Covariance Components Research Articles

Seleção para a produção de ovos em períodos parciais e acumulados em linhagem de frango de corte comercial

Objetivou-se com este estudo estimar parâmetros genéticos para produções parciais e acumuladas de ovos em uma linha fêmea de frangos de corte comercial. Foram considerados 10 períodos mensais entre 25 e 64 semanas, três períodos parciais de 25 a 32, 33 a 48 e 49 a 64 semanas, e três períodos acumulados de 25 até 30, 40 e 50 semanas de idade. Os componentes de covariância e parâmetros genéticos foram obtidos pelo método da máxima verossimilhança restrita, sob o modelo animal considerando o efeito fixo de incubação e os efeitos aleatórios genético aditivo e residual. As estimativas de herdabilidade variaram de 0,12 a 0,41. Evidenciou-se que os períodos anteriores e posteriores ao maior nível de produção apresentam maior variabilidade genética. As correlações genéticas entre os períodos de produção de ovos estudados variaram de -0,12 a 0,98. De modo geral, o padrão de variação foi semelhante entre as estratégias avaliadas, e todas foram geneticamente associadas com a produção total. Os resultados deste estudo mostraram que a melhoria da produção total é viável por meio de seleção de registros parciais. No entanto, caso se considere a eficiência relativa de seleção, o segundo mês e os períodos a partir da quadragésima semana de produção seriam os mais indicados.

Genetic parameters for milk fatty acids, milk yield and quality traits of a Holstein cattle population reared under tropical conditions.

Information about genetic parameters is essential for selection decisions and genetic evaluation. These estimates are population specific; however, there are few studies with dairy cattle populations reared under tropical and sub-tropical conditions. Thus, the aim was to obtain estimates of heritability and genetic correlations for milk yield and quality traits using pedigree and genomic information from a Holstein population maintained in a tropical environment. Phenotypic records (n=36457) of 4203 cows as well as the genotypes for 57368 single nucleotide polymorphisms from 755 of these cows were used. Covariance components were estimated using the restricted maximum likelihood method under a mixed animal model, considering a pedigree-based relationship matrix or a combined pedigree-genomic matrix. High heritabilities (around 0.30) were estimated for lactose and protein content in milk whereas moderate values (between 0.19 and 0.26) were obtained for percentages of fat, saturated fatty acids and palmitic acid in milk. Genetic correlations ranging from -0.38 to -0.13 were determined between milk yield and composition traits. The smaller estimates compared to other similar studies can be due to poor environmental conditions, which may reduce genetic variability. These results highlight the importance in using genetic parameters estimated in the population under evaluation for selection decisions.

GENETIC PARAMETER ESTIMATES FOR PRODUCTION AND REPRODUCTION TRAITS IN DAIRY BUFFALOES

ABSTRACT: The objective of this study was to determine the genetic variation in milk production, milk components, and reproductive traits in dairy buffaloes. A total of 9,318 lactation records from 3,061 cows were used to estimate the heritability of milk yield (MY), fat percentage (%F), protein percentage (%P), lactation length (LL), calving interval (CI), and age at first calving (AFC), as well as genetic and phenotypic correlations between these traits. Covariance components were estimated by Bayesian inference in a multitrait animal model using the GIBBS2F90 program. Contemporary groups and number of milkings (1 or 2) were included as fixed effects, age of dam at calving (linear and quadratic effects) as a covariate, and additive genetic, permanent environmental, and residual effects as random effects. The heritability estimates (± standard deviation) were 0.24 ± 0.02, 0.34 ± 0.05, 0.40 ± 0.05, 0.09 ± 0.01, 0.05 ± 0.01, and 0.16 ± 0.04 for MY, %F, %P, LL, CI, and AFC, respectively. The genetic correlations between MY and %F, %P, LL, CI, and AFC were -0.29, -0.18, 0.66, 0.08, and 0.24, respectively. Milk production and milk components showed sufficient genetic variation to obtain genetic gains through selection. The genetic correlations between MY and milk components were negative, and thus, undesirable because efforts to increase MY may decrease milk quality. Reproductive traits had little genetic influence, indicating that improvement of management would be sufficient to obtain better performance.

