in Northern Thailand.

Genetic parameters and trends for length of productive life (LPL), lifetime number of piglets born alive per year (LBAY), lifetime number of piglets weaned per year (LPWY), lifetime litter birth weight per year (LBWY) and lifetime litter weaning weight per year (LWWY) were estimated using phenotypic records of 3085 sows collected from 1989 to 2013 in a commercial swine farm in Northern Thailand. The five-trait animal model included the fixed effects of first farrowing year-season, breed group and age at first farrowing. Random effects were animal and residual. Heritability estimates ranged from 0.04 ± 0.02 for LBWY to 0.17 ± 0.04 for LPL. Genetic correlations ranged from 0.66 ± 0.14 between LPL and LBAY to 0.95 ± 0.02 between LPWY and LWWY. Spearman rank correlations among estimated breeding values for LPL and lifetime production efficiency traits tended to be higher for boars than for sows. Sire genetic trends were negative and significant for all traits, except for LPWY. Dam genetic trends were positive and significant for all traits. Sow genetic trends were mostly positive and significant only for LPWY and LBWY. Improvement of LPL and lifetime production efficiency traits will require these traits to be included in the selection indexes used to choose replacement boars and gilts in this population.

Genetic correlations between first parity and accumulated second to last parity reproduction traits as selection aids to improve sow lifetime productivity

Objective The objective of this research was to estimate genetic correlations between number of piglets born alive in the first parity (NBA1), litter birth weight in the first parity (LTBW1), number of piglets weaned in the first parity (NPW1), litter weaning weight in the first parity (LTWW1), number of piglets born alive from second to last parity (NBA2+), litter birth weight from second to last parity (LTBW2+), number of piglets weaned from second to last parity (NPW2+) and litter weaning weight from second to last parity (LTWW2+), and to identify the percentages of animals (the top 10%, 25%, and 50%) for first parity and sums of second and later parity traits. Methods The 9,830 records consisted of 2,124 Landrace (L), 724 Yorkshire (Y), 2,650 LY, and 4,332 YL that had their first farrowing between July 1989 and December 2013. The 8-trait animal model included the fixed effects of first farrowing year-season, additive genetic group, heterosis of the sow and the litter, age at first farrowing, and days to weaning (NPW1, LTWW1, NPW2+, and LTWW2+). Random effects were animal and residual. Results Heritability estimates ranged from 0.08±0.02 (NBA1 and NPW1) to 0.29±0.02 (NPW2+). Genetic correlations between reproduction traits in the first parity and from second to last parity ranged from 0.17±0.08 (LTBW1 and LTBW2+) to 0.67±0.06 (LTWW1 and LTWW2+). Phenotypic correlations between reproduction traits in the first parity and from second to last parity were close to zero. Rank correlations between LTWW1 and LTWW2+ estimated breeding value tended to be higher than for other pairs of traits across all replacement percentages. Conclusion These rank correlations indicated that selecting boars and sows using genetic predictions for first parity reproduction traits would help improve reproduction traits in the second and later parities as well as lifetime productivity in this swine population.

Estimation of Genetic Parameters and Trends for Length of Productive Life and Lifetime Production Traits in a Commercial Landrace and Yorkshire Swine Population in Northern Thailand

The objective of this research was to estimate genetic parameters and trends for length of productive life (LPL), lifetime number of piglets born alive (LBA), lifetime number of piglets weaned (LPW), lifetime litter birth weight (LBW), and lifetime litter weaning weight (LWW) in a commercial swine farm in Northern Thailand. Data were gathered during a 24-year period from July 1989 to August 2013. A total of 3,109 phenotypic records from 2,271 Landrace (L) and 838 Yorkshire sows (Y) were analyzed. Variance and covariance components, heritabilities and correlations were estimated using an Average Information Restricted Maximum Likelihood (AIREML) procedure. The 5-trait animal model contained the fixed effects of first farrowing year-season, breed group, and age at first farrowing. Random effects were sow and residual. Estimates of heritabilities were medium for all five traits (0.17±0.04 for LPL and LBA to 0.20±0.04 for LPW). Genetic correlations among these traits were high, positive, and favorable (p<0.05), ranging from 0.93±0.02 (LPL-LWW) to 0.99±0.02 (LPL-LPW). Sow genetic trends were non-significant for LPL and all lifetime production traits. Sire genetic trends were negative and significant for LPL (−2.54±0.65 d/yr; p = 0.0007), LBA (−0.12±0.04 piglets/yr; p = 0.0073), LPW (−0.14±0.04 piglets/yr; p = 0.0037), LBW (−0.13±0.06 kg/yr; p = 0.0487), and LWW (−0.69±0.31 kg/yr; p = 0.0365). Dam genetic trends were positive, small and significant for all traits (1.04±0.42 d/yr for LPL, p = 0.0217; 0.16±0.03 piglets/yr for LBA, p<0.0001; 0.12±0.03 piglets/yr for LPW, p = 0.0002; 0.29±0.04 kg/yr for LBW, p<0.0001 and 1.23±0.19 kg/yr for LWW, p<0.0001). Thus, the selection program in this commercial herd managed to improve both LPL and lifetime productive traits in sires and dams. It was ineffective to improve LPL and lifetime productive traits in sows.