Biomedical subjects

B R Southey

Publications and source records attributed to B R Southey.

5 recordsLinked to original sources

PubMed2003

Discrete time survival analysis of lamb mortality in a terminal sire composite population.

Mortality records of 8,642 lambs from a composite population at the U.S. Meat Animal Research Center during the first year of life were studied using discrete survival analyses. Lamb mortality was studied across periods from birth to weaning, birth to 365 d of age, and weaning to 365 d of age. Animal-time data sets were created for each period using different time intervals: daily, weekly, fortnightly, and monthly. Each data set was analyzed using logistic and complementary log-log sire, animal, and maternal effects models. Explanatory variables included in the models were duration of time interval, sex, type of birth, contemporary group, age of dam, and type of upbringing (nursery or not). Similar estimates of explanatory variables were obtained within the same period across models and different time intervals. Heritability estimates from the complementary log-log models were greater than those from the comparable logistic models because of the difference in variance of the respective link functions. Heritability estimates from the complementary log-log sire model ranged from 0.13 to 0.21 for all periods. These estimates were greater than the complementary log-log animal model estimates that ranged from 0.04 to 0.12. Maternal effects were important early in life, with the maternal heritability slightly greater than the direct additive heritability. Negative correlations (-0.72 to -0.65) between direct additive and maternal effects was estimated. The similarity of results among survival analysis methods demonstrates that the discrete methodology is a viable alternative to estimate variance components in livestock survival data.

Animals↗

PubMed2002

Detection of quantitative trait loci influencing dairy traits using a model for longitudinal data.

A longitudinal-linkage analysis approach was developed and applied to an outbred population. Nonlinear mixed-effects models were used to describe the lactation patterns and were extended to include marker information following single-marker and interval mapping models. Quantitative trait loci (QTL) affecting the shape and scale of lactation curves for production and health traits in dairy cattle were mapped in three U.S. Holstein families (Dairy Bull DNA Repository families one, four, and five) using the granddaughter design. Information on 81 informative markers on six Bos taurus autosomes (BTA) was combined with milk yield, fat, and protein percentage and somatic cell score (SCS) test-day records. Six percent of the single-marker tests surpassed the experiment-wise significance threshold. Marker BL41 on BTA3 was associated with decrease in milk yield during mid-lactation in family one. The scale and shape of the protein percentage lactation curve in family four varied with BMC4203 (BTA6) allele that the son received from the grandsire. Some map locations were associated with variation in the lactation pattern of multiple traits. In family four, the marker HUJI177 (BTA3) was associated with changes in the milk yield and protein percentage curves suggesting a QTL with pleiotropic effects or multiple QTL in the region. The interval mapping model uncovered a QTL on BTA7 associated with variation in milk-yield pattern in family four and a QTL on BTA21 affecting SCS in family five. The developed approach can be extended to random regressions, covariance functions, spline, gametic and variance component models. The results from the longitudinal-QTL approach will help to understand the genetic factors acting at different stages of lactation and will assist in positional candidate gene research. Identified positions can be incorporated into marker-assisted selection decisions to alter the persistency and peak production or the fluctuation of SCS during a lactation.

Animals↗

PubMed2002

Interval and composite interval mapping of somatic cell score, yield, and components of milk in dairy cattle.