Generalized Harmonic Analysis of Computed and Measured Magnetic Fields

In this paper, we present a generalized approach for the harmonic analysis of the magnetic field in accelerator magnets. This analysis is based on the covariant components of the computed or measured magnetic flux density. The multipole coefficients obtained in this way can be used for magnet optimization and field reconstruction in the interior of circular and elliptical boundaries in the bore of straight magnets.

A method for estimating and assessing modes of interannual variability in coupled climate models

The seasonal mean of a climate variable consists of: slow-external; slow-internal; and intraseasonal components. Using an analysis of variance-based method, the interannual variability of the seasonal mean from an ensemble of coupled atmosphere-ocean general circulation model (CGCM) realisations is separable into these three components. Eigenvalue decomposition is applied to the covariance matrices to obtain, for each component, the dominant modes of variability (eigenvectors) and their associated variance (eigenvalues) for the climate variable. Here, a method is described that assesses the modes of interannual variability in CGCMs against those obtained from reanalysis data based on observations. A metric is defined based on the pattern correlation between the observed and modelled modes of variability, and the ratio of their associated variances. This metric is applied to monthly mean southern hemisphere 500 hPa geopotential height from the second half of the 20th century. It is shown that CGCMs have clear differences in the slow-component of modes of interannual variability, related to external forcings and/or slowly-varying internal variability. References C. S. Frederiksen and X. Zheng. Coherent structures of interannual variability of the atmospheric circulation: the role of intraseasonal variability. Frontiers in Turbulence and Coherent Structures , World Scientific Lecture Notes in Complex Systems , Vol. 6, Eds Jim Denier and Jorgen Frederiksen, World Scientific Publications, 87–120, 2007. doi:10.1142/6320 C. E. Leith. The standard error of time-average estimates of climatic means. J. Appl. Meteor. , 12:1066–1069, 1973. 2.0.CO;2>doi:10.1175/1520-0450(1973)012 2.0.CO;2 X. Zheng and C. S. Frederiksen. Variability of seasonal-mean fields arising from intraseasonal variability: part 1, methodology. Clim. Dynam. , 23:177–191, 2004. doi:10.1007/s00382-004-0428-7 C. S. Frederiksen and X. Zheng. Variability of seasonal-mean fields arising from intraseasonal variability. Part 3: Application to SH winter and summer circulations. Clim. Dynam. , 28:849–866, 2007. doi:10.1007/s00382-006-0214-9 S. Grainger, C. S. Frederiksen and X. Zheng. A method for evaluating the modes of variability in general circulation models. ANZIAM J. , 50:C399–C412, 2008. http://journal.austms.org.au/ojs/index.php/ANZIAMJ/article/view/1431 G. A. Meehl, C. Covey, K. E. Taylor, T. Delworth, R. J. Stouffer, M. Latif, B. McAvaney and J. F. B. Mitchell. The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc. , 88:1383–1394, 2007. doi:10.1175/BAMS-88-9-1383 K. E. Taylor, R. J. Stouffer and G. A. Meehl. An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc. 93:485–498, 2012. doi:10.1175/BAMS-D-11-00094.1 X. Zheng, M. Sugi and C. S. Frederiksen. Interannual variability and predictability in an ensemble of climate simulations with the MRI-JMA AGCM. J. Meteor. Soc. Jap. , 82:1–18, 2004. doi:10.2151/jmsj.82.1 H. von Storch and F. W. Zwiers. Statistical Analysis in Climate Research. Cambridge University Press , 484pp, 1999. doi:10.1017/cbo9780511612336 S. Grainger, C. S. Frederiksen and X. Zheng. Modes of interannual variability of Southern Hemisphere atmoshperic circulation in CMIP3 models: assessment and projections. Clim. Dynam. 41:479–500, 2013. doi:10.1007/s00382-012-1659-7 S. Grainger, C. S. Frederiksen and X. Zheng. Estimating components of covariance between two climate variables using model ensembles. ANZIAM J. , 52:C318–C332, 2011. http://journal.austms.org.au/ojs/index.php/ANZIAMJ/article/view/3928 G. P. Compo, J. S. Whitaker, P. D. Sardeshmukh, N. Matsui, R. J. Allan, X. Yin, B. E. Gleason, R. S. Vose, G. Rutledge, P. Bessemoulin, S. Bronnimann, M. Brunet, R. I. Crouthamel, A. N. Grant, P. Y. Groisman, P. D. Jones, M. C. Kruk, A. C. Kruger, G. J. Marshall, M. Maugeri, H. Y. Mok, O. Nordli, T. F. Ross, R. M. Trigo, X. L. Wang, S. D. Woodruff and S. J. Worley. The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc. 137:1–28, 2011. doi:10.1002/qj.776 N. A. Rayner, D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent and A. Kaplan. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. , 108(D14):4407, 2003. doi:10.1029/2002JD002670

