Heterogeneous Residual Variances Research Articles

118 DHGLMF90: A software tool using double hierarchical generalized linear models for estimating the genetic heterogeneity of residual variance

Abstract In animal breeding, there is a growing interest in selecting animals that exhibit uniform responses to environmental conditions, aiming for product consistency as a desirable breeding objective. This could be achieved by modeling heteroskedastic residuals when estimating breeding values through a hierarchical single-trait model that includes a mean and dispersion part. The latter could be influenced by genetic and non-genetic features. Bayesian methods can estimate heteroskedastic residuals using the Metropolis-Hastings algorithm, which is a slow and inefficient approach. Double hierarchical generalized linear models (DHGLM) provide a faster alternative to Bayesian methods. This study aimed to develop software named DHGLMF90 to implement DHGLM using a highly efficient algorithm to accurately estimate heterogeneous residual variances and breeding values, particularly suited for handling large datasets. We improved the reweighted least squares algorithm (IRWLS) to enhance convergence properties compared with prior implementations. IRWLS iteratively computes variance components and leverages them for a bivariate model, covering both the mean and dispersion part of the original model. In DHGLMF90, variance component estimation is performed using REML through BLUPF90+. The software was tested using both simulated and real datasets, the latter from previous studies. Additionally, the simulations were replicated to ensure a comprehensive evaluation of the DHGLMF90 performance. Results indicated that, in most cases, there were no significant differences in estimation compared with results obtained from previous studies. DHGLMF90 converged in order of magnitude faster than Bayesian methods. Future developments will include analyzing datasets with genomic information and multiple-trait models.

Disparate trajectories of cognitive aging among American Indian and Alaskan Native people with and without HIV.

This study describes trajectories of cognitive aging among American Indian/Alaskan Native (AI/AN) adults with and without HIV and the role of immunosenescence longitudinally. We characterized trajectories of cognitive aging in a sample of 333 AI/AN and 309 non-Hispanic White (NHW) adults who were followed longitudinally for up to 20 years by the HIV Neurobehavioral Research Program (HNRP) across six U.S. research sites. We used growth curve modeling with autoregressive Lag-1 structures and heterogeneous residual variances to assess the role of ethnoracial identity and HIV grouping upon decline in trajectories of cognitive aging. HIV- AI/AN adults demonstrated earlier and steeper decline in normative trajectories of cognitive aging on tasks of processing speed, timed tasks of attention/working memory, executive function, and psychomotor speed in comparison to HIV- NHW adults. Accentuated trajectories of cognitive aging were evident in both HIV+ and HIV+ immunosuppressed groups in comparison to HIV- peers and were primarily driven by the role of immunosenescence. AI/AN disparities in trajectories of cognitive aging are evident and are likely explained by the interplay of biopsychosociocultural factors, including immunosenescence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Q&A: Methods for estimating genetic gain in sub-Saharan Africa and achieving improved gains.

Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi-environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin. An illustrative example of estimating realized genetic gain was provided by analyzing historical data on fresh cassava (Manihot esculenta Crantz) yield in West Africa (https://github.com/Biometrics-IITA/Estimating-Realized-Genetic-Gain). This approach can serve as a model applicable to other crops and regions. Modernization of breeding programs is necessary to maximize the rate of genetic gain. This can be achieved by adopting genomics to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing. Tracking operational costs, establishing robust, digitalized data management and analytics systems, and developing effective varietal selection processes based on customer insights are also crucial for success. Capacity building and collaboration of breeding programs and institutions also play a significant role in accelerating genetic gains.

Genomic analysis of feed efficiency traits in beef cattle using random regression models.