Single-marker, interval-mapping (IM) and composite interval mapping (CIM) were used to detect quantitative trait loci (QTL) controlling milk, fat and protein yields, and somatic cell score (SCS). A granddaughter design was used to combine molecular genetic information with predicted transmitting abilities (PTA) and estimated daughter yield deviations (DYD) from eight Dairy Bull DNA Repository Holstein families. Models that included and excluded weights accounting for the uncertainty of the response variable were evaluated in each trait, family and phenotype (DYD and PTA) combination. The genotypic information consisted of 174 microsatellite markers along 29 Bos taurus autosomes. The average number of informative markers per autosome was three and the number of informative sons per family and marker varied between 21 and 173. Within-family results from the least squares single-marker analyses were used in expectation-maximization likelihood IM and CIM implemented with QTL Cartographer. Different CIM model specifications, offering complementary control on the background QTL outside the interval under study, were evaluated. Permutation techniques were used to calculate the genome-wide threshold test statistic values based on 1,000 samples. Results from the DYD and PTA analyses were highly consistent across traits and families. The minor differences in the estimates from the models that accounted for or ignored the uncertainty of the DYD (variance) and PTA (inverse of reliability) may be associated to the elevated and consistent precision of the DYD and PTA among sons. The CIM model best supported by the data had 10 markers controlling for background effects. On autosome (BTA) three, a QTL at 32 cM influencing protein yield was located in family five and a QTL at 74 cM for fat yield was located in family eight. Two map positions associated with SCS were detected on BTA 21, one at 33 cM in family one and the other at 84 cM in family three. A QTL for protein yield was detected between 26 and 36 cM on BTA six, family six, and a QTL for milk yield was detected at 116 cM on BTA seven in family three. The IM and CIM approaches detected a QTL at 3 cM on BTA 14 influencing fat yield in family four. Two map positions on BTA 29 were associated with significant variation of milk (0 cM) and fat yield (14 cM) in family seven. These results suggest the presence of one QTL with pleiotropic effects on multiple traits or multiple QTL within the marker interval. Findings from this study could be used in subsequent fine-mapping work and applied to marker-assisted selection of dairy production and health traits.

Animals↗

PubMed2001

Analysis of nucleotide sequence data using mixed model methodology.

Linear, logistic, and multivariate mixed model analyses were applied to simulated data of five quantitative traits and a binary liability trait to detect associations with sequence variants in seven genes. Infrequent site variants (< 1%) were eliminated and conservative step-wise procedures were used to reduce the number of variants fitted. Random effects accounting for additive genetic relationships between individuals and for common environment effects were fitted to reduce spurious significant results. Five sites in genes 1, 2, and 6 had significant effects (p < 0.0001) on the traits and were found in both replicates studied. Survival analysis using a Weibull model identified two significant sites for disease age at onset. Other less significant sites may be false positives or due to founder effects. This approach was effective in identifying putative sites while accounting for polygenic and environmental sources of variation.

Genetic Predisposition to Disease↗

PubMed2001

Survival analysis of lamb mortality in a terminal sire composite population.

Records of mortality during the first year of life of 8,642 lambs from a composite population at the U.S. Meat Animal Research Center were studied using survival and logistic analyses. The traditional logistic approach analyzes the binary response of whether or not a lamb survived until a particular time point, thus disregarding information on the actual age at death. Survival analysis offers an alternative way to study mortality, wherein the response variable studied is the precise age at death while accounting for possible record censoring. Lamb mortality was studied across five periods based on management practices: birth to weaning, birth to 120 d of age, birth to 365 d of age, weaning to 365 d of age, and 120 to 365 d of age. Explanatory variables included in the models were sex, type of birth, age of dam, and whether or not a lamb was raised in a nursery. The survival analysis was implemented using Weibull and Cox proportional hazards models with sire as random effect. The logistic approach evaluated sire, animal, and maternal effects models. Lambs culled during any period were treated as censored in the survival analyses and were assumed alive in the logistic analyses. Similar estimates of the explanatory variables were obtained from the survival and logistic analyses, but the survival analyses had lower standard errors than the logistic analyses, suggesting a slight superiority of the former approach. Heritability estimates were generally consistent across all periods ranging from 0.15 to 0.21 in the Weibull model, 0.12 to 0.20 in the Cox model, 0.08 to 0.11 in the logistic sire model, 0.04 to 0.05 in the logistic animal model, and 0.03 to 0.07 in the maternal effects logistic model. Maternal effects were important in the early stages of lamb life, but the maternal heritability was less than 0.07 in all the stages studied with a negative correlation (-0.86 to -0.61) between direct and maternal effects. The estimates of additive genetic variance indicate that the use of survival analysis estimates in breeding schemes could allow for effective selection against mortality, thereby improving sheep productivity, welfare, and profitability.

Age Factors↗