Multivariate frailty models for multi-type recurrent event data and its application to cancer prevention trial

Multi-type recurrent event data arise in many situations when two or more different event types may occur repeatedly over an observation period. For example, in a randomized controlled clinical trial to study the efficacy of nutritional supplements for skin cancer prevention, there can be two types of skin cancer events occur repeatedly over time. The research objectives of analyzing such data often include characterizing the event rate of different event types, estimating the treatment effects on each event process, and understanding the correlation structure among different event types. In this paper, we propose the use of a proportional intensity model with multivariate random effects to model such data. The proposed model can take into account the dependence among different event types within a subject as well as the treatment effects. Maximum likelihood estimates of the regression coefficients, variance–covariance components, and the nonparametric baseline intensity function are obtained via a Monte Carlo Expectation–Maximization (MCEM) algorithm. The expectation step of the algorithm involves the calculation of the conditional expectations of the random effects by using the Metropolis–Hastings sampling. Our proposed method can easily handle recurrent event data that have more than two types of events. Simulation studies were used to validate the performance of the proposed method, followed by an application to the skin cancer prevention data.

Multivariate exponentially weighted moving sample covariance control chart for monitoring covariance matrix

In this paper, a control chart is proposed to detect changes in the covariance matrix of a multivariate normal process, when sample size is one. The proposed chart statistic is constructed based on the exponentially weighted form of sample covariance matrix given by individual observation over time. Distance between the values of variance and covariance components in this multivariate exponentially weighted moving sample covariance matrix and, the in-control corresponding elements of process variance-covariance matrix provides a basis for process variability monitoring. The statistical performance of the proposed method is evaluated through the use of a Monte Carlo simulation. The results show the superiority of the proposed control chart performance especially in the case of incremental changes in covariance matrix.

Effect of inclusion or non-inclusion of short lactations and cow and/or dam genetic group on genetic evaluation of Girolando dairy cattle.

The objective of this study was to evaluate the effects of inclusion or non-inclusion of short lactations and cow (CGG) and/or dam (DGG) genetic group on the genetic evaluation of 305-day milk yield (MY305), age at first calving (AFC), and first calving interval (FCI) of Girolando cows. Covariance components were estimated by the restricted maximum likelihood method in an animal model of single trait analyses. The heritability estimates for MY305, AFC, and FCI ranged from 0.23 to 0.29, 0.40 to 0.44, and 0.13 to 0.14, respectively, when short lactations were not included, and from 0.23 to 0.28, 0.39 to 0.43, and 0.13 to 0.14, respectively, when short lactations were included. The inclusion of short lactations caused little variation in the variance components and heritability estimates of traits, but their non-inclusion resulted in the re-ranking of animals. Models with CGG or DGG fixed effects had higher heritability estimates for all traits compared with models that consider these two effects simultaneously. We recommend using the model with fixed effects of CGG and inclusion of short lactations for the genetic evaluation of Girolando cattle.

