QTL mapping in intercross and backcross populations

QTL Mapping for Pest and Disease Resistance in Cassava and Coincidence of Some QTL with Introgression Regions Derived from Manihot glaziovii

Genetic mapping of quantitative trait loci (QTL) for resistance to cassava brown streak disease (CBSD), cassava mosaic disease (CMD), and cassava green mite (CGM) was performed using an F1 cross developed between the Tanzanian landrace, Kiroba, and a breeding line, AR37-80. The population was evaluated for two consecutive years in two sites in Tanzania. A genetic linkage map was derived from 106 F1 progeny and 1,974 SNP markers and spanned 18 chromosomes covering a distance of 1,698 cM. Fifteen significant QTL were identified; two are associated with CBSD root necrosis only, and were detected on chromosomes V and XII, while seven were associated with CBSD foliar symptoms only and were detected on chromosomes IV, VI, XVII, and XVIII. QTL on chromosomes 11 and 15 were associated with both CBSD foliar and root necrosis symptoms. Two QTL were found to be associated with CMD and were detected on chromosomes XII and XIV, while two were associated with CGM and were identified on chromosomes V and X. There are large Manihot glaziovii introgression regions in Kiroba on chromosomes I, XVII, and XVIII. The introgression segments on chromosomes XVII and XVIII overlap with QTL associated with CBSD foliar symptoms. The introgression region on chromosome I is of a different haplotype to the characteristic "Amani haplotype" found in the landrace Namikonga and others, and unlike some other genotypes, Kiroba does not have a large introgression block on chromosome IV. Kiroba is closely related to a sampled Tanzanian "tree cassava." This supports the observation that some of the QTL associated with CBSD resistance in Kiroba are different to those observed in another variety, Namikonga.

Positional Gene Cloning in Experimental Populations

Positional cloning is a technique that identifies a trait-associated gene based on its location in the genome and involves methods such as linkage analysis, association mapping, and bioinformatics. This approach can be used for gene identification even when little is known about the molecular basis of the trait. Vast majority of traits are regulated by multiple genomic loci called quantitative trait loci (QTL). We describe experimental populations and designs that can be used for positional cloning, including backcrosses, intercrosses, and heterogeneous stocks, and advantages and disadvantages of different approaches. Once the phenotype and genotype of each individual in an experimental population have been determined, QTL identification can be accomplished. We describe the statistical tools used to identify the existence, location, and significance of QTLs. These different methods have advantages and disadvantages to consider when selecting the appropriate model to be used, which is briefly discussed.Although the objective of QTL mapping is to identify genomic regions associated with a trait, the ultimate goal is to identify the gene and the genetic variation (which is often quantitative trait nucleotide, QTN) or haplotype that is responsible for the phenotype. By discovering the function of causative variants or haplotypes we can understand the molecular changes that lead to the phenotype. We briefly describe how the genomic sequences can be exploited to identify QTNs and how these can be validated in congenic strains and functionally tested to understand their influence on phenotype expression.

In vitro and in vivo mouse models for pharmacogenetic studies

The identification of causative genes underlying biomedically relevant phenotypes, particularly complex multigenic traits, is of vital interest to modern medicine. Using genome-wide association analysis, many studies have successfully identified thousands of loci (called quantitative trait loci or QTL), some of these associating with drug response phenotypes. However, the determination and validation of putative genes has been much more challenging. The actions of drugs, both efficacious and deleterious, are complex phenotypes that are controlled or influenced in part by genetic mechanisms.Investigation for genetic correlates of complex traits and pharmacogenetic traits is often difficult to perform in human studies due to cost, availability of relevant sample population, and limited ability to control for environmental effects. These challenges can be circumvented with the use of mouse models for pharmacogenetic studies. In addition, the mouse can be treated at sub- and supratherapeutic doses and subjected to invasive procedures, which can facilitate measures of drug response phenotypes, making identification of pharmacogenetically relevant genes more feasible. The availability of multiple mouse genetic and phenotypic resources is an additional benefit to using the mouse for pharmacogenetic studies.Here, we describe the contribution of animal models, specifically the mouse, towards the field of pharmacogenetics. In this chapter, we describe different mouse models, including the knockout mouse, recombinant mouse inbred strains, in vitro mouse cell-based assays, as well as novel experimental approaches like the Collaborative Cross recombinant mouse inbred panel, which can be applied to preclinical pharmacogenetics research. These approaches can be used to assess drug response phenotypes that are difficult to model in humans, thereby facilitating drug discovery, development, and application.

Mapping and confirmation of a major left ventricular mass QTL on rat chromosome 1 by contrasting SHRSP and F344 rats

An abnormal increase in left ventricular (LV) mass, i.e., LV hypertrophy (LVH), represents an important target organ damage in arterial hypertension and has been associated with poor clinical outcome. Genetic factors are contributing to variation in LV mass in addition to blood pressure and other factors such as dietary salt intake. We set out to map quantitative trait loci (QTL) for LV mass by comparing the spontaneously hypertensive stroke-prone (SHRSP) rat with LVH and normotensive Fischer rats (F344) with contrasting low LV mass. To this end we performed a genome-wide QTL mapping analysis in 232 F2 animals derived from SHRSP and F344 exposed to high-salt (4% in chow) intake for 8 wk. We mapped one major QTL for LV mass on rat chromosome 1 (RNO1) that demonstrated strong linkage (peak logarithm of odds score 8.4) to relative LV weight (RLVW) and accounted for ∼19% of the variance of this phenotype in F2 rats. We therefore generated a consomic SHRSP-1(F344) strain in which RNO1 from F344 was introgressed into the SHRSP background. Consomic and SHRSP animals showed similar blood pressures during conventional intra-arterial measurements, while RLVW was already significantly lower (-17.7%, P<0.05) in SHRSP-1(F344) in response to a normal-salt diet; a similar significant reduction of LV mass was also observed in consomic rats after high-salt intake (P<0.05 vs. SHRSP). Thus, a major QTL on RNO1 was confirmed with significant impact on LV mass in the hypertensive background of SHRSP.

solQTL: a tool for QTL analysis, visualization and linking to genomes at SGN database

Background

A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.

Description

The Sol Genomics Network (SGN, http://solgenomics.net) is a primary repository for phenotypic, genetic, genomic, expression and metabolic data for the Solanaceae family and other related Asterids species and houses a variety of bioinformatics tools. SGN has implemented a new approach to QTL data organization, storage, analysis, and cross-links with other relevant data in internal and external databases. The new QTL module, solQTL, http://solgenomics.net/qtl/, employs a user-friendly web interface for uploading raw phenotype and genotype data to the database, R/QTL mapping software for on-the-fly QTL analysis and algorithms for online visualization and cross-referencing of QTLs to relevant datasets and tools such as the SGN Comparative Map Viewer and Genome Browser. Here, we describe the development of the solQTL module and demonstrate its application.

Conclusions

solQTL allows Solanaceae researchers to upload raw genotype and phenotype data to SGN, perform QTL analysis and dynamically cross-link to relevant genetic, expression and genome annotations. Exploration and synthesis of the relevant data is expected to help facilitate identification of candidate genes underlying phenotypic variation and markers more closely linked to QTLs. solQTL is freely available on SGN and can be used in private or public mode.