Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease.
Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease.
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进化遗传算法-机器学习模型研究与早发性冠状动脉疾病相关的候选基因。
DOI：
10.1161/jaha.122.029103
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发表时间：
2023-09-05
影响因子：
5.4
通讯作者：
Lichtarge, Olivier
中科院分区：
文献类型：
--
作者：
Shapiro, Dillon;Lee, Kwanghyuk;Asmussen, Jennifer;Bourquard, Thomas;Lichtarge, Olivier
关键词：
冠状动脉疾病是世界各地的主要死因，有遗传和环境风险因素。尽管全基因组关联研究已经将超过100个独特的基因座与其遗传基础联系起来，但这些只能解释疾病遗传性的一小部分。为了找到冠状动脉疾病的其他基因驱动因素，我们将机器学习应用于心肌梗死遗传学联盟（Myocardial Infarction Genetics Consortium）编码变异对整个外显子组影响的定量进化信息。使用基于集成的监督学习，进化机器学习框架对每个基因对病例和对照样本进行分类的能力进行了排名，并确定了79个显著关联。这些基因与已知的风险位点有关;富含心血管过程，如脂质代谢、凝血和炎症;并在基因敲除小鼠模型中富含心血管表型。其中，INPP 5 F和MST 1 R是潜在的新型冠状动脉疾病风险基因的例子，它们调节免疫信号以响应心脏应激。我们的结论是，基于大量进化信息，对编码变体的功能影响进行机器学习，有能力为心血管生物学的机制和治疗发现提出新的冠状动脉疾病风险基因，并且也应该应用于其他复杂的多基因疾病。
Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome‐wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble‐based supervised learning, the Evolutionary Action–Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.
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影响因子：
4.6
作者：
Kim HS;Li A;Ahn S;Song H;Zhang W
通讯作者：
Zhang W
SAC2通过SAC2调节内吞回收的磷脂酰肌醇4-磷酸的时空控制。
DOI：
10.1083/jcb.201408027
发表时间：
2015-04-13
期刊：
The Journal of cell biology
影响因子：
--
作者：
Hsu F;Hu F;Mao Y
通讯作者：
Mao Y
DOI：
10.1016/j.physa.2010.04.005
发表时间：
2010-08-15
影响因子：
3.3
作者：
Lisewski, Andreas Martin;Lichtarge, Olivier
通讯作者：
Lichtarge, Olivier
影响因子：
4.6
作者：
Hariharan P;Dupuis J
通讯作者：
Dupuis J
影响因子：
5
作者：
Salanti G;Southam L;Altshuler D;Ardlie K;Barroso I;Boehnke M;Cornelis MC;Frayling TM;Grallert H;Grarup N;Groop L;Hansen T;Hattersley AT;Hu FB;Hveem K;Illig T;Kuusisto J;Laakso M;Langenberg C;Lyssenko V;McCarthy MI;Morris A;Morris AD;Palmer CN;Payne F;Platou CG;Scott LJ;Voight BF;Wareham NJ;Zeggini E;Ioannidis JP
通讯作者：
Ioannidis JP