Linear Unbiased Prediction Research Articles

Unraveling genetic variation among white spruce families generated through different breeding strategies: Heritability, growth, physiology, hormones and gene expression.

Tree improvement programs select genotypes for faster growth, at both early and late stages, to increase yields over unimproved material, and the improvement is frequently attributed to genetic control in growth parameters among genotypes. Underutilized genetic variability among genotypes also has the potential to ensure future gains are possible. However, the genetic variation in growth, physiology and hormone control among genotypes generated from different breeding strategies has not been well characterized in conifers. We assessed growth, biomass, gas exchange, gene expression and hormone levels in white spruce seedlings obtained from three different breeding strategies (controlled crosses, polymix pollination, open pollination) using parents grafted into a clonal seed orchard in Alberta, Canada. A pedigree-based best linear unbiased prediction (ABLUP) mixed model was implemented to quantify variability and narrow-sense heritability for target traits. The levels of several hormones and expression of gibberellin-related genes in apical internodes were also determined. Over the first two years of development, the estimated heritabilities for height, volume, total dry biomass, above ground dry biomass, root:shoot ratio and root length, varied between 0.10 and 0.21, with height having the highest value. The ABLUP values showed large genetic variability in growth and physiology traits both between families from different breeding strategies, and within families. The principal component analysis showed that developmental and hormonal traits explained 44.2% and 29.4% of the total phenotypic variation between the three different breeding strategies and two growth groups. In general, controlled crosses from the fast growth group showed the best apical growth, with more accumulation of indole-3-acetic acid, abscisic acid, phaseic acid, and a 4-fold greater gene expression of PgGA3ox1 in genotypes from controlled crosses versus those from open pollination. However, in some cases, open pollination from the fast and slow growth groups showed the best root development, higher water use efficiency (iWUE and δ13C) and more accumulation of zeatin and isopentenyladenosine. In conclusion, tree domestication can lead to trade-offs between growth, carbon allocation, photosynthesis, hormone levels and gene expression, and we encourage the use of this phenotypic variation identified in improved and unimproved trees to advance white spruce tree improvement programs.

Effects of genomic estimated breeding value and dietary energy to protein ratio on growth performance, carcass trait, and lipogenic gene expression in Hanwoo steer

"Genome-based precision feeding" is a concept that involves the application of customised diets to different genetic groups of cattle. We investigated the effects of the genomic estimated breeding value (gEBV) and dietary energy to protein ratio (DEP) on growth performance, carcass traits, and lipogenic gene expression in Hanwoo (Korean cattle) steers. Forty-four Hanwoo steers (BW = 636 kg, age = 26.9 months) were genotyped using the Illumina Bovine 50 K BeadChip. The gEBV was calculated using genomic best linear unbiased prediction. Animals were separated into high gEBV of marbling score or low-gMS groups based on the upper and lower 50% groupings of the reference population, respectively. Animals were assigned to one of four groups in a 2 × 2 factorial arrangement: high gMS/high DEP (0.084 MJ/g), high gMS/low DEP (0.079 MJ/g), low gMS/high DEP, and low gMS/low DEP. Steers were fed concentrate with a high or low DEP for 31 weeks. The BW tended to be higher (0.05 < P < 0.1) in the high-gMS groups compared to the low-gMS groups at 0, 4, 8, 12, and 20 weeks. The average daily gain (ADG) tended to be lower (P = 0.08) in the high-gMS group than in the low-gMS group. Final BW and measured carcass weight (CW) were positively correlated with the gEBV of carcass weight (gCW). The DEP did not affect ADG. Neither the gMS nor the DEP affected the MS and beef quality grade. The intramuscular fat (IMF) content in the longissimus thoracis (LT) tended to be higher (P = 0.08) in the high-gMS groups than in the low-gMS groups. The mRNA levels of lipogenic acetyl-CoA carboxylase and fatty acid binding protein 4 genes in the LT were higher (P < 0.05) in the high-gMS group than in the low-gMS group. Overall, the IMF content tended to be affected by the gMS, and the genetic potential (i.e., gMS) was associated with the functional activity of lipogenic gene expression. The gCW was associated with the measured BW and CW. The results demonstrated that the gMS and the gCW may be used as early prediction indexes for meat quality and growth potential of beef cattle.

Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks.

The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this regard, epistasis models have shown to be capable of increasing the prediction accuracy while their high computational load is challenging. In this study, we investigated the predictive ability obtained in additive and epistasis models when utilizing haplotype blocks versus pruned sets of SNPs by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The prediction accuracies of bivariate GBLUP, ERRBLUP and sERRBLUP were assessed with eight phenotypic traits for 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot. The results indicate that the obtained prediction accuracies are similar when utilizing a pruned set of SNPs or haplotype blocks, while utilizing haplotype blocks reduces the computational load significantly compared to the pruned sets of SNPs. The number of interactions considered in the model was reduced from 323.5/456.4 million for the pruned SNP panel to 4.4/5.5 million in the haplotype block dataset for Kemater and Petkuser landraces, respectively. Since the computational load scales linearly with the number of parameters in the model, this leads to a reduction in computational time of 98.9% from 13.5 hours for the pruned set of markers to 9 minutes for the haplotype block dataset. We further investigated the impact of genomic correlation, phenotypic correlation and trait heritability as factors affecting the bivariate models' prediction accuracy, identifying the genomic correlation between years as the most influential one. As computational load is substantially reduced, while the accuracy of genomic prediction is unchanged, the here proposed framework to use haplotype blocks in sERRBLUP provided a solution for the practical implementation of sERRBLUP in real breeding programs. Furthermore, our results indicate that sERRBLUP is not only suitable for prediction across different locations, but also for the prediction across growing seasons.

Genetic and nongenetic factors affecting body weight in Nili Ravi buffaloes

Live body weight of Nili Ravi buffaloes was studied to investigate the effect of certain environmental sources of variation such as herd, lactation number, lactation stage, season of recording and age of buffaloe at the time of recording alongwith appraisal of genetic parameters of body weight including heritabilities, genetic correlation and phenotypic correlation of body weight with mik yield. Five milking herds belonging to Nili Ravi buffaloe breed in Punjab were utilized in this study. Data recorded during 2010 to 2012 included 839 observations. Estimation of genetic parameters were carried out by Best Linear Unbiased Prediction (BLUP) procedure through ASREML set of coputer program. Least square mean for body weight was found as 523.13±81.63 with a coefficient of variation of 15.60%. Herd, stage of lactation, lactation number, recording season and buffalo’s age at the time of recording significantly affected live body weight. Univariate analysis of body weight indicated that heritability estimate for body weight was 0.33±0.07. Phenotypic correlations of body weight with milk yield in 305 days and milk yield on day of recording were found as 0.071±0.034 and 0.090±0.053 while genetic correlations were found as 0.16±0.00 and 0.20±0.00, respectively. High heritability and positive genetic correlation of body weight with milk yield suggested that body weight should be included in selection program for improved milk yield in Nili Ravi buffaloes.

Optimizing multi-environment testing in potato breeding: using heritability estimates to determine number of replications, sites, and years for field trials

Multi-environment trials (METs) of potato breeding clones and cultivars allow to precisely determine their performance across testing sites over years. However, these METs may be affected by the genotype × environment interaction (GEI) as noted in tuber yield. Furthermore, trials are replicated several times to optimize the predictive value of the data collected because knowledge on spatial and temporal variability of testing environments is often lacking. Hence, the objectives of this research were to use components of variance from METs to estimate broad sense heritability (H2) based on best linear unbiased predictors and use these estimates to determine the optimum number of sites, years, and replications for testing potato breeding clones along with cultivars. The data were taken from METs in southern and northern Sweden comprising up to 256 breeding clones and cultivars that underwent testing using a simple lattice design of 10-plant plots across three sites over 2 years. Percentage starch in the tuber flesh had the largest H2 in each testing environment (0.850–0.976) or across testing environments (0.905–0.921). Total tuber weight per plot also exhibited high H2 (0.720–0.919) in each testing environment or across them (0.726–0.852), despite a significant GEI. Reducing sugar content in the tuber flesh had the lowest, but still medium H2 (0.426–0.883 in each testing environment; 0.718–0.818 across testing environments). The H2 estimates were smaller when their variance components were disaggregated by year and site, instead of lumping them as environments. Simulating H2 with genetic, site, year, site × year, genetic × site, genetic × year, genetic × site × year, and residual variance components led to establish that two replicates at each of two sites in 2-year trials will suffice for testing tuber yield, starch and reducing sugars. This article provides a methodology to optimize the number of testing size and years for METs of potato breeding materials, as well as tabulated information for choosing the appropriate number of trials in same target population of environments.

