美國費城兒童醫(yī)院一個由Hakon Hakonarson主持的研究小組將一種計算機(jī)程序——全基因組關(guān)聯(lián)研究，應(yīng)用到基因標(biāo)記物上,，與傳統(tǒng)的評估患1-型糖尿病概率的方法相比,，該方法有更高的準(zhǔn)確性,。該技術(shù)或許能夠應(yīng)用到某些復(fù)雜的多基因疾病上,，也將促進(jìn)針對患者的基因特征開發(fā)出個性化的治療藥物。該研究報告發(fā)表在10月9日的PLoS Genetic雜志網(wǎng)絡(luò)版上,。
全基因組關(guān)聯(lián)研究(Genome-wide association studies,GWAS)是一種自動基因分型工具,，旨在從人類基因組中尋找致病的基因變異體，使醫(yī)生能夠準(zhǔn)確預(yù)測出個體患某種疾病的可能性,，從而達(dá)到早預(yù)防早治療的目的,。
據(jù)論文作者說，目前,，許多疾病的致病主要基因仍然未被發(fā)現(xiàn),，而一些研究也只是有選擇性的選取小部分基因變異體進(jìn)行研究，所以研究結(jié)果有很大的局限性,。在近期的一些研究中,，研究人員通常利用曲線下面積 (the area under the curve,AUC)來評估患病率，AUC值一般在0.55~0.6之間,，因此臨床應(yīng)用價值不大,。
Hakonarson研究組拓寬基因變異體的研究范圍，廣泛的收集疾病的標(biāo)記物(其中也包括許多未被證實的標(biāo)記物),，從而獲得某個疾病相關(guān)基因之間的統(tǒng)計閾值,，雖然這種方法不能排除假陽性的存在，但總體來說能夠提高預(yù)測結(jié)果的準(zhǔn)確性,。
研究人員將該計算機(jī)程序應(yīng)用到1-型糖尿病GWAS資料組,，并建立了一個評估模型。與對照組相比,，該模型評估的精確度顯著提高,，AUC達(dá)到0.8以上。此外,，研究人員還強(qiáng)調(diào),，選擇合適的疾病研究對象也非常重要，由于1-型糖尿病具有高度遺傳性,，其主要組織相容性復(fù)合體區(qū)域有許多疾病發(fā)病易感性基因存在,。而且，這種疾病風(fēng)險評估模型不適用于大規(guī)?；驋呙?，而只適用于評估患一類疾病的患者。（生物谷Bioon.com）
相關(guān)閱讀：
Nature Genetics：識別出22個影響血細(xì)胞發(fā)育的相關(guān)基因區(qū)域
Nature Genetics：發(fā)現(xiàn)牛皮癬易感基因
生物谷推薦原始出處：
PLoS Genet 5(10): e1000678. doi:10.1371/journal.pgen.1000678
From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes
Zhi Wei1#, Kai Wang2#, Hui-Qi Qu3, Haitao Zhang2, Jonathan Bradfield2, Cecilia Kim2, Edward Frackleton2, Cuiping Hou2, Joseph T. Glessner2, Rosetta Chiavacci2, Charles Stanley4, Dimitri Monos5, Struan F. A. Grant2,6, Constantin Polychronakos3, Hakon Hakonarson2,6*
1 Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, United States of America, 2 Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, 3 Departments of Pediatrics and Human Genetics, McGill University, Montreal, Québec, Canada, 4 Division of Endocrinology, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, 5 Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, 6 Division of Genetics, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of ~0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays.
