Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information

Background Several studies have found that the growth rate of a pig is influenced by the genetics of the group members (indirect genetic effects). Accounting for these indirect genetic effects in a selection program may increase genetic progress for growth rate. However, indirect genetic effects are small and difficult to predict accurately. Genomic information may increase the ability to predict indirect genetic effects. Thus, the objective of this study was to test whether including indirect genetic effects in the animal model increases the predictive performance when genetic effects are predicted with genomic relationships. In total, 11,255 pigs were phenotyped for average daily gain between 30 and 94 kg, and 10,995 of these pigs were genotyped. Two relationship matrices were used: a numerator relationship matrix ($${\mathbf{A}}$$ A ) and a combined pedigree and genomic relationship matrix ($${\mathbf{H}}$$ H ); and two different animal models were used: an animal model with only direct genetic effects and an animal model with both direct and indirect genetic effects. The predictive performance of the models was defined as the Pearson correlation between corrected phenotypes and predicted genetic levels. The predicted genetic level of a pig was either its direct genetic effect or the sum of its direct genetic effect and the indirect genetic effects of its group members (total genetic effect). Results The highest predictive performance was achieved when total genetic effects were predicted with genomic information (21.2 vs. 14.7%). In general, the predictive performance was greater for total genetic effects than for direct genetic effects (0.1 to 0.5% greater; not statistically significant). Both types of genetic effects had greater predictive performance when they were predicted with $${\mathbf{H}}$$ H rather than $${\mathbf{A}}$$ A (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when $${\mathbf{H}}$$ H was used rather than $${\mathbf{A}}$$ A . Conclusions This study provides evidence that: (1) corrected phenotypes are better predicted with total genetic effects than with direct genetic effects only; (2) both direct genetic effects and indirect genetic effects are better predicted with $${\mathbf{H}}$$ H than $${\mathbf{A}}$$ A ; (3) using $${\mathbf{H}}$$ H rather than $${\mathbf{A}}$$ A primarily improves the predictive performance of direct genetic effects.

Family Analysis with Mendelian Imputations

Genotype-phenotype associations can be results of direct effects, genetic nurturing effects and population stratification confounding. Genotypes from parents and siblings of the proband can be used to statistically disentangle these effects. To maximize power, a comprehensive framework for utilizing various combinations of parents’ and siblings’ genotypes is introduced. Central to the approach is mendelian imputation, a method that utilizes identity by descent (IBD) information to non-linearly impute genotypes into untyped relatives using genotypes of typed individuals. Applying the method to UK Biobank probands with at least one parent or sibling genotyped, for an educational attainment (EA) polygenic score that has an R2 of 5.7% with EA, its predictive power based on direct genetic effect alone is demonstrated to be only about 1.4%. For women, the EA polygenic score has a bigger estimated direct effect on age-at-first-birth than EA itself.

Modeling honey yield, defensive and swarming behaviors of Italian honey bees (Apis mellifera ligustica) using linear-threshold approaches

Background Genetic improvement of honey bees is more difficult compared to other livestock, due to the very different reproductive behavior. Estimation of breeding values requires specific adjustment and the use of sires in the pedigree is only possible when mating of queens and drones is strictly controlled. In the breeding program of the National Registry for Italian Queen Breeders and Bee Producers the paternal contribution is mostly unknown. As stronger modeling may compensate for the lack of pedigree information, we tested two models that differed in the way the direct and maternal effects were considered. The two models were tested using 4003 records for honey yield, defensive and swarming behaviors of Italian honey bee queens produced between 2002 and 2014. The first model accounted for the direct genetic effect of worker bees and the genetic maternal effect of the queen, whereas model 2 considered the direct genetic effect of the queen without maternal effect. The analyses were performed by linear (honey production) and threshold (defensive and swarming behavior) single-trait models; estimated genetic correlations among traits were obtained by a three-trait linear-threshold model. Results For all traits, the highest predictability (correlation between breeding values estimated with and without performance records) was obtained with model 2, where direct genetic effect of queens was considered. With this model, heritability estimates were 0.26 for honey yield, 0.36 for defensive behavior, and 0.34 for swarming behavior. Multi-trait estimation resulted in similar or higher heritability estimates for all traits. A low, positive genetic correlation (0.19) was found between honey yield and defensive behavior, whereas the genetic correlation between honey yield and swarming behavior was moderate (0.41). A strong, positive genetic correlation was found between defensive and swarming behaviors (0.62). Predictability for multi-trait evaluations was higher for honey yield (0.46) and defensive behavior (0.30) but almost identical for swarming behavior (0.45) compared to corresponding single-trait predictability. Conclusions Multi-trait evaluation using a model that accounts for the direct genetic effect of queen was the best approach for breeding value estimation of Italian honey bees. The results suggest a new direction for selection of linear and categorical traits in breeding programs where drone origin is unknown.

