Quantitative Trait Locus Mapping Methods for Diversity Outbred Mice

Abstract

Genetic mapping studies in the mouse and other model organisms are used to search for genes underlying complex phenotypes. Traditional genetic mapping studies that employ single-generation crosses have poor mapping resolution and limit discovery to loci that are polymorphic between the two parental strains. Multiparent outbreeding populations address these shortcomings by increasing the density of recombination events and introducing allelic variants from multiple founder strains. However, multiparent crosses present new analytical challenges and require specialized software to take full advantage of these benefits. Each animal in an outbreeding population is genetically unique and must be genotyped using a high-density marker set; regression models for mapping must accommodate multiple founder alleles, and complex breeding designs give rise to polygenic covariance among related animals that must be accounted for in mapping analysis. The Diversity Outbred (DO) mice combine the genetic diversity of eight founder strains in a multigenerational breeding design that has been maintained for >16 generations. The large population size and randomized mating ensure the long-term genetic stability of this population. We present a complete analytical pipeline for genetic mapping in DO mice, including algorithms for probabilistic reconstruction of founder haplotypes from genotyping array intensity data, and mapping methods that accommodate multiple founder haplotypes and account for relatedness among animals. Power analysis suggests that studies with as few as 200 DO mice can detect loci with large effects, but loci that account for <5% of trait variance may require a sample size of up to 1000 animals. The methods described here are implemented in the freely available R package DOQTL.

Genetic regulation of bone strength: a review of animal model studies

“…These include the use of commercially available outbred animals or 'designer' outbred populations, which are created by breeding two or more strains over the course of multiple generations in a manner that minimizes inbreeding. [26][27][28][29] Additional study designs have been used to better define the genetic etiology of bone strength, including the use of congenic and consomic genetic reference populations. 23 A consomic, which is sometimes referred to as chromosome substitution, is a strain wherein all of the alleles for an entire chromosome from one inbred strain of mice or rats have been moved onto an otherwise pure background of a second strain.…”

A Random-Model Approach to QTL Mapping in Multiparent Advanced Generation Intercross (MAGIC) Populations

“…However, when the marker density is high, genome scanning (a single-QTL model) provides a better alternative method for QTL mapping, but the cofactors should be replaced by a polygenic effect, as done in genome-wide association studies (GWAS) (Yu et al 2006). We recently developed a QTL mapping procedure by fitting a polygene using a marker-inferred relationship matrix (replacing cofactors) and demonstrated the robustness of the method (Xu 2013b).Recently, Gatti et al (2014) developed a mixed model for QTL mapping in Diversity Outbred (DO) mice by treating the effects of scanned markers as fixed and a polygenic effect as random. The polygenic effect essentially replaced cofactors to control the genetic background.…”

“…The estimated variance ratio is denoted by b l and will be used as a known constant in the genomic scanning model that follows. File S2 describes the method of estimating l along with the effect of the marker scanned, the so-called exact method (Zhou and Stephens 2012).Fixed model: To test the significance of the kth marker, we first used the fixed-model approach proposed by Gatti et al (2014). The model is…”

“…The method to obtain b j k is called the best linear unbiased prediction (BLUP) and is described in File S2. This revised method is called FIXED-B when compared with other methods.Random model: The fixed-model approach may not be stable when the number of founders is large (Gatti et al 2014), and the design matrix Z k may have variable ranks across different markers. Under the null model, the likelihood-ratio test statistic follows a chi-square distribution with degrees of freedom depending on the number of founders.…”

“…In a p-parent MAGIC population, p 2 1 is the degrees of freedom. When p is large, this test is not convenient and sometimes can be unstable (Gatti et al 2014). For example, if some founder alleles fail to appear in the progeny for some loci, the Z matrices for these loci will not have the same rank as those loci with full representation of all founders.…”