Next generation quantitative genetics in plants

Abstract

Most characteristics in living organisms show continuous variation, which suggests that they are controlled by multiple genes. Quantitative trait loci (QTL) analysis can identify the genes underlying continuous traits by establishing associations between genetic markers and observed phenotypic variation in a segregating population. The new high-throughput sequencing (HTS) technologies greatly facilitate QTL analysis by providing genetic markers at genome-wide resolution in any species without previous knowledge of its genome. In addition HTS serves to quantify molecular phenotypes, which aids to identify the loci responsible for QTLs and to understand the mechanisms underlying diversity. The constant improvements in price, experimental protocols, computational pipelines, and statistical frameworks are making feasible the use of HTS for any research group interested in quantitative genetics. In this review I discuss the application of HTS for molecular marker discovery, population genotyping, and expression profiling in QTL analysis.

Keywords: QTL analysis; RNA-seq; eQTL analysis; genomics; next generation sequencing; plant genetics.

Figure 1

Guide map to the proposed pipelines for SNP identification, genotyping, and molecular phenotyping for QTL analysis in plants. Medium coverage is considered from 20× to 100× the genome or transcriptome size under study. Low coverage is considered under 15× the genome or transcriptome size under study.

Figure 2

Percentage of transcriptome covered versus number of RNA-seq reads used. Eighty-one base pair paired-end RNA-seq reads from S. lycopersicum were randomly sampled in different subset sizes and aligned to the S. lycopersicum genome reference. The percentage of the length of the transcriptome covered by at least one read is represented at different coverages.