Bayesian estimates of genetic relationship between calving difficulty and productive and reproductive performance in Holstein cows

The objective of present study was to estimate genetic correlations between calving difficulty and productive and reproductive traits in Iranian Holsteins. Calving records from the Animal Breeding Center of Iran, collected from 1991 to 2011 and comprising 183 203 first-calving events of Holstein cows from 1470 herds were included in the dataset. Threshold animal models included direct genetic effect (Model 1) or direct and maternal genetic effects with covariance between them (Model 2) were fitted for the genetic analysis of calving difficulty. Also, linear animal models including direct genetic effect were fitted for the genetic analysis of productive and reproductive performance traits. A set of linear-threshold bivariate models was used for obtaining genetic correlation between calving difficulty and other traits. All analyses were implemented by Bayesian approach via Gibbs sampling methodology. A single Gibbs sampling chain with 300 000 rounds was generated by the TM program. Posterior mean estimates of direct heritabilities for calving difficulty were 0.056 and 0.066, obtained from different models. Also, posterior mean estimate of maternal heritability for this trait was 0.018. Estimate of correlation between direct and maternal genetic effects for calving difficulty was negative (–0.44). Posterior mean estimates of direct heritabilities for milk yield, fat yield, protein yield, days from calving to first service, days open and first calving interval were 0.257, 0.188, 0.235, 0.034, 0.042 and 0.050 respectively. The posterior means of direct genetic correlation between calving difficulty and milk yield, fat yield, protein yield, days from calving to first service, days open and first calving interval were low and equal to –0.135, 0.030, –0.067, –0.010, –0.075 and –0.074 respectively. The results of the current study indicated that exploitable genetic variation in calving difficulty, productive and reproductive traits could be applied in designing future genetic selection plans for Iranian Holsteins.

Estimates of genetic parameters and genetic trends for production and reproduction traits in Iranian buffaloes (Bubalus bubalis)

Calving and milk production records from April 1992 to March 2012 comprising 5353 records of age at first calving (AFC), 2972 records of 305-days milk yield (MY) and 2349 records of interval between first and second calving (CI) from the first lactation buffaloes within 785 herds of Iran were analysed using a linear animal model to estimate variance components and heritability for these traits. A linear animal model including direct genetic effect was implemented by Gibbs sampling methodology. A single Gibbs sampling chain with 300 000 rounds was generated by the TM program. Genetic trends were obtained by regressing yearly mean estimates of breeding values on birth year. Posterior mean estimates of direct heritabilities for MY, AFC and CI were 0.46 ± 0.21, 0.20 ± 0.06 and 0.21 ± 0.14, respectively. The posterior means of direct genetic correlation between MY-AFC, MY-CI and AFC-CI were –0.31, 0.01 and –0.17, respectively. Estimates of direct genetic trends for MY, AFC and CI were negative and significant, and their corresponding values were –1.50 ± 0.05 (P < 0.0001), –0.04 ± 0.001 (P < 0.0001) and –0.02 ± 0.005 (P < 0.001), respectively. Medium to high direct heritability estimates for productive and reproductive traits would be due to higher additive genetic variances for these traits and implied that applicable genetic variations observed for productive and reproductive traits could be applied in designing future genetic selection plans for Iranian buffaloes.

