Longitudinal random effects models for genetic analysis of binary data with application to mastitis in dairy cattle

Abstract

-A Bayesian analysis of longitudinal mastitis records obtained in the course of lactation was undertaken. Data were 3341 test-day binary records from 329 first lactation Holstein cows scored for mastitis at 14 and 30 days of lactation and every 30 days thereafter. First, the conditional probability of a sequence for a given cow was the product of the probabilities at each test-day. The probability of infection at time t for a cow was a normal integral, with its argument being a function of "fixed" and "random" effects and of time. Models for the latent normal variable included effects of: (1) year-month of test + a five-parameter linear regression function ("fixed", within age-season of calving) + genetic value of the cow + environmental effect peculiar to all records of the same cow + residual. (2) As in (1), but with five parameter random genetic regressions for each cow. (3) A hierarchical structure, where each of three parameters of the regression function for each cow followed a mixed effects linear model. Model 1 posterior mean of heritability was 0.05. Model 2 heritabilities were: 0.27, 0.05, 0.03 and 0.07 at days 14, 60, 120 and 305, respectively. Model 3 heritabilities were 0.57, 0.16, 0.06 and 0.18 at days 14, 60, 120 and 305, respectively. Bayes factors were: 0.011 (Model 1/Model 2), 0.017 (Model 1/Model 3) and 1.535 (Model 2/Model 3). The probability of mastitis for an "average" cow, using Model 2, was: 0.06, 0.05, 0.06 and 0.07 at days 14, 60, 120 and 305, respectively. Relaxing the conditional independence assumption via an autoregressive process (Model 2) improved the results slightly. mastitis / longitudinal / threshold model

Genetic Analyses of Mastitis Data Using Animal Threshold Models and Genetic Correlation with Production Traits

“…Therefore, alternative modeling is an important area of research. Rekaya et al (1998Rekaya et al ( , 2003 suggested the development of test-day models for longitudinal binary response for the analysis of mastitis field data. The estimates for mastitis heritabilities of those models ranged from 0.05 to 0.11 (Rekaya et al, 1998;Kadarmideen et al, 2000;Heringstad et al, 2003b;Chang et al, 2004).…”

A maximum likelihood approach to functional mapping of longitudinal binary traits

“…Dynamic changes of complex traits including binary traits are a ubiquitous phenomenon in biology and biomedicine (Rekaya et al 2003; Hinrichs et al 2011; Ghahroodi et al 2010). However, the genetic architecture of dynamic binary traits expressed in a time course has been poorly understood, thus limiting our inference about their developmental regulation in relation to important yield traits in agriculture or disease traits in medicine.…”

“…Longitudinal binary response data are common in many fields from plant and animal breeding to biomedical research. For example, the presence or absence of mastitis is longitudinally recorded in the course of lactation to breed healthy and productive cows (Rekaya et al 2003; Hinrichs et al 2011). To test the efficacy of hypnotic drugs in controlling insomnia, a sleep disorder that pervades the human population, a longitudinal clinical trial is designed in which the time of how quickly patients fall asleep after being treated with the drugs is viewed as a binary response, recorded repeatedly in a time course (Ghahroodi et al 2010).…”

Longitudinal threshold model analysis of clinical mastitis using linear splines

“…Clinical mastitis can be analysed as a longitudinal trait in order that multiple treatments and time aspects are accounted (Chang et al, 2004;Heringstad et al, 2003;Rekaya et al, 2003) and information on the trait can be efficiently utilised. So far, in most countries with national dairy health recording schemes, clinical mastitis is defined as a binary trait averaging incidences over lactation or in a few specific lactation periods.…”