Genetic parameter estimates for growth traits at different ages in Nigerian indigenous turkeys

Precise and accurate genetic parameter estimates are crucial for making sound decisions in many stages of animal improvement programme. Genetic parameter estimates for eight growth traits were determined in 308 turkeys generated from mating pure indigenous parents. PROC MIXED of SAS was used to estimate variance and covariance components of the growth traits. The highest heritability estimates for body weight, breast girth, thigh length, shank length, keel length and wing length were observed at week 1, week 8, week 4, week 16, week 4, and a day old-old, respectively. The repeatability estimates ranged from 0.01 to 0.93 with body weight and body length having the least values at week 20 and week 1, respectively. Negative genetic correlations were observed among the growth traits at a day-old. Also, negative correlations were observed among all the growth traits at week 20 except between body weight and shank length, shank length and wing length, shank length and wing span as well as wing length and wing span. Positive phenotypic correlations existed among all the growth traits measured except among some traits at weeks 1 and 16. Phenotypic correlations observed at week 20 were relatively higher than those observed in other weeks. These results suggested that breeders can select for body weight, breast girth, thigh length, shank length, keel length and wing length of Nigerian indigenous turkeys at week 1, week 8, week 4, week 16, week 4, and a day old-old, respectively. Fewer records are required to estimate response to selection of Nigerian indigenous turkeys when selecting for breast girth at a day-old and body length at week 16. The negative genetic correlation observed among most growth traits at week 20 was an indication of unpredictability among these traits at week 20, so tandem method of selection cannot be used in selecting these traits.

Estimation of Genetic Parameters and Trends for Length of Productive Life and Lifetime Production Traits in a Commercial Landrace and Yorkshire Swine Population in Northern Thailand.

The objective of this research was to estimate genetic parameters and trends for length of productive life (LPL), lifetime number of piglets born alive (LBA), lifetime number of piglets weaned (LPW), lifetime litter birth weight (LBW), and lifetime litter weaning weight (LWW) in a commercial swine farm in Northern Thailand. Data were gathered during a 24-year period from July 1989 to August 2013. A total of 3,109 phenotypic records from 2,271 Landrace (L) and 838 Yorkshire sows (Y) were analyzed. Variance and covariance components, heritabilities and correlations were estimated using an Average Information Restricted Maximum Likelihood (AIREML) procedure. The 5-trait animal model contained the fixed effects of first farrowing year-season, breed group, and age at first farrowing. Random effects were sow and residual. Estimates of heritabilities were medium for all five traits (0.17±0.04 for LPL and LBA to 0.20±0.04 for LPW). Genetic correlations among these traits were high, positive, and favorable (p<0.05), ranging from 0.93±0.02 (LPL-LWW) to 0.99±0.02 (LPL-LPW). Sow genetic trends were non-significant for LPL and all lifetime production traits. Sire genetic trends were negative and significant for LPL (−2.54±0.65 d/yr; p = 0.0007), LBA (−0.12±0.04 piglets/yr; p = 0.0073), LPW (−0.14±0.04 piglets/yr; p = 0.0037), LBW (−0.13±0.06 kg/yr; p = 0.0487), and LWW (−0.69±0.31 kg/yr; p = 0.0365). Dam genetic trends were positive, small and significant for all traits (1.04±0.42 d/yr for LPL, p = 0.0217; 0.16±0.03 piglets/yr for LBA, p<0.0001; 0.12±0.03 piglets/yr for LPW, p = 0.0002; 0.29±0.04 kg/yr for LBW, p<0.0001 and 1.23±0.19 kg/yr for LWW, p<0.0001). Thus, the selection program in this commercial herd managed to improve both LPL and lifetime productive traits in sires and dams. It was ineffective to improve LPL and lifetime productive traits in sows.

