This study was conducted to estimate the relative contribution of dominance genetic effects to efficiency-related traits including Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR) in Baluchi sheep. To this end, each trait was analysed with a series of 12 animal models which were identical for fixed and additive genetic effects but differed for combinations of dominance genetic and maternal effects. The Akaike's information criterion (AIC) was used to rank models. (Co)variances between traits were estimated using bivariate analyses. For all traits studied, according to AIC values, models containing the dominance genetic effects provided a better data fit than otherwise identical models. By including dominance genetic effects in the model, additive genetic variance did not change, but a significant decrease was observed in the residual variance (24, 19 and 25% for KR, EF and RGR, respectively). Estimates of dominance heritability $( {\boldsymbol h}_{\boldsymbol d}^ 2 )$ were 0.20 ± 0.05, 0.17 ± 0.05 and 0.19 ± 0.07 for KR, EF and RGR, respectively, more than corresponding estimates of additive heritability ${\bf ( }{\boldsymbol h}_{\boldsymbol a}^{\bf 2} {\bf ) }$ as 0.14 ± 0.02, 0.09 ± 0.03 and 0.13 ± 0.02, respectively. Dominance genetic correlations between traits were 0.89 ± 0.17 (KR-EF), 0.86 ± 0.20 (KR-RGR) and 0.93 ± 0.21 (EF-RGR). Additive genetic correlations between traits were 0.84 ± 0.05 (KR-EF), 0.78 ± 0.04 (KR-RGR) and 0.83 ± 0.04 (EF-RGR). The Spearman correlation between additive breeding values including and excluding dominance genetic effects were close to unity either for all animals or top ranked animals. Since presence of dominance genetic effects increased the model power to fit the data, inclusion of these effects in the genetic evaluation models for Baluchi sheep was recommended.

Although the relationship between gaming addiction (GA) and attention deficit hyperactivity disorder (ADHD) is well established, the causal mechanism of this relationship remains ambiguous. We aimed to investigate whether common genetic and/or environmental factors explain the GA-ADHD relationship. We recruited 1413 South Korean adult twins (837 monozygotic [MZ], 326 same-sex dizygotic [DZ], and 250 opposite-sex DZ twins; mean age = 23.1 ± 2.8 years) who completed an online survey on GA and related traits. Correlational analysis and bivariate model-fitting analysis were conducted. Phenotypic correlation between GA and ADHD in the present sample was 0.55 (95% CI [0.51, 0.59]). Bivariate model-fitting analysis revealed that genetic variances were 69% (95% CI [64%, 73%]) and 68% (95% CI [63%, 72%]) for ADHD and GA respectively. The remaining variances (ADHD: 31%; GA: 32%) were associated with nonshared environmental variances, including measurement error. Genetic and nonshared environmental correlations between ADHD and GA were 0.68 (95% CI [0.62, 0.74]) and 0.22 (95% CI [0.13, 0.30]) respectively, which indicates that shared genes can explain 82% of the phenotypic correlation between ADHD and GA. Our study demonstrated that the ADHD-GA association was largely due to shared genetic vulnerability.

The study was undertaken to estimate the genetic parameters of lactation curve parameters of Wood's function in Jersey crossbred cattle using the Bayesian approach. Data on 33,906 fortnightly test day milk yields of 1,718 lactation records of Jersey crossbred cows, maintained at the ICAR-National Dairy Research Institute in West Bengal, were collected over a period of 40 years. The lactation curve parameters including ‘a’ (initial milk yield after calving), ‘b’ (ascending slope up to peak yield) and ‘c’ (descending slope after peak yield) and lactation curve traits, peak yield (ymax), time of peak yield (tmax) and persistency of milk yield (P) of individual cow for each lactation were estimated using the incomplete gamma function (Wood's model) by fitting the Gauss–Newton algorithm as an iteration method using PROC NLIN procedure of SAS 9.3. Variance components and genetic parameters of lactation curve parameters/traits were estimated by a repeatability animal model using the Bayesian approach. Estimates of heritabilities were found to be 0.18 ± 0.05, 0.09 ± 0.03 and 0.11 ± 0.04 for parameters ‘a’, ‘b’ and ‘c’, respectively and 0.24 ± 0.05, 0.12 ± 0.04, and 0.15 ± 0.05 for ymax, tmax and P, respectively. Repeatability estimates were 0.31 ± 0.03, 0.21 ± 0.04 and 0.30 ± 0.04 for parameters ‘a’, ‘b’ and ‘c’ respectively and 0.39 ± 0.03, 0.24 ± 0.03 and 0.37 ± 0.03 for ymax, tmax and p, respectively. Genetic correlations among lactation curve parameters/traits ranged from −0.75 to 0.95. Existence of genetic correlations among lactation curve parameters/traits indicated substantial genetic and physiological relationships among lactation curve parameters/traits of Jersey crossbred cattle.

