Estimating assortative mating, its history, and its future effect on genetic variance for health, behavioral, and ancestry phenotypes using crosssectionaldata

项目总结/摘要 配对交配（AM）是指父母在遗传上比随机配对更相似。AM可以 导致人口中疾病的流行率和严重程度更高，并可能误导研究人员， 表型之间的关系。该项目的一个关键目标是准确估计父母如何 随着时间的推移进行排序，以描述过去和未来的公共卫生趋势，并显示如何使用这些估计， 在试图缩小对疾病生物根源的搜索时，将AM考虑在内。 这个项目背后的关键思想是AM导致下一代具有更大的遗传变异。 通过多基因评分（PGS）衡量的风险比其他情况下要高。方差越大，范围越大 PGS和极端值的发生率更高。关于AM的一个关键问题是，对于疾病PGS， 与较大方差相关的极值发生率越高，将导致总体发生率越高， 更严重疾病的发病率更高。但方差的增加也提供了一个有价值的 学习AM的工具。例如，如果PGS的方差在总体中发生变化，我们可以使用此信息 来估计AM是如何变化的。父母对的遗传数据目前很少， 数万人，而无关个体的横截面数据则以百万计。 这个项目是开发和部署的方法使用横截面数据在无关的个人研究 不同疾病和性状个体间和对间的交配动态和平均水平 不同的疾病和特征。我们将使用配偶对的数据来验证结果。该项目有三个 具体目标： 1.建立一个通用的理论框架，以了解AM如何影响方差和相关性 个人之间的PGS。重要的是，AM可以影响许多代的遗传风险分布。 2.使用理论框架开发方法，使用以下数据估计AM及其变化： 无关的个人。这将包括研究个体表型的AM和成对表型之间的AM。 表型3.评估AM和交叉表型AM的历史并研究说明性含义 用于健康和健康研究，包括（a）预测AM变化对未来疾病的影响 发病率和未来疾病合并症，（B）将疾病发病率的过去趋势的一部分进行分配， 可以通过AM的变化来解释的合并症，（c）整理出配偶的典型水平的哪一部分 疾病风险的相似性是由于AM，而哪些部分是由于其他因素，例如共同的环境， （d）找出预测疾病的基因之间的相似性部分 而预测另一种疾病或性状的基因是由于AM，因此并不表明AM与其他疾病或性状有共同的生物学特征。 途径，（e）纠正AM的其他关键遗传措施。

PROJECT SUMMARY/ABSTRACT Assortative mating (AM) is when parents are more similar genetically than if pairing were random. AM can lead to a greater prevalence and severity of disease in the population and can mislead researchers studying the relationships between phenotypes. A key objective of this project is to precisely estimate how parents have sorted over time to describe past and future trends in public health—and to show how to use those estimates to take AM into account when trying to narrow down the search for the biological roots of diseases. The key idea behind this project is that AM leads to the next generation having a larger variance of genetic risk—measured by polygenic scores (PGSs)—than it otherwise would. Greater variance leads to a larger range for the PGS and a greater incidence of extreme values. A key concern about AM is that for a disease PGS, a greater incidence of extreme values associated with a larger variance will lead to a greater overall incidence and a greater incidence of more severe forms of the disease. But that increase in variance also provides a valuable tool to study AM. For example, if the variance of a PGS is changing in the population, we can use this information to estimate how AM must also be changing. Genetic data on parent pairs is currently scarce—measuring in the tens of thousands—while cross-sectional data on unrelated individuals is measured in millions. This project is to develop and deploy approaches using cross-sectional data in unrelated individuals to study the dynamics and average level of assortative mating for different individual diseases and traits and across pairs of different diseases and traits. We will validate the results using data on spouse pairs. This project has three specific aims: 1. Develop a general theoretical framework to understand how AM affects the variance and correlation of PGSs across individuals. Importantly, AM can affect the distribution of genetic risk for many generations. 2. Use the theoretical framework to develop methods to estimate AM and its changes using data on unrelated individuals. This will include studying AM for individual phenotypes and AM between pairs of phenotypes. 3. Estimate the history of AM and cross-phenotype AM and study illustrative implications for health and health research, including (a) forecasting the effect of changes in AM on future disease incidence and future disease comorbidities, (b) parceling out the part of past trends in disease incidence and comorbidities that can be accounted for by changes in AM, (c) sorting out what part of the typical level of spousal similarity in disease risk is due to AM and what part is due to other forces, such as a common environment that is generative of that disease, (d) sorting out what portion of the similarity between genes that predict a disease and genes that predict another disease or trait is due to AM and therefore does not suggest a common biological pathway, (e) correcting other crucial genetic measures for AM.