Lettre Guillaume
Montreal Heart Institute, Montreal, Quebec, Canada Faculty of Medicine, Department of Medicine, Université de Montréal, Montreal, Quebec, Canada.
J Med Genet. 2014 Nov;51(11):705-14. doi: 10.1136/jmedgenet-2014-102437. Epub 2014 Sep 3.
In humans, most of the genetic variation is rare and often population-specific. Whereas the role of rare genetic variants in familial monogenic diseases is firmly established, we are only now starting to explore the contribution of this class of genetic variation to human common diseases and other complex traits. Such large-scale experiments are possible due to the development of next-generation DNA sequencing. Early findings suggested that rare and low-frequency coding variation might have a large effect on human phenotypes (eg, PCSK9 missense variants on low-density lipoprotein-cholesterol and coronary heart diseases). This observation sparked excitement in prognostic and diagnostic medicine, as well as in genetics-driven strategies to develop new drugs. In this review, I describe results and present initial conclusions regarding some of the recent rare and low-frequency variant discoveries. We can already assume that most phenotype-associated rare and low-frequency variants have modest-to-weak phenotypical effect. Thus, we will need large cohorts to identify them, as for common variants in genome-wide association studies. As we expand the list of associated rare and low-frequency variants, we can also better recognise the current limitations: we need to develop better statistical methods to optimally test association with rare variants, including non-coding variation, and to account for potential confounders such as population stratification.
在人类中,大多数遗传变异是罕见的,且往往具有人群特异性。虽然罕见遗传变异在家族性单基因疾病中的作用已得到确证,但我们直到现在才开始探索这类遗传变异对人类常见疾病和其他复杂性状的贡献。由于下一代DNA测序技术的发展,此类大规模实验成为可能。早期研究结果表明,罕见和低频编码变异可能对人类表型有很大影响(例如,前蛋白转化酶枯草溶菌素9错义变异对低密度脂蛋白胆固醇和冠心病的影响)。这一发现激发了人们对预后和诊断医学以及基于遗传学开发新药策略的兴趣。在这篇综述中,我描述了一些近期罕见和低频变异发现的结果并给出初步结论。我们已经可以假定,大多数与表型相关的罕见和低频变异具有中等至较弱的表型效应。因此,正如在全基因组关联研究中寻找常见变异一样,我们需要大规模队列来识别它们。随着我们不断扩充相关罕见和低频变异的列表,我们也能更好地认识到当前的局限性:我们需要开发更好的统计方法,以优化对包括非编码变异在内的罕见变异的关联检验,并考虑到诸如人群分层等潜在混杂因素。