Understanding Genomes in Social Contexts

关于社会力量在决定人类生活的各个方面（如健康）中相对于人性的重要性的争论，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是由遗传倾向调节的。然而，人们对社会环境如何影响基因组知之甚少。一个在社会科学中得到广泛证实的发现是，朋友之间是相似的。福勒和克里斯塔基斯的研究表明，这种联系不纯粹是表现型的，而是在基因水平上不相关的朋友之间存在联系。例如，平均而言，朋友的基因型与第四代表亲的基因型相似。事实上，他们利用两种不相关基因型之间的联系来预测友谊。他们认为，这些发现或许可以解释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)活跃的基因与环境相关，3)人们选择与自己有相似表型的朋友，或者4)环境选择具有某些遗传特征的人，例如专业运动队。有证据支持这四种情况的发生。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等人（2017）最近的动物研究提供了实验证据，表明社会网络中的表型相似性也可能直接由周围人的基因型引起。她发现，“社会遗传效应”可以解释高达29%的基因相同或不相同的老鼠的表型变异，这些老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，发生这种情况的确切机制尚不清楚。我提出的项目将研究基因组与社会背景的关系。具体来说，它将：1。通过估算朋友对之间的基因组关联，寻求复制Fowler和Christakis的发现，并根据文献综述，探索这种关联如何依赖于假定的社会因素，如SES，和/或具体的遗传因素。为了测试Baud等人（2017）的实验室发现是否适用于人类。我将通过测试一个假设来做到这一点，即一个人的朋友对各种结果（如抑郁症）的多基因风险评分，应该预测他们自己对这些结果的风险。我希望那时能够使用更复杂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。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。使用雅芳父母和孩子纵向研究中收集的Twitter新数据，我将探索“离线”社交网络的结果是否可以推广到“在线”社交网络。

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.