Biomedical subjects

E A Mäntysaari

Publications and source records attributed to E A Mäntysaari.

10 recordsLinked to original sources

PubMed2006

Genetic evaluation of somatic cell score in dairy cattle considering first and later lactations as two different but correlated traits.

A test-day (TD) random regression model (RRM) was described for the genetic evaluation of somatic cell score (SCS) where first and later lactations were considered as two different but correlated traits. A two-step covariance function procedure was used to estimate variance-covariances and associated genetic parameters. Analysis of estimated breeding values (EBV), ranking of top bulls and cows and some computational aspects were used to compare RRM with TD repeatability model (RPM) and lactation average model (LAM). Residuals were analysed to assess the relative fit of TD models. Comparison between RRM and RPM showed that RRM has lower mean squared error and gave better fit to the data. For young bulls and cows, the standard deviation (SD) of EBVs was highest for RRM and lowest for LAM implying efficient utilization of information on SCS, in terms of revealing more genetic variation. A much lower correlation of EBVs ranging from 0.80 to 0.92 and significant re-ranking of top bulls and cows were observed between RRM and LAM. The lower across-lactation correlation between RRM and LAM indicated that LAM is directed to give more weight to first lactation breeding values. The RRM, where SCS in the first and later lactations was considered as two different but correlated traits was able to make effective use of available information on young bulls and cows, and could offer an opportunity to breeders to utilize EBVs for first and later lactations.

Animals↗

PubMed2006

Feed efficiency of rainbow trout can be improved through selection: different genetic potential on alternative diets.

To assess the genetic potential for selection of increased feed efficiency in rainbow trout (Oncorhynchus mykiss), we estimated the heritabilities and correlations for BW, daily weight gain (DG), and daily feed intake (DFI). Body weight was recorded 5 times, and DG and DFI 3 times during a feeding trial lasting 22 mo. To test the hypothesis that phenotypic and genetic parameters were influenced by a nutritional environment, fish were fed either a modern normal protein diet (NP, 40 to 45% protein and 30 to 33% lipid) or an alternative high protein diet (HP, 50 to 56% protein, 20 to 24% lipid) in a split-family design. Results showed that there were no large differences in heritabilities between the diets. Average heritability for DFI over both diets and different fish ages was low (average h2 = 0.10), indicating that modest genetic changes in response to selection can be obtained. Average heritabilities for BW and DG over both diets and different fish ages were 0.28 and 0.33, respectively. The NP diet enabled fish to express a wide range of BW, as shown by the increased coefficients of phenotypic variation for BW. Fish fed the HP diet showed increased phenotypic variation for DFI in > 750-g fish. On the NP diet, genetic correlations of DFI with DG and BW were very strong for 750- to 2,000-g fish. In contrast, on the HP diet, the respective correlations were moderate to low, revealing more genetic potential to change growth and feed intake simultaneously in opposite directions. An analysis of the predicted selection responses showed that selection solely for high DG improved feed efficiency as a correlated genetic response. Simultaneous selection for high DG and reduced DFI, in turn, may increase genetic gain in feed efficiency by a factor of 1.2 compared with selection solely for DG. However, variation for growth and feed intake and the relationships between these traits were different in different nutritional environments, leading to divergent genetic responses on the alternative diets.

Animal Feed↗

PubMed2005

Genetic and phenotypic relationships among milk yield and somatic cell count before and after clinical mastitis.

This paper studies whether cows with originally lower somatic cell count (SCC) are more susceptible to clinical mastitis (CM) than cows with higher somatic cell count, and evaluates the correlations between CM, SCC, and milk yield. Data were extracted from the Finnish national milk-recording database and from the health recording system. First and second lactation records of 87,861 Ayrshire cows calving between January 1998 and December 2000 were included. Traits studied were incidence of CM, test-day SCC, and test-day milk yield before and following CM. Genetic parameters were estimated using multitrait REML with a sire model. Results did not indicate that cows with genetically low SCC would be more susceptible to CM. The genetic correlation between CM in the first and second lactation was reasonably high (0.73), suggesting that susceptibility to mastitis remains similar across lactations. The genetic correlation between CM and milk yield traits was positive (from 0.38 to 0.56), confirming the genetic antagonism between production and udder health traits. The genetic correlation between SCC and milk was positive in the first lactation, but negative, or near zero in the second lactation. This indicates that breeding for lower SCC might not affect milk production in later lactations. The results of this study support the use of SCC as an indicator of mastitis and a tool for selection for mastitis resistance.

Animals↗

PubMed2004

Genetic associations of prolificacy with performance, carcass, meat quality, and leg conformation traits in the Finnish Landrace and Large White pig populations.

