The future for genetic studies in reproduction.

The future for genetic studies in reproduction.
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DOI:
10.1093/molehr/gat058
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发表时间:
2014-01
影响因子:
4
通讯作者:
Nyholt DR
Nyholt DR
中科院分区:
医学2区
文献类型:
--
作者:
Montgomery GW;Zondervan KT;Nyholt DR

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遗传因素导致许多影响生殖和生育力的常见疾病的风险。近年来,全基因组关联研究(GWAS)的方法已经彻底改变了常见性状和疾病的基因发现。GWAS的结果记录在国家人类基因组研究所的已发表全基因组关联研究目录中,并报告了70多篇与生殖相关的32种性状和疾病的出版物。这些包括子宫内膜异位症,子宫肌瘤,初潮年龄和绝经年龄。通过适当严格显著性水平的结果通常在独立研究中得到很好的复制。影响双胞胎率、不孕症、子宫内膜异位症和初潮年龄的遗传变异的例子表明,生殖特征的疾病相关变异谱与大多数其他常见疾病相似。GWAS“命中”提供了对生物学途径的新见解,这些研究的转化价值在于发现生物标志物的新基因靶点,药物开发和更好地了解导致疾病风险的环境因素。研究结果还表明,遗传数据可以帮助定义疾病的亚型以及与其他特征和疾病的共病。迄今为止,许多关于生殖性状的研究都使用相对较小的样本。未来的遗传标记研究,在大样本的详细表型和临床信息,将产生新的见解疾病的风险,疾病分类和并发症的许多疾病与生殖和不育。
Genetic factors contribute to risk of many common diseases affecting reproduction and fertility. In recent years, methods for genome-wide association studies (GWAS) have revolutionized gene discovery for common traits and diseases. Results of GWAS are documented in the Catalog of Published Genome-Wide Association Studies at the National Human Genome Research Institute and report over 70 publications for 32 traits and diseases associated with reproduction. These include endometriosis, uterine fibroids, age at menarche and age at menopause. Results that pass appropriate stringent levels of significance are generally well replicated in independent studies. Examples of genetic variation affecting twinning rate, infertility, endometriosis and age at menarche demonstrate that the spectrum of disease-related variants for reproductive traits is similar to most other common diseases. GWAS ‘hits’ provide novel insights into biological pathways and the translational value of these studies lies in discovery of novel gene targets for biomarkers, drug development and greater understanding of environmental factors contributing to disease risk. Results also show that genetic data can help define sub-types of disease and co-morbidity with other traits and diseases. To date, many studies on reproductive traits have used relatively small samples. Future genetic marker studies in large samples with detailed phenotypic and clinical information will yield new insights into disease risk, disease classification and co-morbidity for many diseases associated with reproduction and infertility.
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