Residual Random Effects Research Articles

THE IMPACT OF TAXATION ON INTERNATIONAL COMMERCE AND EXCHANGE RATE ON IMPORTS

The globalization of capital markets has accelerated capital movements, turning the world into one large marketplace. International trade has become an important tool in shaping the financial structure of countries. However, elements such as customs duties and import quotas play a decisive role in trade between countries. Taxes, which are a financial policy tool of countries, are used to influence foreign trade, particularly imports. One of the determining factors in foreign trade activities is the exchange rate. The purpose of this study is to examine the effect of taxes on imports and the change in the nominal exchange rate on the import volume of goods and services. In this study, which examined 20 Organization for Economic Co-Operation and Development (OECD) countries with data for the period 1999-2021 taken from the World Bank official website, the model was analyzed with the help of AR (1) Residual Random Effects Generalized Least Squares Regression Model Estimator. The analysis revealed that increases in international trade taxes and exchange rate increases reduce the import volume. This result empirically supports the conclusion that taxes on international trade and reductions in exchange rates increase import volume.

Estimation of the threshold for heat stress and genetic features for milk yield in Mehsana buffaloes in India

Heat stress is one of the primary environmental factors that harm both the productivity and health of buffaloes. The current study was conducted to estimate the threshold of temperature humidity index (THI)1 and genetic features for milk yield of first-lactation Mehsana buffaloes using an univariate repeatability test-day model. The data included 130,475 first lactation test-day milk yield (FLTDMY) records of 13,887 Mehsana buffaloes and the daily temperature and humidity. The statistical model included herd test day as fixed effects, days-in-milk (DIM) classes, age of the animal, as well as random factors such as the additive genetic effect (AGE) of animal in general conditions (intercept), AGE of the buffaloes subjected to heat stress (slope), permanent environmental effect of animal in general conditions (intercept), permanent environmental effect of animal under heat stress conditions (slope) and random residual effect. It was expected that the general effects and the heat-tolerance effects would be correlated, represented by the present investigation's repeatability models. The variance components of FLTDMY in the present study were computed using the REML method. The threshold for THI was 78. At the THI below the threshold, the heritability estimated for the FLTDMY trait was 0.29, and the additive genetic variance (AGV) for heat stress conditions was 0. At THI of 83, AGV for heat stress conditions was highest for FLTDMY. The genetic correlation of general AGE to heat-tolerant AGE was −0.40. The results indicated that a consistent selection for milk production, avoiding the thermal tolerance, may diminish the thermal tolerance capacity of Mehsana buffaloes.

118 Variance Components for Age at First Calving and Yearling Weight When Accounting for Age Differences at Fixed Breeding Dates in a Contemporary Group

Abstract The ability of a heifer to calve earlier in a breeding season has implications on the economic success of heifers in a production system. Heifers that calve earlier in a calving season have increased calf performance, improved subsequent fertility, and a longer productive life in the herd. Production systems typically breed heifers during fixed breeding dates, with heifers of variable ages in the same contemporary group. The objective of this study was to determine if heifer yearling weight (YW) or age differences going into a fixed breeding period influenced the additive genetic variance of age at first calving (AFC) explained in a multi-breed population. Heifer fertility data were obtained from International Genetic Solutions. Females with exposure and calf production records were used to identify individuals calving between 630 and 810 days of age. Females with embryo transfer, multiple birth progeny, and an age differential (DIFF) greater than 100 days were excluded. Age differential was defined as the difference in days between an individual’s birth date and the earliest birth date of an animal in a defined contemporary group. Contemporary group was defined as; breeder, herd, year born, birth and yearling group. After data filtering, there were 149,350 calving records. A 3-generation pedigree file was used containing 365,637 animals, with 37,420 unique sires and 219,533 unique dams. Two bivariate multi-trait animal models for AFC and YW with random additive genetic and residual effects and fixed effects of contemporary group, breed proportion, and % retained hybrid vigor were used. The first model consisted of the aforementioned effects while the second model included DIFF as an additional fixed effect. Analyses were performed using ASReml 3.0. When DIFF was not included as a fixed effect, the additive, residual and phenotypic variances for AFC were 126.1, 456.8, and 582.9 d2, respectively, and the genetic correlation between AFC and YW was 0.36 ± 0.02. When DIFF was included as a fixed effect, the additive, residual, and phenotypic variances for AFC were 10.0, 326.0, and 336.0 d2, respectively. The genetic correlation between AFC and YW was 0.19 ± 0.04. In the absence of DIFF, the heritability estimates for AFC and YW were 0.22 ± 0.01 and 0.44 ± 0.01, respectively but were 0.03 ± 0.003 and 0.44 ± 0.01 respectively, when DIFF was included. Age differential was a significant effect on AFC at –0.86 (P < 0.0001). The low additive variance of AFC when accounting for DIFF suggests that the influence of a female's age going into a fixed breeding date explains much of the variation in AFC. The low heritability estimate for AFC and genetic correlation with YW suggests that selection for improved AFC will be difficult using this method and that producers should instead focus on improved management practices to ensure females calve earlier in a calving season.

