Determining the causal links and clinical significance of rare genetic variants

GWAS已经确定了与各种性状和疾病相关的许多常见遗传变异。然而，在性状水平上，常见变异的大多数个体影响非常小，需要非常大的样本量（即千分之十）才能进行检测，发现的关联对个体对疾病的倾向性或治疗后结果的预测准确性较低。常见变异对中间表型的遗传影响，如基因表达或蛋白质浓度，往往比对性状和疾病的遗传影响大得多，因此这种关联往往在数百或数千个人的较小样本中检测到。在两样本孟德尔随机化研究中，结合来自大型疾病结果研究和较小的转录组学或蛋白质组学研究的信息，可用于提供从DNA变异通过基因表达到疾病结果的因果路径的证据。即便如此，常见变异对表型性状的相对较小的遗传影响使得它们难以在实验室可行的小型功能研究中进行研究。一些遗传变异比GWAS检测到的普通变异具有更大的遗传效应，但由于这些变异对个体适应性的影响更大，因此通过自然选择，这些变异在个体谱系中通常保持在较低的频率。因此，这些变异在无亲缘关系个体的世界性研究中很难被发现，但在纯种群体的研究中可能被发现，特别是在小的创始群体规模和漂移可能会增加其他罕见变异的频率的情况下。尽管如此，这些变异不太可能在LD中与标准阵列上的snp相关联，因此不太可能被捕获。影响较大的罕见变异最有可能位于表达基因内部或附近。因此，利用来自外显子组和邻近区域的DNA序列是捕获这些变异的好策略。在这个项目中，我们建议将蛋白质组学数据与外显子组变异联系起来，以检测局部（即顺式）作用于蛋白质浓度的遗传效应。

GWAS have identified many common genetic variants associated with various traits and diseases. However most of the individual effects of common variants at the trait level are very small, requiring very large sample sizes (i.e. 10's of thousands) for detection, with associations found providing low accuracy predictions of an individual's liability to disease or outcomes after treatment. Genetic effects of common variants on intermediate phenotypes, such as gene expression or protein concentrations, are often much larger than those on traits and diseases and such associations are thus often detectable in smaller samples of hundreds or thousands of individuals. Combining information from large studies of disease outcomes and smaller transcriptomic or proteomic studies in a two-sample Mendelian randomisation study can be used to provide evidence for a causal path from DNA variation through gene expression to a disease outcome. Even so, the relatively small genetic effects of common variants on phenotypic traits make them difficult to study in the small functional studies feasible in the laboratory. Some genetic variants have larger genetic effects than those common variants detected by GWAS, but such variants are often kept at low frequency within individual pedigrees by natural selection as a consequence of their larger effects on individual fitness. Such variants are hence difficult to detect in cosmopolitan studies of unrelated individuals, but may become detectable in studies of pedigreed populations, especially where small founder population size and drift may enhance the frequency of otherwise rare variants. Nonetheless such variants are unlikely to be in LD with and hence captured by associations with SNPs on standard arrays. Rare variants of large effect are most likely to be located within or close to expressed genes. Hence using DNA sequence from the exome and adjacent regions is a good strategy to capture such variants. In this project we propose to link proteomic data with the exome variants to detect locally (i.e. cis) acting genetic effects on protein concentrations.