Sep 23, 1999 - non-linear threshold models, as diseases are recorded as present or absent within lactation (so called binary traits). Linear statistical models ...
Animal Science 2000, 71: 411-419 © 2000 British Society of Animal Science
1357-7298/00/96960411$20·00
Linear and threshold model genetic parameters for disease, fertility and milk production in dairy cattle H. N. Kadarmideen1, R. Thompson2, 3 and G. Simm1 1Animal
Breeding and Genetics Department, Animal Biology Division, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK 2Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS, UK 3IACR, Rothamsted Experiment Station, Harpenden, Hertfordshire AL5 2JQ, UK
Abstract This study provides estimates of genetic parameters for various diseases, fertility and 305-day milk production traits in dairy cattle using data from a UK national milk recording scheme. The data set consisted of 63891 multiple lactation records on diseases (mastitis, lameness, milk fever, ketosis and tetany), fertility traits (calving interval, conception to first service, number of services for a conception, and number of days to first service), dystocia and 305-day milk, fat and protein yield. All traits were analysed by multi-trait repeatability linear animal models (LM). Binary diseases and fertility traits were further analysed by threshold sire models (TM). Both LM and TM analyses were based on the generalized linear mixed model framework. The LM included herd-year-season of calving (HYS), age at calving and parity as fixed effects and genetic, permanent environmental and residual effects as random. The TM analyses included the same effects as for LM, but HYS effects were treated as random to avoid convergence problems when HYS sub-classes had 0 or 100% incidence. Because HYS effects were treated as random, herd effects were fitted as fixed effects to account for effect of herds in the data. The LM estimates of heritability ranged from 0·389 to 0·399 for 305-day milk production traits, 0·010 to 0·029 for fertility traits and 0·004 to 0·038 for diseases. The LM estimates of repeatability ranged from 0·556 to 0·586 for 305-day milk production traits, 0·029 to 0·086 for fertility traits and 0·004 to 0·100 for diseases. The TM estimates of heritabilities and repeatabilities were greater than LM estimates for binary traits and were in the range 0·012 to 0·126 and 0·013 to 0·168, respectively. Genetic correlations between milk production traits and fertility and diseases were all unfavorable: they ranged from 0·07 to 0·37 for milk production and diseases, 0·31 to 0·54 for milk production and poor fertility and 0·06 to 0·41 for diseases and poor fertility. These results show that future selection programmes should include disease and fertility for genetic improvement of health and reproduction and for sustained economic growth in the dairy cattle industry. Keywords: dairy cattle, diseases, fertility, genetic parameters, threshold models.
Introduction
reduced fertility, but this increases cost and therefore a combination of better management and genetic selection for good health and fertility is a more (cost) effective long-term solution. The need for inclusion of health and fertility traits in selection programmes has been strengthened by several findings of an unfavorable genetic correlation with milk production traits (e.g. Simianer et al., 1991; Hoekstra et al., 1994; Pryce et al., 1997; Vandorp et al., 1998; Dematawewa and Berger, 1998; Lindhe and Philipsson, 1998; Lund et al., 1999). Genetic improvement of health and fertility traits could be achieved through their inclusion in national selection indices, for which
Economic loss due to disease and poor fertility is of major concern to dairy industries around the world. For example, the average total cost of milk fever, clinical mastitis and lameness per cow per lactation in the UK is about £220, £218, £273, respectively (Kossaibati and Esslemont, 1995) and an extra day of calving interval costs between £6·22 and £7·44 (Stott et al., 1999). There are also ethical and welfare concerns with regard to therapeutic or hormonal treatment of dairy cows to remedy diseases and improve fertility. Better herd management can temporarily prevent the onset of diseases and 411
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genetic parameters are needed. One of the main objectives of this study was to estimate genetic parameters for various diseases and fertility in the UK dairy cattle population, using records from a national recording scheme. Genetic analysis of many diseases requires the use of non-linear threshold models, as diseases are recorded as present or absent within lactation (so called binary traits). Linear statistical models are not well suited for genetic parameter estimation of binary traits because these models assume normality. Non-linear mixed models based on threshold theory have been studied for the analysis of categorical traits and have been shown to be theoretically better than linear statistical models (e.g. Gianola, 1980 and 1982). Many studies have also adopted threshold models for categorical traits in practice (e.g. McGuirk et al., 1998; Boettcher et al., 1999; Van Tassell et al., 1999). The other objective of this study was to estimate genetic parameters for binary (disease and fertility) traits based on threshold models.
