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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.

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Expected proportion of total additive genetic variance traced by increasing number of markers.
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Figure 2: Expected proportion of total additive genetic variance traced by increasing number of markers.

Mentions: The procedure of [10] were used to estimate the proportion of additive genetic variance that can be explained by markers. This expected proportion (β) is shown in (Figure 2) as a function of number of markers included in the genomic relationship matrix. The expected proportion of additive genetic variance explained by markers is less than 0.85 when the number of markers is below 5 K but increases rapidly until between 15 K and 20 K markers is used in the estimation of genomic variance. Further increases in the number of markers only increase the expected proportion of additive genetic variance explained by markers marginally. The expected proportion of additive genetic variance explained by markers reaches 0.96 when all 44 K markers were used.


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)

Expected proportion of total additive genetic variance traced by increasing number of markers.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Expected proportion of total additive genetic variance traced by increasing number of markers.
Mentions: The procedure of [10] were used to estimate the proportion of additive genetic variance that can be explained by markers. This expected proportion (β) is shown in (Figure 2) as a function of number of markers included in the genomic relationship matrix. The expected proportion of additive genetic variance explained by markers is less than 0.85 when the number of markers is below 5 K but increases rapidly until between 15 K and 20 K markers is used in the estimation of genomic variance. Further increases in the number of markers only increase the expected proportion of additive genetic variance explained by markers marginally. The expected proportion of additive genetic variance explained by markers reaches 0.96 when all 44 K markers were used.

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.

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