We present a general statistical modelling framework for handling multivariate mixed types of outcomes in the context of quantitative genetic analysis. The models are based on the multivariate covariance generalized linear models, where the matrix linear predictor is composed of an identity matrix combined with a relatedness matrix defined by a pedigree, representing the environmental and genetic components, respectively. We also propose a new index of heritability for non-Gaussian data. A case study on house sparrow (Passer domesticus) population with continuous, binomial and count outcomes is employed to motivate the new model. Simulation of multivariate marginal models is not trivial, thus we adapt the NORTA (Normal to anything) algorithm for simulation of multivariate covariance generalized linear models in the context of genetic data analysis. A simulation study is presented to assess the asymptotic properties of the estimating function estimators for the correlation between outcomes and the new heritability index parameters. The data set and R code are available in the supplementary material.

中文翻译：

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在加性遗传模型中对结果的混合类型进行建模。

我们提出了一个通用的统计建模框架，用于在定量遗传分析的背景下处理多元混合类型的结果。这些模型基于多元协方差广义线性模型，其中矩阵线性预测变量由一个恒等矩阵与一个系谱定义的相关性矩阵组合而成，分别表示环境和遗传成分。我们还为非高斯数据提出了新的遗传力指数。以具有连续，二项式和计数结果的麻雀（Passer domesticus）种群为例，以此来激发新模型。多元边际模型的模拟并非无关紧要，因此在遗传数据分析的背景下，我们采用NORTA（Normal to any）算法对多元协方差广义线性模型进行模拟。提出了一个仿真研究，以评估估计函数估计量的渐近性质，以评估结果与新的遗传指数参数之间的相关性。数据集和R代码可在补充材料中找到。