Table 1 Production, fertility, disease and parturition traits used in linear and threshold model analyses, with their abbreviations Traits Milk production traits 305-day milk (kg) 305-day fat (kg) 305-day protein (kg) Fertility traits Calving interval (in days) No. of services per conception (1,2,3,...) Conceived to first service (1/0)† Days to first service (in days) Disease traits‡ Hypocalcaemia/milk fever (1/0)† Mastitis (1/0)† Lameness/foot and leg problems (1/0)† Hypomagnesemia/staggers/tetany (1/0)† Ketosis or acetonaemia (1/0)† Parturition/maternal trait Calving difficulty or dystocia (1/0)†
Abbreviations MILK BFAT PROT CINT NSPC CTFS DTFS FEVR MAST LAME TETY KETO DYST
† Binary trait scored as 1 or 0 based on presence or absence. ‡ All diseases are clinical cases.
Material and methods Data Completed lactation records on Holstein Friesian cows from January 1994 to December 1998 were obtained from one of the UK national recording schemes, offered by Livestock Services UK Ltd (LSUK). Information on various diseases (only clinical cases) and services were recorded by farmers and collected on a monthly basis to coincide with official milk recording visits by LSUK. The recording of disease and service information is voluntary for farms that are milk recording with LSUK. The database contained information on animal identification, diseases identified by a unique numerical code with their dates of occurrence, sire and dam herd book numbers, lactation number, 305day lactation yield of milk, fat and protein and calving and insemination dates. Table 1 describes traits considered in this study and their abbreviations. A data set for analysis of fertility traits was first created according to the following criteria. Records with at least one insemination date were retained in order to compute fertility traits that are based on insemination dates. Because the recording scheme is voluntary, many records (about 50%) did not have insemination dates. Records up to the first five lactations were retained where the cow was sired by a Holstein bull. This resulted in 66383 records. Fertility traits were computed using either calving dates or insemination dates or both. Days to first service (DTFS) was the number of days between calving and first insemination date. Calving interval
(CINT) was the number of days between two successive calving dates. Conception to first service (CTFS) was a binary trait, coded 1 if a cow conceived to its first service and 0 otherwise. Number of services per conception (NSPC) was the number of services for a cow in a given lactation that resulted in a pregnancy. To compute fertility traits such as CTFS and NSPC, information on pregnancy diagnoses were not available, so subsequent calving information was the only way to decide whether an insemination was successful. This meant that cow records had to have at least two calving dates to be selected. Gestation length was also computed using the last service date and calving date in order to validate CTFS and NSPC. Dystocia or calving difficulty was originally recorded as a score trait with numerical codes 1 to 4 for increasing degrees of calving difficulty. For simplicity, all degrees of calving difficulty were grouped into one category as difficult and coded as 1, and 0 otherwise, thereby expressing calving difficulty as a binary trait. Once all fertility traits were computed, the following restrictions were applied on calving interval, days to first service and gestation length. Calving interval was restricted to between 300 and 600 days as calving intervals less than 300 days probably indicated an abortion and calving intervals greater than 600 days would mean an abnormal lactation length (1008 records were lost from the original data set). Days to first service was restricted to between 20 and 200 days, as records outside this range are likely to be physiologically abnormal or wrongly recorded
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Genetic parameters for disease and fertility in UK dairy cows (1484 further records were lost). Gestation lengths were restricted to between 272 and 292 days. This is because the mean gestation length is 282 days and animals are expected to fall within one (21-day) oestrous cycle period around this mean. These restrictions on gestation length were used as an aid in validating CTFS and NSPC scores. An incorrect or abnormal gestation length meant that the exact insemination date that resulted in conception was unknown or missing. Unknown or missing insemination dates could be expected with voluntary recording schemes. Because this data editing on gestation length is arbitrary, other records on animals with abnormal gestation length (out with 282 ± 10 days) were not discarded but CTFS and NSPC for these records were set as ‘missing observations’. The introduction of missing values, in part, accounted for the uncertainty about service information. The standard deviation (s.d.) of true gestation length is about 5 days (Holstein UK and Ireland 2000). Given this s.d., the data editing adopted here also allowed possible outliers (for example, outside the range of mean ± 2·5 s.d., i.e. 282 ± 12·5 days) in the data to be identified and their CTFS and NSPC to be set as missing. The number of records falling outside the range of 282 ± 10 days was 10335 and 282 ± 12·5 was 8279. The distribution of number of herds with complete absence of service dates cross-classified across different proportion of records with missing service dates (from
