近日，来自美国华盛顿大学和HudsonAlpha研究所的研究人员通过研究开发了一种新型的名为CADD（The Combined Annotation–Dependent Depletion）的技术，其可以帮助研究人员在人类基因组中寻找并研究引发疾病的突变，相关研究成果刊登于国际杂志Nature Genetics上。
       研究者Gregory M. Cooper表示，CADD技术将从本质上改进我们寻找引发疾病的突变的能力，该项技术不管是在临床上还是在研究中对于深入理解并研究人类基因组序列非常重要。
       CADD技术可以在人类和黑猩猩的1500万个遗传突变体之中轻松对比并且找出其差异所在，人类机体的突变可以使得其在自然选择中生存下去，当刺激产生的突变没有暴露于自然选择之中，人类机体将会去除有害的致病突变；CADD可以通过对比刺激产生的突变鉴别出产生有害突变的特性。
       研究者Cooper说道，CADD技术可以应用于鉴别基因组中的突变，其对于推进临床上和科学研究中的全基因组测序非常关键。（来源：生物谷Bioon.com）

doi:10.1038/ng.2892
A general framework for estimating the relative pathogenicity of human genetic variants
Martin Kircher, Daniela M Witten, Preti Jain, Brian J O'Roak, Gregory M Cooper & Jay Shendure
Current methods for annotating and interpreting human genetic variation tend to exploit a single information type (for example, conservation) and/or are restricted in scope (for example, to missense changes). Here we describe Combined Annotation–Dependent Depletion (CADD), a method for objectively integrating many diverse annotations into a single measure (C score) for each variant. We implement CADD as a support vector machine trained to differentiate 14.7 million high-frequency human-derived alleles from 14.7 million simulated variants. We precompute C scores for all 8.6 billion possible human single-nucleotide variants and enable scoring of short insertions-deletions. C scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects and complex trait associations, and they highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current single-annotation method.