Severe infections and psychiatric disorders have a large impact on both society and the individual. Studies investigating these conditions and the links between them are therefore important. Most past studies have focused on binary phenotypes of particular infections or overall infection, thereby losing some information regarding susceptibility to infection as reflected in the number of specific infection types, or sites, which we term infection load. In this study we found that infection load was associated with increased risk for attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, schizophrenia and overall psychiatric diagnosis. We obtained a modest but significant heritability for infection load (h2 = 0.0221), and a high degree of genetic correlation between it and overall psychiatric diagnosis (rg = 0.4298). We also found evidence supporting a genetic causality for overall infection on overall psychiatric diagnosis. Our genome-wide association study for infection load identified 138 suggestive associations. Our study provides further evidence for genetic links between susceptibility to infection and psychiatric disorders, and suggests that a higher infection load may have a cumulative association with psychiatric disorders, beyond what has been described for individual infections.

An abnormal alanine aminotransferase (ALT) level is predictive of disease and all-cause mortality and may indicate liver injury. Using twin modeling, the genetic and environmental factors that affect human serum ALT levels have been well studied for the populations in the different countries, and the results showed moderate-to-high heritability. However, the heritability of ALT level has not been explored in Chinese population. Thus, we recruited 369 pairs of twins (233 monozygotic and 136 dizygotic) from the Qingdao Twin Registry in China with a median age of 50 years (40−80 years). Correlation analysis and a structural equation model (SEM) were conducted to evaluate the heritability of ALT level. The data for age, gender, body mass index and alcohol consumption were set as covariates. Intrapair correlation in monozygotic twins was 0.64 (95%CI [.56, .71]) and 0.42 (95% CI [.28, .55]) in dizygotic twins. The SEM analysis indicated that 65% (95% CI [57%, 71%]) of the variation in ALT levels can be explained by additive genetics and 35% (95% CI [29%, 44%]) of the variation is attributed to unique environmental factors or residuals. Shared environmental influences were not significant. In conclusion, serum ALT variations exhibited strong genetic effects. The variation could also be explained by unique environmental factors. However, shared environmental factors have a minor impact on the serum ALT level.

The routine mixing of pigs causes aggression that cannot be greatly reduced by low-cost environmental changes. The variability and heritability of aggressiveness are discussed and both appear adequate to make selection against aggressiveness worthwhile in grower-stage pigs. Selection would require rapid phenotyping of many animals for which a validated indicator genetically correlated to aggressive behaviour is required. Three potential indicators are discussed (attack latency, number of skin lesions and relationship to non-social behavioural traits). Attack latency correlates with post-mixing aggressiveness under research conditions but attacks are delayed under commercial conditions reducing the practicability of the trait for selection. Correlations between aggressiveness and responses to non-social challenges, such as the back-test, are not always consistent. Lastly, the counting of skin lesions is rapid, and the number of lesions has a moderate heritability and is genetically correlated with involvement in aggressive behaviour. The wider effects of selection against post-mixing aggressiveness are discussed. Examining the behavioural strategies of unaggressive pigs, especially their response to defeat, would reveal how selection may alter aggressive tactics. Selection against lesions from mixing is also expected to reduce their number in more stable social conditions, but the implications for aggression between sows and that of sows towards their piglets and humans needs to be investigated. Aggressiveness is genetically correlated with response to handling involving components of social isolation, human presence and novelty. Identifying how unaggressive pigs respond to other challenging situations differing in these components may be worthwhile. Selection against aggression using skin lesions appears to be achievable although the full value of this would benefit from estimations of the genetic correlations with the traits outlined above.

The aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (rg from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from −0.22 to −0.59), between milk yield and milk FA (rg from −0.22 to −0.62), and between protein yield and milk FA (rg from −0.11 to −0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle.

The goal was to study imprinting effects on birth weight (BW), weaning weight (WW), six months weight (W6), pre- and post-weaning growth rate (GRa and GRb), and pre- and post-weaning Kleiber ratio (KRa and KRb) in Baluchi and Makuie sheep breeds. Analyses were done in a two steps process. In the first step, each trait was analysed with a series of 12 univariate animal models, including different combinations of direct and maternal effects and the best model was selected by the AIC criterion. In the second step, three new models were fitted by adding either maternal imprinting effect, paternal imprinting effect, or both of them, respectively, to the best model already selected in the first step, and changes in AIC were monitored. (Co)variances between traits were estimated using bivariate analyses. In Baluchi sheep, for BW, WW and W6, estimates of maternal imprinting heritability (${\boldsymbol h}_{{\boldsymbol mi}}^2$) were 0.12 ± 0.02, 0.08 ± 0.01 and 0.09 ± 0.01, respectively. In Makuie sheep, paternal imprinting heritability (${\boldsymbol h}_{{\boldsymbol pi}}^2$) for BW, WW, W6, GRb and KRb were 0.17 ± 0.05, 0.8 ± 0.05, 0.38 ± 0.10, 0.71 ± 0.15 and 0.65 ± 0.13, respectively. In Baluchi sheep, strong maternal imprinting correlations were observed between BW and WW (0.93 ± 0.05), BW and W6 (0.80 ± 0.07) and WW and W6 (0.91 ± 0.02). In Makuie sheep, paternal imprinting correlations ranged from −0.97 ± 0.33 (BW-GRb) to 0.99 ± 0.47 (GRb-KRb). It was concluded that imprinting effects need to be included in the statistical model to increase the accuracy of genetic evaluation. However, to have accurate and reliable estimates of imprinting effects, the availability of large data sets and deep pedigrees are necessary.

