Meta-analysis of QTL involved in silage quality of maize and comparison with the position of candidate genes

Abstract

A meta-analysis of quantitative trait loci (QTL) associated with plant digestibility and cell wall composition in maize was carried out using results from 11 different mapping experiments. Statistical methods implemented in "MetaQTL" software were used to build a consensus map, project QTL positions and perform meta-analysis. Fifty-nine QTL for traits associated with digestibility and 150 QTL for traits associated with cell wall composition were included in the analysis. We identified 26 and 42 metaQTL for digestibility and cell wall composition traits, respectively. Fifteen metaQTL with confidence interval (CI) smaller than 10 cM were identified. As expected from trait correlations, 42% of metaQTL for digestibility displayed overlapping CIs with metaQTL for cell wall composition traits. Coincidences were particularly strong on chromosomes 1 and 3. In a second step, 356 genes selected from the MAIZEWALL database as candidates for the cell wall biosynthesis pathway were positioned on our consensus map. Colocalizations between candidate genes and metaQTL positions appeared globally significant based on χ(2) tests. This study contributed in identifying key chromosomal regions involved in silage quality and potentially associated genes for most of these regions. These genes deserve further investigation, in particular through association mapping.

Genetic Determinants for Enzymatic Digestion of Lignocellulosic Biomass Are Independent of Those for Lignin Abundance in a Maize Recombinant Inbred Population

“…Several previous expression and QTL studies have focused on genes of lignin biosynthesis (Lorenz et al, 2010;Lorenzana et al, 2010;Thomas et al, 2010;Truntzler et al, 2010;Bosch et al, 2011). Consistent with these studies, we find homologs for almost every gene in the phenylpropanoid synthesis pathway present within at least one of our lignin abundance-or saccharification yieldrelated QTL regions (Supplemental Table S6).…”

“…In contrast, all but one of our QTL for saccharification yield (QTL3) map to at least one of the chromosomal regions associated with proxies of lignin abundance identified in other studies of maize, such as Klason lignin determination (total insoluble lignin) and artificial ruminant digestibility assays (Supplemental Table S6; Barrière et al, 2010;Lorenz et al, 2010;Lorenzana et al, 2010;Thomas et al, 2010;Truntzler et al, 2010). Saccharification yields vary in untreated materials but to a much lesser extent following pretreatments (Mosier et al, 2005).…”

Meta-analysis of QTLs Involved in Pre-harvest Sprouting Tolerance and Dormancy in Bread Wheat

“…Meta-QTL analysis has also been successfully conducted in several other crops like maize Truntzler et al, 2010;Hao et al, 2010;Li et al, 2011;Wang et al, 2006;Coque et al, 2008), cotton (Rong et al, 2007), rice (Ballini et al, 2008;Norton et al, 2008;Khowaja et al, 2009), rapeseed (Shi et al, 2009), potato (Danan et al, 2011), cocoa (Lanaud et al, 2009), soybean (Guo et al, 2006;Sun et al, 2012) and apricot (Marandel et al, 2009). …”

QTLs for oil yield components in an elite oil palm (Elaeis guineensis) cross

“…This limitation is unfortunate, as additional information would facilitate the narrowing of confidence intervals for each QTL and aid in identifying genuine QTLs against spurious ones in overlapping genomic regions. In other crops, comparative mapping has been used to detect real QTLs for traits such as Fusarium head blight resistance (Häberle et al 2009) and plant height in wheat (Griffiths et al 2012), late blight resistance and plant maturity traits in potato (Danan et al 2011) and yield in maize (Truntzler et al 2010;Li et al 2011). …”