Conventional breeding schemes for dairy cattle are based on phenotypic information obtained from individuals and/or their relatives and progeny testing of the young bull candidates. The genetic model used in the evaluation process of the animals does not assume the underlying genes of the quantitative traits to be known. Knowing the chromosomal areas or actual genes affecting the traits would add more information to be used in the selection decisions which would potentially lead to higher genetic response. The first objective of this study was to map quantitative trait loci (QTL) affecting economically important traits: milk production traits, health traits and fertility traits in the Finnish Ayrshire population. The second objective was to investigate the effects of using QTL information in marker-assisted selection (MAS) on the genetic response and the linkage disequilibrium between the different parts of the genome. Whole genome scans were carried out on a grand-daughter design with 12 half-sib families and a total of 493 sons. Twelve different traits were studied: milk yield, protein yield, protein content, fat yield, fat content, somatic cell score (SCS), mastitis treatments, other veterinary treatments, days open, fertility treatments, non-return rate, and calf mortality. A total of 150 markers were used in all other studies except for fertility traits where 171 markers were used. The average spacing of the markers was 20 cM with 2 to 14 markers per chromosome. Associations between markers and traits were analyzed with multiple marker regression. Chromosomes were analyzed separately and by using QTL on other chromosomes as cofactors. Significance was determined by permutation and genome-wise P-values obtained by Bonferroni correction. The benefits from MAS were investigated by simulation: a conventional progeny testing scheme was compared to a scheme where QTL information was used within families to select among full-sibs in the male path. Two QTL on different chromosomes were modelled. The effects of different starting frequencies of the favourable alleles and different size of the QTL effects were evaluated. In the whole genome scans of milk, health and fertility traits of Finnish Ayrshire a large number of QTL, 48 in total, were detected at 5% or higher chromosomewise significance when the chromosomes were analyzed separately. Milk production QTL were found on 8 chromosomes. There are some interesting yield QTL, for example the QTL affecting fat yield on BTA14 which probably is the DGAT1 gene, and the QTL affecting fat yield on BTA12 and protein yield on BTA5, 12, 25. Quantitative trait loci for SCS were found on BTA3, 11, 14, 18, 27, and 29, for mastitis treatments on BTA18 and for other veterinary treatments on BTA2, 14, 16, 22, and 23. Quantitative trait loci for days open were found on BTA1, 2, 5, 12, 20, 25, and 29, for fertility treatments on BTA1, 5, 10, 14, 15, 19, and 25, for calf mortality on BTA4, 6, 11, 15, 18, and 23 and for non-return rate on BTA10 and 14. The use of cofactors revealed a total of 31 possible QTL for milk production traits and 17 for health traits many of which are likely to be false positives however. In the simulation study the total genetic response was faster with MAS than with conventional selection and the advantage of MAS persisted over the studied generations. The rate of response and the difference between the selection schemes reflected clearly the changes in allele frequencies of the favourable QTL. The disequilibrium between the polygenes and QTL was always negative and it was larger with larger QTL size. With lower initial allele frequencies the disequilibrium was slightly higher with MAS but with higher initial frequencies it was lower. When selection was continued for four generations, the MAS scheme resulted first in more negative disequilibrium but the disequilibrium decreased slightly faster with MAS than with conventional selection. The disequilibrium between the two QTL was larger with QTL of large effect and it was somewhat larger with MAS for scenarios with starting frequencies below 0.5 for QTL of moderate size and below 0.3 for large QTL. When selection was continued for four generations, the MAS scheme resulted first in more negative values than the conventional scheme but later in less negative values until close to fixation of the favourable allele when the disequilibrium was close to zero in both schemes. In conclusion, several QTL affecting economically important traits of dairy cattle were detected. Further studies are needed to verify these QTL, check their presence in the present breeding population, look for pleiotropy and fine map the most interesting QTL regions. The results of the simulation studies show that using MAS together with embryo transfer to pre-select young bulls within families is a useful approach to increase the genetic merit of the AI-bulls compared to conventional selection.