Literature DB >> 24671636

The relation between the genetic architecture of quantitative traits and long-term genetic response.

Rostam Abdollahi-Arpanahi1, Abbas Pakdel, Ardeshir Nejati-Javaremi, Mohammad Moradi Shahrbabak, Farhad Ghafouri-Kesbi.   

Abstract

The genetic architecture of a quantitative trait refers to the number of genetic variants, allele frequencies, and effect sizes of variants that affect a trait and their mode of gene action. This study was conducted to investigate the effect of four shapes of allelic frequency distributions (constant, uniform, L-shaped and U-shaped) and different number of trait-affecting loci (50, 100, 200, 500) on allelic frequency changes, long term genetic response, and maintaining genetic variance. To this end, a population of 440 individuals composed of 40 males and 400 females as well as a genome of 200 cM consisting of two chromosomes and with a mutation rate of 2.5 × 10(-5) per locus was simulated. Selection of superior animals was done using best linear unbiased prediction (BLUP) with assumption of infinitesimal model. Selection intensity was constant over 30 generations of selection. The highest genetic progress obtained when the allelic frequency had L-shaped distribution and number of trait-affecting loci was high (500). Although quantitative genetic theories predict the extinction of genetic variance due to artificial selection in long time, our results showed that under L- and U-shapped allelic frequency distributions, the additive genetic variance is persistent after 30 generations of selection. Further, presence or absence of selection limit can be an indication of low (<50) or high (>100) number of trait-affecting loci, respectively. It was concluded that the genetic architecture of complex traits is an important subject which should be considered in studies concerning long-term response to selection.

Mesh:

Year:  2014        PMID: 24671636     DOI: 10.1007/s13353-014-0205-1

Source DB:  PubMed          Journal:  J Appl Genet        ISSN: 1234-1983            Impact factor:   3.240


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