Quantitative trait locus (QTL) mapping in plants dates to the 1980s (Stuber et al. Crop Sci 27: 639-648, 1987; Paterson et al. Nature 335: 721-726, 1988), but earlier studies were often hindered by the expense and time required to identify large numbers of polymorphic genetic markers that differentiated the parental genotypes and then to genotype them on large segregating mapping populations. High-throughput sequencing has provided an efficient means to discover single nucleotide polymorphisms (SNPs) that can then be assayed rapidly on large populations with array-based techniques (Gupta et al. Heredity 101: 5-18, 2008). Alternatively, high-throughput sequencing methods such as restriction site-associated DNA sequencing (RAD-Seq) (Davey et al. Nat Rev Genet 12: 499-510, 2011; Baird et al. PloS ONE 3: e3376, 2008) and genotyping-by-sequencing (GBS) (Elshire et al. PLoS One 6: 2011; Glaubitz et al. PLoS One 9: e90346, 2014) can be used to identify and genotype polymorphic markers directly. Linkage disequilibrium (LD) between markers and causal variants is needed to detect QTL. The earliest QTL mapping methods used backcross and F 2 generations of crosses between inbred lines, which have high levels of linkage disequilibrium (dependent entirely on the recombination frequency between chromosomal positions), to ensure that QTL would have sufficiently high linkage disequilibrium with one or more markers on sparse genetic linkage maps. The downside of this approach is that resolution of QTL positions is poor. The sequencing technology revolution, by facilitating genotyping of vastly more markers than was previously feasible, has allowed researchers to map QTL in situations of lower linkage disequilibrium, and consequently, at higher resolution. We provide a review of methods to identify QTL with higher precision than was previously possible. We discuss modifications of the traditional biparental mapping population that provide higher resolution of QTL positions, QTL fine-mapping procedures, and genome-wide association studies, all of which are greatly facilitated by high-throughput sequencing methods. Each of these procedures has many variants, and consequently many details to consider; we focus our chapter on the consequences of practical decisions that researchers make when designing QTL mapping studies and when analyzing the resulting data. The ultimate goal of many of these studies is to resolve a QTL to its causal sequence variation.