Genetic variability and &lt;i&gt;ex situ&lt;/i&gt; conservation strategies for the neotropical tree &lt;i&gt;Parkia platycephala&lt;/i&gt; Benth.

Parkia platycephala, known commonly as faveira, is a native species that occurs widely throughout Brazil and has high nutritional value and ecological potential. The objective of this study was to estimate the genetic variability in a P. platycephala provenance and progeny test. The test was established using a randomized block design, with 45 progenies from three provenances. The evaluated quantitative traits included height (HEI), diameter at ground level (DGL), and diameter below the first bifurcation (DFB). The data were submitted to Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP) analysis, from which estimates of genetic parameters, genetic correlations, BLUPs, genetic gains, and effective population size were obtained. Estimates of individual narrow sense heritability (h2a) and within progeny heritability (h2w) ranged from low to moderate (0.01 a 0.39), with the highest values observed for DGL (0.32 and 0.39, respectively). The coefficient of individual variation (CVgi(%)) and between progeny genetic variation (CVgp(%)) indicate the existence of genetic variability between and within provenances. The FST vaues demonstrate low to moderate genetic differentiation among the three populations, and genetic correlations between traits were all positive and significant. The results obtained show that there is genetic variability between the studied provenances and progenies. This ensures not only the ex situ conservation of the species, but it can also be exploited in breeding programs.

Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system

BackgroundGenomic selection is widely applied for genetic improvement in livestock crossbreeding systems to select excellent nucleus purebred (PB) animals and to improve the performance of commercial crossbred (CB) animals. Most current predictions are based solely on PB performance. Our objective was to explore the potential application of genomic selection of PB animals using genotypes of CB animals with extreme phenotypes in a three-way crossbreeding system as the reference population. Using real genotyped PB as ancestors, we simulated the production of 100,000 pigs for a Duroc x (Landrace x Yorkshire) DLY crossbreeding system. The predictive performance of breeding values of PB animals for CB performance using genotypes and phenotypes of (1) PB animals, (2) DLY animals with extreme phenotypes, and (3) random DLY animals for traits of different heritabilities ({h}^{2} = 0.1, 0.3, and 0.5) was compared across different reference population sizes (500 to 6500) and prediction models (genomic best linear unbiased prediction (GBLUP) and Bayesian sparse linear mixed model (BSLMM)).ResultsUsing a reference population consisting of CB animals with extreme phenotypes showed a definite predictive advantage for medium- and low-heritability traits and, in combination with the BSLMM model, significantly improved selection response for CB performance. For high-heritability traits, the predictive performance of a reference population of extreme CB phenotypes was comparable to that of PB phenotypes when the effect of the genetic correlation between PB and CB performance ({r}_{pc}) on the accuracy obtained with a PB reference population was considered, and the former could exceed the latter if the reference size was large enough. For the selection of the first and terminal sires in a three-way crossbreeding system, prediction using extreme CB phenotypes outperformed the use of PB phenotypes, while the optimal design of the reference group for the first dam depended on the percentage of individuals from the corresponding breed that the PB reference data comprised and on the heritability of the target trait.ConclusionsA commercial crossbred population is promising for the design of the reference population for genomic prediction, and selective genotyping of CB animals with extreme phenotypes has the potential for maximizing genetic improvement for CB performance in the pig industry.

