Comparison of methods for deriving phenotypes from incomplete observation data with an application to age at puberty in dairy cattle.

BACKGROUND: Many phenotypes in animal breeding are derived from incomplete measures, especially if they are challenging or expensive to measure precisely. Examples include time-dependent traits such as reproductive status, or lifespan. Incomplete measures for these traits result in phenotypes that are subject to left-, interval- and right-censoring, where phenotypes are only known to fall below an upper bound, between a lower and upper bound, or above a lower bound respectively. Here we compare three methods for deriving phenotypes from incomplete data using age at first elevation (> 1 ng/mL) in blood plasma progesterone (AGEP4), which generally coincides with onset of puberty, as an example trait. METHODS: We produced AGEP4 phenotypes from three blood samples collected at about 30-day intervals from approximately 5,000 Holstein-Friesian or Holstein-Friesian × Jersey cross-bred dairy heifers managed in 54 seasonal-calving, pasture-based herds in New Zealand. We used these actual data to simulate 7 different visit scenarios, increasing the extent of censoring by disregarding data from one or two of the three visits. Three methods for deriving phenotypes from these data were explored: 1) ordinal categorical variables which were analysed using categorical threshold analysis; 2) continuous variables, with a penalty of 31 d assigned to right-censored phenotypes; and 3) continuous variables, sampled from within a lower and upper bound using a data augmentation approach. RESULTS: Credibility intervals for heritability estimations overlapped across all methods and visit scenarios, but estimated heritabilities tended to be higher when left censoring was reduced. For sires with at least 5 daughters, the correlations between estimated breeding values (EBVs) from our three-visit scenario and each reduced data scenario varied by method, ranging from 0.65 to 0.95. The estimated breed effects also varied by method, but breed differences were smaller as phenotype censoring increased. CONCLUSION: Our results indicate that using some methods, phenotypes derived from one observation per offspring for a time-dependent trait such as AGEP4 may provide comparable sire rankings to three observations per offspring. This has implications for the design of large-scale phenotyping initiatives where animal breeders aim to estimate variance parameters and estimated breeding values (EBVs) for phenotypes that are challenging to measure or prohibitively expensive.

Estimating Heritabilities and Breeding Values From Censored Phenotypes Using a Data Augmentation Approach.

Time-dependent traits are often subject to censorship, where instead of precise phenotypes, only a lower and/or upper bound can be established for some of the individuals. Censorship reduces the precision of phenotypes but can represent compromise between measurement cost and animal ethics considerations. This compromise is particularly relevant for genetic evaluation because phenotyping initiatives often involve thousands of individuals. This research aimed to: 1) demonstrate a data augmentation approach for analysing censored phenotypes, and 2) quantify the implications of phenotype censorship on estimation of heritabilities and predictions of breeding values. First, we simulated uncensored phenotypes, representing fine-scale "age at puberty" for each individual in a population of some 5,000 animals across 50 herds. Analysis of these uncensored phenotypes provided a gold-standard control. We then produced seven "test" phenotypes by superimposing varying degrees of left, interval, and/or right censorship, as if herds were measured on only one, two or three occasions, with a binary measure categorized for animals at each visit (either pre or post pubertal). We demonstrated that our estimates of heritabilities and predictions of breeding values obtained using a data augmentation approach were remarkably robust to phenotype censorship. Our results have important practical implications for measuring time-dependent traits for genetic evaluation. More specifically, we suggest that data collection can be designed with relatively infrequent repeated measures, thereby reducing costs and increasing feasibility across large numbers of animals.

The Relationships of Experiencing Workplace Bullying with Mental Health, Affective Commitment, and Job Satisfaction: Application of the Job Demands Control Model.

There have been very few theoretical models published to understand the relationship between workplace bullying and different outcome variables. Applying the Job Demands Control (JDC) model, this study analyzed workplace bullying alongside 'traditional' job stressors of role overload and low job control to determine the relative associations of each with mental health and wellbeing. These relative associations have not been well documented. Data were obtained from an organizational climate questionnaire administered to 21 Australian Defence Force units (n = 3193). Results indicated that the correlations between bullying and psychological distress (r = 0.39), job satisfaction (r = -0.28), and affective commitment (r = -0.22) were all significant and for some outcomes greater than those involving the traditional job stressors. Furthermore, for each of these three outcomes, bullying contributed incremental variance after controlling for other job demands. These results support earlier claims that workplace bullying requires the same attention given to traditional work stressors. The JDC model provides a strong theoretical base to investigate workplace bullying. Testing against other stressors allows for consideration of the broader context of workplace bullying when managing the workforce.

