A strategy to identify dominant point mutant modifiers of a quantitative trait

Area of Science:

• Genetics and Genomics
• Cancer Biology
• Quantitative Trait Analysis

Background:

• Identifying genes that modify complex traits, such as cancer, is a central goal in biological research.
• Ethylnitrosourea mutagenesis provides diverse alleles (loss-, gain-, or alteration-of-function) for studying gene modifiers.
• Point mutations offer advantages over strain polymorphisms for detecting heterozygous quantitative phenotypes in both essential and nonessential genes.

Purpose of the Study:

• To analyze strategies for identifying quantitative mutational modifiers of the Apc(Min) cancer phenotype in mice.
• To develop and validate a new panel of isogenic mapping partner lines for efficient genetic modifier discovery.
• To present an evolved strategy that complements existing large-scale genetic analysis initiatives.

Main Methods:

• Utilizing germline point mutagenesis with ethylnitrosourea to generate mutations.
• Employing a cluster of test progeny for statistical significance in modifier identification.
• Developing a mapping panel of six isogenic mapping partner lines for C57BL/6J mice, including the phenotypically neutral B6.SNVg line.
• Performing low-resolution mapping followed by targeted resequencing to identify causative mutations.

Main Results:

• Demonstrated the utility of point mutagenesis for identifying quantitative modifiers of the Apc(Min) phenotype.
• Developed and characterized six isogenic mapping partner lines, with B6.SNVg serving as an effective tool for locating induced mutant modifiers.
• The strategy enables the identification of modifiers unlinked to other potentially confounding genetic variants.

Conclusions:

• The described strategy provides a robust method for identifying quantitative genetic modifiers of complex phenotypes.
• The developed isogenic mapping partner lines, particularly B6.SNVg, are valuable resources for genetic research.
• This approach significantly complements major ongoing initiatives in the genetic analysis of complex systems, including GWAS, the Collaborative Cross, the Knockout Mouse Project, and The Cancer Genome Atlas.