A comprehensive meta-analysis of genetic parameters for resilience and productivity indicator traits in Holstein cattle

Selection for resilience indicator (RIND) traits in Holstein cattle is becoming an important breeding objective as the worldwide population is expected to be exposed to increased environmental stressors due to both climate change and changing industry standards. However, genetic correlations between RIND and productivity indicator (PIND) traits, which are already being selected for and have the most economic value, are often unfavorable. As a result, it is necessary to fully understand these genetic relationships when incorporating novel traits into selection indices, so that informed decisions can be made to fully optimize selection for both groups of traits. In the past 2 decades, there have been many estimates of RIND traits published in the literature, albeit in small populations. To provide valuable pooled summary estimates, a random-effects meta-analysis was conducted for heritability and genetic correlation estimates for PIND and RIND traits in worldwide Holstein cattle. In total, 926 heritability estimates for 9 PIND and 27 RIND traits, along with 362 estimates of genetic correlation (PIND × RIND traits) were collected. Resilience indicator traits were grouped into the following subgroups: Metabolic Diseases, Hoof Health, Udder Health, Fertility, Heat Tolerance, Longevity, and Other. Pooled estimates of heritability for PIND traits ranged from 0.201 ± 0.05 (energy-corrected milk) to 0.377 ± 0.06 (protein content), while pooled estimates of heritability for RIND traits ranged from 0.032 ± 0.02 (incidence of lameness, incidence of milk fever) to 0.497 ± 0.05 (measures of body weight). Pooled estimates of genetic correlations ranged from -0.360 ± 0.25 (protein content vs. milk acetone concentration) to 0.535 ± 0.72 (measures of fat-to-protein ratio vs. milk acetone concentration). Additionally, out of 243 potential genetic correlations between PIND and RIND traits that could have been reported, only 40 had enough published estimates to implement the meta-analysis model. Our results confirmed that the interactions between PIND and RIND traits are complex, and all relationships should be evaluated when incorporating novel traits into selection indices. This study provides a valuable reference for breeders looking to incorporate RIND traits for Holstein cattle into selection indices.

The Causal Interpretation of "Overall Vaccine Effectiveness" in Test-Negative Studies

Test-negative studies are commonly used to estimate influenza vaccine effectiveness (VE). In a typical study, an "overall VE" estimate based on data from the entire sample may be reported. However, there may be heterogeneity in VE, particularly by age. Therefore, in this article we discuss the potential for a weighted average of age-specific VE estimates to provide a more meaningful measure of overall VE. We illustrate this perspective first using simulations to evaluate how overall VE would be biased when certain age groups are overrepresented. We found that unweighted overall VE estimates tended to be higher than weighted VE estimates when children were overrepresented and lower when elderly persons were overrepresented. Then we extracted published estimates from the US Flu VE network, in which children are overrepresented, and some discrepancy between unweighted and weighted overall VE was observed. Differences in weighted versus unweighted overall VE estimates could translate to substantial differences in the interpretation of individual risk reduction among vaccinated persons and in the total averted disease burden at the population level. Weighting of overall estimates should be considered in VE studies in the future.

Computer Algebra and Algorithms for Unbiased Moment Estimation of Arbitrary Order

While unbiased central moment estimators of lower orders (such as a sample variance) are easily obtainable and often used in practice, derivation of unbiased estimators of higher orders might be more challenging due to long math and tricky combinatorics. Moreover, higher orders necessitate calculation of estimators of powers and products that also amount to these orders. We develop a software algorithm that allows the user to obtain unbiased estimators of an arbitrary order and provide results up to the 6th order, including powers and products of lower orders. The method also extends to finding pooled estimates of higher central moments of several different populations (e.g. for two-sample tests). We introduce an R package Umoments that calculates one- and two-sample estimates and generates intermediate results used to obtain these estimators.

Cluster and meta-analyses of genetic parameters for feed intake traits in growing beef cattle

A data set based on 50 studies including feed intake and utilization traits was used to perform a meta-analysis to obtain pooled estimates using the variance between studies of genetic parameters for average daily gain (ADG); residual feed intake (RFI); metabolic body weight (MBW); feed conversion ratio (FCR); and daily dry matter intake (DMI) in beef cattle. The total data set included 128 heritability and 122 genetic correlation estimates published in the literature from 1961 to 2012. The meta-analysis was performed using a random effects model where the restricted maximum likelihood estimator was used to evaluate variances among clusters. Also, a meta-analysis using the method of cluster analysis was used to group the heritability estimates. Two clusters were obtained for each trait by different variables. It was observed, for all traits, that the heterogeneity of variance was significant between clusters and studies for genetic correlation estimates. The pooled estimates, adding the variance between clusters, for direct heritability estimates for ADG, DMI, RFI, MBW and FCR were 0.32 ± 0.04, 0.39 ± 0.03, 0.31 ± 0.02, 0.31 ± 0.03 and 0.26 ± 0.03, respectively. Pooled genetic correlation estimates ranged from -0.15 to 0.67 among ADG, DMI, RFI, MBW and FCR. These pooled estimates of genetic parameters could be used to solve genetic prediction equations in populations where data is insufficient for variance component estimation. Cluster analysis is recommended as a statistical procedure to combine results from different studies to account for heterogeneity.