The Importance of Green Tax in Reducing Carbon Emissions: The Case of G-7 Countries

Global warming and climate change are recognized as one of the most important environmental and economic problems of the last century. For this reason, new studies are added every day in this field. Carbon emissions, which are shown as the most important cause of global warming, result from the intense use of fossil fuels to meet the energy demand. In this context, many countries are trying to limit their carbon emissions with new laws and regulations. One of these regulations is green taxes. In the study, the role of green taxes in reducing carbon emissions that cause global warming and climate change was examined by using linear panel regression methods for the years 1994-2014 in G-7 countries. In the study, firstly specification tests, then deviations from the assumption were tested and parameter estimations were made using AR(1) Residual Random Effects model. When the coefficients obtained in the study are examined, it is seen that there is a positive relationship between economic growth and carbon emissions. On the other hand, it is seen that environmental patent applications, environmental taxation and renewable energy consumption variables, which are indicators for monitoring environmental quality, have a negative relationship with carbon emissions.

16 Genetic Parameter Estimates for Age at First Calving in Simmental and Red Angus Heifers

Abstract The objective of this study was to determine genetic parameter estimates for age at first calving (AFC) in Red Angus and Simmental heifers. The ability for a heifer to calve earlier has implications on calf performance and the heifer's successive re-breeding. Heifer pregnancy data were obtained from International Genetic Solutions, where females with exposure records were included in the population. Females with a subsequent record of calving between 630 and 810 days of age were included in the analysis. Females with embryo transfer or multiple birth progeny were excluded. Records from single-animal and contemporary groups with no variation were eliminated and after data filtering, there were 149,379 Simmental with a mean Simmental breed proportion of 0.58 ± 0.25, and 83,191 Red Angus records with a mean Red Angus breed proportion of 0.80 ± 0.13. A 3-generation pedigree file containing 291,530 Simmental animals with 25,585 unique sires and 161,921 unique dams, and a 3-generation pedigree file containing 174,914 Red Angus animals with 19,733 unique sires and 99,464 unique dams were used for the separate analysis. The mean AFC in Simmental and Red Angus was 724.8 ± 28.64 d, and 724.2 ± 29.93 d, respectively. Data for each breed were modeled separately using a single trait animal model with random additive genetic and residual effects, and the fixed effect of contemporary group using ASReml 3.0. Contemporary group was defined as breeder, herd, year born, birth group and yearling group. The additive, residual, and phenotypic variances for Simmental were 120.08, 520.37, and 640.45 d2, respectively. For Red Angus, 85.67, 543.56, and 629.24 d2 were the additive, residual, and phenotypic variances, respectively. The heritability estimates of AFC for Simmental and Red Angus were 0.19 ± 0.0059 and 0.14 ± 0.0076, respectively. These results demonstrate AFC in days is lowly to moderately heritable. Lower residual and higher additive variance in Simmental compared to Red Angus may suggest the Simmental population has larger variation for underlying fertility influences such as age at puberty. The mean breeding values for AFC in Simmental was 6.1 ± 8.0, with a minimum of -24.4 and maximum of 54.7. Whereas Red Angus breeding values for AFC resulted in a mean of 4.0 ± 5.6 and a minimum and maximum of -16.1, and 37.3, respectively. The comparable heritability estimates, low standard error, and range in breeding values suggest selection for improved AFC can be attained. It is worth further exploring the impact of breed effects and heterosis influences on age at first calving. While this study does not account for heifers that did not calve in their first year, tandem selection for heifer fertility and age at calving should be heavily emphasized due to the economic importance of both traits.

Genome-wide scan for selective sweeps identifies novel loci associated with resistance to mastitis in German Holstein cattle.

Domestication and selection significantly changed phenotypic and behavioural traits in modern domestic animals. In this study, to identify the genomic regions associated with mastitis, genomic data of German Holstein dairy cattle were analysed. The samples were genotyped using the Bovine 50 K SNP chip. For each defined healthy and sick group, 133 samples from 13,276 genotyped dairy cows were selected based on mastitis random residual effects. Grouping was done to infer selection signatures based on XP-EHH statistic. The results revealed that for the top 0.01 percentile of the obtained XP-EHH values, five genomic regions on chromosomes 8, 11, 12, 14 and 26 of the control group, and four regions on chromosomes 3, 4 (two regions) and 22 of the case group, have been under selection. Also, consideration of the top 0.1 percentile of the XP-EHH values, clarified 21 and 15 selective sweeps in the control and case group, respectively. This study identified some genomic regions containing potential candidate genes associated with resistance and susceptibility to mastitis, immune system and inflammation, milk traits, udder morphology and different types of cancers. In addition, these regions overlap with some quantitative trait loci linked to clinical mastitis, immunoglobulin levels, somatic cell score, udder traits, milk fat and protein, milk yield, milking speed and veterinary treatments. It is noteworthy that we found two regions in the healthy group (on chromosomes 12 and 14) with strong signals, which were not described previously. It is likely that future research could link these identified genomic regions to mastitis. The results of the current study contribute to the identification of causal mutations, genomic regions and genes affecting mastitis incidence in dairy cows.