The genomics of linkage drag in inbred lines of sunflower

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here, we analyzed the genomic and phenotypic impacts of wild introgressions in inbred lines of cultivated sunflower to estimate the impacts of linkage drag. First, we generated reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge-regression best linear unbiased prediction (BLUP) model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including >3,000 new genes. While introgressions reduced genetic load at protein-coding sequences, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low-frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

Analysis of advanced generation multistage field trials data in autogamous plant breeding: An evaluation in common Bean

AbstractThe performance of inbred lines in advanced endogamous generation is commonly evaluated in successive generations of testing and selection, which we defined as “multistage field trials” (MSFT). MSFT data routinely exhibit heterogeneity of (co)variances at several levels due to genetic and/or statistical imbalance. Nowadays, mixed models have been widely used to deal with unbalanced data. However, few studies on common bean have addressed the use of a mixed model approach with modeling of (co)variance structures for random effects in MSFT. Furthermore, factor analysis and genotype‐ideotype distance (FAI‐BLUP) selection index was originally proposed using best linear unbiased predictions from individual analysis. In this regard, we aimed to study the implications of modeling (co)variance structures for random effects in the estimation of genetic parameters and evaluate the accuracy and efficiency of inbred line selection by the modeled FAI‐BLUP approach. A total of five trials were evaluated from 2018 to 2020. The results revealed that the unstructured covariance matrix fitted better for grain yield, whereas the matrix with uniform correlation and heterogeneity of variances fitted better for grain aspect and plant architecture. The modeled FAI‐BLUP approach increased the values of selection accuracy and selection efficiency. Our results suggest that modeling the different structures of (co)variances and selecting the best‐performing genotypes by modeled FAI‐BLUP approach should be used in common bean assays involving unbalanced data.

Modeling covariance structures for genetic and non‐genetic effects in cowpea multi‐environment trials

AbstractIn cowpea breeding, multi‐environment trials are conducted to select lines with high yield. The occurrence of genetic and/or statistical imbalance is common in these experiments, in addition to the possibility of (co)variance between genetic and non‐genetic effects. We explore the restricted maximum likelihood/best linear unbiased prediction features to select the model with the most appropriated covariance structure and compare the results with the traditional model (homogenous variances and no covariances). Then, 17 inbred lines and three cultivars were evaluated in six experiments during two crop years in the semiarid zone of Northeast Brazil. The trait evaluated was the 100‐grain weight. We selected the best model considering the Akaike Information Criterion. The model with diagonal structure for the residual effects and heterogeneous compound symmetry for the genetic effects had the best fit. The predicted genetic gain of lines selected in this model was 1.18% higher compared to the traditional model. Modeling different (co)variance structures for genetic and non‐genetic effects is an efficient approach in selecting superior genotypes in multi‐environment trials in cowpea breeding.

Genetic dissection of complex traits in citrus: additive and non-additive genetic variances, inbreeding depression, and single-chromosome heritability

Genetic evaluation in breeding programs generally considers only additive genetic effects; however, non-additive genetic effects may also contribute to phenotypic and genotypic values. Furthermore, the distributions of causal variants in target traits are associated with the efficiency of accumulation of favorable alleles and are therefore important for plant breeding. Despite this, non-additive effects remain to be fully investigated in citrus. Moreover, although quantitative trait loci in citrus have been previously identified, they cannot adequately explain the genetic variance. Therefore, we attempted to estimate non-additive effects, such as dominance and additive-by-additive effects, together with directional dominance as an indicator of inbreeding depression and with chromosome heritability, which reflects the distributions of causal variants for fruit-quality traits in citrus. Phenotypic records of the fruit weight, sugar content, and acid content of 455 cultivars and their progeny were analyzed. Genomic relationship matrices for non-additive effects and single-chromosome genetic effects were constructed using 2319 genome-wide single nucleotide polymorphism loci, which were also used to evaluate directional dominance. These phenotype and genotype records were analyzed using genomic best linear unbiased prediction models. Through this analysis, we showed (1) overestimation of narrow-sense heritability due to substantial contributions of non-additive effects in the tested traits, (2) evidence of inbreeding depression in fruit weight, and (3) highly polygenic architectures in the tested traits. These findings could contribute to precise evaluation of genotypic values and the development of efficient breeding strategies.