Feed efficiency plays a major role in the overall profitability and sustainability of the beef cattle industry, as it is directly related to the reduction of the animal demand for input and methane emissions. Traditionally, the average daily feed intake and weight gain are used to calculate feed efficiency traits. However, feed efficiency traits can be analysed longitudinally using random regression models (RRMs), which allow fitting random genetic and environmental effects over time by considering the covariance pattern between the daily records. Therefore, the objectives of this study were to: (1) propose genomic evaluations for dry matter intake (DMI), body weight gain (BWG), residual feed intake (RFI) and residual weight gain (RWG) data collected during an 84-day feedlot test period via RRMs; (2) compare the goodness-of-fit of RRM using Legendre polynomials (LP) and B-spline functions; (3) evaluate the genetic parameters behaviour for feed efficiency traits and their implication for new selection strategies. The datasets were provided by the EMBRAPA-GENEPLUS beef cattle breeding program and included 2920 records for DMI, 2696 records for BWG and 4675 genotyped animals. Genetic parameters and genomic breeding values (GEBVs) were estimated by RRMs under ssGBLUP for Nellore cattle using orthogonal LPs and B-spline. Models were compared based on the deviance information criterion (DIC). The ranking of the average GEBV of each test week and the overall GEBV average were compared by the percentage of individuals in common and the Spearman correlation coefficient (top 1%, 5%, 10% and 100%). The highest goodness-of-fit was obtained with linear B-Spline function considering heterogeneous residual variance. The heritability estimates across the test period for DMI, BWG, RFI and RWG ranged from 0.06 to 0.21, 0.11 to 0.30, 0.03 to 0.26 and 0.07 to 0.27, respectively. DMI and RFI presented within-trait genetic correlations ranging from low to high magnitude across different performance test-day. In contrast, BWG and RWG presented negative genetic correlations between the first 3 weeks and the other days of performance tests. DMI and RFI presented a high-ranking similarity between the GEBV average of week eight and the overall GEBV average, with Spearman correlations and percentages of individuals selected in common ranging from 0.95 to 1.00 and 93 to 100, respectively. Week 11 presented the highest Spearman correlations (ranging from 0.94 to 0.98) and percentages of individuals selected in common (ranging from 85 to 94) of BWG and RWG with the average GEBV of the entire period of the test. In conclusion, the RRM using linear B-splines is a feasible alternative for the genomic evaluation of feed efficiency. Heritability estimates of DMI, RFI, BWG and RWG indicate enough additive genetic variance to achieve a moderate response to selection. A new selection strategy can be adopted by reducing the performance test to 56 days for DMI and RFI selection and 77 days for BWG and RWG selection.

Genomic predictions and GWAS for heat tolerance in pigs based on reaction norm models with performance records and data from public weather stations considering alternative temperature thresholds.

Genetic improvement of livestock productivity has resulted in greater production of metabolic heat and potentially greater susceptibility to heat stress. Various studies have demonstrated that there is genetic variability for heat tolerance and genetic selection for more heat tolerant individuals is possible. The rate of genetic progress tends to be greater when genomic information is incorporated into the analyses as more accurate breeding values can be obtained for young individuals. Therefore, this study aimed (1) to evaluate the predictive ability of genomic breeding values for heat tolerance based on routinely recorded traits, and (2) to investigate the genetic background of heat tolerance based on single-step genome-wide association studies for economically important traits related to body composition, growth and reproduction in Large White pigs. Pedigree information was available for 265,943 animals and genotypes for 8686 animals. The studied traits included ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off-test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN) and weaning-to-estrus interval (IWE). The number of phenotypic records ranged from 6059 (WN) to 172,984 (TNB). Single-step genomic reaction norm predictions were used to calculate the genomic estimated breeding values for each individual. Predictions of breeding values for the validation population individuals were compared between datasets containing phenotypic records measured in the whole range of temperatures (WR) and datasets containing only phenotypic records measured when the weather station temperature was above 10°C (10C) or 15°C (15C), to evaluate the usefulness of these datasets that may better reflect the within-barn temperature. The use of homogeneous or heterogeneous residual variance was found to be trait-dependent, where homogeneous variance presented the best fit for MDP, BFT, OTW, TNB, NBA, WN and IBF, while the other traits (WW and IWE) had better fit with heterogeneous variance. The average prediction accuracy, dispersion and bias values considering all traits for WR were 0.36 ± 0.05, -0.07 ± 0.13 and 0.76 ± 0.10, respectively; for 10C were 0.39 ± 0.02, -0.05 ± 0.07 and 0.81 ± 0.05, respectively; and for 15C were 0.32 ± 0.05, -0.05 ± 0.11 and 0.84 ± 0.10, respectively. Based on the studied traits, using phenotypic records collected when the outside temperature (from public weather stations) was above 10°C provided better predictions for most of the traits. Forty-three and 62 candidate genomic regions were associated with the intercept (overall performance level) and slope term (specific biological mechanisms related to environmental sensitivity), respectively. Our results contribute to improve genomic predictions using existing datasets and better understand the genetic background of heat tolerance in pigs. Furthermore, the genomic regions and candidate genes identified will contribute to future genomic studies and breeding applications.

