Limits...
Partitioning additive genetic variance into genomic and remaining polygenic components for complex traits in dairy cattle.

Jensen J, Su G, Madsen P - BMC Genet. (2012)

Bottom Line: In models including both genomic (marker) and familial (pedigree) effects most (on average 77.2%) of total additive genetic variance was explained by genomic effects while the remaining was explained by familial relationships.By analyzing the genomic relationship matrix it is possible to predict the amount of additive genetic variance that can be explained by a reduced (or increased) set of markers.For the population analyzed the improvement of genomic prediction by increasing marker density beyond 44 K is limited.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Molecular Biology and Genetics, Centre for Quantitative Genetics and Genomics, Aarhus University, Research Centre Foulum, DK-8830, Tjele, Denmark. Just.Jensen@agrsci.au.dk

ABSTRACT

Background: Low cost genotyping of individuals using high density genomic markers were recently introduced as genomic selection in genetic improvement programs in dairy cattle. Most implementations of genomic selection only use marker information, in the models used for prediction of genetic merit. However, in other species it has been shown that only a fraction of the total genetic variance can be explained by markers. Using 5217 bulls in the Nordic Holstein population that were genotyped and had genetic evaluations based on progeny, we partitioned the total additive genetic variance into a genomic component explained by markers and a remaining component explained by familial relationships. The traits analyzed were production and fitness related traits in dairy cattle. Furthermore, we estimated the genomic variance that can be attributed to individual chromosomes and we illustrate methods that can predict the amount of additive genetic variance that can be explained by sets of markers with different density.

Results: The amount of additive genetic variance that can be explained by markers was estimated by an analysis of the matrix of genomic relationships. For the traits in the analysis, most of the additive genetic variance can be explained by 44 K informative SNP markers. The same amount of variance can be attributed to individual chromosomes but surprisingly the relation between chromosomal variance and chromosome length was weak. In models including both genomic (marker) and familial (pedigree) effects most (on average 77.2%) of total additive genetic variance was explained by genomic effects while the remaining was explained by familial relationships.

Conclusions: Most of the additive genetic variance for the traits in the Nordic Holstein population can be explained using 44 K informative SNP markers. By analyzing the genomic relationship matrix it is possible to predict the amount of additive genetic variance that can be explained by a reduced (or increased) set of markers. For the population analyzed the improvement of genomic prediction by increasing marker density beyond 44 K is limited.

Show MeSH

Related in: MedlinePlus

Estimates of genomic variance (y axis) due to individual chromosomes in relation to chromosome length (x axis) in Mb.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3472176&req=5

Figure 1: Estimates of genomic variance (y axis) due to individual chromosomes in relation to chromosome length (x axis) in Mb.

Mentions: Estimates of variance components due to individual chromosomes are shown in Figure 1. The estimates are plotted against the length of individual chromosomes measured as the number of nucleotides (in Mb) between first and last marker on each chromosome. Note that the scale of the estimated variances varies by trait in Figure 1. Very large variances for milk and fat were found on chromosome 14 where it is known that a gene of very large effect segregates in the Holstein population [24]. As can clearly be seen from the figure the relation between chromosomal variance and chromosome length is weak. If QTL were evenly distributed over the genome it would be expected that chromosomal variance was closely related to chromosome length. The R2 values for a linear regression model of chromosomal variance on chromosome length varied between 0.11 and 0.21 (Figure 1). This indicates that QTL are not evenly distributed over the genome. This is clearly the case for milk and fat that had very low R2 due to the major gene segregating as mentioned above. In general the relation between variance due to individual chromosomes might also be dependent on the model and method used as well as the underlying population structure in the data analyzed.


Partitioning additive genetic variance into genomic and remaining polygenic components for complex traits in dairy cattle.

Jensen J, Su G, Madsen P - BMC Genet. (2012)

Estimates of genomic variance (y axis) due to individual chromosomes in relation to chromosome length (x axis) in Mb.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472176&req=5

Figure 1: Estimates of genomic variance (y axis) due to individual chromosomes in relation to chromosome length (x axis) in Mb.
Mentions: Estimates of variance components due to individual chromosomes are shown in Figure 1. The estimates are plotted against the length of individual chromosomes measured as the number of nucleotides (in Mb) between first and last marker on each chromosome. Note that the scale of the estimated variances varies by trait in Figure 1. Very large variances for milk and fat were found on chromosome 14 where it is known that a gene of very large effect segregates in the Holstein population [24]. As can clearly be seen from the figure the relation between chromosomal variance and chromosome length is weak. If QTL were evenly distributed over the genome it would be expected that chromosomal variance was closely related to chromosome length. The R2 values for a linear regression model of chromosomal variance on chromosome length varied between 0.11 and 0.21 (Figure 1). This indicates that QTL are not evenly distributed over the genome. This is clearly the case for milk and fat that had very low R2 due to the major gene segregating as mentioned above. In general the relation between variance due to individual chromosomes might also be dependent on the model and method used as well as the underlying population structure in the data analyzed.

Bottom Line: In models including both genomic (marker) and familial (pedigree) effects most (on average 77.2%) of total additive genetic variance was explained by genomic effects while the remaining was explained by familial relationships.By analyzing the genomic relationship matrix it is possible to predict the amount of additive genetic variance that can be explained by a reduced (or increased) set of markers.For the population analyzed the improvement of genomic prediction by increasing marker density beyond 44 K is limited.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Molecular Biology and Genetics, Centre for Quantitative Genetics and Genomics, Aarhus University, Research Centre Foulum, DK-8830, Tjele, Denmark. Just.Jensen@agrsci.au.dk

ABSTRACT

Background: Low cost genotyping of individuals using high density genomic markers were recently introduced as genomic selection in genetic improvement programs in dairy cattle. Most implementations of genomic selection only use marker information, in the models used for prediction of genetic merit. However, in other species it has been shown that only a fraction of the total genetic variance can be explained by markers. Using 5217 bulls in the Nordic Holstein population that were genotyped and had genetic evaluations based on progeny, we partitioned the total additive genetic variance into a genomic component explained by markers and a remaining component explained by familial relationships. The traits analyzed were production and fitness related traits in dairy cattle. Furthermore, we estimated the genomic variance that can be attributed to individual chromosomes and we illustrate methods that can predict the amount of additive genetic variance that can be explained by sets of markers with different density.

Results: The amount of additive genetic variance that can be explained by markers was estimated by an analysis of the matrix of genomic relationships. For the traits in the analysis, most of the additive genetic variance can be explained by 44 K informative SNP markers. The same amount of variance can be attributed to individual chromosomes but surprisingly the relation between chromosomal variance and chromosome length was weak. In models including both genomic (marker) and familial (pedigree) effects most (on average 77.2%) of total additive genetic variance was explained by genomic effects while the remaining was explained by familial relationships.

Conclusions: Most of the additive genetic variance for the traits in the Nordic Holstein population can be explained using 44 K informative SNP markers. By analyzing the genomic relationship matrix it is possible to predict the amount of additive genetic variance that can be explained by a reduced (or increased) set of markers. For the population analyzed the improvement of genomic prediction by increasing marker density beyond 44 K is limited.

Show MeSH
Related in: MedlinePlus