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Segregation of a QTL cluster for home-cage activity using a new mapping method based on regression analysis of congenic mouse strains.

ABSTRACT: Recent genetic studies have shown that genetic loci with significant effects in whole-genome quantitative trait loci (QTL) analyses were lost or weakened in congenic strains. Characterisation of the genetic basis of this attenuated QTL effect is important to our understanding of the genetic mechanisms of complex traits. We previously found that a consomic strain, B6-Chr6C(MSM), which carries chromosome 6 of a wild-derived strain MSM/Ms on the genetic background of C57BL/6J, exhibited lower home-cage activity than C57BL/6J. In the present study, we conducted a composite interval QTL analysis using the F2 mice derived from a cross between C57BL/6J and B6-Chr6C(MSM). We found one QTL peak that spans 17.6?Mbp of chromosome 6. A subconsomic strain that covers the entire QTL region also showed lower home-cage activity at the same level as the consomic strain. We developed 15 congenic strains, each of which carries a shorter MSM/Ms-derived chromosomal segment from the subconsomic strain. Given that the results of home-cage activity tests on the congenic strains cannot be explained by a simple single-gene model, we applied regression analysis to segregate the multiple genetic loci. The results revealed three loci (loci 1-3) that have the effect of reducing home-cage activity and one locus (locus 4) that increases activity. We also found that the combination of loci 3 and 4 cancels out the effects of the congenic strains, which indicates the existence of a genetic mechanism related to the loss of QTLs.

SUBMITTER: Kato S

PROVIDER: S-EPMC4220717 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Segregation of a QTL cluster for home-cage activity using a new mapping method based on regression analysis of congenic mouse strains.

Kato S S Ishii A A Nishi A A Kuriki S S Koide T T

Heredity 20140430 5

Recent genetic studies have shown that genetic loci with significant effects in whole-genome quantitative trait loci (QTL) analyses were lost or weakened in congenic strains. Characterisation of the genetic basis of this attenuated QTL effect is important to our understanding of the genetic mechanisms of complex traits. We previously found that a consomic strain, B6-Chr6C(MSM), which carries chromosome 6 of a wild-derived strain MSM/Ms on the genetic background of C57BL/6J, exhibited lower home- ...[more]

PMID: 24781804

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Project description:BACKGROUND:Genomic prediction and quantitative trait loci (QTL) mapping typically analyze one trait at a time but this may ignore the possibility that one polymorphism affects multiple traits. The aim of this study was to develop a multivariate Bayesian approach that could be used for simultaneously elucidating genetic architecture, QTL mapping, and genomic prediction. Our approach uses information from multiple traits to divide markers into 'unassociated' (no association with any trait) and 'associated' (associated with one or more traits). The effect of associated markers is estimated independently for each trait to avoid the assumption that QTL effects follow a multi-variate normal distribution. RESULTS:Using simulated data, our multivariate method (BayesMV) detected a larger number of true QTL (with a posterior probability > 0.9) and increased the accuracy of genomic prediction compared to an equivalent univariate method (BayesR). With real data, accuracies of genomic prediction in validation sets for milk yield traits with high-density genotypes were approximately equal to those from equivalent single-trait methods. BayesMV tended to select a similar number of single nucleotide polymorphisms (SNPs) per trait for genomic prediction compared to BayesR (i.e. those with non-zero effects), but BayesR selected different sets of SNPs for each trait, whereas BayesMV selected a common set of SNPs across traits. Despite these two dramatically different estimates of genetic architecture (i.e. different SNPs affecting each trait vs. pleiotropic SNPs), both models indicated that 3000 to 4000 SNPs are associated with a trait. The BayesMV approach may be advantageous when the aim is to develop a low-density SNP chip that works well for a number of traits. SNPs for milk yield traits identified by BayesMV and BayesR were also found to be associated with detailed milk composition. CONCLUSIONS:The BayesMV method simultaneously estimates the proportion of SNPs that are associated with a combination of traits. When applied to milk production traits, most of the identified SNPs were associated with all three traits (milk, fat and protein yield). BayesMV aims at exploiting pleiotropic QTL and selects a small number of SNPs that could be used to predict multiple traits.

Dataset Information

Segregation of a QTL cluster for home-cage activity using a new mapping method based on regression analysis of congenic mouse strains.

Publications

Segregation of a QTL cluster for home-cage activity using a new mapping method based on regression analysis of congenic mouse strains.

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