The objective of this study was to estimate genetic associations of prolificacy traits with other traits under selection in the Finnish Landrace and Large White populations. The prolificacy traits evaluated were total number of piglets born, number of stillborn piglets, piglet mortality during suckling, age at first farrowing, and first farrowing interval. Genetic correlations were estimated with two performance traits (ADG and feed:gain ratio), with two carcass traits (lean percent and fat percent), with four meat quality traits (pH and L* values in longissimus dorsi and semimembranosus muscles), and with two leg conformation traits (overall leg action and buck-kneed forelegs). The data contained prolificacy information on 12,525 and 10,511 sows in the Finnish litter recording scheme and station testing records on 10,372 and 9,838 pigs in Landrace and Large White breeds, respectively. The genetic correlations were estimated by the restricted maximum likelihood method. The most substantial correlations were found between age at first farrowing and lean percent (0.19 in Landrace and 0.27 in Large White), and fat percent (-0.26 in Landrace and -0.18 in Large White), and between number of stillborn piglets and ADG (-0.38 in Landrace and -0.25 in Large White) and feed:gain (0.27 in Landrace and 0.12 in Large White). The correlations are indicative of the benefits of superior growth for piglets already at birth. Similarly, the correlations indicate that age at first farrowing is increasing owing to selection for carcass lean content. There was also clear favorable correlation between performance traits and piglet mortality from birth to weaning in Large White (r(g) was -0.43 between piglet mortality and ADG, and 0.42 between piglet mortality and feed:gain), but not in Landrace (corresponding correlations were 0.26 and -0.22). There was a general tendency that prolificacy traits were favorably correlated with performance traits, and unfavorably with carcass lean and fat percents, whereas there were no clear associations between prolificacy and meat quality or leg conformation. In conclusion, accuracy of estimated breeding values may be improved by accounting for genetic associations between prolificacy, carcass, and performance traits in a multitrait analysis.

Adipose Tissue↗

PubMed2000

Strategies for estimating the parameters needed for different test-day models.

Currently, most analyses of parameters in test-day models involve two types of models: random regression, where various functions describe variability of (co)variances with regard to days in milk, and multiple traits, where observations in adjacent days in milk are treated as one trait. The methodologies used for estimation of parameters included Bayesian via Gibbs sampling, and REML in the form of derivative-free, expectation-maximization, or average-information algorithms. The first method is simpler and uses less memory but may need many rounds to produce posterior samples. In REML, however, the stopping point is well established. Because of computing limitations, the largest estimations of parameters were on fewer than 20,000 animals. The magnitude and pattern of heritabilities varied widely, which could be caused by simplifications in the model, overparameterization, small sample size, and unrepresentative samples. Patterns of heritability differ among random regression and multiple-trait models. Accurate parameters for large multi-trait random regression models may be difficult to obtain at the present time. Parameters that are sufficiently accurate in practice may be obtained outside the complete prediction model by a constructive approach, where parameters averaged over the lactation would be combined with several typical curves for (co)variances for days in milk. Obtained parameters could be used for any model, and could also aid in comparison of models.

Analysis of Variance↗

PubMed1999

Solving large test-day models by iteration on data and preconditioned conjugate gradient.

A preconditioned conjugate gradient method was implemented into an iteration on a program for data estimation of breeding values, and its convergence characteristics were studied. An algorithm was used as a reference in which one fixed effect was solved by Gauss-Seidel method, and other effects were solved by a second-order Jacobi method. Implementation of the preconditioned conjugate gradient required storing four vectors (size equal to number of unknowns in the mixed model equations) in random access memory and reading the data at each round of iteration. The preconditioner comprised diagonal blocks of the coefficient matrix. Comparison of algorithms was based on solutions of mixed model equations obtained by a single-trait animal model and a single-trait, random regression test-day model. Data sets for both models used milk yield records of primiparous Finnish dairy cows. Animal model data comprised 665,629 lactation milk yields and random regression test-day model data of 6,732,765 test-day milk yields. Both models included pedigree information of 1,099,622 animals. The animal model ¿random regression test-day model¿ required 122 ¿305¿ rounds of iteration to converge with the reference algorithm, but only 88 ¿149¿ were required with the preconditioned conjugate gradient. To solve the random regression test-day model with the preconditioned conjugate gradient required 237 megabytes of random access memory and took 14% of the computation time needed by the reference algorithm.

Algorithms↗

PubMed1996

Relationships between clinical mastitis, somatic cell score, and production for the first three lactations of Finnish Ayrshire.