Evaluation of growth traits of Aceh cattle at the breeding station in Indrapuri District of Indonesia

The present study was conducted using records on four breeding bulls maintained at the Indrapuri’s Breeding and Forages Center (IBFC) of Aceh Cattle, Indrapuri district, Indonesia and 52 of their progeny born from 2010 to 2014 through natural mating. Family selection was practiced in this breeding station. Traits included in this study were birth weight at 0 day (BWc), weaning weight at 205 days of age (WWc), yearling weight at 365 days of age (YWc) and final weight at 550 days of age (FWc) of their progeny. The variance and covariance components were estimated by the Paternal Half-sib Correlation method with the Excel sheets of Microsoft office computer program. The mean body weights of the animals were BWc (13.25+1.18 kg), WWc (47.80+10.00 kg), YWc (73.97+16.79 kg) and FWc (101.87+23.91 kg). Estimated heritability (h2) for BWC was low (h2 &lt; 0.10) and the other traits were high (h2&gt;0.30). Higher standard error (SE) value than h2 caused by less number of calves observation. Research showed that best relative accuracy value (RA&gt;1.00) reached in BWC. but its trait was not effective as selection criterion because of lower h2 value. It was concluded that WWC could be used as selection criterion for breeding bulls based on the similar estimated breeding value (EBV) rank and high h2 value. The EBV of WWC from breeding bull ID: 0752 was calculated and ranked accordingly. The EBVs of three top bulls ID were 100412 (10.32 kg), 100520 (9.38 kg) and 100317 (6.75 kg) and three top heifers were 100526 (9.65 kg), 100408 (7.04 kg) and 100807 (5.20 kg). It can be concluded that these top ranking three bulls and heifers can be selected and used in the breeding programs.Bang. J. Anim. Sci. 2015. 44(2): 85-91

Longitudinal analysis of body weight, feed intake and residual feed intake in F2 chickens

The aim of this study was to estimate covariance functions, using B-spline random regression models for body weight (BW), feed intake (FI) and residual feed intake (RFI) in F2 chickens. Feed intake and body weight were recorded on each bird every week from 2 to 10 weeks of age. The F2 population was derived from a cross between a fast growing Arian broiler line and Iranian native fowl. Sex, hatch and age in week were fitted as fixed effects and additive genetic and permanent environment as random effects. Variance components were estimated by the means of a Bayesian analysis, using Gibbs sampling. Covariance components for cumulative FI and RFI were obtained as a function of week on test. The heritability estimates for BW ranged from 0.19 to 0.34 and were high at the early ages, decreases with age to a moderate value. A similar pattern was observed for FI, where the heritabilities decreased from 0.29 to 0.12 from the second week to 10th week of age. Estimates of heritabilities for RFI along the growth trajectory ranged from 0.10 to 0.29 and increased over the time. Cumulative FI and RFI had medium to high heritability (0.24 and 0.41, respectively). The posterior means of genetic correlations between BW at different age were moderate to high from 2 to 10 weeks of age, ranging from 0.26 to 0.99. These estimates for phenotypic correlations were lower than the corresponding genetic correlations for all the weeks. The posterior means of genetic correlations between different weeks of age ranged from 0.10 to 0.99 for FI and −0.15 to 0.96 for RFI. The genetic and phenotypic correlations consistently decreased as the time interval increased for all traits. However, for the cumulative FI and RFI traits were increased with increasing the age interval from 2 to 9 weeks of age. The genetic correlations and expected response to indirect selection showed that the optimum test length for FI and RFI can be reduced to 2 weeks without a large loss in overall genetic gain in FI or RFI. In general, spline model showed that there was genetic variation in the BW, FI and RFI curve of individual chicken. The results indicated that random regression models can provide a good description of the data and is recommended for genetic selection in chickens.

