The genetic architecture of economic and political preferences
The genetic architecture of economic and political preferences
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DOI：
10.1073/pnas.1120666109
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发表时间：
2012-05-22
影响因子：
11.1
通讯作者：
Visscher, Peter M.
中科院分区：
文献类型：
--
作者：
Benjamin, Daniel J.;Cesarini, David;Visscher, Peter M.
偏好是所有经济和政治行为模型的基本组成部分。我们研究了一个新的综合基因型样本，其中包含了经济、政治偏好和教育程度的数据。我们使用密集单核苷酸多态性（SNP）数据来估计这些性状中由共同SNP解释的变异比例，并进行全基因组关联研究（GWAS）和预测分析。结果的模式与其他复杂特征的发现是一致的。首先，可以用密集SNP阵列解释的表型变异的估计比例，原则上大约是使用双胞胎和家庭样本估计的狭窄遗传力的一半。因此，基于分子遗传学的遗传力估计部分证实了来自行为遗传学研究的显著遗传力的证据。其次，我们的分析表明，这些性状具有多基因结构，遗传变异由许多影响较小的基因解释。我们的研究结果表明，大多数已发表的与经济和政治特征有关的遗传关联研究都明显不够有力，这意味着很高的错误发现率。这些结果传达了一个警示信息，即分子遗传数据是否、如何以及多快能对社会科学研究做出贡献，并有可能改变社会科学研究。我们提出了一些建设性的回应，以解释单个snp的小解释力所带来的推理挑战。
Preferences are fundamental building blocks in all models of economic and political behavior. We study a new sample of comprehensively genotyped subjects with data on economic and political preferences and educational attainment. We use dense single nucleotide polymorphism (SNP) data to estimate the proportion of variation in these traits explained by common SNPs and to conduct genome-wide association study (GWAS) and prediction analyses. The pattern of results is consistent with findings for other complex traits. First, the estimated fraction of phenotypic variation that could, in principle, be explained by dense SNP arrays is around one-half of the narrow heritability estimated using twin and family samples. The molecular-genetic-based heritability estimates, therefore, partially corroborate evidence of significant heritability from behavior genetic studies. Second, our analyses suggest that these traits have a polygenic architecture, with the heritable variation explained by many genes with small effects. Our results suggest that most published genetic association studies with economic and political traits are dramatically underpowered, which implies a high false discovery rate. These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPs.