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A simulation study examined issues important for genetic evaluation of growth in beef cattle by random regression models with cubic Legendre polynomials (RRML) and linear splines with three knots (RRMS) compared with multiple-trait models (MTM). Parameters for RRML were obtained by conversion from covariance functions. Parameters for MTM and RRMS were extracted from RRML at 1,205, and 365 d; parameters for RRMS were the same as MTM for all effects except the permanent environment and the residual. Four data sets were generated assuming RRML included records at 1, 205, and 365 d; at 1, 160 to 250, and 320 to 410 d; at 1, 100, 205, 300, and 365 d; and at 1, 55 to 145, 160 to 250, 275 to 325, and 320 to 410 d. Accuracies were computed as correlations between the true (simulated) and predicted breeding values. With the first data set, excellent agreement in accuracy was obtained for all models. With the second data set, the accuracy of MTM dropped by up to 1.5% compared with the first data set, but accuracy was unchanged for both RRML and RRMS. With the third (fourth) data set, accuracies of RRML were up to 2.4% (2.5%) higher than with the first (second) data set. Small differences in accuracy between RRML and RRMS were found with the third and fourth data sets, which were traced to inflated correlations especially between 1 and 205 d in RRMS; inflation could be decreased by adding one extra knot at 100 d to RRMS. Diagonalization of random coefficients was crucial for RRML but not for RRMS, resulting in approximately six (two) times faster convergence with RRML (RRMS). Reduction of dimensionality in RRML associated with small eigenvalues caused a less accurate evaluation for birth weight. Genetic evaluation of growth by RRM requires careful implementation. The RRMS is simpler to implement than the RRML. Key Words: Growth, Random Regression Model, Splines

The purpose of this study was to quantify the effect of heat stress on genotype by environment interaction among different regions of the United States. The national data consisted of 55.5 million first parity test-day (TD) milk yield records on 5.8 million Holstein cows. Subsets of the national data were from the Northeast (NE) with 12.5 million TD records on 1.3 million cows, and from the Southeast (SE) with 3.5 million TD records on 0.36 million cows. Meteorological data from 202 public weather stations were matched with herds based on distance. Daily mean temperature-humidity index (THI) was calculated from temperature and relative humidity records three days prior to TD. The first model that lacked the effect of heat stress included fixed effects of herd-test date, age at calving class, frequency of milking, and DIM x season class, and random genetic additive (regular breeding value) and permanent environmental effects. The second model that accounted for heat stress included two additional random regressions on degrees of heat stress (t=max[0,THI-72]), one for additive genetic (heat breeding value) and one for permanent environmental effect. Breeding values (BV) were computed by BLUP90IOD. Correlations involved sires with at least 300 daughters in regions being compared. When heat stress was ignored, the correlations of regular BV between NE and SE were 0.86. When the heat stress was considered, the correlation increased by 0.01. The correlation between heat BV for NE x SE was 0.72. Heat stress effect as applied explains only a fraction of differences in sire ranking between SE and the NE. The real impact of heat stress may be higher because THI as used accounts only for a fraction of variability due to heat and low correlations are in part due to limited accuracies. [ABSTRACT FROM AUTHOR]

Abstract Background Effectiveness of genomic selection and fine mapping is determined by the level of linkage disequilibrium (LD) across the genome. Knowledge of the range of genome-wide LD, defined as a non-random association of alleles at different loci, can provide an insight into the optimal density and location of single-nucleotide polymorphisms (SNPs) for genome-wide association studies and can be a keystone for interpretation of results from QTL mapping. Results Linkage disequilibrium was measured by |D'| and r2 between 38,590 SNPs (spaced across 29 bovine autosomes and the X chromosome) using genotypes of 887 Holstein bulls. The average level of |D'| and r2 for markers 40-60 kb apart was 0.72 and 0.20, respectively in Holstein cattle. However, a high degree of heterogeneity of LD was observed across the genome. The sample size and minor allele frequency had an effect on |D'| estimates, however, r2 was not noticeably affected by these two factors. Syntenic LD was shown to be useful for verifying the physical location of SNPs. No differences in the extent of LD and decline of LD with distance were found between the intragenic and intergenic regions. Conclusions A minimal sample size of 444 and 55 animals is required for an accurate estimation of LD by |D'| and r2, respectively. The use of only maternally inherited haplotypes is recommended for analyses of LD in populations consisting of large paternal half-sib families. Large heterogeneity in the pattern and the extent of LD in Holstein cattle was observed on both autosomes and the X chromosome. The extent of LD was higher on the X chromosome compared to the autosomes.

Objectives: The aim of this study was to compare the effectiveness of 5 power lasers (CO(2), diode, KTP, ErCr:YS66, and ND:YVO4) in laser-assisted uvulopalatoplasty., Methods: This is a prospective study of 100 patients who were treated for snoring by laser-assisted uvulopalatoplasty with different lasers., Results: The number of days of pain medication use and the time to return to normal diet were used as indicators of recovery from surgery. Median of pain medication use was 9, 7, 7, 5, and 4 days, and the median of the time to return to normal diet was 8, 7, 7, 6, and 4 days in the CO(2), diode, KTP, Nd:YVO4, and ErCrYSGG groups, respectively., Conclusions: Differences in postoperative recovery were observed. Probands treated with ErCrYSGG laser recovered more quickly than the Nd:YVO4, diode, KTP, and CO(2) groups.