Investigation on presence of major gene for body weight, feed intake and feed efficiency using a segregation analyses in a mice population

Yıl 2019, Cilt: 66 Sayı: 4, 325 - 330, 09.09.2019

Burak Karacaören İssam Moslem

https://doi.org/10.33988/auvfd.445947

Öz

Recent developments in molecular genetics and statistics have
allowed the identification and use of major genes to explain the genetic
variation. In this context, segregation analysis is a fast, reliable and
inexpensive method that uses only phenotype and pedigree information. The aim
of this study was to examine whether body weight, feed intake, and feed
efficiency in a mouse population are directed by major gene in addition to
polygenic and major gene effects by segregation analysis. For this purpose,
previously collected dataset was used (n= 661). In this study, genetic
variance, error variance, major gene variance, additive and dominant gene
effects were estimated by segregation analyses. Dominant variance (1.04) was
found to be smaller than the additive genetic variance (7.32) for body weight.
Polygenic and major gene heritability predicted as 0.29 (± 0.63) and 0.81 (±
0.98) for body weight, 0.35 (± 0.63) and 0.96 (± 0.98) for feed intake and 0.52
(± 0.63) and 0.81 (± 0.98) for feed efficiency respectively. Existence of major
gene was determined by examining the highest probability density regions.
Although the major gene has been identified for body weight and feed intake,
this result is not confirmed by the Mendelian transmission probabilities.

Anahtar Kelimeler

Bayesian analyses, gibbs sampling, major gene, quantitative phenotype, segregation analyses

Kaynakça

• 1. Allan MF, Pomp D, Eisen EJ (2005): Genomic mapping of direct and correlated responses to long-term selection for rapid growth rate in mice. Genetics, 170, 1863-1877.
• 2. Barton NH, Etheridge AM, Véber A (2017): The infinitesimal model: definition, derivation, and implications. Theor Popul Biol, 118, 50-73.
• 3. Ehsani A, Sørensen P, Pomp D, et al (2012): Inferring genetic architecture of complex traits using Bayesian integrative analysis of genome and transcriptome data. BMC Genomics, 13, 456.
• 4. Falconer DS, Mackay TFC (1996): Introduction to Quantitative Genetics. Longman Group Ltd, UK.
• 5. Flint J, Mackay TF (2009): Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res, 19, 723-733.
• 6. Gelman A, Carlin JB, Stern HS, et al (2004): Bayesian Data Analysis. Chapman and Hall, USA.
• 7. Janss LLG, van Arendonk JV, Brascamp EW (1997): Bayesian statistical analyses for presence of single genes affecting meat quality traits in a crossed pig population. Genetics, 145, 395-408.
• 8. Janss LLG (2008): iBay manual version 1.46. Janss Bioinformatics, Lieden, Netherlands.
• 9. Janss LLG (1996): Statistical identification of major genes in pigs. PhD Thesis. Wageningen Agricultural University, The Netherlands.
• 10. Jarvik GP (1998): Complex segregation analyses: uses and limitations. Am J Hum Genet, 63, 942–946.
• 11. Karacaören B, Fırat MZ (2012): Genetik ilerlemelerin hesaplanmasında kullanılan istatistiksel yöntemlerin karşılaştırılması. Ankara Univ Vet Fak Derg, 59, 115-120.
• 12. Karacaören B (2014): Empirical comparison of association and admixture mapping for body weight using F2 mice data set. Archiv Tierzucht, 57, 1-6.
• 13. Medina-Macedo L, Lacerda AEB, Ribeiro JZ, et al. (2014): Investigating the Mendelian inheritance, genetic linkage, and genotypic disequilibrium for ten microsatellite loci of Araucaria angustifolia. Silvae Genet, 63, 234-239.
• 14. Nielsen R, Slatkin M (2013): An Introduction to Population Genetics: Theory and Applications. Sinauer Associates is an imprint of Oxford University Press, UK.
• 15. Sanchez MP, Iannuccelli N, Basso B, et al (2007): Identification of QTL with effects on intramuscular fat content and fatty acid composition in a Duroc × Large White cross. BMC Genet, 8, 55.
• 16. Wolc A, Skotarczak E, Schlote W, et al (2009): Single-gene Effects on Body Weight in Selected and Unselected Mouse Lines Detected by Bayesian Marker-free Segregation Analysis. Scand J Lab Anim Sci, 36, 185-191.