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稀疏线性模型解释表型变异并预测复杂疾病的风险
Sparse Linear Models Explain Phenotypic Variation and Predict Risk of Complex Disease |
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| 课程网址: |
http://videolectures.net/nipsworkshops2011_abraham_disease/
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| 主讲教师: |
Gad Abraham
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| 开课单位: |
澳大利亚国家ICT
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| 开课时间: |
2012-01-23
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| 课程语种: |
英语
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| 中文简介: |
医学遗传学的一个中心目标是建立模型，精确预测给定基因型的复杂疾病。为了最大限度地提高预测价值并识别因果性单核苷酸多态性（SNPs），所有SNPs应同时建模。套索惩罚模型已被证明是一类有用的模型，用于检测因果SNP和建模疾病风险。在这里，我们使用套索惩罚模型对真实病例/对照数据进行综合分析。我们的模型准确区分了乳糜泻和1型糖尿病的病例与对照组，并在独立数据集中进行了强有力的复制，1型糖尿病的验证AUC为0.84，乳糜泻的验证AUC为0.82–0.9，后者在欧洲不同种族的四个独立数据集中进行了验证。该模型还解释了独立验证中的大量表型差异：1型糖尿病为22%，腹腔疾病为21–38%。这项研究表明，监督学习方法可以解决缺失的表型变异，并从基因型可靠地预测乳糜泻和1型糖尿病的发病率。
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| 课程简介: |
A central goal of medical genetics is to create models that accurately predict complex disease given genotype. To maximize predictive value and identify causal single-nucleotide polymorphisms (SNPs), all SNPs should be modeled simultaneously. Lasso penalized models have proven to be a useful class of such models, for detecting causal SNPs and for modeling disease risk. Here, we present a comprehensive analysis of real case/control data using lasso-penalized models. Our models accurately discriminated cases from controls in celiac disease and type 1 diabetes, and strongly replicated across independent datasets with validation AUC of 0.84 for type 1 diabetes and 0.82–0.9 for celiac disease, the latter across four independent datasets of different European ethnicities. The models also explained substantial phenotypic variance in independent validation: 22% for type 1 diabetes and 21–38% for celiac disease. This study shows that supervised learning approaches can address missing phenotypic variance and reliably predict incidence of celiac disease and type 1 diabetes from genotype.
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| 关 键 词: |
医学遗传学; 给定基因型的复杂疾病; 建模疾病风险
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| 课程来源: |
视频讲座网
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| 数据采集: |
2021-12-25：zkj
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| 最后编审: |
2021-12-25：zkj
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| 阅读次数: |
43
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