Estimation of Heritability of Karan Fries Cattle using Bayesian Procedure

  The Bayesian model was applied for analyzing the first lactation in Karan Fries cattle. First lactation data of production (305-day or less milk yield and daily milk yield) were collected from the history-cum pedigree sheet and daily milk yield registers of the division of Dairy Cattle Breeding (DCB), National Dairy Research Institute (NDRI), Karnal. In the Bayesian paradigm, MCMC methods are applied to solve complex mathematical problems to estimate a large number of unknown parameters. Assuming linear mixed model and using the different prior set up, diagnostic of MCMC (Markov Chain Monte Carlo) was carried out graphically as well as by Heidelberg stationarity test. Variance estimates of the random effects (VA) and residual variance estimation (VR) and Variance estimate location effects i.e. fixed effects were calculated along with effective sample size. Finally, heritability (h2) estimates for First lactation 305 days or less milk yield (FL305DMY) was estimated along with its credible interval.

Genetic parameter estimates for feet and leg traits in Red Angus cattle.

The objective of this study was to estimate genetic parameters for feet and leg traits, relationships within feet and leg traits, and relationships between feet and leg traits and production traits in Red Angus cattle. Subjective scores for 14 traits including body condition score (BCS), front hoof angle (FHA), front heel depth (FHD), front claw shape (FCS), rear hoof angle (RHA), rear heel depth (RHD), rear claw shape (RCS), size of hoof (SIZE), front side view (FSV), knee orientation (KNEE), front hoof orientation (FHO), rear side view (RSV), rear view (RV), and a composite score (COMP) were collected by trained evaluators on 1,720 Red Angus cattle. All traits except COMP were scored as intermediate optimum traits. Performance data, and EPD were obtained on all animals measured and a three-generation pedigree was obtained from the Red Angus Association of America (RAAA) that contained 13,306 animals. Data were modeled using a linear bivariate animal model with random additive genetic and residual effects, and fixed effects of age and contemporary group (herd-year) implemented in ASREML 4.0. Heritability estimates of BCS, FHA, FHD, FCS, RHA, RHD, RCS, SIZE, FSV, KNEE, FHO, RSV, RV, and COMP were 0.11, 0.20, 0.17, 0.09, 0.19, 0.25, 0.17, 0.36, 0.16, 0.17, 0.17, 0.30, 0.14, and 0.12, respectively. These results demonstrate feet and leg traits are lowly to moderately heritable. Strong, positive genetic correlations were observed between FHA and FHD (0.89), FHA and RHA (0.88), FHD and RHA (0.85), FHA and RHD (0.85), FHD and RHD (0.94), and FHO and KNEE (0.95), indicating these traits were highly related to each other. Strong and negative genetic correlations were observed between KNEE and FSV (-0.59) and FHO and FSV (-0.75). The strongest Pearson correlation between front limb traits (FHA, FHD, FSV, FHO, KNEE, and COMP) and Stayability EPD (STAY) was FSV (r = 0.16) and for rear limb traits (RHA, RHD, RCS, RSV, RV, and COMP) and STAY was RCS (r = -0.12). This suggests cattle with more slope to the angle of the shoulder and cattle with less evidence of hoof curl may stay in the herd longer. Further investigation into the economic viability of feet and leg trait genetic prediction with a larger population of animals is required to help validate these findings.

Genetic associations between growth traits and age at first calving in Guzera cattle using random regression models

The aim of this study was to estimate genetic associations between body weight (BW), scrotal circumference (SC), and age at first calving (AFC) in Guzera cattle using random regression models. The analyses used 159,366 and 23,780 records of BW and SC obtained between 335 and 724 days of age and divided into classes at intervals of 10 days, as well as 63,596 records of AFC. A multitrait animal model via Gibbs sampling was adopted that included random direct additive genetic, permanent environmental (only for BW and SC), and residual effects. The fixed effects considered were the contemporary group, age of cow at calving (linear and quadratic effect), and the fixed curve to model the average trend of the population on the age classes of the animals. For BW and SC, four models were compared using the deviance information criterion, with Legendre polynomials ranging from one to four coefficients (intercept, linear, quadratic, and cubic) for additive genetic and permanent environmental effects. The model with three coefficients (quadratic) was considered the most appropriate to describe the (co)variance structure of the traits analyzed. The mean heritability estimates ranged from 0.17 to 0.31 for BW and from 0.19 to 0.28 for SC across age classes. The mean heritability for AFC was 0.07. The genetic correlations between BW and SC across age classes were always positive and favorable, ranging from 0.33 to 0.39. These findings indicate that selection for higher BW should result in animals with greater SC. The genetic correlations between BW at different ages and AFC ranged from -0.56 to -0.38, while those between SC at different ages and AFC ranged from -0.55 to 0.08. Except for some genetic correlations close to zero and positive between SC close to 365 days of age and AFC, in general the results showed that the selection for either higher BW or greater SC should result in a reduction of AFC in Guzera cattle. Calculation of the relative selection efficiency indicated a greater correlated response in AFC by selection for SC, especially after 634 days of age, compared to direct selection for AFC.

