The GENIQUEST Project (GENomics Inquiry through QUantitative Trait Loci Exploration with SAIL Technology): is a exploratory DRK12 project that is developing curricular materials for teaching advanced high-school biology students the science of Quantitative Trait Loci (QTL) genetics analysis.
The Maine Mathematics and Science Alliance (MMSA) is prime on this proposal and the Concord Consortium and Jackson Laboratories are collaborating with MMSA. This is an Exploratory project responding to the Discovery Research K-12 program solicitation NSF 06 593, Category B. Development of Resources and Tools, and subcategory 2; Instruction of K-12 Student and Teachers. The project directly addresses the issues in presented in NSF Grand Challenge 3: Cutting- Edge STEM Content in K-12 Classrooms.
QTL analysis uses the science of computational genomics to help uncover the relationship between genetic differences within an organism's genome and the likelihood of a strain expressing clinical phenotype responses in reaction to specific environmental conditions. QTL analysis is done with data generated by exposing strains of model organisms to specific environmental stresses. The result of this analysis is identification of genes or gene networks that are highly correlated with the likelihood of the occurrence of a disease and exposure to specific environmental conditions.
Can QTL analysis help us understand the causes of heart disease in humans?
In order to conduct experiments we need a model organism that has biological mechanisms similar to those in humans. Many advances in understanding the genetic basis of human heart diseases come from conducting QTL analysis on experiments done with mice as a model organism. Epideological data show that a diet high in staurated-fat has a positive correlation with heart disease.
What can we learn from experiments with mice?
Some mice with high levels of saturated fat are susceptible to heart disease. To better uncover the genetic relationship between a diet high in saturated fat and heart disease several strains of mice are selected so that some of the strains are highly resistant to getting heart disease while eating this diet while others are quite susceptible.
What are the genetic differences between mice who get sick and those who don't?
The essential scientific problem at this point is that the differences between the genomes of the mice that stay healthy and the ones that get sick is an extremely large set of genes. These differences include the genes that relate to this specific expression of heart disease (called the clinical phenotype) however they also include many thousands of other differences between the two strains that have nothing to do with this form of heart disease.
Can we narrow down the sets of gene differences we are looking at?
To narrow down the set of genes or gene networks that are related the clinical phenotype the strains are crossbred in a specific manner which mixes up the genes from one strain with another. The mice resulting from this cross-breeding are then exposed to the high-saturated fat diet and at the end of the experiment are measured for indicators of heart disease.
We know which specific mice got sick but the genetic map for each mouse is a jumbled up mix. We could determine the genetic sequence for each mouse – but that would cost too much!
The question now is: We've got about 500 mice and we know which ones got heart disease and how badly. The genetic map for each mouse is a random jumbled up mix from the strains that are resistant and susceptible. If we could compare the genetic map of each mouse to the original pure strains that were resistant and susceptible we could tease out with some statistics which genes or gene networks were most likely to be related to the disease.
Genetic markers can make this easier because they are much easier (and cheaper) to determine.
Here's where another important aspect of conducting QTL analysis comes into play. While it is possible to determine the entire genetic mapping for an individual mouse this is extremely costly. To make this work practical science genetic markers are used instead. Genetic markers are unique easily identifiable points on the genome which are correlated with nearby genes. Each mouse is then measured for the presence of a collection of likely genetic markers and it is this data which is used as a proxy for the actual genetic map.
These data are then input into the QTL analysis scripts which result in a score for each genetic marker correlating the presence of that marker and the presence of the clinical phenotype.
Knowing the the genes that are correlated with disease is helpful but ... that is just the beginning. What part do the proteins these genes express play in the actual biological systems in the mouse and how does this relate to similar diseases in people?
The last essential scientific understanding is that these data do not tell a scientist what causes the disease. The genes or gene networks identified must be related to their effect on the actual biological mechanisms they encode. This can be done by finding existing research on how those genes work or suggesting where new research should take place.
Getting students to both investigate and understand QTL analysis would be a challenge for a full DRK12 project. For GENIQUEST which is a small exploratory DRK12 project we need to be highly selective about where we put our effort to understand and show how students can we can best engage students in deep inquiry in this kind of highly cross-disciplinary science that combines biology, genetics, and statistics.
The importance of this kind of research however cannot be overstated. Progress in this field of science is moving extremely quickly and has an essential basis in both the cross-disciplinary nature of the work and in the collaborative computational tools and datasets shared by researchers in the field. This kind of collaborative computational integration is also driving advances in other sciences. In order to effectively prepare students who will be tomorrow's scientists these kind of learning and inquiry should be scaffolded by changes in the way we teach and integrate science and math at earlier grades.
Geniquest proposal (pdf)
- Instructional Flow & Storylines
- Software Tools and Data (Biologica, QTL, R scripts, ...)
- QTL Science Background
- eMeeting Notes
- Geniquest Schedule and Calendar
- Advisory Board meetings
- New Proposal Solicitations
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