Understanding the "flattening" of gene contributions to human complex trait habitability

全基因组关联研究（GWAS）表明，大多数人类复杂的遗传变异 性状在基因组中广泛分布。这是令人惊讶的，因为我们期望每一个特征都有 特定的机制和途径，其生物学基础，然而，对于大多数性状，这种机制， GWAS的结果并不明显。最近，有人提出， 这种现象可以用自然选择对遗传力的抑制来解释。如果变量在 直接构成性状生物学基础的基因对引起增加的性状具有大的效应大小， 选择，它们只会出现在低频，因此只会对 遗传性这种直觉最近被称为“扁平化”，因为通过保持大效应等位基因， 在低频率下，自然选择改变了遗传力的分布。在这项工作中，我将采取 从基本直觉到完全充实的理论，从理论到第一个基因组的“扁平化”概念- 每个基因每个性状的扁平化程度。首先，我将建立一系列模型来描述 决定扁平化的进化和基因组因素，包括多效性和人类 人口统计学史然后，我将通过直接量化扁平化对 使用英国生物库数据的38种血液和尿液标记物。这些生物标志物提供了一个独特的和令人兴奋的 这是一个测试我的预测的机会，因为我们知道合成背后的调控网络， 这些生物标志物。最后，我将估计每个基因对每个性状的遗传力赤字， 将基因对遗传力的贡献与中性预期进行比较。遗传性赤字将是 这是第一个全基因组测量每个基因每个性状扁平化的方法，我将用它来描述 英国生物样本库特征的扁平化程度。

Genome-wide association studies (GWAS) indicate that heritable variation for most human complex traits is widely distributed across the genome. This is surprising since we expect each trait to have specific mechanisms and pathways underlying its biology and yet, for most traits, such mechanisms and pathways are not evident from GWAS results. Recently, it has been suggested that this phenomenon might be explained by the natural selection’s suppression of heritability. If variants at genes directly underlying trait biology have large effect sizes on the trait which cause increased selection, they will only appear at low frequencies and therefore only make a limited contribution to heritability. This intuition has recently been dubbed “flattening” because, by keeping large effect alleles at low frequencies, natural selection flattens the distribution of heritability. In this work, I will take the concept of “flattening” from basic intuition to fully fleshed out theory and from theory to the first genome- wide measure of flattening per gene per trait. First, I will build a series of models to describe the evolutionary and genomic factors that determine flattening, including pleiotropy and human demographic history. Then, I will validate my models by directly quantifying the effects of flattening on 38 blood and urine markers using UK Biobank data. These biomarkers provide a unique and exciting opportunity to test my predictions because we know the regulatory networks underlying the synthesis of these biomarkers. Finally, I will estimate the heritability deficit of each gene for each trait by comparing the gene’s contribution to heritability to a neutral expectation. The heritability deficit will be the first ever genome-wide measure of flattening per gene per trait and I will use it to characterize the extent of flattening on available UK Biobank traits.