Data on 23,196 cows were extracted from the Finnish system for recording health data and merged with information on SCS and 305-d milk production to study 1) the genetic and phenotypic correlations of clinical mastitis (within 150 d postpartum) and SCS across the first three lactations and 2) the genetic relationships between the traits for individual lactations. (Co)variance components were estimated using linear multitrait REML and the expectation-maximization algorithm. Heritability estimates for separate lactations were distinctly higher for somatic cell score (0.14 to 0.19) than for clinical mastitis (0.02 to 0.05). Genetic correlations of the same traits among different lactations were positive and moderate to high, suggesting that, in practice, clinical mastitis and SCS can be considered as the same traits for different lactations. Genetic correlations of clinical mastitis and SCS varied from 0.37 for first lactation to 0.68 for third lactation, implying that clinical mastitis and SCC monitor different aspects of udder health. A clear, antagonistic genetic association existed between clinical mastitis and milk production, but the genetic correlation of SCS and milk production did not differ from 0.

Animals↗

PubMed1994

Genetic variation of residual feed consumption in a selected Finnish egg-layer population.

The purpose of the study was to estimate the heritability of residual feed consumption (RFC) and the genetic correlations between RFC and economically important traits. The genetic progress after four generations of selection for RFC and the changes in economically important traits were also investigated. A selection experiment for RFC was carried out from 1983 to 1987. The total data consisted of 3,750 birds and 2,661 records. The (co)variance components were calculated using derivative-free bivariate animal model restricted maximum likelihood (REML). Breeding values were estimated for calculating genetic progress in RFC and correlated responses in the other traits. The heritability of RFC calculated from the whole recorded period (16 to 42 wk) and using all 2,661 records was .46 (+/- .04). The genetic correlations between RFC and egg mass, number of eggs, egg weight, and body weight were not significant. The genetic correlation between RFC and feed consumption was .50 (+/- .04). The breeding value estimates indicated a moderate genetic progress in RFC due to selection. Feed consumption was decreased and body weight gain showed reduction in the last two generations. No change could be found in egg mass, number of eggs, egg weight, age at first egg, or body weight.

Animals↗

PubMed1991

Simulation study on covariance component estimation for two binary traits in an underlying continuous scale.

The usefulness of the variance and covariance component estimation methods based on a threshold model was studied in a multiple-trait situation with two binary traits. Estimation equations that yield marginal maximum likelihood estimates of variance components on the underlying continuous variable scale and point estimates of location parameters with empirical Bayesian properties are described. Methods were tested on simulated data sets that were generated to exhibit three different incidences, 25, 15, and 5%. Results were compared with analyses of the same data sets with a REML method based on normal distribution and a linear model. Heritabilities and residual correlations calculated from discrete observations were transformed to underlying parameters. In estimation of heritabilities, all methods performed equally well at all incidence levels and with no detectable bias. As suggested by threshold theory, the genetic correlation was accurately estimated directly from the observations without any need of correction for incidence. Marginal maximum likelihood estimates of genetic correlations were similar to linear model estimates; discrepancies from the true parameters were consistent with both methods. In estimation of residual correlations, the method with the linear model approach yielded satisfactory estimates only at the highest incidence level, 25%. For 5% incidence, the uncorrected estimate of residual correlation was 50% less than the true value, and after correction for incidence, the parameter was overestimated by 90%. The estimates of residual correlation from the threshold model were regarded fair, except at the lowest level of incidence, where the estimate was 27% higher than the true value. Results indicated that when an accurate estimate of residual correlation is needed, the marginal maximum likelihood estimates are superior to the estimates calculated with the linear model. Using correction for the incidence level for residual correlation did not work well except at the highest incidence level.

Analysis of Variance↗

PubMed1991

Clinical ketosis: phenotypic and genetic correlations between occurrences and with milk yield.

The repeatability and heritability of ketosis were estimated using data from 28,277 Finnish Ayrshire cows. A four-trait linear model including community-year, calving age and month, genetic group, and random sire effects was used to describe first and second lactation milk yields and veterinary diagnoses of ketosis. Variance components were estimated using REML. The disease traits were also analyzed with a categorical model including the same effects except that community and year were separate factors. Variance components were estimated with marginal maximum likelihood. Genetic relationships between 339 sires analyzed were included in models. The phenotypic correlation between the first and second lactation was defined as a repeatability of trait. The lactational incidence risk of ketosis was .05 in both the first and the second lactation. Average milk production was 4956 and 5547 kg in the first and second lactations, respectively. Estimates of heritabilities were .09 and .07 for ketosis and .23 and .19 for milk in the first and second lactations, respectively. Genetic correlations between first and second lactation recordings were .64 for ketosis and .93 for milk. Repeatabilities between subsequent lactations were .36 (.13 in linear analysis) for ketosis and .68 for milk. In the first lactation, genetic relationship between milk yield and ketosis was unfavorable, but in the second lactation ketosis and milk yield were genetically and phenotypically unrelated.

Animals↗