Mutation spectrum of hyperphenylalaninemia candidate genes and the genotype-phenotype correlation in the Chinese population

多重连接依赖探针扩增 桑格测序 遗传学 生物 候选基因 表型 苯丙氨酸羟化酶 基因 基因型-表型区分 基因型 高苯丙氨酸血症 DNA测序 外显子 苯丙氨酸 氨基酸
作者
Ruifang Wang,Nan Shen,Jun Ye,Lianshu Han,Wenjuan Qiu,Huiwen Zhang,Lili Liang,Yu Sun,Yanjie Fan,Lili Wang,Yu Wang,Zhuwen Gong,Huili Liu,Jianguo Wang,Hui Yan,Nenad Blau,Xuefan Gu,Yongguo Yu
出处
期刊:Clinica Chimica Acta [Elsevier BV]
卷期号:481: 132-138 被引量:28
标识
DOI:10.1016/j.cca.2018.02.035
摘要

Hyperphenylalaninemia (HPA) is an inherited metabolic disorder that is caused by a deficiency of phenylalanine hydroxylase (PAH) or tetrahydrobiopterin. The prevalence of HPA varies widely around the world.A spectrum of HPA candidate genes in 1020 Chinese HPA patients was reported. Sanger sequencing, next generation sequencing (NGS), multiplex ligation-dependent probe amplification (MLPA) and quantitative real-time PCR (qRT-PCR) were applied to precisely molecular diagnose HPA patients. The allelic phenotype values (APV) and genotypic phenotype values (GPV) were calculated in PAH-deficient patients based on a recently developed formula.Apart from genetic diagnoses confirmed in 915 HPA patients (89.7%) by Sanger sequencing, pathogenic variants were discovered in another 57 patients (5.6%) through deep detections (NGS, MLPA and qRT-PCR). We identified 196, 42, 10 and 2 variants in PAH, PTS, QDPR and GCH1, respectively. And a total of 47 novel variants were found in these genes. Through the APV and GPV calculations, it was found that the new GPV system was well correlated with metabolic phenotypes in most PAH-deficient patients.More HPA candidate variants were identified using new molecular diagnostic methods. The new APV and GPV system is likely to be highly beneficial for predicting clinical phenotypes for PAH-deficient patients.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
爆米花应助王沿橙采纳,获得10
刚刚
小马甲应助177采纳,获得50
1秒前
rhx发布了新的文献求助10
1秒前
cc发布了新的文献求助10
1秒前
斯文败类应助Lucas试剂采纳,获得10
1秒前
2秒前
迷路初兰完成签到,获得积分10
2秒前
华仔应助MissF采纳,获得10
2秒前
汉堡包应助pxh采纳,获得10
3秒前
3秒前
ting完成签到,获得积分20
3秒前
深情安青应助搞怪的思卉采纳,获得10
4秒前
WangXuerong完成签到,获得积分10
4秒前
z123123发布了新的文献求助10
5秒前
5秒前
mount完成签到,获得积分10
6秒前
andre发布了新的文献求助10
6秒前
7秒前
思源应助zz采纳,获得10
7秒前
8秒前
rhx完成签到,获得积分10
8秒前
8秒前
8秒前
callmecjh完成签到,获得积分10
8秒前
啊娴子发布了新的文献求助10
9秒前
scoupsss完成签到,获得积分10
10秒前
10秒前
10秒前
丘比特应助贪玩的鼠标采纳,获得10
11秒前
11秒前
cherish完成签到,获得积分10
11秒前
ewww发布了新的文献求助10
12秒前
全麦面包发布了新的文献求助10
12秒前
177发布了新的文献求助50
12秒前
12秒前
scoupsss发布了新的文献求助10
13秒前
玉堂堂发布了新的文献求助10
13秒前
平常的雨兰完成签到,获得积分10
13秒前
14秒前
研友_VZG7GZ应助momo采纳,获得10
14秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry Applied Machine Learning for IoT and Data Analytics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6443142
求助须知:如何正确求助?哪些是违规求助? 8257058
关于积分的说明 17585007
捐赠科研通 5501690
什么是DOI,文献DOI怎么找? 2900830
邀请新用户注册赠送积分活动 1877812
关于科研通互助平台的介绍 1717461