PSV-6 The Impacts of Heat Stress on Litter Traits of Landrace Population in Taiwan

Abstract Global warming becomes a serious issue in the swine industries of most countries. Sow reproduction suffers the brunt of heat stress problems in tropical and subtropical zones, such as in Taiwan. Litter traits, total number born (TNB) and number born alive (NBA), are essential for sow stability and farm economics. However, these traits are influenced by management, facilities, and environmental conditions. Adjustments to management practices and facilities must be considered environmental conditions, including temperature and relative humidity, particularly in Taiwan under oceanic climates. Such specific climate with high temperature and relative humidity caused heat stress for sows around Taiwan over six months in a year. Therefore, the impact of heat stress at different stages of pregnancy in sows had been analyzed using the best linear unbiased prediction model with heterogeneous residuals. A total of 11,074 records for TNB and NBA from 3,080 Landrace sows were collected from two farms between 2008 and 2021. Simultaneously, weather data were obtained from those weather stations closest to the farms. Sows from mating to farrowing, about four months, had been classified into cool (C) or hot (H) climate condition in each month based on temperature-humidity index equals 70. Hence, there were eight combinations (CCCC, CCCH, CCHH, CHHH, HHHH, HHHC, HHCC, HCCC) of the climate effect in this study. The results of climate effect showed that there was not significant difference among the groups of CCCC, CCCH, CCHH, and CHHH in TNB and NBA, while no significant difference (P > 0.05) was found among the groups of HHHH, HHHC, HHCC, and HCCC in both traits. However, the climate effects of CCCH, CCHH, and CHHH were greater (P < 0.05) than those of HHHC, HHCC, and HCCC in TNB. The climate effects of CCCH and CCHH were greater than those of HHHH, HHHC, HHCC, and HCCC in NBA (P < 0.05). These fluctuations generally indicated when sows experience heat stress on mating and early stage of pregnancy (HHHH, HHHC, HHCC, HCCC), could decrease litter sizes. In the heterogeneous residual variances, the groups of CCCC, CCCH, CCHH, and CHHH were less than the groups of HHHH, HHHC, and HHCC in TNB, significantly, while the groups of CCCC, CCCH, and CHHH were less than the groups of HHHH, HHHC, and HHCC in NBA (P < 0.05). The greatest estimated residual variances were shown in HHCC in both TNB and NBA. These changing of trends indicated high heterogeneous residual variance was discovered when heat stress happened during mating and early pregnancy of sows (HHHH, HHHC, HHCC). In conclusion, heat stress at mating and early stage of sow pregnancy decreased the average and increased the residual variances of TNB and NBA.

Multiple Traits Selection Strategies: A Proposal for Coffee Plant Breeding

Experiments with progenies of perennial species such as coffee are generally affected by the heterogeneity of residual variances between information repeatedly collected in space and time on the same individual. In this study, we propose an index that considers the individual heritability of multiple traits for progeny selection and evaluate the applicability of this index in comparison with other indices in a real dataset. Data from 30 coffee genotypes in the F4:5 generation were used to obtain the individual heritability values (hi2) of progenies that were subjected to factorial analysis to obtain the scores and construct a scatter plot, where graphical analysis (GA) was applied. Genetic gains were obtained for productivity and resistance to Cercospora leaf spot using GA. The best performance among the strategies (−12.11%) was obtained using GA for resistance to Cercospora leaf spot, which has low heritability—contrary to the result obtained using the commonly used index based on the sum of Mulamba and Mock ranks. The GA approach allows an assertive selection to minimize the effects of heterogeneity between seasons, and greater genetic gains are obtained. Its use as a tool for the selection of perennial plant progenies based on multiple characters is promising.

Age-dependent genetic and environmental variance of semen quality in Nordic Holstein bulls

The aim of this study was to estimate genetic and environmental parameters, across bull's age, for semen quality traits including pre- and postcryopreservation semen concentration, sperm motility, and sperm viability as well as ejaculate volume and number of doses per ejaculate. A data set on 96,595 ejaculates from 2,831 Nordic Holstein bulls collected between 2006 and 2019 was used. Genetic and environmental parameters were estimated using a random regression model and applying the average-information REML approach. Spline functions were chosen to fit the additive genetic and permanent environmental effects across bull's age, and the optimal number of knots was chosen using cross validation. Residual variance heterogeneity was assumed in different bull age classes. The estimated repeatabilities of semen quality traits ranged from 0.16 to 0.85 across different ages of bulls. The estimated heritabilities of semen quality traits ranged from 0.02 to 0.56 across different ages of bulls. The results indicate possibilities for genetic improvement of semen quality traits through selective breeding.

