Contrasting genetic architectures in different mouse reference populations used for studying complex traits

Abstract

Quantitative trait loci (QTLs) are being used to study genetic networks, protein functions, and systems properties that underlie phenotypic variation and disease risk in humans, model organisms, agricultural species, and natural populations. The challenges are many, beginning with the seemingly simple tasks of mapping QTLs and identifying their underlying genetic determinants. Various specialized resources have been developed to study complex traits in many model organisms. In the mouse, remarkably different pictures of genetic architectures are emerging. Chromosome Substitution Strains (CSSs) reveal many QTLs, large phenotypic effects, pervasive epistasis, and readily identified genetic variants. In contrast, other resources as well as genome-wide association studies (GWAS) in humans and other species reveal genetic architectures dominated with a relatively modest number of QTLs that have small individual and combined phenotypic effects. These contrasting architectures are the result of intrinsic differences in the study designs underlying different resources. The CSSs examine context-dependent phenotypic effects independently among individual genotypes, whereas with GWAS and other mouse resources, the average effect of each QTL is assessed among many individuals with heterogeneous genetic backgrounds. We argue that variation of genetic architectures among individuals is as important as population averages. Each of these important resources has particular merits and specific applications for these individual and population perspectives. Collectively, these resources together with high-throughput genotyping, sequencing and genetic engineering technologies, and information repositories highlight the power of the mouse for genetic, functional, and systems studies of complex traits and disease models.

Figure 1.

The genetic composition of mouse resources for studying complex traits. The genetic makeup of a typical mouse strain is shown for the Collaborative Cross (A), Chromosome Substitution Strains (B), Outbred Stocks (C), and the Hybrid Mouse Diversity Panel (D). Two sets of chromosomes are shown for the Hybrid Mouse Diversity Panel because it is comprised of both inbred and recombinant inbred strains. Each rectangle represents a chromosome, and each color represents the genetic contribution from a different mouse strain. Mitochondria are not shown.

Figure 2.

QTL intervals frequently contain multiple sub-QTLs. High-resolution mapping of QTL intervals with CSS, congenic, subcongenic, and subsub-congenic strains identified multiple sub-QTLs within QTLs at each level of genetic resolution. QTLs and sub-QTLs were mapped for body weight (A), activity (B), plasma cholesterol (C), body weight (D), and testicular germ cell tumors (TGCT) (E). QTL intervals are represented by horizontal black lines.

Figure 3.

Frequency of monomorphic, monogenic or digenic, and multigenic traits identified with CC (A), CSS (B), OS (C), and the HMDP (D).