Associação genética entre ocorrência de mastite clínica e produção de leite em vacas Holandesas

RESUMO: O objetivo deste trabalho foi estimar os parâmetros genéticos e fenotípicos para ocorrência da mastite clínica (MC) e para a produção de leite acumulada até 305 dias (PR305) e estudar as associações genéticas entre elas, usando informações de 11.738 lactações de 5.084 vacas de um rebanho da raça Holandesa, paridas entre 1995 a 2010. Os componentes de covariância foram obtidos por abordagem Bayesiana, sob modelo animal. As estimativas de herdabilidade para a PR305 e para a MC foram de 0,16 (0,02) e 0,11 (0,02), respectivamente, e as repetibilidades foram 0,34 (0,012) e 0,21 (0,02), para PR305 e MC, respectivamente. A correlação genética entre a PR305 e a MC foi negativa e de baixa magnitude (-0,21±0,13). As estimativas de herdabilidade para PR305 e MC indicam que estas características são influenciadas por fatores ambientais, entretanto há suficiente variabilidade genética para obtenção de ganhos através da seleção.

Contextual Determinants of Childhood Injury: A Systematic Review of Studies With Multilevel Analytic Methods

Background. The definition of injury that underpins the contemporary approach to injury prevention is an etiological definition relating to bodily damage arising from transfer of energy to tissues of the body beyond the limits compatible with physiological function. Causal factors proximal to the energy transfer are nested within a more complex set of contextual determinants. For effective injury control, understanding of these determinants is critical. Objectives. The primary aims of this study were to describe the area-level determinants that have been included in multilevel analyses of childhood injury and to quantify the relationships between these area-level exposures and injury outcomes. Search methods. We conducted a systematic review of peer-reviewed, English-language literature published in scientific journals between January 1997 and July 2014, reporting studies that employed multilevel analyses to quantify the eco-epidemiological causation of physical unintentional injuries to children aged 16 years and younger. We conducted and reported the review in accordance with the PRISMA guidelines. Selection criteria. We included etiological studies of causal risk factors for unintentional traumatic injuries to children aged 0 to 16 years. Methodological inclusion criteria were as follows: Epidemiological studies quantifying the relationship between risk factors (at various levels) and injury occurrence in the individual; Studies that recognized individual exposure and at least 1 higher level of exposure with units at lower levels or microunits (e.g., individuals) nested within units at higher levels or macrounits (e.g., areas or neighborhoods); Injury outcomes (dependent variable) examined at the individual level; and Central analytic techniques belonging to the following categories: multilevel models, hierarchical models, random effects models, random coefficient models, covariance components models, variance components models, and mixed models. We combined criteria from the checklist described by the Cochrane Effective Practice and Organization of Care Review Group with factors in the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement, and we used several quality assessment items from other injury-related systematic reviews to create a quality assessment checklist for this review. Data collection and analysis. Two authors independently extracted data and selected analysis features for the included studies by using preformatted tables. They extracted information as reported in the articles. We determined statistical significance of estimates and effects by using the conventional threshold, P &lt; .05. Any differences in the information extracted were resolved by discussion between authors and by specifically rereading and rechecking the facts as reported in the relevant articles. We tabulated results from the final multilevel model(s) in each of the included articles with key aspects summarized in text. Interpretations of the results and identification of key issues raised by the collated material are reported in the Discussion section of this article. Main results. We identified 11 967 articles from the electronic search with only 14 being included in the review after a detailed screening and selection process. Nine of the 14 studies identified significant fixed effects at both the area and individual levels. The area-level variables most consistently associated with child injury rates related to poverty, education, employment, and access to services. There was some evidence that injury rates were lower in areas scoring well on area-level summary measures of neighborhood safety. There was marked variation in the methods used and in the mapping of measured variables onto the conceptual model of ecological causation. Author conclusions. These results help establish the scope for the public policy approach to injury prevention. More consistent reporting of multilevel study results would aid future interpretation and translation of such findings.