Therapeutic and Safety Outcomes of Intravenous Ketamine for Treatment-refractory Depression in a Veteran Population: A Case Series.

Major depressive disorder is a serious, recurrent, and disabling psychiatric illness. Despite many proven treatments with multiple medications or therapies, approximately 30% of patients fail to achieve remission and are considered to have treatment-refractory depression (TRD). Recently, there has been a growing interest in the use of intravenous (IV) ketamine for the treatment of TRD. There is limited yet increasing evidence to support the use of ketamine, a glutamate receptor antagonist, in the management of depression; however, the lack of data regarding the safety and tolerability of therapy has limited its clinical use. By analyzing a cohort of veterans with TRD and comorbid psychiatric conditions treated with IV ketamine infusions for a 24-month study period, we aim to provide critical information about ketamine's clinical effectiveness and safety. Based on a retrospective chart review, we identified eight veterans with TRD receiving treatment with repeated-dose IV ketamine from 2018 to 2020. The magnitude of clinical response was based on the Beck Depression Inventory self-report scale and the Patient Health Questionnaire-9, both measured at the initial patient consultation and before the beginning of each ketamine infusion treatment. Safety analysis included changes to pre- and post-ketamine infusion on vital signs, effects on alertness and sedation, and potential psychosis-like effects. For all outcomes, we estimated a linear mixed-effects model that allowed heterogeneous residual variances for each veteran. The effect of continuous predictor variables was estimated using restricted cubic splines with knot points specified at the 5th, 35th, 65th, and 95th percentiles. All the analyses were conducted using SAS v.9.4, with P < .05 indicating the statistical significance. This study had institutional review board approval: 1220. During the study period, the median number of ketamine infusions was 15 across a median of 164 days of treatment follow-up with a median time between ketamine infusions of 4 days. For both Beck Depression Inventory and Patient Health Questionnaire-9 scores, there was a statistically significant reduction across infusions (both P < .001), but the strongest reduction occurred before day 40. The change was statistically significant for decreased heart rate (P = .019) but not for systolic blood pressure (P = .612), diastolic blood pressure (P = .942), respiratory rate (P = .822), oxygen saturation (P = .070), and temperature (P = .943). Side effects were reported in six patients (75%); however, the only side effect reported was excessive sedation or dizziness immediately after infusion. In this study, repeated-dose IV ketamine infusions over a 24-month study period resulted in a significant reduction in depression scores in a group of veterans with TRD. The rapid onset of significant response, absence of psychosis-like effects or dissociative symptoms despite psychiatric comorbidities, and minimal effects on vital signs support the clinical efficacy and safety of this exciting new treatment option for patients with TRD. Limitations include a 2-year study period, lack of information on long-term effects, and the retrospective nature of the study. Prospective studies of longer duration are needed to assess the long-term efficacy and safety of IV ketamine for TRD.

A Genome‐Wide Search for Pleiotropy in Cognitive Domain Scores

AbstractBackgroundGenome‐wide association studies (GWASs) have identified loci for Alzheimer’s disease (AD)‐related cognitive traits, and genetic correlations among these traits suggest that a single gene may affect them jointly, i.e., pleiotropy.MethodWe conducted GWASs for scores previously generated by factor analyses for cognitive domains—executive function, language, and memory—using longitudinal data from 7,503 Framingham Heart Study (FHS) and 6,758 National Alzheimer’s Coordinating Center (NACC) subjects who had an average of five and three exams, respectively. We averaged domain scores for each participant by weighting down scores at the visit without normal cognition and used the sum of weights to account for heterogeneous residual variances. The association of each domain score with 8.9 million genetic variants (MAF&gt;0.01), imputed using the TOPMed reference panel (GRCh38), was tested separately in each dataset using linear models including age, sex, education level, and five principal components of ancestry as covariates. Results from FHS and NACC datasets were combined by meta‐analyses using the inverse‐variance method. Pleiotropy for each pair of cognitive domains was evaluated using PLACO (pleiotropic analysis under the composite null hypothesis).ResultScores for paired cognitive domains were moderately genetically correlated—executive function and language (ρP =0.46, ρG =0.69), executive function and memory (ρP =0.39, ρG =0.43), language and memory (ρP =0.44, ρG =0.46). Pleiotropy analyses revealed genome‐wide significant associations for all paired domain scores with many variants in the APOE region (p&lt;3.91×10‐9 for each). For pleiotropy of executive function and language, we identified genome‐wide significant associations with DOK5 (rs28663252, p=4.51×10‐8) and suggestive associations with SOX14 (rs55829884, p=8.03×10‐8), IL20RB (rs11925531, p=9.26×10‐8), and RIMS2 (rs140291972, p=8.76×10‐7). We also found suggestive associations for pleiotropy of language and memory with LHFPL4 (rs2936482, p=5.08×10‐8) and for pleiotropy of executive function and memory with MC4R (rs1943220, p=7.54×10‐8) and CDH20 (rs78756286, p=1.50×10‐7).ConclusionWe identified significant evidence of pleiotropy for multiple cognitive domains, and these findings may improve the interpretation of the genetic basis of cognition and AD‐related traits. These results highlight the benefits of factor scores derived from groups of subjects assessed with various cognitive tests and pleiotropy analysis as a tool for identifying genes that contribute to performance in multiple cognitive domains.

