| 1. |
- Carlborg, Örjan, et al.
(author)
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Simultaneous mapping of epistatic QTL in DU6i x DBA/2 mice.
- 2005
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In: Mammalian Genome. - : Springer Science and Business Media LLC. - 0938-8990 .- 1432-1777. ; 16:7
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Journal article (peer-reviewed)abstract
- We have mapped epistatic quantitative trait loci (QTL) in an F2 cross between DU6i x DBA/2 mice. By including these epistatic QTL and their interaction parameters in the genetic model, we were able to increase the genetic variance explained substantially (8.8%-128.3%) for several growth and body composition traits. We used an analysis method based on a simultaneous search for epistatic QTL pairs without assuming that the QTL had any effect individually. We were able to detect several QTL that could not be detected in a search for marginal QTL effects because the epistasis cancelled out the individual effects of the QTL. In total, 23 genomic regions were found to contain QTL affecting one or several of the traits and eight of these QTL did not have significant individual effects. We identified 44 QTL pairs with significant effects on the traits, and, for 28 of the pairs, an epistatic QTL model fit the data significantly better than a model without interactions. The epistatic pairs were classified by the significance of the epistatic parameters in the genetic model, and visual inspection of the two-locus genotype means identified six types of related genotype-phenotype patterns among the pairs. Five of these patterns resembled previously published patterns of QTL interactions.
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| 2. |
- Günther, Torsten, et al.
(author)
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Where in the genome are significant single nucleotide polymorphisms from genome-wide association studies located?
- 2011
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In: Omics. - : Mary Ann Liebert Inc. - 1536-2310 .- 1557-8100. ; 15:7-8, s. 507-12
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Journal article (peer-reviewed)abstract
- Recent technological progress has permitted the efficient performance of genome-wide association studies (GWAS) to map genetic variants associated with common diseases. Here, we analyzed 2,893 single nucleotide polymorphisms (SNPs) that have been identified in 593 published GWAS as associated with a disease phenotype with respect to their genomic location. In absolute numbers, most significant SNPs are located in intergenic regions and introns. When compared to their representation on the chips, there is essentially overrepresentation of nonsynonymous coding SNPs (nsSNPs), synonymous coding SNPs, and SNPs in untranscribed regions upstream of genes among the disease associated SNPs. A Gene Ontology term analysis showed that genes putatively causing a phenotype often code for membrane associated proteins or signal transduction genes.
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