Modelling Mixed Types of Outcomes in Additive Genetic Models.

IF 1.2 4区数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

International Journal of Biostatistics Pub Date : 2017-07-07 DOI:10.1515/ijb-2017-0001

Wagner Hugo Bonat

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引用次数: 4

Abstract

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.

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加性遗传模型中混合类型结果的建模。

我们提出了一个通用的统计建模框架，用于处理定量遗传分析背景下的多变量混合类型的结果。该模型基于多元协方差广义线性模型，其中矩阵线性预测器由单位矩阵和谱系定义的相关性矩阵组成，分别代表环境和遗传成分。我们还提出了一种新的非高斯数据遗传度指标。以家雀(Passer domesticus)种群为例，采用连续、二项和计数结果来激励新模型。多变量边际模型的模拟不是简单的，因此我们采用NORTA (Normal to anything)算法来模拟遗传数据分析背景下的多变量协方差广义线性模型。通过仿真研究，评估了结果与新遗传指数参数之间相关性的估计函数估计量的渐近性。数据集和R代码在补充资料中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY

CiteScore

2.10

自引率

8.30%

发文量

审稿时长

>12 weeks

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.