Basal Reactivity Evaluated by Infrared Thermography in the "Caballo de Deporte Español" Horse Breed According to Its Coat Color.

Simple SummaryThe importance of the coat color in horses has been present since their domestication. The interactive effect of coat color-determinant genes on other traits, such as behavior, makes it a factor of importance in horse selection, as it determines the way the horse perceives and reacts to its environment. Eye temperature at rest (ETR) assessed with infrared thermography was used as a suitable non-invasive tool to assess basal reactivity. The aim of this study was to evaluate the influence of coat color on basal reactivity assessed with infrared thermography, determine their relationship with the results obtained in show jumping competitions, and to estimate the genetic parameters for ETR to test its suitability for genetic selection. Our results indicated differences in ETR due to six different factors. We also discovered a positive correlation with ranking in show jumping, indicating that less reactive horses were more likely to achieve better rankings. The genetic results indicated that part of the variability present in the basal reactivity due to coat color has an environmental origin. The heritability values indicate that ETR has a suitable genetic basis to be used in the breeding program to select for basal reactivity due to coat color.Horses have been valued for their diversity of coat color since prehistoric times. In particular, the pleiotropic effect that coat color genes have on behavior determines the way the horse perceives and reacts to its environment. The primary aim of this study was to evaluate the influence of coat color on basal reactivity assessed with infrared thermography as eye temperature at rest (ETR), determine their relation with the results obtained by these horses in Show Jumping competitions and to estimate the genetic parameters for this variable to test its suitability for genetic selection. A General Linear Model (GLM) and Duncan post-hoc analysis indicated differences in ETR due to coat color, sex, age, location, and breed-group factors. A Spearman’s rank correlation of 0.11 (p < 0.05) was found with ranking, indicating that less reactive horses were more likely to achieve better rankings. Heritability values ranged from 0.17 to 0.22 and were computed with a model with genetic groups and a model with residual variance heterogeneity. Breeding values were higher with the last genetic model, thus demonstrating the pleiotropic effect of coat color. These results indicate that ETR has a suitable genetic basis to be used in the breeding program to select for basal reactivity due to coat color.

0674 CPAP Adherence and Response to Cognitive Processing Therapy for PTSD in Veterans with Obstructive Sleep Apnea

Abstract Introduction Cognitive processing therapy (CPT) is a recommended first-line psychotherapy treatment for veterans with PTSD. Veterans with PTSD are at higher risk for sleep disorders including obstructive sleep apnea (OSA), and there is evidence that OSA can exacerbate PTSD and vice versa. Several studies have shown that CPAP use is associated with improvement in PTSD symptoms. CPT is an effective, yet time and resource intensive treatment option, making it critical to identify potential health factors impacting its efficacy. This retrospective chart analysis of veterans diagnosed with OSA and PTSD evaluated the effect of CPAP adherence on response to CPT. Methods Medical records of patients seen in a VA Health Care System were reviewed to identify veterans diagnosed with OSA and PTSD that received between 1-12 sessions of CPT with documented weekly PTSD Checklist for DSM-5 (PCL-5) scores, Patient Health Questionnaire-9 (PHQ-9) scores, and were issued a CPAP machine for OSA. Data collected included demographics, CPAP adherence (mean CPAP use, % days with greater than 4 hrs./night of CPAP use, mean residual apnea hypopnea index, mask leak), and PTSD symptoms (PCL-5 and PHQ-9 scores). For each outcome, we estimated a linear mixed-effects model; models for PCL-5 allowed heterogeneous residual variances for each veteran. Results This study identified 25 veterans receiving CPT for PTSD with access to a CPAP machine for OSA. The median age of patients was 43 years, median BMI was 35.6, and median residual AHI was 2.4. Patients received CPT for a median duration of 11.0 weeks. There was significant improvement in weekly PCL-5 and PHQ-9 scores over the course of CPT (both p &amp;lt; .001). CPAP adherence of at least 4 hrs./night for ≥70% of days did not significantly impact PCL-5 and PHQ-9 scores during CPT. Residual AHI and mask leak were not associated with PTSD symptoms during CPT. Conclusion While CPAP nonadherence appears to be a potential contributing factor to the reduced effectiveness of evidence-based treatments for veterans with PTSD, our study revealed significant improvement in PTSD symptoms during CPT regardless of CPAP adherence. Given the retrospective nature and small sample size of this study, further research is needed to expand on these findings. Support (If Any)

