(Peer-Reviewed) Computational Assessment of the Expression-modulating Potential for Noncoding Variants
Fang-Yuan Shi 史方圆 ¹, Yu Wang 王宇 ¹, Dong Huang 黄东 ², Yu Liang ³, Nan Liang 梁楠 ¹, Xiao-Wei Chen 陈晓伟 ² ⁴, Ge Gao 高歌 ¹
¹ Biomedical Pioneering Innovation Center (BIOPIC), Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China
中国 北京 北京大学生物医学前沿创新中心 北京未来基因诊断高精尖创新中心 北京大学生物信息中心 北京大学生命科学学院 蛋白质与植物基因研究国家重点实验室
² State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking University, Beijing 100871, China
中国 北京 北京大学分子医学研究所 生物膜国家重点实验室
³ Human Aging Research Institute, School of Life Science, Nanchang University, Nanchang 330031, China
中国 南昌 南昌大学生命科学学院人类衰老研究所
⁴ Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
中国 北京 清华大学-北京大学生命科学联合中心 北京大学前沿交叉学科研究院
Abstract
Large-scale genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) studies have identified multiple noncoding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge.
Here, we developed CARMEN, a novel algorithm to identify functional noncoding expression-modulating variants. Multiple evaluations demonstrated CARMEN’s superior performance over state-of-the-art tools. Applying CARMEN to GWAS and eQTLs datasets further pinpoints several causal variants other than reported lead single-nucleotide polymorphisms (SNPs). CARMEN scales well with the massive datasets and is available online as a web server at http://carmen.gao-lab.org.
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Opto-Electronic Science
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