Joint linkage and segregation analysis under multiallelic trait inheritance: simplifying interpretations for complex traits

Abstract

Identification of the genetic basis of common traits may be hindered by underlying complex genetic architectures that are inadequately captured by existing models, including both multiallelic and multilocus modes of inheritance (MOI). One useful approach for localizing genes underlying continuous complex traits is the joint oligogenic linkage and segregation analysis implemented in the package Loki. The method uses reversible jump Markov chain Monte Carlo to eliminate the need to prespecify the number of quantitative trait loci (QTLs) in the trait model, thus providing posterior distributions for the number of QTLs in a Bayesian framework. The current implementation assumes QTLs are diallelic, and therefore can overestimate the number of linked QTLs in the presence of a multiallelic QTL. To address the possibility of multiple alleles, we extended the QTL model to allow for a variable number of additive alleles at each locus. Application to simulated data shows that, under a diallelic MOI, the multiallelic and diallelic analysis models give similar results. Under a multiallelic MOI, the multiallelic analysis model provides better mixing and improved convergence, and leads to a more accurate estimate of the underlying trait MOI and model parameter values, than does the diallelic model. Application to real data shows the multiallelic model results in fewer estimated linked QTLs and that the predominant QTL model is similar to one of two predominant models estimated from the diallelic analysis. Our results indicate that use of a multiallelic analysis model can lead to better understanding of the genetic architecture underlying complex traits.

Figure 1

Pedigree used for simulated data.

Figure 2

Comparison of visiting QTL parameters for paired Loki and maLoki runs on two representative simulated data replicates. Panels in left two columns show no. of visiting QTLs vs iteration/3000 for Loki and no. of visiting QTLs vs iteration/1000 for maLoki. Panels in right two columns show the size vs location (cM) of visiting QTLs

Figure 3

Comparison of visiting QTL parameters between Loki and maLoki on the transformed trait data for replicate 2. See Methods for description of data transformation.

Figure 4

Distributional comparisons of key parameters from the Loki and maLoki runs on the simulated data. The columns are for (a) Probability ratio for the chromosome (b) Mean bias in position (c) Mean number of QTLs and (d) Mean number of visiting QTLs. The dotted line at 1 in (d) indicates the true number of linked QTLs. For all boxplots, the whiskers extend to the data point that is closest to and less than 1.5 times the interquartile range.

Figure 5

Results from analysis of HDL levels and APOC3. (a) Histogram of number of visiting QTLs when using Loki (top) and maLoki (bottom). Genotype effects of diallelic QTLs from Loki (b) and maLoki (c). These models are adjusted so that α₃ ≥ 0. The line of points in (c) correspond to the additive QTLs: α₃ = 2α₂. Surface plots of size versus location of visiting QTLs when using Loki (d) and maLoki (e).