Genetic parameters for calving difficulty using complex genetic models in five beef breeds in Australia

Data on Angus (ANG), Charolais (CHA), Hereford (HER), Limousin (LIM) and Simmental (SIM) cattle were used to estimate genetic parameters for calving difficulty (CD), birthweight (BWT) and gestation length (GL) using threshold-linear models and to examine the effect of inclusion of random effect of sire × herd interaction (SxH) in the models. For models without SxH, estimated heritabilities for direct genetic effect of CD were 0.24 (±0.02), 0.22 (±0.04), 0.31 (±0.02), 0.22 (±0.04) and 0.17 (±0.01) for ANG, CHA, HER, LIM and SIM, respectively, whereas maternal heritabilities ranged from 0.13 to 0.20. Estimated heritabilities for direct genetic effect of BWT were 0.38 (±0.01), 0.37 (±0.03), 0.46 (±0.01), 0.35 (±0.02) and 0.36 (±0.01) for ANG, CHR, HER, LIM and SIM, respectively, whereas maternal heritabilities ranged from 0.08 to 0.11. Estimated heritabilities for direct genetic effect of GL were 0.59 (±0.02), 0.42 (±0.04), 0.50 (±0.03), 0.45 (±0.04) and 0.42 (±0.03) for ANG, CHR, HER, LIM and SIM, respectively, whereas maternal heritabilities ranged from 0.03 to 0.09. Genetic correlations between direct genetic effects of CD with BWT were highly positive and with GL were moderately positive for all five breeds. Estimated genetic correlations between direct genetic effects and maternal genetic effects (rdm) ranged across the five breeds from –0.40 (±0.05) to –0.16 (±0.02), –0.41 (±0.03) to –0.27 (±0.08) and –0.47 (±0.10) to –0.06 (±0.12) for BWT, GL and CD, respectively. Fitting SxH interaction as additional random effect significantly increased the log-likelihood for analyses of BWT, GL and CD of all breeds, except for GL of CHA. The estimated heritabilities were less than or equal to the estimates obtained with models omitting SxH. The rdm increased (i.e. became less negative) for BWT, GL and CD of all five breeds. However, the increase for GL was not substantially high in comparison to the increase observed for BWT and CD. Genetic parameters obtained for BWT, GL and CD, by fitting SxH as an additional random effect, are more appropriate to use in the genetic evaluation of calving ease in BREEDPLAN.

Effect of polygenic risk scores on depression in childhood trauma

BackgroundResearch on gene×environment interaction in major depressive disorder (MDD) has thus far primarily focused on candidate genes, although genetic effects are known to be polygenic.AimsTo test whether the effect of polygenic risk scores on MDD is moderated by childhood trauma.MethodThe study sample consisted of 1645 participants with a DSM-IV diagnosis of MDD and 340 screened controls from The Netherlands. Chronic or remitted episodes (severe MDD) were present in 956 participants. The occurrence of childhood trauma was assessed with the Childhood Trauma Interview and the polygenic risk scores were based on genome-wide meta-analysis results from the Psychiatric Genomics Consortium.ResultsThe polygenic risk scores and childhood trauma independently affected MDD risk, and evidence was found for interaction as departure from both multiplicativity and additivity, indicating that the effect of polygenic risk scores on depression is increased in the presence of childhood trauma. The interaction effects were similar in predicting all MDD risk and severe MDD risk, and explained a proportion of variation in MDD risk comparable to the polygenic risk scores themselves.ConclusionsThe interaction effect found between polygenic risk scores and childhood trauma implies that (1) studies on direct genetic effect on MDD gain power by focusing on individuals exposed to childhood trauma, and that (2) individuals with both high polygenic risk scores and exposure to childhood trauma are particularly at risk for developing MDD.

