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Sustainable Food Production pp 127–151Cite as

Animal Molecular Genetics from Major Genes to Genomics

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Animal breeding is a major contributor to the vast improvements in animal production over decades. The main tool in breeding operations is selection; now, more and more attention is also paid to the amount and nature of genetic variation. It is on these topics that the chapter is built on. It starts from the fundamental methods in determining the genetic value of animals . The normal distribution and linear methods stemming from the concept of large number of loci with tiny effects causing variation are the base line. For quite some time, there have been observations on major loci causing deviation in linear prediction of genetic values. There is a part introducing methods to get around such cases and turning them advantageous by directly utilizing the variation mediated by major loci....

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Abbreviations

Additive genetic variance:

is usually the largest part of genetic variation in the quantitative trait and is due to average effects of genes. The change in mean caused by selection is proportional to the additive genetic variance.

BLUP:

is a short name for Best Linear Unbiased Prediction. It is now a norm in estimating breeding values within populations. It uses information on all kind of relatives and corrects the data for differences in production environment.

Breeding value:

of an individual is the expected value of its progeny relative to the population mean.

Candidate gene:

is a possible mutation underlying the mapped QTL. A positional candidate is a gene located in the same region as a mapped QTL. A biological candidate for a QTL is a gene which has a function related to the quantitative trait.

Effective population size (N e ):

is the number of individuals that with random mating result in the same rate of inbreeding as the population itself.

Genetic marker:

is a specific detectable sequence of DNA with a know location in the genome.

Heterosis:

Is the extent to which the performance of crossbred animals is better than the mean of two parental populations.

Infinitesimal model:

assumes the genetic variation of a quantitative trait is due to infinitely many unlinked genes each with an infinitesimally small additive effect, so that selection produces negligible changes in gene frequency and variance at each locus.

Linkage disequilibrium:

is a non-random association of alleles across loci. Recombination between loci will gradually reduce the associations, more slowly the closer the loci are to each other. Adjacent markers with correlated allele frequencies could be used for mapping and selection.

Marker-assisted selection:

is selection on a quantitative trait where also the information on associated markers is used as a selection criterion. Gene-assisted selection is a special case where the marker is at the major gene causing the variation.

Mixed model equations:

are providing a method to simultaneously solve the predicted breeding values (random effects) for animals and estimate the fixed effects due to differences in production environment.

Morgan:

is the unit for a map distance in the genome.

Quantitative trait locus (QTL):

A short genomic region with a large effect on a quantitative trait.

Single nucleotide polymorphism (SNP):

is variation caused by a mutation at a single nucleotide.

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Acknowledgment

I am grateful to colleagues, especially Pekka Uimari, at MTT, for useful comments on the draft of the text, and to Ignacy Misztal for lots of encouragement.

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Authors and Affiliations

  1. Biotechnology and Food Research, MTT Agrifood Research Finland, 31600, Jokioinen, Finland

    Dr. Asko Mäki-Tanila

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  1. Dr. Asko Mäki-Tanila
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Correspondence to Asko Mäki-Tanila .

Editor information

Editors and Affiliations

  1. Department de Produccio Vegetal i Ciencia Forestal, Universitat de Lleida/ICREA, Lleida, Spain

    Paul Christou

  2. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    Paul Christou

  3. Department of Crop and Forest Sciences, University of Lleida, Lleida, Spain

    Roxana Savin

  4. University of New England, Marine Science Center, Biddeford, ME, USA

    Barry A. Costa-Pierce

  5. Department of Animal and Dairy Science Breeding and Genetics, University of Georgia, Athens, GA, USA

    Ignacy Misztal

  6. The Roslin Institute and Royal (Dick) School of Veterinary Studies, Division of Developmental Biology, University of Edinburgh, Midlothian, Scotland, UK

    C. Bruce A. Whitelaw

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Mäki-Tanila, A. (2013). Animal Molecular Genetics from Major Genes to Genomics. In: Christou, P., Savin, R., Costa-Pierce, B.A., Misztal, I., Whitelaw, C.B.A. (eds) Sustainable Food Production. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5797-8_336

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