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Traditional quantitative trait locus (QTL) analysis focuses on identifying loci associated with mean heterogeneity. Recent research has discovered loci associated with phenotype variance heterogeneity (vQTL), which is important in studying genetic association with complex traits, especially for identifying gene-gene and gene-environment interactions. While several tests have been proposed to detect vQTL for unrelated individuals, there are no tests for related individuals, commonly seen in family-based genetic studies. Here we introduce a likelihood ratio test (LRT) for identifying mean and variance heterogeneity simultaneously or for either effect alone, adjusting for covariates and family relatedness using a linear mixed effect model approach. The LRT test statistic for normally distributed quantitative traits approximately follows χ(2)-distributions. To correct for inflated Type I error for non-normally distributed quantitative traits, we propose a parametric bootstrap-based LRT that removes the best linear unbiased prediction (BLUP) of family random effect. Simulation studies show that our family-based test controls Type I error and has good power, while Type I error inflation is observed when family relatedness is ignored. We demonstrate the utility and efficiency gains of the proposed method using data from the Framingham Heart Study to detect loci associated with body mass index (BMI) variability., (© 2014 John Wiley & Sons Ltd/University College London.)

Recent research has revealed loci that display variance heterogeneity through various means such as biological disruption, linkage disequilibrium (LD), gene-by-gene (G × G), or gene-by-environment interaction. We propose a versatile likelihood ratio test that allows joint testing for mean and variance heterogeneity (LRT(MV)) or either effect alone (LRT(M) or LRT(V)) in the presence of covariates. Using extensive simulations for our method and others, we found that all parametric tests were sensitive to nonnormality regardless of any trait transformations. Coupling our test with the parametric bootstrap solves this issue. Using simulations and empirical data from a known mean-only functional variant, we demonstrate how LD can produce variance-heterogeneity loci (vQTL) in a predictable fashion based on differential allele frequencies, high D', and relatively low r² values. We propose that a joint test for mean and variance heterogeneity is more powerful than a variance-only test for detecting vQTL. This takes advantage of loci that also have mean effects without sacrificing much power to detect variance only effects. We discuss using vQTL as an approach to detect G × G interactions and also how vQTL are related to relationship loci, and how both can create prior hypothesis for each other and reveal the relationships between traits and possibly between components of a composite trait.

Since genome size and the number of duplicate genes observed in genomes increase from haploid to diploid organisms, diploidy might provide more evolutionary probabilities through gene duplication. It is still unclear how diploidy promotes genomic evolution in detail. In this study, we explored the evolution of segmental gene duplication in haploid and diploid populations by analytical and simulation approaches. Results show that (1) under the double null recessive (DNR) selective model, given the same recombination rate, the evolutionary trajectories and consequences are very similar between the same-size gene-pool haploid vs. diploid populations; (2) recombination enlarges the probability of preservation of duplicate genes in either haploid or diploid large populations, and haplo-insufficiency reinforces this effect; and (3) the loss of duplicate genes at the ancestor locus is limited under recombination while under complete linkage the loss of duplicate genes is always random at the ancestor and newly duplicated loci. Therefore, we propose a model to explain the advantage of diploidy: diploidy might facilitate the increase of recombination rate, especially under sexual reproduction; more duplicate genes are preserved under more recombination by originalization (by which duplicate genes are preserved intact at a special quasi-mutation-selection balance under the DNR or haplo-insufficient selective model), so genome sizes and the number of duplicate genes in diploid organisms become larger. Additionally, it is suggested that small genomic rearrangements due to the random loss of duplicate genes might be limited under recombination.

It is known that sequencing error can bias estimation of evolutionary or population genetic parameters. This problem is more prominent in deep resequencing studies because of their large sample size n, and a higher probability of error at each nucleotide site. We propose a new method based on the composite likelihood of the observed SNP configurations to infer population mutation rate theta = 4N(e)micro, population exponential growth rate R, and error rate epsilon, simultaneously. Using simulation, we show the combined effects of the parameters, theta, n, epsilon, and R on the accuracy of parameter estimation. We compared our maximum composite likelihood estimator (MCLE) of theta with other theta estimators that take into account the error. The results show the MCLE performs well when the sample size is large or the error rate is high. Using parametric bootstrap, composite likelihood can also be used as a statistic for testing the model goodness-of-fit of the observed DNA sequences. The MCLE method is applied to sequence data on the ANGPTL4 gene in 1832 African American and 1045 European American individuals.

Summary: We present the software implementation of the tree scanning method to detect associations between genetic haplotypes and quantitative traits, utilizing the evolutionary history of the haplotypes, in samples of unrelated individuals., Availability: The program is available free of charge, under the GNU General Public License. A package including C source code, a Makefile, and Windows (DOS) and Macintosh binaries, can be downloaded from http://darwin.uvigo.es