Genetic parameters for performance, feed efficiency, and carcass traits in Senepol heifers

Improving feed efficiency is a key breeding goal in the beef cattle industry. In this study, we estimated the genetic parameters for feed efficiency and carcass traits in Senepol cattle raised in tropical regions. Various indicators of feed efficiency [gain to feed ratio (G:F), feed conversion ratio (FCR), residual weight gain (RG), residual intake and body weight gain (RIG), and residual feed intake (RFI)] as well as growth [final BW, average daily gain (ADG), and DM intake (DMI)], and carcass [rib-eye area (REA), backfat thickness (BF), intramuscular fat score, and carcass conformation score] traits were included in the study. After data editing, records from 1 393 heifers obtained between 2009 and 2018 were used for the analyses. We fitted an animal model that included contemporary group (animals from the same farm that were evaluated in the same test season) as the fixed effect, and a linear effect of animal age at the beginning of the test as a covariate; in addition to random direct additive genetic and residual effects. The (co)variance components were estimated by Bayesian inference in uni- and bivariate analyses. Our results showed that feed efficiency indicators derived from residual variables such as RG, RIG, and RFI can be improved through genetic selection (h2 = 0.14 ± 0.06, 0.13 ± 0.06, and 0.20 ± 0.08, respectively). Variables calculated as ratios such as G:F and FCR were more influenced by environmental factors (h2 = 0.08 ± 0.05 and 0.09 ± 0.05), and were, therefore, less suitable for use in breeding programs. The traits with the greatest and impact on genetic progress in feed efficiency were ADG, REA, and BF. The traits with the greatest and least impact on growth and carcass traits were RG and RFI, respectively. Selection for feed efficiency will result in distinct overall effects on the growth and carcass traits of Senepol heifers. Direct selection for lower RFI may reduce DMI and increase carcass fatness at the finishing stage, but it might also result in reduced growth and muscle deposition. Residual BW gain is associated with the highest weight gain and zero impact on REA and BF, however, it is linked to higher feed consumption. Thus, the most suitable feed efficiency indicator was RIG, as it promoted the greatest decrease in feed intake concomitant with faster growth, with a similar impact on carcass traits when compared to the other feed efficiency indicators.

ImmuneDEX: updated genomic estimates of genetic parameters and breeding values for Australian Angus cattle

Context Immune competence is a proxy trait for general disease resistance and is based on combined measures of an animal’s ability to mount both a cell-mediated immune response (Cell-IR) and an antibody-mediated immune response (Ab-IR). On the basis of previously described arithmetic, we combined these measures into a single proxy trait for immune competence, named ImmuneDEX (IDEX). Aims Using a population of 3715 Australian Angus steers (n = 2395) and heifers (n = 1320) with genotypes for 45 364 single-nucleotide polymorphisms, we provide the latest genomic estimates of heritability and genetic correlations for IDEX and the components Cell-IR and Ab-IR immune competence phenotypes. Accuracy and bias of genomic predictions of breeding values are also presented and discussed. Methods Measures of Cell-IR, Ab-IR and IDEX were analysed jointly in a tri-variate genomic restricted maximum-likelihood model that contained the fixed effects of contemporary group with 80 levels, the linear covariates of age at measurement and change in skin thickness at control site, and the random polygenic (genomic estimated breeding value, GEBV) and residual effects. Following Method LR procedures, we estimate accuracy, bias and dispersion of genomic predictions using a cross-validation scheme based on five year-of-birth cohorts. Key results We report genomic restricted maximum-likelihood model estimates of heritability of 0.247 ± 0.040 for Cell-IR, 0.326 ± 0.059 for Ab-IR, 0.275 ± 0.046 for IDEX. While a small positive genetic correlation (rg) was estimated between Cell-IR and Ab-IR (rg = 0.138 ± 0.095), strongly positive estimates were obtained between IDEX and Cell-IR (rg = 0.740 ± 0.044) and between IDEX and Ab-IR (rg = 0.741 ± 0.036). Averaged across the five validation sets, the accuracy of GEBV for Cell-IR, Ab-IR and IDEX was 0.405, 0.443 and 0.411 respectively. Also, some significant bias or dispersion can be expected depending on the cohort used as the validation population. Conclusions Consistent with previous findings, immune competence phenotypes are moderately heritable and accurate GEBV can be generated to allow the selection of cattle with an improved ability to mount a general immune response. Implications Our analyses suggest that ImmuneDEX will provide a tool to underpin long-term genetic strategies aimed at improving the immune competence of Australian Angus cattle in production systems, which, in turn, is expected to reduce the incidence of disease and our reliance on antibiotics to treat disease.