Genomic evaluation for two-way crossbred performance in cattle

BackgroundDairy cattle production systems are mostly based on purebreds, but recently the use of crossbreeding has received increased interest. For genetic evaluations including crossbreds, several methods based on single-step genomic best linear unbiased prediction (ssGBLUP) have been proposed, including metafounder ssGBLUP (MF-ssGBLUP) and breed-specific ssGBLUP (BS-ssGBLUP). Ideally, models that account for breed effects should perform better than simple models, but knowledge on the performance of these methods is lacking for two-way crossbred cattle. In addition, the differences in the estimates of genetic parameters (such as the genetic variance component and heritability) between these methods have rarely been investigated. Therefore, the aims of this study were to (1) compare the estimates of genetic parameters for average daily gain (ADG) and feed conversion ratio (FCR) between these methods; and (2) evaluate the impact of these methods on the predictive ability for crossbred performance.MethodsBivariate models using standard ssGBLUP, MF-ssGBLUP and BS-ssGBLUP for the genetic evaluation of ADG and FCR were investigated. To measure the predictive ability of these three methods, we estimated four estimators, bias, dispersion, population accuracy and ratio of population accuracies, using the linear regression (LR) method.ResultsThe results show that, for both ADG and FCR, the heritabilities were low with the three methods. For FCR, the differences in the estimated genetic parameters were small between the three methods, while for ADG, those estimated with BS-ssGBLUP deviated largely from those estimated with the other two methods. Bias and dispersion were similar across the three methods. Population accuracies for both ADG and FCR were always higher with MF-ssGBLUP than with ssGBLUP, while with BS-ssGBLUP the population accuracy was highest for FCR and lowest for ADG.ConclusionsOur results indicate that in the genetic evaluation for crossbred performance in a two-way crossbred cattle production system, the predictive ability of MF-ssGBLUP and BS-ssGBLUP is greater than that of ssGBLUP, when the estimated variance components are consistent across the three methods. Compared with BS-ssGBLUP, MF-ssGBLUP is more robust in its superiority over ssGBLUP.

Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame.

Introduction: Sesame is an ancient oilseed crop containing many valuable nutritional components. The demand for sesame seeds and their products has recently increased worldwide, making it necessary to enhance the development of high-yielding cultivars. One approach to enhance genetic gain in breeding programs is genomic selection. However, studies on genomic selection and genomic prediction in sesame have yet to be conducted. Methods: In this study, we performed genomic prediction for agronomic traits using the phenotypes and genotypes of a sesame diversity panel grown under Mediterranean climatic conditions over two growing seasons. We aimed to assess prediction accuracy for nine important agronomic traits in sesame using single- and multi-environment analyses. Results: In single-environment analysis, genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces models showed no substantial differences. The average prediction accuracy of the nine traits across these models ranged from 0.39 to 0.79 for both growing seasons. In the multi-environment analysis, the marker-by-environment interaction model, which decomposed the marker effects into components shared across environments and environment-specific deviations, improved the prediction accuracies for all traits by 15%-58% compared to the single-environment model, particularly when borrowing information from other environments was made possible. Discussion: Our results showed that single-environment analysis produced moderate-to-high genomic prediction accuracy for agronomic traits in sesame. The multi-environment analysis further enhanced this accuracy by exploiting marker-by-environment interaction. We concluded that genomic prediction using multi-environmental trial data could improve efforts for breeding cultivars adapted to the semi-arid Mediterranean climate.

Genome-Wide Association Analysis of Grain Hardness in Common Wheat.

The grain hardness index (HI) is one of the important reference bases for wheat quality and commodity properties; therefore, it is essential and useful to identify loci associated with the HI in wheat breeding. The grain hardness index of the natural population including 150 common wheat genotypes was measured in this study. The phenotypic data diversity of HI based on four environments and the best linear unbiased prediction (BLUP) was analyzed. The results showed that the grain HI of the natural population ranged from 15.00 to 83.00, the variation range was from 5.10% to 24.44%, and the correlation coefficient was 0.872-0.980. BLUP value was used to grade and assign the grain HI to hard wheat, mixed wheat, and soft wheat, and the assigned phenotypes were used for genome-wide association analysis. Two types of grain hardness index phenotypic values were used for genome-wide association analysis (GWAS) using a 55K SNP array. A total of five significant association loci (p < 0.001) were excavated, among which four loci could be detected in three or more environments. They were distributed on chromosomes 1A and 7D, and the phenotypic contribution rate was 7.52% to 10.66%. A total of 48 sites related to grain hardness were detected by the assignment method, among which five were stable genetic sites, distributed on chromosomes 1A(2), 3B(1), 4B(1), and 7D(1), with phenotypic contribution rates ranging from 7.63% to 11.12%. Of the five loci detected by the assignment method, two stable loci were co-located in the phenotypic mapping results of the hardness index. One of the loci was consistent with previous reports and located on chromosome 1A, and one locus was unreported on chromosome 7D. Therefore, it may be a feasible attempt to use the assignment method to conduct genome-wide association analysis of the grain hardness index. In this study, a total of five genetic loci for grain hardness stability were excavated, and two of the loci were located in the two phenotypic values, two of which were not reported.

Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice.

The asymmetric increase in average nighttime temperatures relative to increase in average daytime temperatures due to climate change is decreasing grain yield and quality in rice. Therefore, a better genome-level understanding of the impact of higher night temperature stress on the weight of individual grains is essential for future development of more resilient rice. We investigated the utility of metabolites obtained from grains to classify high night temperature (HNT) conditions of genotypes, and metabolites and single-nucleotide polymorphisms (SNPs) to predict grain length, width, and perimeter phenotypes using a rice diversity panel. We found that the metabolic profiles of rice genotypes alone could be used to classify control and HNT conditions with high accuracy using random forest or extreme gradient boosting. Best linear unbiased prediction and BayesC showed greater metabolic prediction performance than machine learning models for grain-size phenotypes. Metabolic prediction was most effective for grain width, resulting in the highest prediction performance. Genomic prediction performed better than metabolic prediction. Integrating metabolites and genomics simultaneously in a prediction model slightly improved prediction performance. We did not observe a difference in prediction between the control and HNT conditions. Several metabolites were identified as auxiliary phenotypes that could be used to enhance the multi-trait genomic prediction of grain-size phenotypes. Our results showed that, in addition to SNPs, metabolites collected from grains offer rich information to perform predictive analyses, including classification modeling of HNT responses and regression modeling of grain-size-related phenotypes in rice.

Soft calibration for selection bias problems under mixed-effects models

Abstract Calibration weighting has been widely used to correct selection biases in nonprobability sampling, missing data and causal inference. The main idea is to calibrate the biased sample to the benchmark by adjusting the subject weights. However, hard calibration can produce enormous weights when an exact calibration is enforced on a large set of extraneous covariates. This article proposes a soft calibration scheme, where the outcome and the selection indicator follow mixed-effect models. The scheme imposes an exact calibration on the fixed effects and an approximate calibration on the random effects. On the one hand, our soft calibration has an intrinsic connection with best linear unbiased prediction, which results in a more efficient estimation compared to hard calibration. On the other hand, soft calibration weighting estimation can be envisioned as penalized propensity score weight estimation, with the penalty term motivated by the mixed-effect structure. The asymptotic distribution and a valid variance estimator are derived for soft calibration. We demonstrate the superiority of the proposed estimator over other competitors in simulation studies and using a real-world data application on the effect of BMI screening on childhood obesity.

Characterizing Interhospital Variability in Neurosurgical Interventions for Patients with Mild Traumatic Brain Injury and Intracranial Hemorrhage.

The objective of this study was to quantify nation-wide interhospital variation in neurosurgical intervention risk by intracranial hemorrhage (ICH) type in the setting of mild traumatic brain injury (mTBI). This was a retrospective cohort study of adult (≥18 years) trauma patients included in the National Trauma Data Bank from 2007 to 2019 with an emergency department Glasgow Coma Scale score 13-15, diagnosed ICH, no skull fracture. The primary outcome was neurosurgical intervention. Interhospital variation was assessed by examining the best linear unbiased predictors (BLUPs) obtained from mixed-effects logistic regression with random slopes and intercepts for hospitals and covariates for time and 14 demographic, injury, and hospital characteristics; one model per ICH type. Intercept BLUPs are estimates of how different each hospital is from the average hospital (after covariate adjustment). The study population included 49,220 (7%) neurosurgical interventions among 666,842 patients in 1060 hospitals. In 2019, after adjusting for patient case-mix and hospital characteristics, the percentage of hospitals with hemorrhage-specific neurosurgical intervention risk significantly different from the average hospital was as follows: isolated unspecified hemorrhage (0% of 995 hospitals); isolated contusion/laceration (0.54% of 929); isolated epidural hemorrhage (0.39% of 778); isolated subarachnoid hemorrhage (0.10% of 1002); multiple hemorrhages (2.49% of 963); and isolated subdural hemorrhage (16.25% of 1028). In the setting of mTBI, isolated subdural hemorrhages were the only ICH type to have considerable interhospital variability. Causes for this significant variation should be elucidated and might include changing hemorrhage characteristics and practice patterns over time.

