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

Abstract

Libraries of near-isogenic lines (NILs) were used for quantitative trait locus (QTL) detection in model species and economically important crops. The experimental design and genetic architecture of the considered traits determine the statistical properties of QTL detection. The objectives of our simulation study were to (i) investigate the population sizes required to develop NIL libraries in barley and maize, (ii) compare NIL libraries with nonoverlapping and overlapping donor segments and (iii) study the number of QTLs and the size of their effects with respect to the power and the false-positive rate of QTL detection. In barley, the development of NIL libraries with target segment lengths of 10 cM and marker distances of 5 cM was possible using a BC 3 S 2 backcrossing scheme and population sizes of 140. In maize, population sizes larger than 200 were required. Selection for the recipient parent genome at markers flanking the target segments with distances between 5 and 10 cM was required for an efficient control of the false-positive rate. NIL libraries with nonoverlapping donor chromosome segments had a greater power of QTL detection and a smaller false-positive rate than libraries with overlapping segments. Major genes explaining 30% of the genotypic difference between the donor and recipient were successfully detected even with low heritabilities of 0.5, whereas for minor genes explaining 5 !or 10%, high heritabilities of 0.8 or 0.9 were required. The presented results can assist geneticists and breeders in the efficient development of NIL libraries for QTL detection.

Determinants of QTL mapping power in the realized Collaborative Cross

“…Power analyses have been performed previously for a number of RI panels. For biparental RIs, they have been performed analytically in plants (e.g., Kaeppler 1997), animals [e.g., the BXD lines in mice (Belknap et al 1996;Peirce et al 2004)], and in general (Cowen 1988;Soller and Beckmann 1990;Knapp and Bridges 1990), as well as through simulation (Falke and Frisch 2011;Takuno et al 2012). For MPP RIs, they have most often been reported as those resources are introduced to the community.…”

Natural Genetic Variation for Acclimation of Photosynthetic Light Use Efficiency to Growth Irradiance in Arabidopsis

“…To investigate this, it is crucial to identify the genes involved and the effect of alleles of those genes on the photosynthesis phenotype. Identifying causal allelic variation is easier when there is substantial genetic variation for a trait, as quantified by the heritability values (Barton and Keightley, 2002) and the effect size of the quantitative trait locus (Falke and Frisch, 2011). It is hard to predict how much heritability is required before a trait will be amenable to genetic analysis, as this depends on the number of loci that contribute to the heritability (i.e.…”

Stable Carbon Isotope Discrimination Is under Genetic Control in the C4 Species Maize with Several Genomic Regions Influencing Trait Expression

“…To reduce excessive multicollinearity between markers, donor genome fragments were subdivided into nonoverlapping donor genome target regions as described by Falke and Frisch (2011). We identified 164 nonoverlapping donor genome target regions and tested those for significant associations with phenotypic traits.…”

“…Based on the Dunnett (1955) test, putative QTL can be located at the level of introgressed fragments. To identify the locations of genomic regions carrying putative QTL more precisely, donor genome fragments were subdivided into nonoverlapping donor target regions as described by Falke and Frisch (2011). Based on the 77 IL lines tested under field and greenhouse conditions, 164 unique genomic target regions were defined and used as predictor variables in a bidirectional multiple linear regression model (stepwise) to test for significant trait-target region associations.…”