GENIQUEST

The GENIQUEST project is an NSF-funded project of the Maine Mathematics and Science Alliance, The Concord Consortium and The Jackson Laboratory. GENIQUEST aims to bring current bioinformatics concepts and research techniques to secondary science students. In GENIQUEST, we integrate innovative approaches in science instruction from MMSA, data sets and genetics research knowledge from the Jackson Laboratories and a robust genetics modeling environment from the Concord Consortium into an intriguing biology computing environment that spurs student investigation and inquiry.

The GENIQUEST project is an NSF-funded project of the Maine Mathematics and Science Alliance, The Concord Consortium and The Jackson Laboratory. GENIQUEST aims to bring current bioinformatics concepts and research techniques to secondary science students. In GENIQUEST, we integrate innovative approaches in science instruction from MMSA, data sets and genetics research knowledge from the Jackson Laboratories and a robust genetics modeling environment from the Concord Consortium. This results in an intriguing biology computing environment that spurs student investigation and inquiry.

GENIQUEST is an exploratory project funded under the DR-K12 program of the National Science Foundation. GENIQUEST's main goal is to explore the effectiveness of using a digital model-based genetics environment to support secondary school students in learning the concepts underlying cutting-edge bioinformatics research. GENIQUEST familiarizes students with current genetics research, including the advanced technique of Quantitative Trait Loci (QTL) analysis.

We're currently expanding the initial exploratory work you see in the GENIQUEST project into a polished and full-featured project in Geniverse, a new, five-year NSF-funded project. In Geniverse, we're developing a game environment where students can become scientists studying traditional and cutting-edge dragon genetics and bioinformatics.

Principal Investigator

Amy Pallant

Project Inquiries

apallant@concord.org

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GENIQUEST Screen Shot

In GENIQUEST, students breed virtual dragons and manipulate their genes to learn modern genetics content and cutting-edge bioinformatics.

Activity Spotlight

Modern Genetics

Modern Genetics

Students breed dragons to learn concepts in modern genetics.

Learn More
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This material is based upon work supported by the National Science Foundation under Grant No. DRL-0733264. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

How to cite this material.

Usage/Citation

The Concord Consortium (n.d.) GENIQUEST. Retrieved 2014, December 22 from http://concord.org/projects/geniquest

Disclaimer: The Concord Consortium offers citation styles as a guide only. We cannot offer interpretations about citations as this is an automated procedure.

Goals and Objectives

In the GENIQUEST project, we address current goals of science research and education by bringing cutting-edge scientific data and techniques to the high school classroom in an approachable manner. Our long-range goal in the project is to improve students’ understanding of science, scientific research, and the use of evidence in reaching scientific conclusions.

Our exploratory project goals in GENIQUEST are to:

  • Develop and test a tool enabling students to import, manipulate and analyze genomics data.
  • Increase secondary students’ knowledge and skills of data analysis and scientific research.
  • Improve secondary school teachers and students’ access to cutting-edge STEM data sets.
  • Determine the viability of online data as a tool for engaging secondary school teachers and students in scientific inquiry.

As a result of this project, students will be able to:

  • Understand basic principles of quantitative inheritance and QTL analysis
  • Perform basic steps for analysis of QTL genomics data
  • Choose appropriate methods for visualizing and analyzing QTL datasets
  • Generate questions raised by data analysis
  • Further investigate these questions by exploring data or analyzing new datasets
  • Collaborate with other students locally or remotely on data analysis
  • Report results of their analyses to a teacher, class or larger audience

As a result of this project, teachers will be able to use this tool to:

  • Expose students to cutting-edge genomics concepts and data
  • Introduce and improve student data investigation and analysis skills
  • Guide students through the process of scientific inquiry
  • Develop custom data investigations tailored to specific students or courses

The GENIQUEST project curriculum is divided into three units. Each generally occupies several class periods. While the full set of GENIQUEST materials was initially intended for students with prerequisite knowledge about basic genetics, such as advanced or AP Biology courses, many of the activities are appropriate for students in an introductory biology course.

Modern Genetics Introduction

This initial module from the GENIQUEST project introduces the dragons and the inheritance of their traits, then delves into meiosis and its relationship to inherited traits. Students examine the effects of choosing different gametes on dragon offspring, and learn about genetic recombination by creating recombination events to generate specific offspring from two given parent dragons. Students learn about inbred strains and breed an inbred strain of dragons themselves. Lastly, students investigate a mysterious new dragon trait and use the properties of linkage and inheritance to examine the trait's relationship to other, known dragon traits.

View Teacher Guide

Linkage and QTL Explanation

In this module, students learn more about linkage and discover on which chromosome a gene for a previously discovered mystery trait is located. They then attend a virtual conference, where they learn about QTL analysis techniques from top dragon scientists.

View Teacher Guide

Disease Detectives

In the final module, students learn that a disease has struck the dragon population, and that the dragons are depending upon them to help find its source. Fortunately, dragon geneticists have developed a model organism called drakes, which they can use to learn about diseases such as this. In order to pinpoint the location of the disease, students will need to apply their newly gained knowledge about QTL analysis.

The students engage in an interactive primer on QTL analysis techniques and then apply these techniques to various inbred strains of drakes. As the module culminates, students pinpoint the location of the disease to within a handful of mapped genes. In a scaled-down version of the processes undertaken in current genomics science, they must then examine existing knowledge about these genes in a dragon genome browser to determine the exact gene likely to be causing the disease.

View Teacher Guide

Activity Spotlight

Modern Genetics

Modern Genetics

Students breed dragons to learn concepts in modern genetics.

Learn More

Visiting from the NSTA article? See more activities.

For Teachers

Links to the software and teachers' guides are below. To launch the software, visit the project portal.

Teacher Guides

Note: This software relies on the Java programming language. The software resides in a cache on the local computer, and is launched by clicking on a Web page link. The first time the link is clicked to use the software is used on any given computer, a large file must be downloaded into this cache. This cache generally remains intact unless explicitly cleared, so that the software will launch very quickly on any given computer following this initial download.

This has important ramifications for using the software in the classroom – beginning a class – especially a large one – without pre-launching the software may result in very long delays, particularly if the connection to the computers running the software has limited bandwidth. If new software can be installed on the computers you wish to use, you can also avoid this delay by installing the software directly using our GENIQUEST Installer. Whatever you do, before using the GENIQUEST software with a class please *pre-load the software onto each computer that will run it* to avoid potentially extensive delays.

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