Designing, understanding and modelling two-phase experiments with human subjects.

In a recent paper, Jarrett, Farewell and Herzberg discussed a strategy for developing the analysis of a previously published two-phase experiment that investigated the effect of training on pain rating by occupational and physical therapy students. Here, their example is used to illustrate how a multi-step factor-allocation paradigm can be employed (i) to design an experiment, (ii) to understand the confounding in the design and (iii) to formulate linear mixed models, called prior allocation models, for the design. These models are intended as starting models for the analysis of the data, when it becomes available. An understanding of the confounding intrinsic to a design is achieved through an anatomy of the design presented in an analysis-of-variance-style table that can be obtained using functions from the package . The analysis of the pain-rating experiment is re-examined and it is recommended that conclusions be based on a model with heterogeneous residual variances, in addition to the previously proposed block-treatment interactions. The paradigm is also used in producing an alternative design, taking into account the results of the re-analysis.

Longitudinal genetic analysis of semen production traits in Holstein bulls according to a random regression animal models on age.

Here we used random regression animal models (RRAMs) to investigate genetic change over age in the semen volume (VOL) and sperm concentration (CON) of Holstein bulls. We used 35,294 collection records from 1284 Holstein bulls and their 4166 pedigree records. The models included year and month of collection, collection place, collection method, and number of collections attempted for each day and month of age (second-order regressions) as fixed effects; technician as a random effect; and additive genetic and permanent environment as random regressions (first-order regressions). We examined two RRAMs with homogeneous and heterogeneous residual variances (RRAM1 and RRAM2, respectively). By using RRAM1, heritability for VOL and CON increased from 0.08 to 0.61 and 0.18 to 0.57, respectively, between 10 and 126 months of age. By using RRAM2, heritability for VOL increased from 0.11 to 0.28 between 10 and 24 months of age for young bulls and increased from 0.08 to 0.48 between 25 and 126 months of age for mature bulls; heritability for CON ranged from 0.18 to 0.19 for young bulls and increased from 0.10 to 0.48 for mature bulls. Posterior genetic correlations between young ages and older ages were strongly positive for VOLs but weak for CONs.

Genotype-environment interaction effects on weight gain in cattle using reaction norms

The existence of genotype-environment interaction (GEI) using reaction norm models and their impact on the genetic evaluation of Nellore sires for body weight at 120, 210, 365 and 450 days of age was verified. Three models were used: animal model (AM) that disregards GEI and the one-step reaction norm model with homogeneous and heterogeneous residual variance (1SRNMH_het). Bayes Inference via Gibbs Sampling was used to estimate the variance components. The AM model better fits to weights at 120 and 210 days of age, while 1SRNMH_het was more adequate for body weights at 365 and 450 days of age, suggesting the existence of GEI. The posterior means of direct heritability were 0.33±0.01 and 0.36±0.01 and maternal heritability of 0.21±0.01 and 0.19±0.01 for body weights at 120 and 210 days of age, respectively. For body weights at 365 and 450 days of age, posterior means of heritability varied along the environmental gradient, but the ranking of sires based on breeding values was not changed by different environmental gradients. All rank correlations were greater than 0.80, strongly suggesting a scale effect of GEI. Despite the evidence of GEI on post-weaning weight gain, it did not change the ranking of sires. Therefore, it did not have a relevant impact on the genetic evaluation of sires because they are robust to environmental changes.

