1.Discussion on the standard of clinical genetic testing report and the consensus of gene testing industry.
Hui HUANG ; pengzhiyu@bgi.com. ; Yiping SHEN ; Weihong GU ; Wei WANG ; Yiming WANG ; Ming QI ; Jun SHEN ; Zhengqing QIU ; Shihui YU ; Zaiwei ZHOU ; Baixue CHEN ; Lei CHEN ; Yundi CHEN ; Huanhuan CUI ; Juan DU ; Yong GAO ; Yiran GUO ; Chanjuan HU ; Liang HU ; Yi HUANG ; Peipei LI ; Xiaorong LI ; Xiurong LI ; Yaping LIU ; Jie LU ; Duan MA ; Yongyi MA ; Mei PENG ; Fang SONG ; Hongye SUN ; Liang WANG ; Dawei WANG ; Jingmin WANG ; Ling WANG ; Zhengyuan WANG ; Zhinong WANG ; Jihong WU ; Jing WU ; Jian WU ; Yimin XU ; Hong YAO ; Dongsheng YANG ; Xu YANG ; Yanling YANG ; Ying ZHANG ; Yulin ZHOU ; Baosheng ZHU ; Sicong ZENG ; Zhiyu PENG ; Shangzhi HUANG
Chinese Journal of Medical Genetics 2018;35(1):1-8
The widespread application of next generation sequencing (NGS) in clinical settings has enabled testing, diagnosis, treatment and prevention of genetic diseases. However, many issues have arisen in the meanwhile. One of the most pressing issues is the lack of standards for reporting genetic test results across different service providers. The First Forum on Standards and Specifications for Clinical Genetic Testing was held to address the issue in Shenzhen, China, on October 28, 2017. Participants, including geneticists, clinicians, and representatives of genetic testing service providers, discussed problems of clinical genetic testing services across in China and shared opinions on principles, challenges, and standards for reporting clinical genetic test results. Here we summarize expert opinions presented at the seminar and report the consensus, which will serve as a basis for the development of standards and guidelines for reporting of clinical genetic testing results, in order to promote the standardization and regulation of genetic testing services in China.
2.Proteome and genome integration analysis of obesity.
Qigang ZHAO ; Baixue HAN ; Qian XU ; Tao WANG ; Chen FANG ; Rui LI ; Lei ZHANG ; Yufang PEI
Chinese Medical Journal 2023;136(8):910-921
The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.
Humans
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Proteome/metabolism*
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Proteomics
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Prospective Studies
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Obesity/genetics*
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Genomics
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Genome-Wide Association Study