A new advanced backcross tomato population enables high resolution leaf QTL mapping and gene identification

Abstract

Quantitative Trait Loci (QTL) mapping is a powerful technique for dissecting the genetic basis of traits and species differences. Established tomato mapping populations between domesticated tomato (Solanum lycopersicum) and its more distant interfertile relatives typically follow a near isogenic line (NIL) design, such as the S. pennellii Introgression Line (IL) population, with a single wild introgression per line in an otherwise domesticated genetic background. Here, we report on a new advanced backcross QTL mapping resource for tomato, derived from a cross between the M82 tomato cultivar and S. pennellii. This so-called Backcrossed Inbred Line (BIL) population is comprised of a mix of BC 2 and BC 3 lines, with domesticated tomato as the recurrent parent. The BIL population is complementary to the existing S. pennellii IL population, with which it shares parents. Using the BILs, we mapped traits for leaf complexity, leaflet shape, and flowering time. We demonstrate the utility of the BILs for fine-mapping QTL, particularly QTL initially mapped in the ILs, by fine-mapping several QTL to single or few candidate genes. Moreover, we confirm the value of a backcrossed population with multiple introgressions per line, such as the BILs, for epistatic QTL mapping. Our work was further enabled by the development of our own statistical inference and visualization tools, namely a heterogeneous hidden Markov model for genotyping the lines, and by using state-of-the-art sparse regression techniques for QTL mapping.

Deciphering genetic factors that determine melon fruit‐quality traits using RNA‐Seq‐based high‐resolution QTL and eQTL mapping

“…In populations with a different structure, such as introgression lines (IL) and derived backcross inbred lines (BIL), a substantial enhancement of mapping resolution was achieved by increasing the number of lines and marker number. Using the BIL population of tomato (Solanum lycopersicum 9 Solanum pennellii), 446 BIL genotyped by SNP-chip generated 663 bins , and 545 lines genotyped by DNA sequencing generated 1049 bins (Fulop et al, 2016). For comparison, the original 76 IL population comprised 106 bins (Eshed and Zamir, 1995).…”

Morphometric analysis ofPassifloraleaves: the relationship between landmarks of the vasculature and elliptical Fourier descriptors of the blade

“…Another approach, Elliptical Fourier Descriptors (EFDs), treats an outline as a wave connecting back onto itself and subsequently performs a Fourier transform, decomposing the shape into a harmonic series [30–33]. EFDs have been applied to species from both Solanum and Vitis [23, 34–38] as well as the study of leaf asymmetry [25, 39, 40], leveraging the ability to separate symmetric from asymmetric sources of variance.…”

“…Comparing landmark- and contour-based methods not only provides an integrated perspective on leaf morphology, but can also potentially reveal the extent that patterning of the vasculature and blade are correlated in a quantitative fashion. Understanding the complementary features different morphometric methods detect is relevant to a wide variety of fields that use different approaches to extract information content from leaf shapes, including paleobiology and paleoclimate studies [41], ecology [42], evolution [10, 24, 27, 34, 43], genetics [21, 23, 29, 35, 36, 38], developmental biology [10, 18, 20, 24, 25, 34, 36, 39, 40], and plasticity [19, 20, 24, 37]. Heliconius butterflies, too, can even distinguish the shapes of leaves from different Passiflora species, presumably using a learning method yet to be determined [6].…”

Sequencing‐Based Bin Map Construction of a Tomato Mapping Population, Facilitating High‐Resolution Quantitative Trait Loci Detection

“…To use these reduced representation sequencing data in biparental populations, we developed SNPbinner application, which calculates recombination breakpoints using HMMs and generates an amalgamated genotyping table of the entire population. The HMM‐based binning tools that are currently available online provide pipelines that comprised of comprehensive data analyses with multiple steps including SNP calling (Xie et al, 2010; Fulop et al, 2016). SNPbinner, however, is designed specifically for the post‐SNP detection part of the analysis to simplify the binning process and to allow the users to choose the SNP calling platform that best fits their study from the various publicly available tools (Yi et al, 2014).…”