Epistasis affecting litter size in mice

Peripato, Andréa Cristina; Brito, Reinaldo Alves de; Matioli, Sérgio Russo; Pletscher, L. Susan; Vaughn, Ty T.; Cheverud, James M.

doi:10.1111/j.1420-9101.2004.00702.x

Cited by 65 publications

(73 citation statements)

References 44 publications

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“…These results suggested that it was relatively easy to find strong epistasis (for example, h epi 2 47.5%), but difficult to detect weak ones given the population size. Our observations are roughly in line with earlier studies: four interaction-only pairs detected from an F 2 mouse population (size is 510, 166 females) explained about 36% of the total variation in litter size (that is h epi 2 ¼ 9% on average) (Peripato et al, 2004); most epistatic pairs detected for obesity-related traits using 513 F 2 mice had a h epi 2 42% despite the relatively relaxed thresholds that were used (Stylianou et al, 2006). Increasing the population size is a good option to increase power (Carlborg et al, 2003;Ma et al, 2009).…”

Section: Discussionsupporting

confidence: 81%

Controlling false positives in the mapping of epistatic QTL

et al. 2009

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This study addresses the poorly explored issue of the control of false positive rate (FPR) in the mapping of pair-wise epistatic quantitative trait loci (QTL). A nested test framework was developed to (1) allow pre-identified QTL to be used directly to detect epistasis in one-dimensional genome scans, (2) to detect novel epistatic QTL pairs in twodimensional genome scans and (3) to derive genome-wide thresholds through permutation and handle multiple testing. We used large-scale simulations to evaluate the performance of both the one-and two-dimensional approaches in mapping different forms and levels of epistasis and to generate profiles of FPR, power and accuracy to inform epistasis mapping studies. We showed that the nested test framework and genome-wide thresholds were essential to control FPR at the 5% level. The one-dimensional approach was generally more powerful than the two-dimensional approach in detecting QTL-associated epistasis and identified nearly all epistatic pairs detected from the two-dimensional approach. However, only the two-dimensional approach could detect epistatic QTL with weak main effects. Combining the two approaches allowed effective mapping of different forms of epistasis, whereas using the nested test framework kept the FPR under control. This approach provides a good search engine for high-throughput epistasis analyses.

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Section: Discussionsupporting

confidence: 81%

Controlling false positives in the mapping of epistatic QTL

et al. 2009

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“…Selection experiments for increased litter size have been performed in various strains of mice and heritability of litter size is estimated to be relatively low at 15% (Beniwal et al, 1992;Gutiérrez et al, 2006;Peripato et al, 2004). In the present study we showed a slight positive relationship (with a slope of 0.16) between litter size of mothers and their biological daughters, but this relationship did not reach significance.…”

Section: Discussionmentioning

confidence: 34%

Limits to sustained energy intake. XIV. Heritability of reproductive performance in mice

Vaanholt

Sinclair

Speakman

2013

Journal of Experimental Biology

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SUMMARYLimits to sustained energy intake (SusEI) are important because they constrain many aspects of animal performance. Individual variability in SusEI may be imposed by genetic factors that are inherited from parents to offspring. Here, we investigated heritability of reproductive performance in MF1 mice. Food intake, milk energy output (MEO) and litter mass were measured in mothers (F 0 ) and daughters (F 1 ) that were raising litters of 10 pups. Cross-fostering was designed so that half of each litter consisted of biological offspring and the rest came from one unrelated female (i.e. fostered pups). Food intake increased linearly during early lactation and reached a plateau during late lactation (day 9-13, called the asymptotic food intake, FI AS , equivalent to SusEI). Parent-offspring regression showed that FI AS , MEO and litter mass were all positively and significantly related between mothers and their biological daughters, but no significant relationships were found between the same traits for mothers and fostered daughters. FI AS at peak lactation was significantly correlated to adult food intake and body mass when the mice were 6months old and not lactating. In conclusion, a large part of the variation in FI AS could be explained by genetic variation or maternal effects in pregnancy whereas non-genetic maternal effects in lactation were negligible. As a consequence, biological daughters of mothers with high reproductive performance (i.e. high milk production and hence higher litter mass at weaning) had a better reproductive performance themselves, independent of the mother that raised them during lactation.

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“…In fact, by not studying interactions in an F2 cross there is a risk that susceptibility loci might be missed. 20,21 In addition, careful statistical analysis is important. We chose to use the R/qtl software 22 to analyze genetic interactions.…”

Section: Discussionmentioning

confidence: 99%

Identification of epistasis through a partial advanced intercross reveals three arthritis loci within the Cia5 QTL in mice

et al. 2005

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Identification of genes controlling complex diseases has proven to be difficult; however, animal models may pave the way to determine how low penetrant genes interact to promote disease development. We have dissected the Cia5/Eae3 susceptibility locus on mouse chromosome 3 previously identified to control disease in experimental models of multiple sclerosis and rheumatoid arthritis. Congenic strains showed significant but small effects on severity of both diseases. To improve the penetrance, we have now used a new strategy that defines the genetic interactions. The QTL interacted with another locus on chromosome 15 and a partial advanced intercross breeding of the two congenic strains for eight generations accumulated enough statistical power to identify interactions with several loci on chromosome 15. Thereby, three separate loci within the original QTL could be identified; Cia5 affected the onset of arthritis by an additive interaction with Cia31 on chromosome 15, whereas the Cia21 and Cia22 affected severity during the chronic phase of the disease through an epistatic interaction with Cia32 on chromosome 15. The definition of genetic interactions was a prerequisite to dissect the Cia5 QTL and we suggest the partial advanced intercross strategy to be helpful also for dissecting other QTL controlling complex phenotypes.

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Epistasis affecting litter size in mice

Cited by 65 publications

References 44 publications

Controlling false positives in the mapping of epistatic QTL

Controlling false positives in the mapping of epistatic QTL

Limits to sustained energy intake. XIV. Heritability of reproductive performance in mice

Identification of epistasis through a partial advanced intercross reveals three arthritis loci within the Cia5 QTL in mice

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