Geographic variations in dental sealant utilization by Medicaid enrollees.

To investigate geographical variations and their relationship to race/ethnicity in dental sealant utilization for first molars among Wisconsin Medicaid enrollees from 2010 to 2013. Wisconsin Medicaid dental claims for sealants in children aged 6-16 years were analyzed. County-level population density, urban influence code, presence of dental health professional shortage areas, and population-per-dentist ratio were considered as geographic predictors. Descriptive statistics and mixed effects Poisson regression models were used to examine the effect of county level covariates on the number of dental sealants received per person-year (PY) of eligibility adjusting for patient-level characteristics. Over-dispersion was modeled by a random residual effect, and all models adjusted for single-year age and gender interaction and race/ethnicity main effect. Medicaid claims for sealants on first permanent molars for 2010-2013 totaled 288,019 over 1,130,000 PY. The age- and gender-standardized rate of first molar sealant applications per 100 PY were 27.9, 25.7, and 16.6 for White, Hispanic, and Black children, respectively. County-specific rates ranged from a low of 8.9 per 100 PY to a high of 62.6 per 100 PY. In the multivariate analysis after adjusting for geography, compared to Whites, Hispanics had highest rates (rate ratio (RR)=1.33, 95% CI=1.30-1.37) of dental sealant utilization followed by Blacks (RR=1.25, 95% CI=1.21-1.29). Population density was the only significant geographic predictor (RR=0.56 per 10-fold increase, 95% CI=0.45-0.69). Substantial geographic variability in the utilization of sealants for first molars was identified. Lower population density was the main geographical predictor of high sealant utilization.

Assessment of alternative models for genetic analysis of worm and tick infestation in nellore cattle

Worms and ticks are important parasites in beef cattle, especially in tropical areas, causing significant economic and production losses. Understanding animal-to-animal variation on infestation for these parasites might guide genetic selection and improvement of management practices to attenuate its detrimental effects. Statistical models used to analyze such traits usually assume a Gaussian distribution for the observed data. However, this assumption is quite often inappropriate for counting data. Therefore, the objectives of this study were: 1) Estimate genetic parameters for worms and tick infestations in Nellore cattle, and 2) To compare the overall performance of six data analysis approaches for worm and tick infestation in Nellore cattle, using different specifications of generalized linear mixed models (GLMM) and response variables. Data consisted of presence/absence of parasites as well as counting observations for both worms and ticks in a Nellore herd in Brazil. The binary data were analysed with both Gaussian and Threshold models, whereas the counting data were studied using Gaussian models on the original and logarithmic scales, as well as Poisson and Zero-Inflated Poisson (ZIP) models. All models included the systematic effects of contemporary group and age, as well as the random additive genetic and residual effects. Models were compared using four criteria: Deviance Information Criterion (DIC), Spearman's correlation between predicted breeding values from different models, the agreement on the 5 and 50% top-ranked animals across models, and the Mean Squared Error of Prediction (MSEP) assessed via Monte Carlo Cross-Validation (MCCV). The MCCV was performed using parallel computing through the Center for High Throughput Computing (CHTC) at the University of Wisconsin-Madison. The estimates of heritability ranged from 0.15 to 0.40 for worms and from 0.08 to 0.25 for ticks. According to the DIC, non-Gaussian models displayed the best goodness of fit compared to Gaussian models. DIC's results excluded Gaussian models on the logarithmic scale because fairer comparisons involve phenotypes on the same scale. Spearman's correlation and the percentage of agreement on the 5% and 50% top-ranked animals suggested some re-ranking of animals depending upon the model used. Monte Carlos Cross-Validation showed that all models presented similar MSEP with average values of 0.20 (binary data; worms), 0.18 (binary data; ticks), 15.69 (count data; worms), and 14.19 (count data; ticks). Moreover, performing MCCV in parallel had the benefit to deliver results for all models in about 2 days. Heritability estimates indicate that the selection of high merit animals for worms and tick infestation is possible feasible and can potentially contribute to the genetic progress. Furthermore, genetic selection should be performed concomitantly with traditional parasite control approaches. Overall, non-Gaussian models seem to be better suitable for genetic analysis of worm and tick infestation in beef cattle, because such models have lower DIC values with similar predictive performance compared to Gaussian models.

Self-management among pre-teen and adolescent diabetes device users.

