近日，來自美國華盛頓大學(xué)和HudsonAlpha研究所的研究人員通過研究開發(fā)了一種新型的名為CADD（The Combined Annotation–Dependent Depletion）的技術(shù)，其可以幫助研究人員在人類基因組中尋找并研究引發(fā)疾病的突變，相關(guān)研究成果刊登于國際雜志Nature Genetics上。
       研究者Gregory M. Cooper表示，CADD技術(shù)將從本質(zhì)上改進(jìn)我們尋找引發(fā)疾病的突變的能力，該項(xiàng)技術(shù)不管是在臨床上還是在研究中對(duì)于深入理解并研究人類基因組序列非常重要。
       CADD技術(shù)可以在人類和黑猩猩的1500萬個(gè)遺傳突變體之中輕松對(duì)比并且找出其差異所在，人類機(jī)體的突變可以使得其在自然選擇中生存下去，當(dāng)刺激產(chǎn)生的突變沒有暴露于自然選擇之中，人類機(jī)體將會(huì)去除有害的致病突變；CADD可以通過對(duì)比刺激產(chǎn)生的突變鑒別出產(chǎn)生有害突變的特性。
       研究者Cooper說道，CADD技術(shù)可以應(yīng)用于鑒別基因組中的突變，其對(duì)于推進(jìn)臨床上和科學(xué)研究中的全基因組測(cè)序非常關(guān)鍵。（來源：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.