A Comparison of Linear Models for Estimating Co-Variance Components and Genetic Parameters in Holstein Dairy Cattle

A Comparison of Linear Models for Estimating Co-Variance Components and Genetic Parameters in Holstein Dairy Cattle

GENETIC AND PHENOLYTIC PARAMETERS FOR EARLY GROWTH TRAITS ON EGYPTION BALADI CALVES

Data on the pre-weaning growth traits of 815 Egyptian native baladi calves borned from the period 1993 to 2014 were used to study the non-genetic, and genetic phenotypic factors of pre-weaning growth traits. The data were collected from Animal Production Research Station at El-Serw, Ministry of Agriculture. Covariance component for birth weight (W0), weaning weight (WW) and average daily gain from birth to weaning (ADG) were estimated by using least squares and maximum likelihood (LSMLMW) computer program of Harvey (1990). Model included sirs and dams within sires as random effects, year and season of birth, sex of calf and parity of cow as fixed effects. In addition, cow weight at calving and suckling days were used as covariate. The means and standard deviations of body weight at birth, weaning, and average daily gain were 23.47±4.29 kg, 80.59±6.36 kg and 0.477±0.06 g/d, respectively. Statistically season and year of calving and parity of cow did not showed significant effects, but sire and sex of calf had significant effects on studied traits. The values of heritability were 0.15, 0.33 and 0.38 for body weights at birth, weaning and average daily gain from birth to weaning, respectively. The results showed that most estimates of genetic and phenotypic correlations among body weights and daily gain traits were positive.

Core Earnings Uncertainty, Dividend Change Announcements and the Reduction of Covariance Component Risks

We present evidence of two systematic market risk implications associated with core earnings news implicit in dividend change announcements: (1) a decline in firm-market correlation intensity, consistent with reduced investor reliance on overall market movements to value shares, and (2) a downward shift in standard deviation of returns, consistent with increased core earnings information precision. Decoupling these two covariance component risk effects is important because they can offset one another at the firm level, masking unique market influences on total systematic risk. Each is influenced by the information environment in different ways and each is shown to incrementally explain returns in a manner consistent with the capital asset pricing model (CAPM).

Contextual Determinants of Childhood Injury: A Systematic Review of Studies With Multilevel Analytic Methods.

The definition of injury that underpins the contemporary approach to injury prevention is an etiological definition relating to bodily damage arising from transfer of energy to tissues of the body beyond the limits compatible with physiological function. Causal factors proximal to the energy transfer are nested within a more complex set of contextual determinants. For effective injury control, understanding of these determinants is critical. The primary aims of this study were to describe the area-level determinants that have been included in multilevel analyses of childhood injury and to quantify the relationships between these area-level exposures and injury outcomes. We conducted a systematic review of peer-reviewed, English-language literature published in scientific journals between January 1997 and July 2014, reporting studies that employed multilevel analyses to quantify the eco-epidemiological causation of physical unintentional injuries to children aged 16 years and younger. We conducted and reported the review in accordance with the PRISMA guidelines. We included etiological studies of causal risk factors for unintentional traumatic injuries to children aged 0 to 16 years. Methodological inclusion criteria were as follows: Epidemiological studies quantifying the relationship between risk factors (at various levels) and injury occurrence in the individual; Studies that recognized individual exposure and at least 1 higher level of exposure with units at lower levels or microunits (e.g., individuals) nested within units at higher levels or macrounits (e.g., areas or neighborhoods); Injury outcomes (dependent variable) examined at the individual level; and Central analytic techniques belonging to the following categories: multilevel models, hierarchical models, random effects models, random coefficient models, covariance components models, variance components models, and mixed models. We combined criteria from the checklist described by the Cochrane Effective Practice and Organization of Care Review Group with factors in the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement, and we used several quality assessment items from other injury-related systematic reviews to create a quality assessment checklist for this review. Two authors independently extracted data and selected analysis features for the included studies by using preformatted tables. They extracted information as reported in the articles. We determined statistical significance of estimates and effects by using the conventional threshold, P < .05. Any differences in the information extracted were resolved by discussion between authors and by specifically rereading and rechecking the facts as reported in the relevant articles. We tabulated results from the final multilevel model(s) in each of the included articles with key aspects summarized in text. Interpretations of the results and identification of key issues raised by the collated material are reported in the Discussion section of this article. We identified 11,967 articles from the electronic search with only 14 being included in the review after a detailed screening and selection process. Nine of the 14 studies identified significant fixed effects at both the area and individual levels. The area-level variables most consistently associated with child injury rates related to poverty, education, employment, and access to services. There was some evidence that injury rates were lower in areas scoring well on area-level summary measures of neighborhood safety. There was marked variation in the methods used and in the mapping of measured variables onto the conceptual model of ecological causation. AUTHOR CONCLUSIONS: These results help establish the scope for the public policy approach to injury prevention. More consistent reporting of multilevel study results would aid future interpretation and translation of such findings.

