QTL mapping with near-isogenic lines in maize

Abstract

A set of 89 near-isogenic lines (NILs) of maize was created using marker-assisted selection. Nineteen genomic regions, identified by restriction fragment length polymorphism loci and chosen to represent portions of all ten maize chromosomes, were introgressed by backcrossing three generations from donor line Tx303 into the B73 genetic background. NILs were genotyped at an additional 128 simple sequence repeat loci to estimate the size of introgressions and the amount of background introgression. Tx303 introgressions ranged in size from 10 to 150 cM, with an average of 60 cM. Across all NILs, 89% of the Tx303 genome is represented in targeted and background introgressions. The average proportion of background introgression was 2.5% (range 0-15%), significantly lower than the expected value of 9.4% for third backcross generation lines developed without marker-assisted selection. The NILs were grown in replicated field evaluations in two years to map QTLs for flowering time traits. A parallel experiment of testcrosses of each NIL to the unrelated inbred, Mo17, was conducted in the same environments to map QTLs in NIL testcross hybrids. QTLs affecting days to anthesis, days to silking, and anthesis-silk interval were detected in both inbreds and hybrids in both environments. The testing environments differed dramatically for drought stress, and different sets of QTLs were detected across environments. Furthermore, QTLs detected in inbreds were typically different from QTLs detected in hybrids, demonstrating the genetic complexity of flowering time. NILs can serve as a valuable genetic mapping resource for maize breeders and geneticists.

Quantitative trait loci mapping for kernel row number using chromosome segment substitution lines in maize

“…Subsequently, many CSSL populations have been reported in a diverse range of plant species, such as rice (Li et al, 2005;Zeng et al, 2006;Marzougui et al, 2012), soybean (Yamanaka et al, 2005), and barley (Von Korff et al, 2004). Szalma et al (2007) constructed a set of ILs in maize to detect QTL underlying maize flowering time, plant height, and ear height, by introgressing chromosome segments of TX303 to the B73 genome. In another study, Bai et al (2010) detected a set of QTL for plant height and ear height using 98 ILs derived by Zong3 x HB522, from which it was confirmed that the non-additive action was the major genetic basis of both plant height and ear height.…”

“…Most of the initial studies showed that CSSL or IL populations are more effective for QTL identification compared to populations derived from bi-parental F 2 . This difference arises because of the absence of a complex genetic background and because the introgressed segments are the major source of genetic variation compared to the recipient parent (Paterson et al, 1990;Kaeppler, 1997;Szalma et al, 2007).…”

Power and false-positive rate in QTL detection with near-isogenic line libraries

“…These segments cover the entire genome of a donor line, and were introgressed into the genetic background of a recipient line by marker-assisted backcrossing. NIL libraries were suggested to detect quantitative trait loci (QTLs) in tomato (Lycopersicum esculentum, Eshed and Zamir, 1995) and were subsequently developed in Arabidopsis (Keurentjes et al, 2007;Törjek et al, 2008) and in a wide range of crops such as rice (Oryza sativa L., Lin et al, 1998), barley (Hordeum vulgare L., Matus et al, 2003;Schmalenbach et al, 2008), wheat (Triticum aestivum L., Liu et al, 2006), maize (Zea mays L., Ribaut and Ragot, 2007;Szalma et al, 2007) and rye (Secale cereale L., Falke et al, 2009b).…”

Genetic Dissection of Barley Morphology and Development

“…The NILs had greater power than the recombinant inbred line population to detect small-effect quantitative trait loci, at the expense of local resolution (Keurentjes et al, 2007). In maize (Zea mays), a set of 89 NILs was created using marker-assisted selection to analyze flowering-time traits (Szalma et al, 2007). In soybean (Glycine max), an iron-inefficient NIL and differential seed protein content NIL were analyzed using several existing and emerging methodologies for genetic introgression mapping: single-feature polymorphism analysis, Illumina GoldenGate single nucleotide polymorphism (SNP) genotyping, and de novo SNP discovery via RNA-Seq analysis of next-generation sequence data, with the latter being most informative (Severin et al, 2010).…”