Article

Functional genomics: tools for improving farm animal health and welfare.

Institute of Molecular Animal Breeding and Biotechnology, Gene Center of the Ludwig-Maximilian University, Feodor-Lynen-Strasse 25, 81377 Munich, Germany.
Revue scientifique et technique (International Office of Epizootics) (Impact Factor: 0.69). 05/2005; 24(1):355-77.
Source: PubMed

ABSTRACT The first genome sequence assemblies of farm animal species are now accessible through public domain databases, and further sequencing projects are in rapid progress. In addition, large collections of expressed sequences have been obtained, which will aid in constructing annotated transcript maps for many economically important species. Thus, the breeding of farm animals is entering the post-genome era. Functional genomics, defined as applying global experimental approaches to assess gene function, by using the information and reagents provided by structural genomics (i.e. mapping and sequencing), has become the focus of interest. Combining a holistic view of phenotypes at the molecular level with genetic marker data seems a particularly promising approach for improving health and welfare traits in farm animals. These traits are often difficult to define. They suffer from low heritabilities and a corresponding lack of genetic gain in conventional selection and breeding programmes. At the same time, genomic information from micro-organisms and parasites offers the potential for new vaccines and therapeutics. This review describes major functional genomics tools, lists genomic resources available for farm animals and discusses the prospects and challenges of functional genomics in improving the health and welfare of farm animals.

1 Follower
 · 
168 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The objective of this work was to integrate findings from functional genomics studies with genome-wide association studies for fertility and production traits in dairy cattle. Association analyses of production and fertility traits with SNPs located within or close to 170 candidate genes derived from two gene expression studies and from the literature were performed. Data from 2294 Holstein bulls genotyped for 39557 SNPs were used. A total of 111 SNPs were located on chromosomal segments covered by a candidate gene. Allele substitution effects for each SNP were estimated using a mixed model with a fixed effect of marker and a random polygenic effect. Assumed covariance was derived either from marker or from pedigree information. Results from the analysis with the kinship matrix built from marker genotypes were more conservative than from the analysis with the pedigree-derived relationship matrix. From sixteen SNPs with significant effects on both classes of traits, ten provided evidence of an antagonistic relationship between productivity and fertility. However, we found four SNPs with favourable effects on fertility and on yield traits, one SNP with favourable effects on fertility and percentage traits, and one SNP with antagonistic effects on two fertility traits. While most quantitative genetic studies have proven genetic antagonisms between yield and functional traits, improvements in both production and functionality may be possible when focusing on a few relevant SNPs. Investigations combining input from quantitative genetics and functional genomics with association analysis may be applied for the identification of such SNPs.
    Animal Genetics 12/2010; 42(3):251-62. DOI:10.1111/j.1365-2052.2010.02148.x · 2.21 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Microarray technology is a powerful tool for animal functional genomics studies, with applications spanning from gene identification and mapping, to function and control of gene expression. Microarray assays, however, are complex and costly, and hence generally performed with relatively small number of animals. Nevertheless, they generate data sets of unprecedented complexity and dimensionality. Therefore, such trials require careful planning and experimental design, in addition to tailored statistical and computational tools for their appropriate data mining. In this review, we discuss experimental design and data analysis strategies, which incorporate prior genomic and biological knowledge, such as genotypes and gene function and pathway membership. We focus the discussion on the design of genetical genomics studies, and on significance testing for detection of differential expression. It is shown that the use of prior biological information can improve the efficiency of microarray experiments.
    animal 02/2010; 4(2):165-72. DOI:10.1017/S1751731109991054 · 1.78 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Subfertility has negative effects for dairy farm profitability, animal welfare and sustainability of animal production. Increasing herd sizes and economic pressures restrict the amount of time that farmers can spend on counteractive management. Genetic improvement will become increasingly important to restore reproductive performance. Complementary to traditional breeding value estimation procedures, genomic selection based on genome-wide information will become more widely applied. Functional genomics, including transcriptomics (gene expression profiling), produces the information to understand the consequences of selection as it helps to unravel physiological mechanisms underlying female fertility traits. Insight into the latter is needed to develop new effective management strategies to combat subfertility. Here, the importance of functional genomics for dairy cow reproduction so far and in the near future is evaluated. Recent gene profiling studies in the field of dairy cow fertility are reviewed and new data are presented on genes that are expressed in the brains of dairy cows and that are involved in dairy cow oestrus (behaviour). Fast-developing new research areas in the field of functional genomics, such as epigenetics, RNA interference, variable copy numbers and nutrigenomics, are discussed including their promising future value for dairy cow fertility.
    animal 08/2008; 2(8):1158-67. DOI:10.1017/S1751731108002371 · 1.78 Impact Factor