Content and performance of the MiniMUGA genotyping array: A new tool to improve rigor and reproducibility in mouse research

The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well-annotated genome, wealth of genetic resources, and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost-effective, informative, and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole-genome sequencing. Here, we describe the content and performance of a new iteration of the Mouse Universal Genotyping Array (MUGA), MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: (1) chromosomal sex determination, (2) discrimination between substrains from multiple commercial vendors, (3) diagnostic SNPs for popular laboratory strains, (4) detection of constructs used in genetically engineered mice, and (5) an easy-to-interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA, we genotyped 6899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays, both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived, and recombinant inbred lines. Here, we also report the detection of a substantial number of XO and XXY individuals across a variety of sample types, new markers that expand the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. We provide preliminary evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the relevant main stock. We conclude that MiniMUGA is a valuable platform for genetic QC, and an important new tool to increase the rigor and reproducibility of mouse research

Estimation of (Co)Variance Components of Test Day Yields for U.S. Holsteins

peer reviewed(Co)variance components for milk, fat, and protein yields during first lactation were calculated from test-day data from 17,190 cows representing 37 large herds in Pennsylvania and Wisconsin. Initially, four lactation stages of 75 days each were defined, and the test day nearest the midpoint of each interval was used. This approach minimized the number of lactations with missing values. A canonical transformation was used to estimate variance components, which required that all observations with missing values be deleted. Preliminary analysis showed little effect from this selection. Heritabilities usually increased with lactation stage, were highest for milk, and averaged .15. Phenotypic and genetic correlations between milk and protein yields were higher than between milk and fat yields. For each yield trait, the genetic correlation declined from about .90 for adjacent lactation stages to about .75 between lactation stages 1 and 4. When yield traits were from the same lactation stage, the genetic correlation averaged .39 between milk and fat, .78 between milk and protein, and .53 between fat and protein. Phenotypic correlations within lactation stage were >.90 between milk and protein and around .65 between milk and fat and between fat and protein. Estimates for the four 75-day lactation stages were extended to provide estimates for twelve 25-day stages (36 traits) using (co)variance functions, which allowed denser coverage of the lactation

Estimation of (co) variance functions of test day yields in first and later lactations of United States Holstein cows

peer reviewed(Co) variance components for milk, fat, and protein yields during first and second, representing later, lactations were estimated from data for test days from 23,029 Holstein cows from 37 herds in Pennsylvania and Wisconsin. Four lactation stages of 75 d were defined in each lactation, and the test day nearest the center of each interval was used. A total of 9110 observations were available for the final analysis of lactations with test days in all four lactation stages. Data were preadjusted for lactation curves within lactation stages

Correlations between Herd Life and Type Traits in Quebec Holsteins

Long productive life is a primary breeding objective of dairy farmers. Herd life and lifetime profitability are two of the most important factors influencing a cow’s profitability. Various tools have been introduced to aid farmers in obtaining efficient, high producing, sustainable cows, of which type traits, which evaluate an animal’s physical characteristics, are the most popular. Dairy producers have bred for high producing and healthy cows, and many of their decisions are based on a cow’s conformation traits. The objective of this study was to estimate the relationship between various lengths of productive life and type traits for Quebec Holstein cows.[...

Estimation of variance components for cow and parity effects from test-day yields

The initial step in implementation of a US test-day model includes estimation of cow and parity test-day variances needed to calculate lactation stage, age, and pregnancy effects. Single-trait repeatability models were fitted, and variance components were estimated for milk, fat, and protein test-day yields using Method R and a preconditioned conjugate gradient (PCG)equation solver because of large data sets (0.7 to 7.7 million records). Data were obtained for calvings since 1990 for Brown Swiss and Jerseys and for Holsteins from California, Pennsylvania, Texas, and Wisconsin. A minimum of three observations were required per subclass for herd test date and milking frequency. Three parity groups were defined: first, second, and later. Test-day data were adjusted for environmental effects of age, calving season, and milking frequency. Estimated breeding values (EBV)were expressed on a daily basis. To assess effect of adjustments, data also were analyzed without correction. For adjusted data, variance ratios (residual divided by variance of effect)within parity were similar across breeds, subpopulations, and samples: 1.5 to 1.8 for milk, 3.0 to 4.3 for fat, and 1.8 to 2.3 for protein. Variance ratios across parities ranged from 3.5 to 6.8 for milk, 8.7 to 17.6 for fat, and 5.5 to 9.4 for protein. Adjustment for EBV reduced both cow genetic and nongenetic variances. Variance ratios for permanent environment within parity from unadjusted data were nearly identical to those from adjusted data. For unadjusted data, heritabilities ranged from 0.19 to 0.30 for milk, 0.13 to 0.15 for fat, and 0.17 to 0.23 for protein. Although computations took several weeks, use of Method R and a PCG solver enabled estimation of the variance components that will be used for US evaluations based on a test-day model

Estimation of (co)variances of test day yields for first lactation Holsteins in the United States

(Co)variance components for milk, fat, and protein yields during first lactation were estimated from data for test days from 23,029 Holstein cows from 37 herds in Pennsylvania and Wisconsin. Four lactation stages of 75 d were defined, and the test day nearest the center of each interval was used. In four analyses, a lactation stage was added, and observations with missing values were deleted; 17,190 observations were available for the final analysis of lactations with test days in all lactation stages. Missing values were deleted because a canonical transformation was used for estimation of (co)variance matrices. Heritability estimates were similar across analyses, which indicated little effect from selection. Heritabilities usually increased with lactation stage and were highest for milk; mean heritability estimates were 0.19 for milk, 0.14 for fat, and 0.16 for protein. Phenotypic and genetic correlations were higher between milk and protein than between milk and fat. Within a yield trait, genetic correlation declined from ≥0.90 for adjacent stages to 0.75 for milk and protein and to 0.82 for fat between initial and final lactation stages. Within lactation stage, mean genetic correlations were 0.40 between milk and fat, 0.78 between milk and protein, and 0.56 between fat and protein; corresponding mean phenotypic correlations were 0.64, 0.91, and 0.66. The effect of solving the model iteratively was examined with records that had been adjusted using solutions from fitting the full model. Heritabilities for the beginning of lactation increased slightly with the iterative solution, which indicated a better model fit

Deriving lactation yields from test-day yields adjusted for lactation stage, age, pregnancy, and herd test date

Lactation records for milk, fat, and protein yields were calculated from test-day data adjusted for the effects of lactation stage, age, previous days open, days pregnant, and test-day class (herd, test date, and milking frequency). Those lactation records reflect the improved accounting of environmental effects from a test-day model and can be combined with historical lactation records. Test-day data were adjusted with existing lactation multiplicative adjustments to maintain variance characteristics. Then additive adjustments for lactation stage, age, previous days open, and days pregnant were applied. The current multiplicative adjustments for previous days open were not applied because its effect was expected to differ by lactation stage. To remove genetic differences, the estimated breeding value from the previous evaluation divided by 305 was subtracted. Effects of test-day class, and permanent environment within and across parities were estimated within herd. The effect of test-day class was subtracted from adjusted test-day yield, and the breeding value restored. Those deviations then were combined with the best prediction procedure into a lactation measure. Heritabilities and repeatabilities of lactation records that were adjusted for test-day class were higher than for current lactation records. The adjusted records should improve the accuracy of evaluations and allow the use of test-day data as well as provide for the continued use of historical data when test-day data are not available