Understanding Genomes in Social Contexts

关于社会力量相对于人性在决定人类生活的各个方面（如健康）的重要性的争论，是现代学术界最古老的争论之一。最近的证据表明，传统的二元二分法是错误的-结果取决于两者。具体来说，有证据表明，我们对社会环境风险的反应受到遗传倾向的调节。然而，关于社会环境如何影响基因组的研究却知之甚少。在社会科学中，一个被广泛复制的发现是，朋友之间是相似的。Fowler和Christakis的研究表明，这种关联并不纯粹是表型的，而是在基因水平上存在着非相关朋友之间的关联。例如，平均而言，朋友的基因型与第四类人的基因型相似。事实上，他们利用两个无关基因型之间的联系来预测友谊。他们认为，这些发现可能解释了非传染性疾病在社交网络中的聚集和传播。然而，朋友的基因型和表型变得相似的方式仍然不清楚。Fowler和Christakis对他们的发现提出了几种解释：1）这一发现可能是由于人口分层，2）活跃的基因-环境相关性，3）人们选择与他们有相似表型的朋友，或4）环境选择具有某些遗传特征的人，例如职业运动队。有证据支持所有四种情况的发生。例如，博德曼发现，学校的社会经济地位在很大程度上解释了学校朋友之间遗传相似性的差异。Baud et al（2017）最近的动物研究提供了实验证据，表明社交网络中的表型相似性也可能是由周围人的基因型直接引起的。她发现，“社会遗传效应”可以解释基因相同或不相同的笼中鼠对中高达29%的表型变异，这些笼中鼠被迫彼此生活在一起。因此，越来越多的证据表明，社会环境影响基因表达。然而，这种情况发生的确切机制尚不清楚。我提议的项目将研究基因组如何与社会背景相关。具体而言，它将：1。通过估计朋友对之间的基因组关联，试图复制Fowler和Christakis的发现，并在文献综述的基础上，探索这种关联如何依赖于假定的社会因素，如SES，和/或特别是遗传因素。测试Baud et al（2017）的实验室发现是否适用于人类。我将通过检验一个假设来做到这一点，即一个人的朋友对各种结果的多基因风险评分，比如抑郁症，应该预测他们自己对这些结果的风险。我希望能够使用更先进的流行病学方法来评估任何发现的关联的因果效应。例如，尝试使用统计模型，甚至孟德尔随机化来测试因果关系。3.最后，社交媒体已经成为社交网络形成和发展的一个越来越重要的部分。使用新的Twitter数据收集雅芳纵向研究的父母和孩子，我将探讨是否从“离线”社交网络推广到“在线”socialnetworks的结果。

The debate over the importance of social forces relative to human nature in determining aspects ofhuman life, like health, is one of the oldest in modern academia. Recent evidence indicates thatthe traditional binary dichotomy is wrong - outcomes are dependent on both. Specifically, there isevidence that our response to socio-environmental risks is moderated by geneticpredispositions. Less, however, is known about how social contexts affect genomes.A well replicated finding in social sciences is that friends are similar to each other. Research byFowler and Christakis shows that this association is not purely phenotypic, but that there is anassociation between non-related friends at the level of genes. For example, on average, friends'genotypes are as similar as fourth cousins'. Indeed, they used the association between two nonrelatedgenotypes to predict friendships. They stipulate that these findings might explain theclustering and spread of non-communicable diseases in social networks. However, the way inwhich friend's genotypes and phenotypes become similar remains unclear. Fowler and Christakispropose several explanations for their finding: 1) the finding could be due to populationstratification, 2) active gene-environment correlation, 3) people choosing friends who have similarphenotypes to them, or 4) environments which select people with certain genetic traits, e.g. aprofessional sports team. There is evidence to support the occurrence of all four. For example,Boardman found that the socio economic status of a school explained a large part of the variation ingenetic similarity between school friends. Recent animal research by Baud et al (2017) providesexperimental evidence that phenotypic similarities in social networks might also be directly causedby the genotypes of those around them. She found that 'social genetic effects' could explain upto 29% of the phenotypic variation in pairs of genetically identical or non-identical cage mice, forcedto live with each other. There is thus a growing body of evidence which suggests that social contextseffect gene expression. However, the exact mechanism by which this occurs is unclear.My proposed project will examine how genomes relate to social contexts. Specifically, it will:1. Seek to replicate Fowler and Christakis' findings, by estimating the genome wide associationbetween friend pairs, and, subject to a literature review, explore how the association isdependent on putative social factors, like SES, and/or specifically genetic factors.2. To test if Baud et al (2017)'s laboratory findings generalise to humans. I will do this by testing thehypothesis that an individual's friend's polygenic risk score for various outcomes, likedepression, should predict their own risk for these outcomes. I hope to then be able to use moresophisticated epidemiological methods to assess the causal effect of any found association. Forexample, attempting to use statistical modelling, or even Mendelian randomisation, to test acausal relationship.3. Finally, social media has become an increasingly important part of how social networks form andevolve. Using new Twitter data collected in the Avon Longitudinal Study of Parents and Children,I will explore whether the results from 'off-line' social networks generalise to 'on-line' socialnetworks.