Genetic parameters for carcass traits and body size in sheep for meat production.

The aim was to estimate the covariance components and genetic parameters of carcass traits and body size of meat sheep by employing animal models for single and multi-trait analyses. Data were collected from herds of adult Santa Ines sheep. The ribeye area, subcutaneous fat thickness, and rump fat thickness, evaluated in vivo by ultrasound images of the carcass; and morphometric traits: fat depth, rump height, chest circumference, chest depth, body length, and rump length were measured. The covariance components and genetic parameters for these traits were estimated by restricted maximum likelihood methodology, considering the random additive direct-genetic effects of the animal and errors associated with each observation, and the fixed effects of the contemporary group, the type of birth, and the age of the animal classes. Heritability estimates for carcass traits and animal size were higher in the three-trait analyses than in the single-trait analyses. The magnitudes of the values obtained indicate that genetic progress can be achieved by selection based on the traits studied.

Alternative strategies for genetic analyses of milk flow in dairy cattle

Measurements for average milk flow (AMF) in kilograms of milk per minute of milking time from 629,161 Holstein cows from calving years 1990 to 2008 were used to estimate genetic covariance components using a variety of statistical models. For bivariate linear-threshold model applications, Gaussian-distributed AMF (linear sire model) was categorized into 2 distinct classes (threshold sire model) by setting arbitrary thresholds for extremely slow or extremely fast milking cows. In different bivariate runs with the 2 traits, Gaussian AMF and binary AMF, within a Bayesian framework, thresholds for the binary trait were 1.2, 1.6, 2.6, and 2.8 kg/min. Posterior heritabilities for AMF from the linear and the threshold models in all runs were in a narrow range and close to 0.26, and the posterior genetic correlation between AMF, defined as either a Gaussian or binary trait, was 0.99. A data subset was used to infer genetic and phenotypic relationships between AMF with test-day traits milk yield, fat percentage, protein percentage, somatic cell score (SCS), fat-to-protein ratio, and energy-corrected milk using recursive linear sire models, standard multiple trait linear sire models, and multiple trait linear sire models accounting for the effect of a trait 1 on a trait 2, and of trait 2 on trait 3, via linear regressions. The time-lagged 3-trait system focused on the first test-day trait after calving (trait 1), on AMF (trait 2), and on the test-day trait (trait 3) after the AMF measurement. Posterior means for heritabilities for AMF from linear and recursive linear models used for the reduced data set ranged between 0.29 and 0.38, and were slightly higher than heritabilities from the threshold models applied to the full data set. Genetic correlations from the recursive linear model and the linear model were similar for identical trait combinations including AMF and test-day traits 1 and 3. The largest difference was found for the genetic correlation between AMF and fat percentage from the first test day (i.e., −0.31 from the recursive linear model vs. −0.26 from the linear model). Genetic correlations from the linear model, including an additional regression coefficient, partly differed, especially when comparing correlations between AMF and SCS and between AMF and fat-to-protein ratio recorded after the AMF measurement data. Structural equation coefficients from the recursive linear model and corresponding regression coefficients from the linear model with additional regression, both depicting associations on the phenotypic scale, were quite similar. From a physiological perspective, all models confirmed the antagonistic relationship between SCS with AMF on genetic and phenotypic scales. A pronounced recursive relationship was also noted between productivity (milk yield and energy-corrected milk) and AMF, suggesting further research using physiological parameters as indicators for cow stress response (e.g., level of hormones) should be conducted.