Genetic analysis of egg production traits in turkeys (Meleagris gallopavo) using a single-step genomic random regression model

BackgroundEgg production traits are economically important in poultry breeding programs. Previous studies have shown that incorporating genomic data can increase the accuracy of genetic prediction of egg production. Our objective was to estimate the genetic and phenotypic parameters of such traits and compare the prediction accuracy of pedigree-based random regression best linear unbiased prediction (RR-PBLUP) and genomic single-step random regression BLUP (RR-ssGBLUP). Egg production was recorded on 7422 birds during 24 consecutive weeks from first egg laid. Hatch-week of birth by week of lay and week of lay by age at first egg were fitted as fixed effects and body weight as a covariate, while additive genetic and permanent environment effects were fitted as random effects, along with heterogeneous residual variances over 24 weeks of egg production. Predictions accuracies were compared based on two statistics: (1) the correlation between estimated breeding values and phenotypes divided by the square root of the trait heritability, and (2) the ratio of the variance of BLUP predictions of individual Mendelian sampling effects divided by one half of the estimate of the additive genetic variance.ResultsHeritability estimates along the production trajectory obtained with RR-PBLUP ranged from 0.09 to 0.22, with higher estimates for intermediate weeks. Estimates of phenotypic correlations between weekly egg production were lower than the corresponding genetic correlation estimates. Our results indicate that genetic correlations decreased over the laying period, with the highest estimate being between traits in later weeks and the lowest between early weeks and later ages. Prediction accuracies based on the correlation-based statistic ranged from 0.11 to 0.44 for RR-PBLUP and from 0.22 to 0.57 for RR-ssGBLUP using the correlation-based statistic. The ratios of the variances of BLUP predictions of Mendelian sampling effects and one half of the additive genetic variance ranged from 0.17 to 0.26 for RR-PBLUP and from 0.17 to 0.34 for RR-ssGBLUP. Although the improvement in accuracies from RR-ssGBLUP over those from RR-PBLUP was not uniform over time for either statistic, accuracies obtained with RR-ssGBLUP were generally equal to or higher than those with RR-PBLUP.ConclusionsOur findings show the potential advantage of incorporating genomic data in genetic evaluation of egg production traits using random regression models, which can contribute to the genetic improvement of egg production in turkey populations.

Genotype-by-environment interactions for reproduction, body composition, and growth traits in maternal-line pigs based on single-step genomic reaction norms

BackgroundThere is an increasing need to account for genotype-by-environment (G × E) interactions in livestock breeding programs to improve productivity and animal welfare across environmental and management conditions. This is even more relevant for pigs because selection occurs in high-health nucleus farms, while commercial pigs are raised in more challenging environments. In this study, we used single-step homoscedastic and heteroscedastic genomic reaction norm models (RNM) to evaluate G × E interactions in Large White pigs, including 8686 genotyped animals, for reproduction (total number of piglets born, TNB; total number of piglets born alive, NBA; total number of piglets weaned, NW), growth (weaning weight, WW; off-test weight, OW), and body composition (ultrasound muscle depth, MD; ultrasound backfat thickness, BF) traits. Genetic parameter estimation and single-step genome-wide association studies (ssGWAS) were performed for each trait.ResultsThe average performance of contemporary groups (CG) was estimated and used as environmental gradient in the reaction norm analyses. We found that the need to consider heterogeneous residual variance in RNM models was trait dependent. Based on estimates of variance components of the RNM slope and of genetic correlations across environmental gradients, G × E interactions clearly existed for TNB and NBA, existed for WW but were of smaller magnitude, and were not detected for NW, OW, MD, and BF. Based on estimates of the genetic variance explained by the markers in sliding genomic windows in ssGWAS, several genomic regions were associated with the RNM slope for TNB, NBA, and WW, indicating specific biological mechanisms underlying environmental sensitivity, and dozens of novel candidate genes were identified. Our results also provided strong evidence that the X chromosome contributed to the intercept and slope of RNM for litter size traits in pigs.ConclusionsWe provide a comprehensive description of G × E interactions in Large White pigs for economically-relevant traits and identified important genomic regions and candidate genes associated with GxE interactions on several autosomes and the X chromosome. Implementation of these findings will contribute to more accurate genomic estimates of breeding values by considering G × E interactions, in order to genetically improve the environmental robustness of maternal-line pigs.

Direct and maternal genetic components for body weight traits in Markhoz goat.

