2014
DOI: 10.1534/genetics.114.168385
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A New Simple Method for Improving QTL Mapping Under Selective Genotyping

Abstract: The selective genotyping approach, where only individuals from the high and low extremes of the trait distribution are selected for genotyping and the remaining individuals are not genotyped, has been known as a cost-saving strategy to reduce genotyping work and can still maintain nearly equivalent efficiency to complete genotyping in QTL mapping. We propose a novel and simple statistical method based on the normal mixture model for selective genotyping when both genotyped and ungenotyped individuals are fitte… Show more

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Cited by 13 publications
(9 citation statements)
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“…It should be noted that using a small subset of animals from the extreme ends of the phenotypic distribution not only reduce the cost of genotyping, but could retain the power of analysis as proven by simulation [ 31 ] and numerous QTL mapping studies [ 32 ]. Recently, it was also applied for CNV discovery as well, based on animals sampled from the distribution of fatness EBV [ 23 ].…”
Section: Discussionmentioning
confidence: 99%
“…It should be noted that using a small subset of animals from the extreme ends of the phenotypic distribution not only reduce the cost of genotyping, but could retain the power of analysis as proven by simulation [ 31 ] and numerous QTL mapping studies [ 32 ]. Recently, it was also applied for CNV discovery as well, based on animals sampled from the distribution of fatness EBV [ 23 ].…”
Section: Discussionmentioning
confidence: 99%
“…Previously, QTL studies have used the principle of selective genotyping, where genotype information was collected for large phenotypic contrasts to achieve accurate estimation for the effect of marker genotypes. It has been shown that a two‐tailed selective genotyping approach can more efficiently and more precisely detect the QTL, as substantial genetic information resides in individuals with extreme phenotypes (Darvasi & Soller, ; Henshall & Goddard, ; Huang & Lin, ; Lebowitz, Soller, & Beckmann, ; Lee, Ho, & Kao, ; Muranty & Goffinet, ; Sun, Wang, Crouch, & Xu, ; van Gestel, Houwing‐Duistermaat, Adolfsson, Duijn, & Broeckhoven, ; Xu & Vogl, ). However, while genotyping contrasting phenotypes gives more accuracy, the investment in genotypic information on individuals ranked in the bottom may be less useful as these individuals would have a lower relationship with selection candidates in the next generation.…”
Section: Introductionmentioning
confidence: 99%
“…Quantitative trait loci (QTL) detection has been a key step to provide deeper insight into the genetic mechanism of quantitative traits in many areas of biological researches, including crops, evolution, ecology and genetical genomics studies etc. (Lander and Botstein 1989; Haley and Knott 1992; Jansen 1993; Zeng 1994; Kao et al 1999; Sen and Churchill 2001; Broman 2003; Kao 2006; Lee et al 2014; Wei and Xu 2016). In QTL detection, it is often found that some of the genomic regions are relatively enriched in QTL as compared to other regions, and that QTL responsible for correlated traits frequently co-localize in some specific genetic regions (Goffinet and Gerber 2000; Schadt et al 2003; Chardon et al 2004; West et al 2007; Breitling et al 2008; Wu et al 2008; Li et al 2010; Ali et al 2013; Basnet et al 2015).…”
mentioning
confidence: 99%