Screening for Depression and Psychological Distress in a Currently Serving Military Population: The Diagnostic Accuracy of the K10 and the PHQ9.

This study is the first to examine the diagnostic accuracy of two depression screening scales-the Kessler Psychological Distress Scale (K10) and the Patient Health Questionnaire (PHQ)-in an entire regular-serving military population. Currently serving Australian Defence Force personnel (n = 24,481) completed the K10 and PHQ9. Then a targeted subsample (i.e., the analysis sample, n = 1,730) completed a diagnostic interview to identify DSM-IV 30-day disorder. Weighted results represented the entire population (N = 50,049). Both scales similarly showed a good ability to discriminate between personnel with and without depressive disorders. Optimal cutoffs (19 for K10, 6 for PHQ9) showed high sensitivity and good specificity, and were similar to though slightly lower than those recommended in civilian populations. Both scales appear to be valid screens for depressive disorder in the military, using the cutoffs identified. As both performed similarly, scale choice may depend on other factors (e.g., availability of norms).

Screening for Anger and Sleep Difficulties.

INTRODUCTION: Mental health screens are designed to detect individuals at risk of psychological disorders. In the military setting of this study, these disorders were post-traumatic stress disorder (PTSD) and alcohol use. This study extends the literature on deployment-related mental health screening by including measures of sleep difficulties and anger as predictors of postdeployment PTSD and alcohol abuse. Evidence that measures of anger and sleep difficulties contribute incremental validity to the prediction of postdeployment mental health problems, including substance abuse, would be helpful in designing interventions to assist the rehabilitation of returning personnel. MATERIALS AND METHODS: A test battery containing the PTSD Checklist-Civilian (PCL-C) to screen for PTSD, the Kessler 10 to screen for psychological distress, a Sleep Difficulties scale, an exposure to trauma scale, and an anger scale was administered to 212 personnel nearing completion of a deployment to the Middle East. A second battery containing the PCL-C, the Kessler 10, and a measure of alcohol consumption (Alcohol Use Disorders Identification Test [AUDIT]) was administered to the same personnel 3 to 6 months after return to Australia. Hierarchical regression analyses assessed the predictive validity of measures of psychological distress (anxiety and depression), PTSD symptomatology, sleep disturbance, and anger in relation to postdeployment measures of PTSD symptomatology and alcohol use. RESULTS: Time 1 measures predicted 24.4% of the variance in postdeployment PCL-C scores and 13.1% of the variance in AUDIT scores, with the Sleep Difficulties scale contributing to the prediction of the PCL-C score and the anger scale helping to predict AUDIT scores. CONCLUSION: On the basis of these findings, we recommend the inclusion of improved measures of both anger and sleep difficulties in end-of-deployment mental health screens. A less behaviorally specific and more wide-ranging anger scale is recommended for future studies that aim to evaluate the role of anger in screening batteries. Our findings suggest that the Sleep Difficulties scale used in this study would be a worthwhile addition to mental health screening because it is moderately correlated with both Time 1 and Time 2 measures of PTSD symptomatology and psychological distress. Furthermore, there is minimal stigma associated with the experience of sleep difficulties.

Evaluating the merits of using brief measures of PTSD or general mental health measures in two-stage PTSD screening.

Psychological screening of large numbers of personnel returning from deployments should be as brief as possible without sacrificing the ability to detect individuals who are experiencing serious psychological difficulties. This study focused on screening for posttraumatic stress disorder (PTSD) symptomatology in 421 deployed male members of the Australian Army while they were on deployment and again 3 to 6 months after they returned home. The first aim was to evaluate the performance of the Primary Care--Posttraumatic Stress Disorder Screen (PC-PTSD) and a 4-item version of the 17-item Posttraumatic Stress Disorder Checklist (PCL). A second aim was to evaluate the role of the Kessler-10 (K10) in psychological screening. The results indicated that the short form of the PCL was a better substitute for the full PCL than the PC-PTSD. Other results suggested that a more efficient screening process can be achieved using an initial K10 screening followed by more intensive PTSD screening for people identified as high risk. An additional advantage of an initial K10 filter is that other forms of mental illness could also be targeted in the second-stage screening.