Effect of genotype and environment on agronomical characters of alfalfa (Medicago sativa L.) in a typical acidic soil environment in southwest China

Alfalfa (Medicago sativa L.), an important perennial legume forage crop with high nutritional value and forage yield, is widely used in animal husbandry. However, it is very sensitive to aluminum, which severely limits its growth in acidic soils. In this study, we analyzed the genotype variation of each agronomic trait in 44 alfalfa varieties in two acidic soil environments. Then, analysis of variance (ANOVA) of the variance components was performed using the Residual Maximum Likelihood (REML). The best linear unbiased predictor analysis was used to obtain the mean trait of each variety, and the mean values were used to construct the mean matrix of varieties × traits and interaction analysis of varieties × years. The results showed that there was significant (P &amp;lt; 0.05) genotypic variation for each trait of the 44 varieties and the genetic diversity was abundant. The average repeatability (R value) of interannual plant height (PH), stem thickness (ST), number of branches (NS), fresh weight (FW), total fresh weight (TFW), and total dry weight (TDW) was high (0.21–0.34), whereas the genetics were relatively stable. PH, NS, FW, TFW, and dry weight (DW) were positively correlated (P &amp;lt; 0.01) with TDW. Six alfalfa varieties (Algonquin, Xinjiang daye, Trifecta, Vernal, WL354HQ, and Boja) with excellent TDW and TFW were identified in different years, environmental regions, and climatic altitudes. Our research results can provide suggestions and critical information regarding the future improvement and development of new alfalfa strains and varieties that are resistant to acidic soil conditions.

Single-step genomic evaluation for growth traits in a Mexican Braunvieh cattle population.

The objective was to compare (pedigree-based) BLUP, genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population. Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed. The random contemporary group (CG) effect explained about 50, 45, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP. The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.

Genome-wide association and genomic prediction for a reproductive index summarizing fertility outcomes in U.S. Holsteins.

Subfertility represents one major challenge to enhancing dairy production and efficiency. Herein, we use a reproductive index (RI) expressing the predicted probability of pregnancy following artificial insemination (AI) with Illumina 778K genotypes to perform single and multi-locus genome-wide association analyses (GWAA) on 2,448 geographically diverse U.S. Holstein cows and produce genomic heritability estimates. Moreover, we use genomic best linear unbiased prediction (GBLUP) to investigate the potential utility of the RI by performing genomic predictions with cross validation. Notably, genomic heritability estimates for the U.S. Holstein RI were moderate (h2 = 0.1654 ± 0.0317-0.2550 ± 0.0348), while single and multi-locus GWAA revealed overlapping quantitative trait loci (QTL) on BTA6 and BTA29, including the known QTL for the daughter pregnancy rate (DPR) and cow conception rate (CCR). Multi-locus GWAA revealed seven additional QTL, including one on BTA7 (60 Mb) which is adjacent to a known heifer conception rate (HCR) QTL (59 Mb). Positional candidate genes for the detected QTL included male and female fertility loci (i.e. spermatogenesis and oogenesis), meiotic and mitotic regulators, and genes associated with immune response, milk yield, enhanced pregnancy rates, and the reproductive longevity pathway. Based on the proportion of the phenotypic variance explained (PVE), all detected QTL (n = 13; P ≤ 5e - 05) were estimated to have moderate (1.0% < PVE ≤ 2.0%) or small effects (PVE ≤ 1.0%) on the predicted probability of pregnancy. Genomic prediction using GBLUP with cross validation (k = 3) produced mean predictive abilities (0.1692-0.2301) and mean genomic prediction accuracies (0.4119-0.4557) that were similar to bovine health and production traits previously investigated.