Genome-wide associations for fertility using data
association analysis was conducted using a Bayesian from experimental herds in four countries
Stochastic Search Variable Selection (BSSVS) modelthat estimates effects for all SNPs simultaneously. All D.P. Berry1 J.W.M. Bastiaansen2, R.F. Veerkamp2, S.
univariate BSSVS models were run for 50,000 cycles, Wijga2, E. Wall3, E. Strandberg4 and M.P.L. Calus2.
discarding the initial 10,000 cycles for burn-in (i.e., to 1Animal & Grassland Research and Innovation Centre, remove the uncertainty of starting values provided). All Teagasc, Moorepark, Fermoy, Co. Cork. 2Animal Breeding and Genomics Centre, Wageningen UR Agricultural College, Penicuik, The UK. 4Swedish Results and Discussion
University of Agricultural Sciences, Uppsala, Sweden. Heritability estimates for the traditional fertility traitsvaried from 0.03 (PRFS) to 0.16 (CFH). The heritability Introduction
of PPCLA was 0.13. The interval traits (i.e., CFH, CFS, Genome-wide associations for difficult to measure traits are limited by sample population size with accurate correlated (0.37 to 0.99) with each other. The posterior phenotypic data. Fertility phenotypes using information QTL probabilities for the traditional fertility traits were on hormonal profiles are more heritable (Veerkamp et all less than 0.021. Posterior probabilities of >0.04 were al., 2000) than traditional fertility measures thereby observed for PPCLA on BTA2 (BTA-49769-no-rs; increasing the power of genome-wide association probability of 0.060) and BTA21 (BTA-12468-no-rs; studies. The objective of this study was to use data on probability of 0.045). The SNP on BTA2 explained primiparous Holstein-Friesian cows from experimental 0.51% of the genetic variance in PPCLA while the SNP farms in Ireland, the UK, The Netherlands and Sweden on BTA20 explained 0.35% of the genetic variance in to identify genomic regions associated with fertility PPCLA. The Bayes factors of BTA-49769-no-rs and including a fertility phenotype derived from milk BTA-12468-no-rs were 24 and 18, respectively. The posterior QTL probability of 0.060 for PPCLA at SNPBTA-49769-no-rs estimated in the univariate analysis Materials and Methods
increased to 0.094, 0.121, 0.162, 0.662 and 0.162 when Phenotypic data were available on 2,031 primiparous included in a bivariate analysis with CFH, CFS, NS, Holstein-Friesian cows from Ireland, 1,018 cows from CIV and PRFS, respectively. The posterior probability the UK, 725 cows from The Netherlands, and 225 cows of 0.045 for PPCLA at SNP BTA-12468-no-rs on from Sweden. Sampling and determination of milk BTA20 when estimated in the univariate analysis increased to 0.052, 0.152, 0.072, 0.123 and 0.135 when described in detail for the data originating from Ireland included in a bivariate analysis with CFH, CFS, NS, (Horan et al., 2005), the UK (Pollot and Coffey, 2008), The Netherlands (Veerkamp et al., 2000) and Sweden(Petersson et al., 2006). Milk sampling was undertaken Conclusions
two to three times weekly between the years 1991 and Regions of the genome associated with PPCLA were 2005. The traditional fertility traits investigated were identified although no obvious region was associated days from calving to first observed heat (CFH) or first with the traditional fertility measures. This suggests that service (CFS), calving interval (CIV), number of genome wide associations may be more successful if services (NS), and pregnancy rate to first service phenotypes derived from physiological measures, less (PRFS). Post-partum interval to the commencement of influenced by management, are used. The inclusion of luteal activity (PPCLA) was defined as the number of days from calving to the first occurrence of two consecutive test-day records with a milk progesterone FRQFHQWUDWLRQ RI • QJPO *HQHWLF DQG UHVLGXDO Acknowledgements
(co)variances for the fertility traits were estimated using This study is part of the RobustMilk project (which is animal linear mixed models. Fixed effects were country- financially supported by the European Commission experimental treatment-year and country-year-season of under the Seventh Research Framework Programme, calving. For PRFS, CFS was also included as a fixed effect.
Following the removal of animals that did not pass References
parentage verification using the genomic information, as Horan, B, Mee, J.F., O’Connor, P., Rath M., & Dillon,P. (2005) Therio. 63: 950-961 well as the removal of single nucleotide polymorphisms Petersson, K.-J., Strandberg, E., Gustafsson, H., & (SNPs) that had a minor allele frequency of <0.01 ineach Berglund, B. (2006). Anim. Reprod. Sci. 91:201-214 equilibrium, or there was poor quality in calling the Pollot, G.E. & Coffey, M.P. (2008). J. Dairy Sci. 91: genotypes, a total of 37,590 SNPs from the Illumina Bovine50 Beadchip on 1,570 cows from Ireland Veerkamp, R.F., Oldenbroek, J.K., Van Der Gaast, H.J.
(n=319), The UK (n=461), The Netherlands (n=583), & Van Der Werf J.H.J. (2000). J. Dairy Sci. 83:577– and Sweden (n=207) remained. The genome-wide


Fighting Malaria in Kala and Mwange Camps Malaria constitutes a major public health problem in Zambia, exerting the largest toll on vulnerable groups like chil-dren and pregnant women. According to 2008 Ministry of Health bulletin, 3.2 million cases of malaria (confirmed and unconfirmed) were reported countrywide in Zambia with 3,871 deaths. The annual malaria incidence was estimated at 252 c

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