QTL Mapping Using High-Throughput Sequencing

Abstract

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 F2 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.

Combined Use of Genome-Wide Association Data and Correlation Networks Unravels Key Regulators of Primary Metabolism in Arabidopsis thaliana

“…Metabolite levels can be used as quantitative traits, and quantitative trait locus (QTL) mapping of metabolite levels using structured populations facilitates the identification of the genomic regions associated with the metabolic variation [ 7 – 9 ]. However, given the relatively low resolution reached using this approach [ 10 ], the cloning of single causal genes has rarely been achieved. Genome-wide association studies (GWAS), due to the presence of many more meiotic events present in natural populations during historical recombination, allow a more refined QTL resolution [ 11 , 12 ].…”

SNP markers tightly linked to root knot nematode resistance in grapevine (Vitis cinerea) identified by a genotyping-by-sequencing approach followed by Sequenom MassARRAY validation

“…The TASSEL-GBS pipeline was designed for marker discovery and SNP calling in inbred crops with the aim of identifying a large number of markers at low sequencing coverage [ 39 ]. While GBS [ 38 ], as well as restriction site associated DNA (RAD) Sequencing [ 79 , 80 ], are effective platforms for SNP discovery and genotyping in plants, little emphasis has been placed on validating SNPs [ 19 – 21 , 81 ]. Furthermore, in cases where SNPs have been validated, it is difficult to compare results due to differences in next generation sequencing approaches, quality or availability of a reference genome, depth coverage, length of sequence reads, alignment algorithms and SNP calling pipelines [ 82 – 87 ].…”

“…Marker assisted selection is an effective approach to combine desired traits of interest into crop breeding programs [ 19 – 21 ]. Next generation sequencing technologies, such as genotyping-by-sequencing (GBS), provide a cost effective and efficient approach for single nucleotide polymorphism (SNP) discovery and mapping of desirable traits in Vitis spp.…”

Guidelines for Sample Normalization to Minimize Batch Variation for Large-Scale Metabolic Profiling of Plant Natural Genetic Variance

“…However, they suffer from some limitations; only allelic diversity that segregates between the parents of Recombinant inbred lines are generated by selfing an F1 for at least eight generations when full homozygosity is reached. (b) Genome-Wide Association Study (GWAS) for Identification of QTL using natural population; GWAS makes use of natural genotypic variation and enables the analysis of associations between hundreds of thousands of single nucleotide polymorphisms (SNPs) and specific traits the particular F2 cross or within the RIL population can be assayed [27], and second, the amount of recombination that occurs during the creation of the RIL population places a limit on the mapping resolution [40]. The basic principle of genome-wide association studies (GWAS) (Fig.…”