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Rare and common genetic variants underlying the risk of Hirschsprung’s disease

生物 遗传学 疾病 巨结肠病 内科学 医学
作者
Jun Xiao,Chenzhao Feng,Tianqi Zhu,Xuan Zhang,Xuyong Chen,Zejian Li,Jingyi You,Qiong Wang,Didi Zhuansun,Xinyao Meng,Jing Wang,Lei Xiang,Xiaosi Yu,Bingyan Zhou,Jie Tang,Jinfa Tou,Yi Wang,Heying Yang,Lei Yu,Yuanmei Liu,Xuewu Jiang,Hongxia Ren,Mei Yu,Qi Chen,Qiang Yin,Xiang Liu,Zhilin Xu,Dianming Wu,Donghai Yu,Xiaojuan Wu,Jixin Yang,Bo Xiong,Feng Chen,Xingjie Hao,Jiexiong Feng
出处
期刊:Human Molecular Genetics [Oxford University Press]
标识
DOI:10.1093/hmg/ddae205
摘要

Abstract Hirschsprung’s disease (HSCR) is a congenital enteric neuropathic disorder characterized by high heritability (>80%) and polygenic inheritance (>20 genes). The previous genome-wide association studies (GWAS) identified several common variants associated with HSCR and demonstrated increased predictive performance for HSCR risk in Europeans using a genetic risk score, there remains a notable gap in knowledge regarding Chinese populations. We conducted whole exome sequencing in a HSCR case cohort in Chinese. By using the common controls (505 controls from 1KG EAS and 10 588 controls from ChinaMAP), we conducted GWAS for the common variants in the exome and gene-based association for rare variants. We further validated the associated variants and genes in replicated samples and in vitro and vivo experiments. We identified one novel gene PLK5 by GWAS and suggested 45 novel putative genes based the gene-based test. By using genetic variant at RET and PLK5, we constructed a genetic risk score that could identify the individuals with very high genetic risk for HSCR. Compared with patients with zero or one risk allele from the three variants, the risk for HSCR was 36.61 times higher with six alleles. In addition, we delineated a HSCR risk gene landscape that encompasses 57 genes, which explains 88.5% and 54.5% of HSCR in Chinese and European, respectively. In summary, this study improved the understanding of genetic architecture of HSCR and provided a risk prediction approach for HSCR in the Chinese.

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