In Arabidopsis, a variety of mapping populations have been used for the detection of quantitative trait loci (QTLs) responsible for natural variation. In this study, we present an overview of the advantages and disadvantages of the different types of populations used. To do this, we compare the results of both experimental and natural populations for the commonly analysed trait flowering time. It is expected that genome wide association (GWA) mapping will be an increasingly important tool for QTL mapping because of the high allelic richness and mapping resolution in natural populations. In Arabidopsis, GWA mapping becomes ever more facilitated by the increasing availability of re-sequenced genomes of many accessions. However, specifically designed mapping populations such as recombinant inbred lines and near isogenic lines will remain important. The high QTL detection power of such experimental populations can identify spurious GWA associations, and their unique genomic structure is superior for investigating the role of low-frequency alleles. Future QTL studies will therefore benefit from a combined approach of GWA and classical linkage analysis.

Cotton (Gossypium spp.) is an important source of edible oil and protein meals. Complex genetics and strong environmental effects hinder progress in seed quality trait breeding in this species. The use of molecular markers can improve an understanding of the genetic factors conditioning seed quality traits, and is expected to assist in selection of superior genotypes. This study was conducted to identify quantitative trail loci (QTL) associated with seed physical and nutrient traits in cotton. To achieve this objective, a population of 140 BC1S1 lines developed from a cross between ‘TM-1’ and ‘Hai7124’ was evaluated in 2003 and 2004. A linkage map consisting of 918 markers from this population was used to identify QTL using QTLNetwork-2.0 software. Eleven single QTL were identified for kernel percentage, kernel oil percentage, kernel protein percentage and seven amino acids (Asp, Ser, Gly, Ile, Leu, Phe and Arg). Phenotypic variation explained by each individual QTL ranged from 10.89 to 46.28%. Two epistatic QTL for Cys and Leu were detected, explaining 9.55 and 4.43% of the phenotypic variation. These QTL detected for seed quality traits in cotton are expected to be useful for further breeding programmes targeting development of cotton with improved nutrient quality.