Despite increased diabetes device use, few adolescents with type 1 diabetes (T1D) meet glycemic targets. We examine associations between utilization of insulin pumps and continuous glucose monitoring (CGM) and glycemic control. This prospective cohort study included 80 youths (10-18 years of age) with T1D. Multiple linear regression and linear mixed models (LMM) were used to estimate the effects of device self-management on HbA1c and daily time in range (70-180 mg/dL), respectively. Every blood glucose (BG) input/day was associated with a 0.2% decrease in HbA1c (95% CI: -0.297, -0.013), each bolus/day was associated with a 0.2% decrease (-0.327, -0.057), and use of CGM was associated with a 0.5% decrease (-1.00, -0.075). Among CGM users (n = 45) every 10% increase in CGM use was associated with a 0.3% decrease in HbA1c (-0.390, -0.180). In LMM accounting for within subject and between subject variability, there was a negative association between BG input/day frequency (coefficient = -1.880, [-2.640, -1.117]) and time in range. Residual random effects for CGM users were large showing time in range varied between youth with a SD of 15.0% (3 hours and 36 minutes) (SE 2.029, [11.484, 19.530]). Time in range varied significantly from day-to-day with SD of 18.6% (4 hours and 40 minutes) (SE0.455, [17.690, 19.473]). Device self-management behaviors among youth are significantly associated with both HbA1c and time in range. Our findings showing an association between reduced time in range and increased self-management behaviors is novel and deserves further investigation.

Use of Repeated Group Measurements with Drop Out Animals for Variance Component Estimation and Genetic Evaluation: A Simulation Study.

The efficiency of feed utilization plays an important role in animal breeding. However, measuring feed intake (FI) is costly on an individual basis under practical conditions. Using group measurements to model FI could be practically feasible and cost-effective. The objectives of this study were to develop a random regression model based on repeated group measurements with consideration of missing phenotypes caused by drop out animals. Focus is on variance components (VC) estimation and genetic evaluation, and to investigate the effect of group composition on VC estimation and genetic evaluation using simulated datasets. Data were simulated based on individual FI in a pig population. Each individual had measurement on FI at 6 different time points, reflecting 6 different weeks during the test period. The simulated phenotypes consisted of additive genetic, permanent environment, and random residual effects. Additive genetic and permanent environmental effects were both simulated and modeled by first order Legendre polynomials. Three grouping scenarios based on genetic relationships among the group members were investigated: (1) medium within and across pen genetic relationship; (2) high within group relationship; (3) low within group relationship. To investigate the effect of the drop out animals during test period, a proportion (15%) of animals with individual phenotypes was set as the drop out animals, and two drop out scenarios within each grouping scenario were assessed: (1) animals were randomly dropped out; (2) animals with lower phenotypes were dropped out based on the ranking at each time point. The results show that using group measurements yielded similar VCs estimates but with larger SDs compared with the corresponding scenario of using individual measurements. Compared to scenarios without drop out, similar VC estimates were observed when animals were dropped out randomly, whereas reduced VC estimates were observed when animals were dropped out by the ranking of phenotypes. Different grouping scenarios produced similar VC estimates. Compared to scenarios without drop out, there were no loss of accuracies of genetic evaluation for drop out scenarios. However, dropping out animals by the ranking of phenotypes produced larger bias of estimated breeding values compared to the scenario without dropped out animals and scenario of dropping out animals by random. In conclusion, with an optimized group structure, the developed model can properly handle group measurements with drop out animals, and can achieve comparable accuracy of genetic evaluation for traits measured at the group level.

Time class for racing performance of the Quarter Horse: Genetic parameters and trends using Bayesian and multivariate threshold models

The aim of this study was to evaluate racing performance in the Quarter Horse breed in Brazil using genetic parameter estimates, breeding values, and genetic trends for a differentiated trait, time class (TC), at four different distances. The speed records comprising races that occurred between 1981 and 2011 were obtained from hippodromes in the states of São Paulo, Ceará, Brasília, and Mato Grosso do Sul. The final archive analysed contained 4263 horses born to 431 stallions and 1716 dams. These animals participated in 3904 races, providing 17,342 records of final racing time. The four TC traits were defined by 5 scores based on the final times (in seconds) of each animal in each race. The scores for formation of the classes were attributed within each race based on the percent difference from winner's time. Time class was evaluated at distances of 301, 320, 365 and 402 m, generating four phenotypic measures: TC301, TC320, TC365 and TC402, respectively. The genetic parameters were estimated by Bayesian inference in multivariate analysis using a threshold animal model. The model included the fixed effects of race, sex and age class of animal in the race, in addition to additive genetic, animal permanent environmental and residual random effects. The mean heritability estimates were of high magnitude and ranged from 0.45 ± 0.06 (TC402) to 0.56 ± 0.04 (TC365). Repeatability estimates ranged from 0.78 ± 0.06 (TC402) to 0.97 ± 0.01 (TC320). The additive genetic and animal permanent environmental correlations were positive, with mean correlations ranging from 0.33 ± 0.06 between TC301 and TC320 to 0.62 ± 0.06 between TC365 and TC402 and from 0.44 ± 0.08 between TC301 and TC320 to 0.58 ± 0.06 between TC365 and TC402, respectively. The mean breeding values transformed by the cumulative distribution function of the standard normal distribution ranged from 54.6 ± 25.3 (TC365) to 57.8 ± 28.5 (TC320) in the group of animals included in the pedigree and born after 1980. The genetic trends observed indicated that TC301 (301 m) exhibited the greatest evolution between 1981 and 2008. The results suggest the possible use of TC traits as selection criteria since they respond positively to selection, permitting genetic gains in breeding programs of racehorses.

