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A total of 11,815 weight records from 23,94 Japanese Black calves was used to estimate direct, maternal, direct permanent environmental, and maternal permanent environmental effects on growth from birth to 356 d of age. The data were collected from a herd of Japanese Black cattle in Shiroshi city, Miyagi prefecture, Japan. A random regression model, including parity of dam and year-season of calving-sex of calf as fixed effects and animal, dam, animal permanent environmental, and maternal permanent environmental as random effects, was fitted to the data using Legendre polynomials for age of calf. Direct heritability estimates increased from 0.38 at birth to 0.65 at 120 d of age, decreased to 0.38 at 300 d, and then increased again up to 0.47 at 356 d. The ratio of animal permanent environmental variance to phenotypic variance decreased from 0.41 at birth to 0.12 at 90 d, and then increased gradually up to 0.40 at 270 d and oscillated around this value up to the end of the test period. Maternal genetic heritabilities increased from 0.04 at birth to 0.09 at 120 d and then decreased to 0.06 thereafter, whereas the variance ratios due to maternal permanent environment were fairly constant across the age trajectory, fluctuating around the value of 0.03. Direct genetic, phenotypic, maternal genetic, animal permanent environmental, and maternal permanent environmental correlations between different ages were all positive, and they generally decreased as the interval between ages increased. These correlations were lower between weights from nonadjacent ages than those between weights from adjacent ages. Results suggest that selection on preweaning weights would have a positive effect on weights at later ages.

Repeated records of number of services per conception (NSC) were collected on 607 Japanese Black cows. Data were analysed by random regression (RRM) and multiple trait (MTM) models, considering NSC in each parity as a separate trait. The chosen RRM included additive genetic and permanent environmental effects fitted with a third-order Legendre polynomials of parity. Heritabilities (h2) estimated by RRM decreased along the NSC trajectory from 0.15 in the first parity to 0.04 in the sixth parity and then increased up to 0.22 in the 10th parity. The corresponding estimates obtained by MTM ranged between 0.04 in parity 9 and 0.13 in parity 1. Permanent environmental proportions (p2) of the total phenotypic variance estimated by RRM showed similar pattern and magnitude to those of h2 estimated by the same method. On the contrary, the p2 estimated by MTM ranged between 0.04 in the first parity and 0.11 in the 10th parity. Additive genetic (r(G)), permanent environmental (r(P)) and phenotypic (r(PH)) correlations were also estimated. The values estimated by RRM between adjacent parities were higher than those of parities far apart. The corresponding values estimated by MTM were lower than those estimated by RRM with no certain trend. The results indicated that NSC in heifers is more heritable than NSC in cows with different parities. Reproductive traits are economically important traits and hence, they should be considered in breeding goals.

No abstract

BackgroundDifferent levels of evidence related to the variable responses of individuals to drug treatment have been reported in various pharmacogenomic (PGx) databases. Identification of gene-drug pairs with strong association evidence can be helpful in prioritizing the implementation of PGx guidelines and focusing on a gene panel. This study aimed to determine the pharmacogenes with the highest evidence-based association and to indicate their involvement in drug-gene interactions.MethodologyThe publicly available datasets CPIC, DPWG, and PharmGKB were selected to determine the pharmacogenes with the highest drug outcome associations. The upper two levels of evidence rated by the three scoring methods were specified (levels A–B in CPIC, 3–4 in DPWG, or 1–2 levels in PharmGKB). The identified pharmacogenes were further ranked in this study based on the number of medications they interacted with.ResultsFifty pharmacogenes, with high to moderately high evidence of associations with drug response alterations, with potential influence on the therapeutic and/or toxicity outcomes of 152 drugs were identified. CYP2D6, CYP2C9, CYP2C19, G6PD, HLA-B, SLCO1B1, CACNA1S, RYR1, MT-RNR1, and IFNL4 are the top 10 pharmacogenes, where each is predicted to impact patients' responses to ≥5 drugs.ConclusionThis study identified the most important pharmacogenes based on the highest-ranked association evidence and their frequency of involvement in affecting multiple drugs. The obtained data is useful for customizing a gene panel for PGx testing. Identifying the strength of scientific evidence supporting drug-gene interactions aids drug prescribers in making the best clinical decision.