Covariance estimation with Method-R

The objective of the study was to develop algorithms based on MethodR that allow estimation of (co)variance components with large data sets for complex single trait models, e.g., with correlated additive animal effects and/or with dominance effects. Theoretical Method-R formulas were developed for simplified single and bi-variate models. In single trait, the curve of the regression of Method-R was continuous and monotonic, as is described in the literature, and its slope depended on the amount of information on one animal. The curve was flatter as the number of records per animal increased possibly indicating numerical problems with the sire model. For covariance, the curve of the regression was not always monotonic and it had a discontinuity; a regression factor of 1 still corresponded to the correct covariance. Similar curves were observed in analyses of simulated data sets. Due to the observed discontinuity, algorithms implementing Method-R that require continuous regression curve would not work in models with covariances. An alternative algorithm was based on a transformation matrix obtained by multiplying a matrix of numerators with the inverse of a matrix of denominators of the regression factors. This algorithm always converged in models with covariances, but was slow, requiring as many as 1000 rounds to converge. Convergence, faster by 3-10 times, was achieved by applying over-relaxation. Analyses of several simulated and real data sets by Method-R showed that sampling variance of (co)variance estimates with Method-R was higher for covariances or dominance effects than for additive effects. Therefore, larger number of samples is necessary for more complex models to obtain reliable estimates by Method-R

Estimation of genetic covariances with Method R

Method R is a simple and computationally inexpensive method for estimating (co)variances. The objective of the study was to investigate properties of Method R for estimation of (co)variance components with emphasis on covariance estimation. Theoretical Method R formulas were developed for simplified single-variate and bivariate models. In single-trait models, the curve of the regression of Method R was continuous and monotonic and its slope depended on the amount of information on each animal and on the variance ratio. The curve became steeper as the number of records per animal decreased. For covariance, the curve of the regression was monotonic but not continuous. However, a regression coefficient of 1 still corresponded to the correct covariance. Similar curves were observed in analyses of simulated data sets. Because of the observed discontinuity, algorithms implementing Method R that require a continuous regression curve would not work in models with: covariances. An alternative algorithm was based on a transformation matrix obtained by multiplying a matrix of numerators with the inverse of a matrix of denominators of the regression factors. Such an algorithm converged reliably for all models tested. Method R can be modified to estimate covariances in models too large for other methods

Estimation of additive and nonadditive genetic variances in Hereford, Gelbvieh, and Charolais by Method R.

Parameters for direct and maternal dominance were estimated in models that included non-additive genetic effects. The analyses used weaning weight records adjusted for age of dam from populations of Canadian Hereford (n = 467,814), American Gelbvieh (n = 501,552), and American Charolais (n = 314,552). Method R estimates of direct additive genetic, maternal additive genetic, permanent maternal environment, direct dominance, and maternal dominance variances as a proportion of the total variance were 23, 12, 13, 19, and 14% in Hereford; 27, 7, 10, 18, and 2% in Gelbvieh; and 34, 15, 15, 23, and 2% in Charolais. The correlations between direct and maternal additive genetic effects were -0.30, -0.23, and -0.47 in Hereford, Gelbvieh, and Charolais, respectively. The correlations between direct and maternal dominance were -0.38, -0.02, and -0.04 in Hereford, Gelbvieh, and Charolais, respectively. Estimates of inbreeding depression were -0.20, -0.18, and -0.13 kg per 1% of inbreeding for Hereford, Gelbvieh, and Charolais, respectively. Estimates of the maternal inbreeding depression were -0.01, -0.02, and -0.02 kg, respectively. The high ratio of direct dominance to additive genetic variances provided some evidence that direct dominance effects should be considered in beef cattle evaluation. However, maternal dominance effects seemed to be important only for Hereford cattle

Identification of potential genetic variants associated with longevity and lifetime production traits in a Thai Landrace pig population using weighted single-step genome-wide association methods

Longevity and lifetime production traits are of increasing importance in swine breeding schemes worldwide because these traits influence sow productivity and welfare, as well as affecting farm profitability. The Landrace breed makes up one-half of the F1 Large White x Landrace female, which is the most popular maternal line in the breeding herd of commercial pork production systems in Thailand and throughout the world. The objective of this study was to estimate genetic parameters and detect potential genetic variants associated with age at first farrowing (AFF), length of productive life (LPL), lifetime number of piglets born alive (LNBA), lifetime number of piglets weaned (LNW), lifetime wean to first service interval (LW2S) and lifetime pig efficiency (LTP365) in a Thai Landrace pig population. dData were analyzed for 82,346 litters from 12,843 Landrace pigs housed in three farms; all farms were a part of a large commercial production system. Genetic parameters were estimated using a single-step, genomic-BLUP (ssGBLUP) that utilizes general pedigree and genomic relationships. Landrace sows were genotyped with 60K Illumina PorcineSNP60 BeadChip. The genotypes were analyzed by weighted single-step genome-wide association analyses. Heritability estimates for longevity and length of productive life traits were low and ranged from 0.01 to 0.11. The greatest genetic correlations between LPL with LNBA, LNW, LW2S and LTP365 ranged from 0.44 to 0.91. The greatest genetic correlations between LPL and LNBA, LNW, LW2S and LTP365 ranged from 0.44 to 0.91. Based on these results, genetic selection for LPL was not antagonistic with lifetime production. Twenty-seven candidate genes were identified as being associated with one or more traits evaluated in this Landrace pig population. Highlighted genes related to LPL, LNBA, LNW and LTP365 included TMLHE, PDHX and KCNJ6 on SSC13 in this pig population. This constitutes a list of candidate genes that could be incorporated into selection to improve sow longevity and lifetime production traits in the pig industry.This article is published as Plaengkaeo, S., M. Duangjinda, and K. J. Stalder. "Identification of potential genetic variants associated with longevity and lifetime production traits in a Thai Landrace pig population using weighted single-step genome-wide association methods." Genet. Mol. Res 19 (2020): gmr18465. doi: 10.4238/gmr18465. </p

Hsp70 Genotypes and Heat Tolerance of Commercial and Native Chickens Reared in Hot and Humid Conditions

ABSTRACT Heat tolerance in poultry production was obtained attention due to the need for genetic lines that can withstand climate changes. This study aimed at investigating heat tolerance in commercial and native broiler genetics, as well as the physiological and growth performance responses of HSP70 genotypes submitted to heat stress. In Experiment I, heterophil:lymphocyte (H:L) ratio, as an indicator of heat tolerance, was compared between commercial broilers (n = 100) and Thai native chickens (n = 100). Growing chickens (with similar initial weight) of each genetic strain were randomly divided into two groups: 1) thermoneutral environment (26 oC ± 2 oC) and 2) heat stress (36 oC ± 2 oC). The results showed that native chickens originating from a tropical environment presented lower H:L ratio and mortality rate compared with commercial broilers. In Experiment II, HSP70 genotypes were compared. PCR-RFLP was applied to identify the genotypes (C1C1, n = 38; C1C2, n = 38; and C2C2, n = 28). Ten-week-old chickens of each genotype were evaluated in the same environments described in Experiment I. Heat-stress indicators - respiratory rate (RR), cloacal temperature (CT), packed cell volume (PCV), and average daily gain (ADG) - were measured for three weeks. The significant difference in PCV indicated that C2C2 chickens were less tolerant to heat stress compared to other genotypes. The RR, CT, and ADG were not significantly different among all genotypes. Since the C2C2 genotype was shown to be sensitive to heat stress, C1C1 and C1C2 could be used as markers for heat-tolerant genetic strains of Thai indigenous chickens and hybrid commercial lines