Genetic parameters for growth of young beef bulls

The average daily gains of young bulls on test stations (ADGT) were analysed for the most frequent breeds of beef cattle in the Czech Republic using a multiple-trait animal model. Body weights at birth (W0), at 120 days of age (W120) and at weaning at 210 days (WW) were considered in this model as pre-weaning growth. The tested models comprised some of the random effects: direct genetic effect, maternal genetic effect, permanent animal environment effect, permanent maternal environment effect, and some of the fixed effects: dam’s age, sex, herd-year-season, linear and quadratic regression on age at the beginning of the test. For optimization of the models Akaike information criterion (AIC), residual variance and likelihood ratio test were used. Coefficients of direct and maternal heritability across breeds of about 0.25 for W0, about 0.17 for W120, about 0.17 for WW and about 0.29 for ADGT were estimated by all models. All criteria selected models including the permanent animal environment effect, which was the most important effect in the model.

Estimation of genetic parameters for melanoma in the Old Kladruber horse

The aim of this study was to assess the prevalence of melanoma to investigate a possible genetic variation of this trait in the Old Kladruber horse. A total of 564 grey varieties of the Old Kladruber horse, 238&nbsp;males and 326 females, with five generations of ancestors (n = 1245 animals) were analysed. Melanoma status was recorded for different stages. Three different analyses were conducted: a linear animal model (LM) with melanoma classified into five categories, threshold animal model (TM) with melanoma classified into five categories and threshold animal model (TMb) with melanoma classified into two categories (0 = absence, 1 = presence). All models included the fixed effects of year of evaluation, age, line, sex, greying level, random direct genetic effect, and the effect of animal&rsquo;s permanent environment. Heritability for melanoma occurrence was estimated for LM &ndash; 0.09, for TM &ndash; 0.27, and for TMb &ndash; 0.11. The coefficient of repeatability was estimated for LM &ndash; 0.77, for TM &ndash; 0.90, and for TMb &ndash; 0.99. The values of the Pearson&rsquo;s correlation coefficient and Spearmen&rsquo;s rank correlation coefficient among breeding values estimated by LM, TM, and TMb models were from 0.82 to 0.88 and from 0.83 to 0.90, respectively, for data with pedigree information and from 0.77 to 0.84 and 0.77 to 0.88, respectively, for a subset of animals with measurements. Results suggest that additive genetic variation of melanoma occurrence in the Old Kladruber horse seems large enough to be exploited in a specific breeding programme. &nbsp;

Genetic association between cows’ stayability to three years of age and juvenile growth traits in Canadian Angus herds

Bivariate linear animal models were fit using MTDFREML programs for the analysis of cows’ stayability to 3 yr (STAY3, n = 1, 703) as a binary scored trait paired with body weights at birth (BW, n = 6,116), 205-d weaning (WW, n = 5,360,) and 1 yr of age (YW, n = 5250) in Angus cattle. For STAY3, the model included a fixed effect due to herd ×year of cow’s birth along with a random direct genetic effect. For each of BW, WW and YW, the model included fixed effects due to herd ×birth year, birth season, birth type, calf’s sex and the age of dam (as linear and quadratic covariates), while the random effects were direct and maternal genetic effects and permanent maternal environmental effects. Survival analysis revealed that the risk of cows, 10 yr or less in age, being culled from five Canadian Angus herds was highest between 2 and 3 yr of age. The direct heritability (h2) estimates for BW, WW and YW were 0.54, 0.73 and 0.47, respectively, and corresponding maternal heritability estimates were 0.14, 0.33 and 0.13, respectively. Furthermore, the direct h2 estimate for STAY3 from three bivariate analyses was 0.23–0.24. Estimates of direct-maternal genetic correlations of BW, WW and YW were -0.18, -0.70 and -0.39, respectively. The direct genetic correlations of STAY3 with BW, WW and YW were -0.15 to -0.09 indicating that selection for growth would have less detrimental influence on the stayability trait of cows. The correlations of direct genetic effects of STAY3 with maternal genetic effects of BW, WW and YW were between 0.20 and 0.25, indicating their favorable relationships as correlated traits. Key words: Stayability, growth traits, heritability, genetic correlations, beef cattle