The aim of the study was to estimate the (co)variance components and genetic trends of direct and maternal genetic effects in Markhoz goat using random regression (RR) models. A total of 16,283 body weight trait records from birth to 400 days of age were obtained from 3476 Markhoz kids. Kids were from 223 bucks and 1217 does raised in the Markhoz goat breeding station in Sanandaj, Iran. The fixed effects were gender, year of birth (1992-2014), age of dam (2 to 7 years old), and type of birth (single or twin). RR models based on Legendre polynomials of age at weight recording included direct and maternal genetic and permanent environmental effects. Heterogeneous residual variances were considered along age of recording. According to the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the likelihood ratio test (LRT), adding covariance between direct and maternal genetic effects, resulted in significant improvement in the level of fit. After examining models with different order of fits, the most suitable model consisted of the fourth-order polynomial for direct genetic effect and the third-order polynomial for maternal genetic, individual permanent environmental, and maternal permanent environmental effects. The direct heritabilities ranged from 0.21 at birth to 0.55 at 400 days of age. The largest (0.25) and the lowest (0.17) maternal heritabilities were estimated at birth and 60 days of age, respectively. Generally, negative direct-maternal genetic correlations were estimated between different ages, which might cause fluctuations of direct-maternal genetic trends along the years. It was concluded that selection at 100 days of age could reduce the impact of the antagonist relationship between maternal and direct genetic effects.

Genetic evaluation models for resistance to ticks in a crossbred Angus-Nellore population

ABSTRACT: This research identified the animal model that best describes the genetic and residual variations for tick counts in yearlings from a crossbred Angus-Nellore population of 6,951 animals that are progenies of 382 bulls and 6,198 cows. Genetic values were predicted by the Bayesian inference methodology. The models tested were: Traditional Animal, and Crossbred Animal with and without segregation, considering residual homoscedastic and heteroscedastic variances. The criteria of choice were the number of parameters, deviance information, and predictive order, which indicated the best fitfor the Traditional Animal model and Crossbred Animal model (with segregation), both with residual heteroscedastic Gaussian variance. The mean values of fixed genetic effects were positive and similar in the both models, indicating that animals with higher proportion of the Angus breed had greater infestation, and the Nellore breed was an important addition for resistance to ticks. The estimated genetic variation by the heteroscedastic Gaussian Animal model for the Nellore breed was 4.54-fold higher than that estimated for the Angus breed. The estimates of heritability of the different genetic groups ranged from 0.12 to 0.15 and from 0.01 to 0.35, respectively, for the Traditional Animal model and for the heteroscedastic Gaussian crossbred model. The Spearman’s rank-order correlation for the predicted genetic values was 0.94, considering all sires. However, when considering the top 10%, 20%, and 30% sires, differences in ranking were more evident (0.28 to 0.67). The Crossbred Animal model with segregation and heterogeneous residual variances was the most appropriate for genetic evaluation of tick counts on animals from Angus-Nellore crossings.

Genetic analysis of litter size and number of kids born alive across parities in Spanish goat breeds using a random regression model

Genetic parameters of litter size (LS) and number of kids born alive (NBA) were estimated across parities in Florida and Payoya goat breeds using a threshold random regression model (RRM). We analysed the reproductive records for the Florida and Payoya breeds separately, and a total of 130.849 and 67.478 reproductive records from the first to the seventh parities from Florida and Payoya, respectively, were included in the analysis. Random regressions on Legendre polynomials of standardised parity were included for permanent environmental and additive genetic effects. We based our estimation of all the covariance components on Bayesian inference under categorical distribution and considered heterogeneous residual variances in the model. The estimates of heritabilities ranged from null to 0.37 for LS and from 0.02 to 0.20 for NBA, while the repeatability estimates were between 0.008–0.60 and 0.03–0.44 for LS and NBA, respectively. Phenotypic correlations within-trait along parities ranged widely from −0.31 to 0.97, while the genetic correlations within-trait through parities showed a wider range and magnitude of values, from −0.98 to 0.99 for LS and from −0.72 to 0.99 for NBA. The estimates of genetic correlations between LS and NBA in the different parities were positive in the first two parities and negative in the later ones. Due to the noticeable changes in variance and the incomplete genetic correlations, the use of a random regression model to analyse LS and NBA is recommended in goat breeds. HIGHLIGHTS A bivariate threshold random regression model has been used for the first time to estimate genetic parameters of litter size and number of kids born alive across parities in Spanish goat breeds. The evolution of genetic parameters along parities is different between Florida and Payoya that have different production systems, being more intensive in the case of the Florida breed. Estimates of variance were not constant across parities for both traits and breeds. Genetic correlations within-trait across parities were different from unity. Thus, LS and NBA should be treated as different traits in successive parities. Given that the estimates of variance and covariances are not constant across parities for both traits and breeds, the use of RRM is highly recommended.