Detecting major genetic loci controlling phenotypic variability in experimental crosses

Abstract

Traditional methods for detecting genes that affect complex diseases in humans or animal models, milk production in livestock, or other traits of interest, have asked whether variation in genotype produces a change in that trait's average value. But focusing on differences in the mean ignores differences in variability about that mean. The robustness, or uniformity, of an individual's character is not only of great practical importance in medical genetics and food production but is also of scientific and evolutionary interest (e.g., blood pressure in animal models of heart disease, litter size in pigs, flowering time in plants). We describe a method for detecting major genes controlling the phenotypic variance, referring to these as vQTL. Our method uses a double generalized linear model with linear predictors based on probabilities of line origin. We evaluate our method on simulated F₂ and collaborative cross data, and on a real F₂ intercross, demonstrating its accuracy and robustness to the presence of ordinary mean-controlling QTL. We also illustrate the connection between vQTL and QTL involved in epistasis, explaining how these concepts overlap. Our method can be applied to a wide range of commonly used experimental crosses and may be extended to genetic association more generally.

Figure 1.—

Distance (cM) between simulated and detected QTL for F₂ (10,000 replicates) and the CC (1000 replicates).

Figure 2.—

Relationship between log P-values for false vQTL and marker information content (SIC) when simulating a mean-controlling QTL in a CC population. For each of 200 simulations, ordered along the x-axis by their most significant vQTL peak, the plot shows the mean and standard deviation of SIC for 1000 mice. The SIC statistics are stationary, indicating no apparent tendency for marker uncertainty to produce false vQTL signals.

Figure 3.—

Scan for QTL controlling the mean (top) and the variance (middle) of body weight at 200 days of age on chicken chromosome 1 in an F₂ cross between Red Jungle Fowl and White Leghorn with 756 F₂ offspring. (Bottom) Marker information contents (SIC). Genome-wide significance threshold calculated using 1000 permutations.

Figure 4.—

Estimates of vQTL effects given as percentage change in residual variance for allele substitutions for body weight at 200 days of age on chicken chromosome 1 in an F₂ cross between Red Jungle Fowl and White Leghorn. Solid lines: Maximum-likelihood estimates from the full marginal likelihood. Shaded lines: DGLM estimates. Close up for 90–130 cM shown in bottom figure.