Binary Trait Mapping in Experimental Crosses With Selective Genotyping

Selective genotyping is an efficient strategy for mapping quantitative trait loci. For binary traits, where there are only two distinct phenotypic values (e.g., affected/unaffected or present/absent), one may consider selective genotyping of affected individuals, while genotyping none or only some of the unaffecteds. If selective genotyping of this sort is employed, the usual method for binary trait mapping, which considers phenotypes conditional on genotypes, cannot be used. We present an alternative approach, instead considering genotypes conditional on phenotypes, and compare this to the more standard method of analysis, both analytically and by example. For studies of rare binary phenotypes, we recommend performing an initial genome scan with all affected individuals and an equal number of unaffecteds, followed by genotyping the full cross in genomic regions of interest to confirm results from the initial screen.WE consider the problem of mapping genetic loci contributing to a binary trait in an experimental cross with selective genotyping. There are two clear approaches for linkage analysis with a binary trait. Typically, we compare the proportion of affected individuals across genotype groups (Xu and Atchley 1996). Alternatively, we can compare genotype frequencies between affected and unaffected individuals, similar to Henshall and Goddard (1999). Beyond these two basic approaches, binary trait mapping has seen fundamental advances in regression models (McIntyre et al. 2001; Deng et al. 2006), extensions to multiple-QTL mapping (Coffman et al. 2005; Chen and Liu 2009), and the development of Bayesian algorithms (Yi and Xu 2000; Huang et al. 2007). However, the original data structure and approach have remained intact. Existing methods for binary trait mapping largely require the availability of genotype and phenotype data for a representative sample of both affected and unaffected individuals, and we have not yet seen a well-developed framework for binary trait mapping in the presence of selective genotyping.It is not uncommon to see genotype data on affected individuals only, in which case the above methods cannot be used. Instead, we can compare observed genotype frequencies to the expected segregation ratios given the cross type, in a test for segregation distortion (see Faris et al. 1998; Lambrides et al. 2004). For example, the expected segregation proportions for an intercross are 1:2:1. The observed genotypes can then be described by a multinomial model, and statistically significant deviation from the expected segregation ratios among the genotyped affected individuals would suggest genotype–phenotype association. Gene mapping approaches that model genotypes rather than phenotypes have been developed extensively in the analysis of affected human relative pairs (see, for example, Risch 1990; Holmans 1993; Hauser and Boehnke 1998). In the analysis of experimental crosses, however, this type of approach has been developed primarily for the identification of monogenic mutants (Moran et al. 2006).Once all affected individuals are genotyped, an investigator may go on to genotype unaffected individuals. With this genotyping strategy in mind, we present several potential methods of analysis that might be applied in this context. First, we consider a standard analysis of the genotyped individuals, with disease proportions compared across genotype groups (Xu and Atchley 1996). Having omitted ungenotyped individuals, this method of analysis appears invalid because the estimated disease proportions are biased upward, reflecting an overrepresentation of affecteds in the set of genotyped individuals under consideration. As an alternative, we develop a reverse approach with genotype frequencies compared across phenotype groups. Because selective genotyping does provide a representative sample of genotypes for each phenotype group, this reverse approach does not face the bias in parameter estimation seen with the standard approach. We further extend the reverse approach to incorporate a segregation assumption, as is necessary for an affecteds only analysis. Finally, we present a full-likelihood analysis accounting for selective genotyping, similar to that suggested by Lander and Botstein (1989) for quantitative traits. We develop the full-likelihood approach both with and without incorporating an assumption on the genotype segregation proportions.Having put forth each of these methods, we derive analytic relationships among them. These relationships provide important insight regarding application of the presented methods under selective genotyping. Most notably, we find that making a segregation assumption can lead to spurious evidence of a QTL, but is necessary to treat the case of affecteds only genotyping. We demonstrate properties of the methods in an analysis of recovery from infection by Listeria monocytogenes in intercross mice and further compare power of the methods through computer simulations. Finally, we synthesize our analytical and simulation results to offer more general suggestions for the analysis of binary trait data with selective genotyping.