Recipient Organization
UNIV OF WISCONSIN
21 N PARK ST STE 6401
MADISON,WI 53715-1218
Performing Department
Dairy Science
Non Technical Summary
The transition to lactation period in dairy cattle, defined as 3 weeks prior to and 3 weeks after calving, is known to be the most challenging period in the dairy cow life cycle, specifically in terms of metabolic disorders. Ketosis, is defined as elevated ketone bodies in the blood and is a critical challenge to transition dairy cows that has negative impacts to milk production, animal health, and profitability. Cows with sub-clinical ketosis (SCK) produce less milk, are more likely to develop a displaced abomasum, and are more likely to be culled from the herd. The overall goal of the proposed research is to develop and validate practical tools for use by producers to aid in identification and treatment of SCK. Our central hypothesis is that cowside tests, milk analysis, and genomic markers can be used for early detection and reduction of negative impacts of SCK at the individual-animal, pen, and herd levels. This research focuses on developing and validating a SCK predictor index using genetic risk scores correlated with SCK incidence.To achieve this objective, we will collect repeat blood samples from 750 early lactation dairy cows to determine if the cow is healthy or ketotic in terms of ketosis. Cows will also be genotyped and genetic information used to determine genetic markers for ketosis predisposition. Having a strong disease diagnosis and the genotype will allow for analysis of genetic risks scores and will be used to develop a SCK predictor index. This will be a valuable tool for producers, nutritionists, and geneticists as it will allow for cows at increased risk of SCK to be identified and managed or treated differently.
Animal Health Component
50%
Research Effort Categories
Basic
(N/A)
Applied
50%
Developmental
50%
Goals / Objectives
The overall goal of the proposed research is to develop and validate practical tools for use by producers to aid in identification and treatment of sub-clinical ketosis (SCK). Our central hypothesis is that cowside tests, milk analysis, and genomic markers can be used for early detection and reduction of negative impacts of SCK at the individual-animal, pen, and herd levels. At the completion of this project, it is our expectation that we will have conducted a genome wide association study (GWAS) that identifies loci associated with SCK incidence in a comprehensive data set, allowing for use of these loci as a marker for SCK predisposition. The specific aim of the research is to develop and validate a SCK predictor index using genetic risk scores correlated with SCK incidence.
Project Methods
Repeated blood samples will be collected from 750 cows at commercial dairy farms to determine the incidence of SCK. Our collaborative group has been performing SCK testing at more than 10 dairy herds and has an ongoing relationship with these herds. At each farm, blood sampling will be conducted twice a week after morning milking, during morning feeding. Blood samples will be collected for blood BHBA concentrations at four timepoints between 4 and 17 DIM for each cow.Justification of the collection of four samples per cow is based on posthoc analysis of a dataset from the collaborative group, examining the frequency of testing required to determine a definitive diagnosis of SCK vs. healthy. Posthoc analysis of previously collected data (Oetzel, unpublished data). Blood BHBA was quantified by Precision Xtra meter from 1868 cows (across 4 herds) at 6 time points between 4 and 17 DIM to determine incidence of SCK and absolute ketosis status (positive or negative) for each cow. A positive SCK diagnosis required at least one BHBA >1.2 mmol/L between 3 and 16 DIM. Sensitivity is defined as the number of cows ketotic on that test day divided by the total number of cows determined to be ketotic during the trial, with 100% sensitivity being ideal and 90% sensitivity being a robust test. Within the whole dataset, the sensitivity of a single blood Precision Xtra test was 44%. These data highlight that accurate determination of SCK status requires repeat testing; however, the number of test required has not yet been defined. Without adequate testing timepoints, a cow can be classified as nonketotic merely because she was not tested at the correct timepoint and not because she had truly remained below the cutpoint for SCK through the entire fresh period. To determine how many timepoints are needed, the dataset was manipulated to include only 4 tests per cow between 4 and 17 DIM to determine if 4 tests per cow is adequate to determine overall SCK incidence. The sensitivity of the 4-test approach was 91% compared with the 6-test approach. This analysis supports that a 4-test approach to ketosis testing provides adequate information to determine a definitive ketosis status.Blood samples will be collected at each of the four timepoints via venipuncture of the coccygeal vein or artery into evacuated tubes (Becton Dickinson, Franklin Lakes, NJ). Determination of blood BHBA cowside will be by Precision Xtra meter (Abbott Laboratories, Abbott Park, IL) using blood (100 μL) from the evacuated tube. Use of the cowside test will allow for same-day feedback of information regarding SCK status to the producer for treatment decisions.Herd DairyComp 305 records and individual cow health and treatment records will also be collected. While there is variation in the consistency and accuracy of recorded data in DairyComp 305 across farms the data still presents a useful insight into possible risk factors and impacts of SCK. Objective data (parity, calving date, pregnancy confirmed by veterinarian, services to conception, etc.) will be considered reliable inputs into the regression models. In addition to data collected from DairyComp305 records during the testing period, outcomes data will be collected over the next six-months post-sampling. Milk production (at 30 DIM and cumulative at 30, 90, and 120 DIM, and previous lactation 305 mature equivalent milk), health incidences (specifically ketosis and displaced abomasum), culling, and reproductive efficiency data (DIM at first breeding, services to conception, and days to pregnant) will be collected to examine differences in outcomes based on SCK status. In addition to the blood samples, a hair sample will be collected for genotyping (Genex, Verona, WI). Low-density genotypes (19K SNP chip) will be analyzed against SCK status, as a continuous variable (blood BHBA concentration) and a categorical variable (healthy, sub-clinical ketosis, ketosis), to determine loci associated with SCK. These genetic risk scores can then be used to identify animals that are at increased risk for developing SCK. Output of GWAS will be used to identify individual or sets of SNP that can be used to predict an individual cow's genetic predisposition (risk) for SCK. Each cow in our study will have one of three possible genotypes at each of the 19,000 SNP loci: AA, AB, or BB, where alleles A and B refer to alternative alleles or variants at a given location on the chromosome. If the frequency of one allele (e.g., allele A) is higher among cows that are affected with SCK than among cows that are unaffected, then it is "associated" with the disease. Such an association occurs when the SNP is located on the same chromosome near an unknown functional mutation in the genome that affects a biological process related to SCK (e.g., some aspect of triglyceride synthesis). Even though the exact location and mechanism of these mutations are unknown, the SNP genotype at that location can be used as a "marker" for selection, and by changing the frequency of the SNP allele we will also change the frequency of the underlying mutant allele that increases SCK susceptibility. In the current project, our aim is not genetic selection, but rather a genetic diagnostic tool that can identify cows with high SCK susceptibility that should be targeted for a management intervention. Although the example described above refers to the use of a single SNP as a marker for SCK susceptibility, extension to multiple SNP is extremely simple, and in our study we anticipate using several dozen SNPs simultaneously when evaluating a cow's genetic predisposition for SCK. An alternative, which will also be explored in the proposed study, is to use all 19,000 SNP simultaneously. This approach has been used successfully in our lab for prediction of residual feed intake and related traits in lactating Holstein cows based on the SNP genotype, health history, or SNP genotype plus health history for each cow (Yao et al., 2014). Similar approaches have been used in humans to compute genetic risk scores for various diseases. For example, Grassman et al. (2012) developed a genetic risk prediction model for age-related macular degeneration (AMD) based on age, smoking, and 13 risk variants and reported that 87.4% of patients in the highest risk category were expected to develop AMD by 85 years of age, as compared with roughly 50% of the general population and only 2.2% of patients in the lowest risk category. One issue that can lead to false-positive associations between SNP genotypes and disease status, particularly in humans, is population stratification (e.g., people from different racial/ethnic groups may differ in disease frequency while also differing in allele frequency by chance), so we will check for population substructure prior to our GWAS analysis and make adjustments as needed. This genomic testing will allow for identification of cows that are at greater risk for SCK onset, through development of a genetic risk score. This information will allow producers to identify specific cows that need more intensive management or observation during the transition to lactation period. This tool represents something that is currently not available to producers and will improve our ability to make informed management and nutrition decisions regarding SCK on each specific farm and with each specific cow.