Genetics of Factor VIII Inhibitor Development in Hemophilia Patients: Novel Statistical Approaches in the PATH Study

Here we apply state-of-the-art statistical genetic approaches toward investigating the genetic architecture of factor VIII (FVIII) inhibitor (FEI) development in Hemophilia A (HA). A total of 442 North American HA patients (237 Whites and 205 Blacks; 88% severely affected) enrolled in the PATH Study were: 1) ImmunoChip genotyped at ~167,000 single nucleotide polymorphisms (SNPs) in genes previously implicated in autoimmune disease risk; 2) Evaluated by DNA sequencing and assays for the recurrent intron (I)1 and I22 inversions to identify their causative F8 mutations; and 3) Tested with the Bethesda assay to determine their FEI status. The ImmunoChip genotypes were used to construct a genetic relationship matrix (GRM), denoted by K, following our previously published method,1 and the F8 sequence data along with results from the I1 and I22 inversion assays were used to construct a shared F8-mutation matrix, denoted by F. We analyzed a dichotomous FEI variable under the statistical genetic threshold/liability model (a probit regression in the fixed effects) in conjunction with a variance components model for the FEI liability phenotypic covariance matrix, denoted by P, to model potentially important random effects. For the latter, we specifically assumed independent additive genetic, F8-mutation, and residual environmental random effects. By the independence assumption, the covariance matrix is then decomposable as a sum of the additive genetic (Va), F8-mutation (Vf), and residual environmental (Ve) variances respectively structured by K, F, and the identity matrix I. The variance component model is given as: P = K*Va + F*Vf + I*Ve. Heritability, denoted by h2, is defined as the ratio of Va to the total phenotypic variance (Vp): h2 = Va / Vp. We can further speak of the total heritability given as: h2t = h2r + h2f + h2snp, where the subscripts t, r, f, and snp respectively denote total, residual additive genetic, F8-mutation-specific, and SNP heritabilities. Using eigenstructure methods,2 we can compute power under a simpler model in which Va and Vf are combined as a single variance component. We computed power to detect genetic association as measured by SNP-specific heritability for a set of 403 SNPs in or near 14 candidate immune response genes previously implicated in FEI risk. To account for multiple hypothesis testing, power was computed at the Bonferroni-adjusted significance level of 0.05/403 = 1.2 × 10-4. Under the simplified model, we computed the statistical power to detect causal SNPs for our sample and study design for the sample FEI prevalences, denoted by Kp, for Whites (22.5%) and Blacks (45%), across a range of total heritabilities, h2t = 15%, 35%, and 55%, where the lattermost total heritability was observed for FEI liability in the current study (Figure 1). It should be noted that because the liability heritability is known to be biased upward, we applied the Dempster-Lerner correction to both the total and SNP-specific heritabilities.3 Close inspection of Figure 1 reveals that varying h2t from 15% to 35% to 55% results in slight decreases in power due to the decreasing ratio of the SNP-specific heritability to the total heritability. However, as seen in all three panels, the more important determinant of power is clearly the FEI prevalence in that the power curve for a Kp of 45% is associated with greater power than the power curve for a Kp of 22.5% across the range of total heritabilities examined. As seen in Figure 1, we have adequate power to detect SNP heritabilities as low as 5% and 6%, respectively, for a Kp of 45% and 22.5%. As noted above, we observed a FEI liability total heritability of 55% consisting of a 47% residual additive genetic heritability (p = 0.019) and 8% F8-mutation specific heritability (p = 0.005). This is the first study to use a GRM based on genotype data and a shared causal F8 mutation matrix to model additive genetic and F8-mutation specific effects.Almeida M, Peralta J, Farook V, …, Blangero J. Pedigree-based random effect tests to screen gene pathways. BMC Proc. 2014; 8(Suppl 1 Genetic Analysis Workshop): S100.Blangero J, Diego VP, Dyer T, …, Göring H. A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees. Adv Genetics. 2013; 81: 1-31.Glahn D, Williams J, McKay D, …, Blangero J. Discovering schizophrenia endophenotypes in randomly ascertained pedigrees. Biol Psychiatry. 2015; 77(1): 75-83. [Display omitted] DisclosuresChitlur:Baxter, Bayer, Biogen Idec, and Pfizer: Honoraria; Novo Nordisk Inc: Consultancy. Dinh:Haplomics Biotechnology Corporation: Employment, Equity Ownership. Howard:Haplomics Biotechnology Corporation: Equity Ownership, Other: Chief Scientific Officer, Patents & Royalties: Patent applications and provisional patent applications ; CSL Behring: Research Funding.