Schizophrenia and bipolar disorder display clinical similarities and dissimilarities. We investigated whether the genetic factor differentiating schizophrenia from bipolar disorder is genetically associated with cognitive phenotypes and hippocampal volumes. We revealed genetic overlaps of the genetic differentiating factor with low general cognitive ability, low childhood IQ, low educational attainment and reduced hippocampal volumes. The genetic correlations with low general cognitive ability and reduced hippocampal volumes were associated with risk of schizophrenia, whereas the genetic correlations with high childhood IQ and educational attainment were associated with risks of bipolar disorder. These findings suggest these disorders have disorder-specific genetic factors related to clinical phenotypes.

The present study aimed to determine the genetic and environmental etiology of the association between childhood negative emotionality (NE) and hyperactivity/inattention problems (HIP) using South Korean elementary school twins (mean age = 10.19 years, SD = 1.79 years). Telephone interviews were given to mothers of 919 twins (229 monozygotic males: 112 pairs and 5 individuals; 148 dizygotic males: 73 pairs and 2 individuals; 180 monozygotic females: 87 pairs and 6 individuals; 103 dizygotic females: 50 pairs and 3 individuals; 259 opposite-sex dizygotic twins: 127 pairs and 5 individuals) to assess their children’s NE and HIP. Consistent with prior studies, the phenotypic correlation between NE and the HIP was moderate (r = .29; 95% CI = .24, .34). Model-fitting analysis revealed that additive genetic and nonshared environmental influences on NE were .45 (95% CI [.34, .54]) and .55 (95% CI [.46, .66]), respectively, and that additive and nonadditive genetic, and nonshared environmental influences on HIP were .08 (95% CI [.03, .26]), .41 (95% CI [.21, .51]) and .51 (95% CI = .42, .61), respectively. In addition, the additive genetic correlation between NE and HIP was 1.0 (95% CI [.52, 1.00]), indicating that additive genetic factors are entirely shared between the two phenotypes. Nonadditive genetic influences were unique to HIP and not responsible for the NE-HIP association. Nonshared environmental correlation was significant but modest (re = .18, 95% CI [.06, .30]).

Our objective was to evaluate the genetic merit of Holstein cattle population in southern Brazil in response to variations in the regional temperature by analyzing the genotype by environment interaction using reaction norms. Fat yield (FY) and protein yield (PY) data of 67 360 primiparous cows were obtained from the database of the Paraná Holstein Breeders Association, Brazil (APCBRH). The regional average annual temperature was used as the environmental variable. A random regression model was adopted applying mixed models with Restricted Maximum Likelihood (REML) algorithm using WOMBAT software. The genetic merit of the 15 most representative bulls, depending on the temperature gradient, was evaluated. Heritability ranged from 0.21 to 0.27 for FY and from 0.14 to 0.20 for PY. The genetic correlation observed among the environmental gradients proved to be higher than 0.80 for both traits. Slight reranking of bulls for both traits was detected, demonstrating that non-relevant genotype by environment interaction for FY and PY were observed. Consequently, no inclusion of the temperature effect in the model of genetic evaluation in southern Brazilian Holstein breed is required.

Loneliness is related to mental and somatic health outcomes, including borderline personality disorder. Here, we analyze the sources of variation that are responsible for the relationship between borderline personality features (including four dimensions, affective instability, identity disturbance, negative relationships, self-harm and a total score) and loneliness. Using genetically informative data from two large nonclinical samples of adult twin pairs from Australia and the Netherlands (N = 11,329), we estimate the phenotypic, genetic and environmental correlations between self-reported borderline personality features and loneliness. Individual differences in borderline personality and loneliness were best explained by additive genetic factors with heritability estimates h2 = 41% for the borderline personality total score and h2 = 36% for loneliness, with the remaining variation explained by environmental influences that were not shared by twins from the same pair. Genetic and environmental factors influencing borderline personality (total score and four subscales separately) were also partial causes of loneliness. The correlation between loneliness and the borderline personality total score was rph = .51. The genetic correlation was estimated at rg = .64 and the environmental correlation at re = .40. Our study suggests common etiological factors in loneliness and borderline personality features.