Variance components due to direct and maternal effects for birth weight of Brown Swiss cattle

This study was conducted to estimate variance components and genetic parameters for birth weights of Brown Swiss calves. Experimental data were gathered from the records of a State Farm of Turkey (Konuklar) covering the years between 1995 and 2005. Random effects considered in this study included direct and maternal additive genetic effects, maternal permanent environmental effects with direct-maternal genetic covariance and random residual effects. AI-REML algorithm of WOMBAT software was used to estimate variance and covariance components, genetic parameters and breeding values. The general mean for birth weights of all calves was calculated as 38.12±0.006 kg. The least square for mean birth weights was calculated as 38.99±0.007 kg for male calves and as 37.26±0.008 for female calves. Direct heritability (hD2), maternal heritability (hM2), total heritability (hT2), rAM and cAM estimates were respectively calculated as 0.15, 0.10, 0.39, 0.96 and 0.13

Genetic parameters for cheese-making properties and milk composition predicted from mid-infrared spectra in a large data set of Montbéliarde cows

Cheese-making properties of pressed cooked cheeses (PCC) and soft cheeses (SC) were predicted from mid-infrared (MIR) spectra. The traits that were best predicted by MIR spectra (as determined by comparison with reference measurements) were 3 measures of laboratory cheese yield, 5 coagulation traits, and 1 acidification trait for PCC (initial pH; pH0PPC). Coefficients of determination of these traits ranged between 0.54 and 0.89. These 9 traits as well as milk composition traits (fatty acid, protein, mineral, lactose, and citrate content) were then predicted from 1,100,238 MIR spectra from 126,873 primiparous Montbéliarde cows. Using this data set, we estimated the corresponding genetic parameters of these traits by REML procedures. A univariate or bivariate repeatability animal model was used that included the fixed effects of herd × test day × spectrometer, stage of lactation, and year × month of calving as well as the random additive genetic, permanent environmental, and residual effects. Heritability estimates varied between 0.37 and 0.48 for the 9 cheese-making property traits analyzed. Coagulation traits were the ones with the highest heritability (0.42 to 0.48), whereas cheese yields and pH0 PPC had the lowest heritability (0.37 to 0.39). Strong favorable genetic correlations, with absolute values between 0.64 and 0.97, were found between different measures of cheese yield, between coagulation traits, between cheese yields and coagulation traits, and between coagulation traits measured for PCC and SC. In contrast, the genetic correlations between milk pH0 PPC and CY or coagulation traits were weak (-0.08 to 0.09). The genetic relationships between cheese-making property traits and milk composition were moderate to high. In particular, high levels of proteins, fatty acids, Ca, P, and Mg in milk were associated with better cheese yields and improved coagulation. Proteins in milk were strongly genetically correlated with coagulation traits and, to a lesser extent, with cheese yields, whereas fatty acids in milk were more genetically correlated with cheese yields than with coagulation traits. This study, carried out on a large scale in Montbéliarde cows, shows that MIR predictions of cheese yields and milk coagulation properties are sufficiently accurate to be used for genetic analyses. Cheese-making traits, as predicted from MIR spectra, are moderately heritable and could be integrated into breeding objectives without additional phenotyping cost, thus creating an opportunity for efficient improvement via selection.

Bayesian inference of genetic parameters for reproductive and performance traits in White Leghorn hens

This study estimated the genetic parameters for reproductive and performance traits and determined which ones can be used as selection criteria for egg production in laying hens using the Bayesian inference. The data of 1894 animals from three generations of White Leghorn laying hens were analyzed for fertility (FERT), hatchability (HATC), and birth rate measurements at 60 weeks of age (BIRTH), body weight at 16 and 60 weeks of age (BW16 and BW60), age at sexual maturity (ASM), egg height/width ratio, weight, and density at 28, 36, and 40 weeks of age (RHW28, RHW36, RHW40, WEGG28, WEGG36, WEGG40, DENS28, DENS36, and DENS40, respectively) traits. The genetic parameters were estimated by the Bayesian inference method of multi-trait animal model. The model included the additive and residual genetic random effects and the fixed effects of generation. The a posteriori mean distributions of the heritability estimates for reproductive traits ranged from 0.14 ± 0.003 (HATC) to 0.22 ± 0.005 (FERT) and performance from 0.07 ± 0.001 (RHW28) to 0.42 ± 0.001 (WEGG40). The a posteriori mean distributions of the genetic correlation between reproductive traits ranged from 0.18 ± 0.026 (FERT and HACT) to 0.79 ± 0.007 (FERT and BIRTH) and those related to performance ranged from –0.49 ± 0.001 (WEGG36 and DENS36) to 0.75 ± 0.003 (DENS28 and DENS36). Reproductive and performance traits showed enough additive genetic variability to respond to selection, except for RHW28. This trait alone would have little impact on the genetic gain because environmental factors would have a higher impact compared to those from the additive genetic factors. Based on the results of this study, the selection applied on the BIRTH trait can be indicated to improve FERT and HATC of eggs. Furthermore, the use of the WEGG40 could improve egg quality in this population.